The proceedings of the 16th Annual Conference of China Electrotechnical Society: Volume I (Lecture Notes in Electrical Engineering, 889) 981191527X, 9789811915277
142 38 196MB
English Pages [1465]
Contents
Optimal Design of Coupling Coils for Wireless Power Transfer System Featuring High Misalignment Tolerance
1 Introduction
2 Transmission Characteristics of Two-Coil Wireless Power Transmission System
3 Coil Optimization Design
3.1 The Uniformity of Mutual-Inductance and Magnetic Flux Density
3.2 Structure of Transmitting Coil
3.3 Calculation Method of the Magnetic Flux Density of the Combined Coil
3.4 Objective Function of Uniform Magnetic Flux Density
3.5 The Methods and Results of Optimization
4 Simulation and Experimental Verification
5 Conclusion
References
Auto-Distrubance-Rejection-Controller Design for VSCF Wind Turbine
1 Introduction
2 ADRC
2.1 TD
2.2 ESO
2.3 NLSF
3 Controller Design for VSCF Wind Generator System
3.1 Model
3.2 Controller Designment
3.3 Digital Designs
4 Simulation and Experiments
4.1 Simulations
4.2 Experiments
5 Conclusion
References
Functional Demonstration and Power Optimization of a Monitoring System for Arc Blowout Lightning Protection Devices
1 Introduction
2 System Architecture and Working Principles
2.1 System Architecture
2.2 System Working Principles
3 System Functional Test
4 Conclusion
References
Distribution Network Line Loss Allocation Method Taking into Account Distributed Power Sources and Economic Operation Intervals
1 Preface
2 Economic Operation Range of Distribution Network
3 Distribution Network Loss Allocation Method with DG
3.1 Improved Average Network Loss Coefficient Method
3.2 Correction Calculation Considering Economic Operation Interval
4 Method Application
5 Conclusion
References
Study on Macroscopic Performance and Characteristics of DC Arc in Condition of Short Gap
1 Introduction
2 Experiments and Measuring Method
2.1 An Experimental Platform
2.2 DC Controllable Power Supply
2.3 ZVS Driver
2.4 Linear Output Transformer
2.5 Measuring System
2.6 Rod-Rod Electrodes
3 Experimental Results and Analysis
3.1 Macroscopic Performances
3.2 Arc Extinguishing Characteristics
3.3 Electrode Ablation
4 Conclusions
References
Improved Feedback Compensated Closed Loop Stator Flux Observer
1 Introduction
2 Mathematical Models of Flux Linkage and Back EMF
3 Traditional Flux Observation Methods
3.1 Pure Integral Observer
3.2 Low-Pass Filter Flux Observer
3.3 Saturation Inhibited Flux Observer
4 Improved Feedback Compensated Closed Loop Flux Observer
4.1 Feedback Compensated Closed Loop Flux Observer
4.2 Improved Feedback Compensated Closed Loop Flux Observer
4.3 Analysis of Algorithm Principle and Proof of Convergence
5 Simulation Analysis and Verification
5.1 Comparison of Several Flux Observers
5.2 Simulation of Direct Torque Control
6 Conclusion
References
Influence of Long-term Cooling and Heating Cycles on the Interface Pressure of Cable Accessories
1 Introduction
2 Experimental Settings
2.1 Test System and Experimental Platform
2.2 Test Method
3 Experimental Results and Analysis
4 Simulation Verification
5 Conclusion
References
Experimental Study on SF6Degradation by Dielectric Barrier Discharge Filled with Zirconia
1 Introduction
2 Experiment
2.1 Experiment Platform
2.2 Parameter Calculation
3 Experimental Results
3.1 Electrical Parameters
3.2 DRE and EY
4 Products Analysis
4.1 Emission Spectrum
4.2 Product Component
5 Conclusion
References
Reactive Power Optimization of PSO-DBN Based on IoT Technology
1 Introduction
2 IoT Technology and Analysis of Reactive Power Optimization Problems
3 PSO-DBN Reactive Power Optimization Strategy
3.1 PSO-DBN Theory
3.2 PSO-DBN Reactive Power Optimization Scheme
4 Case Study
4.1 PSO-DBN Reactive Power Optimization Scheme
4.2 Anal Sizes of This Results
5 Conclusion
References
A Novel Structure for Switched Reluctance Motor to Be Driven by Full-Bridge Power Converter
1 Introduction
2 Theoretical Analysis
2.1 Operating Principle
2.2 Calculation of Circulating Current
3 Simualtion Analysis
4 Validation
5 Conclusion
References
Simulation and Experimental Study on Muzzle Flow Field of Electromagnetic Energy Equipment
1 Introduction
2 Simulation Model
2.1 Flow Field Control Equation and Turbulence Model
2.2 Model Building
2.3 Overset Meshing
3 Simulation Results and Analysis
4 Experimental Study
4.1 Muzzle Shock Wave Measurement
5 Conclusion
References
Study on PSS Parameter Setting of Point-to-Network Transmission Mode Considering Regional Oscillation
1 Introduction
2 Overview of Long Distance Point to Grid Transmission Mode
3 Basic Principle of PSS Tuning Considering Regional Oscillation
4 Simulation Analysis
4.1 Simulation Analysis System
4.2 Simulation Analysis Results
5 Engineering Application
5.1 No Compensation Characteristic Test of Excitation System
5.2 PSS Parameter Calculation Considering Regional Oscillation
5.3 Test Verification
6 Conclusion
References
Multi-objective Optimization of Electromagnetic Devices Based on Improved Jaya Algorithm and Kriging Model
1 Introduction
2 Selection of Approximation Model
3 Improved Jaya Algorithm
3.1 Initialize Population Based on Skew Tent Mapping
3.2 Neighborhood Update Strategy for MOEA/D
3.3 Strategy of Introducing Levy Flight
3.4 Flow Chart Improve Algorithm
3.5 Numerical Test and Result Analysis
4 Description of TEAM 22 Problem
5 Optimizing Method
6 Conclusion
References
Numerical Wind Tunnel Simulation of Breeze Vibration and Response Analysis Under Random Frequency
1 The Introduction
2 Numerical Wind Tunnel Simulation of Transmission Line Breeze Vibration Phenomenon
2.1 Simulation Model of Fixed Transmission Line Turbulence
2.2 Simulation Results
3 Calculation of Anti-vibration Hammer Installation Position
3.1 Calculation Method of Anti-shock Hammer Installation Position
3.2 Optimal Arrangement Method of Shockproof Hammer
4 Response Analysis of Breeze Vibration Under Random Frequency
4.1 Probability Distribution of Wind Direction
4.2 Frequency Probability Distribution
5 Response Analysis of Breeze Vibration Under Random Frequency
6 Conclusion
References
Experimental Study on the Partial Discharge Inception Characteristics of Different Pressboards for Electrical Purpose
1 Introduction
2 Test Platform
2.1 Test Circuit
2.2 Test Voltage
2.3 Strong Vertical Component Electric Field Test
2.4 Test Data Processing
3 Measurement Results and Analysis
3.1 Comparisons of Different Batches from the Same Manufacturer
3.2 Comparisons of Different Manufacturers
4 Conclusion
References
Analysis of the Influence of Meteorological Factors on Electric Vehicle Charging Load and Mixed Regression Forecasting Model
1 Introduction
2 Analysis of the Influence of Meteorological Factors on Electric Vehicle Charging Load
2.1 Analysis of Charging Load Characteristics of Electric Vehicles
2.2 Identification of Major Meteorological Factors
3 Building the Mixed Regression Forecasting Model
4 Example Analysis
4.1 Model Training
4.2 Model Evaluation
5 Conclusion
References
Cross-platform Communication Simulation System Frame-Design and Modelling Strategy
1 Demand Modeling of Communication Simulation System
1.1 Analysis of Core Requirements of Communication Simulation System
2 Abstract Framework Modeling of Communication Simulation System Based on Reflection Technology
2.1 The Basic Concept of Reflection
2.2 Basic Functions of Reflection
2.3 Design and Implementation of Abstract Framework of Communication Simulation System Based on Reflection Technology
References
Closed-Loop Control System of a Contactor Based on Single-Board RIO
1 Introduction
2 Closed-Loop Control Strategy
2.1 Double Closed-Loop Structure in the Making Process
2.2 Single Current Closed-Loop Structure in the Closing Process
2.3 Principle of Closed-Loop Control
2.4 Cosimulation Analysis
3 Embedded System Based on Single-Board RIO
3.1 Embedded System Structure
3.2 System Hardware
4 Experimental Validation
4.1 Validation of Control Strategy
4.2 Comparative Analysis of Experimental Results
5 Conclusion
References
Analysis of a Misoperation of the Ratio Differential Protection of Main Transformer
1 Preface
2 Introduction to Failure
3 Analysis of Accident Doubts
3.1 Doubtful Point: Whether the Main Transformer is Faulty
3.2 Doubt 2: Why the Main Transformer Protection Does not Operate During the First Failure
3.3 Why There is No Warning Signal for the Three Main Transformer Protection
4 Hardware Detection
4.1 Failure to Reproduce the On-Site Return Plug-In
4.2 Board Hardware Detection
5 Conclusions and Corrective Measures
References
Analysis of a Local Overheating Fault of UHV Converter
1 Introduction
2 Exception Introduction
3 Test Chamber and Treatment Edge
3.1 Live Detection
3.2 Power Failure Test
3.3 Welding Condition of Box Edge
4 Site Inspection and Disassembly
4.1 On Site Inspection
4.2 Factory Return Inspection
5 Simulation Calculation and Cause Analysis
5.1 Distribution of Magnetic Leakage Field
5.2 Circulation Distribution of Structural Parts
5.3 Cause Analysis
6 Conclusions
References
Single Branch Experimental Research on Phase Change Cooling in Power Module of Vehicle Traction Converters
1 Introduction
2 The Design and Analysis of Test Module
3 Experimental Setup and Measurement Procedure
4 Experimental Results and Discussion
4.1 Temperature Distribution and Flow Characteristics for Type I
4.2 Periodic Dynamic Operation Condition
4.3 A Hybrid Cooling Scheme for Type II
5 Conclusions
References
Experimental Study on Acoustic Emission and Ultrasonic Testing Technology with Fiber Bragg Gratings Sensing
1 Introduction
2 Acoustic Emission Detection Experiment
3 Detection of Artificial Damage on the Surface of Gas Tank Under Ultrasonic Excitation
4 Conclusion
References
Research on Key Technology of 10 kV Mechanical DC Circuit Breaker
1 Introduction
2 Topology and Technical Scheme
2.1 Topology and Principle
2.2 Key Technology Research
3 Circuit Simulation
4 Breaking Test
5 Conclusion
1. References
Structure Design and Electric Field Simulation of a Compact -150 kV/10 mA High Voltage Power Supply
1 Introduction
2 Design of CW High Voltage Circuit and Consideration
3 Structure of the Compact HV Power Supply
4 Electric Field Simulation of the Compact HV Power Supply
5 Conclusion
References
A DC Microgrid System Architecture and Control Strategy for Aerospace Applications
1 Introduction
2 Proposed DC MG System and Operating Strategy
3 Working Principle and Control Strategy of GCC
4 Small Signal Modelling of GCC
5 Simulation and Experimental Results
5.1 Simulation Results and Analysis of GCC
5.2 Experimental Results and Analysis
6 Conclusion
References
Research on Key Technology of Parallel Breaking for DC Circuit Breaker
1 Introduction
2 Structure and Working Principle
2.1 Overall Topology and Principle
2.2 Topology and Principle of Power Electronic Switch
3 Influence of Stray Inductance on Parallel Breaking
3.1 Parallel Busbar Scheme
3.2 Laminated Busbar Scheme
4 Influence of Driving Performance on Parallel Breaking Characteristic
5 Prototype Development and Application
6 Conclusion
References
Adaptive Field Weakening Control Algorithm of Surface-Mounted Permanent Magnet Synchronous Motors with Low Carrier Ratio
1 Introduction
2 Traditional Field Weakening Algorithm of SPMSM
3 Adaptive Field Weakening Algorithm of SPMSM
4 Experimental Verification and Analysis
5 Experimental Verification and Analysis
References
A High-Precision Method for High-Power Electric Heating Element Insulation Inspection
1 Introduction
2 Frequency Zero Sequence Current Analysis Before and After the Fault
3 Principle of High Precision Insulation Resistance Calculation
4 Simulation Analysis
5 Conclusions
References
Dynamic Hierarchical Collaborative Optimal Scheduling in Energy Internet Based on Cooperative Game
1 Introduction
2 Hierarchical Architecture of Typical Park and Regional Level Energy Internet
3 Hierarchical Collaborative Optimal Scheduling Model of Park and Regional Level Energy Internet
3.1 Energy Efficiency Optimization and Scheduling of Lower Level Park Energy Internet
3.2 Economic Cost Optimization and Scheduling of Upper Level Regional Energy Internet
4 Hierarchical Cooperative Optimal Scheduling Based on Cooperative Game
4.1 Mathematical Description of Multi Objective Optimization Problems
4.2 Solving Multi Objective Optimization Problems Based on Cooperative Game Method
5 Case Study
5.1 System Description
5.2 Results and Analyses
6 Conclusion
References
Improving Transmittable Active Power Capability of VFT with Optimal Stator Reactive Power Reference
1 Introduction
2 Behaviors of VFT System with a Series Converter
2.1 VFT Configuration with a Series Converter
2.2 VFT Operation
3 Active Power Transfer in VFT System
3.1 Magnitude of Stator Reactive Power
3.2 Magnitude of Slip
4 Optimal Stator Reactive Power Reference for VFT
5 Hardware-in-Loop Experimental Studies
6 Conclusions
References
Simulation Research on Train LKJ Split Simulation Operation Equipment System
1 Introduction
2 Overview of LKJ2000 Analog Equipment
3 System Composition
3.1 LKJ2000 Display Simulation
3.2 LKJ Receiving Control Terminal Operation
4 Switching Between LKJ and Modes
5 Supervisory Simulation Operation
6 Conclusion
References
Application of Pulse Width Modulation to Structure Optimization of Permanent Magnet Synchronous Linear Motor
1 Introduction
2 Permanent Magnet Width Modulation Method
3 Simulation Analysis
3.1 Magnetic Field Calculation
3.2 Harmonic Analysis of No-Load Back Electromotive Force
4 Experiment
5 Conclusion
References
High-Efficiency Drive for IPMSM Based on Finite Model Predictive Control
1 Introduction
2 IPMSM Drive Analysis
2.1 IPMSM Model
2.2 Voltage Source Inverter (VSI)
2.3 Power Loss
3 Drive System
3.1 High-Efficiency Strategy (HES)
3.2 FMPC-HED
4 Experiments
4.1 General Behavior
4.2 Efficiency Map
5 Conclusion
References
Comparison of Two Drive Topologies for Ironless-Stator Permanent Magnet Motor Driven by Square Wave
1 Introduction
2 Configuration of BLDCM Control System
3 Comparison of Performances
3.1 Drive Topology Inherent Characteristics
3.2 Control Regulation
3.3 Inductance
3.4 Current
4 Conclusion
References
Research of Ultra-High Voltage DC Generator Based on Neural Network PID
1 Introduction
2 Control Model of Ultra-High Voltage DC Generator
2.1 Structure of Ultra-High Voltage DC Generator
2.2 Control Model System
3 Improved BP Neural Network
3.1 BP Neural Network
3.2 Optimization of BP Neural Network
4 Simulation Analysis
5 Application Tests
5.1 Main Test Parameters
5.2 Test Methods
5.3 Test Results
6 Conclusion
References
High Frequency Single Phase Induction Motor Driver Based on Half Bridge Circuit and Soft Switch Control
1 Introduction
2 Background
2.1 Driver Circuit
2.2 Control Strategy and Soft Switch
3 Results
4 Conclusion
References
Research and Application of Partial Discharge Inspection Instrument for Converter Transformer Based on High Frequency Coupling Method
1 Introduction
2 High Frequency Partial Discharge Detection Principle of Converter
3 Design of High Frequency Pulse Current Sensor
3.1 Equivalent Circuit of High Frequency Sensor
4 Design of Field Interference Suppression Circuit
5 Design and Development of High Frequency Inspection Instrument
6 Field Application
7 Conclusions
References
Research on Mobile Unit of Refrigerated Truck Based on STM32
1 Introduction
2 Overall Design
2.1 Communication Method
2.2 Implementation
3 Hardware Design
3.1 MCU Module
3.2 Refrigerator Compartment Temperature Monitoring Module
3.3 Refrigerator Battery Voltage Monitoring Module
3.4 Refrigerated Truck Collision Monitoring Module
3.5 GSM Communication Module
4 Software Design
4.1 Main Program Design
4.2 Temperature Monitoring Program for Refrigerated Compartment
4.3 Design of Monitoring Program for Refrigerated Truck Battery
4.4 Collision Monitoring Program Design
4.5 Communication Subprogram Design
5 Conclusion
References
Design Method for Power Unit of Power Conversion System Based on SiC MOSFET
1 Introduction
2 Circuit Principle of Power Unit
3 Key Structure Design of Power Unit
3.1 Overall Design Scheme
3.2 Low Inductance Design Method
3.3 Heat Dissipation Design Method
4 Experimental Verification
5 Conclusion
References
Impedance-Based Synchronization of Active Rectifier in Inductive Power Transfer Systems
1 Introduction
2 Mathematical Model of IPT System
2.1 Circuit Model
2.2 Instantaneous Rectifier Input Impedance
3 Active Rectifier Control
3.1 Introduction of the PWM Generation Scheme
3.2 Impedance-Angle-Based Phase Synchronization Algorithm
3.3 “Tooth Missing” Phenomenon
4 Experimental Results
4.1 Steady-State System Smoothness
4.2 Dynamic Performance
4.3 Discussions
5 Conclusion
References
Arc Simulations of a 252 kV Self-blast Circuit Breaker for Different Currents Considering Gas Control Valves
1 Introduction
2 Modelling
2.1 Arc Model
2.2 Load Analysis of Check Valve
2.3 Load Analysis of Relief Valve
3 Results and Discussions
4 Conclusion
References
Novel Parameter Mismatch Impact Elimination Strategy for IM FCS-MPCC Using Sliding Mode Perturbation Observation and Compensation Techniques
1 Introduction
2 IM Modelling for FCS-MPTC
2.1 State-Space Model of IM
2.2 Impacts of Parameter Mismatch on Performance
3 Proposed Sliding Mode Disturbance Observer
3.1 Sliding Mode Observer
3.2 Stability Analysis
4 HIL Testing Results
4.1 FCS-MPCC Performance Without Disturbance Observer
4.2 FCS-MPCC Performance with Disturbance Observer
5 Conclusion
References
Direct Power Control of Dual-Active-Bridge Three-Phase Shift Modulation Featuring Multi-order Harmonic Current Minimization
1 Introduction
2 DAB Analysis of MRS Modulation Strategy
3 Direct Power Control of DAB
4 Simulation Results
5 Conclusion
References
Effect of Oxygen Concentration on the Current-Carrying Friction and Wear Performance of C/Cu Contact Pairs
1 Introduction
2 Experimental Details
2.1 Experimental Equipment
3 Experimental Methods and Parameters
4 Results and Discussion
4.1 Effect of Oxygen Concentration on Wear
4.2 Effect of Oxygen Concentration on Coefficient of Friction
4.3 Effect of Oxygen Concentration on Contact Resistance
5 Conclusion
References
Direct Torque Control of Squirrel Cage Motor Based on Sector Optimization
1 Introduction
2 Basic Theory of Direct Torque Control
2.1 The Effect of Voltage Space Vector on Flux Linkage and Torque
2.2 Basic Principles of Direct Torque Control
3 Optimized 30-Sector Voltage Vector Table
4 Simulation Analysis
5 Conclusion
References
Characteristic Analysis of Fast Vacuum Switch Based on Electromagnetic Repulsion and Permanent Magnet Holding Mechanism
1 Introduction
2 Working Principle of Electromagnetic Repulsion and Permanent Magnetic Holding Mechanism
3 Simulation Analysis of the Permanent Magnet Retaining Actuator
3.1 Simulation Calculation
3.2 Simulation Results
3.3 Influence Analysis of Different Structures of Permanent Magnet Mechanism on Electromagnetic Force (Suction) of Z Axis
4 Simulation Analysis of Motion Characteristics Based on Electromagnetic Repulsion-Permanent Magnet Retaining Mechanism
4.1 Simulation Model
4.2 Mathematical Model
4.3 Comparison Between Simulation and Experiment Results
5 Conclusion
References
Discussion on Mechanism of the Gas Medium on Self-breakdown Probability of High-Power Gas Switch
1 Introduction
2 Experiment Setup
2.1 Structure of MMGS
2.2 Test Platform of Self-breakdown Voltage
3 Experimental Results
3.1 Self-breakdown Voltage of Single Switch Gap
3.2 Self-breakdown Probability of MMGS
4 Mechanism Explanation
4.1 Role of Corona Needles
4.2 Self-breakdown Process
4.3 Pure N2
4.4 Mixture of N2and Trace Electro-negative Gas
4.5 Pure SF6
5 Conclusion
6 Data Availability
References
The Study of High-Order Force on Electromagnetic Vibration of PMSMs
1 Introduction
2 Teeth Chopping Effect
3 Analysis of Exciting Force
4 Simulation Results
4.1 6p/36s IPMSM (Type I)
4.2 6p/36s SPMSM (Type II)
4.3 6p/36s SPMSM with Narrow Slot Width (Type III)
5 Experiments
6 Conclusion
References
Research and Application of Fibre Channel Status Online Sensing Technology
1 Introduction
2 Overview of the Overall Technical Route
2.1 Traditional Offline Fault Location Method
2.2 Fault Location Method Based on Standard Optical Interface Protocol
3 The Concrete Realization of the Technical Solution for Online Perception of Channel Status
4 Key Technologies for Online Perception of Channel Status
5 Scheme Implementation
6 Experimental Test Plan
7 Summary
References
Research of Two-Vector Model Predictive Flux Control Based on Flux Error
1 Introduction
2 Mathematical Models
3 Proposed TV-MPFC
3.1 Reference Values Calculation
3.2 Voltage Vectors Selection
3.3 Durations Time Calculation
3.4 Control Method Comparison
4 Simulation Results
5 Conclusion
References
Speed and Position Estimation Algorithm of Permanent Magnet Synchronous Motor Based on Extended Kalman Filter
1 Introduction
2 Mathematical Model of PMSM
3 State Estimation Based on EKF
4 Experimental Results and Analysis
5 Conclusion
References
High-Accuracy Torque Estimation and Multi-closed-Loop Control of PMSM for Electric Vehicles
1 Introduction
2 PMSM Model and Factors Affecting the Accuracy
2.1 PMSM Model
2.2 Dq Frame Deviation
2.3 Calculation of Motor Losses
3 Torque Estimation and Multiple Closed-Loop Control
3.1 Torque Estimation
3.2 Multi-closed-Loop Control Strategy
4 Simulation
5 Experiment
6 Conclusion
References
The Introduction of Dissociation Term in Numerical Simulation of Trichel Pulses in Air
1 Introduction
2 The Model
2.1 Governing Equations
2.2 Swarm Parameters
2.3 Geometric Dimensions and Initial Conditions
3 Dissociation Term
3.1 Dissociation Reaction
3.2 Dissociation Coefficient
4 Simulation Result
4.1 Single Pulse Discharge
4.2 Multiple Pulse Discharge
5 Conclusion
References
A High Sensitivity Sensor for Reconstruction of Conductivity Distribution in Region of Interest
1 Introduction
2 Mathematical Model of EIT
3 Sensor Design
4 Results and Discussions
4.1 Imaging of an Inclusion with High Conductivity
4.2 Imaging of Inclusions with High Conductivity and Low Conductivity
5 Conclusion
References
Fault Location of T-type Line with Double-Circuit Line on the Same Tower with Asymmetrical Parameters
1 Introduction
2 Analysis of Different Line Characteristics
2.1 T-shaped Transmission Line with Asymmetrical Parameters on the Same Tower Double-Circuit Line
2.2 Principle of Fault Location for Double Circuit Asymmetrical Line
3 Fault Location of Transmission Line
3.1 Introduction of RWPSO Algorithm Model
3.2 Optimal Solution of Ranging Equation
3.3 Ranging Result Analysis and Line Selection Criterion
4 Simulation
4.1 Line Parameters and Error Calculation
4.2 Simulation Results Under Different Transition Resistances
4.3 Comparison of Fitness Curves of Different Algorithms
5 Conclusion
References
Fault Location of Distribution Network Based on Stacked Autoencoder
1 Introduction
2 Stack Autoencoder
3 Scheme of Fault Location Based on Stack Autoencoder
3.1 Data Collection
3.2 Data Characteristics
3.3 Network Construction
3.4 Selection of Loss Function
3.5 Fine-Tuning of the Model
3.6 Model Prediction and Fault Node Judgment
4 Result Analysis
5 Conclusion
References
Prediction of Failure Probability of Overhead Lines in Distribution Network Based on Historical Statistics and Meteorological Monitoring Data
1 Introduction
1.1 Classification of Fault Types for Overhead Lines in Distribution Networks
1.2 The Meaning of the Internal Fault of the Overhead Line of the Distribution Network
1.3 The Meaning of the External Fault of the Overhead Line of the Distribution Network
2 Dividing the Operating Status of Overhead Lines in the Distribution Network
2.1 Calculation of the Internal Failure Rate of Overhead Lines Based on Exponential Function Fitting
2.2 Calculation of External Failure Rate Based on Stacked Autoencoder Network
3 Establishment of a Fault Prediction Model for Overhead Lines in a Distribution Network
3.1 Fokker-Planck Equation Theory
3.2 Fokker-Planck Equation Theory
4 Example Analysis
4.1 Calculation Example of External Failure Rate Based on SAE Algorithm
4.2 Fitting Calculation Example of Health Index and Internal Failure Rate
4.3 Overhead Line Real-Time Failure Probability Value
5 Conclusion
References
Research on Image Segmentation of Power Line Based on Encoder-Decoder Network
1 Introduction
2 PowerLine Image Segmentation Network
2.1 EDN-Light
2.2 Network Architecture
3 Network Training
3.1 Dataset
3.2 Network Training Parameters
3.3 Training Process
3.4 Evaluation Index
4 Results
5 Conclusion
References
Design of Low-Cost Micro-PMU Based on e-IpDFT
1 Introduction
2 Micro-PMU Synchronous Measurement Method
2.1 The DN Signal Model
2.2 Design of Synchronous Measurement Method
3 Device Implementation
4 Micro-PMU Device Test
5 Conclusion
References
Electro-thermal Failure of Insulation in AC Submarine Cable Subjected by the Transient Overvoltage
1 Introduction
2 Simulation Model and Parameters
3 Simulation Results and Analysis
4 The Influence of Single-Phase Grounding on the Physical Field Characteristic of Cable Insulation
4.1 Influence of Overvoltage on Electro-thermal Performance of Insulation
4.2 Influence of Large Overcurrent on Electro-thermal Performance of Insulation
5 Discussion
6 Conclusion
References
A Control Strategy for Modified the Stability of Weak Grid Integrated Wind Power on a Large Scale Through MMC-HVDC Transmission
1 Introduction
2 Topology and Operation Mode of Weak Grid Integrated Wind Power via MMC-HVDC
2.1 System Topology
2.2 Operation Mode
3 MVSG Control Model
3.1 MVSG Active Power-Frequency Loop
3.2 MVSG Reactive Power - Voltage Loop
4 Coordinated Power Support Control Strategy for Weak AC Grid
4.1 Strategy for Wind Turbine Overspeed Load Shedding Reserve
4.2 A Coordinated Control Strategy for Improving the Stability of Weak AC Grid
5 Case Anlysis
6 Conclusion
References
A Novel Control Strategy for Series Converter of VFT Under Dual-Side Harmonically Distorted Grid Voltages
1 Introduction
2 Modeling of VFT Under Distorted Grid Voltages
3 Novel Control Strategy for Series Converter
4 Hardware-in-Loop Experimental Studies
5 Conclusions
References
Temperature Calculation Method of Dry-Type Transformer Based on Fractional Order Thermal Circuit Model
1 Introduction
2 Heat Transfer Analysis of Dry-Type Transformer
2.1 Heat Dissipation Mode of Dry-Type Transformer
2.2 Thermoelectric Analogy Theory
3 Fractional Thermal Circuit Model of Dry-Type Transformer
3.1 Fractional Calculus Theory
3.2 Fractional Order Characteristics of Heat Capacity
3.3 Fractional Thermal Circuit Model of Dry-Type Transformer
3.4 Thermal Circuit Parameters
4 Solution of Fractional Order Thermal Path
4.1 An Overview of the Prediction-Correction Method
4.2 Simulation of Fractional Order Thermal Circuit Model
5 Conclusion
References
Measurement of the AC Resistance of Power Cable with Large Cross-section Conductor
1 Introduction
2 Theoretical Calculation of AC Resistance
3 Measurement of AC Resistance
3.1 Method of Measurement
3.2 Measuring System
4 Measurement of the Cable
4.1 Measurement Results of the Aluminum Rod
4.2 Measurement Results of the Segmental Conductors
5 Conclusion
References
Design of a Fast-Tracking Differential Observer
1 Introduction
2 Fast Tracking Differential Observer Design
3 Tracking Performance Analysis of Differential Observer
4 Simulation Results
5 Conclusion
References
Identification of Transient Power Quality Disturbances Based on S-transform Feature Extraction and Random Forest Classification
1 Introduction
2 Feature Extraction Based on S-transform
2.1 S-transform
2.2 Feature Extraction
3 Transient Power Quality Disturbances Identification
3.1 Theory of Random Forest Classifier
3.2 Importance Selection of Features
3.3 Selection of the Number of Classifier Units
4 Simulation and Analysis
5 Concluding Remarks
References
Dynamic Potential Analysis of Dyneema Ropes Above Transmission Line Based on Three-Dimensional Simulated Charge Method
1 Introduction
2 Calculation Principle
2.1 Calculation Principle of 3DCSM
2.2 Catenary Equation
3 3DCSM Calculation Example
3.1 Calculation Example Parameters
3.2 Calculation Example Results
4 FEM Calculation Example
4.1 Calculation Example Model
4.2 Calculation Example Results
5 Analysis and Conclusion
References
Research on Control of Grid-Forming Converters Based on Virtual Oscillator Control
1 Introduction
2 Principle of Virtual Oscillator Control
2.1 Basic Principle of Nonlinear Oscillator
2.2 Decentralized VOC Control
2.3 System Structure Using VOC
3 Droop Property of VOC Control
4 Case Study Simulations
5 Conclusions
References
Motion Artifact Removal Based on Stationary Wavelet Transform and Adaptive Filtering for Wearable ECG Monitoring
1 Introduction
1.1 Background
1.2 Artifact and Noise in ECG Monitoring
2 Algorithm
2.1 Stationary Wavelet Transform
2.2 Adaptive Filtering
2.3 Proposed Hybrid Algorithm
3 Measurement and Results
4 Conclusion
References
Exploration of Macroscopic Characterization for Low-Voltage AC Arc State
1 Introduction
2 Design of AC Arc Spectral Measurement System
2.1 Construction of the Spectral Measurement System
2.2 Processing of the Spectroscopic Measurement Data
2.3 Spectral Measurements Over a Continuous Time Domain
3 Low-Voltage AC Arc Spectroscopy Analysis
4 Application of the Macroscopic Characterization Parameters
5 Conclusion
References
Cluster Division in Wind Farm Based on DTW and KL-GMM
1 Introduction
2 Calculate Clustering Indexes Based on DTW and KL-GMM
2.1 Error Analysis of Calculating Similarities Between Fans Based on Traditional Euclidean Distance
2.2 DTW: Calculating the Similarity of Fans
2.3 Divied Wind Generator Groups Based on KL-GMM
3 Analysis of Cases
4 Conclusion
References
An Integrated Boost Micro-inverter for PV Generation System
1 Introduction
2 Working Principle of Micro-inverter
2.1 Structure and Operating Principles
2.2 Voltage Gain (G)
3 Experimental Results
4 Conclusion
References
Parameter Identification and Co-simulation Verification of Dynamic Inductance in Electromagnetic Switch
1 Introduction
2 Dynamic Inductance Identification Model
2.1 Current Closed Loop Control
2.2 Inductance Mathematical Model
2.3 Coil Resistance Self-correction
3 Co-simulation Model of Electromagnetic-Mechanical Field
3.1 Research Object
3.2 Dynamic Equation of Motion
3.3 Multiphysics Coupling Calculation
4 Experimental Verification and Analysis
4.1 Experimental Verification of Co-simulation Model
4.2 Verification of Inductance Identification Model
5 Conclusion
References
Review on Applications of Artificial Intelligence in Relay Protection
1 Introduction
2 Development of Power Grids
2.1 The Developments at Source Side
2.2 The Developments at Network Side
2.3 The Developments at Load Side
3 Relay Protection Problems Due to the Developments of Power Grid
4 Application of AI in Relay Protection
4.1 Relay Protection Setting and Online Checking
4.2 New Principle of Protection
4.3 Fault Location and Diagnosis
5 Conclusions and Prospects
References
Voltage Adaptive Dynamic Partition Method Considering Reactive Power Margin
1 Introduction
2 Full Dimension Voltage and Reactive Power Sensitivity
3 Modular Function Index Considering Reactive Power Margin
4 Simulation and Analysis
4.1 Computing Method
4.2 Case Simulation Analysis and Results
5 Conclusion
References
Optimal Allocation of Capacity for Vehicle Charging Stations with Wind-PV Microgrid
1 Introduction
2 Car Charging Station Microgrid System
2.1 Mathematical Model of System
2.2 Mathematical Model of Charging Load
2.3 Objective Function
2.4 Restrictions
3 Analysis
3.1 Simulation Calculation of Electric Vehicle Charging Load
3.2 Multi-level Optimization
3.3 Sensitivity Analysis
4 Conclusion
References
Effect of Thermo-oxidative Ageing on Physicochemical and Electrical Properties of Liquid Silicone Rubber
1 Introduction
2 Experiment
2.1 Sample Preparation
2.2 Experiments
3 Effect of Thermal Oxidative Ageing on Microstructure Properties
3.1 Molecular Structure (FTIR)
3.2 Cross-Section Morphology
4 Effect of Thermal Oxidative Ageing on Macroscopic Performance
4.1 Mechanical Properties
4.2 DC Breakdown Field Strength
4.3 High Field DC Conductivity
4.4 Terahertz Time Domain Spectroscopy
5 Discussion
6 Conclusion
References
Design of Control Loop of Three-Phase Z-source Inverter
1 Introduction
2 Working Principle of ZSI
3 Mathematical Model Establishment
3.1 Mathematical Model of Z-source System
3.2 Mathematical Model of Three-Phase Inverter Circuit
4 Design of Control System
4.1 Inductor Current Inner Loop Control
4.2 Capacitor Voltage Outer Loop Control
5 Simulation Circuit Design and Result Analysis
6 Conclusion
References
Effect of Surface Treatment on Surface Flashover Performance and Multi-factor Aging Characteristics of Epoxy Resin
1 Introduction
2 Experimental Preparation
2.1 Sample Preparation and Surface Treatment
2.2 Electric-Heat-Gas Combined Aging Experiment System
2.3 Properties Test
3 Results and Discussion
3.1 Experimental Results
3.2 Discussion
4 Conclusion
References
Analysis of the Influence of Active Filter on the Unstable Resonance for Traction Power Supply System
1 Introduction
2 Unified Mathematical Model of Traction Power Supply System
2.1 Mathematical Model of Active Filter
2.2 Traction Net-Locomotive Mathematical Model
2.3 Unified Mathematical Model
3 Analysis of the Resonance Stability of the Active Filter to the Traction Power Supply System
3.1 The Impact of Active Filter Access on the Resonance Stability of the Traction Power Supply System
3.2 The Influence of the Parameters of the Active Filter on the Resonance Stability of the Traction Power Supply System
4 Simulation
5 Conclusion
References
Research on Digital Vehicle Inverter Power Supply
1 Introduction
2 The Main Circuit
2.1 DC-DC Boost Circuit
2.2 Push-Pull Circuit Schematic Diagram
2.3 Design of DC/AC Inverter Circuit
3 System Control Strategy and Slmulation
3.1 Push-Pull Circuit Control
3.2 SPWM Inverter Circuit Control Strategy
4 Analysis of Simulation Results
4.1 Push-Pull Circuit Simulation Analysis
4.2 Inverter Circuit Simulation Analysis
5 Experimental Results Analysis
5.1 Analysis of Drive Waveform of Switching Tube
6 Conclusion
References
Effects of Rail Shape on Recoil of Electromagnetic Energy Equipment
1 Introduction
2 Rail Force Analysis of Electromagnetic Energy Equipment
3 Simulation Analysis
3.1 3-D Finite Element Model
3.2 Analysis of Simulation Results
4 Influence of Rail Shape on Recoil
4.1 Rail Shape Design
4.2 Analysis of Simulation Results
5 Conclusion
References
Low-Intensity Pulsed Ultrasound for Killing Tumor Cells: The Physical and Biological Mechanism
1 Introduction
2 Materials and Method
2.1 LIPUS Experimental Platform
2.2 Parameters Setting
2.3 Cells Lines and Media
3 Results
3.1 Tumor Cell Distribution
3.2 Physical Mechanism of the Different Distribution of Tumor Cells
3.3 Biological Mechanism of Tumor Cell Killing by LIPUS
4 Discussion
5 Conclusions
References
Credible Capacity Evaluation Method of Distributed Generation in Distribution Network Based on Power Supply Reliability
1 Introduction
2 Introduction Credible Capacity Evaluation Method Based on the Same Power Supply Reliability
2.1 Output Model of Distributed Generation
2.2 Reliability Evaluation Based on Sequential Monte Carlo Simulation
2.3 Credible Capacity Evaluation Based on the Same Power Supply Reliability
3 Case Study
3.1 Evaluation Result of Power Supply Reliability
3.2 Credible Capacity Evaluation Results
4 Conclusion
References
Violation Detection of Transmission Line Construction Based on YOLO Network
1 Introduction
2 The Application of Deep Learning YOLO Network in Violation Detection of Transmission Line Construction
2.1 YOLO Network Structure
2.2 Violation Detection of Transmission Line Construction
3 YOLO Network Uses Different Intersection Over Union Algorithms
3.1 Intersection Over Union Algorithm GIoU
3.2 Intersection Over Union Algorithm DIoU
3.3 Intersection Over Union Algorithm CIoU
3.4 The Impact of Using Different Intersection Over Union Algorithms on the Precision of YOLO Networks
4 YOLO Network Uses K-means Clustering Algorithm
4.1 K-means Clustering Algorithm
4.2 The Impact of K-means Clustering Algorithm on the Precision of YOLO Network Prediction
5 Conclusion
References
Optimization Design for 500 kV Four-Circuit Transmission Lines on Same Tower
1 Introduction
2 Calculation Model and Initial Results
3 Structure Adjustment and E-field Optimization
3.1 Optimization of Shielding Ring
3.2 Optimization of Connection
4 Conclusion
References
A New 3-D Multi-segment Modelling Method for Stator Transposition Bar and Its Application in Calculating the Circulating Current Loss of Large Hydro-generators
1 Introduction
2 The Proposed Modelling Method
2.1 3-D Multi-segment Modelling Method
2.2 Boundary Condition and Coupling Circuit
3 Experimental Validation
4 Calculation Results and Comparison with Traditional 2.5-D Multi-slice Method
4.1 Traditional 2.5-D Multi-slice Method
4.2 Magnetic Field
4.3 Circulating Current Loss
5 Conclusion
References
Study of Decentalized Trade Based on Blockchain and Rolling Aggregation Mechanism in V2G
1 Introduction
2 Decentralized Trading Mechanisms and Models
2.1 Decentrized Trading Model in V2G
2.2 Decentralized Trading Rules Based on Rolling Aggregation
3 Smart Contract Based on Hyperchain
3.1 Smart Contract Based on Hyperchain and Blockchain Technology
3.2 V2G Power Trading Smart Contract
4 Simulation
5 Conclusion
References
Hysteresis Loop Measurement for Steel Sheet Under PWM Excitation Condition
1 Introduction
2 Experimental Device
2.1 Epstein Frame Parameters
2.2 Experiment Platform
3 Principle of the Experiment
3.1 PWM Excitation Generation Method
3.2 Experimental Measurement Principle
4 Result
4.1 Measurement of Hysteresis Loop at Different Frequencies
4.2 Influence of High Order Harmonics
5 Conclusion
References
Influence of Trace SF6on Surface Corona Characteristics in SF6/N2Mixtures Under DC Voltages
1 Introduction
2 Experimental Setup and Method
2.1 Experimental Setup
2.2 Experimental Method
3 Experimental Results and Discussion
3.1 Light Emission Patterns Under Different SF6Contents
3.2 Discharge Characteristics Under Different SF6Contents
4 Conclusion
References
The Influence of Different Fillers on the Properties of Carbon-Matrix Composites
1 Introduction
2 Experimental
2.1 Materials
2.2 Preparation of Composite Sample
2.3 Characterization
3 Results and Discussion
3.1 Characterization of the Raw Materials
3.2 Microstructure and Mechanical Properties of Composites
3.3 Thermal and Electrical Conductivity
4 Conclusion
References
Study on the Mechanism of Electrodynamic Behavior of Metal Particles in DC GIL
1 Introduction
2 Electrodynamic Behavior of Linear Particles
3 Electrodynamic Simulation of Linear Particles
3.1 Initiation Analysis
3.2 Analysis of Air Gap Breakdown Caused by Motion
4 Conclusion
References
Analysis of the Surrounding Magnetic Field Distribution Under the Parallel Condition of AC and DC Cable Lines
1 Introduction
2 Tunnel Model and Calculation of Cable Magnetic Field
3 Simulation Analysis
4 Conclusion
References
Parameter Design and Simulation of Unsymmetrical 18-Pulse Phase-Shifting Autotransformer
1 Introduction
2 Research Methods
2.1 Principle of Rectifier
2.2 Transformer Design
3 Results
4 Conclusion
References
Research on the Influence of AC Cable Lines on the Electric Field Intensity of Parallel DC Cable Lines
1 Introduction
2 Submarine Tunnel Model and Research Method
3 Simulation Analysis
4 Conclusion
References
Cause Analysis and Preventive Measures for Breaking of Strain Clamp in 500 kV Transmission Line
1 Introduction
2 Failure Overview
3 Material Inspection
3.1 Macroscopic Morphology Analysis
3.2 Microscopic Morphology Analysis
3.3 X-ray Digital Imaging Inspection
3.4 Chemical Composition Detection
4 Failure Cause Analysis
4.1 Material Inspection Result Analysis
4.2 Connection Structure Analysis
4.3 Operating Environment Analysis
4.4 Comprehensive Analysis
5 Preventive Measures
5.1 Governance Method
6 Preventive Measures
7 Summary
References
Comprehensive Evaluation of Diversified and High Elastic Power Grid Based on Entropy-AHP-TOPSIS Method
1 Introduction
2 Research Strategy and Methods
2.1 Diversified and High Elastic Grid Evaluation Index System
2.2 Theoretical Model
3 Example Analysis
4 Conclusion
References
A Dual-Loop Control Strategy for Interlinking Converters in Hybrid AC/DC Microgrids
1 Introduction
2 Research on Microgrid Control Strategy
2.1 Introduction of ACMG Control Strategy
2.2 Introduction of DCMG Control Strategy
3 Research on Control Strategy for the IC
3.1 Realization of Outer Loop Control Strategy for the IC
3.2 Realization of Inner Loop Control Strategy for the IC
4 Simulation Analysis
4.1 Simulation Parameters
4.2 Simulation Results
5 Conclusion
References
Force Simulation of Four Bundle Spacer Under Short Circuit Condition
1 Introduction
2 Modeling of Spacer
2.1 Physical Model
2.2 Parameters and Loads
3 Results and Analysis
3.1 Boundary Condition Setting
3.2 Force Analysis
3.3 Comparison of Results
4 Conclusion
References
Numerical Simulation of Diffusion Characteristics of SF6Decomposition Products in Gas-Insulated Switchgear
1 Introduction
2 Simulation Model
2.1 The 3D Model Geometry
2.2 The Governing Equations and Transfer Parameters
3 Simulation Results and Discussion
3.1 The Concentration Distribution
3.2 The Effects of Defect Location on Diffusion Concentration
3.3 The Time-Domain Characteristic Curves
4 Conclusion
References
Electromagnetic Transient Calculation and Experiment of Intelligent Transformer Under DC Bias Magnetic Field
1 Introduction
2 Generating Mechanism of DC Magnetic Bias
3 Harmonic Analysis
4 Finite Element Modeling of DC Bias
4.1 Excitation Current and Harmonic Analysis
4.2 Magnetic Density Distribution Under DC Bias
4.3 Transformer Core Loss Distribution
5 DC Magnetic Bias Simulation Experiment
6 Conclusion
References
Torque Density Optimization of an Axial Flux Permanent Magnet Synchronous Machine Using Genetic Algorithm Combined with Simplex Operator
1 Introduction
2 Analytical Model
2.1 Magnetic Field
2.2 Motor Weight
2.3 Output Torque Density
3 Optimization Model and GA Algorithm Design
3.1 Optimization Model
3.2 Chromosome Design
3.3 GA with Constraints
3.4 Combining Simplex Operator
4 Optimization Result and 3-D FEA Result
5 Conclusion
References
Application of Knowledge Mapping and Fault Diagnosis in Power Communication Network
1 Introduction
1.1 Background
1.2 The Purpose and Significance of This Paper
1.3 Research Status at Home and Abroad
1.4 Chapter Arrangement of the Thesis
2 The Theoretical Research of Knowledge Mapping Algorithm
2.1 Theoretical Research on Knowledge Mapping Algorithm
2.2 Key Points and Difficulties of Research
3 Construction and Implementation of Knowledge Map and Fault Diagnosis Model
3.1 Main Research Contents
3.2 Construction and Implementation of Knowledge Map and Fault Diagnosis Model
4 Application and Effect of the Model
5 Summarizes and Prospects
5.1 Summary
5.2 Expectation
References
A Mix Peer-To-Peer Energy Trading Hierarchy Modal for Prosumers
1 Introduction
2 Structured Peer-To-Peer Energy Trading Hierarchy Analysis
2.1 Pure P2P Trading Model
2.2 Mix P2P Trading Model
3 Structured Peer-To-Peer Energy Trading Hierarchy Construction
3.1 Mix P2P Trading Hierarchy
3.2 Mix P2P Trading Algorithms
3.3 Two-Stage Marching
4 Case Study
5 Conclusion
References:
Research and Application System of Power Communication Network Data Mining and Intelligence Based on Regulatory Cloud
1 Introduction
2 Characteristics and Development Requirements of Power Grid Communication
3 Construction of Communication Intelligent Scheduling Based on Regulation Cloud
3.1 State Monitoring Technology of Communication Map Based on Regulation Cloud
3.2 Automatic Business Analysis and Intelligent Fault Disposal Technology
3.3 Inspection and Intelligent Assistant Disposal Technology of Communication Hidden Danger Based on Artificial Intelligence
3.4 Build Communication Operation Simulation and Event Handling Drilling Platform
3.5 The Intelligent Dispatching Command System of Communication Support Based on Regulation Cloud
4 Conclusion
References
Research on Simulation Training System of Substation Based on HTC Vive
1 Introduction
2 Overall Design
2.1 Selection of Modeling Software
2.2 Virtual Reality Engine Unity
2.3 Virtual Reality Development Tool HTC Vive
3 Function Introduction
3.1 The Site Inspection
3.2 Cognitive Learning
3.3 Operating Practice
3.4 Fault Handing
4 Function Implementation
4.1 The Site Inspection
4.2 Cognitive Learning
4.3 Operating Practice
4.4 Fault Handing
5 Conclusion
References
Development of Vehicle Charger with High Power Factor Operation
1 Introduction
2 Main Index and Topological Structure
3 Control Theory Analysis
3.1 Power Factor and Harmonics
3.2 Theoretical Analysis of Boost APFC Control
3.3 Main Circuit Parameter Designs
4 Prototype and Experimental Analysis
4.1 Prototype Production
4.2 Analyses of Experimental Results
5 Conclusions
References
A Novel Adaptive Low Voltage Locking Protection Strategy for Distributed Generation Access
1 Introduction
2 Background
2.1 Basic Principle of Low Voltage Locking
2.2 Influence of DG Access on Low Voltage Locking
3 Adaptive Low Voltage Locking Strategy
3.1 Operating Characteristic Curve of Strategy
3.2 Protection Logic Design
4 Simulation and Analysis
5 Conclusion
References
The Influence of Load Model on the Accuracy of Power Grid Simulation
1 Introduction
2 Research Strategy and Methods
2.1 Load Model
2.2 Theoretical Analysis of Static Load Effect
3 Results
3.1 Influence of Engineering Load Model on Simulation Results
3.2 Engineering Calculation Considering Load Model Error
4 Conclusion and Suggestion
4.1 Conclusion
4.2 Suggestion
References
Research on Recognition Strategy of Oil-paper Insulation Aging State Based on Deep Residual Network
1 Introduction
2 Theory
3 Algorithm and Strategy
3.1 Data Expansion
3.2 Image Preprocessing
3.3 Deep Residual Network
4 Experimental Simulation
4.1 Construction of Test Platform
4.2 Network Evaluation Index
4.3 Identification Results
5 Conclusion
References
Leading Phase Operation Strategy of Multi-generators Based on Equivalent Impedance Method
1 Introduction
2 Generator Leading Phase Operation
2.1 Leading Phase Operation Mechanism Under Classical Model
2.2 Leading Phase Operation Considering Excitation Control
3 Multi-generators Leading Phase Operation Coordination Under Constant Reactive Power Control
3.1 Equivalent Model of Reactive Power Distribution
3.2 Reactive Power Control Coordination of Multi-generators Leading Phase
3.3 Equivalent Impedance Calculation Based on Branch Current
3.4 Steps of Multi-generators Leading Phase Operation Strategy
4 Case Application and Verification
4.1 Description of an Actual Case
4.2 Verification of the Multi-generators Leading Phase Strategy
4.3 Comparison of Multi-generators Leading Phase and Single Generator
5 Analysis of Multi-generators Leading Phase Coordination Strategy
5.1 Importance of Reactive Power Control Mode
5.2 Adaptability of Equivalent Impedance Method
6 Conclusion
References
Improved Degaussing Power Supply Applied to Ship Degaussing System
1 Introduction
2 Bipolar Degaussing Power Supply Structure
2.1 Working Mode Analysis of Bipolar DC-DC Converter
2.2 Equivalent Analysis Based on Buck-Boost Circuit
3 Parameter Design
3.1 Parameter Design of Inductance L1and Capacitance C1
3.2 Parameter Design of Inductance L2and Capacitance C2
4 MATLAB Simulation
5 Conclusion
References
Optimal Control Strategy of Fatigue-Oriented Wind Turbine Yaw System
1 Introduction
2 Structure and Load Analysis of Yaw System
2.1 Structure of Yaw System
2.2 Fatigue Characterization Parameters of Yaw System
2.3 Load Experiments of Yaw System
3 Design of Fuzzy Controller for Yaw System
3.1 Overall Design of Fuzzy Controller
3.2 Input and Output Variables and Membership Function
3.3 Fuzzy Rules
3.4 Defuzzification
4 Case Studies
4.1 Load Analysis Under Case 1
4.2 Load Analysis Under Case 2
4.3 Load Analysis Under Case 3
5 Conclusion
References
The Fabrication Technology and Test Results of the NbTi Superconducting Racetrack Magnets
1 Introduction
2 Coil Design
2.1 Magnetic Field Analysis
2.2 Force Analysis of Superconducting Coils
2.3 The Wingding of Racetrack NbTi Superconducting Magnets
2.4 The Vacuum Pressure Impregnation of the Magnets
3 Results and Discussions
3.1 Excitation Tests
4 Conclusions
References
Cluster Analysis Based Eigenvalue Extraction and Dynamic Time Regulation for Electricity Anomaly Detection
1 Introduction
2 Data Processing
2.1 Obtain User Electricity Consumption Data Please Note
2.2 Delete and Complete Data
3 Cluster Analysis
3.1 Data Dimensionality Reduction
3.2 Canopy Clustering Algorithm and Implementation
3.3 K-Medoids Clustering Algorithm and Its Implementation
3.4 DBI Clustering Number Effect Analysis
4 Extract User Power Consumption Characteristic Value Curve
4.1 Obtaining the Characteristic Value Curve of Electricity Consumption of the User to Be Tested
4.2 Obtaining user’s Typical Electricity Consumption Characteristic Value Curve
5 Electricity Abnormality Detection and Analysis
6 Conclusion
References
Research on the Novel Nonlinear Robust Control Strategy of Power Grid Low Frequency Oscillation Suppression Based on CSMES
1 Introduction
2 Nonlinear Robust Control Based on ESO
3 System Principle and Control
4 Simulation Results
5 Conclusions
References
Research on the Data Security Enhancement Method Based on Encryption Paradigm
1 Introduction
2 Related Research
2.1 Cryptography
2.2 Industry Security Rules
2.3 Cyber Attacks
2.4 Relevant Concepts of Network Security
3 Symmetric and Asymmetric Encryption
3.1 Asymmetric Encryption
3.2 Symmetric Key Encryption
4 Attribute Based Ring Signature Scheme
4.1 Algorithm Description
4.2 Encryption Algorithm
4.3 Decryption and Verification Algorithm
5 Conclusion
References
Research on Early Warning and Disposal Technology of Intelligent IoT Terminal Security Threat
1 Introduction
2 Related Works
3 Intelligent IoT Terminal Security Platform Construction
3.1 Overall Architecture Design
3.2 Core Technology Research
3.3 Technical Framework
3.4 Logic Deployment
4 Conclusion
References
Mechanism Analysis and Suppression Strategy of Ultra-low Frequency Oscillation in DC Asynchronous Networking
1 Introduction
2 State-Space Model with Hydro-Turbine Regulator Effect
2.1 System Description
2.2 Converter Model
2.3 Rectifier-Side Controller Model
2.4 Inverter-Side Controller Model
2.5 DC Transmission Line Model
2.6 AC Network Model
2.7 State-Space Model of the Whole System
3 The Influence of FLC on Ultra-low Frequency Oscillation Characteristics
3.1 Proportional Link
3.2 Integral Link
3.3 Differential Link
4 Coefficient Setting of FLC
4.1 Proportional Link Coefficient
4.2 Integral Link Coefficient
4.3 Differential Link Coefficient
5 Conclusion
References
Research on Transformer Fault Diagnosis Technology Based on Adaboost-Decision Tree and DGA
1 Introduction
2 Adaboost-Decision Tree
3 Results and Analysis
4 Conclusion
References
The Application of Pulsed Corona Discharge Plasma Technology in Air Pollution Control
1 Introduction
2 Experiment
2.1 Experimental Device
2.2 Experimental Method
3 Results
3.1 Electric Field Intensity
3.2 Secondary Voltage and Power Curve
3.3 Ozone Yield and Power at Different Voltages
3.4 Power and Ozone Concentration
4 Conclusion
References
Research on Visualization Technology of GIM Format File for Power System Engineering that Based on Model Lightweight Technology
1 Introduction
2 Lightweight Technology Characteristics and Status Quo
3 Advantages of Applying LOD Technology to GIM Analysis
4 Algorithm for LOD Parsing GIM File
5 Present Scheme Based on GIM Format Model
6 Application Effectiveness
6.1 Substation Equipment Model
6.2 Substation Engineering
6.3 Transmission Line Equipment Model
6.4 Transmission Line Engineering Model
7 In Conclusion
References
Design of Intelligent Contactors Development Platform
1 Introduction
2 Experimental Platform Principle
2.1 Hardware Architecture
2.2 Software Architecture
3 Embedded Control and Data Acquisition
3.1 Driving Circuit Design
3.2 Process Control Module
3.3 Closed-Loop Control Strategy
3.4 Data Acquisition
3.5 Data Processing
4 Experimental Verification
5 Conclusion
References
Multi-scale LBM-FDM Analysis on Natural Convection Heat Transfer and Metal Particle Movements Inside Gas Insulated Switchgear
1 Introduction
2 Numerical Modeling
2.1 Force Analysis of Metal Particle Inside GIS
2.2 Natural Convection Heat Transfer Process
2.3 LBM-FDM Numerical Procedure
3 Mathematical Model
3.1 Calculation Procedure
3.2 Calculation Procedure
4 Mathematical Model
5 Conclusion
References
Smooth Switching Control Method for Important Loads of Distribution Network Based on Fast Response of Energy Storage
1 Introduction
2 Microgrid Structure and Control
2.1 Microgrid Structure
2.2 Control of Energy Storage Inverter
2.3 Control Strategy of Grid-Connected to Islanding of Energy Storage Inverter
2.4 Control Strategy of Islanding to Grid-Connected of Energy Storage Inverter
3 Simulation Examples and Analysis
3.1 Grid-Connected to Island Simulation
3.2 Island to Grid-Connected Simulation
4 Conclusion
References
Study on Substation High Reliable Communication and Deterministic-Delay
1 Introduction
2 Principle of Storm Suppression
3 CRC Inverse Operation and Correction Code Method
4 Design of Reliability Network Topology
5 Deterministic Delay Performance Test
6 Conclusion
References
Optimal Control Method of Important Load Voltage Based on Model Prediction and Multi-device Collaboration
1 Introduction
2 Fast State Estimation of a Power Grid
3 MPC-Based Voltage Control
3.1 Model Prediction and Rolling Optimization
3.2 Feedback Control
3.3 Voltage Control Process Based on MPC
4 Mathematical Model of Controllable Equipment
4.1 Battery Energy Storage
4.2 Electric Vehicle
4.3 Super Capacitor
4.4 Wind Turbine
4.5 Photovoltaic Cell
4.6 SVC Model
5 Example Analysis
5.1 Basic Data
5.2 Simulation Results and Analysis
6 Conclusion
References
Research on Evaluation Index System of Science and Technology Innovation Ability of Electric Power Information and Communication Enterprises
1 Introduction
2 Literature Review
3 Overview of Research Methods
3.1 Analytic Hierarchy Process
3.2 Build a Hierarchical Structure
3.3 Build a Hierarchical Structure
3.4 Consistency Check
4 Evaluation of Science and Technology Innovation Ability of Electric Power Information and Communication Enterprises
4.1 Construction of Evaluation Index System for Electric Power Information and Communication Ability
4.2 Use the AHP Method to Determine the Weight of the Evaluation Index
5 Conclusion and Suggestions
5.1 Research Conclusions on the Evaluation Index System of Science and Technology Innovation Capability of Electric Power Information and Communication Enterprises
5.2 Countermeasures and Suggestions for Cultivating Technological Innovation Ability of Electric Power Information and Communication Enterprises
References
Design of High Torque Density Motor with Permanent Magnet/Reluctance Hybrid Double Rotor
1 Introduction
2 Motor Structure and Principle of Operation
3 Motor Electromagnetic Design
3.1 Motor Power Calculation
3.2 Design of Pole-Slot Combinations
3.3 Rotor Structure Design
3.4 Research on the Method of PMRHDRM to Obtain the Maximum Rated Output Torque
4 Simulation and Performance Analysis
4.1 Back Electromotive Force and Magnetic Field Analysis
4.2 Analysis of Rated Output Torque
5 Conclusions
References
Parametric Analysis and Performance Comparison of a Novel Brushless Double-Fed Generator with Series Cage Bar Assisted Magnetic Barrier Rotor
1 Introduction
2 Structure of the Proposed BDFG
3 Rotor Structure Paramter Analysis
3.1 Magnetic Barrier Structure
3.2 Polar Arc Coefficient
3.3 Number of Permeable Layers
3.4 Series Cage bar Parameters
4 Comparative Analysis of Different Rotors
5 Conclusion
References
Analysis and Mitigation of Middle Frequency Resonance for Grid-Connected Inverter Under Weak Grid
1 Introduction
2 Middle-Frequency Resonance Analysis of Inverter Integrated in Weak Grid
3 Participation Sensitivity Analysis and Mitigation of Middle-Frequency Resonance
4 Experimental Results
5 Conclusion
References
Conductor Selection of UHV Half-Wavelength AC Transmission Line
1 Introduction
2 Parameter Model of HWTL
3 Voltage Characteristics of UHV HWTL
4 Research on Conductor Selection
4.1 Principles for Selection of Conductor Cross-Sections and System Conditions
4.2 Economic Current Density and Total Conductor Cross-Section
4.3 Conductor Splits and Split Spacing
4.4 Primary Selection of Conductor Combination
5 Electromagnetic Environment of Conductors
5.1 Calculation of Em/E0
5.2 Radio Interference Calculation
5.3 Audible Noise Calculation
6 Comparison of Mechanical Properties
7 Economic Comparison
8 Conclusion
References
Dynamic Reactive Power Optimization of Power System Considering Load Demand Side Response
1 Introduction
2 Flexible Load Optimization Model Under Demand-Side Response
2.1 Demand-Side Response
2.2 Mathematical Model
3 Flexible Loads Participate in Reactive Power Optimization of Distribution Network Under Demand Side Response
3.1 Reactive Power Output Mathematical Model of Distributed Power Generation
3.2 Objective Function
3.3 The Constraints
4 Experimental Analysis
4.1 Parameter Settings
4.2 Results Analysis
5 Conclusion
References
A LLC Soft-Start Control Strategy Based on PSM and PFM
1 Introduction
2 LLC Resonant Converter Topology
3 The Soft-Start Control Strategy Based on PSM and PFM
3.1 PSM Soft-Start with Power Function
3.2 Soft-Start Control Strategy Based on PFM
3.3 Hybrid Soft-Start Control Strategy Based on PSM and PFM
4 Simulation and Experimental Verification
4.1 Related Parameters
4.2 Simulations
4.3 Experiments
5 Conclusion
References
Research and Design of 72.5 kV Environmental Protection Circuit Breaker
1 Introduction
2 Structural Characteristics of 72.5 kV Environmental Protection Circuit Breaker
3 Structural Design of 72.5 kV Vacuum Interrupter
3.1 Analysis and Calculation of Insulation Performance of Vacuum Interrupter
3.2 Magnetic Field Simulation Analysis of Vacuum Interrupter
4 Simulation Analysis of External Insulation Performance
5 Type Test Verification
5.1 Insulation Test
5.2 Mechanical Life Test
5.3 Short Circuit Making and Breaking Test
6 Conclusion
References
Influence of Via Stubs on Signal Integrity of Multi-layer PCB Boards
1 Introduction
2 The Via Hole Model of a PCB Board
2.1 Types of the Via Hole
2.2 Establishing the Via Hole Model
3 Simulation of Signal Integrity of Transmission Lines in the PCB Board
3.1 Simulation Steps
3.2 Simulation Results
3.3 Influence of Stub Length on Signal
4 Solutions of Stub Influence
5 Conclusions
References
Predictive Fuzzy Control Using Particle Swarm Optimization for Magnetic Levitation System
1 Introduction
2 Analysis and Modeling of MLS
3 Design of PSO-PFPID Controller
3.1 Generalized Predictive Control System Design
3.2 Fuzzy PID Control Design
3.3 Particle Swarm Optimization Algorithm
4 Simulation and Experiment Analysis
4.1 Simulation Analysis
4.2 Experiment
5 Conclusion
References
Effect of Defect Location on Decomposition Components Detection in SF6Gas Under Partial Discharge
1 Introduction
2 Experimental Platform and Method
2.1 Experimental Platform
2.2 SF6Gas In-Situ Detection Device
2.3 SF6Gas Detection and Analysis Methods
2.4 Experimental Method
3 Experimental Results and Discussion
4 Conclusion
References
Effect of DC Bias on the Operation Characteristics and Magnetic Circuit Difference of Single-Phase Transformer and Three-Phase Transformer
1 Introduction
2 Formation Mechanism of DC Bias in Transformers
3 Magnetic Circuit Analysis of Single-Phase Transformer and Three-Phase Column Transformer
4 Simulation Analysis of Transformer DC Bias Current
4.1 Analysis of Excitation Current and Harmonic Simulation Results of Single-Phase Transformer
4.2 Analysis of Excitation Current and Harmonic Simulation Results of Three-Phase Three-Column Transformer
5 Conclusion
References
Unify Control for Bidirectional Buck-Boost Converter Used in Supercapacitor Energy Storage System of Crane
1 Introduction
2 Topology Analysis
3 Control Strategy
4 Simulation Results
5 Conclusion
References
Parameter Modification Method and Influence Analysis of Double-Circuit Transmission Lines on the Same Tower
1 Introduction
2 State Estimation and Actual Measurement Analysis
3 Parameter Correction Method and Analysis
3.1 Calculation of Parameters of Double-Circuit Transmission Lines on the Same Tower
3.2 Comparative Analysis of Different Phase Sequence Arrangements of Double-Circuit Transmission Lines on the Same Tower
4 Simulation Analysis
4.1 Line Modeling on the Double-Circuit Transmission Lines on the Same Tower
4.2 Simulating Calculation of Double-Circuit Transmission Lines on the Same Tower
5 Analysis of the Influence of Resistance on Reactive Power
6 Conclusion
References
Analysis of Inverter Commutation Failure Caused by Background Harmonics
1 Introduction
2 Commutation Process and Commutation Failure Mechanism
2.1 Commutation Process
2.2 Commutation Failure Mechanism
3 Inducing Factors for Commutation Failure
3.1 Commutation Voltage Amplitude Reduction
3.2 Forward Movement of the Zero-Crossing Point of the Commutation Voltage
3.3 Voltage Waveform Distortion
4 Simulation Verification of Commutation Failure Caused by Harmonics
4.1 Normal Operation
4.2 Failure Analysis
4.3 AC System Background Harmonics
5 Conclusion
References
A Novel AC/DC Residual Current Sensor for Power Electronic-Enabled Devices
1 Introduction
2 The Operation Principle of the Proposed AC/DC Residual Current Sensor
2.1 The Topology of the Proposed AC/DC Residual Current Sensor
2.2 The DC and Low-Frequency Residual Current Measurement
2.3 The High-Frequency Residual Current Measurement and Compensation
3 Modeling and Simulation of the Proposed AC/DC Residual Current Sensor
3.1 Modeling of the Proposed Residual Current Sensor
3.2 Simulation Results and Analysis
4 Experimental Verification
5 Conclusion
References
Analysis of the Influence of Bore Spacing Variation on the Electromagnetic Launching Process
1 Introduction
2 Bore Spacing Measurement and Shape Analysis
3 Establishment of Launch Model Under Typical Bore Spacing
4 Influence of Bore Spacing on Armature
4.1 Force Analysis of Armature Under the Action of Bore Spacing
4.2 Analysis of Armature Vibration Under the Action of Bore Spacing
5 Analysis of the Influence of Bore Spacing on the Contact Between Armature and Rail
6 Conclusion
References
Author Index
Recommend Papers
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Lecture Notes in Electrical Engineering 889
Qingxin Yang Xidong Liang Yaohua Li Jinghan He Editors
The proceedings of the 16th Annual Conference of China Electrotechnical Society Volume I
Lecture Notes in Electrical Engineering Volume 889
Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Yong Li, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Luca Oneto, Dept. of Informatics, Bioengg., Robotics, University of Genova, Genova, Genova, Italy Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Walter Zamboni, DIEM - Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA
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Qingxin Yang Xidong Liang Yaohua Li Jinghan He •
•
•
Editors
The proceedings of the 16th Annual Conference of China Electrotechnical Society Volume I
123
Editors Qingxin Yang Tianjin University of Technology Tianjin, Tianjin, China Yaohua Li Institute of Electrical Engineering Beijing, Beijing, China
Xidong Liang Department of Electrical Engineering Tsinghua University Beijing, China Jinghan He Beijing Jiaotong University Beijing, Beijing, China
ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-19-1527-7 ISBN 978-981-19-1528-4 (eBook) https://doi.org/10.1007/978-981-19-1528-4 © Beijing Paike Culture Commu. Co., Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Contents
Optimal Design of Coupling Coils for Wireless Power Transfer System Featuring High Misalignment Tolerance . . . . . . . . . . . . . . . . . . Yukuo Zhang, Naming Zhang, Gaoyang Pan, Bin Yang, Shi Zhou, and Shuhong Wang Auto-Distrubance-Rejection-Controller Design for VSCF Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan-ning Zhang and Chen-dong Duan Functional Demonstration and Power Optimization of a Monitoring System for Arc Blowout Lightning Protection Devices . . . . . . . . . . . . . . Yaojing Luo, Jufeng Wang, Yanlei Wang, Renbao Yan, Yiyi Zhang, Kezhu Guo, Ping Huang, Yuheng Xu, Yang Lu, and Jiqiang Li Distribution Network Line Loss Allocation Method Taking into Account Distributed Power Sources and Economic Operation Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaojun Li, Siming Zeng, Liang Meng, Anyi Li, and Jifeng Liang
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Study on Macroscopic Performance and Characteristics of DC Arc in Condition of Short Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruiyang Guan, Xinlao Wei, Bo Zhu, and Zhichao Xue
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Improved Feedback Compensated Closed Loop Stator Flux Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuyu Wang, Zhenpeng Luo, Siqing Zhang, Yue Han, and Xiaolong Wang
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Influence of Long-term Cooling and Heating Cycles on the Interface Pressure of Cable Accessories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingtao Huang, Yu Jin, Tao Zhou, Jin Yang, Yuming Wu, Pengfei Meng, and Kai Zhou
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Experimental Study on SF6 Degradation by Dielectric Barrier Discharge Filled with Zirconia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chang Zhou, Yufei Wang, Guozhi Zhang, Jingsong Yao, and Xiaoxing Zhang Reactive Power Optimization of PSO-DBN Based on IoT Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Xia, Qian Zhang, and Guoli Li A Novel Structure for Switched Reluctance Motor to Be Driven by Full-Bridge Power Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haitao Sun, Yan Chen, Jiquan Liu, Zhiwei Yan, Xiangdong Yu, and Yinke Dou
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Simulation and Experimental Study on Muzzle Flow Field of Electromagnetic Energy Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Chen Miao, Ying Zhao, Wen Tian, Guochao Li, Weikang Zhao, Weiqun Yuan, and Ping Yan Study on PSS Parameter Setting of Point-to-Network Transmission Mode Considering Regional Oscillation . . . . . . . . . . . . . . . . . . . . . . . . . 114 Fanchao Meng Multi-objective Optimization of Electromagnetic Devices Based on Improved Jaya Algorithm and Kriging Model . . . . . . . . . . . . . . . . . 125 Shuangsheng Huang, Bing Yan, and Bin Xia Numerical Wind Tunnel Simulation of Breeze Vibration and Response Analysis Under Random Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Chujun Fu, Qi Liu, Jianli Zhao, Baofeng Yan, Bei Wang, and Jialin Qin Experimental Study on the Partial Discharge Inception Characteristics of Different Pressboards for Electrical Purpose . . . . . . . 143 Xin Liu, Congcong Chen, Hanbing Hao, Taiping Wang, Xueyou Zhang, Chunjia Gao, Bo Qi, and Chengrong Li Analysis of the Influence of Meteorological Factors on Electric Vehicle Charging Load and Mixed Regression Forecasting Model . . . . . . . . . . . 151 Longlong Shang, Ran Hu, Jingyu Lu, Wei Xiao, Jun Jia, and Ji Zhao Cross-platform Communication Simulation System Frame-Design and Modelling Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Meng Yu and Zilan Zhao Closed-Loop Control System of a Contactor Based on Single-Board RIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Wei Chen and Longfei Tang
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Analysis of a Misoperation of the Ratio Differential Protection of Main Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Tianying Chen, Xianzhi Wang, Yuhao Zhao, Tiecheng Li, Ze Li, and Yangjun Hou Analysis of a Local Overheating Fault of UHV Converter . . . . . . . . . . . 194 Xiu Zhou, Qing-ping Zhang, Xu-tao Wu, Wei-feng Liu, Yan Luo, Xiu-guang Li, Ning-hui He, Yang Wu, Ying Wei, and Tian Tian Single Branch Experimental Research on Phase Change Cooling in Power Module of Vehicle Traction Converters . . . . . . . . . . . . . . . . . 209 Wei Hao, Biao Chen, Huitao Li, Guangkun Lian, and Jiayi Yuan Experimental Study on Acoustic Emission and Ultrasonic Testing Technology with Fiber Bragg Gratings Sensing . . . . . . . . . . . . . . . . . . . 220 Lijun Meng, Han Zhang, Qianpeng Han, and Junjie Huo Research on Key Technology of 10 kV Mechanical DC Circuit Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Zhongjian Song, Wengang Xie, Zhicheng Zhang, Fengliang Xiao, Wei Li, Guangrong Luo, and Bin Xie Structure Design and Electric Field Simulation of a Compact −150 kV/10 mA High Voltage Power Supply . . . . . . . . . . . . . . . . . . . . . 237 Xiaolong Lu, Shangwen Chen, Zhiming Hu, Dapeng Xu, and Zeen Yao A DC Microgrid System Architecture and Control Strategy for Aerospace Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Yinghua Dou, Tao Liu, Baolei Dong, Wei Xie, and Aiwei Yang Research on Key Technology of Parallel Breaking for DC Circuit Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 Wengang Xie, Bo Liu, Guangrong Luo, Wei Li, Zhongjian Song, Zhicheng Zhang, and Gongyi Zhang Adaptive Field Weakening Control Algorithm of Surface-Mounted Permanent Magnet Synchronous Motors with Low Carrier Ratio . . . . . 272 Chao Wu, Xiangdong Sun, and Jianyuan Wang A High-Precision Method for High-Power Electric Heating Element Insulation Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 Tiejun Zeng, Jianhui Liu, Song Yu, and Jiaqi Yang Dynamic Hierarchical Collaborative Optimal Scheduling in Energy Internet Based on Cooperative Game . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Yongjie Zhong, Yuping Li, Wei Zhang, Bing Hu, Yinian Qi, and Dong Chen
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Improving Transmittable Active Power Capability of VFT with Optimal Stator Reactive Power Reference . . . . . . . . . . . . . . . . . . . . . . . 298 Jiahao Lu, Yun Zeng, Jielong Chen, Xiangxuan Kong, and Sizhe Chen Simulation Research on Train LKJ Split Simulation Operation Equipment System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Qingsheng Shi, Shuaishuai Liu, and Qingsong Shang Application of Pulse Width Modulation to Structure Optimization of Permanent Magnet Synchronous Linear Motor . . . . . . . . . . . . . . . . . 316 Song Huang, Shuhong Wang, Nana Duan, Weijie Xu, and Bowen Shang High-Efficiency Drive for IPMSM Based on Finite Model Predictive Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 Yunfei Zhang and Rong Qi Comparison of Two Drive Topologies for Ironless-Stator Permanent Magnet Motor Driven by Square Wave . . . . . . . . . . . . . . . . . . . . . . . . . 334 Haoyan Li, Haiping Xu, Xi Chen, Tao Guan, and Zengquan Yuan Research of Ultra-High Voltage DC Generator Based on Neural Network PID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Hongda Zhang, Lingjie Xu, Xiao Chen, Peng Guo, Xunan Ding, and Xinghui Jiang High Frequency Single Phase Induction Motor Driver Based on Half Bridge Circuit and Soft Switch Control . . . . . . . . . . . . . . . . . . 356 Yu Wang, Xupeng Fang, Ying Zang, and Dengjun Yan Research and Application of Partial Discharge Inspection Instrument for Converter Transformer Based on High Frequency Coupling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Xiu Zhou, Qingping Zhang, Yan Luo, Yunlong Ma, Xiuguang Li, Jin Bai, Lu Tian, and Yuhua Xu Research on Mobile Unit of Refrigerated Truck Based on STM32 . . . . 375 Ying Zhang, Shuchen Liu, and Ningning Ren Design Method for Power Unit of Power Conversion System Based on SiC MOSFET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Ning Xie, Yanjun Zhao, Wei Zhao, Jingpeng Yue, Wei Wang, and Chiye Zhang Impedance-Based Synchronization of Active Rectifier in Inductive Power Transfer Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Guodong Zhu and Dawei Gao
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Arc Simulations of a 252 kV Self-blast Circuit Breaker for Different Currents Considering Gas Control Valves . . . . . . . . . . . . . . . . . . . . . . . 405 Zhijun Wang, Jianying Zhong, Shengwu Tan, Yongqi Yao, Hao Zhang, and Yinghui Chai Novel Parameter Mismatch Impact Elimination Strategy for IM FCS-MPCC Using Sliding Mode Perturbation Observation and Compensation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 Yaofei Han, Chao Gong, Zhixun Ma, Jinqiu Gao, Guozhen Chen, and Zhiming Liao Direct Power Control of Dual-Active-Bridge Three-Phase Shift Modulation Featuring Multi-order Harmonic Current Minimization . . . 433 Ziwei Liu, Zhaolong Sun, Baolong Liu, and Zhixin Li Effect of Oxygen Concentration on the Current-Carrying Friction and Wear Performance of C/Cu Contact Pairs . . . . . . . . . . . . . . . . . . . 443 Ziran Ni, Zhijiang He, Hong Wang, Guoqiang Gao, Zefeng Yang, and Wenfu Wei Direct Torque Control of Squirrel Cage Motor Based on Sector Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Xiaolong Wang, Zhenpeng Luo, Siqing Zhang, and Shuyu Wang Characteristic Analysis of Fast Vacuum Switch Based on Electromagnetic Repulsion and Permanent Magnet Holding Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 462 Anying Cao, Yao Liu, Jianfu Chen, Xu Cheng, Wei Li, Zhongjian Song, Guanxin Qiu, and Huaihao Cheng Discussion on Mechanism of the Gas Medium on Self-breakdown Probability of High-Power Gas Switch . . . . . . . . . . . . . . . . . . . . . . . . . . 474 Xianfei Liu and Xuandong Liu The Study of High-Order Force on Electromagnetic Vibration of PMSMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Jianfeng Hong, Shanming Wang, Yuguang Sun, and Haixiang Cao Research and Application of Fibre Channel Status Online Sensing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Tao Zhang, Jin Li, Mingjun Xue, Hao Zhang, Yuping Li, and Minglei Bao Research of Two-Vector Model Predictive Flux Control Based on Flux Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 Jie Luo, Zhiyong Lan, Xunhua Luo, and Shufan Ye Speed and Position Estimation Algorithm of Permanent Magnet Synchronous Motor Based on Extended Kalman Filter . . . . . . . . . . . . . 515 Yanhao Li, Zhiyong Lan, Xiaoyang Su, and Shanqi Dai
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High-Accuracy Torque Estimation and Multi-closed-Loop Control of PMSM for Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Qingyu Song, Feifan Ji, Yanjun Li, Longfei Zhao, and Xiaolong Li The Introduction of Dissociation Term in Numerical Simulation of Trichel Pulses in Air . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 Mengting Han, Ziqing Guo, Qizheng Ye, and XiaoFei Nie A High Sensitivity Sensor for Reconstruction of Conductivity Distribution in Region of Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547 Zhiwei Tian, Yanyan Shi, Feng Fu, Yuehui Wu, Zhen Gao, and Yajun Lou Fault Location of T-type Line with Double-Circuit Line on the Same Tower with Asymmetrical Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Zhongan Yu, Junjun Wu, and Da Deng Fault Location of Distribution Network Based on Stacked Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 XinTong Li, LiWen Qin, YongZan Li, Xin Yang, and XiaoYong Yu Prediction of Failure Probability of Overhead Lines in Distribution Network Based on Historical Statistics and Meteorological Monitoring Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 LiWen Qin, XiaoYong Yu, WenLin Liu, HaiTao Gui, and LiFang Wu Research on Image Segmentation of Power Line Based on Encoder-Decoder Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Haotian Sun, Zhijian Fang, Zhiguo Wei, and Fei Xie Design of Low-Cost Micro-PMU Based on e-IpDFT . . . . . . . . . . . . . . . 596 Shiying Zheng, Shuai Liang, Zelong Yu, Zaiji Yuan, Zhigang Cao, Renjie Ding, and Shuguang Li Electro-thermal Failure of Insulation in AC Submarine Cable Subjected by the Transient Overvoltage . . . . . . . . . . . . . . . . . . . . . . . . . 604 Weiwang Wang, Xilin Yan, Yong Feng, and Hantao Wang A Control Strategy for Modified the Stability of Weak Grid Integrated Wind Power on a Large Scale Through MMC-HVDC Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 Wenbo Qi, Changjiang Wang, Xianchao Liu, Wei Fan, and Fangwei Duan A Novel Control Strategy for Series Converter of VFT Under Dual-Side Harmonically Distorted Grid Voltages . . . . . . . . . . . . . . . . . . 621 Jiahao Lu, Jielong Chen, Yun Zeng, Xiangxuan Kong, and Sizhe Chen Temperature Calculation Method of Dry-Type Transformer Based on Fractional Order Thermal Circuit Model . . . . . . . . . . . . . . . . . . . . . 630 Guangyu Zhang, Hui Zhong, and Xue Li
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Measurement of the AC Resistance of Power Cable with Large Cross-section Conductor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638 Weining Wu and Xiaolei Qu Design of a Fast-Tracking Differential Observer . . . . . . . . . . . . . . . . . . 647 Jianli Zhao, Ningping Yuan, Zhuo Liu, Zheng Kou, Yilin Wang, and Liang Wang Identification of Transient Power Quality Disturbances Based on S-transform Feature Extraction and Random Forest Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 Qinqin Wang and Wang Guo Dynamic Potential Analysis of Dyneema Ropes Above Transmission Line Based on Three-Dimensional Simulated Charge Method . . . . . . . . 666 Xuehuan Wang, Nana Duan, and Shuhong Wang Research on Control of Grid-Forming Converters Based on Virtual Oscillator Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Lei Huang Motion Artifact Removal Based on Stationary Wavelet Transform and Adaptive Filtering for Wearable ECG Monitoring . . . . . . . . . . . . . 683 Zhengyi Xu, Yifeng Wang, Xingchen Tian, Xinlei Zheng, and Jiangtao Li Exploration of Macroscopic Characterization for Low-Voltage AC Arc State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Zhi-ang Huang, Xin Zheng, and Xiaojie Shan Cluster Division in Wind Farm Based on DTW and KL-GMM . . . . . . . 701 Fengrui Liu, Xiaojing Li, Xiaorui Hu, Shuxuan Li, Yunlian Liu, and Qiang Shi An Integrated Boost Micro-inverter for PV Generation System . . . . . . . 708 Xuefeng Hu, Zikang Long, Chenjin Fei, Zhenhai Yu, and Kunshu Mu Parameter Identification and Co-simulation Verification of Dynamic Inductance in Electromagnetic Switch . . . . . . . . . . . . . . . . . . . . . . . . . . 716 Liang Meng, Jierong Zhuang, and Zhihong Xu Review on Applications of Artificial Intelligence in Relay Protection . . . 724 Ming Dai, Guomin Luo, Zhenlin Wang, and Qihui Chen Voltage Adaptive Dynamic Partition Method Considering Reactive Power Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734 Rui Zhang, Kecheng Liu, Xiaoming Li, Jianhu Guo, Zhe Wang, and Jifeng Liang Optimal Allocation of Capacity for Vehicle Charging Stations with Wind-PV Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742 Zhongan Yu, Da Deng, and Junjun Wu
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Effect of Thermo-oxidative Ageing on Physicochemical and Electrical Properties of Liquid Silicone Rubber . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 Mengqi Wang, Jiachen Yu, Hao Liu, Jiacai Li, Wei Shen, and Shengtao Li Design of Control Loop of Three-Phase Z-source Inverter . . . . . . . . . . . 764 Sisi Bai, Yingna Guo, Zhao Ma, and Huan Cheng Effect of Surface Treatment on Surface Flashover Performance and Multi-factor Aging Characteristics of Epoxy Resin . . . . . . . . . . . . . 772 Bingnan Li, Huan Niu, Mingru Li, Zhen Li, Yafang Gao, Shengtao Li, and Hangyin Mao Analysis of the Influence of Active Filter on the Unstable Resonance for Traction Power Supply System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 Chen Niu and Guo Wang Research on Digital Vehicle Inverter Power Supply . . . . . . . . . . . . . . . . 791 Zhaohua Yu, Gaili Yue, and Luyao Li Effects of Rail Shape on Recoil of Electromagnetic Energy Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799 Guochao Li, Weiqun Yuan, Wen Tian, Chen Miao, Weikang Zhao, Ying Zhao, and Ping Yan Low-Intensity Pulsed Ultrasound for Killing Tumor Cells: The Physical and Biological Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . 812 Jianhao Lin, Shoulong Dong, Wencheng Peng, Hongmmei Liu, Penghao Zhang, Haoxiang Lv, Liang Yu, and Chenguo Yao Credible Capacity Evaluation Method of Distributed Generation in Distribution Network Based on Power Supply Reliability . . . . . . . . . 821 Bing Sun, Jiahao Chen, Xin Li, Zhicheng Liu, and Yunfei Li Violation Detection of Transmission Line Construction Based on YOLO Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830 Lingjia Zhang, Lizhou Luo, Libin Chen, Jian Zeng, Xiaoyu Xin, Zhongshu Liu, and Nana Duan Optimization Design for 500 kV Four-Circuit Transmission Lines on Same Tower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838 Gang Song, Haoyu Zhang, Chuang Liu, Zhiwei Ye, Yiyu Guo, Peng Liu, and Zongren Peng A New 3-D Multi-segment Modelling Method for Stator Transposition Bar and Its Application in Calculating the Circulating Current Loss of Large Hydro-generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 847 Chenguang Wang, Yanping Liang, Lei Ni, Xu Bian, and Dongmei Wang
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Study of Decentalized Trade Based on Blockchain and Rolling Aggregation Mechanism in V2G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 862 Sixiang Zhao, Yachao Wang, Hanji Ju, Tianshu Hu, and Fengming Chu Hysteresis Loop Measurement for Steel Sheet Under PWM Excitation Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 872 Xinyang Gao, Nana Duan, and Shuhong Wang Influence of Trace SF6 on Surface Corona Characteristics in SF6/N2 Mixtures Under DC Voltages . . . . . . . . . . . . . . . . . . . . . . . . . 880 Yanliang He, Wei Ding, Anbang Sun, and Guanjun Zhang The Influence of Different Fillers on the Properties of Carbon-Matrix Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891 Qichen Chen, Guangning Wu, Zefeng Yang, Jiahui Lin, Wenfu Wei, Guoqiang Gao, Hao Li, Guofeng Yin, and Chunmao Li Study on the Mechanism of Electrodynamic Behavior of Metal Particles in DC GIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 900 Han Cheng, Wei Wei, Li Zhang, Tong Zhao, and Liang Zou Analysis of the Surrounding Magnetic Field Distribution Under the Parallel Condition of AC and DC Cable Lines . . . . . . . . . . . . . . . . . 907 Jianjun Yang, Zhijie Zhu, Jingyi Li, Nana Duan, Ke Wang, Shuhong Wang, and Xuehuan Wang Parameter Design and Simulation of Unsymmetrical 18-Pulse Phase-Shifting Autotransformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915 Jiahui Zhang, Dongsheng Yuan, and Shuhong Wang Research on the Influence of AC Cable Lines on the Electric Field Intensity of Parallel DC Cable Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . 923 Zhijie Zhu, Jianjun Yang, Nana Duan, Jingyi Li, Shuhong Wang, Hongke Li, and Xuehuan Wang Cause Analysis and Preventive Measures for Breaking of Strain Clamp in 500 kV Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . 931 Jiankun Zhao, Jianli Zhao, Liang Xu, Jialin Qin, and Kaiyue An Comprehensive Evaluation of Diversified and High Elastic Power Grid Based on Entropy-AHP-TOPSIS Method . . . . . . . . . . . . . . . . . . . 941 Hanyun Wang, Tao Wang, Xinyi Wang, Bing Li, and Congmin Ye A Dual-Loop Control Strategy for Interlinking Converters in Hybrid AC/DC Microgrids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 Yuwei Zhang, Qian Xiao, Zhipeng Jiao, Wenbiao Lu, Jin Xu, Yunfei Mu, and Hongjie Jia
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Force Simulation of Four Bundle Spacer Under Short Circuit Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965 Wang Lei, Zhao Jianli, Wang Liang, and Zhao Zijian Numerical Simulation of Diffusion Characteristics of SF6 Decomposition Products in Gas-Insulated Switchgear . . . . . . . . . . . . . . 974 Yifan He, Chen Li, Yanliang He, Xiaoxin Chen, Wei Ding, Anbang Sun, and Guanjun Zhang Electromagnetic Transient Calculation and Experiment of Intelligent Transformer Under DC Bias Magnetic Field . . . . . . . . . . . . . . . . . . . . . 984 Dongliang Lan, Minghua Zhu, Tengteng Hou, Zhiwei Chen, Fenglinzi Dan, and Beibei Liang Torque Density Optimization of an Axial Flux Permanent Magnet Synchronous Machine Using Genetic Algorithm Combined with Simplex Operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994 Jiayue Zhou, Jianyun Chai, Xi Xiao, Haifeng Lu, and Chaosheng Huang Application of Knowledge Mapping and Fault Diagnosis in Power Communication Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 Yi Zhang, Can Qi, Ye Zhao, Kunrui Tong, Zilan Zhao, and Lin Zhang A Mix Peer-To-Peer Energy Trading Hierarchy Modal for Prosumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1020 Songnong Li, Lefeng Shi, and Song Wang Research and Application System of Power Communication Network Data Mining and Intelligence Based on Regulatory Cloud . . . 1029 Zhao Zilan, Yu Ran, Yu Meng, Zhang Jiaojiao, Zhang Yi, Wan Ying, and Xu Hongfei Research on Simulation Training System of Substation Based on HTC Vive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038 Yuhan Wang and Xiaohui Liao Development of Vehicle Charger with High Power Factor Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 Fucun Li, Zhou Wang, Yan Zhang, Danwen Yu, Lijun Liu, and Guanglei Li A Novel Adaptive Low Voltage Locking Protection Strategy for Distributed Generation Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054 Zhiren Liu, Weilin Tong, Jinghua Xie, Cheng Li, Yajuan Lv, and Tianyu Fang The Influence of Load Model on the Accuracy of Power Grid Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1063 Xiang-yu Liu, Hui-bin Li, Xiao-ming Li, Shuai Li, Ning Gong, and Shi-bo Yang
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Research on Recognition Strategy of Oil-paper Insulation Aging State Based on Deep Residual Network . . . . . . . . . . . . . . . . . . . . . . . . . 1076 Tao Li, Zihao Wang, Yongdao Wang, Linfeng Mao, Zhensheng Wu, and Deling Fan Leading Phase Operation Strategy of Multi-generators Based on Equivalent Impedance Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 Xiang-yu Liu, Shuai Li, Xiao-ming Li, Hui-bin Li, Shi-bo Yang, and Ning Gong Improved Degaussing Power Supply Applied to Ship Degaussing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1100 Chao Huang, Shengdao Liu, Zhixin Li, and Ziwei Liu Optimal Control Strategy of Fatigue-Oriented Wind Turbine Yaw System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 Zhiheng Liu, Qi Yao, and Bo Ma The Fabrication Technology and Test Results of the NbTi Superconducting Racetrack Magnets . . . . . . . . . . . . . . . . . . . . . . . . . . . 1123 Wanshuo Sun, Lei Wang, Jinshui Sun, Junsheng Cheng, Shunzhong Chen, and Qiuliang Wang Cluster Analysis Based Eigenvalue Extraction and Dynamic Time Regulation for Electricity Anomaly Detection . . . . . . . . . . . . . . . . . . . . 1130 Jingjing Jiang, Xinming Liu, Wenzhuang Chen, and Aikun Mao Research on the Novel Nonlinear Robust Control Strategy of Power Grid Low Frequency Oscillation Suppression Based on CSMES . . . . . . 1139 Kaiji Li, Zhongjian Kang, and Zheng Chang Research on the Data Security Enhancement Method Based on Encryption Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1152 Xiangsen Xu, Shuo Li, and Jing Zeng Research on Early Warning and Disposal Technology of Intelligent IoT Terminal Security Threat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159 Shijun Zhang, Shuo Li, and Jing Zeng Mechanism Analysis and Suppression Strategy of Ultra-low Frequency Oscillation in DC Asynchronous Networking . . . . . . . . . . . . 1168 Qingsong Liu, Lingfang Yang, Qingming Xin, Ziying Wang, Jun Deng, Shunliang Wang, and Chuang Fu Research on Transformer Fault Diagnosis Technology Based on Adaboost-Decision Tree and DGA . . . . . . . . . . . . . . . . . . . . . . . . . . . 1179 Xiu Zhou, Tian Tian, Pengcheng Zhang, Yi Wang, Yan Luo, Yunlong Ma, Xiuguang Li, Ninghui He, and Jun Sun
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The Application of Pulsed Corona Discharge Plasma Technology in Air Pollution Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1190 Lingang Weng, Xiaodong Shi, Qing Ye, Keji Qi, Shuai Zhang, Licheng Zheng, and Yujie Liu Research on Visualization Technology of GIM Format File for Power System Engineering that Based on Model Lightweight Technology . . . . 1199 Jialin Qin, Liang Xu, Bo Wang, and Guowen Li Design of Intelligent Contactors Development Platform . . . . . . . . . . . . . 1207 Hao Chen and Longfei Tang Multi-scale LBM-FDM Analysis on Natural Convection Heat Transfer and Metal Particle Movements Inside Gas Insulated Switchgear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217 Hongtao Li, Jinggang Yang, Shan Gao, and Ke Zhao Smooth Switching Control Method for Important Loads of Distribution Network Based on Fast Response of Energy Storage . . . 1226 Yuanliang Zhao, Weijie Qiu, Hujun Shi, Xiaodong Xu, Xiaolei Zhao, Liangzhong Yao, and Siyang Liao Study on Substation High Reliable Communication and Deterministic-Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1237 Qian Zhao, Xianjun Zhang, Zhongwei Meng, Pingzhong Yan, Zhibao Liang, and Rui Yang Optimal Control Method of Important Load Voltage Based on Model Prediction and Multi-device Collaboration . . . . . . . . . . . . . . . 1248 Yuanliang Zhao, Weijie Qiu, Hujun Shi, Xiaodong Xu, Pengyu Wang, Liangzhong Yao, and Siyang Liao Research on Evaluation Index System of Science and Technology Innovation Ability of Electric Power Information and Communication Enterprises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1260 Qionglan Na, Dan Su, Yixi Yang, Huimin He, Jing Lou, and Jing Zeng Design of High Torque Density Motor with Permanent Magnet/Reluctance Hybrid Double Rotor . . . . . . . . . . . . . . . . . . . . . . . . 1274 Xiang Li, Zhaoyu Zhang, Yujiang Sun, Siyang Yu, and Fengge Zhang Parametric Analysis and Performance Comparison of a Novel Brushless Double-Fed Generator with Series Cage Bar Assisted Magnetic Barrier Rotor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1283 Zhenyu Diao, Siyang Yu, and Fengge Zhang Analysis and Mitigation of Middle Frequency Resonance for Grid-Connected Inverter Under Weak Grid . . . . . . . . . . . . . . . . . . . 1292 Gaoxiang Li and Hongzhi Pan
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Conductor Selection of UHV Half-Wavelength AC Transmission Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305 Jialin Qin, Haiyan Mei, Jingwei Su, and Daping Liu Dynamic Reactive Power Optimization of Power System Considering Load Demand Side Response . . . . . . . . . . . . . . . . . . . . . . . 1316 Jie Chen, Changchun Cai, Shuqin Wang, Zengmao Cheng, and Shenshen Zhuo A LLC Soft-Start Control Strategy Based on PSM and PFM . . . . . . . . 1324 Yuxing Li, Jingkai Niu, Xiaomin Xin, Peng Liu, Yunfeng Guo, and Yu Lu Research and Design of 72.5 kV Environmental Protection Circuit Breaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1332 Canjiang Yao, Longyong Sun, Gang Xu, Yingying Liu, Panke Yuan, Guang Yang, Senran Li, and Wuyang Chen Influence of Via Stubs on Signal Integrity of Multi-layer PCB Boards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1340 Xueyan Lin, Yongsheng Zhou, and Xin An Predictive Fuzzy Control Using Particle Swarm Optimization for Magnetic Levitation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353 Fanqi Bu and Jie Xu Effect of Defect Location on Decomposition Components Detection in SF6 Gas Under Partial Discharge . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1370 Yifan He, Xianjun Shao, Xiaoxin Chen, Yanliang He, Wei Ding, Yuancheng Liu, Chen Li, Anbang Sun, and Guanjun Zhang Effect of DC Bias on the Operation Characteristics and Magnetic Circuit Difference of Single-Phase Transformer and Three-Phase Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1381 Beibei Liang, Zhiwei Chen, Landong Liang, Fei Xia, Qipei Zhou, and Yingying Zhang Unify Control for Bidirectional Buck-Boost Converter Used in Supercapacitor Energy Storage System of Crane . . . . . . . . . . . . . . . . 1390 Qinghua Lin Parameter Modification Method and Influence Analysis of Double-Circuit Transmission Lines on the Same Tower . . . . . . . . . . 1398 Jingyuan Dong, Xiaoming Li, Xiangyu Liu, Tengkai Yu, Tianying Chen, and Rui Zhang Analysis of Inverter Commutation Failure Caused by Background Harmonics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1406 Yue Wang, Jun Wen, Tian Chen, Zhiyong Yu, and Zhengang Lu
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A Novel AC/DC Residual Current Sensor for Power Electronic-Enabled Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1416 Yao Wang, Tongtong Ma, Chenguang Hao, Zhizhou Bao, and Yi Wu Analysis of the Influence of Bore Spacing Variation on the Electromagnetic Launching Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1430 Pengchao Pei, Bin Cao, Xia Ge, and Mingtao Li Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1441
Optimal Design of Coupling Coils for Wireless Power Transfer System Featuring High Misalignment Tolerance Yukuo Zhang1 , Naming Zhang1 , Gaoyang Pan1 , Bin Yang2 , Shi Zhou1 , and Shuhong Wang1(B) 1 State Key Laboratory of Electrical Insulation and Power Equipment, Faculty of Electrical
Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China [email protected] 2 State Grid Shaanxi Electric Power Research Institute, Xi’an 710049, Shaanxi, China
Abstract. The misalignment between the transmitting coil and the receiving coil will lead to the deterioration of the transmission characteristics of the wireless power transfer system. In order to reduce the adverse effect of coupling coils misalignment on the wireless power transfer system, this paper presents a design and optimization method of the combined transmitting coil with high misalignment tolerance. Firstly, the important influence of mutual-inductance on wireless power transfer system is analyzed. Next, we analyzed the uniformity of mutualinductance and magnetic flux density. Thirdly, the structure of the combined coil is proposed, the calculation equation of the magnetic flux density generated by the coil is established, and the optimization objective function model is constructed. Furthermore, the particle swarm optimization (PSO) algorithm is used to calculate the optimization model. Finally, simulation and experiments show that the proposed combined coil has higher misalignment tolerance than the traditional uniform spiral coil, which verifies the correctness and accuracy of the design. Keywords: Combined coil · High misalignment tolerance · Wireless power transfer (WPT) · Uniform magnetic flux density · Particle swarm optimization (PSO)
1 Introduction The wireless power transfer (WPT) technology makes up for the defects of traditional power supply mode, and has a wide application prospect in UAVs, electric vehicles, biomedical and other aspects [1–3]. In WPT system, when the coupling coils are misaligned, the mutual-inductance between the coupling coils will produce varying degrees of fluctuation, resulting in the deterioration of the transmission characteristics of the system. It is a vital method to design the coupling coil featuring high misalignment tolerance to improve the system’s ability to resist misalignment. In this paper, the two-coil © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1–10, 2022. https://doi.org/10.1007/978-981-19-1528-4_1
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structure of coupling coil with unequal size is adopted, which application background is UAV, small robot and other medium-power, small size electronic equipment. Some researchers have focused on the research of high misalignment tolerance WPT system, and have made some important progress. C. Rong et al. [4] proposed the optimization design method of resonance coils with high misalignment tolerance for drone wireless charging based on Genetic algorithm, which can reach 56.23% in contrast with traditional coils. In [5], a multi extended transmitting coil in WPT system with effectiveconfiguration for UAV large area charging was designed. However, the complex structure reduces the practicability. Chunwei Cai et al. [6] developed a wireless charging system based on a novel orthogonal magnetic structure, which can deliver 500 W with a dc-to-battery efficiency of 90.8%. In [7], two methods, the valley-filling method (VFM) and peak-clipping method (PCM), are introduced to modulate the magnetic flux based on a uniform magnetic field. However, the above research generally has the defects of complex structure and large amount of calculation. This paper tries to establish a simple and scientific design method of the coil featuring high misalignment tolerance. The analytic equation of the magnetic flux density generated by the transmitting coil in space was established, and the objective function of uniform magnetic field was established. The number of turns and the size of the coil were optimized by using particle swarm optimization (PSO) algorithm [8]. The theoretical calculation, simulation and calculation show that the mutual inductance between the coupling coils is stable within a large misalignment distance.
2 Transmission Characteristics of Two-Coil Wireless Power Transmission System The traditional two-coil mutual-inductance circuit model (Series-Series type) is shown in Fig. 1. The circuit model is a typical simplified circuit of the two-coil WPT system. U in is the AC supply voltage; C 1 , C 2 are the resonant capacitors; L 1 , L 2 are the selfinductance of transmitting coil and receiving coil; R1 , R2 are the AC resistance for the transmitting coil and receiving coil; M is the mutual-inductance between transmitting coil and receiving coil.
Fig. 1. The two-coil mutual-inductance circuit model.
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When the resonance condition (1) is satisfied, the power transfer efficiency (PTE) and output power can be calculated by the following Eq. (2), (3): 1 1 =√ ω= √ L1 C1 L2 C2
(1)
(ωM )2 RL (r2 + RL ) r1 (r2 + RL ) + (ωM )2
(2)
(ωM )2 RL Uin2 Output Power = 2 r1 (r2 + RL ) + (ωM )2
(3)
PTE =
It can be seen from Eq. (2) and (3) that when the mutual-inductance (M) between coils changes, it will cause the fluctuation of PTE and output power, which has a very adverse impact on the working process of wireless charging system. Therefore, maintaining the stability of M is a necessary prerequisite to ensure the efficient and sustainable transmission of the WPT system. In fact, the M between two coils will change with the relative change of space position, and the coil misalignment has a great influence on the mutual inductance. To improve the misalignment tolerance of the coil, the mutual-inductance is required to be stable from the perspective of the circuit.
3 Coil Optimization Design 3.1 The Uniformity of Mutual-Inductance and Magnetic Flux Density Maintaining the stability of mutual-inductance between coils is a necessary condition to improve the misalignment tolerance of coils. However, it is difficult to optimize the mutual inductance directly. The schematic diagram of electromagnetic induction and coupling effect of the twocoil model is shown in the Fig. 2. The current flowing into the transmitting coil is i1 , and the open-circuit voltage of receiving coil is u2 . According to Faraday’s law of electromagnetic induction, u2 can be calculated by (4). In addition, according to mutual-inductance circuit model, the open-circuit voltage (u2 ) can also described by (5). N d dψ =− u2 = − Bi · Si dt dt Si
(4)
i=1
u2 = M
di1 dt
(5)
Where N is the number of turns of the receiving coil; Bi (i = 1, 2…N) is the magnetic induction at the position of each turn of the receiving coil; Si (i = 1, 2…N) is the area of receiving coil per turn; M is the mutual-inductance between transmitting coil and receiving coil.
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Fig. 2. Schematic diagram of electromagnetic induction and coupling effect of the two-coil model.
We can obtain (6) by (4) and (5): N d di1 =− M Bi · Si dt dt Si
(6)
i=1
Obviously, for the determined transmitting coil and receiving coil, the stability of B also means the stability of M and vice versa. Therefore, the study of coil mutual-inductance (M) can be transformed into the study of the magnetic flux density (B) produced by the transmitting coil at the position of the receiving coil. That is to say, the key to high misalignment tolerance of the coupling coils is that the transmitting coil generates uniform magnetic flux density (B) at the height of the receiving coil. 3.2 Structure of Transmitting Coil Due to the skin effect and proximity effect of the coil, it is not advisable to wind the coil tightly at high frequency, which will lead to large AC loss and reduce the transmission efficiency of the system. In the meanwhile, it is still very difficult for the coil to produce uniform magnetic flux density in space by uniform and sparse winding. In this paper, a combined planar spiral transmitting coil is proposed. The coil consists of a large multi-turn uniform spiral coil and several single turn spiral coils with different starting radii, as shown in Fig. 3. The outer part of the combined coil is a uniform spiral coil with N0 turns and s turns spacing, and the inner part is a number of single turns with N1 turns of different sizes. 3.3 Calculation Method of the Magnetic Flux Density of the Combined Coil When the transmitting coil is placed in the xy plane, the BZ (z-axis component of the magnetic flux density) of the combined coil, actually, determines the coupling effect between the coupling coils. According to the Biot-Savart law (7) and the superposition theorem, the calculation Eq. (8) of BZ produced by the combined transmitting coil can
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Fig. 3. The structure diagram of the combined planar spiral coil.
be deduced [9]. B= N1
μ0 I 4π
dl × er r2
(7)
L
Bz (x, y, z)_coil_i i=1 2π N ( s cos θ − ( s θ + R ) sin θ) · (y − ( s θ + R ) sin θ) − ( s sin θ + ( s θ + R ) cos θ) · (x − ( s θ + R ) cos θ) μ I 0 2π 0 0 0 0 2π 2π 2π 2π 2π = 0 · dθ 3 4π 0 s θ + R ) cos θ)2 + (y − ( s θ + R ) sin θ)2 + z 2 2 (x − ( 2π 0 0 2π
Bz (x, y, z) = Bz (x, y, z)_coil_0 +
+
N1 2π s cos θ − ( s θ + R ) sin θ) · (y − ( s θ + R ) sin θ) − ( s sin θ + ( s θ + R ) cos θ) · (x − ( s θ + R ) cos θ) ( 2π μ0 I i i i i 2π 2π 2π 2π 2π · dθ 3 4π s θ + R ) cos θ)2 + (y − ( s θ + R ) sin θ)2 + z 2 2 i=1 0 (x − ( 2π i i 2π
(8) Where BZ (x, y, z)_coil_0 is the z-axis component of the magnetic flux density produced by the outer part of the combined coil; BZ (x, y, z)_coil_i (i = 1, 2, …N1 ) is the z-axis component of the magnetic flux density produced by the inner part; µ0 is the permeability of vacuum; I is the current flowing through the transmitting coil, A. 3.4 Objective Function of Uniform Magnetic Flux Density Assuming that the plane height of each turn of the receiving coil is different, the comprehensive deviation rate (d) of the magnetic field of the transmitting coil at the position of the receiving coil is defined by (9), and then the optimal Ri (i = 1, 2, … N 1 ) is obtained by PSO algorithm.
N
2 (B (0, l, h ) − B (0, 0, h ))
z j z j
j=1
(9) d =
N 2
B (0, 0, h )
z j
j=1 Where, N 2 is the number of turns of the receiving coil; hj (j = 1, 2, … N 2 ) is the plane height of each turn of the receiving coil.
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The above functions satisfy the constraints (10): Ri − Ri+1 ≥ s 0 ≤ Ri ≤ R0
(10)
3.5 The Methods and Results of Optimization Particle swarm optimization (PSO) is an evolutionary computing technology. It comes from the study of birds’ predation behavior. The basic idea of particle swarm optimization algorithm is to find the optimal solution through the cooperation and information sharing among individuals. The advantage of PSO is that it is simple and easy to implement, and there are not many parameters to adjust. It has been widely used in function optimization, neural network training, fuzzy system control and other genetic algorithms. Figure 4 shows the coupling coil optimization process. To start with, considering the size and volume of the receiving device in WPT system, the size and structure of the receiving coil are determined. For example, the receiving coil is determined to be space spiral coil, with 8 turns, 5 mm turn spacing, 70 mm minimum transmission height, and whose radius is 70 mm. Then, according to the actual demand, determine the size and number of turns of the outer section of the transmitting coil. For instance, R0 = 260 mm, N0 = 8, s = 5 mm. Furthermore, refer to the relevant data and requirements, the objective function and constraint equation are established. Finally, the PSO algorithm is used to Start Considering practical requirement, determine the turns (N2), turn spacing (s), radius (R) of receiving coil Determine the R0, N0 Establish the objective function (d) and constraint equation Use the PSO algorithm to optimize the NO radius (Ri) of multiple single turn spiral wires
Meet the design requirements YES
Save the optimization results Ri END
Fig. 4. Coupling coil optimization process based on PSO algorithm.
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Fig. 5. The relationship between the total BZ and the misaligned distance.
solve the above functions and equations, and the optimal RI is obtained when the required conditions are satisfied. Through the optimization of PSO algorithm, it is found that when N1 = 3, that is, R1 = 245 mm, R2 = 214.8 mm, R3 = 50.03 mm, the comprehensive magnetic field deviation rate (d) of the receiving coil plane is the smallest. And the relationship between the total BZ generated by the optimized transmitting coil at the receiving coil position and misaligned distance is shown in Fig. 5. It can be seen from Fig. 5 that the magnetic flux density of the transmitting coil at the receiving coil is stable within the range of misalignment distance (−200 mm, 200 mm).
4 Simulation and Experimental Verification Based on the optimization results, the 3D simulation model of coupling coils is established in the COMSOL Multiphysics, as shown in Fig. 6. The simulation results show that the self-inductance of the combined transmitting coil is 103.2 µH.
Perfectly Matched Layers
Air
Receiving coil
Transmitting coil
Fig. 6. The 3D simulation model of the coupling coils in COMSOL.
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(a)
(b)
Fig. 7. Two different transmitting coil models. (a) The model of the combined transmitting coil. (b) The model of the transmitting coil with a traditional uniform spiral structure.
In order to illustrate that the proposed combined transmitter coil has a high tolerance for misalignment, a traditional uniform spiral coil model is established for comparison, as shown in Fig. 7. The self-inductance of the two coils is consistent, and the minimum radius and maximum radius are consistent. Through the simulation analysis of the optimized coupling coil, and the magnetic field distribution of the coupling coils under different misalignment distance is obtained, as shown in Fig. 8.
(a)
(b)
(d) Fig. 8. Magnetic field distribution of the coupling coils at different misalignment distance. (a) 0 mm. (b) 100 mm. (c) 200 mm. (b) 250 mm.
Optimal Design of Coupling Coils
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It can be found that the coupling effect of the coupling coil remains stable within the misalignment distance of 200 mm and weakens at 250 mm. Furthermore, the mutualinductance values of two kinds of transmitting coils and receiving coils with different misalignment distance are calculated by COMSOL, and the physical models is built to measure the mutual-inductance, as shown in Fig. 9.
(a)
(b)
Fig. 9. The physical models of the two different coupling coils. (a) The combined transmitting coil. (b) The traditional uniform spiral structure.
Figure 10 shows the simulation and experimental results of M between two kinds of transmitting coils and receiving coil.
Fig. 10. The simulation and experimental results of M between two kinds of transmitting coils and receiving coil. (S-result, E-result represent the simulation results and the experimental measurement results, respectively; Opt coil, traditional coil represents the combined transmitting coil and the traditional uniform spiral structure, respectively).
Figure 10 shows the simulation and experimental results of M between two kinds of transmitting coils and receiving coil. Obviously, compared with the traditional uniform spiral emission coil, the mutual-inductance between the combined transmitter coil and the receiving coil can be basically unchanged at a large misalignment distance. When the misalignment distance is increased, the mutual-inductance decreases more slowly. Such characteristic makes the combined coupling coil have higher misalignment tolerance.
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5 Conclusion In this paper, a combined planar spiral transmitting coil is proposed and designed by means of theoretical derivation, objective function establishment and particle swarm optimization algorithm. The coil can produce magnetic flux density at the receiving coil, and then make the mutual inductance of coupling coil stable under large misalignment distances. Simulation and experimental measurement show that the combined coupling coils meet the requirement of high misalignment tolerance compared with the traditional coil. In the future, the design method proposed in this paper can be applied to practical coil design.
References 1. Zakerian, A., Vaez-Zadeh, S., Babaki, A.: A Dynamic WPT system with high efficiency and high power factor for electric vehicles. IEEE Trans. Power Electron. 35(7), 6732–6740 (2020) 2. Luo, Z., Wei, X.: Analysis of square and circular planar spiral coils in wireless power transfer system for electric vehicles. IEEE Trans. Industr. Electron. 65(1), 331–341 (2018) 3. Rozman, M., et al.: Smart wireless power transmission system for autonomous EV charging. IEEE Access 7, 112240–112248 (2019) 4. Rong, C., et al.: Optimization design of resonance coils with high misalignment tolerance for drone wireless charging based on genetic algorithm. IEEE Trans. Ind. Appl. 58, 1242–1253 (2021) 5. RamRakhyani, A.K., Lazzi, G.: Multicoil telemetry system for compensation of coil misalignment effects in implantable systems. IEEE Antennas Wirel. Propag. Lett. 11, 1675–1678 (2012) 6. Cai, C., Wu, S., Jiang, L., Zhang, Z., Yang, S.: A 500-W wireless charging system with lightweight pick-up for unmanned aerial vehicles. IEEE Trans. Power Electron. 35(8), 7721– 7724 (2020) 7. Wang, W., Xu, C., Zhang, C., Yang, J.: Optimization of transmitting coils based on uniform magnetic field for unmanned aerial vehicle wireless charging system. IEEE Trans. Magn. 57(6), 1–5 (2021) 8. Wei, G., Jin, X., Wang, C., Feng, J., Zhu, C., Matveevich, M.I.: An automatic coil design method with modified AC resistance evaluation for achieving maximum coil-coil efficiency in WPT systems. IEEE Trans. Power Electron. 35(6), 6114–6126 (2020) 9. Gupta, M., Agarwal, P.: To model magnetic field of RF planar coil for portable NMR applications. In: 2017 International Conference on Inventive Computing and Informatics (ICICI), pp. 490–494 (2017)
Auto-Distrubance-Rejection-Controller Design for VSCF Wind Turbine Yan-ning Zhang(B) and Chen-dong Duan School of Electronic and Control Engineering, Chang’an University Xi’an, Xi’an, China [email protected]
Abstract. The variable- speed constant frequency (VSCF) wind turbine is developing rapidly with the electrics development. It is well known all that VSCF wind turbine control system is nonlinear, so many control technologies are employed to purse the good control result. The auto-disturbance-rejection-controller (ADRC) is presented for it doesn’t need the experimental setup. The core is the extended state observer that get all variation of load resistance. The nonlinear parts of the wind generator are dealed as the disturbances that are estimated by additional states. The controller is designed to pursue the max power point of wind turbine under the rating wind speed in the paper. The lab system of wind turbine generator are carried out and the series program are executed. The results confirm that the controller tracks the max power points well. Keywords: ADRC · VSCF · Wind turbine · Simulation · Experiment
1 Introduction The growth of the people and the development of the society need more and more energy. The consumption of fossil energy is along with the environmental pollution. The better choice is going to exploit renewable green energy. The wind power is converted to the electric power by wind generator system. The pressure difference and the rotation of the earth take the wind, hence the wind varies in an indefinite pattern and strength. The key knowledge of wind turbulence in wind turbines is clear. Random loads and stresses cause power fluctuations and fatigue life of the generators. An issue of wind turbine has been held in recent [1], which concerned research on wind power system is the enhancement of innovative control algorithms for smoother and more effective operation. The most wind generator system run at fixed speeds except starting and stopping [2]. As we all know, running at a constant speed means that the maximum coefficient of performance can only be obtained at a specific wind speed. A low coefficient of performance is observed, which reduces the energy output below the expected energy output for variable speed operation [3–6]. The relation of wind speeds and output power of turbine can be shown as: Pw = 1/2ρv13 SCp © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 11–24, 2022. https://doi.org/10.1007/978-981-19-1528-4_2
(1)
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The explanation of parameters and variable in the Eq. (1) are listed as follows, the parameter ρ represents the air density, the variable v1 is the speed for up wind, S is the past through area, Cp is the coefficient of power gain. The variation of Cp is shown in Fig. 1. The parameter Cp is described as: Cp = f (v, β)
(2)
The vibration of Cp can be described as Fig. 1.
Fig. 1. Curve of Cp
Fig. 2. Curve of Cp (λ)
If the coefficient β is fixed, the relation between the Cp and λ is shown as Fig. 2. It can be seen that if the coefficient Cp is to be maximum, the ration between the wind speeds and the rotor speeds of the wind turbine are constant at value 9. Therefore, it is concluded that when high output efficiency is to be achieved, the rotor speed of the wind turbine should be adjusted together with the wind speed. The method of linearization approach applies the linear system theory to the design and analysis of the control system, the shortage of this way is the low reliability [7–9]. Many teams are working on the variable speed control method of the wind turbine system [10]. A method for the control of VSWT (variable speed wind turbin) is presented. The target of the method is get high efficiency when the rotor speed track the desired speed. The excitation winding voltage of the generator is automatically tuned through the developed auto disturbance rejection controller algorithm.
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2 ADRC ADRC is proposed to avoid the complex modelling of real control system. The primary purpose of feedback control is to solve the uncertainties of the plant dynamics and interference from outside. It is shown in Fig. 3
Fig. 3. One-order ADRC
Figure 3 shows the first order ADRC. It composed by tracking first order trace differentials (TD), extended states observer (ESO) and the non-line system feed (NLSF). 2.1 TD The TD is to get the requisite signals when the input signal passes through, it produces two or more output signals. one of these signals remains the same as the input signal while others are the (n-1)th order differential signals. The TD of one-order ADRC can be shown as Eq. (3). z˙1 = −rfal(v1 − v, a, δ)
(3)
The signal v1 is same as the input signal v. The TD is described by the theorem 2.1. The partner of TD is described by Eq. (4). ⎧ ⎨ z1 = z1 (4) z = z˙1 ⎩ 2 z˙2 = fun(z1 , z2 ) when z1 , z2 meet as:
⎧ ⎨ lim z1 = 0 t→∞
⎩ lim z2 = 0
(5)
t→∞
⎧ x1 = x1 ⎪ ⎪ ⎨ x2 = x2 ⎪ x =x −v ⎪ ⎩ 1−2 2 1 x˙ 2 = R f (x1−2 , x2 /R)
(6)
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The solution of the system with constant parameter T and Eq. (6) is described as follows: T |x1 (t) − v(t)| = 0 lim (7) R→∞ 0
2.2 ESO The first researcher of ESO method for on-linear system is Han. The ESO combines together the external disturbance and the internal nonlinear dynamics, it is the key of ADRC. The results obtained from the ESO are important to the controller of system. Second-order ESO is presented as: e = z1 − x 1 z˙1 = z2 − β1 fal(e1 , a1 , δ) + bu(t) z˙2 = −β2 fal(e2 , a2 , δ)
(8)
The variable z1 is to get the space state y, and the variable z2 is to get the extended state which reflects the disturbance. 2.3 NLSF When the controller states are observed by the ESO the controller input is described as: ⎧ ⎨ e1 = v1 − z1 (9) u = kfal(e1 , a, δ) ⎩ 0 u = u0 − z2 /b The function fal(e1 , a, δ) is nonlinear, the parameter k is the control value.
3 Controller Design for VSCF Wind Generator System The rotor speed of the wind generator is tuned by the ration of Boost-bucker which is presented as Fig. 4. The key of this method is to obtain the reaction torque of the generator through the change of voltage and output current, so as to adjust the rotor speed of the generator according to the change of wind speed. 3.1 Model The power electronic equipment between the wind turbine and the DC bus is fixed in order to obtain the maximum gain from the wind. When controlling a fixed-distance wind turbine, the torque obtained by wind energy is expressed as: Pf υ3 1 = Cp (λ · β) ρπ R2 ωg 2 ωg Cp (λ · β) 1 = ρπ R3 υ 2 2 λ
Tf =
(10)
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Fig. 4. VSCF wind turbine
where the constant ρ is air density, the constant R is blade diameter; the variable v is the wind speed; the constant Cp is the coefficient. The relation of ωg and T f can be shown as: Jg
d ωg = Tf − Tg − Bωg dt
(11)
Here is the definition of Tg : Tg = pφig = kg igq
(12)
The whole model of wind turbine is presented as ⎧1 Ta = w(t) ⎪ ⎪ ⎨J ω˙ g = − BJ ωg + w(t) + ⎪ u(t) = Te ⎪ ⎩ Te = kt Idc
−1 J u(t)
(13)
The parameter k in Eq. (13) shows the proportion of transformer, and the variable igq shows electric current. 3.2 Controller Designment The optimal variable ω∗ is the referenced input for specified speed. The rule is designed which make the real ω to track ω∗ , thus the wind turbine could the maximal power of wind. In Eq. (13) it is concluded that the system is one order control system. The Eq. (13) is compared to Eq. (14) x˙ = −5x + w(t) + bu(t)
(14)
The variable Tf /J is regarded as w(t) which is observed at ESO. The variable Tg in Eq. (12) is the control value of the system. The controller design model is shown as Fig. 5.
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Fig. 5. Controller design model
The ADRC of VSCF wind turbine designment is described as. 1) Designment of TD The TD of the system in Eq. (14) is designed as: x˙ 1 = −rfal(x1 − v, a, δ)
(15)
The variable v is the signal, the variable x1 follows up v. The value of r is determined by Eq. (16). r ≥ 2 max(f ) Where, f is the frequency of input signal. 2) Designment of ESO The first order ESO is designed as z1 = z2 − β1 fal(z1 − ω, a, δ) + u(t) z2 = −β2 fal(z1 − ω, a, δ)
(16)
(17)
where the parameter z1 is to track ωg , z2 is to observe the torque. The value of β1 , β2 is determined by Eq. (18) β1 ≥ 2ξ ωc , β2 ≥ ωc2 ξ = 0.707 3) Designment of non-linear combination The first order NLSF is designed as ⎧ ⎨ e1 = v − z1 u = K1 fal(a, δ, e1 ) ⎩ 0 u = u0 − z3
(18)
(19)
The linear model proposed is shown below. ⎧ ⎪ ⎨ e1 = v − z1 ⎪ ⎩
u0 = K1 e1 u = u0 − z3
(20)
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Usually, K1 is calculated by the transfer function and comply with the rule of TD gains r, the K1 is designed as K1 ≥ ωc
(21)
3.3 Digital Designs The controller is designed on the core of dsp2812, and then the ADRC algorithm is transformed to the digital design. The digital designs are shown as following. Digital TD is presented as Eq. (22). v1 (k + 1) = v1 (k) − rhfal(v1 (k) − v(k), a0 , δ0 )
(22)
Where, v(k) is the input signal, h is the numerical integration step. The digital ESO is presented as Eq. (23). e (k) = z1 (k) − x(k) 1 z1 (k + 1) = z1 (k) + h z2 (k) − β1 fal(e1 , a1 , δ) + bu(k) z2 (k + 1) = z2 (k) − hβ2 fal(e1 , a2 , δ)
(23)
The digital control value is shown as Eq. (24). e1 (k) = z1 (k) − x(k) z1 (k + 1) = z1 (k) + h z2 (k) − β1 fal(e1 , a1 , δ) + bu(k) z2 (k + 1) = z2 (k) − hβ2 fal(e1 , a2 , δ)
(24)
The control input is presented as Eq. (25). u = u0 − (−J )z2
(25)
In Eqs. (23), (24), (25) the input signal v(k) and the coefficient b are shown as v(k) = ωref (26) b = (−1/J )
4 Simulation and Experiments The ADRC design for wind turbine is verified not only by simulation and also by experiments. 4.1 Simulations The simulation model in MATLAB is shown as Fig. 6.
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Fig. 6. Simulation model
The simulation parameters are set up as v = 15 m/s, D = 20 m, J = 1270 kg · m2 . The controlled result is presented as Fig. 7.
Fig. 7. Rotor speed controlled by ADRC
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The rotor speed can quickly follows the designed speed. And the controlled figure at the varied wind speed is presented in Fig. 8.
Fig. 8. Rotor speed controlled by ADRC
The real rotor speed curve matches the referred optimal rotor speed curve well. The simulations verify that the ADRC of VSCF wind turbine is valued in theory. 4.2 Experiments The experiments are carried out on the experiment system in the lab. The experiment system is presented as Fig. 9. It is composed by main electric circuits, control electric circuits and the computer. The main electric circuits contain the permanent magnetism synchronic motor generator, drive shaft coupling, permanent magnetism synchronic generator, rectifier and Boost-Bucker.
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Fig. 9. Experiment system
The rotor speeds experiments are carried out in the paper. The current and voltage are get by TDS1002b oscilloscope and transferred to computer by National Instruments Lab View software. The rotor speeds of wind turbine are calculated by the measured current and voltage. The experiments from the starting to the rating speed are shown as Fig. 10, Fig. 11, Fig. 12 and Fig. 13. The PID control experiments are shown as Fig. 10 and Fig. 11.
Fig. 10. Current and Voltage of DC bus when PID
The ADRC experiments of are shown as Fig. 12 and Fig. 13.
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Fig. 11. Rotor speeds when PID
Fig. 12. Current and Voltage of DC bus when ADRC
In the Fig. 10 and Fig. 12 it is concluded that current and voltage of DC bus when ADRC are better than them when PID. The time from the starting to the rating speed when ADRC is shorter than it when PID. The errors when ADRC are smaller than them when PID. The experiment that there are strong disturbance when the wind turbine is running is carried out. The results are shown as Fig. 14 and Fig. 15.
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Fig. 13. Rotor speeds when ADRC
Fig. 14. Current and Voltage of DC bus
In the Fig. 14 the voltages can keep but the currents drop to the low point when there are strong disturbance in the system. In Fig. 14 and Fig. 15 the currents and rotor speeds rise quickly to the original value.
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Fig. 15. Rotor speeds when disturbance
5 Conclusion ADRC is used to track the optimum rotor speed of wind turbine, the controlled results of ADRC for VSCF wind generator are described in Sect. 4. The simulations verify that the respond time of system when ADRC is short and the actual rotor speed differs very little from the design rotor speed. The experiments are carried out. The ADRC is better than PID. However, the real controller of real wind turbine is not presented in the paper. The paper presents the good advices when the real controller is going to be made.
References 1. Abouri, H., Guezar, F.E., Bouzahir, H.: Advanced control strategies for wind energy systems. In: 2020 International Conference on Electrical and Information Technologies (ICEIT) (2020) 2. Zhang, Y., Cao, B., Kang, L., Xu, J.: Controller design for small wind generator with ADRC. Procedia Environ. Sci. 11(Part C), 1128–1134 (2011) 3. Beltran-Pulido, A., Cortes-Romero, J., Coral-Enriquez, H.: Robust active disturbance rejection control for LVRT capability enhancement of DFIG-based wind turbines. Control. Eng. Pract. 77, 174–189 (2018) 4. Salameh, J.P., Cauet, S., Etien, E., Sakout, A., Rambault, L.: PMSG-based wind turbine torque harmonic reduction through LPV control of EKF-based disturbance estimation research reported in this publication was supported by FEDER program Poitou-Charentes of the European Union under award number PC158. IFAC-PapersOnLine 52(28), 196–201 (2019) 5. Ren, L., Mao, C., Song, Z., Liu, F.: Study on active disturbance rejection control with actuator saturation to reduce the load of a driving chain in wind turbines. Renew. Energy 133, 268–274 (2019) 6. Ravanji, M.H., Canizares, C.A., Parniani, M.: Modeling and control of variable speed wind tur-bine generators for frequency regulation. IEEE Trans. Sustain. Energy 11(2), 916–927 (2019) 7. Han, J.: A class of extended state observers for uncertain systems. Control Dec. 10(1), 85–88 (1995)
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8. Cheikh, R., Menacer, A., Chrifi-Alaoui, L., et al.: Robust nonlinear control via feedback linearization and Lyapunov theory for permanent magnet synchronous generator- based wind energy conversion system. Front. Energy 14(1), 180–191 (2018) 9. Alkhabbaz, A., Yang, H.S., Weerakoon, A., et al.: A novel linearization approach of chord and twist angle distribution for 10 kW horizontal axis wind turbine. Renew. Energy 178, 1398–1420 (2021) 10. Yin, X., Zhang, W., Zhao, X.: Current status and future prospects of continuously variable speed wind turbines: a systematic review. Mech. Syst. Signal Process. 120, 326–340 (2019)
Functional Demonstration and Power Optimization of a Monitoring System for Arc Blowout Lightning Protection Devices Yaojing Luo1(B) , Jufeng Wang1(B) , Yanlei Wang1 , Renbao Yan1 , Yiyi Zhang1 , Kezhu Guo2 , Ping Huang1 , Yuheng Xu1 , Yang Lu1 , and Jiqiang Li3 1 School of Electrical Engineering, Guangxi University, Nanning 530000, China
[email protected]
2 Power Transmission Inspection Center,
State Grid Tacheng Power Supply Company, Tacheng 834700, China 3 Zoucheng Power Supply Company, State Grid Shandong Electric Power Company, Zoucheng 273500, China
Abstract. A smart online monitoring system based on a self-organized wireless sensor network (WSN) is developed and successfully implemented to monitor multiple operational conditions of an arc blowout lightning protection device (ABLPD) in a modern transmission tower line system. Combining a local-area WSN with a wide-area communication network technology, the sensor data could be efficiently delivered to users. The monitoring system comprises a data transfer unit (DTU), a main node, and multiple sub nodes. The real-time awareness of the ABLPD information, including latitude and longitude, local trigger flag bit, total trigger times, trigger failure times, continuous trigger times, device statement, update time, and trigger data set are critical to maintaining a reliable and stable operation of ABLPDs. The monitoring system is powered with solar energy, which is especially suitable for outdoor WSN applications on the Internet of Things (IoT). Power optimization is also considered at the hardware and software design levels. Via real-world testing, the system is demonstrated as a cost effective and simple platform that is suitable for use with ABLPDs. Keywords: ABLPD · WSN · DTU · IoT · Power optimization · Real-world testing
1 Introduction Nowadays, with the higher stability requirement of power line system, the overvoltage phenomenon caused by lightning strokes is becoming a more and more severe problem that threatens power grid operation. Most of the power grid trip-out accidents are caused by lightning overvoltage [1]. To ease the problem, an arc blowout lightning protection device (ABLPD) based on arc blowout theory and technology is rising [2]. The ABLPDs must be installed on parallel gaps that are nearby insulators. At the moment of an impact © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 25–32, 2022. https://doi.org/10.1007/978-981-19-1528-4_3
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arc caused by lightning overvoltage acting on ABLPD, the arc blowout gas will spout within 1 ms, so that the arc blowout action is completed and trip-out rate can be limited. With the development of remote area communication methods and technologies, more and more platforms for monitoring power girds have been developed. In 2017, a machine to machine (M2M) communication network-based monitoring system was developed that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication [3]. In the same year, a wide area monitoring system (WAMS) is overviewed and presented its operation experiences in electric power grid wide area monitoring, which is considered as a pivotal component of future electric power grids [4]. Recently, an improved power grid monitoring system has been proposed. The system uses wireless local area network (WLAN) for data transmission and uses the improved AES encryption algorithm to improve the security of data transmission, which verifies the correctness and feasibility of the system through verification [5]. However, there are no Internet of Things (IoT) platforms that support remote monitoring for the operational conditions of ABLPDs at present. Based on this, we put forward the ABLPD monitoring system that is based on the fourth-generation (4G) cellular network, which provides information when an ABLPD is triggered. The ABLPD monitoring system combines a 4G data transfer unit (DTU) [6], a main node and multiple sub nodes. By applying a micro control unit (MCU) as the center controller [7], a self-organized wireless sensor network (WSN) between sub nodes and the main node is fulfilled through a 2.4-GHz wireless technology [8]. Using a positioning module, the location data of every sub node can be obtained if an ABLPD is triggered. In other words, each sub node is an isolated unit that can automatically collect the real-time trigger data of ABLPD. Along with the hardware and software design, the wireless signal losses analysis and power optimizing test show that the monitoring system implementation satisfies the necessary requirements for 24 h online operation, which is the basic requirement of an all-whether monitoring system. In this paper we are mainly concerned with evaluating the trigger times detection accuracy and battery life of the ABLPD monitoring system after optimization. The evaluation is carried out in a real-world functional test using an impulse voltage generator. In summary, reasonable ABLPD trigger detection sensitivity allows a sub node to obtain effective information in order to make feasible in monitoring ABLPD, as well as remote data collection and delivery.
2 System Architecture and Working Principles This section elaborates in detail the sub/main node operational scheme of our monitoring system. We also present the working principle of each modules in the ABLPD monitoring system herein. 2.1 System Architecture The architecture of the ABLPD monitoring system is illustrated in Fig. 1, where RS232, RS485 are serial communication standards and transistor-to-transistor (TTL) is a digital logic design [9]. The circuit of the sub/main node is custom-made with the same
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printed circuit board (PCB) configuration, which is mainly composed of three functional modules: a trigger detection module, a local data transmission module and a power control module. Besides, a commercial available positioning module, which is used for providing the location data when a sub node detects a trigger event.
Fig. 1. Monitoring system architecture and operational pattern.
2.2 System Working Principles Detailed information about the functional modules in the ABLPD monitoring system follows. Trigger Detection Module: This module is used to detect the light of arc blowout action through a flame sensor which is an isolated peripheral circuit component attached for the ABLPD trigger times monitoring. Combing with a trigger times counting algorithm, the data of the ABLPD trigger times can be extracted and transmitted to a MCU in the form of level pulses by detecting the TTL signal fluctuation from digital and analog output pin of a flame sensor. The low level of the pulses will be detected, counted, and stored in a data array, including three main variables: total trigger times, continuous trigger and trigger failure times. The continuous trigger times variable denotes the trigger events counting within a counting period. For instance, an ABLPD is first triggered twice in 1 s; hence, the total/continuous trigger times are both counted on 2. Next time, if an
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ABLPD is triggered thrice in 1 s, the total/continuous trigger times will be counted as 2 + 3 and 3 times, respectively, and the trigger failure time is 0. We obtained the ABLPD triggering waveform from a flame sensor (Fig. 2).
Fig. 2. Digital and analogy waveform when a flame sensor is triggered.
In Fig. 2, channels 1 and 2 of an oscilloscope were connected to the flame sensor’s digital and analog output pins, respectively, to compare the output signal features of the flame sensor. The output of the flame sensor is normally at a high level. When light appeared, and the flame sensor was triggered, the digital signal immediately switched to a low level that much faster and became more stable than the analog signal. Therefore, using a digital signal for trigger times counting is more suitable compared to using an analog signal. Local Data Transmission Module: Compared with 433-MHz communication technology, we chose 2.4-GHz technology for trigger data delivery because the 2.4-GHz technology has several advantages, such as a universal public channel for free use, a higher data transmission rate at 250 kb/s (433-MHz technology at 100 kb/s), a good anti electromagnetic interference (EMI) performance, especially nearby high voltage (HV) power lines. However, the main defect of the 2.4-GHz technology is that the signal strength easily weakens. The signal transmission loss [10] is calculated as follows: Sl = 20[log10 (ft ) + log10 (d )] − 27.552
(1)
where, S l (dB) is the wireless signal transmission loss; d (m) is the transmission distance in free space; and ƒ t (MHz) is the signal transmission frequency. Figure 3 depicts the relationship between S l and d. The higher the transmission frequency, the larger the
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signal transmission loss. In fact, the wireless signal coverage radius of the sub nodes must be greater than the direct distance between two transmission towers in practical application.
Fig. 3. Signal transmission loss spectra under different communication frequencies.
Power Control Module: Sometimes, the ABLPD monitoring system must shut down unnecessary parts (e.g., local data transmission module and positioning module) to ensure power duration. Turning them on all the time is unwise. Therefore, a twinchannel p-channel metal oxide semiconductor (P-MOS) transistor chip was applied to control their power supply [11]. Through reasonable software power control, two main approaches can be used to save electricity. First, the positioning module will automatically be turned off after the location data is collected successfully. Second, by making use of timer in the MCU, the local data transmission module will be shut down for several seconds (determined by the counting cycle of timer) if the new received trigger data is the same with the last trigger data for certain times. Considering the power limitation of solar energy, the power consumption of positioning and the local data transmission module should be optimized. Here, we conducted a power optimization test which reflects the influence of power consumption on battery life. Based on this, the sub node uses an independent Li-ion battery which has a capacity of 2500 mA/h and a rated voltage of 5 V. The following equation is used to estimate the battery life of the sub node: TSN =
CS IS
(2)
where, T SN (hour) is the battery life of a sub node; C S (mA/h) is the nominal value of the battery capacity; and I S the operational current of a sub node. Theoretically speaking, the
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battery life of a sub node can reach 42 h according to (2). After power optimization, the battery life of a sub node can be improved to 50 h, which is meaningful for an all-weather monitoring platform. The relationship between T SN and C S is shown in Fig. 4.
Fig. 4. Battery life of a sub node before/after power optimization.
3 System Functional Test To demonstrate the functions of the monitoring system, we conducted a system functional test. For better understanding, Fig. 5 (a) shows the test scheme and illuminates the direction of the impact arc. In Fig. 5 (b), a sub node is put on the top of the ABLPD that was colored in white to make the trigger moment easier to be observed and depicted the moment when the ABLPD was triggered. As we can see, an impact arc breaks down the air and enters into the arc blowout passageway of the ABLPD from bottom to top, then the ABLPD was triggered in an instant. Meanwhile, the sub node would detect a trigger event. The magnitude of the impact current is calculated by: Ii =
Vl · Ni Vi = Ri Ri
(3)
where I i is the magnitude of impact current, V l is the voltage magnitude of each level’s capacity in the impulse voltage generator, Ni is the number of capacitance, V i is the magnitude of impact voltage, Ri is the total loop resistance of the impulse voltage generator. Here, Ni = 15, V l = 37 kV and Ri = 935 , so that the V i = 555 kV and I i ≈ 594 A.
Functional Demonstration and Power Optimization of a Monitoring System
31
Figure 6 illustrates the user interface on a PC. The following information can be found in the figure: display of latitude and longitude; total trigger times; trigger data set; trigger failure times; continuous trigger times; device statement; and update time. For the proposal of reducing the probability of electromagnetic interference. All information is packed in a data array which can be sent via a local data transmission module.
Fig. 5. Experimental scheme and spot: (a) test scheme; (b) ABLPD trigger moment.
Fig. 6. Information software user interface on a PC.
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4 Conclusion In this paper, a specialized monitoring system for ABLPD has been developed. Using a flame sensor, whenever an ABLPD is triggered, a corresponding sub node will send a message to the main node then to the DTU, and the data includes the status of the ABLPD trigger moment. The power optimization is considered in hardware and software design level, which ensures the battery life of the system. In system functional test, trigger photography is collected and significant trigger data of the ABLPD is sent to the user terminal successfully. In conclusion, although the ABLPD monitoring system has been examined in the real-world functional test, however, we show in our research that the challenge is not the data collection function of our system, but rather how to guarantee the stability of system operation under a strong electromagnetic pulse environment. Acknowledgement. This work was supported by the Science and Technology Project of Guangxi Key Research and Development (AB18126105) and the Special Fund Project of Guangxi Innovation-Driven Development (AA18242050).
References 1. Fernando, H.S., Alberto, D.C., Silverio, V.: Lightning overvoltage due to first strokes considering a realistic current representation. IEEE T. Electromagn. C. 52(4), 929–935 (2010) 2. Shangshi, H., Jufeng, W., Yuheng, X., Zijian, L., Renbao, Y.: The effect of a multi–fracture compression airflow arc–extinguishing structure interrupting the power frequency follow current. AIP Adv. 10(3), 035129 (2020) 3. Gharavi, H., Hu, B.: Wireless infrastructure M2M network for distributed power grid monitoring. IEEE Network 31(5), 122–128 (2017) 4. Liu, Y., You, S., Yao, W., Hesen, L.: A distribution level wide area monitoring system for the electric power grid–FNET/GridEye. IEEE Access. 5, 2329–2338 (2017) 5. Yang, C., Wu, J., Wang, L., Xiaolan, Z., Liangshu, L., Shan, L.: Smart Grid Monitoring Systems based on Advanced Encryption Standard and Wireless Local Area Network. IOP Conf. Ser. Mat. Sci Eng. 719(1), 012056 (2020) 6. Kai, L., Wei, N., Lingjie, D., Mehran, A., Jianwei, N.: Wireless power transfer and data collection in wireless sensor networks. IEEE T. Ven. Technol. 67(3), 2686–2697 (2017) 7. Shinlun, C., Minchun, T., Hoyin, L., Tinglan, L.: VLSI implementation of a cost–efficient micro control unit with an asymmetric encryption for wireless body sensor networks. IEEE Access. 5, 4077–4086 (2017) 8. Liang, X., Qiben, Y., Wenjing, L., Guiquan, C.Y., Thomas, H.: Proximity–based security techniques for mobile users in wireless networks. IEEE T. Inf. Foren. Sec. 8(12), 2089–2100 (2013) 9. Liu, L., et al.: Information collection system of duck products based on IoT. EURASIP J. Wirel. Commun. Netw. 2018(1), 1 (2018). https://doi.org/10.1186/s13638-018-1144-z 10. Norton, K.A.: Transmission loss in radio propagation. In: Proceedings of the IRE, pp. 146–152 (1953) 11. Zhang, J., Wang, Z.-J., Quan, Z., Yin, J., Chen, Y., Guo, M.: Optimizing power consumption of mobile devices for video streaming over 4G LTE networks. Peer-to-Peer Netw. Appl. 11(5), 1–14 (2017). https://doi.org/10.1007/s12083-017-0580-6
Distribution Network Line Loss Allocation Method Taking into Account Distributed Power Sources and Economic Operation Intervals Xiaojun Li(B) , Siming Zeng, Liang Meng, Anyi Li, and Jifeng Liang State Grid Hebei Electric Power Research Institute, No.200 Xing’an Avenue, Yu Hua District, Shijiazhuang, China [email protected]
Abstract. In the medium and low voltage distribution network, the access capacity of distributed power sources continues to increase, and the traditional method of distribution network loss coefficients is no longer in line with market rules and balanced development. The method in this paper analyzes the relationship between the operation status change of the distribution network and the line loss after the distributed power supply is connected, and establishes a line loss allocation model considering the economic operation time correction. The correction calculation depends on the operation of the distributed power supply network in a certain period of time. The change of economic time does not involve human participation and discards the influence of subjective factors. Taking an actual distribution network as an example, the method is applied, and the influence of distributed power access on the line loss is presented in time periods. The method is compared with the improved average network loss coefficient method to verify the effectiveness and rationality of the method. Keywords: Distribution network line loss · Distributed power generation · Allocation method · Economic operation interval
1 Preface With the proposal of China’s energy policy, green and clean energy has ushered in development opportunities and has broad room for development in the future. New energy accounts for an increasing proportion of the energy structure. In the medium and low-voltage power distribution network, the penetration rate of distributed generation (DG) is getting higher and higher, the power supply pattern is changing, and new energy power and continuous consumption It is becoming an important technical feature of the power system [1]. At present, there have been many related studies on the problem of transmission network loss allocation at home and abroad. The proposed methods of loss allocation include the loss coefficient method, the theory of abandonment, graph theory, power flow tracking and other methods [2–11]. Literature [4] proposes that the average network loss © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 33–43, 2022. https://doi.org/10.1007/978-981-19-1528-4_4
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coefficient method is limited to only a single distribution of network loss to power nodes (excluding distributed power) or load nodes. It cannot achieve multi-role distribution and cannot be applied to power distribution with distributed power. Literature [12–15] introduces nucleolus theory, superposition theorem, and game theory methods to improve the line loss allocation method under bilateral transactions. From the research situation at home and abroad, due to the radial distribution network with many branches and large number of nodes, it is difficult to realize the loss allocation method of the transmission grid in the distribution network [16]. In the distribution of network loss, there is still no universal and generally acceptable method of distribution of line loss. At present, distributed power sources are connected to the grid and do not bear the network loss. However, as the scale of access continues to expand, the power supply companies of the grid will bear more and more network losses. From a market-oriented perspective, it is not conducive to the development of the power grid and the quality of service. Both the power grid and distributed power users should assume an equal role in power supply services. Therefore, from the perspective of the background of power market reform and the scientific management of reliable grid connection, the research on the loss allocation of distributed power sources has important research value.
2 Economic Operation Range of Distribution Network The economic operation of the distribution network is to maintain a reasonable transmission capacity under the rated power supply capacity to reach the operation interval corresponding to the minimum loss of the distribution network. The duration of the economic operation state is the economic operation time (t e ). The economic operation of the distribution network is Operation is the goal of planning and operation, and it is also an important guarantee for achieving energy saving and loss reduction in the power system. The economic running time (t e ) can be calculated by the equivalent resistance method [12]. The total loss of the distribution network (W t ) is Wt =
P0,i t +
i
K 2 SL2 tReq × 10−3 U 2 cos2 α
(1)
Where: i P0,i is the sum of no-load losses of all transformers in the distribution network; t is a certain period of time during the operation of the distribution network; K is the ratio of the root mean square load to the average load; S L is the average load in time t; Req is the total equivalent resistance of the distribution network transformer and the distribution network line; U is the rated voltage of the distribution network; cosα is the operating power factor. When the distribution network load collection does not meet the conditions, a day can be divided into several time periods, and the power supply collected in each time period can be equivalently replaced. If the power supply in a certain period of time is W 0 , then S L = W 0 /t. Bring S L into Eq. (1), then Wt =
i
P0,i t +
K 2 W02 Req × 10−3 U 2 tcos2 α
(2)
Distribution Network Line Loss Allocation Method
35
It can be seen from the above formula that the total loss of the distribution network is composed of two parts. The first part is a fixed loss, which is only related to operating time; the latter part is a variable loss, which is related to both operating time and power supply. Taking the derivative of Eq. (2) with respect to t and setting it to zero, it is obtained that when the total loss of the distribution network is the smallest, the optimal running time t e of the distribution network is: Req × 10−3 KW0 te = (3) U cosα i P0,i Incorporating Eq. (3) into Eq. (2), the minimum total loss of the distribution network W min is obtained as 2KW0 P0,i Req × 10−3 (4) Wmin = U cosα i
That is, when the total fixed loss of the distribution network is equal to the variable loss, the total loss of the distribution network is the smallest. Due to the discontinuity and instability of the output of distributed power sources, the connected transformers may be connected to the grid or the two-way flow of the off-grid. It is advisable to calculate the loss of the distribution network with distributed power sources in time periods to ensure that the calculated distribution in each time period The direction of the flow of distributed power in the power grid is basically unchanged. On the basis of the equivalent resistance method, the equivalent electric quantity method can be used to correct the equivalent resistance of the original distribution network, and the corrected equivalent resistance Req of the distribution network is:
Req = ReqL + ReqR
(5)
among them, n
m
ReqL =
i=1 Wi +
j=1
n
j
i=1 Wi
+
dj
k=1 Wpsk
d
k=1 Wpsk
2 Rj
2
(6)
Where: ReqL is the equivalent resistance of the conductors of the distribution network; m is the number of distribution line sections; nj is the total number of distribution transformers that flow through the j-th line section; W i is the active power of the ith distribution transformer Electricity; d j is the number of DGs connected to the j-th distribution line (take the first section of the distribution line as the front and the end as the back); W psk is the k-th DG active energy (W psk takes a negative value when the DG power is upload to the grid, and it takes a positive value when the DG power is down
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the current); Rj is the resistance of the j-th line segment; n is the number of distribution transformer nodes; d is the number of DG nodes.
n Pk,i Wi2 U2 • 103
2 2 d S i i=1 i=1 Wi + k=1 Wpsk
ReqR = n
(7)
Where: ReqR is the equivalent resistance of all distribution transformer windings in the distribution network; U is the rated voltage of the high-voltage side of the distribution transformer; Pk,i is the rated load loss of the distribution transformer carried by the i-th node; S i is the rated capacity of the distribution transformer carried by the i-th node; other symbols are the same as formula (6). Due to load changes and considering the uncertainty of distributed power output, the distribution network cannot operate at a fixed economic operating point for a long time. In order to achieve the minimum loss, a “micro increment W” can be set to determine its economic operating range. Where W represents the increase of the network loss value of a certain operating point relative to the theoretical minimum network loss W min , that the best economic operating point, and W is calculated as follows: W = λWmin
(8)
In the formula: λ is the “optimization cost factor”, which represents the incremental percentage of the theoretical minimum network loss W min , which can be set according to actual loss requirements [15]. Usually, the value is λ = 3%–5%. The smaller the value is, the more ideal the network loss characterizes the operating point and the closer to the minimum network loss. If the power supply amount W t in a certain period of time, according to formula (3), the economic operation interval of this period can be obtained as [t 1 , t 2 ]. t 1 is the lower limit of the economic operation interval, and t 2 is the upper limit of the economic operation interval. If λ is 4%, the value of the micro increment W is determined, then according to the definition of the micro increment, Wmin − Wt1 = −λWmin
(9)
Wt2 − Wmin = λWmin
(10)
Incorporating Eq. (4) into Eq. (9), and combining with Eq. (3), the lower limit t 1 of the economic operation interval is: (11) t1 = te (1 + λ) − te (1 + λ)2 − 1 In the same way, it can be obtained that the upper limit t 2 of the economic operation interval is: (12) t2 = te (1 + λ) + te (1 + λ)2 − 1 Figure 1 shows a schematic diagram of the division of economic operation intervals of the distribution network.
Distribution Network Line Loss Allocation Method
37
Fig. 1. Economic operation range of distribution network
It can be seen from Fig. 1 that the total loss of the distribution network is the smallest in the economic operation interval [t 1 , t 2 ]. If the distributed power supply is connected, the operation of the distribution network will deviate from the economic operation range, which will increase the loss and affect the economics of the operation of the distribution network, which should be considered in the online loss allocation.
3 Distribution Network Loss Allocation Method with DG 3.1 Improved Average Network Loss Coefficient Method Traditional line loss allocation methods such as average coefficient method, power flow tracing method and marginal network loss coefficient method are difficult to apply in the distribution network line loss allocation containing DG [7]. Based on the current medium and low voltage distribution network measurement data acquisition and distribution network management foundation, it is relatively difficult to collect the power data of each network node in time periods. Therefore, this paper proposes to adopt an improved average network loss coefficient method to calculate the distribution network line loss. The improved average network loss coefficient method takes distributed power output as a part of the network power supply capacity and introduces it into the network loss allocation. After the improvement, the network loss is allocated according to the power of the power supply and the distributed power supply [9]. The apportionment method proposed in this paper is to first distribute the network loss of the distribution network according to the total power of the power source and the distributed power source; then, consider the changes in the economic operation interval of the distribution network caused by the access of the distributed power source. The network loss coefficient of the power supply is corrected; finally, based on the power output of the power node and the distributed power supply, the corrected network loss coefficient is used for line loss allocation. Applying the improved average network loss coefficient method, the average network loss coefficient of power supply nodes and distributed power sources is: WL n W j=1 Gj + i=1 WGi
δL = m
(13)
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Among them, j is the power supply node number, m is the number of power supply nodes, WGj is the power generation power of power supply node j; i is the DG node number, n is the number of DGs, WGi is the power generation power of DG node i; W L is power distribution allocate network loss during a certain period of time. 3.2 Correction Calculation Considering Economic Operation Interval Taking into account the impact of distributed power sources on the economic operation interval of the distribution network, the economic operation evaluation coefficient is introduced, and the calculation formula is as follows: kwi =
t i t e
(14)
Where: kwi is the economic operation evaluation coefficient of the i-th distributed power supply; t i is the economic operation time of the distribution network after the i-th distributed power supply is connected (hours); t e is the economic operation time of the distribution network before the distributed power supply is connected (hours); when t i > t e , it means that the distributed power access increases the economic running time of the distribution network, and the evaluation coefficient is k wi > 1; when t i < t e , indicating that distributed power access reduces the economic running time of the distribution network, k wi < 1. Considering the correction of the economic operation interval of the distribution network, the line loss of DG node i is apportioned as follows: WLGi = γi δL WGi
(15)
γi is the correction coefficient of the i-th DG considering the economic operation interval of the distribution network, which is calculated as follows: (16)
The first type of state change refers to the change of the distribution network from economic operation to non-economic operation during the calculation period; the second type of state change refers to the change of the distribution network from non-economic operation to another non-economic operation during the calculation period; the third type state change refers to the transformation of the distribution network from non-economic operation to economic operation during the calculation period. Due to changes in the distribution of distributed power line loss, the line loss distribution coefficient of the power node is corrected accordingly. After the line loss of the distributed power node is allocated, it is not difficult to obtain the line loss distribution coefficient of power node j as: WL − i WLGi (17) δL = j WGj
Distribution Network Line Loss Allocation Method
Bring Eq. (13) and Eq. (15) into Eq. (17), get δL j WGj + i WGi − i γi W Gi δL = j WGj
39
(18)
Further sorting out, get
δL = δL 1 + Then, let γj =
1+
(1−γi )W Gi i j WGj
i
(1 − γi )W Gi j WGj
(19)
, the line loss apportionment of power node j
becomes: WLGj = γj δL WGj
(20)
It can be seen that γj is a function of the DG economic operation correction factor (γi ), the power node and the distributed power supply. In order to correspond to the correction factor of the DG node, γj is called the economic operation correction factor of the power supply node.
4 Method Application Taking the actual operation of the distribution network line in a certain place as an example, the method proposed in this paper is used to apportion the line loss of the distributed power generation. The topology of the distribution network is shown in Fig. 2. The voltage level is 10 kV, 22 nodes in total, and the power factor is 0.95. This example assumes that the distributed access points are the 11th node and the 19th node. The rated capacity is 600 kW and 100 kW respectively, and the power factor is set to 0.98.
Fig. 2. The network topology of a distribution network
A typical day is selected for analysis, and the low-voltage load and distributed power operating data are collected at one-hour intervals. The relevant parameters are calculated according to the method in this article. The economic running time of the distribution network before the distributed power is connected is shown in Table 1. Since the light
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X. Li et al. Table 1. Distribution network parameter calculation of 7–18 h load
Time/h
Distribution network DG active load active power/kW power/kW
Equivalent resistance Req /
Economic running time t e /h
7
462.767
78.299
1.275
0.904
8
491.830
153.422
1.475
1.034
9
531.609
279.387
1.501
1.127
10
648.492
369.845
1.533
1.389
11
755.726
477.533
1.589
1.649
12
828.890
570.966
1.736
1.890
13
736.807
597.321
1.581
1.603
14
733.892
563.557
1.573
1.593
15
762.053
437.864
1.602
1.669
16
758.858
255.332
1.627
1.675
17
729.467
143.117
1.568
1.581
18
694.153
65.661
1.245
1.340
intensity is weak between 1–6 h and 19–24 h, the output of distributed power sources is close to zero, so Table 1 only lists the data series in the case of distributed output. Similarly, according to the above parameters and DG active power, the method in this paper is used to calculate the economic operation interval of the distribution network after the distributed power is connected. After comparison, the result of the economic operation interval change shown in Fig. 3 can be obtained.
Fig. 3. Diagram of changes in economic operation interval caused by DG access
Distribution Network Line Loss Allocation Method
41
At 7–9 o’clock, the DG output gradually increases, the economic operation state of the distribution network changes from economic operation to a lower economic operation interval; at 10–15 o’clock, the output of distributed power is relatively high, and the operation state of the distribution network crosses from a point higher than the economic operation interval to a lower economic operation interval point; at 16–18 o’clock, the operation status of the distribution network dropped from a point higher than the economic operation interval to the economic operation interval. Applying the method of this article to distribute the line loss of distributed power sources, first calculate the correction coefficient of each segment time distributed power source. According to formula (16), it can be seen that at 10–15 o’clock, the distribution network is not economical running status before and after the DG is connected, so γi = 1; at 7–9 o’clock, γi = 1/kwi . According to calculation, the correction coefficients are 1.205, 1.452 and 1.605 respectively; 16–18 o’clock, γi = kwi , the correction coefficients are 0.548, 0.679 and 0.856. The calculation results and method comparison are shown in Table 2. Table 2. The results comparison of DG apportioning line loss by time period (kWh) Time
Distribution network loss
Average network loss coefficient method
Improved average network loss coefficient method
Method of this article
7–9
22.9
0.0
7.6
11.3
10–15
37.9
0.0
25.3
25.3
16–18
36.1
0.0
7.1
4.6
194.9
0.0
40.0
41.2
Full time
The time DG apportioned line loss totals 40.0 kWh, and the difference between the two is 1.2 kWh. However, it should be noted that the results of DG’s apportionment of line loss by time period vary greatly, and the amount of change depends on the change of DG access to the economic running time. In addition, if 10–15 points change from non-economic operation to economic operation, the DG apportioned line loss will be reduced by 16.1 kWh, and the total loss of the distribution network will be reduced by 23.8 kWh, affecting the distribution network loss rate by 0.25% points.
5 Conclusion The method proposed in this paper conforms to the orientation of DG access capacity optimization and energy-saving operation of the distribution network, and is of great significance to the safe and stable operation of the distribution network. The application examples in this article verify the rationality of the model method. Compared with the method of artificially formulating reward and punishment measures, it discards the influence of subjective factors and avoids disputes or possible disputes. There is little difference between the two methods for allocating the line loss of DG
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during the whole time period. This is because the power generation of DG in the time period just makes the distribution network present two operating states, causing the line loss of DG to be allocated in one time period to increase, while the other time period. Decrease, the increase and decrease of the two are equivalent. However, it should be noted that the DG apportioned line loss varies greatly depending on the time period. The main reason is that the DG access changes the operation status of the distribution network, which will directly affect the line loss rate. Full consideration of the impact of DG access is conducive to the marketization of DG trading. And the healthy development of the distribution network. Acknowledgment. This research was supported by S&T Program of Hebei (20314307D), and partially funded by State Grid Hebei Electric Power Company (kjcb-2020–36).
References 1. Li, P., Zhang, C., Wu, Z., et al.: Distributed adaptive robust voltage/var control with network partition in active distribution networks. IEEE Trans. Smart Grid 11(3), 2245–2256 (2020) 2. Jabr, R.: Robust volt/var control with photovoltaics. IEEE Trans. Power Syst. 34(3), 2401– 2408 (2019) 3. Hu, W., Wang, J., He, Z., et al.: Study on line loss calculation of intelligent distribution network based on genetic algorithm optimized neural network. Internet Things Technol. 10(1), 40–43, 47 (2020). (in Chinese) 4. Xiang, H., Sen, O., Keying, W.: Review of energy losses allocation method of distribution system including distributed generation. POWER DSM 20(5), 28–32 (2018). (in Chinese) 5. Kryonidis, G., Demoulias, C., Papagiannis, G.: A two-stage solution to the bi-objective optimal voltage regulation problem. IEEE Trans. Sustain. Energy 11(2), 928–937 (2020) 6. Li, Z., Hou, H., Jiang, S., et al.: Line loss calculation and power theft analysis based on artificial neural network. Southern Power Grid Technology 13(2), 7–12, 50 (2019). (in Chinese) 7. Quankun, J., Yingna, L., Chuan, L.: Distribution network line loss calculation method based on improved PSO optimization RBF network. Inf. Technol. 43(5), 61–64 (2019). (in Chinese) 8. Yitao, Z., Zezhong, W., Liping, L., et al.: Line loss prediction of 10 kV distribution network based on grey relational analysis and improved neural network. Power Grid Technol. 43(4), 1404–1410 (2019). (in Chinese) 9. Jian, L., Junming, H., Jianquan, Z., et al.: Line loss calculation of distribution network based on process state characterization. Power Syst. Prot. Control 45(10), 55–61 (2017). (in Chinese) 10. Chao, Z., Shaorong, W., Yilu, L., et al.: A novel RNN based load modelling method with measurement data in active distribution system. Electric Power Syst. Res. 166, 112–124 (2019) 11. Wang, B., Liu, C.X., Sun, K.: Multi-stage holomorphic embedding method for calculating the power-voltage curve. IEEE Trans. Power Syst. 33(1), 1127–1129 (2018) 12. Yao, R., Sun, K., Shi, D., et al.: Voltage stability analysis of power systems with induction motors based on holomorphic embedding. IEEE Trans. Power Syst. 34(2), 1278–1288 (2019) 13. Weijiang, W., Lilin, C., Zhou, Y., et al.: Benchmarking daily line loss rates of low voltage transformer regions in power grid based on robust neural network. Appl. Sci. 9(24), 32–37 (2019) 14. Lei, W., Zhang, J., Yu, K., et al.: Study on line loss calculation method of distribution network based on typical load curve. Smart Power 48(3),124–130 (2020). (in Chinese)
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15. Wei Meifang, H., Bizheng, P.W., et al.: Study on line loss calculation method of distribution network based on dynamic three-phase unbalance degree. Smart Power 48(2), 104–108 (2020). (in Chinese) 16. Yingmei, Z., Genghuang, Y., Xiayi, H., et al.: Research on prediction model of line loss rate in transformer district based on LM numerical optimization and BP neural network. IOP Conf. Ser. Earth Environ. Sci. 252(3), 56–71 (2019)
Study on Macroscopic Performance and Characteristics of DC Arc in Condition of Short Gap Ruiyang Guan(B) , Xinlao Wei, Bo Zhu, and Zhichao Xue School of Electrical Engineering, Harbin University of Science and Technology, No.52 Xue Fu Road, Nan Gang District, Harbin, Heilongjiang, China [email protected]
Abstract. Arc development process can be directly reflected by the macroscopic performance of DC arc in condition of short gap. In this paper, a testing platform was established. The DC arc was taken as the research object in the short rodrod electrodes. Taking the gap length of 20 mm and 5 mm as examples, the macroscopic performances of arc were captured. The arc extinguishing current and arc conductance when the arc was extinguishing were measured and the formulae were fitted. Finally, the electrodes ablations were presented. The results show that: when the gap is 20 mm, the color of arc changes from blue, purple to yellow, and the arc state changes from unstable and soundable into stable and soundless. The flame stage is the most vigorous and the diameter of arc column increases to 3 times as large as before. While if the gap is 5 mm, the arc state is stable, silent and flameless, and no abrupt change in arc diameter. The arc extinguishing current and conductance are both linear functions of the gap length. Comparing with the electrode connecting to the high voltage, the electrode connecting to the ground is ablated worse. Keywords: DC Arc · Arc Characteristics · Macroscopic Performance · Short gap
1 Introduction DC arc is a difficulty and focused problem in the field of electrical engineering. In DC system, DC fault arc may be caused by breakdown of arcing horn, poor wire contact or insulation damage. But the main problem is that there is no zero point for DC current. If the fault arc failed to be extinguished, the high temperature can lead to electrical equipment damage, fire, even can cause a great influence to power system [1–3]. Therefore, the study on DC arc characteristics and performance has an important practical significance. Some researches on the DC arc in short gap have been done. Reference [4] studied the image characteristics of different gas arcs under small DC current, providing a reference for the understanding of experimental phenomena and a support for the study of environmental protection of gas arc characteristics. Reference [5] studied the morphology © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 44–51, 2022. https://doi.org/10.1007/978-981-19-1528-4_5
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characteristics of arc in the combustion process, and provided a basis for the selection of the charging voltage parameters of inductor and capacitor in the DC switching process. Reference [6] built a DC arc simulation experimental platform to study the influence of power supply voltage, current and gap length on arc characteristics. In reference [7], the temperature and shape of the arc in the circuit breaker are tested, and the test results are consistent with the actual conditions, providing a guideline for the design of circuit breaker contacts. While reference [8] studied the self-extinguishing characteristics of the latent arc of AC transmission line based on the classical Mayr arc model, and proposed the arc resistance criterion for the self-extinguishing of the AC arc. Takashi Chino, a Japanese scholar, has studied an arcing horn device which can be used to extinguish single-phase grounding arc in transmission lines [9]. Tsinghua University studied the arc’s Volt-Ampere characteristics and arc resistance characteristics in the condition that the parallel gap distance is 1m, 2m and 4m [10]. However, the actual power system has a high rated operating current and high voltage level, and the fault arc in the power system belongs to a long-gap high-current DC arc, which is a very complex and rapid time-varying process, and it is greatly affected by the randomness of discharge and external factors. Under the condition of short gap, the low current DC arc is more stable and the test results have better repeatability. In this paper, DC arc under the condition of short gap is taken as the research object to build a DC arc test platform. The extinguishing characteristics of DC arc at different gap distances were tested. The arc extinguishing current and arc extinguishing conductance were fitted. Various macroscopic performances were captured to study the evolution process of DC arc.
2 Experiments and Measuring Method 2.1 An Experimental Platform The testing platform includes a controllable DC power supply, a ZVS driver (zero-voltage switching driver (ZVSD)), a linear output transformer, a pair of rod-rod electrodes, an arc-measuring system and a grounding line, as shown in Fig. 1.
UH DC DC: ZVS AC: converter DC: AC: power 0~30V driver 0~24V transformer 0~20kV U L 220V/50Hz supply 0~6A 0~50kHz r
Fig. 1. Experimental platform
The DC voltage output by the DC power supply is converted into high-frequency AC voltage by the ZVS driving circuit, which is then sent to the linear output transformer
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to increase the AC voltage, and then loaded to the rod-rod electrode in the form of DC high voltage to generate DC arc. In the testing process, the DC arc is firstly established by using the DC high voltage to break down the gap, then the voltage and current of the arc are adjusted in two different ways respectively, and the characteristics and macroscopic performance of the arc are recorded. The two adjustment methods are as follows: 1, The current adjustment method, it keeps the voltage tap at the maximum position and gradually adjusts the current tap so that the arc current rises from the minimum value to the maximum value, and then gradually adjusts back from the maximum value until the arc extinguished. 2, the voltage adjustment method, it keeps the current tap in the maximum position, gradually adjusts the voltage tap, make the arc voltage from the minimum value increase to the maximum value, and then gradually adjusts the arc voltage back from the maximum value until the arc extinguished. 2.2 DC Controllable Power Supply The DC equipment adopts YS-RLD-3006D dual-channel output controllable DC power supply, which is produced by Shanghai Yisheng Electronic Technology Co., Ltd.. The DC power supply can realize the separate control of voltage and current with low ripple and low noise characteristics. Its rated parameters: input AC 220 V/50 Hz, output DC 0–30 V/0–6 A. 2.3 ZVS Driver ZVS driver (Zero-Voltage Switching Driver) is a driver module based on zero voltage switching technology. Because of the rapid conversion technology of the circuit, it can convert DC voltage into high-frequency AC voltage, and it is low heating, simple and reliable. It is used to drive the linear output transformer. Rated parameters of ZVS driver circuit are: input DC 12–30 V/5 A, output frequency 30–50 kHz. 2.4 Linear Output Transformer The voltage booster used in this paper is actually a linear output transformer, which is commonly used as an electronic acceleration element in the television tube, it is also known as the high-voltage package. The BSC25-NO446 high voltage package, which is produced by Baisheng Electronic Co., Ltd, can generate a DC high voltage of 20 kV to make the short gap break down and produce arc. Its input parameters are: 12–24 V/5–10 A, the highest output high voltage: DC 10–20 kV. 2.5 Measuring System The measuring system is composed of DPO-3032 oscilloscope, P6015A high voltage probe, sampling resistor R, camera and FLIR-T55901 infrared thermal imager. Oscilloscope with high voltage probe is used to measure arc voltage U D ; The arc current I D is calculated by the voltage and resistance values from two terminals of the sampling resistor, as shown in Formula (1). The camera is used to record the whole process of the test
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including arc starting, arc burning and arc extinguishing. Electrode ablation temperature was measured by FLIR-T55901 infrared thermal imager. UD = UH − UL (1) ID = UL /r Where, U H is the voltage between the ground and the high-voltage electrode. And U L is the voltage of the sampling resistor, as shown in Fig. 1. 2.6 Rod-Rod Electrodes The rod-rod electrodes are used in this test. The angle of the two electrodes can be adjusted in order to make the gap length vary from 0 mm to 25 mm. And the diameter of the copper electrode is 2 mm.
3 Experimental Results and Analysis 3.1 Macroscopic Performances When the gap distance is 20 mm and 5 mm respectively, the macroscopic shape of the arc is different. When the gap is 20 mm, the arc can be divided into 8 typical stages according to the different arc performance, while if the gap is 5 mm, the arc has only 4 forms, as shown in Fig. 2 and Fig. 3 (in the figure, the left electrode is grounded and the right electrode is connected to DC high voltage). Figure 2 and Fig. 3 are both obtained by the current adjustment method.
(a)
(b)
(e)
(f)
(c)
(g)
Fig. 2. Macroscopic performances of DC arc in 20 mm gap
(d)
(h)
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When the applied DC voltage is not high enough to break down the entire gap, and the electric field intensity on the electrode surface is greater than the ionization electrical intensity of the air, the corona discharge is locally sustained, and high frequency corona sound can be heard. When the voltage is slightly increased, it can be observed that small discharge points appear from the extreme points of the high voltage, which gradually grow longer and develop towards the grounding electrode. The tip of the discharge points is slightly blue. At this time, the sneezing sound with a higher frequency than corona appears, which can be considered as the initial stage of arc discharge, as shown in Fig. 2 (a). If the voltage continues to rise to the critical breakdown voltage of the gap, it can be seen that the thin, light blue arc channel rapidly connect the two electric tips, and then quickly goes out, and then re-discharged, accompanied with a crisp breakdown sound, as shown in Fig. 2 (b). Once the arc is established, the voltage between the two electrodes drops quickly, and with the increasing energy injected into the arc, the ionization model changes from electric ionization to thermal ionization. The heat accumulation and the number of ions increase dramatically in the arc channel, which makes the arc channel expand outwards and become thicker. The state is gradually stable, the sound disappears, the arc channel is purple, but the arc root at both ends still remains light yellow, as shown in Fig. 2 (d) and (e). As the energy further increases and is affected by heat, the air around the arc channel is ionized and a plasma layer is gradually formed covering the blue arc column. The plasma layer is yellow with a high temperature. Figure 2 (f) shows the critical state between the purple arc and the yellow arc. The plasma layer keeps gathering and accelerates the intensification of thermal ionization. The arc appears yellow with slightly thicker and more stable arc channel, as shown in Fig. 2 (g). When the arc current rises to 50 mA, the arc column diameter increases sharply, and the arc becomes into yellow flame state, burning stably and brightly like a candle, as shown in Fig. 2 (h). Under the action of thermal buoyancy and electromagnetic force, the arc is always extended upward in an arch shape at each stage. When the gap is 5 mm, there is no corona discharge and spark discharge stage. When the applied voltage get close to the gap breakdown voltage, a blue and stable arc is generated with very thin arc channel and no sound, as shown in Fig. 3 (a). If the current is slightly increased, the blue arc channel becomes slightly thicker and the surrounding area is slightly purple. The arc root on the grounding side is shorter and slightly thicker, while the arc root on the high voltage side is slightly thinner, as shown in Fig. 3 (b). As the arc current continues to increase, the arc channel gradually changes. Under the action of thermal ionization, a large amount of plasma gathers around the arc channel which makes the arc channel appear yellow. Meanwhile, blue light can be observed from the arc root at ground side, as shown in Fig. 3(c). When the current is larger than 50 mA, the state of the arc channel does not change significantly, but the color of the arc root’s emission on the ground side changes from blue to purple, while the one on the high voltage side does not change significantly, as shown in Fig. 3 (d). In summary, when the gap is 20 mm, the color of the arc channel changes from blue to purple, then to yellow. The state of arc is changed from unstable and soundable to stable and silent. The most vigorous stage is flame-like, and the diameter of arc column increases 3 times as large as before. While if the gap is 5 mm, the color of the arc channel still follows the rule of blue to purple, then to yellow, and the arc root on the
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(a)
(b)
(c)
(d)
49
Fig. 3. Macroscopic performances of DC arc in 5 mm gap
high voltage side has no obvious light. Arc state is stable, no sound, no flame stage, and the arc column’s diameter gradually increases. 3.2 Arc Extinguishing Characteristics Figure 4 shows the characteristics of the arc extinguishing current and arc extinguishing conductance according to different gap distances. 25
6 testing data
20
15
10
5 4.5 4 3.5
5 4
fitting line
5.5
testing data arc conductance/µS
extinguishing current/mA
fitting line
8
12 gap distance/mm
16
20
3
5
10 15 gap distance/mm
(a)
20
(b)
Fig. 4. Arc extinguishing characteristics
It can be seen that the relationship between arc extinguishing current and gap distance is a linear function. If the gap distance increases, the arc extinguishing current
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increases linearly. The reason is that: the smaller the gap is, the smaller the maintenance energy is required by the arc, leading to a smaller arc extinguishing current. The conductivity of arc depends on the percentage of arc channel ionization. When the gap distance increases, the arc extinguishing current increases, the percentage of arc channel ionization increases, and more charged particles gathered in the arc channel. Therefore, the electric conductivity of arc is larger. 3.3 Electrode Ablation After a large number of tests, it is found that the ablations of the high-voltage electrode and the grounding electrode are different. The surface of the grounding electrode is severely ablated and appears black, while the high-voltage electrode has almost no obvious erosion. The infrared thermal imaging device was used to test the temperature of the two electrodes. It was found that the temperature of the ground electrode was as high as 658 °C, while the temperature of the high voltage electrode was slightly higher than the room temperature, as shown in Fig. 5. The reason is that a large number of electrons are emitted from the grounding electrode under the action of high electric field intensity, and the electron concentration is large, so the collision is violent, which causes the temperature of the grounding electrode higher.
Fig. 5. Electrode ablation
4 Conclusions In this paper, the macroscopic performance and extinguishing characteristics of DC arc in the condition of short gap are studied. 1, When the gap is 20 mm, the color of the arc changes from blue to purple, then to yellow. The state of arc is changed from unstable and soundable to stable and soundless, and the most vigorous stage is flame-like, in which the diameter of arc channel increases 3 times as large as before. While if the gap is 5 mm, the arc state is stable, no sound, no flame stage, and arc’s diameter gradually increase. 2, The relationship between arc extinguishing current, conductance and gap distance are linear functions.
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3, The electrode temperature on the ground side is as high as 658 °C, and the electrode ablation is serious. The temperature of the high voltage electrode is just slightly higher than the room temperature, and the electrode has no obvious ablation. Acknowledgments. This work is funded by University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2018198).
References 1. Chino, T., Iwata, M., Imoto, S.: Development of arcing horn device for interrupting ground fault current of 77 kV overhead lines. IEEE Trans. Power Delivery 20(4), 2570–2575 (2005) 2. Ruiyang, G., Zhidong, J.: Performance and characteristics of a small-current DC arc in a short air gap. IEEE Trans. Plasma Sci. 47(1), 746–753 (2019) 3. Wang, J., Wu, D.: Development of an arc-extinguishing lightning protection gap for 35 kV overhead power lines. IET Gener. Transm. Distrib. 11(1), 2897–2901 (2017) 4. Zhibin, C., Xu, J., Zongqian, S.: Image characteristics analysis of different gas arcs under small DC current. High Voltage Apparatus 52(12), 199–203 (2016). (in Chinese) 5. Taotao, Q., Zhihui, H., Enyuan, D.: Study on dynamic influencing factors of shape evolution of DC vacuum arc. High Voltage Apparatus 51(11), 59–63 (2015). (in Chinese) 6. Lan, X., Zeyu, Z., Jun, Y.: Mathematical model and characteristics of low current DC fault arc. Transactions of China Electrotechnical Society 16(3), 1–11 (2012). (in Chinese) 7. Su, B., Haiyun, L., Yonggang, G.: Arc shape and arc temperature measurements in SF6 highvoltage circuit breakers using a transparent nozzle. IEEE Trans. Plasma Sci. 46(6), 2120–2125 (2018) 8. Xianglian, Y.: Study on self-extinguishing characteristics of single-phase grounding arc of AC transmission line. China Electric Power Research Institute, Beijing China (2009). (in Chinese) 9. Chino, T., Iwata, M., Imoto, S.: Development of arcing horn device for interrupting groundfault current of 77 kV overhead lines. IEEE Trans. Power Delivery 20(4), 2570–2575 (2005) 10. Yu, Z., Yu, J., Rong, Z.: Arc characteristics of parallel gap for composite insulator. in Proceedings on International Conference on Lightning Protection, pp. 107–111, Shanghai, China (2014)
Improved Feedback Compensated Closed Loop Stator Flux Observer Shuyu Wang, Zhenpeng Luo(B) , Siqing Zhang, Yue Han, and Xiaolong Wang Inner Mongolia Key Laboratory of Electromechanical Control, Inner Mongolia University of Technology, Mongolia, China [email protected]
Abstract. Aiming at the DC bias and integral drift of pure integral stator flux observer, an improved feedback compensated closed-loop flux observer is designed. Firstly, the orthogonal relationship between the flux linkage and the back electromotive force (EMF) is used to control the cosine of the angle between them to zero. Then through simple multiplication operation, the dynamic compensation of flux linkage is realized, and the correct integration result is obtained. Theoretical analysis shows that the DC component in the integration result can be completely eliminated. At the same time, the simulation results prove the good performance of the improved observer and the correctness of the theoretical analysis. Theoretical analysis shows that, the DC component in the integration result can be completely eliminated. The simulation results verify the good performance of the improved observer and the correctness of the theoretical analysis. Keywords: Flux observer · Stator flux observer · Induction Motor (IM) · Direct Torque Control (DTC)
1 Introduction DTC uses the measured stator voltage and current to calculate the position of the current motor stator flux vector, and then selects the appropriate voltage vector through the numerical difference between the flux and torque and the given value, so as to realize the IM control. Calculating the position of stator flux vector means observing the amplitude and phase of stator flux, which is the core part of the whole DTC and the key to realize the performance of direct torque control [1, 2]. At present, the voltage model observer which is widely used has obvious pure integral problems, such as DC bias and integral drift [3, 4]. Therefore, this paper analyzes the traditional flux observation method and feedback compensation closed loop flux observer, and improves the feedback compensation closed loop flux observer. Compared with the original feedback compensation closed loop flux observer [5], the improved structure adjusts the input value of PID controller, that is, the cosine value of the angle between the flux and the back EMF vectors, which makes the physical meaning of the feedback compensation control value clearer, and uses simple multiplication operation to replace the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 52–61, 2022. https://doi.org/10.1007/978-981-19-1528-4_6
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original complex vector decomposition and synthesis process. The structure of the flux observer is simplified, the response speed is accelerated, and the dynamic performance of the induction motor is improved, which is easier to realize in engineering.
2 Mathematical Models of Flux Linkage and Back EMF In steady state, two phases are stationary αβ In coordinate system, stay α and β The back EMF component of the stator esα and esβ can be expressed as: esα = esαa + c1 (1) esβ = esβa + c2 where, esαa and esβa are the sinusoidal components of the stator back EMF on the α and β axes respectively; c1 and c2 represent the DC components in esα and esβ . The sinusoidal components of the stator back EMF on the α and β axes can be expressed as: esαa = Em cos(ω1 t + ϕ0 ) (2) esβa = Em sin(ω1 t + ϕ0 ) where, E m is the amplitude; ω1 is the angular frequency; ϕ 0 is the initial phase. esα and esβ of the stator back EMF on the α and β axes can be obtained by integrating respectively, the component of the stator flux linkage on the α and β axes Ψ sα and Ψ sβ : Ψsα = Ψsαa + Ψsα0 + c1 t (3) Ψsβ = Ψsβa + Ψsβ0 + c2 t Where, Ψ sαa and Ψ sβa are sinusoidal components of Ψ sα and Ψ sβ , respectively, which satisfy the equation: e Ψsαa = Eωm1 sin(ω1 t + ϕ0 ) = ωsβa1 (4) Ψsβa = − Eωm1 cos(ω1 t + ϕ0 ) = − eωsaa1 Ψ sα0 and Ψ sβ0 are DC offsets of Ψ sα and Ψ sβ respectively, and their expressions are as follows: Ψsα0 = − Eωm1 sin ϕ0 (5) Ψsβ0 = Eωm1 cos ϕ0 c1 t and c2 t are the integral drift components of Ψ sα and Ψ sβ , respectively [6].
3 Traditional Flux Observation Methods 3.1 Pure Integral Observer According to the above mathematical formulas of the back EMF and flux linkage, the direct integration method can not get the expected result. Although the error of the initial value of the integral can be eliminated by changing the initial phase of the back electromotive force, the DC component still exists [7].
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3.2 Low-Pass Filter Flux Observer The Laplace transform expression of the pure integral flux observer is: Ψs =
1 es S
(6)
By introducing a low-pass filter, the expression of the low-pass filter flux observer can be obtained: Ψs =
1 ωc es + Ψs s + ωc s + ωc
(7)
where, ωc is the cutoff frequency. The structure of the low-pass filtered flux observer is shown in Fig. 1 [8]. The low-pass filter flux observer 1/(s + ωc ) is used to calculate the pure integrator and filter or suppress the DC component generated by the integration. The performance of the low-pass filtered flux observer is better than that of the pure integral observer, but the disadvantage is that the amplitude and phase of the output flux will change with the input ωc , so it is more suitable for use at low speed.
es
Ψs
Fig. 1. Structure diagram of low-pass filter flux observer
3.3 Saturation Inhibited Flux Observer
esα
Ψsα
|Ψ |
P/C
esβ
θ
C/P Ψsβ
Fig. 2. Structure diagram of saturation suppression flux observer
The structure of the saturation inhibition flux observer is shown in Fig. 2. The method of limiting the amplitude is adopted to suppress the integral drift. The method of limiting amplitude is used to restrain integral drift. This method improves the observation accuracy of flux linkage to a certain extent, and eliminates the deviation between actual state and actual value [9].
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4 Improved Feedback Compensated Closed Loop Flux Observer 4.1 Feedback Compensated Closed Loop Flux Observer The improved amplitude-limited integral observer has poor dynamic performance, and is only applicable to the occasions where the amplitude of stator flux linkage is unchanged. When the load changes, the error of the observation results is large. In order to solve the problem of poor dynamic performance of stator flux linkage, some scholars put forward the idea of feedback compensation control, that is, compensation control through the orthogonality of flux linkage vector and back EMF vector. The structure is shown in Fig. 3 [10]. esαΨsα +esβΨsβ
esα
PID
÷
P/C
Ψsα
|Ψ |
θ
C/P
esβ
Ψsβ
Fig. 3. Initial model of feedback compensated closed loop flux observer
The flux observer can improve the dynamic performance of the stator flux, but the physical meaning of the feedback compensation control is not clear. 4.2 Improved Feedback Compensated Closed Loop Flux Observer The relationship between the two vectors of stator flux flux and back electromotive force can be expressed as: Ψ
esα Ψsα + esβsβ =0 coses , Ψ s = 2 + e2 · Ψ 2 + Ψ 2 esα sα sβ sβ
(8)
If the orthogonal relationship of two vectors is destroyed due to DC bias or integral drift, Eq. (8) will not be equal to zero. Therefore, the closed loop feedback control is designed to make Eq. (8) equal to zero, so as to suppress the influence of DC bias or integral drift. Based on the expression of pure integral observer, the feedback compensation vector Z is introduced, and the expression of feedback compensation closed loop flux observer is as follows: Ψs =
1 ωc es + Z s + ωc s + ωc
Where, Z is the feedback compensation signal.
(9)
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When Z = 0: that is, Ψ s = Ψ LPF , is a first-order low-pass filter. Ψs =
1 es = Ψ LPF s + ωc
(10)
when Z = Ψ s , it is a pure integral observer, as shown in Eq. (6). It can be seen that different values of Z will lead to different types of integrators. According to the orthogonal relationship between back EMF and flux linkage, the feedback compensation vector Z should make cos converges to zero. Based on this relationship, the difference between cos and zero is used as the input of the PID regulator, and the output of the PID regulator adjusts the feedback compensation amount Z sα and Z sβ , so as to obtain the expression (11) and structure diagram of the improved feedback compensation closed loop flux observer, as shown in Fig. 4. ⎧ ωc 1 Ψsα = s+ω esα + s+ω Zsα ⎪ ⎪ c c ⎪ ω 1 ⎪ c ⎪ Ψ = e + Z ⎪ ⎨ sβ s+ωc sβ s+ωc sβ e Ψ +esβ Ψsβ ki Zsα = kP + s + kd s · 2 sα sα · Ψsα (11) 2 · Ψ 2 +Ψ 2 ⎪ esα +esβ sα sβ ⎪
⎪ ⎪ esα Ψsα +esβ Ψsβ ⎪ k ⎪ ⎩ Zsβ = kP + si + kd s · 2 2 2 2 · Ψsβ esα +esβ · Ψsα +Ψsβ
esα Ψsα
Zsα
Ψsα
PID
0 Ψsβ
esβ
Zsβ
Ψsβ
Fig. 4. Structure diagram of improved feedback compensated closed loop flux observer
According to the vector schematic diagram in Fig. 5, under ideal conditions, Ψ s and es are orthogonal, the output of formula (8) is zero, that is, the control quantity is the same as the given value, and the output of PID controller is zero, so the feedback does not work. If the initial value of the integral is not zero or the integral drifts, The orthogonal relationship between Ψ s and es is broken, which makes is greater than 90°, at this time, the calculated output of Eq. (8) is less than zero, and the output of PID controller is reduced. By reducing the amplitude of Z, let Ψ s ’ approach Ψ s until the orthogonal relationship between Ψ s and es was restored. When is less than 90°, feedback adjustment can also restore the orthogonality between Ψ s and es .
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β
es
ΨLPF
φ Ψs
α Ψ´ s
Z Fig. 5. Vector diagram of improved feedback compensation closed loop flux observer
4.3 Analysis of Algorithm Principle and Proof of Convergence Because the goal of feedback compensation control is to get the correct flux observation results, that is, the feedback compensation control is to get the correct flux observation results Ψ sα and Ψ sβ converge to Ψ sαa and Ψ sβa that is cos converge to zero. The Laplace transformation is taken for the opposite formula (1) and formula (3), and the formula (12) and formula (13) are obtained. Take formula (12) and formula (13) into formula (8), and arrange available formula (14). ⎧ ⎨ L(esα ) = Em · s cos ϕ02−ω12 sin ϕ0 + c1 s s +ω1 (12) ⎩ L esβ = Em · s sin ϕ02+ω12cos ϕ0 + cs2 s +ω 1
⎧ ⎨ L(Ψsα ) = Em · s sin ϕ02+ω12cos ϕ0 − Em sin ϕ0 + c21 ω1 sω1 s s +ω1 s cos ϕ0 −ω1 sin ϕ0 Em cos ϕ0 E m ⎩ L Ψsβ = − ω · + sω1 + cs22 1 s2 +ω2
(13)
1
L[coses , Ψs ] =
1 2 2 2 +2E cos ϕ sc +ω c +2E sin ϕ sc −ω c sEm m m 0 1 1 2 0 2 1 1 + c1 +c2 2 2 s2 s +ω1
×
×
1 2 +2sE cos ϕ sc +ω c +2sE sin ϕ sc −ω c s 2 Em m m 0 1 1 2 0 2 1 1 + c2 + c2 1 2 s2 +ω12
2ω1 Em cos ϕ0 (sc1 + ω1 c2 ) − ω1 Em sin ϕ0 ω1 (c1 + c2 ) + 2sω1 c2 Em sin ϕ0 + s2 Em sin ϕ0 (c1 − c2 )
ω1 s2 + ω12
(14) According to the final value theorem: lim coses , Ψ s = lim sL[coses , Ψ s ] = 0
t→∞
s→0
(15)
From Eq. (15), it can be known that cos converges to zero, that is, Ψ sα and Ψ sβ can converge to Ψ sαa and Ψ sβa , that is, the improved flux observer of theoretical analysis is correct and feasible, and the expected flux linkage results can be obtained.
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5 Simulation Analysis and Verification 5.1 Comparison of Several Flux Observers In order to observe the operation effect of the improved feedback compensation closed loop flux observer, the above five flux observer models are built in the simulation platform. The simulation time is set at 20 s, ωc is set as 100 rad/s, and the input is set as the following two different functions: 0–10 s: esα = sin(3t) + 0.01
(16)
esβ = cos(3t) + 0.01 10–20 s: esα = 0.4 sin(6t + 30) + 0.01 esβ = 0.4 cos(6t + 30) + 0.011
(17)
At 0 s, the input back EMF amplitude is 1 V, the frequency is 3 Hz, the initial phase is 0, and the DC offset is set to 0.01. At 10 s, the amplitude of the input back EMF changes to 0.4 V, the frequency changes to 6 Hz, the initial phase changes to 30, and the DC offset remains unchanged. The simulation and comparison waveforms of the five observers are obtained, as shown in Fig. 6. Feedback closed loop observer Improved feedback closed loop observer Pure integral observer
Ψ/(Wb)
1 0.8 0.6 0.4 0.2 0 -0.2 -0.4
Saturation rejection observer
low-pass filter -0.6 Ideal flux linkage
0
2
4
6
8
10
t/(s)
12
14
16
18
20
Fig. 6. Simulation comparison waveforms of five kinds of flux observers
As can be seen from Fig. 6, the output waveform offset effect of the pure integral observer is the most obvious, and its DC component becomes larger and larger as time goes on; The introduction of low-pass filter can effectively suppress the DC offset of the output waveform, but also produce the phase and amplitude deviation; Before 10 s, the output waveform of the saturation suppression observer tracks the ideal curve. After 10 s, the output waveform has amplitude deviation due to the sudden change of input, but it is also gradually approaching the ideal curve; Compared with the former three flux observer, the feedback closed loop flux observer can track the ideal flux waveform better; The improved feedback closed loop flux observer further optimizes the output waveform, so that it can quickly coincide with the ideal curve from the beginning, and
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output stably. After the input changes, it can also respond quickly, coincide with the new ideal curve, and then output stably, reflecting good stability and dynamic response performance. 5.2 Simulation of Direct Torque Control In the direct torque control system of three-phase IM, the pure integral observer and the improved feedback compensation closed loop flux observer are used to observe the flux chain respectively, and the simulation results are compared. The parameters of the IM are shown in Table 1 below. The system simulation structure is shown in Fig. 7. In order to observe the performance difference between the improved flux observer and pure integral flux observer in the simulation environment, the back EMF DC bias is manually set to 0.2 V; The simulation time is set as 2 s; The control target of the system is to control the speed of asynchronous motor to 1000 r/min; The pure integral flux observer is used to observe the flux in the first second, and the improved flux observer is used in the second. The performance of the improved observer is analyzed by comparing the output flux and speed waveforms of two different observers. Table 1. Induction motor parameters Parameters
Value
Parameters
Value
Rated power/(Kw)
20
Stator inductance/(mH)
4
Rated frequency/(Hz)
50
Rotor inductance/(mH)
2
Number of pole pairs
2
Stator resistance/()
0.435
Mutual inductance/(mH)
69.31
Rotor resistance/()
0.816
Te PI
1000
ω
Ψ s*
ΔTe ΔΨs φ Ψs Te
Three position hysteresis Two position hysteresis
Orthogonal feedback flux observation
Voltage vector selection
isα isβ usα αβ usβ abc
sA sB sC isa isb usa usb
IM Fig. 7. System simulation structure diagram
By analyzing the comparison chart of flux waveform in Fig. 8 and speed waveform in Fig. 9, it can be seen that when the pure integral flux observer is used, the output flux
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waveform is asymmetric up and down, and the integral drift phenomenon is obvious. Although the speed waveform is controlled to a given value of 1000 r/min, the fluctuation amplitude is large, and the dynamic response speed is slow; When the improved closed loop flux observer is used, the output flux waveform has no integral drift, and the speed can quickly return to the given value and remain stable.
1.5
Pure integral observer Improved feedback closed loop observer
1
Ψ/(Wb)
0.5 0
-0.5 -1 -1.5 0
0.2 0.4 0.6 0.8
1
t/(s)
1.2 1.4 1.6 1.8
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Fig. 8. Flux contrast waveform
n/(r·min-1)
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t/(s)
Fig. 9. Speed contrast waveform
It can be seen that the pure integral observer can not solve the problem of DC bias of back EMF, which leads to the integral drift of flux linkage observation and obvious jitter of speed waveform; The improved flux observer can eliminate the DC offset error, avoid the integral drift phenomenon, and make the speed track the given value quickly and stably. At the same time, the feasibility of the improved feedback closed loop flux observer is verified.
6 Conclusion Based on the feedback closed loop flux observer, this paper uses the difference between the cosine of the angle between the flux and the back EMF and zero as the control variable, and uses simple multiplication operation to replace the original complex vector
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decomposition and synthesis process to compensate the flux. Theoretical analysis and simulation results verify the correctness and feasibility of the improved observer, that is, it can eliminate the influence of the initial integral value and DC offset, improve the accuracy of the flux observer and the dynamic performance of the speed control system, and is more suitable for engineering applications.
References 1. Ji, Z.: Direct torque speed control system based on optimized flux observer. J. Lanzhou Coll. Arts Sci. (Nat. Sci. Ed.) 35(01), 60–64 (2021). (in Chinese) 2. Wang, D., Wang, R., Lai, C.: Improvement and Simulation of traditional asynchronous motor direct torque control. Electric Drive 48(03), 9–12 (2018). (in Chinese) 3. Xin, Z.: A novel stator flux observer of induction motor for robot. Mech. Des. Manuf. 09, 205–209 (2020). (in Chinese) 4. Taherzadeh, M., Hamida, M.A., Ghanes, M.: A new torque observation technique for a PMSM considering unknown magnetic conditions. IEEE Trans. Ind. Electr. 68(3), 1961–1971 (2021) 5. Hu, J., Wu, B.: New integration algorithms for estimating motor flux over a wide speed range. IEEE Tran. Power Electron. 13(5), 969–977 (1998) 6. Jie, Z., Jianyun, C., Xudong, S.: An improved integral algorithm for the voltage model of induction motors based on the orthogonality of back EMF and flux. Acta Electrotechn. Sinica 29(03), 41–49 (2014). (in Chinese) 7. Colin, S.: Adaptive speed identification for vector control of induction motor without rotational transducers. IEEE Trans. Ind. Appl. 28, 1054–1061 (1992) 8. Li, C.E., Wang, J., Zhang, Y.: Flux linkage observation method for significantly improving dynamic performance of induction motor. Electric Drive 43(09), 3–7 (2013). (in Chinese) 9. Zhang, Y.: Research on the key technology of asynchronous motor torque control system based on indirect vector control. HeFei University of Technology (2019). (in Chinese) 10. Xiao, X.: Dual PWM Variable Frequency Speed Control System Integrated Control Strategy. University of Science and Technology Beijing (2017). (in Chinese)
Influence of Long-term Cooling and Heating Cycles on the Interface Pressure of Cable Accessories Jingtao Huang1 , Yu Jin2 , Tao Zhou2 , Jin Yang2 , Yuming Wu2 , Pengfei Meng1(B) , and Kai Zhou1 1 College of Electrical Engineering, Sichuan University, Wu Hou District, Chengdu, China
[email protected]
2 Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming, China
[email protected]
Abstract. The interface pressure between the cable accessories and body seriously affects the stable and safe operation of cable. The interface pressure test system was designed to explore the changing rule of the cable accessories interface pressure under long-term cooling and heating cycle. The control mode of high and low temperature cycle was used to simulate the cooling and heating load running state of cable, and the dynamic thermo-mechanical analysis of silicone rubber materials was carried out. The results show that the elastic modulus of silicone rubber increases obviously at the test temperature. In addition, as aging time increases and aging temperature increases, unrecoverable deformation occurs in cable accessories, resulting in reduced interface pressure and reduced sealing performance. At the same time, the experimental results was verified by finite element simulation of interface pressure based on cable accessory parameters. Keywords: Cable accessories · Cooling and heating cycles · Interface pressure · Silicone rubber
1 Introduction Cable joints and cable terminals are collectively referred to as cable accessories [1], which are essential components of cable lines. Meanwhile, cable accessories are also weak links and fault prone parts of the cable system [2]. Cable terminal can be divided into outdoor terminal, indoor terminal and equipment terminal [2]. The types of cable joints are mainly including of cold shrinkage, heat shrinkage, casting, wrapping and prefabrication [3]. In the natural state, the inner diameter of the cold-shrinkable intermediate joint is smaller than the outer diameter of the cable body. When the cold-shrinkable tube leaves the factory, the plastic support bar is used to hold it open so that its inner diameter is greater than the outer diameter of the cable [4–6]. In use, the cold shrinkable tube is first covered outside the cable body, then the plastic support strip is pulled out, the cold shrinkable tube is shrunk back, and interface pressure is generated between the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 62–73, 2022. https://doi.org/10.1007/978-981-19-1528-4_7
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interference fit and the cable body, so that the cold shrinkable tube is firmly installed to the cable body [5, 6]. The interface pressure between the cable body and the accessories will affect the electrical strength of the cable accessories, and the interface pressure is also the key to determining the interface tightness of the cable accessories and ensuring the long-term safe and reliable operation of the cable accessories [7, 8]. Many scholars have done a lot of research on the interface pressure of cable accessories. Literature [9] established the theoretical calculation model of the interface structure of the cable cold shrinkage intermediate joint and obtained the theoretical calculation method of the interface pressure. Literature [10] used the method of combining field measurement and numerical simulation to point out that during the installation and operation of cables, the insulation interface pressure at the cable terminal increases with the increase of voltage. Literature [11] analyzed the electrical and mechanical properties of three cables with different service life and studied the influence of elastic modulus change of insulating layer on interface pressure of aging cables. In reference to a cable fault case, the influence of the amount of interference and the thickness of the silicone rubber insulation layer on the interface stress was studied by theoretical analysis and finite element simulation [12]. Although some scholars believe that temperature had an influence on the interface pressure of cable accessories in previous research [13, 14], no scholars have focused on the effect of the interface pressure of cable accessories on long-term cooling and heating load cycles. Therefore, the paper will study the influence of long-term cooling and heating load cycles on the interface pressure, and then explore the key factors that affect the sealing performance of the accessory interface.
2 Experimental Settings 2.1 Test System and Experimental Platform The holding force of cable accessories is produced by the interference fit between silicone rubber (SR), the insulation material of the accessory, and XLPE (the insulation of the cable body). In the production process of cold-shrinkable cable accessories, plastic support strips are used to make the accessory in an expanded state [5, 6]. In the process of accessory installation, the plastic support bar is drawn out, and the silicone rubber insulation is tightened on the cable insulation surface. Due to the expansion state of F21 SR
XLPE
F1
F2 2 Pressure Sensor
Voltage Conversion Signal Acquisition
Fig. 1. Test system of interface pressure
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SR, radial stress F1 and tangential stress F21 and F22 are generated. The built interface pressure testing system for cable accessories is shown in Fig. 1. The clamping force of cable joints is collected by built-in pressure sensors. The test system is composed of two parts, including hardware measuring device and software analysis system. Hardware measuring device mainly consists of pressure sensor, measuring bridge, A/D conversion module, single chip microcomputer and power supply module. Its work is that the strain value measured by the pressure sensor on the cable accessory is output in the form of resistance value, and then the resistance analog signal is converted into voltage digital signal that can be collected by the SCM through the voltage conversion module and A/D conversion module. At last, the data is uploaded to the upper computer through the serial port mode of the SCM. The software analysis system is composed of the built-in mechanical model of cable joints and the algorithm program of material parameters under different conditions. Its function is to calculate the interface pressure of cable accessories by combining the strain measured by the measuring device with the mechanical model and material parameters of the accessories. To explore the influence of temperature and aging time on the interface sealing performance of cable accessories, a cooling and heating cycle aging platform was built for cable accessories, and a long-term cooling and heating cycle aging experiment was conducted on actual cable accessories. In the aging experiment of cold and heating cycle, the high and low temperature alternating humidity and heating test chamber is used to control the temperature change of the cable joint and realize the cold and heating cycle. The RGDJS-800 test box produced by Surui Electronic Equipment Co., Ltd was selected for the high and low temperature alternating humidity and heat test box. Because the electrical structure of the insulation interface between the cable intermediate joint and the cable terminal is similar, the 10 kV 95 mm2 single-core indoor cold-shrinkable terminal produced by Suzhou Xirong Electric Co., Ltd. is selected as the experimental object for the interface pressure test. The schematic diagram of the cold and heating cycle experimental system is shown in Fig. 2. The pressure sensor selected for the cooling and heating cycle aging test is the FlexiForce HT201 thin-film pressure sensor produced by Tekscan, as shown in Fig. 3. The sensor is suitable for high temperature applications and can measure pressure in an environment up to 200 °C. The sensitivity of the sensor in the detection range of 0–200 N is 0–50 lb, and the interface pressure of the cable accessories is generally 0.1–0.5 MPa, so the sensor meets the measurement range of the interface pressure of the stress cone.
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Fig.2 Diagram of test system
Fig. 3. FlexiForce HT201 thin film pressure sensor
In the experiment, the thin film pressure sensor is attached to the interface of cable accessories. When the interface produces strain, the thin film pressure sensor pasted on it will undergo the same mechanical deformation, causing corresponding changes in the resistance of the strain gauge. The resistance strain gauge will convert the mechanical quantity into the resistance variation output. 2.2 Test Method Before installing the cold shrinkable tube of the terminal joint, the sensor’s induction area is attached to the position where the stress cone contacts with the main insulation of the cable, which is about 10 mm above the truncation point of the outer semi-conductive layer. Fig. 4 is the installation position diagram of the thin film pressure sensor.
Pressure Sensor
Cold Shrinkable Cable Terminal
Fig. 4. Installation diagram of pressure sensor
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Semi ü conductive Layer
XLPE
Ground Line PVC ü tape
Thin Film Pressure Sensor
Outer Semi ü conductive Layer
Sensor Data Line
Fig. 5. Interface pressure test diagram
The sensor is fixed on the outer sheath of the cable b adhesive tape to fix the sensor. Then the cold shrinkable tube is covered and the support bar in the cold shrinkable tube is slowly drawn out, so that the thin film pressure sensor is as flat as possible attached to the surface between the stress cone and the main insulation to ensure the detection effect. The cable terminal with a thin film pressure sensor installed on the interface is shown in Fig. 5. The aging temperature of the cooling and heating load cycle experiment was set at 130 °C, and every 24 h was a cycle. Each cycle was aged at 130 °C for 8 h and stood at room temperature at 25 °C for 16 h. The aging process was all carried out in the oven as shown in Fig. 2. In each cycle, the current interface pressure was quickly recorded several times after the temperature was stable in the heating and cooling stages, and the average value was obtained to avoid accidental errors.
3 Experimental Results and Analysis The cable terminal samples with the built-in thin film pressure sensor were subjected to 54 cycles of cooling and heating aging. Fig. 6 shows the change curve of the interface pressure values of the cable accessories measured at 25 °C during the cooling stage in the cooling and heating cycles for 54 days. Figure 7 shows the change curve of the interface pressure value of the cable accessories measured at 130 °C in the heating stage during the cooling and heating cycle for 54 days. To eliminate the interference in the testing process and better reflect the changing trend, the measured results were fitted exponentially, and the fitting results were shown as the smooth curves in Fig. 6 and Fig. 7. As can be seen from Fig. 6 and Fig. 7, in the same cooling and heating load cycle, the interfacial pressure at 130 °C is about 0.54 MPa, which is higher than that at 25 °C. It can be found from the fitting results that, with the increase of the cooling and heating cycle aging time, the interface pressure of the first
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Interface Pressure (MPa)
0.52 0.50 0.48 0.46 0.44 Interface Pressure Fitting Result
0.42 0.40 0
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Fig. 6. Change law of interface pressure at 25 °C
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0.58 Interface Pressure Fitting Result
0.57 0.56 0.55 0.54 0.53 0.52 0.51 0
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Fig. 7. Change law of interface pressure at 130 °C
20 aging cycles is basically unchanged at 25 °C, while the interface pressure of the last 34 aging cycles decreases rapidly from about 0.5 MPa to about 0.4 MPa. This indicates that the stress relaxation phenomenon has obviously occurred in the material. At 130 °C, the interfacial pressure is maintained at about 0.54 MPa with the aging time. May be the main reasons for this phenomenon is that when the temperature is higher, the main factors of affecting the interface pressure is no longer the elastic modulus of material, but material coefficient of thermal expansion[15]. Due to the high sensitivity to thermal silicone rubber materials, therefore under 130 °C measured interface pressure always maintain the level of high and unstable. After the experiment, the cable accessories were peeled. Figure 8 is the accessories before and after aging. It is observed that the aging cable accessories produced obvious relaxation phenomenon, which also verified from the side that the silicone rubber material underwent stress relaxation under the cooling and heating cycle.
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Fig. 8. Accessories before (left) and after (right) aging
4 Simulation Verification To further verify the law of interface pressure relaxation of cable accessories under the cooling and heating cycle, the silicon rubber material of cable accessories before and after aging was sliced, and the sample was analyzed by dynamic thermomechanical (DMA) analysis. The sample is a 6 mm × 25 mm rectangular sample. The elastic modulus of the material at different temperatures is measured, and the interface pressure of the cable accessory is simulated and analyzed. DMA test was conducted on the new cable accessories and the cable accessories after 54 days of cyclic aging under heating and cooling loads. The test results are shown in Fig. 9. It can be seen from Fig. 9 that the elastic modulus of silicone rubber insulation material for cable accessories before and after aging decreases gradually with the increase of temperature. The elastic modulus of silicone rubber increases significantly in the temperature range of 25–130 °C after 54 days of cyclic aging of cable accessories under cooling and heating loads. At 25 °C, the elastic modulus increases from 1.675 MPa to 1.959 MPa, while at 130 °C, the elastic modulus increases from 1.444 MPa to 1.632 MPa. 2.0 1.959
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Dimensions of the accessories in the initial state and after aging (all without support bars) were measured. The inner diameter and thickness of the accessories in the initial state and after aging were shown in Table 1. Where R is the inner diameter of the accessory; D is the thickness of the accessory; d is the size of diameter expansion, which is the cable insulation radius (measured 10.8 mm) minus the inner diameter of the accessory; E is the elastic modulus, which refers to the DMA measurement results. Table 1. Parameters of accessories State of the accessories Normal Aging 54 days
r/mm
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9.0
9.1
1.8
1.665
10.2
8.0
0.6
1.942
As can be seen from Table 1, the diameter expansion and thickness of the cable accessories decreased after aging. The diameter expansion decreased from 1.8 mm before aging to 0.6 mm after aging, and the thickness decreased from 9.1mm before aging to 8.0 mm. The reason for this phenomenon is the “respiration effect” [16] of silicone rubber materials due to repeated thermal expansion and contraction under the cold-heat cycle. Literature [16] show that repeated respiration can accelerate the stress relaxation of polymer materials, which is consistent with the experimental results in Table 1. In addition, it is also related to the residual crosslinking agent in silicone rubber and the evaporation of small molecular products produced in the aging process [17]. Since the cable accessory is a cylindrical symmetrical structure [18], the diameter expansion model of silicone rubber parts is built with the help of simulation software, as shown in Fig. 10. As the cable accessory has a cylindrically symmetrical structure, a 1/4 ring simulation model is made in the simulation software, and the model boundary classification and mesh division are shown in Fig. 11. The model data were adjusted by referring to the measured data in Table 1. The density and Poisson’s ratio of silicone rubber before and after aging were ignored. The density of SR material was set as 1.08 g/cm3 and the Poisson’s ratio was 0.499. Symmetric constraints are imposed on boundary 1 and boundary 2, and displacement boundary conditions are imposed on boundary 3.
Fig. 10. The model of Cable accessory
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Fig. 11. Boundary condition (left) and mesh subdivision (right)
The built-in solid mechanical field of the simulation software is used to conduct mechanical analysis of the cable accessories at normal temperature before and after aging [19]. The simulation results of the interface pressure of the cable accessories are shown in Fig. 12. It can be found from the simulation results that the interface pressure of the cable joint decreases from 0.376 MPa to 0.278 MPa after the cyclic aging of the cooling and heating load for 54 days. The real interface pressure of the accessories decreases from 0.5 MPa to 0.4 MPa. Although there is a certain error between the simulation value and the measured value, it can be found that the interface pressure of the accessories decreases with the aging time. The above errors are mainly caused by bending when the thin film pressure sensor is attached to the insulating surface of the cable, and its pressure-resistance curve will be offset. It is difficult to eliminate the errors of measuring surface pressure with this type of sensor through calibration. In addition, the assumption of simulation is that silicone rubber is a single uniform component. In actual industrial production, silica and other components will be doped into silicone rubber to enhance its various properties [20]. As silica is not uniformly distributed, various parameters in the simulation process cannot be fully fitted with the real data. It can be concluded that
Interface Pressure (MPa)
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0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 -5
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Fig. 12. Simulation results of interface pressure
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the simulation results are consistent with the actual measurement results taking these factors into consideration. In summary, the following conclusions can be drawn from the research and analysis. Under the action of long-term cooling and heating cycle, cable accessories on the one hand will accelerate the stress relaxation of silicone rubber material and produce irreversible deformation, leading to the decrease of interface pressure after contraction. On the other hand, it will also affect the elastic modulus of the material, resulting in the change of interface pressure. In addition, stress relaxation phenomenon is more obvious when cable accessories resume working at low temperature under the action of cooling and heating cycle [21, 22]. Relevant studies have shown that at low field strength, there is a large amount of charge accumulation at the interface between silicone rubber and crosslinked polyethylene [23, 24]. Therefore, the status of low load and low operating temperature of cable is worth paying close attention to in the process of on-site electrified operation. The aging temperature and time of the cyclic aging of the cooling and heating load are the main factors affecting the change of the interface pressure of the cable joint.
5 Conclusion The article design the interface pressure measurement system based on thin film pressure sensor, built aging experiment platform of cooling and heating cycles, measured the cable accessories interface pressure through the built-in thin film pressure sensor, and analyzed dynamic thermal mechanical of the cable accessories before and after aging. Finally, the interface pressure of cable accessories is simulated and analyzed by finite element method, and the simulation results are in good agreement with the experimental results. The study drew the following conclusions: (1) The experimental results show that the cooling and heating cycle of cable accessories during normal operation will accelerate the aging of silicone rubber materials, resulting in stress relaxation of the materials. With the increase of aging time and aging temperature, the cable accessories produce unrecoverable deformation, the interface pressure drops, and the interface tightness decreases. (2) The experimental and simulation results show that the cause of stress relaxation of accessory materials is the change of elastic modulus of the material and the diameter expansion rate of the accessory, and the cause of this change is the "respiration effect" caused by repeated thermal expansion and contraction of the material. (3) Under the condition of cooling and heating cycle, the stress relaxation of cable accessories in low temperature state is more obvious than that in high temperature state in each cycle. This indicates that the cable accessory is easier to detect the fault in the state of low load and low operating temperature.
Acknowledgements. We would like to thank the participants who take part in this study. This work was supported by National Natural Science Foundation of China (51877142) and Basic Applied Study of Sichuan Province (2021YJ0538).
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References 1. Suh, K.S., Nam, J.H., Kim, J.H., et al.: Interfacial properties of XLPE/EPDM laminates. IEEE Trans. Dielectrics Electr. Insul. 7(2), 216–221 (2000) 2. Fournier, D.: Aging of defective electrical joints in underground power distribution systems. In: Electrical Contacts - 1998. Proceedings of the Forty-Fourth IEEE Holm Conference on Electrical Contacts (Cat. No.98CB36238), pp. 179–192 (1998) 3. Yuzhong, C., Zhongwei, X.: Technology and application of cold shrinkage power cable accessories. Insul. Mat. 06, 60–63 (2004) 4. Libin, H., Zhang, C., Tan, X., et al.: Research on microstructure and charge characteristics of decommissioned cable accessories. Proc. CSEE 41(02), 770–781 (2021) 5. Yahua, T.: Application of silicone rubber cold shrinkage power cable accessories. East China Electric Power 02, 63–64 (2006) 6. Amyot, N., Fournier, D.: Influence of thermal cycling on the cable-joint interfacial pressure. In: ICSD 2001. Proceedings of the 20001 IEEE 7th International Conference on Solid Dielectrics (Cat. No.01CH37117), pp. 35–38 (2001) 7. Cui Jiangjing, Y., Dong, W., et al.: Research progress of interface pressure measurement method between cable body and accessories. Insul. Mat. 51(03), 1–6 (2018) 8. Liu, C., Hui, B., Fu, M., et al.: Influence of mechanical stress on operation reliability of silicone rubber high-voltage cable accessories. High Volt. Technol. 44(02), 518–526 (2018) 9. Jia, Z., Zhang, Y., Fan, W., et al.: Calculation and Analysis of Interface Pressure of Cold Shrinkage Intermediate Joint of 10kV XLPE Cable 10. Ma, Y., Zheng, P., Han, X., et al.: Determination and numerical studies for insulation interfacial pressure of HV cable termination. In: 2011 Asia-pacific Power and Energy Engineering Conference, Wuhan, China, pp. 1-4. IEEE (2011) 11. Du, B.X., Gu, L.: Effects of interfacial pressure on tracking failure between XLPE and silicon rubber. IEEE Trans. Dielectr. Electr. Insul. 17(6), 1922–1930 (2010) 12. Xie, Q., Wang, X., Fu, M., et al.: High voltage cable joint interference fit and mechanical properties calculation of silicone rubber accessories. High Volt. Technol. 44(02), 498–506 (2018) 13. Wang Xia, Y., Dong, D., et al.: Discussion on several key problems in the design of highvoltage cable accessories. High Volt. Technol. 44(08), 2710–2716 (2018) 14. Song, L., Jiakang, P., Xia, W., et al.: Analysis of influencing factors of interface pressure of high-voltage cable accessories. Insul. Mat. 46(06), 86–89 (2013) 15. Hamdan, M.A., Pilgrim, J.A., Lewin, P.L.: Thermo-mechanical analysis of solid interfaces in HVAC cable joints. IEEE Trans. Dielectr. Electr. Insul. 26(6), 1779–1787 (2019) 16. Li, Y.: Effect of cooling and heating cycles on microstructure and properties of SiCp/Al composites. Harbin Institute of Technology (2019). (in Chinese) 17. Wang, R., He, Y., Kang, H., et al.: Cable joints with silicone rubber insulation thermal aging and ultrasonic features [J/OL]. High voltage: 1-9 [2021-05-09]. https://doi.org/10.13336/j. 1003-6520.hve.20201160. 18. Chen, Q., Qin, Y., Shang, N., et al.: Analysis of the influence of temperature on the distribution of electric field in the intermediate joint of HVDC cable. High Volt. Technol. 40(09), 2619– 2626 (2014) 19. Daigo, Y., Takehisa, H., Shigeki, S., et al.: Electric field optimization of the power cable joint by using evolutionary calculation of the power cabled joint by using evolutionary calculation method. Electr. Eng. Jpn 150(4), 44–53 (2005) 20. Yan, Z., Yang, K., Wang, S., et al.: Study on the influence mechanism of silicone grease on the degrading characteristics of silicone rubber electric branches. In: Proceedings of the CSEE 39(02), 604–611+657 (2019)
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21. Fournier, D., Lamarre, L.: Effect of pressure and temperature on interfacial breakdown between two dielectric surfaces. In: [Proceedings] 1992 Annual Report: Conference on Electrical Insulation and Dielectric Phenomena, pp. 229–235 (1992) 22. Funing, Q., Guanglei, S., Zhonglei, Z., et al.: Physical and chemical properties of silicone rubber insulation for cable intermediate joint. Insul. Mat. 53(12), 44–49 (2020) 23. Xia, W., Xia, L., Mingbo, Z., et al.: Mechanism of space charge formation at the interface between silicone rubber and crosslinked polyethylene under temperature gradient field. High Volt. Technol. 37(10), 2424–2430 (2011) 24. Choo, W., Chen, G., Swingler, S.G.: Electric field in polymeric cable due to space charge accumulation under DC and temperature gradient. IEEE Trans. Dielectr. Electr. Insul. 18(2), 596–606 (2011)
Experimental Study on SF6 Degradation by Dielectric Barrier Discharge Filled with Zirconia Chang Zhou1(B) , Yufei Wang1 , Guozhi Zhang1 , Jingsong Yao2 , and Xiaoxing Zhang1 1 Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid
Equipment, Hubei University of Technology, Wuhan, China [email protected] 2 State Grid Hubei Electric Power Company Maintenance Company, Wuhan, China
Abstract. Sulfur hexafluoride (SF6 ) has a strong greenhouse effect, and the industrial treatment of its waste gas has gradually become a research hotspot. In this paper, based on the packed bed dielectric barrier reactor, the effect of dielectric barrier discharge (DBD) on the degradation of SF6 was studied when the diameter of ZrO2 was 1.8 mm, 3 mm and 5.8 mm, respectively. The results show that the filament discharge of the reaction system becomes more obvious with the increase of zirconia size, which promotes the formation of active substances in the reaction system. With the increase of input power, the destruction and removal efficiencies of SF6 increased gradually, while the energy yield decreased. At the same time, the increase of zirconia size significantly changes the destruction and removal efficiencies and energy yield rate of SF6 . When the diameter of zirconia is 5.8mm, the highest degradation rate is 60.1%, and the energy yield is 7.88 g/kwh, which is much higher than that when the diameter of zirconia is 1.8 mm. The main decomposition products are SOF2 , SO2 F2 and SOF4 . These results indicate that the appropriate zirconia size can effectively promote the industrial degradation of SF6 . Keywords: SF6 · Dielectric barrier discharge · Packing bed reactor · Zirconia
1 Introduction Sulfur hexafluoride (SF6 ) has excellent physical and chemical properties and is widely used in power equipment, metal smelting, semiconductor processing and other fields [1– 3] Because SF6 has good arc extinguishing performance, about 80% of SF6 gas is used as insulating gas for high-voltage equipment [4, 5]. However, SF6 was listed as one of the six strong greenhouse gases in the 1997 Kyoto Protocol, because its Global Warming Potential (GWP) is approximately 23,500 times that of CO2 , and its atmospheric lifetime is approximately 3200 years [6, 7]. In recent years, the harmless treatment of SF6 waste gas has gradually become a research focus. As an emerging technology, low-temperature plasma technology has been widely used in the field of waste gas treatment, such as the removal of volatile organic compounds © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 74–85, 2022. https://doi.org/10.1007/978-981-19-1528-4_8
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and nitrogen oxides [8–10]. In the low-temperature plasma system, the overall kinetic temperature of the gas is kept low, and the electron energy is very high, reaching 1–10 eV, which can break the chemical bonds of gas molecules during the reaction and produce a large amount of active substances. At present, the commonly used low-temperature plasma generation methods include radio frequency discharge, microwave discharge, corona discharge, and dielectric barrier discharge (DBD). At the same time, scholars have used these methods to treat SF6 exhaust gas [11–14]. Radio frequency and microwave discharge have a high SF6 degradation rate, but require a high input power, which reduces the energy utilization rate. Corona discharge and dielectric barrier discharge have simple structures and can produce a large number of active particles at low input power, but their degradation rate and degradation energy efficiency still need to be improved. In order to further improve the DBD reaction process, increase the degradation rate and degradation energy efficiency of waste gas treatment, filling a suitable packing medium in the DBD reactor to form a Packing Bed Reactor (PBR) is considered a promising method. At present, adding filler materials in the process of DBD decomposition of CO2 can effectively improve the degradation rate and energy utilization of the reaction. Zhou et al. filled zirconia and glass bead particles in the DBD reaction and found that the energy efficiency of the system was improved after the zirconia was filled. It is 3.3%–7% [15]. Van et al. found that filling the DBD reactor with zirconia can increase the CO2 conversion efficiency and energy efficiency by 1.9 and 2.2 times, respectively [16]. Cui et al. improved the degradation rate and energy efficiency of SF6 by filling glass beads and alumina particles in the DBD reactor. Especially in the alumina filled system, the degradation rate was increased by 115.14% compared with the unfilled system [17, 18]. However, there is no relevant report on the study of filling zirconia particles into a DBD reactor and using them for SF6 degradation. In this article, we carried out an experimental study on the degradation of SF6 by DBD by zirconia particles of different sizes, and recorded the discharge characteristics of different sizes of zirconia, including voltage and current waveforms and discharge Lissajous patterns. The degradation rate and energy utilization rate of different zirconia sizes under different input powers were compared, and the increase of zirconia size effectively improved the degradation rate and energy utilization rate of the reaction. At the same time, the rule of product generation under the filling of zirconia is analyzed. Related research results provide an experimental basis for the industrial degradation of SF6 waste gas.
2 Experiment 2.1 Experiment Platform The schematic diagram of the experimental platform is shown in Fig. 1. The DBD reactor is a double-layer cylindrical reactor, and the medium is made of quartz glass. The inner tube of the reactor has an outer diameter of 4 mm and a thickness of 2 mm. The inner diameter of the outer tube of the reactor is 10 mm and the thickness is 2.5 mm. The discharge gap between the inner and outer tubes is 6mm. The inner electrode is a copper rod, which is placed in the inner tube of the reactor, and the outer electrode is a stainless steel mesh, which is wound on the outside of the outer tube of the reactor. The length of
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Fig. 1. Experiment platform
the reactor is 300 mm, the length of the discharge area is 200 mm, and the volume of the discharge area is about 52 cm3 . In this paper, zirconia balls with diameters of 1.8 mm, 3 mm, and 5.8 mm are selected as the filling material to fill the discharge area. The reactor power supply is composed of a voltage regulator and a plasma power supply CTP2000K produced by Nanjing Suman Electronic Technology Company. The output voltage of the voltage regulator is 0–250 V, and the voltage regulator is adjusted to control the input power of the plasma reaction. The output waveform of the plasma power supply is quasi-sine, the output power is controlled within 0–150 W, and the output voltage is 0–15 kV. The input power can be obtained from the plasma power supply side, and the plasma discharge power is measured by the voltage-charge method [18]. In the experiment, the Tektronix TDS 320 digital oscilloscope produced by Tektronix measured the discharge voltage, current and Lissajous figure. The test gas was provided by Wuhan Newruide Company, 10% SF6 and 99.999% Ar. Using Ar as the background gas can promote the DBD discharge process and produce more active particles [17], while diluting the SF6 gas to prevent the excessive concentration of SF6 from inhibiting the discharge process. The mixed gas is configured by a dynamic gas distributor (GC500) produced by Jiangsu Tanggao Electric Technology Co., Ltd. The maximum dilution ratio of the gas distributor is 300:1, and the accuracy is ±1% F.S.. During the experiment, the SF6 gas concentration was 2%, and the gas flow rate was 150 mL/min. The remaining concentration of SF6 is measured by gas chromatograph, and the final degradation rate is calculated. The qualitative analysis of gas products adopts the Fourier infrared analyzer (IR Tracer-100) produced by Shimadzu Corporation, the detection band is 400–2000 cm−1 , and the detection accuracy is 1 nm. The detection gas cell optical path is 10 cm, and the gas cell window adopts KBr lens to increase the infrared light
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transmittance. In this experiment, the MX2500 + three-channel spectrometer produced by Ocean Optics Company was used to obtain the emission spectrum during the plasma reaction. The detection range of the spectrometer was 300–800 nm and the resolution was 0.1 nm. 2.2 Parameter Calculation The oscilloscope can detect multiple electrical signals in the DBD discharge process, including discharge voltage, discharge current, etc. Figure 2 shows a typical Q-U Lissajous figure during the DBD discharge process [17]. The entire parallelogram is divided into two stages. The AB and CD segments are the discharge cut-off stages. There is no plasma discharge in the air gap of the reactor, but there is a displacement current to charge the reactor [18]. The slope of the AB segment corresponds to the slope of the discharge cut-off phase Ccell . The AD and BC sections are the discharge phases, and the gas in the air gap breaks down, resulting in a plasma discharge process. The slope of the BC section corresponds to the equivalent capacitance Ceff of the reactor during the discharge process. In this paper, the SF6 degradation rate (Destruction And Removal Efficiencies, DRE) is calculated by the following formula: DRE(%) =
Cin − Cout Cin
(1)
In the formula, Cin is the initial concentration of SF6 , and Cout is the concentration of SF6 after DBD treatment. Define energy utilization rate (Energy Yield, EY) as the degradation mass of SF6 per unit input energy, and the calculation formula is shown in (2): mSF6 EY (g kWh) = Pi · t
(2)
The degradation mass of SF6 is g, Pi is the reactor input power, and the unit of EY is g/kWh.
Fig. 2. Typical lissajous figure
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3 Experimental Results 3.1 Electrical Parameters
(a)1.8mm
(b)3mm
(c)5.8mm
Fig. 3. Discharge voltage and current waveforms under different sizes of zirconia
Figure 3 shows the discharge voltage and current waveforms of different sizes of zirconia filled systems when the input power is 100 W and the gas flow rate is 150 mL/min. It can be seen from the figure that under different size filling systems, the voltage and current shapes are different. In the zirconium oxide filled system with a diameter of 5.8 mm, there are a large number of burrs in the discharge current, which corresponds to the filament discharge in the discharge process. As the size of the filled zirconia decreases, the number of discharge filaments in the reactor also decreases significantly, and the discharge current curve becomes smoother. This may be due to the larger surface area provided by the small particles of zirconia. Promotes micro-discharge on the surface and suppresses the generation of filament discharge. The existence of filament discharge in the reactor can promote the production of more active particles in the reactor and promote the degradation of SF6 . Figure 4 shows the Lissajous graphs under different sizes of zirconia. At this time, the input power of the point placement system is 100 W, the SF6 concentration is 2%, and the gas flow rate is 150 mL/min. The different sizes of zirconia change the shape of the Lissajous figure. The Lissajous patterns in the three systems are all approximately elliptical. Obviously, the Lissajous pattern changes due to the addition of filled zirconia, which is a typical filling effect. As the filling size decreases, the slopes of the AD and BC sections of the discharge phase increase, corresponding to the increase in the equivalent capacitance Ceff of the filling system during the discharge process.
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Fig. 4. Lissajous figure in different sizes of zirconia
3.2 DRE and EY This paper carried out SF6 degradation experiments under different zirconia size filling systems. Considering that SF6 is a strong electronegative gas, too high SF6 concentration will affect the SF6 degradation rate and energy utilization. Therefore, this paper uses argon as the diluent gas to control SF6 The gas concentration is 2% for degradation experiments. Keeping the gas flow rate at 150 mL/min, the changes of SF6 degradation rate and energy utilization rate under different input powers were studied. The SF6 degradation rate and energy utilization rate are calculated by formulas (1) and (2), and the change curves are shown in Figs. 5 and 6.
Fig. 5. Destruction and removal efficiencies
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Figure 5 shows the variation of SF6 degradation rate with input power under different zirconia sizes. With the increase of the input power, the electric field in the reaction system increases, and more active particles are produced, and more active materials collide with SF6 molecules to decompose, which promotes the degradation rate of SF6 . At the same time, as the size of zirconia decreases, the overall degradation rate of SF6 has been significantly reduced. Because zirconia has a relatively stable chemical property, it is considered that the change of the filling size will affect the gas residence time during the reaction. As the size of zirconia decreases, the discharge gap of the reactor decreases, and the residence time of the gas in the discharge area is gradually shortened, which may lead to a decrease in the degradation rate of SF6 . During the reaction, zirconia may not be able to increase the gas residence time by adsorbing the reaction gas. As the size of zirconia decreases from 5.8 mm to 1.8 mm, the gas residence time decreases from 7.4 s to 4.4 s. Zirconia has the effect of enhancing the discharge in the discharge area, and the change in gas residence time due to dimensional changes is obviously a key factor affecting the degradation rate of the reaction. When the diameter of zirconia is 1.8 mm, as the input power increases, the degradation rate gradually rises from 28.6% at 60 W to 44.9% at 100 W. When the diameter of zirconia increases to 5.8 mm, the degradation rate has reached 40.3% at 60 W. As the input power rises to 100 W, the degradation rate increases to 60.1%. Compared with 1.8 mm when the diameter of zirconia is 5.8 mm, the degradation rate has been significantly improved.
Fig. 6. Energy yield
Figure 6 shows the variation curve of the energy utilization rate with the input power under the system filled with different sizes of zirconia. As the input power increases, the energy utilization rate in systems with different zirconia sizes gradually decreases. The increase in input power in Fig. 5 is accompanied by an increase in degradation rate. As the input power increases, more heat loss is generated in the reaction system, which in turn leads to a decrease in energy utilization. In a system with a size of 5.3 mm
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zirconia, the energy utilization rate of 60 W input power can reach 7.88 g/kWh. As the input power reaches 100 W, the energy utilization rate drops to 7.05 g/kWh. In a system with a zirconia size of 1.8 mm, the energy utilization rate at 60 W is 5.7 g/kWh. As the input power increases, the energy utilization rate decreases to 5.13 g/kWh at 100 W. Obviously, when the size of zirconia is 5.8 mm, the energy utilization rate is significantly higher than 1.8 mm, and an appropriate increase in the size of zirconia is beneficial to improve the energy utilization rate of the reaction.
4 Products Analysis 4.1 Emission Spectrum Figure 7 shows the emission spectra of systems with different sizes of zirconia. At this time, the input power in the system is 100 W and the gas flow rate is 150 mL/min. The spectra of the three systems are collected by an emission spectrometer under no light conditions, and the lighting focusing mirror is located 5 cm directly in front of the discharge area of the reactor. The types of active particles in systems with different sizes of zirconia are the same, mainly concentrated in the 600–800 nm band. Among them, the stronger spectral lines are Ar (695.45 nm), Ar (708.68 nm), F (738.95 nm), Ar (750.29 nm), Ar (763.42 nm), Ar (794.88 nm), and the weaker spectral lines are Si II (639.17 nm), S II (757.93 nm), O (777.09 nm). The overall intensity of the emission spectrum of the system with a size of 5.8 mm zirconia is higher than that of the other two systems, indicating that as the size of zirconia increases, the generation of active particles in the reaction system is promoted, and the degradation rate of the reaction system is increased. Since the proportion of Ar gas in the reaction system is 98%, the intensity of the Ar spectrum in the reaction system is high. In addition to the Ar spectrum, there are also S and F spectrum lines. SF6 gas molecules generate F radicals after breaking the SF bond, and S radicals require SF6 to break the SF bond six times to be generated, resulting in the intensity of the S spectrum. It should be lower than the F spectral line. There is a small amount of Si spectra in the reaction system, which may be due to the etching reaction between the F radicals produced by the reaction and the SiO2 in the reactor, resulting in a small amount of Si ions. There is a small amount of O spectrum in the reaction system, but there is no O2 in the system. The O spectrum may come from the etching reaction of O in ZrO2 and SiO2 .
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(a)Zirconia 1.8 mm in diameter
(b) Zirconia 3 mm in diameter
(c) Zirconia 5.8 mm in diameter
Fig. 7. Emission spectra under different zirconia size systems
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4.2 Product Component Figure 8 is the FTIR diagram of the degradation products when the diameter of zirconia is 3 mm. At this time, the SF6 concentration is 2%, the input power is 100 W, and the gas flow rate is 150 mL/min. Considering that the filling materials are all zirconia, only the 3 mm zirconia system map is selected as the analysis object. The degradation products are concentrated in 400–1600 cm−1 , and the main products are SOF2 , SO2 F2 , SOF4 , SO2 , SiF4 , etc. Compared with the product without filling medium in the literature [17], it is found that the filling of zirconia does not affect the composition of the product. The presence of SiF4 indicates that SiO2 in the glass tube of the reactor participated in the reaction.
Fig. 8. FTIR diagram of degradation products when the size of zirconia is 3 mm
5 Conclusion In this paper, a packed bed dielectric barrier discharge experiment platform was built to study the effect of zirconia on the degradation of SF6 by DBD when the size of zirconia is 1.8 mm, 3 mm, and 5.8 mm. The following conclusions are obtained: (1) The change of the size of the filled zirconia can significantly change the discharge parameters of the reactor. The increase of the size, the more obvious the discharge of the filament. In the 5.8 mm zirconia system, the filament discharge is the most intense. (2) As the input power increases, the degradation rate of the reaction system continues to increase, while the energy utilization rate decreases. At the same time, the increase in the size of zirconia can also significantly improve the degradation rate and energy utilization. When the SF6 concentration is 2% and the input power is 100 W, the maximum degradation rate is 60.1% and the energy utilization rate is 7.05 g/kWh in a system with a size of 5.8 mm zirconia.
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(3) Compared with the case of no filling medium, the size change of zirconia does not affect the degradation products. The main by-products after the reaction are SOF2 , SO2 F2 , SOF4 , and SO2 . Acknowledgments. We would like to thank participants and companies who take part in the study. This research was carried out within the Hubei Provincial Key Laboratory Project (Project No.: HBSEES201906), and partially funded by Natural Science Foundation of Hubei Province. We thank them from the bottom of our hearts for their support and help.
References 1. Lin, S., Li, X., Xu, J., Shan, C.: Research on numerical computation of SF6 breakdown voltages and spectral experiment in uniform electric fields. Proc. CSEE 36(1), 301–309 (2016). (in Chinese) 2. Xiao, H., Zhang, X., Xiao, S., Hu, X.: Experiment of effects of ambient medium on sulfur hexafluoride degradation for a double dielectric barrier discharge reactor. Trans. China Electrotechn. Soc. 32(20), 20–27 (2017). (in Chinese) 3. Fang, X.K., et al.: Sulfur Hexafluoride (SF6) emission estimates for China: an inventory for 1990–2010 and a projection to 2020. Environ. Sci. Technol. 47(8), 3848–3855 (2013) 4. Rueping, M., Nikolaienko, P., Lebedev, Y., Adams, A.: Metal-free reduction of the greenhouse gas sulfur hexafluoride, formation of SF5 containing ion pairs and the application in fluorinations. Green Chem. 19 (2017) 5. Zhang, X., Xiao, H., Huang, Y.: A review of degradation of SF6 waste by low temperature plasma. Trans. China Electrotechn. Soc. 31(24), 16–24 (2016). (in Chinese) 6. Yan, X., Wang, C., Ji, Y., Zhang, Z., Song, H., Yang, R.: Modeling of the relation between SF6 decomposition products and interior faults in gas insulated equipment. Trans. China Electrotechn. Soc. 30(22), 231–238 (2015). (in Chinese) 7. Li, Y., Zhang, X., Cui, Z., Xiao, H., Zhang, G.: Experiment of effect of ammonia on degradation of sulfur hexafluoride by dielectric barrier discharge. Trans. China Electrotechn. Soc. 34(24), 5262–5269 (2019). (in Chinese) 8. Wang, Y., Zhang, R., Sun, J., Zhang, J., Wang, Q., Wang, D.: Simulation study of the removal of NO from N2 /NO mixture by pulsed dielectric-barrier discharge at atmospheric pressure. High Volt. Eng. 2, 405–413 (2016). (in Chinese) 9. Gong, X.: Heat effect of dielectric barrier discharge and its application in methanol reforming for hydrogen production. Dalian University of Technology (2018). (in Chinese) 10. Mustafa, M.F., et al.: Application of non-thermal plasma technology on fugitive methane destruction: configuration and optimization of double dielectric barrier discharge reactor. J. Clean. Prod. 174, 670–677 (2018) 11. Piemontesi, M., Pietsch, R., Zaengl, W.: Analysis of decomposition products of sulfur hexafluoride in negative dc corona with special emphasis on content of H2 O and O2 . In: Conference Record of the 1994 IEEE International Symposium on Electrical Insulation. IEEE (1994) 12. Shih, M., Lee, W., Chen, C.Y.: Decomposition of SF6 and H2 S mixture in radio frequency plasma environment. Ind. Eng. Chem. Res. 42(13), 2906–2912 (2003) 13. Yan, S., Li, H., Renxi, Z., Huiqi, H.: Decomposition of SF6 by dielectric barrier discharge. Environ. Chem. 26(3), 275–279 (2007). (in Chinese) 14. Zhou, A., Dong, C., Ma, C., Yu, F., Dai, B.: DBD plasma-ZrO2 catalytic decomposition of CO2 at low temperatures. Catalysts 8(7), 256 (2018)
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15. Bogaerts, A., Van Laer, K.: Improving the conversion and energy efficiency of carbon dioxide splitting in a zirconia-packed dielectric barrier discharge reactor. Energy Technol. 3, 1038– 1044 (2015). Generation,Conversion,Storage,Distribution 16. Cui, Z., Zhang, X., Tian, Y., Pan, X., Luo, Y., Tang, J.: Plasma-assisted abatement of SF6 in a dielectric barrier discharge reactor: investigation on the effect of packing materials. J. Phys. D Appl. Phys. 53(2), 025205 (2019) 17. Cui, Z., Zhang, X., Tian, Y., Li, Y., Tang, J.: Effects of glass beads packing on SF6 abatement by packed bed plasma. Plasma Chem. Plasma Process 40, 43–59 (2020) 18. Butterworth, T., Elder, R., Allen, R.: Effects of particle size on CO2 reduction and discharge characteristics in a packed bed plasma reactor. Chem. Eng. J. 293, 55–67 (2016)
Reactive Power Optimization of PSO-DBN Based on IoT Technology Peng Xia1,2(B) , Qian Zhang1,3 , and Guoli Li1,4 1 School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China
[email protected]
2 Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control,
Anhui University, Hefei, China 3 Engineering Research Center of Power Quality, Ministry of Education, Anhui University,
Hefei, China 4 Anhui Industrial Power Saving and Electricity Safety Laboratory, Hefei, Anhui, China
Abstract. When the large amount of Distributed Generations (DGs) are connected to the grid, the whole distributed power network lost line, and the voltage fluctuates are obvious. In recent years, the concepts of power Internet of Things (IoT)technology and energy Internet are proposed. Therefore, power grid technology has entered a new stage. The IoT technology is characterized by high bandwidth, high transmission rate, and low latency. In addition, with the collected data are increased, IoT becomes an effective way to improve the computing power of edge nodes in edge computing. In order to reduce network losses and voltage fluctuations, reactive power optimization is need to be implement in distribution network system. This paper proposes a distributed power voltage regulation strategy based on the particle swarm optimization and deep belief network (PSO-DBN) under the IoT. Firstly, IoT technology and characteristics are introduced, and the current research status of voltage regulation technology are analyzed in distribution network. Secondly, the weight of DBN is improved with PSO. Finally, the IEEE33 bus system is taken as a simulation case, the effects of daily reactive power optimization and the amount of historical data is analyzed. It is concluded that the method proposed in this paper has obvious effects on reducing voltage fluctuation and network loss. Keywords: Distributed generation · IoT · PSO-DBN · Reactive power optimization
1 Introduction In recent large times, large scale and high-penetration distributed power sources are connected to the distribution network [1, 2]. At the same time, the construction of related energy companies has become a focus. As an important part of the smart grid construction. IoT [3] plays a vital role in the smart grid construction. With the rapid © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 86–93, 2022. https://doi.org/10.1007/978-981-19-1528-4_9
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development of big data technology in various industries, experts in various fields have paid more and more attention to big data technology [4–6]. Data-driven refers to the application of deep learning technology to analyze a large amount of data to obtaining effective data information, which is used to guide the subsequent reactive power optimization process of the distribution network. In the field of reactive power optimization, data-driven technology is mainly divided into two categories: and. The similarity matching algorithms mainly includes random matrix theory [7], correlation law [8], etc. The classifier training algorithms mainly includes support vector machines(SVM) [9], deep belief networks(DBN) [10]. The calculation time problem of traditional reactive power optimization methods is effectively solved by the above methods. An PSO-DBN method is proposed in this paper. The PSO-DBN model is established to reactive power optimization. The method in this paper is applied to IEEE33 nodes system. This method combined with PSO, SVM and DBN. The result is that this paper has a better adjustment effect on voltage fluctuation and network loss.
2 IoT Technology and Analysis of Reactive Power Optimization Problems The Internet of Things was first proposed by Professor Ashton of the MIT Auto-ID Center when he was studying RFID in 1999 [11]. Regarding the definition of the Internet of Things, the more commonly accepted definition is: An Internet based on information carriers such as the Internet, radio and television networks, and telecommunications networks, so that all universal physical objects that can be independently addressed are interconnected. The Internet of Things can realize the connection between anything and anyone anytime, anywhere, and ideally can provide any path or any service under the network (Fig. 1).
Fig. 1. IoT architecture diagram
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3 PSO-DBN Reactive Power Optimization Strategy 3.1 PSO-DBN Theory The training steps of DBN mainly include pre-training of RBM and adjustment of DBN parameters. Pre-training refers to train the RBM layer by layer from bottom to top through input. The model parameters are trained and optimized repeatedly in the network [12] (Fig. 2).
ᮦᦤ䗉ޛ
Fig. 2. DBN structure
PSO [13] as a heuristic algorithm, it needs to be updated in speed and position of each particle. When the end condition is achieved, the optimal solution, particle speed, and position update formula can be obtained. As shown in (1) and (2): t+1 t t t Vim = ωVim + c1 r1 (Pim − Xim ) + c2 r2 (Gim − Xim )
(1)
t+1 t+1 t Xim = Xim + Vim
(2)
Where ω represents the weight of the particle, c1 and c2 represent the learning factor, r1 and r2 represent the random number between 0 and 1, i represents the number of iterations, Vim and Xim represent the individual and group extreme values. 3.2 PSO-DBN Reactive Power Optimization Scheme The PSO-DBN model is used to learn the mapping relationship between the load, the topology of the node and the reactive power optimization strategy. The real-time data is processed by CNN. The real-time power equipment control strategy is obtained through the learned mapping relationship. The reactive power optimization flowchart is shown in Fig. 3.
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1. The parameters of the PSO are set. Such as the particle swarm size, learning factor, inertia weight, and the maximum number of iterations. The connection weight between each layer in the DBN network is used as the vector of PSO. 2. The fitness function value of the particle f are calculated, the function is here: N m
f =
(pij − tij )2
i=1 j=1
(3)
N
Where N represents the number of samples, m represents the particle dimension, pij and tij represent the reconstructed value and actual value of the dimensional particle in the sample. 3. The fitness value of each particle are compared with the group extreme value, if f > pbest , the current fitness value are replaced, else the group extreme value are kept. 4. According to formulas (3), the velocity and position of the particles are updated. 5. The dimension of the group extreme after PSO is used to the initial weight of the DBN network. The DBN model is trained and the parameter is adjusted until the end of DBN training.
Establish reactive power optimization model of distribution network
DBN is used to connect weights as vectors of PSO
Initialize the DBN network structure
Calculate the fitness of the particle
Setting network parameters The particle is selected as initial weight of DBN
YES
F>P No
Start DBN network training
Determine individual and global optima
Update particle velocity and position
Test set
Trainin g set
Determine the DBN structure
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Obtain the input and output
Fig. 3. PSO-DBN reactive power optimization flow chart
4 Case Study 4.1 PSO-DBN Reactive Power Optimization Scheme The IEEE 33-node system are used as the research object. Reactive power compensation is increased between the 22nd node and the 33th node of the system. The transformers
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ratio is regulated between 16 to 17 branches, 19 to 20 branches, 24 to 25 branches, and 26 to 27 branches. The improved node topology is shown in Fig. 4. The size of the historical data is 1440.
Fig. 4. Improved IEEE33 node topology
4.2 Anal Sizes of This Results In this paper, the optimized voltage fluctuation is used the objective function, and the reactive power optimization model of the distribution network is established. Comprehensive function is used to the optimization index. The function of a function represents the network loss of a certain amount of time. The expression method of the comprehensive function is shown in the formula:
du − du du n un − ui du = u n F =p+
(4)
(5)
i=1
Where p represents the network loss, du and du represents the voltage deviation before reactive power optimization and after reactive power optimization, n represents the total number of nodes, ui represents the voltage of node, un represents the rated voltage. Is the prove the effectiveness of the method is compared with PSO, SVM, and DBN in this paper. The formula (5) and (6) are used as the optimization index. The resulting insight is always as seen in Fig. 5, 6 and 7.
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Fig. 5. Voltage deviation on someday
Fig. 6. Loss on someday
From the Fig. 5, 6, Fig. 7, It is shown that as the following conclusions: 1. The comprehensive function of PSO and SVM is relatively large. It is indicated traditional heuristic optimization algorithms are difficult to obtain the nonlinear function relationship between distribution network load data and reactive power optimization strategies. 2. PSO-DBNs comprehensive function, voltage deviation, and network loss are all smaller than other algorithm proposed. It can be concluded that the optimization performance of PSO-DBN is better than of DBN.
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Fig. 7. Comprehensive function on someday
5 Conclusion This article analyzes the reactive power optimization problem of the power design system. The shortcomings of the traditional reactive power optimization method are indicated, the PSO-DBN model is used to adjust the voltage and losses of the distribution network. Simulation experiments are carried out with the IEEE33-node distribution network system as the research object. The PSO-DBN model is better than other methods in the regulation of voltage fluctuations and network losses. Acknowledgment. This work was supported in part by the National Natural Science Foundation of China under Grant (52077001).
References 1. Brucue, R., Gemstenar, W.: The landscape index ADAPTS to the neural network activity population combined with the emission model. J. Neurophysiol. 94, 3637–3642 (2015) 2. Kerilasm, S., Tuskophim, P.: Application of integrated Internet of Things technology for agricultural development. In: Proceedings of the IEEE 16th International Conference on Communication Technology (ICCT), pp. 26–31. IEEE (2015) 3. Spaho, E., Mino, G., Barolli, L., Xhafa, F.: Goodput and PDR analysis of AODV, OLSR and DYMO protocols for vehicular networks using CAVENET. Int. J. Grid Util. Comput. 2(2), 130–138 (2011) 4. Liu, Y., Sakamoto, S., Matsuo, K., Ikeda, M., Barolli, L., Xhafa, F.: A comparison study for two fuzzy-based systems: improving reliability and security of JXTA-overlay P2P platform. Computer 20(7), 2677–2687 (2015) 5. Li, Z., Kucukkoc, I., Nimlakstann, J.V.: Evaluation of the corresponding framework for the overall design of bidirectional balanced power supply voltage. Comput. Oper. Res. 84, 146– 161 (2017)
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6. Maker, M.R.C.A., Rashid, M.E.F.A.: A solution for balancing the overall assembly conditions bilaterally. Int. J. Adv. Manuf. Technol. 89, 1743–1763 (2016) 7. Qiu, Q.H., Ren, Y.: Suganthana PN Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft. Comput. 54, 246–255 (2017) 8. Acmchker, P.H., Sanatary, S.K.: Manikandam MS (2016) Analysis of disturbance influencing factors in grid-connected global power quality system with variational mode factor decomposition and decision tree. IEEE Trans. Smart Grid 9(4), 3122–3132 (2016) 9. Lin, B.-M., Tmsi, M.-C.: A hierarchical control strategy for wavelet neural probabilistic networks based on classification. IEE Proc. Gener. Transm. Distrib. 152(6), 969–976 (2005) 10. Mozcsfari, K., Amiraboucd, M.: An inverter DC rectifier with high reliability and high efficiency. IEEE Trans. Power Electron. 35(9), 7525–7531 (2019) 11. Suvcender Retty S.: Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors. Electr. Eng. 100(3), 1297–1303 12. Dedmicyc, A., Filipmsoky, S., Dedinec, A., et al.: Deep belief network based electricity load forecasting: an analysis of Macedonian case. Energy 115, 1688–1700 (2016) 13. Zhaobo, L.: The adaptive and improved particle swarm optimization algorithm and BP network are summarized together. Geod Geodyn 39(05), 528–532 (2019)
A Novel Structure for Switched Reluctance Motor to Be Driven by Full-Bridge Power Converter Haitao Sun1,2,3(B) , Yan Chen2 , Jiquan Liu3 , Zhiwei Yan3 , Xiangdong Yu1 , and Yinke Dou2 1 National Engineering Laboratory for Coal Mining Machinery, Taiyuan, China
[email protected]
2 College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan,
China 3 Coal Technology and Engineering Group, Tai Yuan Research Institute of China, Taiyuan,
China
Abstract. A modified driving topology that uses a ring structure for the windings of the switched reluctance motor is proposed, with which it is possible to drive the SRM by a full-bridge power converter instead of an asymmetric H-bridge circuit that is currently the most commonly used driving system for the SRM. Based on the existing ring structure, the improved topology uses a flyback converter to control the circulating current. The theoretical analysis is made to define the relationship between and value of different types of current. The proposed topology is also tested by simulation tool and experimental setup, using the trapezoidal current method, the results show that the circulating current can be controlled within a certain level with the proposed topology that works well for the SRM. Keywords: Switched reluctance motor · Current control · Driving system · Power converter · Full-bridge circuit
1 Introduction A switched reluctance motor usually uses the asymmetric H-bridge as the standard driving topology (Fig. 1a), which drives SRM by using the pulse winding current that is the main reason of the torque ripple. Compared to an asymmetric H-bridge, a conventional full-bridge power converter (Fig. 1b) is known as the driving system for PMSM, and has internal freewheel diodes. A full-bridge power converter is supposed to export low harmonics, high voltages, as well as the high power, which are required for the large drives [1, 2]. As a result, the full-bridge circuit is a better choice than the conventional converter for driving SRM. In order to apply the full-bridge power converter to the SRM drive, there are two types of solution proposed by the previous researchers: modifying the SRM inner structure, and improving the SRM driving system. A variant of the full pitch SRM is proposed in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 94–101, 2022. https://doi.org/10.1007/978-981-19-1528-4_10
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Fig. 1. a. Asymmetric H-bridge circuit; b. Full-bridge circuit
the paper [3], which describes a reluctance motor called vernier motor that is driven by a full-bridge power converter. The slots and windings in the motor are modified as that in a conventional induction motor. By changing the inner structure, the operation principle of the motor is also changed. The authors in [4] proposed a bipolar SRM that is driven as a synchronous reluctance motor, by improving the winding distribution. A disadvantage of this approach is that the change of inductance between the terminals is less than in the case of asymmetric H-bridges. The article [5] modifies the SRM power converter, by using two three-phase fullbridge circuits and a one-leg IGBT power module. The SRM with 8/6 structure can be driven by the proposed converter. Essentially, the novel power converter uses the same number of power tubes as the conventional converter. It can be found that for the changing SRM structure methods, the cost of motor production is higher, it means that changing the driving system can be a more economical way to drive an SRM by the full-bridge converter. But for the existing methods that change the driving system, they all can be considered as a variant of the conventional power converter. Different with the existing methods, we have proposed a novel topology that uses a ring structure to drive SRM by a full-bridge power converter, as is described in Fig. 2. The proposed idea is to connect the windings in series that builds a delta structure, at the same time, there is a constant voltage source inserted between two windings. As a result, the three-phase windings and the constant voltage source make a ring structure. The inserted voltage source keeps the polarity of the winding currents the same.
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ia ib ic
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With the extra power supply, this type of structure, however, generates extra power losses in the SRM. Even the value of the inserted voltage source is chosen well, the winding current cannot be controlled with a certain value, the winding current may be higher than required, which leads to unnecessary copper losses. In order to modify the ring structure, an improved topology is proposed in this paper. By replacing the inserted voltage source with a flyback converter, the winding current is supposed to be controlled at a suitable level, the power losses in the driving system can be reduced as well.
2 Theoretical Analysis 2.1 Operating Principle As is described in [6], an extra voltage source generates a circulating current, which is acted as a component in the winding currents. Another component is the current generated and controlled by the full-bridge converter. It can be found that the winding current cannot be controlled well without controlling the circulating current in the ring structure. + SRM
i1 Circulating Current (IDC)
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ia i2 ib i3
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As is shown in Fig. 3, by replacing the DC voltage source with a flyback converter, it is possible to control the circulating current directly. Here we define the IDC as the circulating current in the ring structure. It can be found that by controlling the transistor Q7 in the flyback converter, the circulating current can be kept constant with a setting reference value. According to Fig. 3, the operating principle of the proposed structure can be described as that, the combination of the circulating current and the leg current generated by the full-bridge power converter contributes to the winding current. Based on the description above, the inserted circulating can be set with a constant value, here we use a trapezoidal current for driving the SRM, and the flux linkage-current diagram of the SRM is shown in Fig. 4. Figure 5 shows a more intuitive trapezoidal current waveform with the corresponding working points. Via Fig. 4, the operating principle of the SRM driven by the proposed topology can be explained: starting from WP1, the working point moves along the arrow’s path, the rotor teeth are totally unaligned to the stator teeth at WP1, and moves to the aligned position, then back to the initial position.
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Fig. 4. The operational principle of the proposed topology
2.2 Calculation of Circulating Current For an SRM, there should be a maximum winding current limitation (I max ) for protecting the windings. A graphical illustration of the several defined currents is shown in Fig. 5. i (A) Imax IDC+Ipeak
WP3
WP4
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Fig. 5. The trapezoidal current waveform and the relationship between different types of current
In order to keep the winding current positive, based on Fig. 5, I DC should have a range within Ipeak ≤ IDC ≤ Imax − Ipeak
(1)
(1) means that if I DC is higher than I max –I peak , the maximum value of the winding current will be higher than its limitation I max . And if I DC is lower than I peak , the current of some working points will be lower than 0 A, then the winding in the SRM cannot work in the working area described in Fig. 4. In (1), we assume that the absolute peak values of the winding current without I DC in the negative part and positive part are the same.
3 Simualtion Analysis We choose Matlab/Simulink as the simulation tool for analysis. The full model is described in Fig. 6. It can be deduced from the proposed topology that by controlling the full-bridge circuit, the speed of the machine can remain constant, by controlling the flyback converter, the circulating current can be kept with a fixed value.
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+
+
+
-
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In order to compare the simulation results to the validation results, we set the parameters of the Simulink models based on the datasheet of the practical system. Figure 5 and (1) indicate that for the proposed topology, the reference value of the circulating current should be higher than the peak value of the winding current, for avoiding the negative value of the winding current. We define the difference between the minimum winding current value and 0A as I offset , as a result, I DC is IDC = Ioffset + Ipeak
(2)
At the same time, based on Kirchhoff’s current law, in the trapezoidal current waveform, the relation of the peak value between the winding current and leg current is Ipeak =
2 Ilegpeak 3
(3)
Where I leg peak is the peak value of the leg current. In the practical application of an electric vehicle, the set value of the leg current in the driving circuit can be set by controlling its throttle position. Note that in Fig. 6, we use a speed control, of which the output of the speed controller is the reference value of I leg peak , the I DC is 2 IDC = Ioffset + Ilegpeak 3
(4)
Under the condition of 500 rpm and 2N * m, we set I offset as 0.5 A. The simulated leg currents are shown in Fig. 7.
Fig. 7. The simulated leg current in the full-bridge power converter
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It can be found in Fig. 7 that the full-bridge inverter generates the leg currents with trapezoidal waveform. A current commutation occurs every 60 electrical degrees. Because in the SRM, the winding inductance changes nonlinearly with the constant rotor speed, the leg currents are asymmetric. Figure 8 shows the SRM winding currents generated by the proposed topology.
Fig. 8. The simulated SRM winding current
It can be found that in Fig. 8, the winding current keeps positive. The winding current ranges from 0.5 A to 3 A, which means that the circulating current lifts the winding currents to a higher value than 0.5 A. Consequently, the SRM winding current avoids 0 value, which contributes to the stability of the proposed driving system. Figure 8 also shows that a slope occurs in every flat stage of the trapezoidal waveform, this is because in the circulating current, there are third harmonics generated, which lead to the slopes in the waveform. As is shown in Fig. 9.
Fig. 9. The simulated circulating current in the ring structure
In Fig. 9, the circulating current is set as 1.75 A, there are third harmonics in the waveform. Here we declare that the circulating current is calculated to lift the winding current, it does not exist in the ring structure physically.
4 Validation The practical system of the proposed topology is shown in Fig. 10. It contains a dSpace platform, full-bridge converter, and an SRM with 6/4 structure.
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Fig. 10. The practical system of the proposed topology
The parameters of the practical system correspond to the simulation system. The proposed topology is also tested by using the trapezoidal current waveform in the setup. Similar to the simulation, we set the offset current as 0.5 A, with the condition of 500 rpm and 2N * m. The experimental leg current waveform is shown in Fig. 11.
Fig. 11. The experimental leg current waveform
It can be found from Fig. 11 that even with some measured errors, the practical leg current shows a trapezoidal waveform, which can match the result in Fig. 7 well. Figure 12 describes the experimental winding current by using the proposed method.
Fig. 12. The experimental winding current waveform
Similar to Fig. 8, the winding current in Fig. 12 has a trapezoidal waveform, in 360 electrical degrees, the winding current has 6 stages. It proves that the proposed topology
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can drive the SRM with the trapezoidal current driving method. The experimental results have a similar trend as the simulated results.
5 Conclusion This article proposes a novel driving topology for the three-phase switched reluctance motor, with which a three-phase full-bridge power converter can be used for driving the SRM. This mass-produced and cheaper full-bridge inverter has the benefit that the cost for driving the SRM reduces, and the internal structure of the SRM does not need to be changed. By modifying the ring structure, the circulating current can be controlled directly. The theoretical analysis of the improved topology is then made. Based on the obtained equations, the combination of the circulating current and leg current contributes to the winding current. With the simulation tool, the proposed topology is tested by the trapezoidal current driving method. An experimental system is also used to verify the application of the proposed topology. The results show that the practical waveforms match the simulated results well, under the same conditions.
References 1. Peng, F.Z., Lai, J.-S., McKeever, J., VanCoevering, J.: A multilevel voltage-source inverter with separate DC sources for static VAr generation. In: IAS 1995. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting, Orlando, FL, USA, vol.3, pp. 2541–2548 (1995) 2. Tolbert, L.M., Peng, F.Z., Habetler, T.G.: Multilevel converters for large electric drives. IEEE Trans. Ind. Appl. 35(1), 36–44 (1999) 3. Takano, M., Shimomura, S.: Study of variable reluctance vernier motor for hybrid electric vehicle. In: 2013 IEEE ECCE Asia Downunder, Melbourne, VIC, pp. 1341–1347 (2013) 4. Liu, X., et al.: Performance comparison between unipolar and bipolar excitations in switched reluctance machine with sinusoidal and rectangular waveforms. In: 2011 IEEE Energy Conversion Congress and Exposition, Phoenix, AZ, pp. 1590–1595 (2011) 5. Hu, K., Yi, P., Liaw, C.: An EV SRM drive powered by battery/supercapacitor with G2V and V2H/V2G capabilities. IEEE Trans. Industr. Electron. 62(8), 4714–4727 (2015) 6. Sun, H., et al.: A control method with ring structure for switched reluctance motor. In: 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), Nottingham, pp. 1–5 (2018)
Simulation and Experimental Study on Muzzle Flow Field of Electromagnetic Energy Equipment Chen Miao1,2 , Ying Zhao2,3(B) , Wen Tian1,2 , Guochao Li1,2 Weikang Zhao2,3 , Weiqun Yuan2,3 , and Ping Yan2,3
,
1 University of Chinese Academy of Science, Beijing 100049, China 2 Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
{miaochen19,zhao_ying2001}@mail.iee.ac.cn 3 Key Laboratory of Power Electronics and Electric Drive, Chinese Academy of Science,
Beijing 100190, China
Abstract. In order to study the characteristics of the muzzle flow field during the dynamic launch of electromagnetic energy equipment, based on Navier-Stokes equation and Realizable k − ε turbulence model, in this paper, a two-dimensional unsteady compressible turbulence model of electromagnetic energy equipment is established by using overset mesh method and UDF secondary development. A high-frequency dynamic pressure transmitter was used to measure the pressure change of the shock wave after the armature was discharged. The simulation results show that the muzzle flow field is a complex shock wave system flow field in the process of electromagnetic energy equipment dynamic launching, and presents different parameter changes at different times, forming the shock wave in front of the armature, spherical shock wave with moving center in the muzzle and the coronal shock wave successively. The experimental results show that when the armature initial velocity ranges from 420 m/s to 450 m/s, the shock pressure varies from 52 kPa to 58 kPa (170 mm in the transverse direction and 80 mm in the longitudinal direction from the muzzle Center), and the rising edge is steep and the falling edge is slow. Keywords: Electromagnetic energy equipment · Muzzle flow field · Overset mesh method · Pressure transmitter · Shock wave
1 Introduction Electromagnetic energy equipment is a new type of device that uses high-power pulse current to generate ampere force and push the armature to supersonic speed [1]. Compared with traditional artillery, it has the advantages of large export kinetic energy, long range, precise and controllable range, and has become a recent research hotspot [2]. The armature of electromagnetic energy equipment is pushed out by Ampere force, which makes the high-pressure and high-temperature gas push out from the bore [3]. Along © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 102–113, 2022. https://doi.org/10.1007/978-981-19-1528-4_11
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with the mixture of aluminum vapor and air and plasma, it impacts the devices near the bore and produces a certain recoil force, which affects the service life and performance of electromagnetic energy equipment [4–6]. The design of insulating panels for electromagnetic energy equipment requires full consideration of material insulation, high temperature, and aerodynamic effects. In addition, the design of dynamic launch test sites and the formulation of protective measures for test facilities require the development of high-temperature flow field in the muzzle. Therefore, it is very necessary to study the muzzle flow field of electromagnetic energy equipment. For the muzzle flow field of traditional artillery, scholars at home and abroad have done a lot of work in experiments and simulation calculations, and the theoretical system is relatively mature [7–9]. In recent years, some scholars have carried out the simulation calculation of the muzzle flow field of electromagnetic energy equipment. Chen Liang established a three-dimensional unsteady model of electromagnetic energy equipment launching by using dynamic grid technology, and numerically simulated the flow field in the bore and muzzle by controlling the motion law of projectile through UDF [10]. Du Peipei used the method of overlapping multi-block structured mesh, updated the velocity of projectile in real time through Java program, and realized the flow field analysis of projectile dynamic launching process [11]. Gao Yuan et al. Simulated the muzzle flow field of electromagnetic energy equipment considering arc plasma and backflow, and established a three-dimensional numerical model of arc plasma according to the MHD equation [12, 13]. At present, the research on the flow field of electromagnetic energy equipment is mainly focused on numerical simulation, and the research on the test method is relatively few. The muzzle region of electromagnetic energy equipment armature is a flow field with complex shock wave system [11]. In order to explore the development law of the flow field and the distribution of pressure and other parameters in the muzzle region of the armature, ICEM is used for structured mesh generation, and the two-dimensional unsteady transient turbulence model of the armature muzzle region is established by using the overlapping mesh method. In addition, the pressure change of shock wave in the muzzle is measured by high frequency dynamic pressure sensor. Comparing the experimental results with the simulation results, it is found that there is a good consistency.
2 Simulation Model 2.1 Flow Field Control Equation and Turbulence Model The armature is driven by Ampere force in the orbit, and the velocity reaches supersonic in a very short time. The gas at the armature head is compressed rapidly. In the ideal gas case, the two-dimensional compressible unsteady equation is ∂F ∂G ∂U + + =J ∂t ∂u ∂ν
(1)
Where u and ν are the velocity components in X and Y directions respectively. The definitions of parameters U , F and D can be seen in reference [10].
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Realizable k − ε model is used to solve the model. The transport equations of k and ε are given in reference [11] ∂ ∂(ρk) ∂(ρkui ) μt ∂k = (2) μ+ + Gk − ρε + ∂t ∂xi ∂xj σk ∂xj ∂(ρε) ∂(ρεui ) ε2 ∂ μt ∂ε = (3) μ+ + ρC1 Eε − ρC2 + √ ∂t ∂xi ∂xj σε ∂xj k + νε μt = ρCμ Cμ =
k2 ε
1 A0 + As U ∗ k/ε
(4) (5)
Where Prandtl number of turbulent kinetic energy σk = 1.0, the turbulent Dissipation η , η = Sk/ε, the above parameters Rate σε = 1.3, C2 = 1.9, C1 = max 0.43, η+5 are assigned during model selection. Other parameter definitions can be found in the literature [14]. 2.2 Model Building The electromagnetic energy equipment is composed of a conductive track and an insulating support surrounding the outside. The muzzle area has a complex structure and an arc suppression device. When the armature and the rail are separated, the local high voltage generated by the disconnection of the power circuit will generate an arc in the muzzle area. The tail of the electromagnetic energy equipment is in direct contact with the atmosphere. Therefore, the simulation process only concerns the change of the flow field of the muzzle, and does not consider the factors that have little influence on the simulation results. The following assumptions are made in this simulation: • The electromagnetic energy equipment is equivalent to two parallel tracks, ignoring the influence of insulation support shell and arc suppression device in muzzle area; • The armature is equivalent to a rectangular block to satisfy a good nesting relationship in the overlapping grid. • The phenomenon of temperature rise caused by hypervelocity friction and transition is not considered. • Assuming that the entire air field is an ideal gas, the ionization phenomenon caused by the high voltage in the muzzle area after the armature exits the chamber is not considered. The two-dimensional electromagnetic energy equipment motion model used in this paper is shown in Fig. 1. The armature moves with variable acceleration in the x-axis direction. In this paper, AC and BD replace the guide rails on both sides, ABCD is the air area inside the guide rail, and EGFH is the outside air area. Considering the open structure of the electromagnetic energy equipment in our research group, there is an air gap of 1mm between the armature and the guide rail.
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Fig. 1. Two-dimensional motion model of electromagnetic energy equipment
In this model, the fluid region is set as ideal gas, AB as the pressure inlet, GH, FH as the pressure outlet, both of which are a standard atmospheric pressure, and the rest of the boundary is set as wall. The density-based solver is used to solve the high-speed compressible flow. The armature is accelerated under the action of ampere force, and the peak acceleration exceeds 20000 g. The speed obtained by the electromagnetic energy equipment circuit simulation model of the research group is used to control the movement of the armature. The movement curve is shown in Fig. 2, the armature exits the muzzle at 1.76 ms, and the initial velocity is 1377 m/s. UDF programming is used to import 500 velocity data into the model, Other data points are obtained by linear interpolation. speed
speed (m/s)
2000
1500
1000
500
0 0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 0.0030
t(s)
Fig. 2. Armature speed simulation curve
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2.3 Overset Meshing Because of the relative movement between the armature and the guide rail, it is necessary to use a dynamic grid to simulate the movement of the armature. In this paper, the overlapping grid method is used to simulate the movement of the armature in the internal and external air areas. The overlapping grid consists of two parts: the background grid containing the air domain; the component grid constructed by the armature part. The nested grid after the two grids overlap in FLUENT. The nested grid will not produce negative volume during the movement of the component, and can better handle the narrow gap between the armature and the guide rail, and always maintain a high grid quality during the solution. In order to avoid all kinds of problems caused by unreasonable meshing, this paper uses ICEM to deal with unstructured grid. The near wall surface of the guide rail is locally densified to avoid mesh loss, and the armature motion area (CI, DJ) in the external air domain is densified. Considering that the armature has little influence on EG and FH regions after being discharged from the chamber, and in order to reduce the number of grids and improve the operation speed, the grid size gradually increases from CI (DJ) to EG (FH). Finally, the whole fluid domain is divided by quadrilateral unstructured mesh. The number of background meshes is 892345, and the mesh quality is greater than 0.95. The number of nested regions is 5600.
3 Simulation Results and Analysis According to the simulation results, the development of electromagnetic energy equipment flow field is divided into four stages: when the armature moves in the bore, the gas in the bore is compressed to form shock wave; when the armature is close to the muzzle, the initial shock wave at the muzzle begins to form; When the armature exits the muzzle, the spherical shock wave expands; The armature moves in the external air and forms a coronal shock wave. Figure 3 shows the pressure contours of the shock wave when the armature is 121 mm away from the muzzle. When the armature moves in the air region of the guide rail, it compresses the front gas to form a weak compression wave. With the increase of the armature speed, the speed of the rear compression wave gradually increases, catching up with and squeezing the front compression wave (as shown in Fig. 4 the velocity contours, the shock wave speed generated in the rear is greater than that in the front). The pressure value of the front compression wave is always less than that of the rear, and finally a pressure discontinuity shock wave is formed. The intensity of shock wave mainly depends on the change of armature velocity, when the armature velocity is 1165 m/s, the peak pressure of shock wave reaches 2.148 MPa. When the shock wave moves in the bore, it is a plane wave. When the shock wave moves from the bore to the muzzle, it expands into a flat initial shock wave at the muzzle.
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Fig. 3. Shock wave pressure contour
Fig. 4. Shock wave velocity contour
Figure 5 shows the pressure contour of the initial shock wave at the muzzle. As the armature moves toward the muzzle, the diameter of the initial shock wave expands continuously and becomes ellipsoid, and the compressed air behind it also expands outward continuously. At this time, the initial shock wave presents a stable ellipsoid. At the same time, there is a negative pressure area on the side of the muzzle, which is mainly due to the rapid “suction” effect of supersonic gas in the rail. Before the armature comes out of the bore, the gas area in front of the armature is always the maximum pressure area, and the maximum pressure value first increases and then decreases. As shown in Fig. 6, when the front part of the armature comes out of the bore, the gas in the bore expands rapidly in the external air region. At this time, the pressure of the armature head is lower than that when it moves in the bore. This is because the pressure of the armature head is released due to the expansion of the shock wave in the bore, forming a spherical shock front. When the armature tail exits the muzzle (Fig. 7), the diameter of the spherical shock wave increases further, while the pressure of the shock front changes little.
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Fig. 5. Pressure contour of muzzle shock wave
Fig. 6. Pressure contour of armature head at muzzle
Fig. 7. Pressure contour of armature tail at muzzle
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Figure 8 shows the pressure contour of the armature moving in the external air region. At this stage, the pressure of the armature head gradually tends to be stable, and the shock wave at this time is a spherical shock wave with a moving spherical center. At this time, the middle part of the armature head protrudes outward, forming an obvious coronal shock wave. When the pressure contour map of the armature head is enlarged, it can be found that the shock wave of the armature head is symmetrical bow distribution, and the pressure gradient of the armature head is large (Fig. 9).
Fig. 8. Pressure contour of armature moving in air region
Fig. 9. Shock wave pressure contour of armature head
In order to analyze the influence of the far-field shock wave on the flow field after the armature exits the muzzle, 16 pressure monitoring points are placed at equal intervals in the muzzle area. The coordinates of the monitoring point 1 is (1050, 50), and the coordinates of the other monitoring points are spaced 100 mm in the x direction and 50 mm in the y direction. The pressure change is shown in Fig. 10. Taking (a) as an example, the calculation results show that: the initial values of the four pressure monitoring points are all 0, and then increase sharply, and the rise time of the four monitoring points is 40 ms. However, the decrease rate decreases with the increase of axial distance of armature. This is due to the large pressure gradient in front of the wave front. When the shock wave passes through the detection point, the pressure reaches the peak in a short time, but the pressure gradient behind the wave front is small, so the decline rate is low.
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Fig. 10. Pressure variation at 16 monitoring points
4 Experimental Study 4.1 Muzzle Shock Wave Measurement Because the rising edge of muzzle shock wave of electromagnetic energy equipment is in microsecond level, the spectrum characteristic is greater than 1 MHz. Therefore, the pressure measurement system should meet the following requirements: good dynamic response characteristics of the system; The system is stable and can adjust zero automatically; The system can be calibrated; In addition, the measurement sensor should have high frequency response, and the volume should be as small as possible to reduce the interference to the muzzle flow field. Based on the above considerations, the integrated high-frequency dynamic pressure transmitter is used in this experiment. The response frequency of the transmitter is 200 kHz, and the measurement range is 0–200 kPa. In order to avoid the possible electromagnetic radiation generated by the electromagnetic energy equipment, the sensor outlet line is equipped with an electromagnetic shielding layer.
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Table 1 shows the shock pressure values measured in 9 tests. Except for the first test, the other tests have measured the values. The charging voltage of this series of tests is 1150 V. Due to the low repeatability of electromagnetic emission test, the armature initial velocity is different, and the shock wave pressure is also different. Table 1. Nine tests were conducted with high frequency dynamic pressure transmitter. Test number
Muzzle peak voltage/V
Initial velocity(m/s)
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1
193
454
/
2
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452
57.64
3
190
449
56.85
4
185
419
52.87
5
195
432
52.73
6
202
445
52.12
7
203
441
55.00
8
194
447
55.63
9
205
449
56.24
Considering the bearing capacity of the power supply system, the charging voltage of this experiment is 1150 V. The test point is 170 mm in the transverse direction and 80 mm in the longitudinal direction from the muzzle center. Taking test 9 as an example, according to the data measured by probe B, the armature initial velocity is 449 m/s. As shown in Fig. 11 (b), the actual measured muzzle shock wave pressure is 56.24 kPa. The characteristic of the curve is that it rises to the peak value in 40 ms, then falls in 1 ms. Figure (a) shows the simulation value of shock wave pressure in muzzle flow field when the initial velocity of armature is 450 m/s. The shock wave begins to rise at 3.17 ms and reaches 58.17 kPa amplitude after rising edge. By comparing the experimental value with the simulation value, it can be seen that the difference of shock pressure amplitude between the simulation value and the experiment value is 1.93 kPa; In addition, the falling edge time of simulation is 20 ms slower than that of experiment; The main reasons for the above differences include: in the test, B probe is used to measure the armature initial velocity, which makes the armature initial velocity measurement inaccurate, and the pressure sensor itself has 5% measurement error. In the simulation, the influence of metal oxide impurities produced by the friction between armature and rail on muzzle flow field is not considered. After the armature is out of the bore, the simulation assumes that the armature moves at a uniform speed, while in the actual operation process, the armature will slow down due to the influence of air resistance after the armature is out of the bore.
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0.0030
t(s)
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0.0035
Fig. 11. Comparison of muzzle pressure values obtained from simulation and experiment
5 Conclusion In this paper, the flow field of electromagnetic energy equipment in dynamic launching process is calculated by using overset mesh method and UDF code. The change and development of flow field parameters in the process of armature launching are studied. In addition, the integrated high-frequency dynamic sensor is used to test. The main conclusions are as follows: 1. The dynamic launching process of electromagnetic energy equipment is a flow field with complex shock wave system, which mainly includes four stages: when the armature moves in the bore, the gas in the bore is compressed to form shock wave; when the armature is close to the muzzle, the initial shock wave at the muzzle begins to form; When the armature exits the muzzle, the spherical shock wave expands; The armature moves in the external air and forms a coronal shock wave. 2. The results of sensor measurement show that the shock pressure produced by armature high-speed motion is a waveform with steep rising edge and gentle falling edge, and there is a second peak. When the armature initial velocity ranges from 420 m/s to 450 m/s, the shock pressure varies from 52 kPa to 58 kPa. The simulation results are in good agreement with the test results.
Acknowledgment. This work was supported by the National Natural Science Foundation of China 51875546 and 52173089.
References 1. Ma, W., Lu, J.: Electromagnetic emission technology. J. Natl. Univ. Defense Sci. Technol. 38(06), 1–5 (2016). (in Chinese) 2. Li, J., Yan, P., Yuan, W.: Development and current situation of electromagnetic energy equipment launching technology. High Volt. Technol. 40(04), 1052–1064 (2014). (in Chinese)
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3. Merlen, A.: Generalization of the muzzle wave similarity rules. Shock Waves 9(5), 341–352 (1999) 4. Merlen, A., Dyment, A.: Similarity and asymptotic analysis for gun-firing aerodynamics. J. Fluid Mech. 225(1), 497–528 (1991) 5. Weimer, J.J., Singer, I.L., et al.: Temperatures from spectroscopic studies of hot gas and flame fronts in a railgun. IEEE Trans. Plasma Sci. 39(1), 174–179 (2011) 6. Zhao, W., Che, Y., Kong, Y., et al.: Muzzle blowback and its effects on degradation of bore insulator. IEEE Trans. Plasma Sci. 48(6), 2254–2260 (2020) 7. You, G., Wei, Q.: Numerical simulation of muzzle flow field. Explos. Shock 9(3), 254–260 (1989). (in Chinese) 8. Jiang, X., Jiang, B., Jiang, H.: Numerical study of muzzle flow field with moving mesh based on ALE equation. J. Comput. Mech. 8(04), 563–567 (2008). (in Chinese) 9. Fang, J., Li, Q., Ru, Z.: Numerical simulation of muzzle flow field of a new muzzle device. J. Missile Guid. 31(06), 152–154 (2011). (in Chinese) 10. Chen, L., Liu, K., He, X., et al.: Numerical study on transient flow field of electromagnetic energy equipment muzzle based on dynamic grid technology. Ship Sci. Technol. 41(17), 147–150 (2019). (in Chinese) 11. Du, P., Lu, J., Feng, J., Li, X.: Flow field analysis of electromagnetic orbital launcher dynamic launching process based on multi block overlapping grid. Acta Armamentarii 39(02), 234–244 (2019). (in Chinese) 12. Gao, Y., Xiao, H., Ni, Y., et al.: Simulation and analysis of the railgun muzzle flow field considering the arc plasma. IEEE Trans. Plasma Sci. 47(5), 2242–2249 (2019) 13. Gao, Y., Ni, Y.J., Xiao, H., et al.: Modeling and simulation of muzzle flow field of railgun with metal vapor and arc. Defence Technol. 16(4), 802–810 (2019) 14. Shih, T.H., Liou, W.W., Shabbir, A., et al.: A new k-ε eddy viscosity model for high Reynolds number turbulent flows. Comput. Fluids 24(3), 227–238 (1995) 15. Yi, Y.: Analysis and Research of Gear Profile Modification Based on Thermal Fluid Solid Coupling. Beijing Jiaotong University, Beijing (2019) 16. Li, H.: Intermediate Ballistics. Beijing University of Technology Press, Beijing (2015).(in Chinese)
Study on PSS Parameter Setting of Point-to-Network Transmission Mode Considering Regional Oscillation Fanchao Meng(B) State Grid HeBei Electric Power Research Institute, No. 238 TiYu Street, Shijiazhuang, China [email protected]
Abstract. In this paper, a method of setting PSS parameters for the transmission mode of point-to-network considering the regional oscillation is proposed. Three methods of theoretical analysis, simulation calculation and field test are integrated to set and verify the PSS parameters. Firstly, the calculation of PSS center frequency based on PSS transfer function is introduced, and then local oscillation frequency and interval oscillation frequency under three kinds of disturbance of voltage stepping, removing lines and removing unit is calculated using actual power grid data in three kinds of operation mode of normal way, no PSS and no compensation; Finally, the field test is carried out to test the non-compensation characteristic of the excitation system, and the PSS parameters with regional oscillation are calculated. The effectiveness of the method is demonstrated by the combination of simulation calculation and field test. Keywords: Low frequency oscillation · Power System Stabilizer (PSS) · Prony analysis · Regional oscillation
1 Introduction In recent years, the development of UHV power grid, the large-scale grid connection of new energy and the application of long-distance point-to-point transmission mode have greatly increased the complexity of power grid operation in China [1–3]. The stability characteristics of power grid in China have undergone great changes. The possibility of low-frequency oscillation in power grid is gradually increasing, the oscillation modes are more and more, and the oscillation frequency is lower and lower [4, 5]. In order to suppress the occurrence of low-frequency oscillation, it has become the most economical, direct and effective means to widely use PSS in excitation system, so the research of PSS also puts forward higher requirements [6, 7]. Considering whether its parameters are properly selected is very important to suppress the low-frequency oscillation of power grid: only when the PSS time constant can have a good compensation effect on the phase lag caused by the excitation system and generator, and its gain is just right, can PSS provide a good damping for the low-frequency oscillation of power grid [8]. Therefore, its parameter optimization design has always been one of the hot issues in academic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 114–124, 2022. https://doi.org/10.1007/978-981-19-1528-4_12
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and engineering circles. References [9] introduce the field test method, that is, in the operation of the unit, by testing the open-loop response characteristics of the unit and its excitation system, and then setting the PSS control parameters; References introduces the simulation analysis method, which is to design and verify PSS parameters through theoretical derivation or time domain simulation through the mathematical model of units and power grid. The limitation of the field test method is that the PSS control parameters can only be set and verified under a few operation modes and oscillation modes; The defect of model analysis is that the accuracy of the model is questionable. In this paper, a PSS parameter optimization method is proposed, which combines the field test method and the simulation analysis method to fully consider the regional oscillation and local oscillation [10–12].
2 Overview of Long Distance Point to Grid Transmission Mode In this paper, the long-distance point to network transmission mainly refers to the construction of thermal power plants in energy concentrated areas, and the power load is transmitted to the load center through 500 kV transmission line. The transmission line from Shanxi Jinjie power plant and Fugu power plant to Hebei South power grid is one of them. Jinjie power plant and Fugu power plant are large capacity power sources in the north of Shaanxi Province. Six 600 MW units of Jinjie power plant are connected to 500 kV Xindu switching station through 500 kV Jinxin third line, with a length of about 251 km. Four 600 MW units of Fugu power plant are connected to 500 kV Xindu switching station through 500 kV Fu Xin double circuit line, with a length of about 192 km. Xindu switching station is connected to 500 kV Shibei station of Hebei South power grid through 500 kV Xin Shi four circuit line, with a length of about 209 km. Series compensation capacitors (hereinafter referred to as series compensation) exist in Jinxin third line, Fuxin double line and Xinshi fourth line. The transmission system of Jinjie power plant and Fugu power plant forms a long-distance point to network transmission mode of “10 generators and 9 lines”.
3 Basic Principle of PSS Tuning Considering Regional Oscillation There are two modes of low frequency oscillation: local oscillation and interval oscillation. Operation experience shows that it is not uncommon for a generator unit to participate in two oscillation modes at the same time, and all power system stabilizers have the ability to suppress both oscillation modes. In the local oscillation mode, the stabilizer installed in a single unit can play a leading role in restraining the oscillation. For the interval oscillation mode, the stabilizer can only provide partial damping, and the proportion of this part of damping is often related to the proportion of the capacity of the generator unit in the whole system capacity. Therefore, the design of power system stabilizer should ensure that under various operation modes, especially in the long-distance point to grid transmission mode, it can not only provide enough damping for local oscillation mode, but also play a greater role in stabilizing interval oscillation, so as to ensure that the system has better operation performance.
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Other ΔTe0 ΔTm
Δω
1/Tjs
ws/s
Δδ
ΔTep GEP
PSS
Fig. 1. PSS block diagram
As shown in Fig. 1, it shows the change of electromagnetic torque without PSS; Represents the electromagnetic torque generated by the action of PSS; GEP is the transfer function of the equipment elements that PSS must pass through; PSS is the transfer function of power system stabilizer. As can be seen from Fig. 1,
PSS = KS
Tep = GEP × PSS ω
(1)
(1 + T1 S)(1 + T3 S) TS S [ ] 1 + TS S (1 + T2 S)(1 + T4 S)
(2)
KS is the amplification factor of PSS, TS is the time constant of PSS direct link, T1, T2, T3 and T4 are time constants of PSS leading lag compensation. Set t1/t2 = t3/t4 = 10, then the compensation center frequency is: √ 10 1 fe = = (3) √ 2π T1 2π T1 T2
ΔTep Δω ΔTe
Δδ ΔTe0
Fig. 2. Phasor diagram of PSS electromagnetic torque
Figure 2 shows the change of electromagnetic torque after installing PSS. Before PSS is installed, the electromagnetic torque is in the fourth quadrant, which will cause negative damping and instability. If a signal which is in phase with the shaft is input at the excitation phase adding point, a positive electromagnetic torque which is almost
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in phase with the shaft will be generated, and the total electromagnetic torque will be obtained by adding it to the phasor. In the first quadrant, the negative damping torque is compensated and the system tends to be stable. According to Fig. 2, combined with vector, in order to make PSS not only suppress local oscillation, but also play a maximum role in interval oscillation mode, the parameters of lead lag link of PSS should be set so that the lag phase corner is in the first quadrant. From the above analysis, it can be concluded that the PSS setting method considering regional oscillation in this paper is to calculate the center frequency of local oscillation and interval oscillation through simulation, calculate the PSS phase compensation angle, and then superimpose the measured phase angle without PSS compensation to judge whether the PSS phase compensation meets the standard requirements.
4 Simulation Analysis 4.1 Simulation Analysis System 4.1.1 Unit Grid Model In order to verify the PSS setting method considering regional oscillation, the off-line data of actual power grid is used for simulation analysis in this section. The generator, excitation system, PSS and speed control system in the off-line data are modeled according to the actual power grid model, and the parameters are engineering measured parameters. A few units that are not measured adopt typical parameters, and BPA software is used to calculate the center frequency of power grid oscillation under different operation modes. 4.1.2 Model and Parameters of Excitation System and PSS
Fig. 3. IEEE static excitation system (ST1A)
AVR of unit G1 excitation system has IEEE ST1A standard model, and its transfer function is shown in Fig. 3. The typical parameters of AVR are: TC = 3S, TB = 4S, tb1 = 0.06 s, Tc1 = 0.03 s, KF = 0, TF = 1 s, Ka = 56.25, Ta = 0.02 s. PSS of unit G1 has IEEE pss2b standard model, and its transfer function is shown in Fig. 4.
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Fig. 4. IEEE dual input power system stabilizer (PSS2B)
The typical parameters of PSS are: Ks1 = 7, Ks2 = 6.8, Ks3 = 1.2, T1 = 0.2 s, T2 = 0.02 s, T3 = 0.2 s, T4 = 0.02 s, T5 = 0.1s, T10 = 0.1, T6 = 0.2s, M = 2, N = 1, Tw1 = 6 s, Tw2 = 6 s, Tw3 = 6, Tw4 = 6 s, T7 = 6 s, T8 = 0.6 s, T9 = 0.09 s; US1 is the feedback quantity of the system with the speed deviation as the feedback quantity, US2 represents the feedback quantity of the system by the power deviation, and only the power is taken as the feedback amount of the system in this case. 4.2 Simulation Analysis Results 4.2.1 Load Step Simulation Calculation In normal mode, PSS out of operation and series compensation out of operation, the reference voltage step of excitation regulator of unit 1 in Jinjie power plant is set as 4%. Prony method is used to analyze the active power oscillation of unit 1, and the corresponding oscillation curve section is 1 s–10 s. Prony analysis with 150 points equal step sampling and 20 steps is used. The analysis results are shown in Table 1. Table 1. Load step simulation value Operation mode Oscillation frequency (Hz)
Damping ratio
Oscillation times
Normal mode
1.73
0.12
3
No PSS
1.74
0.04
5
No Series compensation
1.73
0.11
3
According to Fig. 5, under the three modes of normal mode, PSS exit operation and serial compensation exit operation, the active power oscillation frequency is 1.73 Hz. 4.2.2 Simulation Calculation of the Cutting Line In normal mode and PSS exit operation, one circuit of Jinjie Xin is cut off respectively. Prony method is adopted to analyze the active power oscillation of unit 1. The corresponding oscillation curve is 1 s–10 s, and the 150 point equal step sampling is used for 20-order Prony analysis. See Table 1 for the analysis results; In the mode of serial compensation exit operation, Jinjie Xindu line, unit 1 active power divergent oscillation (Table 2).
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no pss
119
no sc
660 650
P(MW)
640 630 620
610 600
0
1
2
3
4
5 Time(sec.)
6
7
8
9
10
Fig. 5. Load step simulation curve
Table 2. Numerical simulation of cutting line Operation mode
Oscillation frequency (Hz)
Damping ratio
Oscillation times
Normal mode
0.91
0.07
5
No PSS
0.95
0.03
9
It can be seen from Fig. 6 that the active power oscillation frequency is 1.01 Hz in normal mode and PSS out of operation mode. After PSS out of operation, the active power damping ratio decreases and the oscillation times increase. 4.2.3 Simulation Analysis of Unit Removal After Fault Under normal mode, PSS out of operation and series compensation out of operation, unit 1 of Jinjie power plant is cut off respectively. Prony method is used to analyze the active power oscillation of unit 2. The corresponding oscillation curve section is 1 s–10 s. Prony analysis with 150 point equal step sampling and 30 order is adopted. The analysis results are shown in Table 3. According to the analysis in Fig. 7, under the normal mode and PSS out of operation mode, the active power oscillation frequency is 1.0 Hz, and the active power damping ratio decreases and the oscillation times increase after PSS out of operation; When the series compensation is out of operation mode, the active power oscillation frequency is 0.9 Hz, the active power damping ratio further decreases, and the oscillation frequency further increases.
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1,500
1,000
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0
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2
4
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Time(sec.)
(a) No Series compensation 900 normal
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600 500 400 300 200 100 0
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5 Time(sec.)
6
7
8
9
10
(b) normal and no PSS
Fig. 6. Numerical simulation curve of cutting line
Table 3. Numerical simulation of cutting generator Operation mode
Oscillation frequency (Hz)
Damping ratio
Oscillation times
Normal mode
0.97
0.10
4
No PSS
1.01
0.08
5
No Series compensation
0.92
0.07
7
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No Sc
680
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660
640
620
600
0
1
2
3
4
5 Time(sec.)
6
7
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9
10
Fig. 7. Numerical simulation curve of cutting generator
5 Engineering Application 5.1 No Compensation Characteristic Test of Excitation System Unit 1 of Shanxi Guohua Jinjie Energy Co., Ltd. is a 600 MW steam turbine generator unit. The excitation regulator adopts unitrol-5000 microcomputer excitation regulator produced by ABB company. Electric power and rotor angular velocity are used as input signals of PSS. Under the condition of 566 MW active power, 80 MVar reactive power and 19820 V terminal voltage, the excitation non compensation characteristics of unit 1 are measured. The excitation regulator operates automatically in a single cabinet and PSS exits. The microcomputer excitation regulator samples the white noise signal from hp35670a spectrum analyzer and adds the sampling signal to the reference voltage of AVR. Slowly increasing the level of white noise signal makes the generator voltage fluctuate less than 2%. Using hp35670a spectrum analyzer to measure the frequency characteristic between the white noise signal output by the spectrum analyzer and the generator voltage is the no compensated frequency characteristic of the excitation system, and the phase angle in the phase frequency characteristic is measured, see Table 4. Table 4. The parameters of the dominant power oscillations obtained with Prony analysis Frequency (Hz)
Phase (°)
Frequency (Hz)
Phase (°)
Frequency (Hz)
Phase (°)
0.1
−35.1
0.8
−71.4
1.5
−111
0.2
−51.0
0.9
−73.0
1.6
−119
0.3
−62.0
1
−72.2
1.7
−131
0.4
−64.0
1.1
−85.0
1.8
−111
0.5
−61.8
1.2
−86.0
1.9
−114
0.6
−69.9
1.3
−95.0
2
−108
0.7
−69.8
1.4
−113
–
–
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5.2 PSS Parameter Calculation Considering Regional Oscillation According to part 3.2 of the simulation results, the oscillation frequency of Jinjie and Fugu power plants is 1.0 Hz relative to the Southern Hebei Power Grid. According to the measured non compensation characteristics of excitation system, the local oscillation frequency of unit 1 is 1.7 Hz. It can be seen from Eq. 4. √ 10 (4) T1 = 2π fe The estimated PSS parameters are as follows: T1 = 0.5 s, T2 = 0.05 s, T3 = 0.5 s, T4 = 0.05 s, TW1 = 6S, TW2 = 6S, TW3 = 6, T7 = 6S. Considering that the change of mechanical power in Fig. 3 is zero, the model shown in Fig. 3 can be simplified as a PSS with single power input signal. p =π/2 − arctan(ωTw3 ) − arctan(ωT7 ) + arctan(ωT1 ) − arctan(ωT2 ) + arctan(ωT3 ) − arctan(ωT4 ) The calculated PSS compensation phase is shown in the table (Table 5). Table 5. The parameters of the dominant power oscillations obtained with Prony analysis Frequency (Hz)
Phase of PSS(°)
Compensation phase (°)
Frequency (Hz)
Phase of PSS(°)
Compensation phase (°)
0.1
−39.74
−74.84
1.1
14.51
−70.49
0.2
−35.60
−86.60
1.2
15.18
−70.82
0.3
−24.75
−86.75
1.3
15.44
−79.56
0.4
−14.55
−78.55
1.4
15.38
−97.62
0.5
−6.19
−67.99
1.5
15.07
−95.93
0.6
0.33
−69.57
1.6
14.54
−104.46
0.7
5.25
−64.55
1.7
13.84
−117.16
0.8
8.89
−62.51
1.8
13.01
−97.99
0.9
11.51
−61.49
1.9
12.06
−101.94
1.0
13.33
−58.87
2
11.03
−96.97
5.3 Test Verification According to part 3.2 of the simulation results, the oscillation frequency of Jinjie and Fugu power plants is 1.0 Hz relative to the Southern Hebei Power Grid. According to the measured non compensation characteristics of excitation system, the local oscillation
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frequency of unit 1 is 1.7 Hz. Due to the limitation of field test conditions, only the PSS damping effect of local oscillation frequency is verified. When the generator is in grid connected operation, the active power of the generator is about 575 MW and the reactive power is about 80 MWar, the PSS exits, and the threephase voltage and three-phase current of the secondary side of the generator PT and CT are connected to the WFLC electric quantity analyzer for wave recording. In this case, the load voltage step test is carried out, and the WFLC wave recording is started at the same time to record the swing amplitude and times of the active power. Then put PSS into operation and repeat the above test under the same working condition. When PSS is not put into operation, the number of active power swings is about 4. When the PSS is put into operation, the number of active power swing is about once. It can be seen that the amplitude and times of active power oscillation should be reduced, and PSS has a significant inhibitory effect on local oscillation. According to the oscillogram, the local oscillation frequency of the unit is about 1.7 Hz.
6 Conclusion In this paper, a PSS parameter tuning method for point to grid transmission mode considering regional oscillation is proposed. The PSS parameters are tuned and verified by combining simulation calculation with field test. The conclusions are as follows. 1) Using simulation analysis method, the local oscillation frequency is calculated by voltage step disturbance, and the interval oscillation frequency is calculated by threephase short circuit line removal and unit removal disturbance. 2) According to the center frequency of local oscillation and interval oscillation, the PSS phase compensation angle is calculated, and then the measured phase angle without PSS compensation is superimposed to judge whether the PSS phase compensation meets the standard requirements. 3) The accuracy of setting PSS parameters is verified by the method of load voltage step.
References 1. Chen, J., Jin, T., Mohamed, M.A., et al.: An adaptive TLS-ESPRIT algorithm based on an S-G filter for analysis of low frequency oscillation in wide area measurement systems. IEEE Access 7, 47644–47654 (2019) 2. Rezaei, N., Mohammad-Nasir, U., Ifte-Khairul, A., et al.: Genetic algorithm-based optimization of overcurrent relay coordination for improved protection of DFIG operated wind farms. IEEE Trans. Ind. Appl. 55(6), 5727–5736 (2019) 3. Routray, A., Rajeev-Kumar, S., Ranjit, M.: Harmonic reduction in hybrid cascaded multilevel inverter using modified grey wolf optimization. IEEE Trans. Ind. Appl. 56(2), 1827–1838 (2020) 4. Liu, X., Lu, P., Chen, D., et al.: Impact of operation conditions and engineering eqivalent on PSS test. Power Syst. Prot. Control 47(10), 96–103 (2019). (in Chinese)
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5. Jiang, C., Zhou, J., Shi, P., et al.: Ultra-low frequency oscillation analysis and robust fixed order control design. Int. J. Electr. Power Energy Syst. 104, 269–278 (2019) 6. Meng, F.: Disposition, configuration, parameter calculation and commissioning of PSS. Electr. Power 37(10), 8–13 (2004). (in Chinese) 7. Meng, F., Dong, X., Gao, Z., Li, G., Xie, X.: an online evaluation method of power system stabilizer based on wide-area monitoring system. Electric Power Autom. on Equip. 32(10), 146–149 (2012). (in Chinese) 8. Yixin, N., Shousun, C., Baolin, Z.: Theory and Analysis of Dynamic Power System. Tsinghua University Press, Beijing (2002).(in Chinese) 9. Liu, Q.: Stability of Power System and Excitation Control of Generator. China Electric Power Press, Beijing (2007).(in Chinese) 10. Kundur, P.: Power System Stability and Control. McGraw-Hill, New York (1994).(in Chinese) 11. Dasu, B., Mangipudi, S., Rayapudi, S.: Interconnected multi-machine power system stabilizer design using whale optimization algorithm. Prot. Control Modern Power Syst. 4(1), 13–23 (2019). https://doi.org/10.1186/s41601-019-0116-6 12. Hang, N., Lan, Z., Gan, D., et al.: A survey on applications of wide-area measurement system in power system analysis and control. Power Syst. Technol. 29(10), 46–51 (2005). (in Chinese)
Multi-objective Optimization of Electromagnetic Devices Based on Improved Jaya Algorithm and Kriging Model Shuangsheng Huang1 , Bing Yan2 , and Bin Xia1(B) 1 School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870,
China [email protected] 2 TBEA Shenyang Electric Technology Consulting Co., Ltd., Shenyang 110025, China
Abstract. In this paper, the multi-objective optimization of TEAM22 problem was proposed in electromagnetic equipment as an example for the inverse problem of electromagnetic equipment always been the focus and difficulty of our country’s research. Firstly, the accuracy of the BP neural network, support vector regression model and kriging model were compared. The results showed that the kriging model has a fairly high accuracy. Then, based on the advantages of the standard Jaya algorithm, the Jaya algorithm is improved. The NSGAII, MOEAD, and improved Jaya algorithm are compared to verify with the performance of classic test function. The verification result shows that the improved Jaya algorithm performed better. Finally, the improved Jaya algorithm combined with the Kriging model is used to optimize the multi-objectives. The result shows that it has the advantages of fast convergence and high accuracy. Keywords: Electromagnetic device · Improved Jaya algorithm · Kriging model · Multi-objective optimization
1 Introduction With the rapid development of science and technology, electromagnetic equipment has been used in many related fields. The requirements for the inverse problem of electromagnetic equipment are higher and strict requirements are put forward for its design. For example, permanent magnets in medical equipment, permanent magnet synchronous motors, transformers, superconducting magnetic energy storage systems and circuit breakers in electrical engineering as well as antennas in the aerospace or military fields [1]. Among them, the more prominent TEAM22 benchmark problem of electromagnetic equipment is used for multi-objective optimization, which has relative reference value and practical significance in engineering. Nowadays, the most popular multi-objective optimization algorithm of engineering applications is the intelligent optimization algorithm. The intelligent optimization algorithm is a collection of various algorithms inspired by the evolution of some biological © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 125–132, 2022. https://doi.org/10.1007/978-981-19-1528-4_13
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searches in nature. The swarm intelligence optimization algorithm belongs to one of the optimization algorithms of intelligent optimization algorithms. The algorithm is an abbreviation based on the organizational behavior of individuals in the population and shows relatively independent intelligence. The related concept of swarm intelligence can be understood as the mutual influence between individuals and individuals with the environment to form intelligent behaviors. There is no forced control center in the population, and the final result is the intelligence of the entire [2]. The warm intelligence optimization algorithm is in a complex environment, the entire population is highly adaptable to the environment with the characteristics of rapidity and flexibility. The population will not affect the normal work of the overall population for some special individuals, so it has a strong robustness. Based on the above advantages of swarm intelligence, a large number of scholars are attracted to in-depth research on swarm intelligence optimization algorithms. Current swarm intelligence optimization algorithms include particle swarm optimization algorithm, ant colony algorithm, cuckoo algorithm and firefly algorithm [3]. These optimization algorithms have fast calculation speeds and strong global search capabilities, so it is widely applied to multi-objective and high-dimensional optimization problems. Nowadays, some algorithms based on other principles have also appeared, such as ant-lion algorithm, weed algorithm [4]. The Jaya algorithm is considered to be a new random heuristic algorithm. The algorithm is a swarm intelligence optimization algorithm proposed by an Indian scholar Venkata Rao in 2016 [5]. In recent years, the algorithm has been applied to many global optimization problems. There is a big difference between Jaya and other optimization algorithms. Jaya does not need to adjust its control parameters. And it is a Sanskrit word that translates into Chinese to mean “victory”. The algorithm is a simple and powerful global optimization algorithm. Based on this concept, the Jaya algorithm can optimize the solution of the problem for a given corresponding goal, so that its solution should be close to the best solution and far away from the worst solution. In the standard Jaya algorithm, a relatively uniformly distributed formula is used to update the solution. However, in recent years, some scholars have proposed some improved strategies. This paper proposes an improved Jaya algorithm to optimize multi-objective problems.
2 Selection of Approximation Model In this paper, under the ANSYS simulation software, the 76 sets of data obtained from the simulation of the TEAM22 problem are used to establish an approximate model. Through the accuracy of the more common BP neural network, support vector regression (SVR) and Kriging model, the root mean square error (RMSE), The root mean square error (RMSE), the average value of the relative percentage error (MAPE) and the coefficient of determination (R2 ) are common model calculation indicators as the evaluation indicators of the approximate model, the calculation results show that the Kriging model has higher accuracy than the other two models. The accuracy evaluation indicators of the three models are shown in Table 1.
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Table 1. Calculate the evaluation index of approximation model. Approximation model
RMSE
MAPE
R2
BP
16.5095
2.0714
−1.1130
SVR
9.8540
3.0409
0.24679
Kriging
3.5199
0.1774
0.90391
3 Improved Jaya Algorithm 3.1 Initialize Population Based on Skew Tent Mapping In the optimization of the target problem, the standard Jaya algorithm has high requirements for the initial solution. The quality of the initial solution will affect the accuracy of the solution. However, the use of the Skew Tent chaotic map can make the initial population distribute very uniform, which can improve the performance of the algorithm [6]. The Skew Tent chaos map generation initialization population can be divided into three steps. Firstly, random x 0 is generated between (0, 1), then, population x 0 uses Skew Tent chaotic mapping to generate x 1 , finally, the initial solution x initial is generated by applying the corresponding transformation to the population x 1 , and the corresponding formula for generating x 1 and the initial solution x initial are as follows: x0 0 < x0 < t t (1) x1 = 1−x 0 1−t t < x0 < 1 xinitial =xmin + x1 · (xmax − xmin )
(2)
wt is a random number of (0, 1), and x min and x max are the upper or lower limits of the objective function solution. 3.2 Neighborhood Update Strategy for MOEA/D Since the standard Jaya algorithm is not suitable for multi-objective optimization, a MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) neighborhood update strategy is introduced. The idea is to use the information obtained from several adjacent sub-problems so as to optimize each sub-problem accordingly, so that each sub-problem evolves in the best direction [7]. In addition, by calculating the Euclidean distance of the weight vector, these sub-problems are used to complete the co-evolution. During the evolution process, the weight vector ω = {1, 2… N} is initialized to divide the neighborhood. For the neighborhood of the i weight vector of the individual x i (i = 1, 2… N), it is recorded as B(i) = {i1 , i2 …, ik }, Initialize the individual x i and calculate the corresponding objective function value, denoted as F(x i ). Find the reference point, that is, the minimum value of each objective function, denoted as z = {z1 , z2 …, zm }T , where m is the dimension of the objective function. Then, the individual is updated accordingly for the individual x i (i = 1, 2,…, N) in the neighborhood B(i) and two individuals x j and x k are chose to generate a new individual y through differential
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evolution. Finally, it is needed to generate a new reference point for j = 1… M, if Z j > f j (y), then Z j is equal to f j (y), and to update the neighbors in the form of Tchebycheff aggregation function Domain optimal solution finally. For j ∈ B(i), the aggregate function value of x j is greater than the aggregate function value of new individual y, then x j is equal to y, and the corresponding objective function value is replaced at the same time, F(x i ) is equal to F(y).The formula of aggregation function is as follows: (3) min g te (x|w, z ∗ )= max wi fi (x) − zi∗ 1≤i≤m
where f i (x) is the i-th is the objective function and z* is the minimum value of the objective function in the corresponding dimensions of the multi-objective. By comparing the value of the objective function and the new solution of historical neighborhood optimal solution, the value of objective function is replaced to select the best accordingly. At the same time it is need to update to remove the “bad solution” in the neighborhood, as shown in Fig. 1.
Fig. 1. Schematic of neighborhood update strategy.
3.3 Strategy of Introducing Levy Flight Levy Flight proposes a random search method that includes most short step lengths and occasionally long step lengths. For this purpose, it can assist the optimization algorithm to perform detailed local search, and finally jump out of the local optimal value to reach the global optimal value, thereby enhancing the ability of this algorithm to optimize [8]. The corresponding Levy Flight update formula is as follows: xnew =xk + L(β1 ) ⊕ (xbest − |xk |) − L(β2 ) ⊕ (xworst − |xk |)
(4)
where x best and x worst are the best and worst individuals in the current iteration, ⊕ is pointto-point multiplication, L (β 1 ) and L (β 2 ) are different random variables conforming to the standard normal distribution of μ or ν. The expression is L(·) ∼
ϕ·μ |ν|1/2
(5)
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(1 + β) · sin(π · β/2) ϕ= (1 + β)/2 · β · 2(β−1)
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1/β (6)
According to the experience, β usually takes 1.5 better. 3.4 Flow Chart Improve Algorithm The flow chart of the improved Jaya algorithm is shown below. Step1: the relevant parameters of the algorithm are set, including dimension m of design variable, number of objective function n and population size p. Step2: Initialize the weight vector of each particle ω = {1, 2… N} and initialize the population use the Skew Tent chaotic map and calculate the corresponding objective function value. Step3: The j-th individual in the domain is presented. According to the MOEA/D neighborhood update strategy, the objective function values of domain individuals are weighted to obtain the aggregate function values and the optimal and the worst solutions are found. Step4: The new individuals are generated when the population is renewed. Meanwhile, the new individual is subjected to appropriate probability variation. The weight function value of the new solution under the current weight is calculated and compared with the old solution. Step5: the weight function value of the new solution under the current weight value is calculated and compared with the old solution. If the old solution is good, it remains unchanged. Otherwise, it replaces the old solution with the new solution. If the disaggregation meets the requirements, output the optimal disaggregation, otherwise return to step 3. 3.5 Numerical Test and Result Analysis In this paper, four classical ZDT series test functions are selected to compare and verify the three optimization algorithms of NSGAII, MOEAD and improved Jaya algorithm. These four test functions are ZDT1, ZDT2, ZDT3 and ZDT4 respectively, and the corresponding optimization algorithms are (a), (b), (c) and (d) in comparison with Pareto diagram. The above four classical test functions were used to verify and compare the three optimization algorithms. The comparison of the Pareto graph of the four test functions obtained by simulation showed that the Pareto graph of the improved Jaya algorithm was distributed evenly, and it was easy to jump out of the local optimal solution, and the convergence speed was fast, so as to ensure the diversity of understanding.
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Fig. 2. Pareto frontier comparison of three optimization algorithms of ZDT1, ZDT2, ZDT3 and ZDT4 test functions.
4 Description of TEAM 22 Problem According to reference [9], the current directions of two solenoid coils in the superconducting magnetic energy storage system are opposite. The inner solenoid coil has the function of system energy storage. The actual energy storage E 0 is 180 MJ, and the outer solenoid coil is to reduce the stray field around the system. That is to say, the smaller 2 of the superconducting magnetic energy storage system, the better, the stray field Bstray and the formula of the average stray field is shown in (7). 22
2 Bstray =
2 Bstray,i
i=1
22
(7)
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When stray field of the system satisfies smaller, the energy storage value of the system is as close to 180 MJ as possible. Two optimization goals are established for optimization and the expressions of two objective functions are as follows: F1 =
2 Bstray 2 Bnorm
F2 = E − Eref
(8) (9)
The three-parameter discrete optimization problem of the TEAM22 problem is studied in this paper. Where Bnorm is 3 mT, Eref = E0 = 180 MJ.
5 Optimizing Method This paper utilized ANSYS software to establish a finite element model for the TEAM22 problem and simulated, then utilized the Latin hypercube method to perform parametric scanning to obtain 76 sets of sample points for constructing the kriging model. The DACE file of the MATLAB toolbox was used as the kriging model. In addition, the improved Jaya algorithm is used to optimize the constructed kriging model. In the improved Jaya algorithm, the population size is 40 and the maximum number of iterations is 500. The Pareto solution set obtained is shown in Fig. 3.
Fig. 3. Pareto frontier solution set of the objective function.
In this paper, Kriging algorithm, MOEA/D algorithm and improved Jaya-Kriging algorithm (IJK) are used to solve TEAM22 respectively. The solution results of several different algorithms are shown in Table 2. It can be seen from Fig. 3 and Table 1 that the combination of the improved Jaya algorithm and the Kriging model is used to optimize the TEAM22 problem with multiple objectives, which improves the efficiency of the optimization and the accuracy of the algorithm.
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Algorithms
R2 (m)
H 2 /2(m)
D2 (m)
F1
F2
Iterations
Kriging
3.120
0.241
0.378
0.195
3.879
800
MOEA/D
3.126
0.243
0.389
0.164
4.135
1200
IJK
3.094
0.239
0.396
0.158
3.701
500
Reference [9]
3.090
0.272
0.345
0.159
3.956
2000
6 Conclusion This paper compared the accuracy of the approximate model and analyzed the performance of the optimization algorithm in the classic test function. It shows that the Kriging model has high accuracy. At the same time, the improvement of the Jaya algorithm can avoid the local optimum and achieve the global optimum to ensure the diversity of population. In the TEAM22 optimization design problem, an improved Jaya algorithm combined with the Kriging model is proposed to optimize multi-objectives. The results showed that the combination of two methods can not only improve the optimization efficiency but the accuracy of the algorithm.
References 1. Hong, M.: Solving electromagnetic fields by general reflection transmission method for coaxialcoil antenna in cylindrically multilayered medium. IEEE Geo. Remote Sens. Lett. 15(6), 912– 916 (2018) 2. Shen, H., Liu, G., Chandler, H.: Swarm intelligence based file replication and consistency maintenance in structured P2P file sharing systems. IEEE Trans. Comput. 64(10), 2953–2967 (2015) 3. Zhang, Z., Wang, D.: Study on the intentional choice mechanism of course selection based on swarm intelligence algorithm. Sci. Program. 3(1), 1–6 (2021) 4. Marine, L., Gawain, J., Romain, F., et al.: Unsupervised classification algorithm for early weed detection in row-crops by combining spatial and spectral information. Remote Sens. 10(5), 761–762 (2018) 5. Rao, R.V., Saroj, A.: Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm. Energy Syst. 9(2), 305–341 (2016). https://doi.org/10.1007/s12667-016-0221-9 6. Demidova, L., Gorchakov, A.: A study of chaotic maps producing symmetric distributions in the fish school search optimization algorithm with exponential step decay. Symmetry 12(5), 784–785 (2020) 7. Chen, X., Shi, C., Zhou, A., et al.: On balancing neighborhood and global replacement strategies in MOEA/D. IEEE access 48(9), 1–2 (2019) 8. Chegini, S., Bagheri, A., Najafi, F.: A new hybrid PSO based on sine cosine algorithm and levy flight for solving optimization problems. Appl. Soft Comput. 73(2), 697–726 (2018) 9. Wang, L.: Research on stochastic optimization algorithm based on population and its application in inverse problems of electromagnetic field. In: 6th Zhejiang University, pp. 46–52. Zhejiang University, Zhejiang (2016). (in Chinese)
Numerical Wind Tunnel Simulation of Breeze Vibration and Response Analysis Under Random Frequency Chujun Fu1(B) , Qi Liu1 , Jianli Zhao1 , Baofeng Yan1 , Bei Wang2 , and Jialin Qin2 1 Inner Mongolia Electric Power Research Institute, Hohhot 010010, China
[email protected] 2 China Electricity Council Power Construction Technology and Economic Consulting Center,
Beijing, China
Abstract. TA two-dimensional numerical model was used to simulate the force of the transmission line in the breeze. The fluid analysis module of ANSYS Fluent was used to simulate the force of the transmission line in the breeze. The size of the ACSR GROSBEAK lead was taken as an example to get the change of the lift coefficient and drag coefficient of the lead with the wind speed under different wind speeds. At the same time, the fatigue life of the conductor under random frequency is obtained by studying the influence of wind direction and wind speed. Keywords: Wind tunnel simulation · Random frequency · Fatigue life
1 The Introduction Under the action of wind, the transmission line (ground conductor) is always in a state of vibration. According to the difference of frequency and amplitude, the vibration of power transmission line can be roughly divided into three kinds: the breeze vibration of high frequency and small amplitude, the wake vibration of medium frequency and medium amplitude and the large wave of low frequency. The three kinds of vibration can cause damage to the transmission line, and the breeze vibration is the most frequent one [1, 2]. High voltage overhead lines will produce breeze vibration under the action of Karman Vortices, which will cause wear of conductors, ground wires (including optical fiber composite overhead ground wire OPGW) and metal tools. Long time Under the action of wind, the transmission line (ground conductor) is always in a state of vibration. According to the difference of frequency and amplitude, the vibration of power transmission line can be roughly divided into three kinds: the breeze vibration of high frequency and small amplitude, the wake vibration of medium frequency and medium amplitude and the large wave of low frequency. Aeolian vibration is the most frequent
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type of vibration wear will lead to fatigue break or fracture of conductors and ground wires, as well as damage of transmission line tower poles and metal tools, seriously threatening the safety of line operation [3–5]. In order to carry on the thorough research to the wire breeze vibration phenomenon, the various countries scholars put forward the different method to study the transmission line breeze vibration phenomenon. At present, wind tunnel experiment is widely used to obtain the wind energy input power curve of the conductor. However, due to the high cost of wind tunnel experiment and limited experimental conditions, it cannot be used on a large scale.
2 Numerical Wind Tunnel Simulation of Transmission Line Breeze Vibration Phenomenon Wind excitation is the main cause of transmission system breeze vibration, and under different wind speed conditions, will bring a relatively large difference in vibration cloud dynamic characteristics. 2.1 Simulation Model of Fixed Transmission Line Turbulence Since the transmission line is a long and thin structure in the breeze vibration, its aerodynamic characteristics are mainly determined by the shape of the cross section, so the two-dimensional numerical model can be used to simulate the force of the transmission line in the breeze. The ANSYS Fluent fluid analysis module was used to simulate the size of the ACSR GROSBEAK lead as an example, where the diameter of the lead was D = 25.16 mm. In the simulation, the computing domain is set as a rectangle (as shown in Fig. 1), the computing domain size is set as: upstream 12D, downstream 20D, and the width is 24D. The fluid is air. In the simulation, the air flows from left to right, the left boundary is the speed control inlet, the right boundary is the pressure outlet, and the upper and lower boundary and the surface of the conductor model are set as the wall
Fig. 1. Grid division of flow field
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surface. Wind speeds in simulation are 10 m/s, 11 m/s, 12 m/s, 13 m/s, 14 m/s and 15 m/s, respectively. The turbulence model adopts the SST model in the K-ω model, which is suitable for both low and high Reynolds number flow regions. Since the accuracy of the calculation is highly dependent on the mesh density, the mesh near the wire is refined during the mesh division. 2.2 Simulation Results In the simulation, periodic vortex shedding was obtained after about 1 s of iterative calculation. The velocity cloud diagram of the extracted simulation area is shown in Fig. 2. As you can see from the image, the Karmen vortex formed behind the power lines and alternately fell off. Figure 3 shows the time history of lift coefficient of the conductor when the wind speed is 10–15 m/s. As can be seen from the figure, for different wind speeds, the average lift coefficient of the wire is 0, and its amplitude increases with the increase of wind speed. Figure 4 shows the time history of the drag coefficient of the conductor when the wind speed range is 10–15 m/s. It can also be seen from the figure that the drag coefficient increases with the increase of wind speed.
Fig. 2. Instantaneous flow field near the transmission line when the wind speed is 10 m/s
The amplitude of lift coefficient varies with the wind speed, as shown in Table 1, and its amplitude increases with the increase of the wind speed. The average resistance coefficient changes with the wind speed, as shown in Table 2. The resistance coefficient increases with the increase of the wind speed.
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Fig. 3. Time history diagram of lift coefficient at different wind speeds: (a) 10 m/s; (b) 11 m/s; (c) 12 m/s; (d) 13 m/s; (e) 14 m/s; (f) 15 m/s.
Fig. 4. Time history diagram of resistance coefficient at different wind speeds: (a) 10 m/s; (b) 11 m/s; (c) 12 m/s; (d) 13 m/s; (e) 14 m/s; (f) 15 m/s
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Table 1. The amplitude of lift coefficient varies with the wind speed. Wind speed (m/s)
10
11
12
13
14
15
Cl amplitude
0.49
0.58
0.68
0.79
0.90
1.04
Table 2. Average resistance coefficient varies with wind speed Wind speed (m/s)
10
11
12
13
14
15
The average Cd
0.58
0.69
0.81
0.93
1.07
1.22
3 Calculation of Anti-vibration Hammer Installation Position For the safe operation of the line, the anti-vibration hammer is generally used for antivibration. The anti-vibration hammer is used for anti-vibration of high voltage overhead wire. It is necessary to choose the appropriate anti-vibration hammer according to the actual situation and install it reasonably so as to reduce the dynamic bending stress of the transmission line and improve the fatigue life of the transmission line. Breeze vibrations on power lines are directly affected by local meteorological conditions, especially the wind speed [6–9]. Therefore, it is necessary to determine the selection and installation strategy of anti-vibration hammer according to the local meteorological conditions [10]. 3.1 Calculation Method of Anti-shock Hammer Installation Position The general method for calculating the installation position of the anti-vibration hammer is to assume that the anti-vibration protection should be carried out in the whole range from the minimum wavelength (the highest frequency) to the maximum wavelength (the lowest frequency) within the range of the breeze vibration of the transmission line, and the probability of the occurrence of each wavelength is equal or normally distributed. When installing an anti-vibration hammer, the installation point should be in equal proximity to the wave-belly of the minimum half wavelength and the maximum half wavelength. Therefore, we can get the installation position calculation of the national defense vibration hammer as formula (1). D Tav (1) b1 = 2.25 ∼ 2.375 VM q In order to avoid when the frequency is an integer multiple of the first anti-vibration hammer at the wave node and can not play a full role, often use the non-equidistant installation method, generally the second anti-vibration hammer installation distance is 1.75 times the first one. However, the above installation method considers the installation method of the whole frequency band, and the protection of the low frequency band is not sufficient.
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Therefore, it is necessary to carry out differentiated installation layout method according to the local meteorological conditions. Consider that not all frequencies of vibration are dangerous vibrations, and the probability of occurrence of each vibration frequency is different. Therefore, a more reasonable and optimal method to calculate the installation position is to only calculate the vibration frequency band that needs protection, and consider the probability of each vibration frequency in the frequency band. In this way, the frequency range that needs to be protected is reduced, and the installation position of the anti-vibration hammer is avoided to be close to the wavy position that does not need to be protected or has a small probability of occurrence, so as to achieve the best protection effect of the anti-vibration hammer [11–13]. The breeze vibration has the characteristic of randomness. The principle of probability and statistics is used to calculate the weighted amplitude ratio by considering the probability of occurrence of dangerous vibration frequency, so as to determine the optimal installation position of the anti-vibration hammer. In general, when a transmission line vibrates at a certain natural frequency, it can be considered that the transmission line vibrates in the form of the main mode corresponding to that frequency. Therefore, the vibration displacement of the wire can be expressed as Eq. (2). (2) Yi = Ai sin 2π x λi sin(2π fi t) For a standing wave whose vibration frequency is fi or wavelength is λi , the amplitude of each point of the transmission line is expressed in Eq. (3). (3) Yi = Ai sin 2π x λi Thus, the sum of squares of the amplitude ratio can be expressed in Eq. (4). 2 Yi Ai = sin2 2π x λi
(4)
For different values x, the larger the number, the greater the limiting effect on the frequency of the wave by installing the damper in this position. (Y i /Ai )2 For a frequency band requiring anti-vibration hammer, considering the probability of occurrence of each vibration frequency in the frequency band, the weighted amplitude ratio is defined as Eq. (5).
Yi 2 ) Ai 2π x = p(fi − f ≤ fi ≤ fi + f ) sin2 ( ) λi fx =
p(fi − f ≤ fi ≤ fi + f )(
(5)
The larger f x is, the greater the comprehensive energy consumption of the antivibration hammer installed at point x in this protective frequency band. Based on this, related programs are written to calculate the maximum value f x corresponding to the point x.
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3.2 Optimal Arrangement Method of Shockproof Hammer (1) Calculate the breeze vibration of the power transmission line without the antivibration hammer first, determine the vibration frequency band that needs the key protection according to the dynamic bending strain limit, and then select the antivibration hammer that can effectively cover the protection frequency; (2) According to the local meteorological data, determine the probability distribution of wind speed and calculate the probability of each vibration frequency in the key protection frequency band; (3) Calculate the weighted amplitude ratio of the frequency band, and determine the best installation position of the anti-vibration hammer by its maximum value; (4) If multiple anti-vibration hammers are needed, the installation position of the nearest anti-vibration hammer of the off-line clamp can be determined first, and then the transmission line with the anti-vibration hammer whose installation position has been determined is calculated, the protection frequency band is redetermined, and the weighted amplitude ratio is calculated, so that the installation position of other anti-vibration hammers can be determined in turn.
4 Response Analysis of Breeze Vibration Under Random Frequency Breeze vibration can cause fatigue damage of wire, and then affect the service life of transmission wire. It is difficult to evaluate the fatigue life accurately because there are many factors affecting the fatigue life of transmission lines. The traditional design method of transmission line breeze vibration adopts the infinite life design method, also known as the single limit method, but it only qualitatively describes whether the structure is safe or not, and can not consider the influence of different wind environment, in fact, the distribution of wind speed and direction will also affect the fatigue life of the conductor. 4.1 Probability Distribution of Wind Direction Wind direction has a great influence on the vibration of overhead lines. When the Angle between the wind direction and the overhead line is between 45° and 90°, the stable vibration of the overhead line can be observed within the range of the vibration speed of the breeze. When the included Angle is between 30° and 45°, the vibration time is short, and the vibration time is sometimes nonexistent and does not last. When the included Angle is less than 20°, the vibration of the overhead line can not be observed basically because the wind input energy is insufficient. This is because the more perpendicular the wind direction is to the power transmission line, the more likely it is to cause Karman vortex street. When the Angle between the two is less than 45°, the wind component will greatly reduce the energy of Karman vortex street.
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4.2 Frequency Probability Distribution A large number of measured data show that the wind speed distribution meets the Weibull distribution, and the wind speed fitting is very effective, which is suitable for processing the wind speed data and can estimate the velocity distribution at different heights. Weibull distribution expression is shown in Eq. (6), and the probability of wind speed 0.5–10 m/s and frequency 10–100 Hz can be calculated.
k p(f ≤ fc ) = 1 − exp −(fc D/St cw )
(6)
In practical calculation, the frequency range of possible breeze vibration (10–100 Hz) is divided into several small frequency bands. For example, the vibration probability calculation formula of a frequency range is expressed in Eq. (7).
k
k p(fi−1 ≤ f ≤ fi ) = exp −(fi−1 D/Si c − exp fi D/Si c
(7)
The stress on the transmission line does not belong to the symmetrical cycle. The Goodman line in finite life is used. The equivalent alternating stress amplitude is calculated by the formula (8). σεq =
σa σb σa − σm
(8)
σb is the strength limit of the transmission line; σa Is the dynamic stress amplitude of breeze vibration; σm Is the average stress of breeze vibration. Put the calculated equivalent alternating stress amplitude into Eq. (9), and then the life N under random vibration failure can be calculated. σ = 450N −0.200 , N ≤ 2 × 107 σ = 263N −0.168 , N > 2 × 107
(9)
According to the probability distribution diagram of wind direction, the wind direction probability f (d) of the circuit is calculated. According to the wind speed probability f (v) caused by the breeze vibration in Eq. (7), the probability of the occurrence of breeze vibration can be expressed as f (d,v) = f (d)f (v), so the annual average time and average number of cycles of the breeze vibration can be obtained.
5 Response Analysis of Breeze Vibration Under Random Frequency Fatigue damage theory is used to predict the fatigue life of conductor under random frequency. The theory of cumulative fatigue damage is the accumulation law and criterion of fatigue damage under the action of variable amplitude fatigue load. At present, fatigue damage theory is divided into linear theory and nonlinear theory. Linear theory is easy to calculate and more research, coupled with a simple theoretical basis for metal fatigue life estimation more applications.
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The linear fatigue cumulative damage considers that the fatigue damage is linear superposition and each stress is independent and uncorrelated. The typical linear cumulative damage theory is Miner’s theory. The Miner’s theory states that DAMSiIs defined as the damage rate under stress level I, and the damage rate caused by different stress levels can be superimposed to obtain the total damage rate. When the total damage rate reaches 1.0, the component is considered to be damaged. As shown in Eq. (10): Dam =
Dami =
i
n(σi ) N (σi )
(10)
i
Where, n(σi ) is the number of cycles when the stress variation range is σi ; N (σi ) can be the number of alternating stress cycles required for transmission line failure. The calculation formula for the annual cumulative damage of transmission line breeze vibration is as follows: The calculation formula for the annual cumulative damage of transmission line breeze vibration is as follows: n (11) Dyear = N When the damage accumulates, the transmission line is considered to be damaged by fatigue. D = 1, Then, the service life calculation formula of the power transmission line is (12). Life =
1 Dyear
(12)
Then, the fatigue life of the transmission line system can be calculated according to Eqs. (11) and (12).
6 Conclusion In this paper, by means of numerical simulation, the phenomenon of breeze vibration of two dimensional transmission lines is studied, and its vibration mechanism is analyzed. It is concluded that the amplitude of lift and drag coefficients of wires increases with the increase of wind speed. Based on the Miner linear damage accumulation theory, the fatigue characteristics of the material were studied considering the probability distribution of wind speed and direction. The fatigue damage theory was used to predict the fatigue life of the conductor under the action of random frequency.
References 1. Huang, X.: On-line Monitoring and Fault Diagnosis of Transmission Lines. China Electric Power Press, Beijing (2008). (in Chinese) 2. Chen, Y., Wan, Q., Gu, L.: Discussion on the structure of UHV wire and pole tower in China. High Volt. Technol. 30(6), 38–41 (2004). (in Chinese)
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3. Luo, X., Zhang, Y., Xie, W.: Research and application of detection and evaluation system for anti-vibration hammer performance. Guangdong Electric Power 28(12), 119–123 (2015). (in Chinese) 4. Luo, X., Zhang, Y., Xie, S., et al.: Experimental research on nonlinear impedance and parameter identification of anti-vibration hammer. J. Vib. Shock 32(11), 182–185 (2013). (in Chinese) 5. Chen, J., Li, J.: Anti-vibration of fiber optic composite overhead ground wire. Commun. Electr. Power Syst. 27(167), 6–9 (2006). (in Chinese) 6. Lu, M.L., Chan, J.K.: An efficient algorithm for aeolian vibration of single conductor with multiple dampers. IEEE Trans. Power Delivery 22(3), 1529–1822 (2007) 7. Kalombo, R.V., Araújo, J.A., Ferreira, J.L.A., et al.: Assessment of the fatigue failure of an All Aluminium Alloy Cable (AAAC) for a 230 kV transmission line in the center-west of Brazil. Eng. Fail. Anal. 61, 77–87 (2015) 8. Keyhan, H., Mcclure, G., Habashi, W.G.: Dynamic analysis of an overhead transmission line subject to gusty wind loading predicted by wind-conductor interaction. Comput. Struct. 122, 135–144 (2013) 9. Liu, S., Sun, N., Yin, Q., et al.: Study of new vibration suppression devices for application to EHV transmission line ground wires. Energy Procedia 12(3–4), 313–319 (2011) 10. Shao, T.: Mechanical Calculation of Overhead Transmission Line, 2nd edn., pp. 312–354. China Electric Power Press, Beijing (2003). (in Chinese) 11. Zhang, D.: Design Manual of High Voltage Transmission Circuit for Electric Power Engineering, pp. 229–231. China Electric Power Press, Beijing (2003). (in Chinese) 12. He, X., Li, H.: Engineering calculation of the installation position of anti-vibration hammer in overhead transmission line. J. Comput. Phys. 5(17), 588–592 (2000). (in Chinese) 13. Sun, L.: Research on the Performance of Overhead Transmission Line Vibration Absorber and Accurate Calculation of Installation Position, vol. 5, pp. 49–57. Hefei University of Technology (2003). (in Chinese)
Experimental Study on the Partial Discharge Inception Characteristics of Different Pressboards for Electrical Purpose Xin Liu1 , Congcong Chen2 , Hanbing Hao1 , Taiping Wang1 , Xueyou Zhang1 , Chunjia Gao2(B) , Bo Qi2 , and Chengrong Li2 1 State Grid Anhui Maintenance Company, Hefei 230000, People’s Republic of China 2 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,
North China Electric Power University, Beijing 102206, People’s Republic of China [email protected]
Abstract. Partial discharge inception characteristics is one of the important performance indicators of electrical pressboard, which can effectively characterize the corona field strength and determine the allowable field strength of insulating materials. In this paper, a partial discharge detecting platform based on IEC 60641–2 was established, and utilizing which, the PDIVs (Partial Discharge Inception Voltage) of different pressboard belonging to various suppliers were captured. The experimental revealed that the maximum deviation of the PDIV of pressboard from different manufacturers is 26.87%, and the maximum deviation of the PDIV of different batches of pressboard from the same manufacturer is 18.82%. Although some manufacturers’ pressboards have higher PDIV, their pressboard data has a greater dispersion. The average values of the PDIV of the insulating pressboards produced by manufacturers A, B, C, D, and E are 18.45 kV, 18.9 kV, 16.5 kV, 14.9 kV, and 16.8 kV, respectively, and the corresponding average standard deviations are 1.07 kV, 1.26 kV, 1.50 kV, 1.66 kV, 1.62 kV. Keywords: Partial discharge inception voltage · Pressboard for electrical purpose · Performance dispersion
1 Introduction Power transformers, as one of the important apparatuses in power girds, mainly adopt oilpressboard composite insulation as the main insulating structure, while the performance of which could be closely related to the safe and reliable operation of grid [1–4]. Partial discharge inception characteristics can effectively reflect the insulation performance of the material, and it is an indispensable basis for determining the allowable field strength of electrical pressboard [5, 6]. Dutch scholars Q. Zhuang conducted experimental studies on the epoxy-paper insulation of an Oil-immersed transformer through partial discharge analysis, and some representative PD phenomena were obtained from the experimental results [7]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 143–150, 2022. https://doi.org/10.1007/978-981-19-1528-4_15
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With the increasing number of manufacturers of pressboards for electrical purpose, it is very necessary to conduct a systematic comparative study on the products of different paperboard manufacturers to objectively reflect the difference in insulation performance between different paperboard products, which provided a basic database for the structural design and optimization of oil-pressboard insulation equipment.
2 Test Platform 2.1 Test Circuit The test platform is composed of a test chamber, an AC power supply, et al., and the internal temperature of the test chamber is controlled at the indoor temperature during the test.
Fig. 1. Test circuit
The detection test is carried out under AC voltage, and the specific test circuit is shown in Fig. 1. 2.2 Test Voltage
Fig. 2. AC voltage 60s step-by-step boost mode AC voltage 60s step-by-step boost mode
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The test voltage is AC voltage. Combining with the performance of the test equipment, a 60-s step-up method is selected as the voltage method. The voltage is boosted by 0.5 kV per minute, and the withstand voltage of the test product at each level is also one minute, as shown in Fig. 2. 2.3 Strong Vertical Component Electric Field Test For the measurement of the strong vertical component electric field, column-plate electrodes are used. The plate electrode is designed according to the IEC 60243–1:2013 standard [8]. The upper electrode of the column electrode is a cylinder with a diameter of 25 mm, and the lower electrode is a circular plate with a diameter of 75 mm as shown in Fig. 3. The sample is sandwiched between the metal electrodes, the distance between the two metal plates is the thickness of the sample, and the voltage is applied to the upper metal electrode. The edge of the electrode is rounded (the radius is 3.0 ± 0.2 mm) to avoid flashover.
Fig. 3. Electrode structure diagram
2.4 Test Data Processing The average calculation process is as follows: x1 + x2 + · · · + xn n The maximum value calculation process is as follows: X=
XM ax = Max{x1 , x2 , · · ·, xn }
(1)
(2)
The minimum value calculation process is as follows: XM in = Min{x1 , x2 , · · ·, xn } The sample standard deviation calculation process is as follows: 2 Xi − X S= N −1 Among them, x is the test data and n are the number of samples.
(3)
(4)
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3 Measurement Results and Analysis Partial discharge inception characteristics of 15 kinds of insulating pressboard samples produced by electrical insulating pressboard manufacturers A to E were measured at 20 °C. Next, we will discuss the test results of PDIV and PDIDC (Partial Discharge Inception Discharge Capacity) of different batches of insulating paperboard samples from various manufacturers.
The partial discharge inception voltage (kV)
25 20 15 10 5 0
A1 A2 A3 A4 A5 A6
The partial discharge inception discharge capacity (pC)
3.1 Comparisons of Different Batches from the Same Manufacturer
10 9 8 7 6 5 4 3 2 1 0
A1 A2 A3 A4 A5 A6
Pressboard type
Pressboard type
(a) Partial discharge inception voltage (b) Partial discharge inception discharge capacity Fig. 4. Partial discharge initiation characteristics of manufacturer A insulating pressboard
The partial discharge inception voltage (kV)
25 20 15 10 5 0 B1
B2
Pressboard type
The partial discharge inception discharge capacity (pC)
The PDIV of pressboard-A1 is the largest. The average value is 19.03 kV and the standard deviation is 0.91 kV. PDIDC of pressboard-A5 is the smallest, with an average value of 6.30 pC. and a standard deviation of 1.26 pC. 10 8 6 4 2 0
B1
B2
Pressboard type
(a) Partial discharge inception voltage (b) Partial discharge inception discharge capacity Fig. 5. Partial discharge initiation characteristics of manufacturer B insulating pressboard
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20 18 16 14 12 10 8 6 4 2 0
C1
C2
The partial discharge inception discharge capacity (pC)
The partial discharge inception voltage (kV)
The PDIV of pressboard-B1 is relatively large. The PDIDC of pressboard-B2 is relatively small. 8 7 6 5 4 3 2 1 0
C1
C2
Pressboard type
Pressboard type
(a) Partial discharge inception voltage (b) Partial discharge inception discharge capacity Fig. 6. Partial discharge initiation characteristics of manufacturer C insulating pressboard
The partial discharge inception discharge capacity (pC)
The partial discharge inception voltage (kV)
The PDIV of pressboard-C2 is relatively large, while the PDIDC of pressboard-C2 is relatively small.
18 16 14 12 10 8 6 4 2 0 D1
D2
Pressboard type
9 8 7 6 5 4 3 2 1 0 D1
D2
Pressboard type
(a) Partial discharge inception voltage (b) Partial discharge inception discharge capacity Fig. 7. Partial discharge initiation characteristics of manufacturer D insulating pressboard
The PDIV of the two batches of pressboard of manufacturer D is basically the same and the data dispersibility is better, only the discharge amount has a certain difference, pressboard-D2 is 1.94 times that of pressboard-D1.
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(a) Partial discharge inception voltage (b) Partial discharge inception discharge capacity Fig. 8. Partial discharge initiation characteristics of manufacturer E insulating pressboard
The PDIV of pressboard-E2 is relatively large, while the PDIDC of pressboard-E2 is relatively small. Analyzing Fig. 4, 5, 6, 7 and 8, it can be found that the partial discharge initiation characteristics of the six batches of pressboard from manufacturer A, the two batches of pressboard from manufacturers B, C, D, and E are not much different, and the data is well dispersed, which shows that the products of different batches of various manufacturers have stable performance in terms of partial discharge characteristics. The PDIDC of 14 batches of insulating pressboards were all greater than 2 pC, indicating that the judgment of the initiation of partial discharge was correct. There is no direct relationship between the PDIV and the PDIDC. 3.2 Comparisons of Different Manufacturers In order to horizontally compare the inception characteristics of the partial discharge of the insulating pressboards of various manufacturers, find the average and standard deviation of the PDIV and the PDIDC of different batches of pressboard of each manufacturer, and draw the following histogram:
The partial discharge inception voltage (kV)
25 20 15 10 5 0 A
B
C
D
E
Pressboard type
Fig. 9. Comparison of the partial discharge inception voltage of pressboard from different manufacturers
The partial discharge inception discharge capacity (pC)
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10 8 6 4 2 0
A
B
C
D
E
Pressboard type
Fig. 10. Comparison of the partial discharge inception discharge capacity of different manufacturers of pressboard
The PDIV of the insulating pressboard of manufacturer B is the largest, with an average value of 18.89 kV. The PDIV of the insulating pressboard of manufacturer D is the smallest, with an average value of 14.89 kV. B is 1.23 times of D. It can be seen from Fig. 9 that the PDIV of the pressboards produced by manufacturers A and B is higher than that of the pressboards produced by manufacturers C, D, and E, but the difference is not significant. It shows that the insulation performance of insulation pressboard produced by manufacturers A and B is still slightly better than that of pressboard produced by manufacturers C, D, and E, but the insulation performance of pressboard produced by manufacturers C, D, and E is enough to meet the insulation conditions of ordinary transformers at room temperature. Insulating pressboard produced by manufacturers B has the largest PDIDC with an average value of 7.45 pC. Insulating pressboard produced by manufacturers D has the smallest PDIDC with an average value of 5.48 pC, which is 1.36 times that of D. It can be seen from Fig. 10 that the PDIDC of the insulating pressboards of the manufacturers A and B is basically smaller than that of the manufacturers C, D, and E, which is reasonable because the PDIV of the manufacturers A and B is greater.
4 Conclusion The partial discharge inception characteristics of samples from different electrical insulating paperboard manufacturers were measured. The partial discharge inception characteristics of different batches of cardboard from the same manufacturer were compared, and the partial discharge inception characteristics of different cardboard manufacturers at home and abroad were compared. The results show that: The standard deviation of the average value of PDIV of the six batches of insulation pressboard of manufacturer A is 1.07 kV, and the difference between different batches is small, the range is only 1.70 kV. In the case of manufacturers B, C, D, and E, when the number of inspection batches is less than that of manufacturer A, the standard deviations of the average PDIV of the insulating pressboards reached 1.26 kV, 1.50 kV, 1.66 kV and 1.62 kV, respectively. The insulating pressboard produced by manufacturer A is significantly better than other manufacturers in terms of the quality control of the PDIV.
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PDIV of insulating pressboards produced by manufacturers A and B reached 18.47 kV and 18.89 kV, respectively, which were higher than those of the three other manufacturers. Considering that the PDIV is higher, it is reasonable that the PDIDC of manufacturers A and B is slightly higher than that of other manufacturers. Based on the analysis of the measurement results of the inception characteristics of the partial discharge, it is not difficult to find that the insulating pressboards produced by manufacturers A and B are ahead of the other three in terms of the inception characteristics of the partial discharge. But the fifteen batches of pressboard from six manufacturers can all meet the insulation application of high-voltage transformers. Acknowledgments. The reported research is supported by the Key Scientific and Technological Projects of State Grid Anhui Electric Power Co. Ltd., grant 2021110045002456.
References 1. Cui, L., Chen, W., Vaughan, A.S., et al.: Comparative analysis of air-gap PD characteristics: vegetable oil/pressboard and mineral oil/pressboard. IEEE Trans. Dielectr. Electr. Insul. 24(1), 137–146 (2017) 2. Tang, C., Liao, R., Chen, G., et al.: Research on the feature extraction of DC space charge behavior of oil-paper insulation. Sci. China Technol. Sci. 54(5), 1315–1324 (2011) 3. Damoah, R., et al.: A case study of pyro-convection using transport model and remote sensing data. Atmos. Chem. Phys. 6(1), 173–185 (2006) 4. Zhou, Y.X., Sun, Q.H., et al.: Effects of space charge on breakdown and creeping discharge of oil-paper insulation. Trans. China Electrotechn. Soc. 26(2), 27–33 (2011) 5. Saha, T.K., Purkait, P.: Advanced Signal Processing Techniques for Partial Discharge Measurement. Wiley-IEEE Press, Hoboken (2017) 6. Chen, X., Morshuis, P. H. F., Zhuang, Q., et al.: Aging of oil-impregnated transformer insulation studied through partial discharge analysis. In: 10th IEEE International Conference on Solid Dielectrics (ICSD), pp. 1–4 (2010) 7. Chen, X., Morshuis, P. H. F., Zhuang, Q., et al.: Aging of oil-impregnated transformer insulation studied through partial discharge analysis. In: 10th IEEE International Conference on Solid Dielectrics (ICSD), pp. 1–4(2010) 8. IEC Standard, IEC 60243–1:2013, Electric strength of insulating materials - Test methods Part 1: Tests at power frequencies
Analysis of the Influence of Meteorological Factors on Electric Vehicle Charging Load and Mixed Regression Forecasting Model Longlong Shang1 , Ran Hu1 , Jingyu Lu2(B) , Wei Xiao3 , Jun Jia3 , and Ji Zhao3 1 Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518000, China 2 Jilin University, Jilin 130012, China
[email protected] 3 Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610042, China
Abstract. Electric vehicle charging load forecasting is the basis for carrying out research on electric vehicle charging infrastructure planning and construction, power system optimization and scheduling, etc. Since the electric vehicle charging behavior is random in terms of time and space, a lot of complicated factors can influence the charging load forecasting, and different charging load forecasting models and results will be obtained if it is considered from different perspectives. In this paper, the author first researches the influence of meteorological factors on electric vehicle charging load with the correlation analysis method, and particularly distinguishes the difference between the influence of meteorological factors on conventional electric load and on electric vehicle charging load. The analysis results reveal that the response of electric vehicle charging load to meteorological factors is relatively lagging behind, and thus the author proposes a mixed regression forecasting model based on meteorological factors. In this model, indicators of multiple meteorological factors are considered and the idea of similar date method is combined. The example analysis shows the forecast accuracy of this model is obviously higher than that of the single factor forecasting model. Finally, this paper further points out the possible research direction of electric vehicle charging load based on meteorological factors. Keywords: Electric vehicle · Load forecasting · Correlation analysis · Mixed regression model
1 Introduction With the adjustment of the national energy strategy, the penetration rate of electric vehicles in cities is gradually increasing. The charging requirement of electric vehicles will definitely increase to a level that cannot be ignored in the distribution network. Unlike conventional electric load, electric vehicle charging load is random and intermittent in terms of time and space. On the one hand, this will give an impact to the operation of the distribution network, causing serious negative impacts on power quality, network © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 151–165, 2022. https://doi.org/10.1007/978-981-19-1528-4_16
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loss, voltage fluctuation or three-phase unbalance, or making other power grid operating indicators worse. On the other hand, as a flexible and controllable load, if the electric vehicle charging load can be flexibly scheduled, regulated and controlled under the guidance of new technologies and new policies, its negative impact can be eliminated, and the coordination and interaction between electric vehicles and the power grid, other energy systems and transportation system can also be realized, bringing economic and environmental benefits to users, power grids and the society. This is a new opportunity brought by the joint development of the new national energy strategy and electric vehicle technologies. Under such a background, electric vehicle charging load forecasting is of great significance for the research on power generation optimization, power grid dispatching, power market transaction, charging station planning and construction, and convenient and economic travel of users [1–3]. The electric vehicle charging load is affected by multiple factors. Scholars at home and abroad have done a lot of research work on electric vehicle charging load forecasting. Currently, the forecasting methods mainly include the charging period based Monte Carlo load forecasting model, charging probability based statistical load forecasting model, and trip chain based load forecasting model considering temporal-spatial distribution. Reference [4] proposes an electric vehicle charging load forecasting method considering temporal-spatial distribution on the basis of simulating the driving, parking and charging behaviours of electric vehicles with Monte Carlo method. Reference [5], on the other hand, proposes a daily taxi charging load forecasting model based on the segmented probability estimation method by considering the influence of shifts, meals and driving at night on the taxi charging start moment and daily driving mileage from the perspectives of taxi operating mode and driving characteristics. In Reference [6], a random real-time dynamic path simulation is carried out based on the trip chain and Markov decision process, which avoids the problem that electric vehicles with the same start and end points get charged at fixed locations, and reflects the randomness of electric vehicles in spatial movement. In References [7, 8], travel spaces are classified by activity purposes and the spatial movement characteristics of vehicles are obtained by using the trip chain and Markov primary state transition matrix. In References [9–13], the trip chain of vehicles is randomly simulated, and the driving and charging behaviors are described by with Markov process. This integrates the geographic information, grid nodes, and vehicle charging, and further develops the general feature structure of the trip chain, and the distribution of travel time, driving time, dwell time, driving distance, and driving destination in different scenarios such as weekdays and weekends and their interrelationship. With the research on electric vehicle charging load forecasting going deeper, the trend of future research will be comprehensively considering more influencing factors. In the smart grid big data environment where a large amount of real-time load data and meteorological information can be obtained, for improving the load forecasting accuracy, it is particularly important to explore the influence of key meteorological factors on the load. However, most researches tend to focus on the influence of meteorological factors on conventional electric load. They believe meteorological factors are the dominant factors that affect the conventional electric load due to their influence on the use of air conditioning or electric heating equipment in household electricity, but ignore the
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influence of meteorological factors on the electric vehicle charging load. Thus, there is a lack of analysis on the influence of meteorological factors in the current researches, let alone electric vehicle charging load forecasting model dominated by meteorological factors. To address these problems, this paper proposes a mixed regression forecasting model dominated by meteorological factors. The next Sections are arranged as follows: Sect. 2 first analyzes the influence of meteorological factors on the electric vehicle charging load with the correlation analysis method; Sect. 3 builds a mixed regression forecasting model based on the analysis results in Sect. 2, and optimizes the regression constant with the idea of similar date method; Sect. 4 verifies the forecasting effect of this model with specific engineering examples; Sect. 5 summarizes relevant conclusions of the analysis of the influence of meteorological factors on electric vehicle charging load, and further points out the possible research direction of the meteorological factors based electric vehicle charging load.
2 Analysis of the Influence of Meteorological Factors on Electric Vehicle Charging Load 2.1 Analysis of Charging Load Characteristics of Electric Vehicles Characteristics describing the electric vehicle charging load are divided into two categories: electric quantity characteristics and electric power characteristics. The former includes the average charging quantity of vehicles, power supply quantity of charging station, charging quantity of each type of vehicle, while the latter includes the maximum load, minimum load, peak-valley difference, load rate, load curve, etc. Clarifying these characteristics is the basis for analyzing the influence of meteorological factors on the electric vehicle charging load. For the daily load curve involved in this paper, Table 1. Characteristics describing the electric vehicle charging load Characteristics
Details
Maximum daily load
ymax = max1≤t≤n yt
Minimum daily load
ymin = min1≤t≤n yt
Average daily load
n y = 1n yt
Daily peak-valley difference
L = ymax − ymin
Daily load rate
γ = y/ymax
Variance
1 S 2 = n−1
t=1
Daily electric quantity Daily load utilization
n
(yt − y)2
t=1
Q = 024 yt dt
24
y dt
t η = y Q×24 = y 0 ×24 max max
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let’s set the load sequence of a day as yt (t = 1, 2, ..., n), where n = 24, 48, 96, 288, corresponding to sampling intervals of 60 min, 30 min, 15 min and 5 min respectively. These characteristics are calculated as follows (Table 1): Previous researches have shown that meteorological factors have a direct influence on conventional electric loads. Some meteorological conditions have a significant influence on conventional loads and electricity consumption, for example, when the weather gets cold/hot dramatically, a large number of heating/cooling equipment will be put into operation; rainfall/drought will decrease/increase the electricity load of local agricultural irrigation. Due to the specificity of electric vehicle charging behavior, meteorological factors will not directly influence the electric vehicle charging behavior, but influence the travel pattern of electric vehicles and the application of the equipment inside vehicles. This will affect the consumption of electric power, and then influence the charging load. For example, the continuous high-temperature weather will increase the utilization frequency of air conditioners in electric vehicles, thus increasing the power consumption, shortening the charging cycle, and increasing the charging probability in the following period of time, but will not directly cause electric vehicles to be charged at the moment. Therefore, unlike conventional loads, the influence of meteorological factors on electric vehicle charging load will not manifest immediately. The daily load curve of a charging station is a charging load curve reflected by the maximum daily load, minimum daily load, average daily load, etc. It is directly shaped by daily charging behavior. The daily electric quantity curve is shaped by the cumulative energy consumption in the previous days. It is a characteristic related to the charging frequency and can reflect the influence of meteorological factors on the electric vehicle charging load more reasonably. In view of the above considerations, this paper analyzes the influence of meteorological factors based on three research objects: maximum daily load, average daily load, and daily electric quantity (daily charging quantity of electric vehicle clusters or daily power supply of charging stations), and takes daily electric quantity as the forecast target of the mixed regression forecasting model. 2.2 Identification of Major Meteorological Factors Meteorological characteristics of a region in a period of time include temperature, humidity, rainfall, and wind speed. In order to quantitatively analyze the relationship between electric vehicle charging load and each type of meteorological factors, we first consider that meteorological factors influence the charging load in the following two aspects: 1. Influencing the use of vehicular electrical equipment: for example, continuous high/low-temperature weather in the previous days increases the utilization frequency of air conditioners in vehicles, thus increasing the power consumption and the charging probability. 2. Influencing the travel pattern of users: For example, typhoon, stormy and heavy fog weather will decrease the travel of private cars, and accordingly, decrease the charging frequency.
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The sunny/rainy weather conditions of a day will not directly cause electric vehicles charged, but will influence the travel of vehicle users on that day. Therefore, the daily meteorological factors should also be considered. Thus, the idea of analyzing the influence of meteorological factors on electric vehicle charging load is as follows (Fig. 1):
Fig. 1. Idea of analyzing the influence of meteorological factors on electric vehicle charging load
In order to obtain more targeted analysis results, in the research, we further divide the temperature in the meteorological factors into maximum daily temperature, minimum daily temperature, and average daily temperature, and then simplify the indicators of humidity and rainfall as weather indicators, which are specifically classified into 8 tags: sunny, cloudy, overcast, light rain, shower, moderate rain, heavy rain, and rainstorm. In the followings, we will analyze the influence of the above meteorological factors on load characteristics from two aspects respectively by taking the meteorological data (including maximum daily temperature, minimum daily temperature, average daily temperature, and weather data) from July to December in a place and the historical load data of a charging station in that place as an example: Influence of Temperature First, we believe that temperature mainly influences the charging load by influencing the utilization of vehicular electrical equipment, so we first observe the relationship between daily electric quantity and temperature over time through images. According to the analysis in Sect. 1, we know that the influence of the utilization of vehicular electrical equipment on the charging load is lagging behind in terms of time. Therefore, the indicators of temperature factors here are not the values of the load characteristics on a day, but the average values of the temperature data of the load characteristics in the previous days. Besides, since the original temperature data cannot be compared with the daily electric quantity, their per-unit values are taken and then visualized: xt∗ =
xt xmax
(1)
xt is the variable needing per-unit normalization. After the daily electric quantity and temperature characteristics are brought in respectively, the relationship curves between
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the per-unit value of daily electric quantity and the per-unit value of the maximum temperature, minimum temperature and average temperature are shown in Fig. 2.
Fig. 2. Per-unit curve of daily electric quantity and temperature characteristics over time
The changes of daily electric quantity and temperature characteristics over time in Fig. 2 show that: 1. The change trend of daily electric quantity is generally similar to those of the maximum, minimum and average temperatures. When the temperature rises, the daily electric quantity rises too. When the temperature decreases, the daily electric quantity also shows a decreasing trend. 2. The changes in daily electric quantity are not completely consistent with the changes in temperature on some days. This shows some extreme weather conditions greatly influence users’ travel. As mentioned earlier, the temperature directly affects the use of vehicular electrical equipment, but the impact on charging load is lagged in time, and the lag time is related to the battery characteristics of the vehicle itself and the travel pattern of users. Now, we further analyze the charging lag time with the correlation analysis method from a macroscopic perspective. The partial correlation coefficient between the average temperature and daily electric quantity in the first 3, 5 and 7 days are calculated as follows
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respectively: ρX ,Y =
cov(X , Y ) E[(X − μX )(Y − μY )] = σX σY σX σY
(2)
where, X and Y indicate two variables. Taking the maximum daily temperature as an example, we plot the partial correlation matrix of daily electric quantity (I 1 ) and the average daily temperature in the past 3, 5, 7 days (represented by I 2 , I 3 , I 4 respectively) as follows (Table 2): Table 2. Partial correlation matrix of daily electric quantity and temperature Indicator
I1
I2
I3
I4
I1
1.000
0.676
0.721
0.684
I2
0.676
1.00
0.952
0.958
I3
0.721
0.952
1.000
0.994
I4
0.684
0.958
0.994
1.000
The results of the partial correlation analysis show that all temperature indicators are strongly correlated, and this is in line with the actual conditions. The correlation coefficient between daily electric quantity and the average temperature in the past 5 days is the largest, and their correlation is the strongest. Thus, we can infer that the charging cycle of the electric vehicle clusters in that region is about 5 days. Based on this, we further analyze the direct correlation between the daily electric quantity and the average maximum temperature, average minimum temperature, and average daily temperature in the past 5 days, and plot curves as shown in Fig. 3.
Fig. 3. Relationships between daily electric quantity and the average maximum temperature, average minimum temperature, and average daily temperature
The figure shows that the relationships between daily electric quantity and the average maximum temperature, average minimum temperature, and average daily temperature
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in the past 5 days are close to the shape of a quadratic curve. This is a very important insight for the establishment of the mixed regression forecasting model later. Influence of Weather Conditions Next, we will analyze the influence of weather conditions on the daily charging load from the perspective of influencing the travel pattern of users. According to the previous analysis, we believe that extreme weather will affect the travel of electric vehicle users, thus directly influencing the daily charging load curve. Firstly, we use visually observe the relationship between weather conditions and charging load, and then plot a scatter diagram as shown in Fig. 4(a) by taking the daily peak load and average daily load as two load characteristics, and indicating weather conditions with 8 tags, i.e. sunny, cloudy, overcast, light rain, shower, moderate rain, heavy rain, and rainstorm.
Fig. 4. Relationships between charging load and weather conditions and month
Figure 4(a) shows that the charging loads are not high in heavy rain and rainstorm weather conditions, but not fall in the lowest charging load area. Moreover, the charging loads are at a low level in sunny and cloudy weather conditions that are more suitable for travel, but at a high level in rainy weather conditions. This is inconsistent with the aforesaid analysis and supposition. To explore the reasons for this, we further plot a scatter diagram of the relationship between charging load and month, as shown in Fig. 4(b). Figure 4(b) indicates that charging load is closely related to month, showing a decreasing trend from summer to winter. In that region, it is hot and rains a lot in summer, while it is cool and dry in autumn and winter. Clearly, compared with weather conditions, temperature is the dominant factor that influences electric vehicle charging load. This verifies the analysis results as described above, and also explains the influence of extreme weather on users’ travel and charging load. Compared with other days in July, the average load and peak load on July 31 are obviously lower because of heavy rain, with the peak load and average load decreased to 73.3% and 86.2% of the average values in that month respectively. Due to rainstorm, the average load and peak load on August 24 are obviously lower than those on other days in August, with the peak load
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and average load decreased to 92.2% and 38.1% of the average values in that month respectively. Based on the above analysis, we can draw a qualitative conclusion that among all meteorological factors, temperature becomes the main factor affecting the charging load by affecting the utilization of vehicular electrical equipment, while weather conditions will not obviously affect charging load, but extreme weather such as rainstorm can significantly affect the charging load by affecting users’ travel. Please note that the above conclusion is only for the climate characteristics of the city mentioned herein, but the analysis method is applicable to cities with any climate.
3 Building the Mixed Regression Forecasting Model Based on the analysis results of the influence of meteorological factors on electric vehicle charging load in Sect. 2, we know that temperature is the main factor influencing the electric vehicle charging load, and this influence is relatively lagging behind and will manifest after a period of time. In the following, we will build a temperature-dominated single regression forecasting model and mixed regression forecasting model respectively by taking the daily electric quantity forecasting as an example. According to the analysis in Subsect. 2.2, we know that daily electric quantity is strongly correlated with the maximum temperature, minimum temperature and average daily temperature in the past 5 days, and their relationship diagrams are close to a quadratic curve. Therefore, for the single regression forecasting model, the square of the highest temperature, lowest temperature and daily average temperature (represented by I 5 , I 6 , I 7 respectively) is used. The partial correlation matrix is shown in Table 3. Table 3. Partial correlation matrix of daily electric quantity and the square of temperature Indicator
I1
I5
I6
I7
I1
1.000
0.707
0.737
0.756
I5
0.707
1.000
0.913
0.975
I6
0.737
0.913
1.000
0.978
I7
0.756
0.975
0.978
1.000
The partial correlation matrix shows that the square of the average daily temperature in the past 5 days has the largest correlation coefficient and the strongest correlation with the daily electric quantity, so in the single regression model, we choose the relationship between the square of the average daily temperature in the past 5 days and the daily electric quantity as the quadratic function relationship. As analyzed above, the charging load is also strongly correlated with the month. Meanwhile, since the difference in extreme weather, working days and non-working days should also be reflected in the model as far as possible, we introduce regression constants that can reflect different months, weekday categories and extreme weather on the basis of the quadratic function,
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and the single regression forecasting model is as follows: 2 Qn = aTmean + bTmean +
12 7
s(i, m)ci,m + dn
(3)
i=1 m=1
Where, n is the date, Qn is the daily electric quantity, Tmean is the average daily temperature in the first 5 days, i is the specific weekday, m is the month, a and b are constant coefficients, and s(i, m) is a sign function. When the weekday category of date n is exactly i and the month is exactly m, the value of s(i, m) is 1, otherwise its value will be 0. ci,m indicates the regression constant when the weekday category is i and the month is m. dn indicates the extreme weather index of the nth day. For the above single regression model, the major influence factor is the average daily temperature in the past 5 days, and its influence is considered separately. However, when we carry out the correlation analysis, we note there are only small differences among the correlation coefficients between the square of the maximum temperature, the square of the minimum temperature and the square of the average daily temperature and the daily electric quantity respectively. In this context, we comprehensively consider the three variables, assign weights according to the magnitude of the correlation coefficients, and then construct the following weighted mixed regression forecasting model: 2 2 +b T 2 Qn = Wmean (a1 Tmean + b1 Tmean ) + Wmax (a2 Tmax 2 max ) + Wmin (a3 Tmin + b3 Tmin ) +
12 7
s(i, m)ci,m + dn
i=1 m=1
(4) where, Tmax is the average maximum temperature in the past 5 days, Tmin is the average minimum temperature in the past 5 days, Wmean , Wmax and Wmin are the weights of the average daily temperature, maximum temperature, and minimum temperature that are related to the correlation coefficients respectively, a1 , a2 , a3 , b1 , b2 and b3 are the regression constant coefficients, and the remaining symbols have the same meaning as above.
4 Example Analysis In this Section, we verify the effectiveness of the regression forecasting model proposed herein through specific engineering examples. Through Shenzhen Power Supply Bureau, we obtained the daily load data of an electric vehicle charging station in Shenzhen from July to December, with 96 points each day. At the same time, we also got the meteorological data of Shenzhen City on the corresponding dates through websites, including the maximum temperature, minimum temperature, and weather conditions. Considering some data should be used for the model test, we take the data in the 4th week (if it is not a complete week, the day(s) will be taken forward) of each month as the test set. These data will not participate in the model training, and the remaining data will be used as the training set for solving the model parameters.
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4.1 Model Training First, we carry out the basic quadratic function curve fitting in the single regression forecasting model with the average daily temperature in the first 5 days as the variable, and the fitting result is shown in Fig. 5.
Fig. 5. Basic quadratic function curve fitting
Through parameter estimation, we get the values of the regression constant coefficients a and b, being 29.72 and -926.1 respectively, and the estimated value of extreme weather index dn is: 10953.4, Non extreme weather d= (5) 8716.1, Extreme weather The estimated value of the regression constant ci,m related to the weekday category and month is shown in Table 4.
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Week
Month 07
08
09
10
11
12
Monday
1257.36
232.33
−148.76
−943.16
7.06
−447.94
Tuesday
1023.20
−80.06
−15.78
−737.99
−106.08
−140.04
806.79
912.33
−730.96
−1003.54
267.29
410.15
Thursday
1476.60
−242.67
204.96
−896.59
69.74
−298.50
Friday
1643.63
−42.14
196.64
−1128.74
−107.70
−357.59
Saturday
2443.90
182.29
895.08
−171.11
610.87
152.09
Sunday
919.23
−544.93
120.46
−335.93
666.94
361.72
Wednesday
Next, we estimate the parameters of the mixed regression model. First, we calculate the weights of the three temperature characteristics according to the correlation coefficients, then carry out the quadratic curve fitting of each temperature characteristic, and finally estimate the regression constants. The parameter estimation results are shown in Table 5. Table 5. Parameter estimation of the mixed regression model Parameter
Temperature T mean
T max
T min
W
0.344
0.321
0.335
a
30.19
25.00
29.72
b
−1105.64
−992.09
−926.1
The estimated value of the extreme weather index d n is as follows: 12754.5, Non extreme weather d= 10651.3, Extreme weather
(6)
The estimated value of the regression constant ci,m related to the weekday category and month is shown in Table 6.
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Table 6. Estimation results of regression constants in the mixed regression model Week
Month 07
08
09
10
11
12
Monday
1157.72
76.28
−141.43
−1337.11
−428.54
−392.15
Tuesday
960.99
−333.50
−237.04
−907.34
−534.41
−65.84
Wednesday
597.50
644.65
−1037.15
−1241.52
−164.25
471.03
Thursday
1812.65
−521.21
−105.50
−1225.08
−300.75
−244.42
Friday
1926.59
−102.35
−50.83
−1592.19
103.59
−344.43
Saturday
2506.36
181.70
644.49
−668.95
585.96
179.03
Sunday
1047.71
−569.42
−221.18
−852.90
438.07
474.25
4.2 Model Evaluation We forecast the daily electric quantity of that charging station in the last week of each month from July to December in the above single regression forecasting model and the weighted mixed regression forecasting model respectively, and compare the data with the actual daily electric quantity. The forecast results are shown in Fig. 6.
Fig. 6. Forecast results of the single regression model and the mixed regression model
In order to evaluate the forecasting effects of the two models, the average relative errors between the forecast values and actual values is shown in Table 7. From the above forecast results, we can see that the temperature-dominated regression forecasting method can forecast the changing trend of daily electric quantity with the month more accurately. Especially in hot summer, when the temperature difference in a day and the temperature fluctuations among adjacent days are small, a lower average relative error can be obtained, while in autumn and winter, when the temperature difference is large and weather conditions become unstable, the average relative error also increases. However, in the mixed regression model, the average relative error is obviously
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Model
Month 07
08
09
10
11
12
Average
Single model
5.70%
9.96%
16.00%
8.49%
12.40%
14.86%
11.23%
Mixed model
7.29%
8.91%
17.72%
7.78%
11.24%
6.38%
9.89%
decreased and the forecasting accuracy is improved because the maximum, minimum and average temperatures in a day are considered. On the whole, the temperature-dominated mixed regression forecasting model proposed herein has a high forecasting accuracy in practical industrial applications, and the average relative error of its forecast result is less than 10%.
5 Conclusion For the problem that meteorological factors are ignored in the current researches on electric vehicle charging load, this paper analyzes the influence of meteorological factors on electric vehicle charging load, identifies the dominant influencing factors, and reveals that the influence of meteorological factors on electric vehicle charging load is lagging behind in terms of time. On this basis, this paper proposes a weighted mixed regression forecasting model dominated by meteorological factors for daily electric quantity forecasting. This model takes temperature as the main variable, and optimizes the regression constants where similar dates and extreme weather are considered. At the same time, we also build a temperature-dominated single regression forecasting model as a control, and then through specific example analysis, verify the validity of the mixed regression model. This is of great importance for the application of meteorological factors related regression analysis methods on electric vehicle charging load forecasting. The work in this paper still has some limitations, including the rough quantification of extreme weather and the small scale of training samples, which limit the accuracy of the model, and will be solved in our next researches. Acknowledgment. The work is supported by the China Southern Power Grid Key Project (090000KK52190181).
References 1. Zhao, J., Wen, F., Yang, A., et al.: Influence of electric vehicle on power system and its scheduling and control problems. Power Syst. Autom. 35(14), 2–10 (2011). (in Chinese) 2. Hu, Z., Song, Y., Xu, Z., et al.: Influence and utilization of electric vehicles connected to power grid. Chin. J. Electr. Eng. 32(4), 1–10 (2012). (in Chinese) 3. He, J., Xie, Y., Ye, H., et al.: Influence of electric vehicle charging mode on active distribution network. Electric Power Constr. 36(01), 97–102 (2015). (in Chinese)
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4. Quinn, C., Zimmerle, D., Bradley, T.H.: An evaluation of state-of-charge limitations and actuation signal energy content on plug-in hybrid electric vehicle, vehicle-to-grid reliability, and economics. IEEE Trans. Smart Grid 3(1), 483–491 (2012) 5. Karfopoulos, E.L., Hatziargyriou, N.D.: Distributed coordination of electric vehicles providing V2G services. IEEE Trans. Power Syst. 31(1), 329–338 (2015) 6. Zhang, Q., Wang, Z., Tan, W., et al.: Space time distribution prediction of electric vehicle charging load based on MDP random path simulation. Power Syst. Autom. 42(20), 59–66 (2018). (in Chinese) 7. Wang, D., Guan, X., Wu, J., et al.: Analysis of multi-location PEV charging behaviors based on trip chain generation 2014. IEEE (2014) 8. Chen, L., Nie, Y., Zhong, Q.: Electric vehicle charging load forecasting model based on travel chain. Acta Electrotechnics Sinica 30(04), 216–225 (2015). (in Chinese) 9. Tang, D., Wang, P.: Probabilistic modeling of nodal charging demand based on spatialtemporal dynamics of moving electric vehicles. IEEE Trans. Smart Grid 7(2), 627–636 (2016) 10. Wen, J., Tao, S., Xiao, X., et al.: Analysis of electric vehicle charging demand based on stochastic simulation of travel chain. Power Grid Technol. 39(06), 1477–1484 (2015). (in Chinese) 11. Shun, T., Kunyu, L., Xiangning, X., et al.: Charging demand for electric vehicle based on stochastic analysis of trip chain. IET Gener. Transm. Distrib. 10(11), 2689–2698 (2016) 12. Zhao, S., Zhou, J., Li, Z., et al.: Analysis method of electric vehicle charging demand based on travel chain theory. Electr. Power Autom. Equip. 37(08), 105–112 (2017). (in Chinese) 13. Iversen, E.B., Moller, J.K., Morales, J.M., et al.: Inhomogeneous Markov models for describing driving patterns. IEEE Trans. Smart Grid 8(2), 581–588 (2017)
Cross-platform Communication Simulation System Frame-Design and Modelling Strategy Meng Yu(B) and Zilan Zhao State Grid Jibei Information and Telecommunication Company, Beijing, China [email protected]
Abstract. With the rapid development of electricity automation, information system design and communication resources management, we’ve made great progress in communication simulation. As a comprehensive platform for resource management, auxiliary analysis and simulation, the communication simulation system measures the feasibility of the scheme through simulation results, selects the most reasonable system configuration and parameter settings, and then applies it to the actual systems. The simulation method can make better use of design space, combine digital and empirical models easily, and combine the characteristics of equipment and real signals for analysis and design, which can effectively reduce costs. This paper starts with prototype iteration based on frame design in electricity work experience, and does a deep research on software engineering, requirement engineering and reflection coding. After analyzing the key requirements, we propose the core strategy of communication resource frame design and core code. Through simulation experiments, the framework design and modeling strategy proposed in the paper are verified, and the experimental results are quantitatively analyzed. Keyword: Communication resource simulation
1 Demand Modeling of Communication Simulation System 1.1 Analysis of Core Requirements of Communication Simulation System In the first step of the construction phase of the communication simulation system, it is necessary to do a good job of modeling and optimizing system requirements. In addition to manual semantic analysis of the required text, the Chinese language segmentation tool LTP-Cloud [2] developed based on the maximum entropy classification theory [1] can also be used to further confirm the accuracy of the system. Before determining the detailed requirements of the communication simulation system, we must first determine the basic requirements of the system. To ensure the accurate acquisition of requirements, this paper uses manual analysis combined with the method of word segmentation technology based on maximum entropy theory to complete the work. The communication simulation system is segmented, and the following results are obtained (Fig. 1): © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 166–176, 2022. https://doi.org/10.1007/978-981-19-1528-4_17
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Fig. 1. Word segmentation results-1 of communication simulation system
First look at the first grammatical analysis diagram. The core of the whole phrase is “system”. Its direct modifiers are “communication” and “simulation”, and the modifier relationship is ATT (Attribute, definite relationship) (Figs. 2 and 3).
Fig. 2. Word segmentation results-2 to measure the feasibility of the program through simulation results.
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Fig. 3. Word segmentation results-3 to choose the most reasonable system configuration and parameter settings.
Through “word segmentation result-2” and “word segmentation result-3”, it can be determined that “the feasibility of the program is measured by simulation results. The most reasonable system configuration and parameter settings are selected.” The keywords are “feasibility” and “system” Configuration” and “Parameter Settings” [3]. Combining the above semantic analysis, it can be deduced that the core requirements of the communication simulation system is that the communication simulation system can measure the feasibility of the scheme, and provide the necessary system configuration and parameter setting information, and ultimately serve the user’s reasonable decisionmaking.
2 Abstract Framework Modeling of Communication Simulation System Based on Reflection Technology 2.1 The Basic Concept of Reflection The reflection mechanism is in the running state, for any class, you can know all the properties and methods of this class; for any object, you can call any of its methods; the approach to dynamically acquire information and dynamically calling the objects is called the reflection mechanism. The so-called reflection can be understood as the operation of obtaining object type information at runtime. The traditional programming method requires the programmer to decide the type to be used in the compilation stage, but with the help of reflection, the programmer can dynamically obtain this information, so as to write more portable code [4]. The reflection mechanism mainly provides the following functions: Judging the class to which any object belongs at runtime; constructing an object of any class at runtime; judging the member variables and methods of any class at runtime; calling any object at runtime; generate dynamic proxy.
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In the Java reflection system, a part of the program is used to describe and calculate the problem domain, which is called application program, and a part of the program is used to express and control the computing behavior of the application program, which is called metaprogram, which is the program of the program [5]. The level of meta-programs in the entire system is called meta-level. Correspondingly, programs that directly perform application functions are at the application level or more commonly called base-level [7]. In short, the basic level is the level where people are familiar with common application objects, and is used to describe the problem domain to be solved by the computing system, while the meta level is the level that describes and controls the basic level of computing behaviour (Fig. 4).
Fig. 4. Reflection concept map
2.2 Basic Functions of Reflection (1) Get the attributes of an object. Obtain the class of an object through the getClass() method, then instantiate a Field object, receive the attributes declared by the class, and finally obtain an instance of the attribute through the get() method. (2) Get the static properties of a certain class. First obtain the class according to the class name, just like obtaining the properties of an object, by instantiating a Field object, the properties declared by the class are directly obtained from the class. (3) The method of executing an object. It is also necessary to obtain the class of the object first, then configure the array of classes and use it as the condition of the search method. Get the method to be executed through the getMethod() method. (4) Execute a static method of a certain class. (5) Create an instance of the new class.
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2.3 Design and Implementation of Abstract Framework of Communication Simulation System Based on Reflection Technology Geographic Information Systems (Geographic Information Systems, referred to as GIS) is a system that manages all kinds of geographic information-related resources in the real and objective world and the attribute data describing the characteristics of these resources. It is widely used in water conservancy, geology, transportation, electric power, telecommunications and other industries. The GIS system is applied to the communication department, and it can manage all communication resources, such as switches, optical communication equipment, carrier equipment, monitoring equipment, optical distribution frames, digital distribution frames, and audio distribution frames [8]. Visually and dynamically display the geographic location, usage and relationship of communication resources in graphical information. This paper takes the communication resource management system as an example to discuss the design and implementation of an abstract framework for communication simulation system based on reflection technology. 2.3.1 Abstract Frame Design of Communication Simulation System 2.3.1.1 Basic Geographic Information Management It mainly manages and stores resource information with spatial attributes, including electronic maps, area information, site information, computer building information, and computer room information [6]. Geographical information management is manifested in the form of electronic maps in the system. The establishment of electronic maps helps to establish a distribution map of equipment based on the actual geographic background [9]. Especially for some data with strong geographic attributes, such as the distribution map of the transfer box, the establishment of an electronic map can give the actual geographic attributes of the device itself, which is convenient for the user to grasp the distribution of the equipment in a certain area (Fig. 5). 2.3.1.2 Connection Management The connection relationships between resources in the communication network resource management system are: – The relationship between the devices: there is a mutual connection relationship between each two devices. – The relationship between ports: there are directly connected ports or the port connection relationship between the required networks elements. – The relationship between the port and the terminal: the relationship between the physical port of the device and the terminal of the connected device (such as ODF). – Terminal jumper relationship: jumper relationship between terminals. – The relationship between the terminal of the connected device and the core of the cable: such as the connection relationship between the ODF and the core. – The relationship between logical network elements and physical ports: the physical ports included in a logical network element, their connection relationship is established through the corresponding relationship between the network element and the machine disk where the port is located.
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Fig. 5. Example diagram of basic information management
– The relationship between the map and the object: The relationship between the map and the object, on the one hand, has a unique indication of the device resource in the map, and on the other hand, there is a relationship table of the map to which each object on the map belongs.
2.3.1.3 Device Management The equipment management module of the computer room mainly completes the management of adding, editing, and deleting connection equipment resources such as racks, transmission equipment, switching equipment, access equipment, patch panels, etc. installed in the computer room, and can generate the above-mentioned equipment statistical reports [10]. The optical cable equipment management module completes the management of optical cable and related equipment resources [11]. It can input the relevant information of the optical cable, can specify the use of optical core pairs by color, and can display the position of the optical cable on the rack and the user information of the optical core at the same time. It can query the optical fiber equipment connected to the optical fiber of a specified optical cable and its routing information, including number, passing well or rod, pipe hole number, whether there is a connector box, etc., and can generate a print report (Fig. 6).
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Fig. 6. Device management example diagram
2.3.2 Pseudo-Code Implementation of Abstract Framework of Communication Simulation System The communication simulation system involves “point element simulation”, “line element simulation” and “point-line relationship simulation”. A complete and usable communication simulation system requires requirements analysis, data structure design, attribute design, method design, and functional verification of the above three types of elements. On this basis, general abstract classes and interfaces are designed to provide standard templates for subsequent development work. Java language has very good cross-platform features [12]. This paper is based on Java language to implement a preliminary pseudo-code implementation of the abstract framework of the communication simulation system. 2.3.2.1 “Point Element” Pseudo-Code Design The “point elements” of the communication simulation system cover GIS points, communication sites, communication equipment rooms, communication equipment rooms, communication equipment, frames, slots and boards, etc. The basic attributes involved are: (1) GIS coordinates-used to mark the spatial location of point elements. (2) Sub-elements-Any site may contain one or more devices. These devices are called sub-elements of the site. In the same way, any communication device may contain multiple frames, slots and boards.
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(3) Parent element-a single device may belong to a certain site, and a single frame may belong to a certain device. (4) Other additional attributes-commissioning time, maintenance special responsibility, affiliated management department, equipment brand, planned return time. (5) log information-recording various events, alarm information and operation during the survival period of the point element Dimensional information. According to the above-mentioned basic attributes, the pseudo-code design of the point element is carried out, and its main attributes and methods are as follows:
//Point element core class public class Node{ private String nodeId;//The unique identifier of the point element private String gisX;//The x coordinate of the point element private String gisY;//The y coordinate of the point element private String gisZ;//The z coordinate of the point element //Child element private ArrayList childNodes=new ArrayList(); //Parent element private ArrayList parentNodes=new ArrayList(); //Additional attributes Map extraAttributes=new HashMap(); } //Point element core operation interface public interface NodeOperation{ String getNodeId();//Get point element id String getGisX();//Get the x coordinate of the point element String getGisY();//Get the y coordinate of the point element String getGisZ();//Get the z coordinate of the point element String getExtraAttribute(String attributeName);//Get the specified attribute value ArrayList getParentNodeList();//Get a collection of parent elements ArrayList getChildNodesList();//Get a collection of child elements }
After designing the core class and core operation interface of the point element, for any point element NewNode, its basic structure is as follows: public class NewNode extends Node implements NodeOperation{ //Specific operation code }
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When calling a specific point element for operation, it is necessary to load the specified point element through the “class name” through the Java reflection technology and call the method with the specified name through the invoke method. Through the reflection technology, multiple point element classes can be managed uniformly, and the system update operation can be completed only by slightly modifying the code after creating a new point element class, which provides strong support for the realization of a cross-platform communication simulation system. Taking the newly created NewNode as an example, the template code for object loading is: Class nodeClass=Class.forName("NewNode"); Object nodeObject=nodeClass.newInstance();
After loading the NewNode, use the invoke method to call a specific method to complete the operation. Method gisXMethod=nodeClass.getMethod("getGisX"); String gisX=gisXMethod.invoke(nodeObject);//Call the parameters Method attributeMethod= nodeClass.getMethod("getExtraAttribute",String.class); String department= attributeMethod.invoke(nodeObject, “department”);
method
without
2.3.2.2 “Line Element” Pseudo Code Design The “line elements” of the communication simulation system cover cables, optical cables, virtual connections, transmission channels, sub-channels, etc. The basic attributes involved are: (1) Starting point-the starting point of the line element. (2) End point-the end point of the line element. (3) Sub-element-any transmission channel may contain multiple time slots or occupy multiple optical fibers. (4) Parent element-any transmission channel may belong to a higher-level channel. (5) Other additional attributes-commissioning time, maintenance special responsibility, affiliated management department, equipment brand, planned return time. (6) log information—Record all kinds of events, alarm information and operation and maintenance information that occurred during the survival period of the line element. According to the above-mentioned basic attributes, the pseudo-code design of the line elements is carried out, and the main attributes and methods are as follows:
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//Line element core class public class Line{ private String lineId;//The unique identifier of the line element private Node startNode;//The starting point of the line element private Node endNode;//The end of the line element //Child element private ArrayList childNodes=new ArrayList(); //Parent element private ArrayList parentNodes=new ArrayList(); //Additional attributes Map extraAttributes=new HashMap(); } //Line element core operation interface public interface LineOperation{ String getNodeId();//Get line element id Node getStartNode();//Get the starting point coordinates String getEndNode();//Get the end point coordinates String getExtraAttribute(String attributeName);//Get the specified attribute value ArrayList getParentNodeList();//Get a collection of parent elements ArrayList getChildNodesList();//Get a collection of child elements } After designing the core class and core operation interface of the line element, for any line element NewLine, its basic structure is as follows: public class NewLine extends Line implements LineOperation{ //Specific operation code }
When calling a specific line element to operate, the basic steps are as shown in Sect. 2.3.1. The class is still loaded through Class.forName (“class name”), and the corresponding method is called through invoke to perform the operation.
References 1. Liu, T., Che, W., Li, S.: Semantic role labeling with maximum entropy classifier. J. Softw. 18(3), 565–573 (2007). (in Chinese) 2. Liu, T., Che, W., Li, Z.: Language technology platform. J. Chin. Inf. Process. 25(6), 53–62 (2011). (in Chinese) 3. Wang, L.N.: MATLAB and communication simulation (1999). (in Chinese) 4. Qin, C., Wang, N., Ren, H., Duan, X., Zhang, K.: Simulation and study on data communication in digital substation based on virtual local area network. Power Syst. Prot. Control 41(2), 126–130 (2013). (in Chinese) 5. Cai, Z., Ren, X., Zou, L.: A simulated communication system for distributed multi-robots. CAAI Trans. Intell. Syst. 4(4), 309–313 (2009). (in Chinese)
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6. Xie, P., Li, Q.: Simulation analysis off virtual network among multi routers based on Boson Netsim. Res. Explor. Lab. 28(8), 90−93 (2009). (in Chinese) 7. Xie, S., Xu, Z., Hu, T.: Study on testing platform for train control center-simulation of communication sub-system. Comput. Technol. Dev. 18(1), 243−246 (2008). (in Chinese) 8. Guan, W., Lv, W., Dai, Y.: Simulation on power line communication using wavelet based multi-carrier modulation technique. In: Proceedings of the CSU-EPSA, April 2006, vol.18, no. 2, pp. 67−70 (2006). (in Chinese) 9. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011) 10. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, USA, pp. 655–665. Association for Computational Linguistics (2014) 11. Medelyan, M.D., Witten, I.H.: Mining domain specific thesauri from Wikimedia: a case study. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 442–448. IEEE Computer Society (2006) 12. Nakayama, K., Hara, T., Nishio, S.: A thesaurus construction method from large scale web dictionaries. In: 21st International Conference on Advanced Information Networking and Applications 2007. AINA 2007, pp. 932–939. IEEE (2007)
Closed-Loop Control System of a Contactor Based on Single-Board RIO Wei Chen1 and Longfei Tang1,2,3(B) 1 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116,
Fujian, China [email protected] 2 Fujian Province Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University), Fuzhou 350108, Fujian, China 3 Fujian Province University Engineering Research Center of Smart Distribution Grid Equipment, Fuzhou, China
Abstract. The intelligent development of a contactor has taken an increasingly high proportion in the electrical power system. In order to adapt to the high-speed and complex real-time control strategy, the rapid control prototype technology based on single-board RIO controller is introduced into the multi-intelligent control system of a contactor in this paper. The double closed-loop control structure of the flux linkage and current is adopted in the making process of the contactor, so that the coil current can be adjusted naturally in terms of the air gap between the cores, effectively reducing the energy loss and inhibiting the contact bounces. In the closing process, a simple and single current closed-loop control is adopted. And then, the contactor is demagnetized at a full negative voltage. The hardware platform of the embedded system based on single-board RIO is built, including the driving module and the data acquisition module. The system uses the LabVIEW software platform as the upper computer and carries out real-time control of the contactor according to the calculation of the acquired signal by the FPGA module of the single-board controller, which verifies the control strategy designed in this paper. It provides an efficient research and platform for the development of more complex intelligent control technology of contactors. Keywords: Contactor · Closed-loop control · Flux linkage · Contact bounces · Single-board RIO
1 Introduction Contactors in the power system are mainly used to take on the functions of power transmission, control and protection, which directly affects the stability and reliability of power supply and distribution system. However, traditional alternating current (AC) contactors still have many problems, such as serious contact bounces, high maintenance power consumption, poor resistance to voltage sags, etc. [1, 2]. Therefore, recently, many © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 177–186, 2022. https://doi.org/10.1007/978-981-19-1528-4_18
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scholars introduce complex control strategies into the contactor to effectively optimize the dynamic process and improve the performance of switches. At present, pulse width modulation (PWM) control technology has been widely used in the contactor, in which the coil voltage and current are used as closed-loop feedback to adjust the excitation [3, 4]. Moreover, the sensorless displacement control was also proposed in Literature [5, 6], which inhibited the contact bounces. Literature [7] used the slope of current drop to indirectly control the speed of the movable core. In Literature [8], a cascade control strategy with flux linkage feedback was proposed to adjust the coil current in the making process until the cores were closed stably, which greatly reduced the energy loss. These control strategies can all effectively improve the service life and enhance the reliability and stability of the contactor. However, for the complex intelligent control scheme, the mainboard with a single control chip is difficult to achieve. Some scholars have used the NI CompactRIO industrial controller to build a fast control system, which realized the intelligent control of contactors and other objects. CompactRIO controller has a Field Programmable Gate Array (FPGA) module, which can execute a variety of tasks in parallel and simplify the complexity of the program. But the controller is expensive and large. Therefore, a single-board reconfigurable input/output (sb-RIO) controller is adopted in this paper. Real-time processor, FPGA module, analog and digital Input/Output (I/O) ports are integrated on a single board, which has a smaller shape size and is suitable for deployment applications. In addition, customized I/O for high capacity deployments can be retained through RIO mezzanine card (RMC) connector, enabling direct access to I/O ports for certain processor functions [9]. In this paper, a hardware platform built by a sb-RIO controller is used to complete the design of closed-loop control strategy for a contactor. The flux linkage is obtained by using the voltage balance equation of the electromagnetic system during the making process. Therefore, the double closed-loop control structure of the flux linkage and current is adopted in the making process, enabling to adjust the coil current naturally in terms of the air gap between the cores. In order to avoid the problem that the integrated flux linkage error will gradually increase over time in practice, a single and low-current closed loop is adopted in the closing process. The control strategy is proposed for the first time, of which the system is simple, direct, and has good dynamic performance. This control strategy can effectively inhibit the contact bounces of the contactor and reduce the energy loss during the action process, and has practical significance in engineering.
2 Closed-Loop Control Strategy 2.1 Double Closed-Loop Structure in the Making Process In the electromagnetic system of the contactor, when the magnetic field is considered to be concentrated and evenly distributed in a small gap, the electromagnetic force according to Maxwell’s formula can be expressed as follows: 2 Fx = 2μψSN 2 0 (1) ψ = Licoil
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where ψ is the flux linkage, L is the inductance, icoil is the coil current, F x is the electromagnetic force of the contactor, μ0 is the air permeability, S is the net sectional area of the core, and N is the coil number. As shown in (1), there is a direct relationship between the electromagnetic force and the flux linkage. So the electromagnetic force F x can be indirectly controlled by the flux linkage ψ, so as to realize a reasonable match between F x and reaction force, enabling to control the speed of the movable core. As the flux linkage is related to the coil current and the inductance of the magnetic circuit, the flux linkage will increase rapidly when the air gap between the cores is decreasing during the making process. A single current closed loop is difficult to effectively control ψ and F x can not also be reliably controlled. Therefore, an outer loop structure of the flux linkage control is added, and the flux linkage controller gives the current reference value according to the flux linkage feedback. So that, the current reference value of the inner loop can be adjusted to reduce the coil current as the flux linkage gradually increases to the reference value, enabling to reduce the terminal speed of the movable core and inhibit the contact bounces. This control strategy greatly shortens the duration of large current and reduces the energy loss during the making process. The voltage equation of the electromagnetic mechanism of the contactor is written as follows: dψ = ucoil − icoil Rcoil dt
(2)
By further integrating both sides of (2), the following expression can be obtained: t ψ= (ucoil − icoil Rcoil )dt (3) t0
where ucoil is the coil voltage, Rcoil is the coil resistance, and t is the present moment of the dynamic process. The initial values of ψ, ucoil and icoil at t 0 are 0. In (3), Rcoil can be measured off-line. ucoi and icoil can be also measured in real time from sensors, so ψ can be obtained by integral calculation as the feedback of the outer flux linkage closed-loop control. The current reference value is given according to the comparison between the flux linkage calculation value and the flux linkage reference value, and then the current controller outputs the PWM signal to the electronic switches of the driving circuit, so that the contactor can quickly meet the making requirements in the making process and smoothly turn into the closing process. 2.2 Single Current Closed-Loop Structure in the Closing Process In the method of further integral of the voltage balance equation to calculate the flux linkage, the small measurement error of sensors and the error caused by the system interference will gradually increase with the accumulation of the long running time in practice, so that the final flux linkage calculation value will be far away from the real value. However, the making time of a contactor is less than 100 ms, and the shorttime integral calculation of ψ will be accurate in the making process. Therefore, when the contactor making process ends and enters the stable closing process, the system is switched into the single current closed-loop control strategy.
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Because the air gap between the cores is invariable in the contactor closing process, and according to the binary one-to-one nonlinear correspondence relation among the flux linkage, coil current and the displacement of the movable core of the electromagnetic mechanism, the coil current under the outer flux linkage closed-loop control will be naturally adjusted to the minor value which the flux linkage reference value and the closed displacement correspond to. Then, the control strategy is switched into the single current closed-loop control, and the coil current during the closing process will be readjusted to the current reference value, enabling to maintain at a fixed minor current and achieve the reduction of energy loss. When the breaking signal is received, a full negative voltage is adopted to rapidly demagnetize the cores and then the contactor is broken. 2.3 Principle of Closed-Loop Control The principle of the closed-loop control strategy in the whole process of the contactor is shown in Fig. 1, which is divided into two parts including the coil driving circuit and the closed-loop control strategy.
S1
Rectifying bridge Input Voltage
C1
Driving circuit Coil
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The coil driving circuit part is expounded that: after the input voltage passes through rectifying bridge D1 and filter capacitor C 1 , the direct current (DC) voltage is output; three states can be defined according to the voltage at both ends of the coil as follows. When the electronic switches S 1 and S 4 are switched on at the same time, the coil is energized with a positive voltage; this is considered as state +1. When S 4 is switched on and S 1 is switched off, icoil flows through the fast recovery diode D3 and S 4 , so the coil voltage is the sum of the conduction voltage between D3 and S 4 , which is approximately
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0 V; this is considered as state 0. When S 1 and S 4 are switched off at the same time, the coil current feeds energy back to capacitor C 1 through the fast recovery diodes D2 and D3 , so the coil is acted at a full negative voltage; this is considered as state −1. The control strategy is expounded as follows. The action of the contactor is controlled by sequential module of the process control in the system. During the making process, ucoil and icoil measured by the sensor in the driving circuit are fed into the integral calculation to obtain the observed flux linkage value ψˆ of the magnetic circuit. Then the current reference value of the inner current loop is output under outer flux linkage closed-loop control. Finally, the PWM duty cycle is output under the current hysteresisloop control. At the same time, the switches of S 1 and S 4 are both determined to be on or off, making the driving circuit in the state +1 or state −1, so as to achieve fast excitation. After switching into the stable closing process, the single current closed-loop control is adopted to continue to maintain under the new and minor current reference value. At this time, S 4 is set to the closed state all the time in the closing process, and the driving circuit which is between the state +1 and state 0 reduces the number of conduction cycles of electronic switches and achieves energy-saving effect. At last, a negative voltage with state −1 is carried out to demagnetize the coil according to the process control until the coil current drops to 0. 2.4 Cosimulation Analysis A cosimulation system is built to complete the construction of the contactor model and the simulation verification of the control strategy [10]. The simulation waveforms of the closed-loop control for a contactor are shown in Fig. 2. The flux linkage reference value ψ ref is set to 2 WB to ensure the reliable movement of the movable core; the maximum imax and minimum imin current reference value of the inner current loop is set to 4 A and −1 A during the making process, respectively; and the current reference value iref of the single current closed-loop control in the closing process is set to 0.3 A. The contactor enters the making process at t 0 . At this time, the flux linkage obtained by integral calculation is smaller than ψ ref , so the current reference value of the inner loop is set to imax , and the coil is acted by the voltage of state +1, so that icoil increases at a high rate of speed. At t 1 , the flux linkage reaches the reference value of 2 WB, and then the driving circuit of the coil is transformed in the state +1 or state −1 to maintain the flux linkage. With the reduction of the air gap, the coil current icoil is continuously adjusted and decreases to its mimmum. The cores are reliably closed at t 2 . According to the process control, the single current closed loop structure is selected during the closing process at t 3 . icoil is quickly stabilized around 0.3 A at t 4 under the voltage of state +1, and then the driving circuit of the coil is transformed between only state +1 and state 0 to achieve the energy-saving effect in the closing process. As can be seen from the magnifying diagram of the current details in Fig. 2, the coil current is adjusted from 0.2 A to 0.3 A under the single current closed-loop control, while the electromagnetic force increases greatly, which effectively improves the closed reliability of the contactor. At t 5 , full negative voltage is applied to the coil, and the contactor breaking process is completed at t 6 .
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Fig. 2. The simulation waveforms of the closed-loop control for a contactor
3 Embedded System Based on Single-Board RIO 3.1 Embedded System Structure The control system designed in this paper adopts NI single-board RIO-9627 controller, a board-level embedded device, as the core. It also includes the computer, the coil driving circuit of the contactor, sensors, extended NI C-series Analog Input (AI) module and the oscilloscope which displays the experimental results. The overall structure of the embedded system is shown in Fig. 3. C-series AI module collects the coil voltage and coil current of the contactor through the sensors. After the collected signals are calculated by the intelligent control strategy, the Digital Input/Output (DIO) ports of the controller outputs the PWM signals, which provide the excitation voltage for the contactor after the power amplification of the driving circuit. The Analog Output (AO) ports convert the internal digital parameters into analog signals, which are convenient for oscilloscope measurement. Finally, according to the coordination of the system hardware and programming of LabVIEW, the closedloop control strategy in the whole process of the contactor can be quickly tested and verified.
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3.2 System Hardware NI sbRIO-9627 is a CompactRIO board with an area of only 15 cm * 10 cm, of which the design is solid, reliable and flexible. The control core of the system consists of a dual-core 667 MHz ARM Cortex-A9 processor and a Xilinx Zynq-7000 FPGA chip with a maximum clock rate of 40 MHz. In addition, the board has a real-time clock, hardware monitoring timer, 512 MB of storage, 256 MB of RAM and so on. It also has 16 channels of 16 bit AI ports, 4 channels of 16 bit AO ports, and 4 DIO ports. In order to improve the frequency and accuracy of the data sampling, the system uses RMC connector to match the custom sub-card, and expands the built-in analog I/O through NI C-series module (this system chooses NI 9223). The data collected by the voltage sensor and current sensor are converted into the signals within the allowable input range of NI 9223, and then fed into the controller. NI 9223 has a 4-channel AI ports with a maximum sampling rate of 1 MS/s for a single channel. After buffering and conditioning, the input signal of each channel is sampled by the analog-to-digital converter to realize synchronous sampling of all channels.
4 Experimental Validation 4.1 Validation of Control Strategy A CJ20-630A AC contactor is used as the experimental prototype, and the sb-RIO is used as the core controller to carry out the experimental validation of the control strategy of the contactor. The experimental waveforms of the making, closing and breaking process are shown in Fig. 4. ψ ref is set to 2 WB; imax is set to 4 A; imin is set to −1 A; and iref in the closing process is set to 0.3 A in keeping with the simulation. At t 0 , the contactor coil is excited, and the coil current increases rapidly under the outer flux linkage closed-loop control
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After the contactor is closed steadily, it turns into the single current closed-loop control of the closing process at t 3 . As can be seen from Fig. 4, under the single current closed-loop control at t 3 to t 4 during the closing process, ψ obtained by integral calculation will gradually decrease with the increase of time due to the accumulation of the measurement error. Therefore, it is not appropriate to use integrated flux linkage as the feedback in the long-term closing process of a contactor. With the driving circuit of state −1 at t 4 , the contactor is quickly demagnetized until the breaking is completed. 4.2 Comparative Analysis of Experimental Results The experimental waveforms of a contactor using traditional current closed-loop control in the whole process is shown in Fig. 5. Compared with the double closed-loop control as shown in Fig. 4, the bounce time of the contactor increases from 3.6 ms under the flux linkage closed-loop control strategy to 11.6 ms under the constant current closed-loop control. After many continuous contrast experiments, the constant flux linkage control schemes all have obvious bounces inhibition effect. During the action of the contactor, the inductance L increases fleetly with the decrease of the air gap between the cores. So under the single current closed-loop control, the coil current is difficult to be automatically adjusted. Therefore, the electromagnetic force F x is too large, resulting in the hign collision speed of cores and the deterioration of the contact bounces. However, under the outer flux linkage closed loop, it can be concluded according to (1) that the coil current will automatically and evenly decrease in order to maintain the constant ψ. This can make the contactor naturally transit to the closing process, which reduces the energy loss and inhibits the cantact bounces of the contactor.
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5 Conclusion In this paper, an embedded control system with single-board RIO controller as the core is developed, and the control strategy of double closed loop of the flux linkage and current in the contactor making process and single current closed loop in the closing process is completed. The effectiveness and reliability of the control strategy are verified by the experiments. Compared with the traditional single current closed-loop control, the coil current of the contactor can be adjusted naturally in the making process according to the decrease of the air gap between the cores under the flux linkage control, reducing the energy loss of making current, and obtaining better inhibition effect of the contact bounces. It can be used to optimize the dynamic process and provide the valuable experience for a contactor. Acknowledgements. Natural Science Foundation of Fujian Province (2020J05134), Joint Foundation of Fujian Innovation Center (CXZX-202004), and Scientific Research Project of Science and Education Development Center of Fuzhou University in Jinjiang (2019-JJFDKY-30).
References 1. Zheng, S., Niu, F., Li, K., Huang, S., Liu, Z., Wu, Y.: Analysis of electrical life distribution characteristics of AC contactor based on performance degradation. IEEE Trans. Componen. Packag. Manuf. Technol. 8(9), 1604–1613 (2018). https://doi.org/10.1109/TCPMT.2018.284 1425 2. Zheng, Z., Ren, W., Wang, T.: Experimental investigation of the breaking arc behaviour and interruption mechanisms for AC contactors. IEEE Trans. Plasma Sci. 49(1), 389–395 (2021). https://doi.org/10.1109/TPS.2020.3042545 3. Zhuang, J., Xu, Z.: The multivariate feedback control strategy for electromagnetic contactor holding. Proc. Chin. Soc. Electr. Eng. 39(05), 1516–1526 (2019). (in Chinese)
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4. Luo, H., Xu, Z.: Research on intelligent individual-phase control technology for combinedswitches. Electr. Energy Manag. Technol. 01, 14–21 (2016). (in Chinese) 5. Wang, X., Lin, H., Fang, S., Ren, Q., Jin, P.: Analysis of flux-weakening control and dynamic characteristic for making process of sensorless intelligent permanent magnet contactor. Proc. Chin. Soc. Electr. Eng. 31(18), 93–99 (2011). (in Chinese) 6. Ramirez-Laboreo, E., Sagues, C., Llorente, S.: A new model of electromechanical relays for predicting the motion and electromagnetic dynamics. IEEE Trans. Ind. Appl. 52(3), 2545– 2553 (2016) 7. Tang, L., Xu, Z.: A slope closed-loop control technology of AC contactors. Proc. Chin. Soc. Electr. Eng. 37(3), 988–997 (2017). (in Chinese) 8. Zhang, C., Xu, Z.: A cascade control strategy for intelligent AC contactors based on flux linkage feedback. Proc. Chin. Soc. Electr. Eng. 40(04), 1329–1338+1424 (2020). (in Chinese) 9. Zhu, Z., Xiao, X., Wang, W., You, P., Zhong, L.: Development of ultrasonic guided wave testing system based on SB-RIO. Eng. J. Wuhan Univ. 51(10), 939–940 (2018). (in Chinese) 10. Xu, Z., Tang, L.: Co-simulation and digital control technology of the intelligent AC contactor. Proc. Chin. Soc. Electr. Eng. 35(11), 2870–2878 (2015). (in Chinese)
Analysis of a Misoperation of the Ratio Differential Protection of Main Transformer Tianying Chen1(B) , Xianzhi Wang1 , Yuhao Zhao1 , Tiecheng Li1 , Ze Li1 , and Yangjun Hou2 1 Electric Power Research of State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021,
China [email protected] 2 Hebei Huadian Shijiazhuang Thermal Power Co., Ltd., Shijiazhuang 050041, China
Abstract. This article introduces a three-phase trip fault of the main transformer after a single-phase short circuit in the line. First, according to the protection action when the fault occurs, it is preliminarily judged that the trip of the main transformer is a malfunction of the protection device. Secondly, by analyzing the internal principle of the protection device and the device hardware Detect and determine that the cause of the fault is the damage of the AD plug-in of the protection device, and finally put forward corresponding rectification measures for this fault to ensure the reliable and stable operation of the transformer differential protection. Keywords: Transformer · Ratio differential · AD plugin · Relay protection
1 Preface Transformer differential protection is one of the main protections for internal faults of the transformer. Its protection scope includes the transformer itself, current transformer and transformer’s lead wire, etc. The faulty tripping of transformer protection will seriously affect the reliability of power supply and cause the area of power failure to increase [1]. Studies have shown that most of the protection malfunctions are caused by unbalanced currents, loop failures, poor insulation, electromagnetic interference, etc. In recent years, the research on trip accidents has mostly focused on the external circuits of relay protection and automatic devices. Faults are rarely involved [2–4]. This paper takes a fault-free tripping accident of a main transformer protection device as an example, deeply researches the internal principles of the protection device, analyzes the three doubtful points of this fault and hardware detection, finds the cause of the transformer protection malfunction, and proposes corresponding rectification measures.
The scientific and technological project number of State Grid Hebei Electric Power Co., Ltd. is kj2020-048; The funding fund number is 20314301D. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 187–193, 2022. https://doi.org/10.1007/978-981-19-1528-4_19
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2 Introduction to Failure At 15:34:56 on July 2, 2020, the 110 kV A line in the 220 kV M station has a C-phase permanent ground fault, the 110 kV A line protection operates quickly in 13 ms, the 58 ms three-phase switch is tripped, 1574 ms coincides with the fault, and the 1656 ms three-phase The switch trips again, 1717 ms 220 kV No. 1 main transformer A set protection (CSC-326D) B, C differential action outlet in 220 kV M station, B-phase differential current is 0.980 A, braking current is 1.055 A, C-phase differential current is 0.980 A, system The dynamic current is 0.883 A, and the three-phase main transformer No. 1 trips in 1768 ms, and the main transformer protection device has no alarm during the fault process. There are three doubtful points in this failure: 1. Is the No. 1 main transformer of M station faulty? Why is the protection of No. 1 main transformer A set of protection activated but B set of protection not activated? 2. If the protection of No. 1 main transformer A set of M station works without failure, why the protection of No. 1 main transformer does not work when A line fails for the first time? 3. If the protection of No. 1 main transformer A set of M station works without failure, is the protection device normal? Why is there no warning signal?
3 Analysis of Accident Doubts 3.1 Doubtful Point: Whether the Main Transformer is Faulty Analysis of fault recorder Reading the fault record chart, it is found that when the No. 1 main transformer A set of protective action exits, the magnitude of the fault current is basically the same as that of the first fault, and the sum of the currents on the high, middle and low sides of the No. 1 main transformer is basically 0, which is a zone. External fault characteristics. Analysis of protection devices When reviewing the internal recording of the protection device of No. 1 main transformer, it was found that: 1) The A and B phases of the high voltage 1 side of CPU1 showed normal fault current waveforms, and the C phase current recording of the high voltage 1 side was about 145 A DC. When there is no external input for the three-way current on the high-voltage side 2, the DC quantity of about 145 A is also collected, and the waveform is abnormal, as shown in Fig. 1. 2) The high voltage 1 side waveform of CPU2 presents a normal fault current waveform, and the high voltage 2 side current channel presents a normal zero-drift current waveform, as shown in Fig. 2. Among them, the recording channel I1A/I1B/I1C represents the current on the high voltage side 1 of the main transformer, and the recording channel I2A/I2B/I2C represents the current on the high voltage side 2 of the main transformer (note: the external circuit has no wiring). According to the internal recording analysis of the No. 1 main transformer protection device, the three-phase current sampling of the high-voltage 1 side C-phase current and the high-voltage side 2 of the A set of protection CPU1 is abnormal. It is suspected that the AD chip is damaged.
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Fig. 1. Set A protects the recorded data of CPU1 (high voltage 1 side current and high voltage 2 side current)
Fig. 2. Set A protects the recorded data of CPU2 (current on side 1 of high voltage and current on side 2 of high voltage) I dz
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The action curve of the differential protection is shown in Fig. 3. The current amount sampled from the three sides of the main transformer is calculated. The braking current of phase B at the time of protection action is 1.055A, and the corresponding action current threshold is calculated according to the differential action criterion. The value is. Idz = S2 (Izd − 0.6Ie ) + 0.6Ie S1 + Icd = 0.5 ∗ (1.055 − 0.6Ie ) + 0.2 ∗ 0.6Ie + 0.45 = 0.814A = 1.732180000 among them, Ie = √ S ∗ 230 ∗500 = 0.904, The differential current at the 3 ∗ Un ∗ nct moment of action is 0.980 A, which is greater than the corresponding action threshold, so the CPU1B phase differential protection action exit. (The same reason as C). At the same time, due to the failure of the A line, the main transformer A set protection feels a sudden change in current, so the CPU2 starts and meets the protection action conditions, and the main transformer A set protection trips the outlet. Based on the above analysis, it is determined that the main transformer has not failed. The main transformer A set of protection should be a fault-free operation, and the B set of protection shall not operate as a normal behavior. 3.2 Doubt 2: Why the Main Transformer Protection Does not Operate During the First Failure The main transformer differential protection device CSC-326D is designed with dual CPU + dual A/D. CPU1 is responsible for the protection logic, and CPU2 is responsible for starting. In order to further enhance the reliability of the protection, an action curve (differential protection action value) is also set in the starting CPU2 0.7 times the fixed value). After the differential current of CPU2 enters the action area on the ratio braking curve, it will send the “differential start flag” to the protection CPU1, and the protection CPU1 will receive the “differential start flag” sent by the start CPU2 after its own differential protection has met the operating conditions. In addition, in order to prevent the communication problem between the two CPUs from affecting the protection action, protect the CPU1 under the conditions of differential action, but does not receive the “differential start flag” sent by the startup CPU2. It will act out after a delay of 60 ms [5–7]. Although in the two failures, the No. 1 main transformer A set protection CPU1 differential protection is active, but the sampling channel of CPU2 is normal, so CPU2 only starts, but the differential protection in CPU2 does not operate, so the “differential protection” is not sent to CPU1. According to the action logic, the protection of No. 1 main transformer A set of protection should be delayed 60 ms to export. When the A line fails for the first time, the fault is removed within 58 ms, and the 60 ms required for the action is not reached, so the main transformer A set of protection does not operate. 3.3 Why There is No Warning Signal for the Three Main Transformer Protection According to the logic of the protection device, the differential current limit value Is = MAX (the differential current limit threshold value, the differential current limit bottom value), considering the sampling accuracy of the CT, the program sets the differential
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current limit limit value 0.06In (In Is the secondary rated current), the minimum accurate working current of the device is 0.08In [8–10]. The fixed value of the differential starting current of this station is 0.45 A, the highvoltage side CT transformation ratio is 2500/5, the differential current limit value Is = MAX(0.3 * 0.45, 0.06 * 5) = 0.3 A, through the A set of main transformer protection Start the wave recording and analyze that the differential current during the operation is basically maintained at about 0.1 A, which is less than the limit value of the differential current. Therefore, the protection device did not judge the differential current over-limit alarm.
4 Hardware Detection 4.1 Failure to Reproduce the On-Site Return Plug-In Build a device environment consistent with the site, and reproduce the plug-in failure. After power-on, use the tester to increase the amount, the high-voltage side 1 C-phase current sampling is zero, and the scene phenomenon is completely reproduced. 4.2 Board Hardware Detection Visual inspection of CPU plug-in Visually inspect the appearance of the 5SF.004.071/V2 plug-in. The components and connectors on the plug-in are in good condition, and there is no bumping damage. Microscopic examination The solder joints of the component D10 chip (AD7865) on the CPU plug-in were inspected under a microscope, and the solder joints were well soldered, eliminating the problem of the soldering quality of the AD chip. Single board function test Test the power on the single board, test the AD chip power (vcc) output voltage of 5.06 V, test the positive and negative power output of the op amp chip +12.01 V and −11.98 V are normal. Troubleshoot and test the analog sampling abnormal channel Use a signal generator to apply power frequency 1.0vpp signals to the input terminals pin-3, pin-10, pin-12, and pin-14 of the component number N3 chip (Note: OP497), and track them with an oscilloscope respectively The output signal corresponding to the test component N3 chip (ie: component D10-AD7865 analog signal input) is normal. Through this test method, it is preliminarily inferred that the op amp chip functions normally. Test the external characteristics of the D10 chip In order to further judge the failure mode of the AD chip, use a transistor graphic instrument, according to the analog input IV curve of the AD chip (component number D9) as the reference source (note: the external circuit of D9 and D10 are exactly the same), compare and test the AD chip (component Number D10) IV curve of 4 analog
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input terminals. Comparing the test results, the input I-V curves of the two AD chips are exactly the same. Therefore, it is ruled out that the AD chip fails due to electrical overstress, and the internal data processing error of the AD chip is presumed. Internal output simulation of the chip Use the emulator to check the original code value output by the AD chip, and find that the sampling of the high-voltage 1 side C-phase and the high-voltage 2 side A/B/C phase sampling occurs full deviation, as shown in Fig. 4. Through the check, it corresponds exactly to the three-way current of the high-voltage side 1 side C-phase and the highvoltage side 2 side. After checking the hardware, it was confirmed that the internal circuit of the AD chip was damaged, and it was judged that the internal reference voltage of the AD chip was abnormal, which caused the data to shift.
Fig. 4. AD chip output original code value
5 Conclusions and Corrective Measures After fixed value calculation and hardware analysis, it is determined that the cause of the malfunction of the A set protection of the No. 1 main transformer in the M station is that the AD chip of the CPU1 (component number D10) is internally damaged, resulting in abnormal sampling of the C-phase current on the high voltage side 1. When the differential current reaches the action value, the action outlet is protected. 1) In terms of protection software, combined with on-site conditions and professional inspection requirements, a zero-drift over-limit alarm function is added to the subsequent generation device. The zero-drift over-limit alarm function criterion is: when the DC component content of a current channel continues 1 When the minute is greater than 20% In, the device reports “Zero drift over limit alarm”. The zero drift over limit alarm
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can effectively detect the damage of the AD chip, and the AD chip is damaged, and the current sampling is full and has a very sensitive recognition ability. 2) The AD chip manufacturer is ADI (the world’s most famous AD manufacturer) company, which is widely used in relay protection, with a cumulative use of millions of pieces. From the statistics, the chip failure rate is basically the same as that provided by the supplier. (0.4PPM) is approaching. Next, we will continue to track and conduct failure mechanism analysis with chip manufacturers. If there is a problem that affects the safe operation of the power grid, measures will be taken and users will be notified in time.
References 1. Yang, H.: Research and design of integrated microcomputer relay protection device based on dual CPU. Donghua University, Shanghai (2015). (in Chinese) 2. Jiali, H.: Principles of Power System Relay Protection. China Electric Power Press, Beijing (2010).(in Chinese) 3. Tang, J.: Research on transformer differential protection based on second harmonic restraint. Anhui University of Science and Technology, Anhui (2018). (in Chinese) 4. Abdullah, A.M., Butler-Purry, K.: Distance protection zone 3 misoperation during system wide cascading events: the problem and a survey of solutions. Electr. Power Syst. Res. 23(16), 56–59 (2018) 5. Haowen, L.: Analysis on the cause of a fault-free tripping accident of a main transformer protection device. Electr. Technol. 25(10), 101–103 (2017). (in Chinese) 6. Chang, X.: Research on transformer differential protection device based on dual CPU. Anhui University of Science and Technology, Anhui (2013). (in Chinese) 7. Deng, F., Wang, F.: Misoperation monitoring and early warning during startup and shutdown of petrochemical units. J. Loss Prev. Process Ind. 16(12), 176–178 (2020). (in Chinese) 8. Wei, L., Zhang, J., Su, X.: Analysis and preventive measures of line protection misoperation caused by defects of current secondary circuit. Autom. Electr. Power Syst. 43(19), 113–115 (2019). (in Chinese) 9. Aboutaleb, H., Evangelos, F., Ilhan, K.: Impact of inverter based resources on system protection. Energies 31(20), 33–35 (2021) 10. Guarda, F.G.K., Cardoso, G., Bezerra, U.H.: Minimising direct-coupled distributed synchronous generators impact on electric power systems protection. IET Gener. Transm. Distrib. 32(25), 232–234 (2019)
Analysis of a Local Overheating Fault of UHV Converter Xiu Zhou(B) , Qing-ping Zhang, Xu-tao Wu, Wei-feng Liu, Yan Luo, Xiu-guang Li, Ning-hui He, Yang Wu, Ying Wei, and Tian Tian State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China [email protected]
Abstract. In this paper, a low-end Y/Y converter in lingshao HVDC project was put into operation soon, and the abnormal chromatogram data of the main body oil was found to be related to the abnormal temperature of the converter box edge. During the comprehensive inspection, the heating of the box edge was treated by welding the box edge. After the converter transformer was put into operation again, the abnormal gas production of the converter transformer was not eliminated In the later stage, through live and power failure test, on-site core drilling and factory disassembly inspection, the local heating causes of the converter transformer were found, and corresponding rectification suggestions were put forward. Keywords: UHV converter · Local overheating · Fault analysis oil chromatogram
1 Introduction DC Transmission Line of ±800 kV Power Transmission Project from lingzhou to shaoxing is one of the ten UHV projects of “four AC and six DC” in the national air pollution control action plan. The total length of the transmission line is about 1720 km, the rated voltage is ±800 kV, and the rated transmission power is 8 million KW. The converter transformer of the station is one of the core equipment of lingshao HVDC project, and its healthy operation is directly related to the power transmission of Ningxia electric power. If the converter transformer body is abnormal or fails, it is not only necessary to adjust the power operation of lingshao HVDC, but also bring great trouble to the field staff. Therefore, it is very important for lingshao HVDC project to analyze and find out the abnormality of converter transformer. This paper introduces a local overheating fault of UHV converter body in sending terminal station of lingshao UHVDC project [1–3]. Through test detection, on-site box entry and factory disassembly inspection, the cause of abnormal overheating of converter transformer is confirmed, and corresponding rectification suggestions are put forward.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 194–208, 2022. https://doi.org/10.1007/978-981-19-1528-4_20
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2 Exception Introduction Shortly after the lingshao HVDC project was put into operation, the online oil chromatographic data of a converter transformer at the low end of Y/Y type at the sending end station showed abnormal growth. About three years after the project was put into operation, the total hydrocarbon content of the converter transformer body reached the attention value of DL/T 722-2014 “guide for analysis and judgment of dissolved gas in transformer oil”. During the four years of operation of converter transformer, hydrocarbon gas in converter transformer has been increasing step by step, while carbon monoxide and carbon dioxide have been increasing slowly [4]. It is preliminarily suspected that the abnormal chromatogram data of converter transformer body oil has a certain relationship with the box edge heating. During the comprehensive inspection of the third year of operation, the box edge heating was treated by welding. Although the temperature of converter transformer box edge dropped by 20 °C, the chromatogram data of converter transformer body oil still increased [5]. After four years of operation, the total hydrocarbon value doubled, that is 539.9 µL/L, according to DL/T 722-2014 “guide for analysis and judgment of dissolved gas in transformer oil” three ratio method is used to determine the converter transformer has high temperature overheating fault, and David triangle method is used to determine that the thermal fault temperature is greater than 700 °C. In order to ensure the safe and stable operation of lingshaote HVDC project and converter transformer, according to the operation and maintenance strategy formulated in the earlier stage, the converter transformer is immediately withdrawn from the system operation on the same day to find the converter transformer The gas content of converter transformer body is shown in Fig. 1. and Fig. 2.
Fig. 1. Hydrocarbon gas content of converter transformer
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Fig. 2. CO and CO2 gas content of converter transformer
3 Test Chamber and Treatment Edge 3.1 Live Detection The converter transformer is tested regularly with high frequency partial discharge and ultra high frequency partial discharge without power failure. The test spectrum and data are qualified, and no abnormality is found. 3.2 Power Failure Test During the comprehensive inspection of converter station, routine tests of insulation resistance, DC resistance, dielectric loss of winding and bushing, withstand voltage of transformer oil, micro water and dielectric loss of converter transformer were carried out in turn. The test data were qualified and no abnormality was found. 3.3 Welding Condition of Box Edge Remove the short wiring between the original bolts, and then seal the gap between the boxes with wet asbestos rope after fastening the bolts again, clean the paint along the weld area of the box; fill the cracks along the bolts, nuts and boxes around the weld with asbestos rope and paint nearby; weld by “section welding” and “intermittent welding”, and offset 2.85 m from the center line of the tank to the casing side of the mesh side As the starting point, the welding direction is 6m towards the casing at the valve side [6]. During the welding process, the temperature of the sealing strip is monitored by infrared thermograph. The welding shall be stopped immediately after the position of the sealing strip exceeds 105 °C, and welding shall continue under 90 °C of the temperature drop; after the welding, the welding parts shall be cleaned and painted as shown in Fig. 3. and Fig. 4.
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Fig. 3. South box edge not welded
Fig. 4. The situation of the south side box after welding
Before the welding of the box edge, the axial-flow fan was used to cool the hot spot during the operation of the converter transformer. After the welding, the axial-flow fan was shut down to verify the effect, and the cooling effect of the fan was about 20 K [7]. According to the temperature comparison before and after the welding of the box edge, the heating problem of the box edge was alleviated after the welding, but the oil chromatogram data of the converter transformer still increased, and there was no evidence to show that the converter transformer The abnormal oil chromatogram is related to the heating of box edge [8]. The infrared temperature measurement of box edge after welding is shown in Fig. 5.
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Fig. 5. Temperature measurement map of box edge after welding
4 Site Inspection and Disassembly 4.1 On Site Inspection After the converter transformer is out of operation, the relevant personnel enter the box at the converter station to check the fault prone parts of the converter transformer one by one. Inspection of converter tap changer and its connecting parts. It is found that the position of the terminal on the static contact 12 of the switch selector on the upper part of the switch is abnormal. Shake the lead by hand, and the connection position can be rotated about 10°. Then open the mechanical connection position between the lead and the terminal on the static contact 12, and it is found that the copper washer is dark red, as shown in Fig. 6. Then one by one, it was found that the connection of 10 upper, 11 upper and 11 lower static contacts was slightly loose, and the copper washer was also dark red. Through analysis, the abnormality had no direct relationship with the oil chromatogram data of converter transformer body.
Fig. 6. Contact surface on lead side of copper washer of terminal on static contact 11
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Check the inner box edge of net valve side and tap side with Nomex Paper, and there are three yellow signs on both sides, as shown in Fig. 7. It is analyzed that the abnormality is not directly related to the abnormality of oil chromatogram data of converter transformer body.
Fig. 7. Abnormal color of valve side box edge
Check the two shielding wires at the position of the iron core side support plate at the lower part of the outgoing line at the valve side. It is found that there is virtual connection between the two shielding wires [9]. The two shielding wires share a common grounding base, and the two terminals are overlapped and connected at a certain angle. During the factory manufacturing, the insulation length of one of the terminals is long, resulting in the insulation paper part stuck between the two terminals, resulting in poor grounding The abnormality is not directly related to the abnormality of oil chromatogram data of converter transformer. Based on the above inspection and analysis, it can not be determined that the abnormality is directly related to the abnormality of oil chromatogram data of converter transformer body. Therefore, after full discussion and discussion, it was decided to return to the plant for further inspection and analysis of the converter. 4.2 Factory Return Inspection From January to February 2021, the relevant inspection of the returned converter was carried out in the converter manufacturer. The inspection is divided into six steps. (1) Inspection of converter transformer oil tank. The appearance inspection of the outer surface of the oil tank shows no abnormality. There are three erasable yellow marks on the inner edge of the upper box along the tapping side in the welding area of the original box. The analysis shows that the yellow marks are rust like substances, which are not related to the total hydrocarbon generation rate and quantity of the operating chromatography, as shown in Fig. 8.
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Fig. 8. The lower surface of the upper oil tank is partially attached with traces that can be wiped off
(2) Check the body of converter transformer before de oiling. The switch and valve side lead wire were preliminarily inspected before oil removal, and no abnormality was found, as shown in Fig. 9.
Fig. 9. Transformer body before deoiling
(3) Inspection of relevant parts of converter transformer switch. Remove the switch head cover and lift out the switch core for appearance inspection. Erasable Brown marks were found on the surface of the two insulating parts at the lower part of the grading ring, as shown in Fig. 10. the color of the connecting part between the switch oil chamber and the tap changer was not completely consistent with that of other parts, and the color turned blue, which was considered to be caused by the switch manufacturing process, as shown in Fig. 11.
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Fig. 10. There are brown marks on the insulating parts of tap changer
Fig. 11. Partial drawing of tap changer
(4) Inspection of converter transformer body after de oiling. There is no abnormality in the upper valve side lead a, the coil outlet and lead of column 1 and column 2, the “hand-in-hand” connecting line between columns on the grid side, the inner part of the pressure equalizing ball and its lead of the formed lead on the grid side, and the upper pressing plate of column 1 and column 2. There is only a large area of gray trace near the coil outlet of column 1 and column 2, near the insulating part on the clamp side and one side of the formed angle ring, as shown in Fig. 12. (5) Inspection on disassembly of complete set of coil of converter transformer. Gray marks were found on the outside of the first layer of paperboard on the inner diameter side of the net coil. At the same time, gray marks were found on the upper surface of the paperboard on the lower end ring of the net coil, and no abnormality was found in other parts, as shown in Fig. 13.
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Fig. 12. The lower valve side lead, the cardboard between the lead and the clamp
Fig. 13. Inner diameter side paper cylinder of net coil
(6) Inspection of converter transformer core and its structural parts. When removing the support plate of the upper clamp, overheat marks were found at the corner of the upper support plate pressing block on the side yoke of column 1 valve. After removal, overheat marks were found on the connection surface of the support plate, overheat marks were found on the screw rod of a pull plate on the opposite side of the support plate, partial burning damage was found on the screw thread, and burning marks were also found on the inner hole of the corresponding support plate. No abnormality was found in other supporting plates, as shown in Fig. 14.
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Fig. 14. Photos of insulation damage of phase C support
Before the four pull-down bands of yoke were removed, no discoloration was found on the wrinkle paper on the non core side. The two pull belts between the 1-pillar and 2 columns were removed and found that the inside of the strap was carbonized with wrinkled paper, and the corresponding step pad also had overheating marks. Remove the seriously carbonized wrinkle paper, and the drawing film has no obvious color change, as shown in Fig. 15, Fig. 16. The other two tension bands were not abnormal. Check the lower end face of the lower yoke without any abnormality.
Fig. 15. Photos of insulation damage of phase C support
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Fig. 16. Photos of insulation damage of phase C support
5 Simulation Calculation and Cause Analysis 5.1 Distribution of Magnetic Leakage Field The coil of the converter transformer is a two column parallel structure, the core is a four column core, two main columns and two side columns. The order of coil arrangement is core valve side coil net side coil voltage regulating coil box wall (side column), and a core shielding cylinder is set between the valve side winding and the core [10]. The oil tank is equipped with 4 mm copper shield. As shown in Fig. 17, 18 and Fig. 19.
Fig. 17. Distribution nephogram of magnetic flux leakage of clamped plate frame
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Fig. 18. Drawing of upper clamp outside the window
Fig. 19. Drawing of lower clamp outside the window
5.2 Circulation Distribution of Structural Parts The circulating current of upper and lower clamps, pull plate, pull belt and support plate of converter transformer is analyzed, as shown in Fig. 20 and Fig. 21.
Fig. 20. Circulation diagram of clamp, tension plate, tension belt and brace
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Fig. 21. Circulation diagram
After calculation: the maximum current of the two tension belts in the middle of the lower frame is 130.4 A; the maximum current of the two tension belts in the middle of the upper frame is 95.7 A; the maximum current in the tension plate is 8.3 A. When the converter transformer is under load operation, the current in the tension belt and plate meets the design requirements. 5.3 Cause Analysis (1) The operation data shows that the fault characteristic gas is continuously generated during the operation of the equipment, the total hydrocarbon content continues to increase, the ethylene content in the fault characteristic gas is higher, a small amount of acetylene gas, CO and CO2 continue to increase. The fault type shall be medium high temperature overheating higher than 700 °C, and the overheating fault involves solid insulation [11, 12]. (2) According to the inspection, it is judged that there are two fault points on the iron core, one is the upper and lower yoke window pulling belt between the main columns, and the other is the upper support plate of the yoke beside the column I. The phenomena of two fault points are high temperature overheating, which is consistent with the growth data of methane, ethylene, acetylene and other characteristic gases in operation. The above-mentioned naked metal over hot spot is the direct reason for the abnormal increase of total hydrocarbon in oil chromatography. The high temperature overheating caused the cracking of transformer oil to produce carbon black particles. (3) The content of the characteristic gas has a change in the value. Acetylene is high before July 2017, and the acetylene content fluctuates in the range of 1.4 ± 0.3 for more than 3 years from July 25, 2017 to September 15, 2020. Ethylene has been increasing continuously in recent two years, with a volume of 227 µL/L. The results show that the contact surface of the strip and the brace plate is directly related to the change of the hot contact surface. The contact surface is smaller at the beginning of overheating, the temperature is higher than 700 °C, and the content of acetylene in the chromatography is large. With the increase of superheat time, the contact area of the over hot spot increases, and the temperature of the superheat decreases to some extent, and ethylene is the main characteristic gas.
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(4) The local carbonization of the strip insulation wrinkle paper and laminated wood cushion block is consistent with the CO and CO2 gas growth data. The carbon of the paper and the block of laminated wood is the direct reason for the growth of CO and CO2 in oil chromatography. (5) Through simulation calculation (under load condition, the maximum circulation value in the belt is 130.4 A), the quality of the material of the belt and the pulling plate is traced back, and the material of the belt is confirmed to be the 631 high-strength steel of the military industry, which meets the requirements of the drawings. The reason of design structure and raw material material is excluded. It can be confirmed that the manufacturing process is dispersive in the tension belt, the flatness of the end face of the pulling plate, the angle of the screw hole of the belt pulling and the bolt fastening. (6) The yellow material of the tank cover is considered to be rust, and no evidence of characteristic gas produced by overheating of the tank is found. After the local welding treatment of the box in 2020, the phenomenon of overheating along the retired rheometer has been eliminated. (7) The insulation around the valve side coil has black material, and the judging composition is carbon particle. The gray trace is on the side of the coil on the back side of the valve. It is concluded that the carbon black particles produced in the core pulling belt and support plate flow with transformer oil circulation. Under the action of DC electric field of coil at valve side, carbon black particles are absorbed to the surface of some insulation parts [13, 14]. The adhesion degree of carbon black particles is positively proportional to the field strength of DC field and the oil flux on the insulation surface.
6 Conclusions (1) The tension strap connection of the upper and lower yokes of the iron core is changed from the conduction structure to the insulation structure at one end. The lower yoke tension belt is wrapped with wrinkled paper and removed, and the corresponding step cushion block is slotted to improve the heat dissipation condition of the tension belt. (2) Install copper tape at the contact surface of upper strut plate beside column 1 to increase the effective contact area and ensure effective conduction. Nomex Paper is added at the screw to ensure the insulation between the screw and the fixed plate. (3) Replace the insulation parts with gray marks; reassemble the body, Repeated drying, assemble and vacuum oil injection treatment, restore the local welding along the tank edge; increase the large flow hot oil flushing during hot oil circulation.
References 1. Zhou, X., Wu, X., Luo, Y., et al.: Application of partial discharge monitoring technology in defect detection of 750kV main transformer. Transformer. 058(2), 62–65 (2021). (in Chinese)
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2. Zhang, J., Tan, Z., Li, K.: Analysis and improvement of core grounding mode of oil immersed transformer. Transformer 054(5), 72–73 (2017). (in Chinese) 3. Zhang, Z., Liu, Q., Xu, Y.: A 220 kV transformer clamp grounding fault and its treatment. Transformer 050(10) (2013). (in Chinese) 4. Zhou, X., Tian, T., Luo, Y., et al.: Cause analysis and treatment of an abnormal 330kV main transformer. Transformer 057(007), 86–87 (2020). (in Chinese) 5. Zhou, X., Wu, X., Ding, P., et al.: Research on transformer partial discharge UHF pattern recognition based on CNN-LSTM. Energies 13, 6 (2019) 6. Zhou, X., Liu, W., Luo, Y., et al.: Analysis of Abnormal Chromatogram of low end YD-A phase converter rheologic oil in UHVDC. Transformer 057(082), 73–76 (2020). (in Chinese) 7. Xu, R.: Analysis and field treatment of a 500 kV main transformer core clamp grounding fault. Transformer 042(3), 43–44 (2005). (in Chinese) 8. Liang, C., Deng, J., Zhou, H., Zhang, J., Liu, Q., Pan, Z.: Typical insulation defect cases of converter transformer. Transformer 056(10), 80–84 (2019). (in Chinese) 9. Song, H., Lei, Z.: Cause analysis and improvement of acetylene overproof in non fault condition of converter transformer. Ningxia Electr. Power 001, 51–56 (2019). (in Chinese) 10. Lang, J.: Transformer internal fault diagnosis based on oil gas chromatography. North China Electric Power University (2016). (in Chinese) 11. Tang, H., Wu, G., Deng, J., et al.: Electro-thermal comprehensive analysis method for defective bushings in HVDC converter transformer valve-side under multiple-frequency voltage and current harmonics. Int. J. Electr. Power Energy Syst. 130, 106777 (2021) 12. Chen, M., Liu, X., Wu, Z., et al.: Novel heat pipe current-carrying tube of RIP valve-side bushing in converter transformer. Electr. Power Syst. Res. 184(C), 106344 (2020) 13. Tang, H., Wu, G., Chen, M., et al.: Analysis and disposal of typical breakdown failure for resin impregnated paper bushing in the valve side of HVDC converter transformer. Energies 12(22), 4303 (2019) 14. Li, Y., Zhou, K., Zhu, G., et al.: Study on the influence of temperature, moisture and electric field on the electrical conductivity of oil-impregnated pressboard. Energies 12(16), 3136 (2019)
Single Branch Experimental Research on Phase Change Cooling in Power Module of Vehicle Traction Converters Wei Hao(B) , Biao Chen, Huitao Li, Guangkun Lian, and Jiayi Yuan Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China [email protected]
Abstract. The power density, reliability and compactness of the installation space of electronic devices have been developed and improved. The load change and impact during dynamic operation also put forward higher requirements for the cooling system. Therefore, increasing the heat dissipation capacity, absorbing the load impact and improving reliability and security of the whole system are main objectives of this study. By setting up a single branch power module phase change cooling system, the heat transfer effect and flow resistance of forced circulation evaporative cooling for different geometry structures of cold plates are researched in this paper. At the same time, the cooling performance of the whole system is analyzed and discussed according to the actual vehicle operation process which varies between two operation conditions to verify that the cooling system can adapt to the heat load impact. Finally, a hybrid cooling scheme with mini-channel and micro-jet impingement is carried out to improve the temperature uniformity. These experimental results show that the heat exchange capacity and reliability of the single branch phase change cooling system can meet the requirements of vehicle traction converters, and provide guidance for the next multiple branches design and application of the whole system. Keywords: Power module · Phase change cooling · Dynamic response · Temperature uniformity
1 Introduction With the improvement of the performance and integration of electronic devices, the problem of heat dissipation has become increasingly prominent [1]. IGBT, the core component of power module traction converters, will produce power loss during operation. The operation performance of the power device is sensitive to the temperature, and the sharply change of the temperature will affect the device’s opening and closing process [2]. According to the literature, the reliability of electronic devices reduces 5% when the temperature raises every 1 °C above the working limit temperature [3]. Therefore, in order to solve the heating problem of the power device and meet the safe IGBT operation, it is very important to design the reasonable structure of heat dissipation components, and analyze the cooling capacity and dynamic response process of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 209–219, 2022. https://doi.org/10.1007/978-981-19-1528-4_21
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different heat exchange structures to keep the junction temperature below Tjmax , even during overload conditions. Based on disadvantages of the forced convection air cooling and single-phase liquid cooling, two-phase cooling technology is applied to vehicle traction converter system in this paper. The phase change heat transfer can not only make full use of the latent heat of the cooling medium, but also ensure that the temperature of the heat source is uniform [4]. Under the condition of absorbing the same heat, two-phase cooling needs less medium and has higher heat exchange efficiency, which can greatly reduce the heat exchange area and the volume of the whole cooling system. Besides these advantages, in some circulation systems with less resistance, the density difference between gas and liquid can make the system achieve self-circulation without additional power consumption [5]. A review of the open literature reveals that the geometry of the cold plate will greatly affect the heat transfer efficiency. It is generally known that the design of internal structures, such as channel dimensions and configuration conditions, are crucial to the cold plate in the power module. By reducing thermal resistance, increasing area-to-volume ratios and make the flow more uniform, the overall performance of heat exchangers is improved gradually [6]. Costa and Lopes increased the number of fins and reduced the fin thickness to achieve a considerable temperature decrease [7]. Tran et al. concluded that rising the channel height and decreasing the channel length can improve the heat transfer performance [8]. On the other hand, Li and Chen carried out the experiment to show the relationship between Rth and Re [9]. Larger Re generally means high liquid velocity and better heat transfer performance according to many experimental correlations. In terms of the flow velocity uniformity and temperature distribution, the effect of different relative positions of inlet and outlet and header shapes have been analyzed and concluded. Xia et al. found out that the rectangular header shapes and entrance and exit perpendicular to the base plate could provide better flow uniformity [10]. At the same time, Hung et al. observed that an enlarged channel outlet was benefit to the thermal performance [11]. After changing the geometry parameters and optimizing the fluid characteristics, some researchers added the micro-jet impingement to the micro-channel cooling method to enhance the local heat transfer capacity [12]. Jet impingement cooling is particular suitable for steady heat loads and periodic dynamic heat loads [13]. It is known that free jet impinging to a heated surface could form four different flow fields. Between these fields, the flow rate changes most dramatically and the large impact momentum on heated surface maybe not acceptable for some delicate devices [14]. Also, the feature that flow direction changes from normal to radial to the surface influences the process of bubble nucleation and departure [15]. In this paper, by setting up two-phase cooling experimental platform, the fluid flow resistance and the heat transfer performance of the cold plate are researched and the system’s dynamic response process is verified when operation power changes. Additionally, by comparing different channel geometry of the cold plate and flow pattern of the cooling medium, it is concluded which of these models are more suitable for engineering design and application. Finally, this research aims to optimize the system components, including the dimensions of mini-channels, flow pattern and operation conditions from two aspects, lowering the maximum temperature of heating device and improving the temperature uniformity, respectively. The innovation of this paper lies in applying hybrid
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micro-channel/micro-jet impingement cooling method to solve the problem of uneven wall temperature in long distance flow boiling process.
2 The Design and Analysis of Test Module Based on the above literature, according to the power module size and installation position, two kinds of cold plates with different internal structures are designed for research, respectively named type I and type II, as shown in Fig. 1. For type I, there are three different sizes of channel wall thickness and spacing denoted as type I-A, I-B and I-C in Fig. 2 to compare the heat dissipation capacity between them. For three kinds of structures of type I, the channel length 300 mm and wall height 10 mm remain unchanged, except the fin thickness 4 mm, 2.5 mm and 1.5 mm and channel width 3 mm, 2 mm, 1.5 mm for type I-A, I-B and I-C, respectively. The rectangular header shapes of the inlet/outlet are made to keep the flow uniformity. Because the larger outlet size is benefit to the two-phase flow, the hydraulic diameter of the inlet and outlet are set to 19 mm and 25 mm. On the basis of type I, type II is composed of two aluminum plates with vertical channels and one micro porous interlayer between them. The vertical channels are formed by cutting 1.5 mm wide and 10 mm deep slots equidistantly within the test surface area of the cold plate. Along the flow direction, the distance between circular jet holes increases gradually from 3 mm to 10 mm. Every unit cell consists of a row of jet holes and the single corresponding micro channel. There are thirty rows with 3 mm spacing, twenty rows with 5 mm spacing and ten rows with 10 mm spacing of 1 mm diameter circular holes drilled into the micro porous interlayer from the bottom to the top. Different jetting holes spacing contributes the better flow distribution and keep a relatively uniform temperature distribution along the flow direction.
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Fig. 1. Structural diagram of (a) type I and (b) type II
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Fig. 2. Dimensions of (a) type I-A, (b) type I-B and (c) type I-C channel wall
3 Experimental Setup and Measurement Procedure Figure 3 is the flow chart of two-phase cooling system in power module. The experimental device consists of four major parts: power module, circulating pump, condenser and data measurement equipment. The power module is composed of heating devices (used to replace IGBT devices) arranged on both sides and the cold plate (hollow cavity with mini flow channels). The heating devices are machined from copper blocks within cartridge heaters to supply heat to the test surface. Epoxy resin insulation board is installed outside the heat source to reduce heat loss. The cold plate is made of two separate aluminum alloy blocks and fixed together by bolts. This arrangement provides the convenience of replacing the structure of the cold plate for different studies.
Fig. 3. Power module phase change cooling system flow chart
Figure 4 shows the installation position of heating devices 1–3 on A side corresponds to the heating devices 4–6 on B side. The dimension of heat source on both sides of the cold plate and heating power under different operating conditions are shown in Table 1. In the experimental process, the heat flux of heating modules are regulated by voltage regulators. Although the internal structures of different cold plates are different, the shape and dimensions of cold plates are the same with size of 380 mm × 315 mm × 28 mm.
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Fig. 4. Schematic of the cold plate (a) A side and (b) B side
Copper-constantan (T-type) thermocouples as thermal measurement tools are installed a small distance below the test surface to get detailed temperature data. Because heating power and heat dissipation circumstance of the corresponding positions on both sides of the cold plate are basically the same, this paper takes the temperature data on one side of the cold plate as the example to analyze and discuss. Thermal conductive silicone grease is filled between the cold plate and each heat source module to reduce thermal resistance. In the circulation loop, the opening of the valve control the flow rate and pressure. When the operating condition reaches a steady state, key parameters such as pressure, temperature and total volumetric flow rate are measured and recorded. Table 1. Module size and heat power Single power module IGBT number
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In our study, CFC-113 and HCFC-141b are applied as working fluids for two phase cooling. In the experiment, cooling medium enters the cold plate and flows along internal channels of the chamber at a certain speed. It absorbs heat from power modules of the cold plate and gradually vaporizes from liquid to vapor at the saturation temperature Tsat . Then vapor-liquid mixture passes through the top collecting header and enters the condenser which is a plate fin heat exchanger. Forced air convection is formed by an external fan to dissipate the heat carried by the cooling working medium into ambient environment. The condensed coolant then exits from the condenser and flows along
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connecting pipes to the circulating pump. After being pressurized by the circulating pump, the cooling medium enters the cold plate to begin the next cycle.
4 Experimental Results and Discussion 4.1 Temperature Distribution and Flow Characteristics for Type I As is well known, the fluid flow and heat transfer are influenced by the system pressure, fluid properties and internal structure of the cold plate. First of all, three kinds of cold plates with different channel structures are compared and researched. Figure 5 shows temperature distribution in different positions of the cold plate for type I. It can be seen that the maximum temperature occurs behind 1# heating device at the top of the cold plate, while the minimum temperature occurs at the bottom near the flow inlet. Also, the temperature behind heat sources is slightly higher than other locations at the same height. This result shows that heat is mainly taken away by channels behind heat sources and the heat transfer is the most intense in this region. It is also found that with channel wall thickness and spacing decreasing, type I-C had the minimum temperature 78 °C and the best heat transfer characteristic at the same flow flux. This result demonstrates that decreasing channel wall thickness and spacing would improve the fluid velocity in channels and increase the heat transfer area between the coolant and channels. Theoretically, these changes increase the average Re number so as to cause high heat transfer coefficient and lower cold plate temperature. Also, it can be observed that the temperature T2 and T4 are higher than T1 and T3, respectively, which demonstrate that more fluid flows along channels near the location of inlet/outlet because of the low flow resistance. It is because of the flow maldistribution so that the temperature is uneven. Moreover, the bigger temperature drop from type I-A to type I-B can illustrate that decreasing channel sizes has a great effect on heat transfer within certain limits.
Fig. 5. Temperature distribution for different geometry structures of channels
By setting different valve opening to adjust the coolant flow, we compare the cooling effect and flow resistance with the circulation pump flow rising. Figure 6 shows the
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relation between the difference of temperature and pressure and the system mass flow flux. With the coolant flow increase, the temperature at every point gradually decreases but the temperature difference between T1 and T3, T2 and T4 almost remain unchanged which is 12.5 K and 17 K, respectively. It is just for the reason that the outlet vapor quality decreases with the flow flux increase leading to the temperature decreasing and better heat transfer characteristic. However, the same temperature drops at the same vertical location demonstrate that the change of the flow flux in each channel is consistent within the scope of this study. It is also observed that the total pressure drop increases from 19.1 kPa at the flow flux 1142.2 kg/m2 ·s to 30.7 kPa at the flow flux 1873.2 kg/m2 ·s. The reason for the above trend can be explained that the wall friction between the channel and the vapor-liquid coolant is more intense with the total mass flow flux increase. The faster the fluid velocity, the greater the flow resistance. This means increasing Re can improve the heat transfer performance at the cost of higher pump consumption.
Fig. 6. The curves of temperature and pressure at different flow flux
4.2 Periodic Dynamic Operation Condition In Fig. 7 and 8, several groups of cycle tests under different working conditions are carried out for the type I-C to simulate the actual dynamic process. One operation cycle consists of three minute operation condition I with total power 12 kW and three minute operation condition II with total power 2.64 kW. Once the heat load changes, the temperature and pressure of each measuring point rise or drop instantaneously and the proportion of twophase medium at the outlet respectively varies with the heat load. This result shows that phase change cooling is sensitive to the dynamic process and is able to adapt to the heat load impact. Specifically, the experimental data curves indicate that T4 has the maxmium temperature rise rate 0.16 K/s but T2 still has the highest temperature during operation condition I. As for pressure changes, the rise rate of inlet pressure 0.28 kPa/s is slightly higher than outlet pressure. As is known to all, with the increase of heat load, more coolant absorbs heat and vaporizes to make the whole system pressure rise up quickly.
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Although the temperature and pressure doesn’t reach the steady state, the parameter’s curve and upper and lower limits of each measuring point are consistent under each operation cycle. It is verified that evaporative cooling can cope with the actual operation condition and meet the temperature requirement of vehicle traction converters.
Fig. 7. Temperature response curve with time under the periodic change of heat
Fig. 8. Pressure response curve with time under the periodic change of heat
4.3 A Hybrid Cooling Scheme for Type II Although the heat transfer effect and the cold plate temperature can meet requirements, the larger temperature difference in different positions of the cold plate will also conversely decrease the performance and reliability of electronic devices. Therefore, based on the above research and analysis, type II is applied to improve the temperature uniformity on the cold plate. Figure 9 shows thermocouple readings of each point on the cold plate by using HCFC-141b as working fluid. Obviously, there is a better temperature distribution in the
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micro porous jet structure compared with those of mini-channels. In the type I-C, T3 and T8 have nearly the same minimum temperature which are increased by 10 K in the type II. The regions between the dashed lines indicate the range of temperature changes which is observed that the maximum temperature gradient is 9.5 K in the type II nearly twice smaller than 18 K occurred in the type I-C. By comparing the dispersion of the two groups of temperature data from the following standard deviation formula. N 1 2 (Ti − T ) (1) Tδ = N i=1
where T is the mean temperature of the cold plate. It is concluded that the temperature distribution is more uniform in the hybrid cooling scheme with Tδ = 2.86 than the mini-channel heat sink with Tδ = 6.21.
Fig. 9. Comparison of temperature uniformity with different geometry structures
In this hybrid cooling method, liquid supplied into the mini-channels from circular holes flows along the vertical channel and undergoes bubble growth and departure. At high jet velocities, the influence of jet impingement on cooling performance is greater than that of micro-channel. Through theoretical analysis, the heat transfer mechanism of this hybrid cooling scheme is concluded and shown in Fig. 10. In conventional minichannel flow, bubbles grow from the heated surface and merge with each other along the flow direction. However, the bubble dynamics are fundamentally different for this hybrid cooling scheme. The most remarkable difference is that the mixing of subcooled fluid of the micro jet with the upstream hot fluid will decrease the overall void fraction of mini-channels. By increasing bubbles departure frequency and reducing the wall thermal boundary layer, the heat is taken away efficiently and continuously. At the same time, micro-jet impingement can increase the flow velocity to improve the fluid turbulence and then enhance heat transfer coefficients. More importantly, the pattern of micro porous jets shows that the liquid entering from the bottom holes could absorb more heat along the channel than those from the middle and top. So, different flow flux is required at the various regions of mini-channels by setting different vertical spacing between micro porous to keep the temperature uniform. In conclusion, applying both micro-jet
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impingement and mini-channel flow can avoid flow maldistribution and uneven flow resistance between the mini-channels and contribute to get greater wall temperature uniformity.
Fig. 10. The mechanism of mini-channel flow and hybrid flow with micro-jet impingement and mini-channel
5 Conclusions In this paper, flow resistance and two-phase heat transfer characteristics in the different mini-channel heat sinks with two working fluids were explored. The heat transfer efficiency and the temperature uniformity of the whole cold plate were improved by optimizing the channel size and applying a hybrid cooling method. At the same time, the periodic operation process was simulated to investigate the dynamic response and reliability of the two-phase cooling system. In general, the following conclusions can be drawn: 1. For type I, decreasing channel wall thickness and spacing or increasing the flow flux in some extent would improve the fluid velocity in channels so as to enhance the heat transfer coefficient. On the other hand, the reduction of channel sizes increase the heat exchange surface area between the coolant and channels to absorb more heat. Although, these methods can effectively reduce the maximum temperature to meet the requirements of vehicle traction converters, pressure losses and the cost of pump power of the whole system can not be ignored. 2. In the actual operation process, the periodic change of heat load will affect the temperature of the cold plate and the pressure of the cooling system directly and quickly. The result shows that phase change cooling is sensitive to the dynamic operation process and is able to adapt to the heat load impact. 3. A cooling mode combining mini-channel and micro-jet impingement was carried out to compare the flow characteristic and the temperature uniformity. The experimental results showed that, on the one hand, micro-jet impingement can reduce void fraction along the channel by creating rapid condensation and collapse of bubbles, as well as to reduce the wall temperature of the cold plate; On the other hand, setting different micro jets spacing along the channel to distribute the flow is benefit to the wall temperature uniformity. Although, applying different geometry structures and working fluids can achieve the requirements of vehicle traction converters which is lower wall temperature and better temperature uniformity, the stability and reliability of the whole system during
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the vehicle moving deserves further studying. In addition, the characteristics of multiple power modules in parallel are our future research direction. Acknowledgments. The work was supported in part by the National Natural Science Foundation of China under Grant 51907189 and in part by the Institute of Electrical Engineering, CAS under Grant E1551501.
References 1. Wang, L., Yang, L., Li, S.R.: Research and analysis of cooling performance of IGBT module for high power converters. Power Electron. 52(8), 56–58 (2018) 2. Malu, N., Bora, D., Nakanekar, S., Tonapi, S.: Thermal management of an IGBT module using two-phase cooling. In: 14th IEEE ITHERM Conference, Orlando, pp. 1079–1085. IEEE (2014) 3. Ahmed, H.E., Salman, B.H., Kherbeet, A.S., Ahmed, M.I.: Optimization of thermal design of heat sinks: a review. Int. J. Heat Mass Transf. 118, 129–153 (2018) 4. Yoram, Z.: A review of natural circulation loops in pressurized water reactors and other systems. Nucl. Eng. Des. 67, 203–225 (1981) 5. Szczukiewicz, S., Borhani, N., Thome, J.R.: Two-phase flow operational maps for multimicrochannel evaporators. Int. J. Heat Fluid Flow 42, 176–189 (2013) 6. Wu, J.M., Zhao, J.Y., Tseng, K.J.: Parametric study on the performance of double-layered microchannels heat sink. Energy Convers. Manag. 80, 550–560 (2014) 7. Costa, V.A.F., Lopes, A.M.G.: Improved radial heat sink for led lamp cooling. Appl. Therm. Eng. 70(1), 131–138 (2014) 8. Tran, N., Chang, Y.J., Teng, J.T., Dang, T., Greif, R.: Enhancement thermodynamic performance of microchannel heat sink by using a novel multi-nozzle structure. Int. J. Heat Mass Transf. 101, 656–666 (2016) 9. Li, H.Y., Chen, K.Y.: Thermal performance of plate-fin heat sinks under confined impinging jet conditions. Int. J. Heat Mass Transf. 50(9–10), 1963–1970 (2007) 10. Xia, G.D., Jiang, J., Wang, J., Zhai, Y.L., Ma, D.D.: Effects of different geometric structures on fluid flow and heat transfer performance in microchannel heat sinks. Int. J. Heat Mass Transf. 80, 439–447 (2015) 11. Hung, T.C., Huang, Y.X., Yan, W.M.: Thermal performance of porous microchannel heat sink: Effect of enlarging channel outlet. Int. Commun. Heat Mass Transf. 48, 86–92 (2013) 12. Sung, M.K., Mudawar, I.: Single-phase and two-phase heat transfer characteristics of low temperature hybrid micro-channel/micro-jet impingement cooling module. Int. J. Heat Mass Transf. 51, 3882–3895 (2008) 13. Fan, S.M., Duan, F.: A review of two-phase submerged boiling in thermal management of electronic cooling. Int. J. Heat Mass Transf. 150, 119324 (2020) 14. Meyer, M.T., Mudawar, I., Boyack, C.E., Charles, A.H.: Single-phase and two-phase cooling with an array of rectangular jets. Int. J. Heat Mass Transf. 49(1/2), 17–29 (2006) 15. Wolf, D.H., Incropera, F.P., Viskanta, R.: Jet impingement boiling. Adv. Heat Transf. 23, 1–132 (1993)
Experimental Study on Acoustic Emission and Ultrasonic Testing Technology with Fiber Bragg Gratings Sensing Lijun Meng1(B) , Han Zhang1 , Qianpeng Han1 , and Junjie Huo2 1 College of Intelligent Manufacturing, Jianghan University,
No. 8 San Jiao Hu, Caidian District, Wuhan, China [email protected] 2 Wuhan City Vocational College, No. 127 Nan Li, Hongshan District, Wuhan, China
Abstract. The combination of fiber Bragg gratings (FBG) sensing with acoustic emission and ultrasonic detection can make full use of the advantages of various technologies to realize the comprehensive damage detection of equipment in harsh environment. Firstly, the acoustic emission signals of the gas tank under different impact heights caused by steel ball were detected by FBGs, and the signals were analyzed by using Fourier transform and Hilbert-Huang transform method. The results showed that the acoustic emission intensity measured by FBG increases with the impact height and internal pressure; the main frequencies of Hilbert Huang transform spectrum were not obviously affected by the impact height; while the main frequency band of Hilbert Huang signals was increased after inflation. Then, this FBG was used to detect the ultrasonic wave propagating in the gas tank before and after the artificial defect. The results reflect that the main frequencies before and after the defect was basically the same, but the signal amplitude decreased and the main frequency wave packet dispersed and lagged a lot when there was a defect. It provides experimental basis and effective signal processing method for the simultaneous measurement of acoustic emission and ultrasonic detection with FBG. Keywords: FBG · Acoustic emission · Ultrasonic detection · Hilbert-Huang transform
1 Introduction Large equipment with advanced technology and complex structure, such as water turbine generator, pressure vessel, and aeroengine, plays an extremely important role in the national economic construction. The real-time monitoring and diagnosis of their operating states is of great significance to ensure their safe operation and nonoccurrence of serious accidents. Currently, there are dozens of nondestructive testing methods commonly used, such as leakage testing, magnetic particle testing, ultrasonic testing, acoustic emission testing, and ray testing [1, 2]. However, single testing technology has its own © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 220–227, 2022. https://doi.org/10.1007/978-981-19-1528-4_22
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limitations and often cannot identify all forms of damage. The integration and innovation of various detection technologies is a development direction in the future. Some researchers combine acoustic emission and ultrasonic technology to study the deformation stage in metals, the fracture of friction-stir-welded joints, gas tungsten arc welding quality and so on [3–6]. Acoustic emission technique can be used for internal crack extension of continuous dynamic real-time detection and recognition, and ultrasonic testing technology can be employed to locate static damage and measure damage size by using the reflection, scattering and other signal characteristics of the ultrasonic wave artificially launched or generated inside the specimen, therefore the technology combining these two methods to detect pressure vessel damage can help us to understand the damage state and the damage evolution mechanism more comprehensively [7]. However, the current acoustic emission and ultrasonic composite detection technology mainly uses piezoelectric ceramics to detect acoustic signals, and it has disadvantages such as large volume, easy to be affected by electromagnetic interference, etc. Fiber Bragg grating (FBG) sensors have become the preferred component instead of piezoelectric ceramics due to their high sensitivity, strong anti-interference ability, good insulation, compact structure and easy construction of sensor network [8]. The current researches mainly focus on detecting Lamb waves or artificial acoustic emission sources in thin plates [9–11], and there are few studies on actual equipment structures. Based on FBG sensing, it is studied the acoustic emission produced by steel ball impact and ultrasonic waves before and after man-made damage in the gas tank, and the signals detected by FBG is analyzed with Fourier transform and Hilbert-Huang transform method.
2 Acoustic Emission Detection Experiment The experimental principle is shown in Fig. 1. The FBG was glued on the surface of the gas tank with epoxy resin adhesive, and the acoustic emission signal was generated by the steel ball free-fall impact from high to the fixed position of the gas tank. FBG was arranged along the acoustic propagation path to measure the acoustic emission signal. The wavelength demodulation system based on a tunable laser was established. The light emitted by the tunable laser reached the FBG through the coupler, and then the photodetector received the reflected light and converted the light intensity change into electrical signal which was displayed by the oscilloscope. In the experiment, a steel ball with a diameter of 6 mm was used, the length of the fiber grating was 3 mm, and the distance from the impact point to the fiber grating center was 100 mm. The adjustable laser was Yenista-T TLS-AG-C, and Thorlabs-PDA-10CS was chosen as photodetector; digital oscilloscope was Tektronix TBS-1102. Experiments were carried to detect the acoustic emission under different steel ball impact heights with FBG, when there is no gas or a certain high-pressure gas in the gas storage tank. Figure 2 showed the FBG signal when there was no gas in the storage tank and the impact height was 700 mm. Its main frequency in Fourier transform result was around 195.3 kHz. The impact acoustic emission is the superposition of guided wave with multiple modes. It is difficult to analyze the impact acoustic emission only by time domain or simple frequency domain analysis owing to waveform transformations and
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Fig. 1. Schematic diagram of steel ball impact acoustic emission experiment
Fig. 2. Acoustic emission signal and its Fourier transform result of no-load gas tank with 700 mm impact height
signal attenuation in the propagation process. Therefore, Hilbert-Huang transform was used to study the impact acoustic emission. Figure 3 showed the empirical modal analysis results of the signal in Fig. 2. In Fig. 3, the signal was decomposed into imf1-imf7 modal function and residual component res. Removing low intensity components and noise, it mainly analyzes the modal components of imf3-imf6 in this paper. The instantaneous envelope and instantaneous frequency curves of the imf3-imf6 component were mainly concentrated around 800 kHz, 600 kHz, 400 kHz, and 120 kHz. In order to better reflect the signal frequency change with time, the Hilbert-Huang spectrum line was obtained in Fig. 4. Form Fig. 4, the acoustic emission was composed of many mode waves with different frequencies. During the initial phase, each frequency band energy was relatively low; as time goes by, the high-frequency energy increased rapidly and gradually conversed to a relatively low high-frequency signal. After the peakto-peak point of the acoustic emission signal (after 8us in Fig. 4), the high-frequency signal fluctuated in different mode waves, and the energy gradually decreased. Finally, with the further loss of energy, it oscillated greatly in all frequency range. The center frequency of the 120 kHz–50 kHz band signal existed throughout the whole period. The guided wave energy of each frequency was continuously converted over time. Therefore, the peak-to-peak value in time domain was used to reflect the impact intensity, and the Hilbert-Huang spectrum was used to analyze the guided wave mode change. First, the response of FBG to the acoustic emission propagating on gas tank surface with no compressed gas was studied. During the experiment, steel balls were freely
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Fig. 3. Empirical mode decomposition results
falling from different heights to the fixed position of the gas tank. Figure 5 showed the peak-to-peak change of the FBG acoustic emission signal increased with the impact height. Its amplitude characteristic can reflect the intensity change of the surface acoustic emission. Figure 5 also showed the dominant frequency of higher energy and relatively long duration in the Hilbert-Huang spectrum at different impact strengths, and the guided waves dominant frequency remained basically unchanged (mainly 80 kHz–330 kHz) at different impact heights, which was consistent with the constant impact of the same steel ball. These analyses showed that the FBG frequency domain characteristics can be used to identify and detect the acoustic emission source, and the signal amplitude can reflect its impact intensity. Then, the response of the FBG to the acoustic emission propagating on loaded gas tank of 400 kPa pressure was studied. The steel balls were freely falling from 300 mm to 900 mm to the gas tank surface. The same FBG was used to measure each acoustic emission in turn. Figure 6 showed the changes of signal peak-to-peak value and the main frequency in Hilbert Huang spectrum with the impact height. The peak-to-peak value fluctuated with impact height, and the main frequencies of the Hilbert Huang spectrum were basically stable in the range of 110 kHz–430 kHz, and these main frequencies were increased compared with the no-load ones. This indicates that when there is compressed gas in the gas tank, the guided wave frequencies of the acoustic emission signal measured by the FBG pasted on the surface will be increased.
Fig. 4. Hilbert-Huang spectrum
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Fig. 5. Analysis results under different impact heights when the gas tank is empty
Fig. 6. Analysis results under different impact heights with pressure of 400 kPa
In addition, acoustic emission experiments under different gas pressures pressure were studied. The gas tank was filled with different compressed gas from 100 kPa to 600 kPa pressure, and the steel ball fell at 700 mm. The response signals of the FBG to the impact acoustic emission were recorded in turn. The signal results were shown in Fig. 7. From the figure, the peak-to-peak value increased slightly non-linearly with the internal pressure, while the of the main frequency change with gas pressure was not obvious.
Fig. 7. Analysis results under different gas pressures with 700 mm impact height
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3 Detection of Artificial Damage on the Surface of Gas Tank Under Ultrasonic Excitation The experimental setup was similar to Fig. 8. The ultrasonic transmitter and receiver card was used to generate high-voltage pulse signals to stimulate the probe to generate ultrasonic wave on the surface of the gas tank. The duration of the high-voltage pulse signal was 1 us, and the voltage amplitude was 300 V. A double crystal oblique probe of 1.5 MHz was adopted, and the distance between the ultrasonic incident point and the FBG center was 50 mm. First, the FBG was used to measure the ultrasonic wave when there was no damage on the gas tank surface. Then an artificial damage was made by AB glue in the path of ultrasonic propagation. The man-made damage was a slender rectangle 25 mm away from the incident point, and its direction was perpendicular to the ultrasonic axis. Then the FBG was employed to measure the ultrasonic wave again. The ultrasonic signals measured before and after the man-made defects were shown in Fig. 8. From the Fourier transform and the Hilbert yellow spectrum shown in Fig. 8 and Fig. 9, the ultrasonic wave with the center frequency of 1.2 MHz was detected, which was basically the same as the center frequency of the probe. It can be seen from the main frequencies and response amplitude of Hilbert-Huang spectrum that the main frequencies of the response signal before and after the damage were basically in the range
Fig. 8. Time-domain and FFT signal of fiber grating before and after man-made damage
Fig. 9. Hilbert-Huang spectrum of FBG before and after man-made damage
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of 1–1.5 MHz, while the signal energy showed a significant drop. After the damage, the dominant frequency of the detected signal was more dispersed and lagged. This should be due to the delay caused by ultrasound scattering after encountering man-made damage, bypassing the damage edge and propagating along the acoustic axis. Therefore, the damage can be judged by the signal amplitude change, and the damage position can be further determined by analyzing the delay of the main frequency wave packet.
4 Conclusion The impact acoustic emission and ultrasonic wave detection of gas storage tanks using FBG were studied. The signals were compared and analyzed by the Fourier transform and Hilbert-Huang transform methods. By using free-falling steel ball to hit the outer surface of the gas tank to generate acoustic emission, the effects of impact height of steel ball and charging pressure of gas tank on impact acoustic emission measured by FBG were studied experimentally. It showed that the FBG peak-to-peak value increased with the impact height, and the effect of charging pressure on peak-to-peak value was not obvious; the dominant frequencies of Hilbert spectrum signal were basically not affected by the impact height, while the dominant frequency of the Hilbert spectrum increases as the gas tank is filled. In addition, the same FBG was used to detect the ultrasonic wave on the surface of the gas tank. The dominant frequencies of the ultrasonic guided waves measured by the FBG were basically the same before and after the man-made damage, while the response amplitude decreased significantly and the arrival time of the dominant guided wave has a certain delay when there was damage. Acknowledgments. This study was supported by the Natural Science Fund of Hubei Province in China under grant No. 2019CFB590 and National Natural Science Foundation of China under grant No. 51505187.
References 1. Kumpati, R., Skarka, W., Ontipuli, S.K.: Current trends in integration of nondestructive testing methods for engineered materials testing. Sensors 21(18), 6175 (2021) 2. Huan, H.T., Liu, L.X., Mandelis, A., Peng, C.L., Chen, X.L., Zhan, J.S.: Mechanical strength evaluation of elastic materials by multiphysical nondestructive methods: a review. Appl. Sci.-Basel 10(5), 1588 (2020) 3. Vetrone, J., Obregon, J.E., Indacochea, E.J., Ozevin, D.: The characterization of deformation stage of metals using acoustic emission combined with nonlinear ultrasonics. Measurement 178, 109407 (2021) 4. Stepanova, K.A., Kinzhagulov, I.Y., Yakovlev, Y.O., Kovalevich, A.S., Ashikhin, D.S., Alifanova, I.E.: Applying laser-ultrasonic and acoustic-emission methods to nondestructive testing at different stages of deformation formation in friction stir welding. Russ. J. Nondestruct. 56(3), 191–200 (2020) 5. Zhang, L., Basantes-Defaz, A.C., Ozevin, D., Indacochea, E.: Real-time monitoring of welding process using air-coupled ultrasonics and acoustic emission. Int. J. Adv. Manuf. Technol. 101(5–8), 1623–1634 (2019)
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6. Thiyagarajan, J.S.: Non-destructive testing mechanism for pre-stressed steel wire using acoustic emission monitoring. Materials 13(21), 5029 (2020) 7. Li, H.R., Dong, Z.K., Ouyang, Z.L., Liu, B., Yuan, W., Yin, H.W.: Experimental investigation on the deformability, ultrasonic wave propagation, and acoustic emission of rock salt under triaxial compression. Appl. Sci.-Basel 9(4), 635 (2019) 8. Wu, Q., Okabe, Y., Yu, F.: Ultrasonic structural health monitoring using fiber Bragg grating. Sensors 10, 3395 (2018) 9. Yu, F., Okabe, Y.: Linear damage localization in CFRP laminates using one single fiber-optic Bragg grating acoustic emission sensor. Compos. Struct. 238, 1119929 (2020) 10. Tsuda, H., Lee, J.-R., Guan, Y., et al.: Investigation of fatigue crack in stainless steel using a mobile fiber Bragg grating ultrasonic sensor. Opt. Fiber Technol. 03, 209–214 (2007) 11. Zhu, Y.K., Chong, B., Lin, X.M.: Acoustic emission localization method for damage of composite material sheet based on intensity-type optical fiber sensing technology. Non-destruct. Monit. 33, 56–60 (2011)
Research on Key Technology of 10 kV Mechanical DC Circuit Breaker Zhongjian Song, Wengang Xie(B) , Zhicheng Zhang, Fengliang Xiao, Wei Li, Guangrong Luo, and Bin Xie Shandong Taikai High-Voltage Switchgear Co., Ltd., Tai’an, China [email protected]
Abstract. Due to its significant advantages in distributed energy and DC load access, flexible network interconnection, power quality and other aspects, multiterminal flexible DC system has become an important development trend of power grid in the future. DC circuit breakers that can break large fault current within a few milliseconds are needed in order to ensure the safe, stable and reliable operation of the multi-terminal flexible DC system. To meet the application demand of multiterminal flexible DC power network, this article designs a 10 kV mechanical DC circuit breaker. Firstly, its topology and working principle are explained. Then, the key technologies, including the selection of mechanical switch, the parameter design of transfer branch, and the insulation design of the whole prototype, are introduced. Moreover, a simulation model is built based on finite element software and the parameters are modified and optimized on this basis. Finally, a test circuit is set up to verify the bidirectional breaking capability of the developed 10 kV mechanical DC circuit breaker prototype. The results show that the prototype successfully breaks the 10 kA short-circuit current within 10ms, which is basically consistent with the simulation results, meets the engineering requirements, and also provides a reference for the subsequent design of DC circuit breakers. Keywords: DC circuit breaker · Parameter design · Oscillation test circuit
1 Introduction With the rapid development of economy, power grid construction is facing more and more challenges. Especially in terms of large-scale distributed renewable energy access, island power supply, offshore wind farm clusters and new urban power grid construction, a new type of transmission network is urgently needed to solve the problems [1–4]. With the continuous deepening of power electronics technology research, DC transmission has developed rapidly due to its unique advantages. The hybrid and solid DC circuit breaker use power electronic devices, and the breaking speed is faster than that of the mechanical DC circuit breaker. However, when the rated voltage and breaking current are high, a large number of devices are required to be connected in series and parallel, and the main branch of some topologies also contains power electronic switches, which are complex in structure and expensive in cost. For the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 228–236, 2022. https://doi.org/10.1007/978-981-19-1528-4_23
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mechanical DC circuit breaker, its main branch adopts mechanical switch with very low loss and the transfer branch adopts resonant capacitor and resonant inductor, which have relatively low cost but long breaking time. In the design of the DC circuit breaker, factors such as breaking time, breaking current and cost should be considered comprehensively. According to the requirements of the Dongguan Park Demonstration Project, which is a national key special project of AC and DC hybrid distributed renewable energy technology, this paper proposes a design scheme of a 10 kV mechanical high voltage DC circuit breaker. The design of its key parameters is calculated and analyzed, and then the rationality of the developed prototype parameters is verified through simulation and breaking test.
2 Topology and Technical Scheme 2.1 Topology and Principle According to the requirements of the Dongguan project, this paper designs a mechanical DC circuit breaker adopting LC resonant forced commutation theory. It’s composed of the mechanical switch branch (or main branch), the LC resonant branch (or transfer branch, consisting of solid-state switch S, resonant capacitor C, resonant inductor L and bypass switch K) and the energy absorption branch (MOV arrester), as shown in Fig. 1. In the traditional topology, high voltage ball gap switch is generally used to put LC resonant branch, but it will generate sparks during conduction, which will pose a threat to the oil insulation equipment around the converter station, and its reliability is low [5–8]. To avoid the above problems, solid state switch S is adopted in this paper to replace the high voltage ball gap switch. In addition, due to the requirement of bidirectional breaking, the solid-state switch adopts 5 groups of reverse parallel thyristors (S 1 –S 5 ) in series, parallel static equal-voltage resistance Rp and dynamic resistance-capacitance absorption branch Rs –C s at both ends of the thyristor. During maintenance, bypass switch K is used to release energy from the resonant capacitor. the mechanical switch M
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During normal operation, the mechanical switch M is in closing state, the solid state switch S and the bypass switch K are not conducted, the current flows through the mechanical switch branch, and the resonance capacitor C in the LC resonance branch is
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pre-charged to a certain voltage. When a short circuit occurs in the system, the mechanical switch is first opened. When its contact moves to a distance sufficient to withstand the transient overvoltage, the thyristor is triggered to conduct, and the oscillating current generated by resonant capacitor C and resonant inductance L is superimposed on the mechanical switch branch to make its current cross zero. Then the current is transferred to the LC resonant branch to charge the resonant capacitor, and the total voltage between the terminals of the circuit breaker increases. When it rises to the operating voltage of the arrester, the current is transferred to the energy-consuming branch, and the line energy is dissipated by the MOV. 2.2 Key Technology Research 2.2.1 Mechanical Switch Design of the mechanical switch mainly includes the arc extinguishing chamber, the operating mechanism, the buffer mechanism and the holding mechanism. Generally, vacuum arc extinguishing chamber and SF6 arc extinguishing chamber are used and the main characteristics of them are shown in Table 1. Compared with the SF6 arc extinguishing chamber, the vacuum arc extinguishing chamber has lighter contact quality, shorter overtravel, smaller opening distance and faster insulation recover speed. In addition, it is easy to realize fast opening and closing operation, and has the characteristics of safety and reliability, long service life, small maintenance workload, and environment free from pollution [9, 10]. Therefore, a vacuum interrupter is adopted in the design of the mechanical switch branch in this paper. It requires that a 10 kA short-circuit current can be broken within 10 ms in the Dongguan project. The breaking time is relatively long, and the opening speed of the mechanical switch is not very high. So the vacuum switch driven by the conventional permanent magnet mechanism can meet the requirement. However, if a faster opening speed is required, the electromagnetic repulsion mechanism can be considered. Table 1. Comparison between vacuum interrupter and SF6 interrupter Characteristic
Vacuum interrupter
SF6 interrupter
Contact quality
Light
Heavy
Contact stroke
10–30 mm
100–220 mm
Contact overtravel
Short
Long
Insulation recovery speed
Faster
Fast
Life
Longer
Long
Environmental protection
Yes
No
Maintenance workload
Small
Large
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2.2.2 Parameter Design of the Transfer Branch There are three key parameters need to be determined for the transfer branch: the capacitance value, the charging voltage of the capacitor and the inductance value of the reactor, which satisfy the following formula: 1 √ 2π LC C I= U0 L
f =
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In the above formula: f is the frequency of the transfer branch, L is the inductance value of the resonant inductor, C, U 0 are the capacitance and charging voltage value of the resonant capacitor, and I is the magnitude of the reverse current. From Eqs. (1) and (2), it can be seen that to determine these three parameters, the oscillation frequency of the transfer branch and the amplitude of the reverse current should be determined first. In the design of the transfer branch, the determination of the oscillation frequency is very critical. The higher the f , the greater di/dt before the vacuum switch current crosses zero, and the higher the voltage stress that the mechanical switch bears. The lower the f , the more the time of the mechanical switch current crossing zero is and the longer the total breaking time. In addition, in the reverse breaking, the oscillating current will be superimposed on the main branch. The lower the f , the higher the energy between the arc extinguishing chamber contacts during arcing, which is not conducive to the recovery of the dielectric strength of the vacuum medium after arcing. At the same time, the value of f is directly related to the overall cost of the DC circuit breaker. The higher the f , the smaller the resonance capacitance and resonance inductance, and the lower the cost. Therefore, the selection of the oscillation frequency f requires comprehensive consideration of cost and breaking performance. Studies have shown that it is more moderate to select a few thousand Hz for f [11–13]. The amplitude of the reverse current should be selected according to the maximum expected short-circuit current value of the system to ensure that the mechanical switch current can reliably cross zero and the arc can be extinguished under all working conditions. In addition, in the case of bidirectional breaking, the direction of superposition current is the same as that of mechanical switch branch. It is necessary to ensure that the peak value of the first wave of the oscillating current in 3/4 period is not less than the rated breaking current value with a certain margin. The cost of the resonant capacitor is proportional to C and U 0 . Therefore, the selection of capacitors should refer to the specification of shunt capacitors used in AC system, and the cost and overvoltage should be considered comprehensively. 2.2.3 Insulation Design The insulation parameters of the 10 kV mechanical DC circuit breaker in Dongguan project are as follows: (1) the rated DC withstand voltage: 30 kV/60 min (terminal to ground), 15 kV/60 min (between the terminals) (2) the rated operating impulse voltage:
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42 kV (terminal to ground), 18.4 kV (between the terminals) (3) the rated lightning impulse voltage 75 kV (terminal to ground), 23 kV (between the terminals). In the insulation design, the air safety net distance of each component can be based on “GB 311.1–2012 Insulation co-ordination-Part 1: definitions, principles and rules”. According to the difference of atmospheric environment, air gap type and impulse withstand voltage value, the g-parameter method can be used for calculation [14]. The air safety net distance required by lightning impulse voltage and operation impulse voltage should be calculated separately, and the larger value should be selected. According to the above principles, considering cost, performance and other factors, this article selects C = 200 µF, U 0 = 5 kV, L = 20 µH, f = 2.5 kHz.
3 Circuit Simulation
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The common fault current test circuit can be realized by the discharge of the precharged capacitor or inductance, as shown in Fig. 2 [15], or through a low-frequency alternator to provide energy, as shown in Fig. 3 [16]. n1_left
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(a) Integral simulation mode
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Fig. 4. DC circuit breaker simulation model
In order to verify whether the parameter design in Sect. 2.2 is reasonable, this paper builds a simulation model using finite element software based on the pre-charged capacitor test circuit, and simulates the process of 10 kA forward and reverse current breaking for DC circuit breakers respectively. The results are shown in Fig. 4, in which Fig. 4(a) is the overall simulation model, and Fig. 4(b) is the packaged circuit breaker sub-module DCCB.
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17 9 13 5 t/ms (e) voltage between the terminals of the circuit breaker 1
Fig. 6. Simulation results of 10 kA reverse interruption
At t = 1 ms, the line current I 0 and the mechanical switch branch current I 1 start to rise. At t = 3 ms, the mechanical switch starts to open. At t = 9 ms, the contact distance of the mechanical switch is sufficient to withstand the transient overvoltage after breaking.
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At this time, the thyristor of the transfer branch is triggered and the pre-charged capacitor discharges to the mechanical switch branch through the reactor. In forward breaking, the direction of superposition current is opposite to that of the mechanical switch branch and the current of the mechanical switch branch directly drops to zero. In the same way, the current direction of reverse breaking is the same and the current of the mechanical switch branch will rise in positive superposition first and then oscillate to zero. The the resonant capacitor is charged by the transferring current. The transfer branch current I 2 rises rapidly, the voltage between the circuit breaker E CB gradually rises, and the peak value of TRV can reach 16.0 kV. Finally, the arrester is turned on to release energy. It can be seen from the above simulation that the 10 kV mechanical DC circuit breaker designed according to the principle of Sect. 2.2 is fully capable of breaking the forward and reverse 10 kA short-circuit current.
4 Breaking Test For the 10 kV mechanical DC circuit breaker, if the direct test method is adopted, the test circuit needs to provide a current of more than 10 kA and a transient recovery voltage of more than 16 kV, and the cost will be very high. An equivalent breaking test circuit is used in this paper, and its principle diagram is shown in Fig. 7. K test object K2 K1
K0 C U
L
DCCB
K3
R1
Fig. 7. Principle diagram of interruption test circuit
1 4 2
5
3
6
1-Energy-consuming branch MOV; 2-Resonance inductance; 3-Mechanical switch; 4-Solid state switch; 5-Resonance capacitor; 6-Solid state switch MOV1 Fig. 8. 10 kV mechanical DC circuit breaker prototype
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According to the parameters and simulation results mentioned above, a prototype of 10 kV mechanical DC circuit breaker is designed. Its structure is shown in Fig. 8. Based on the test circuit shown in Fig. 7, 10 kA forward and reverse current breaking tests are conducted on the designed 10 kV mechanical DC circuit breaker prototype. The test results are shown in Fig. 9 and Fig. 10. Among them, TRIGGER, IALL, VALL, ICB and ILC represent the opening signal of DC circuit breaker, the line current, the total voltage, the mechanical switch branch current and the transfer branch current respectively.
Fig. 9. 10 kA positive current interruption waveform
Fig. 10. 10 kA reverse current interruption waveform
It can be seen from Fig. 9 and 10 that: in forward breaking, the breaking time is 8.3 ms, the full current breaking time is 12.3 ms, and the peak transient interruption voltage is 16.0 kV; in reverse breaking, the breaking time is 8.2 ms, the full current breaking time is 12.2 ms, and the peak transient interruption voltage is 16.0 kV. The variation of current and voltage in the process of current breaking is basically consistent with the previous simulation results, which fully conforms to the requirements of Dongguan project. At the same time, the rationality of parameter design is verified.
5 Conclusion According to the requirements of the demonstration project in Dongguan Park, this paper designs a 10 kV mechanical HVDC circuit breaker, which can successfully break the forward and reverse 10 kA fault current within 10 ms. The principle of parameter design and insulation design at different branch is given. Then based on circuit simulation and breaking test, the ability of dc circuit breaker to break 10 kA short-circuit current and the rationality of parameter selection are verified. It meets the engineering requirements, and also provides a reference for the subsequent design of DC circuit breakers.
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1. References 1. Gao, J., Dan, S., Gu, W.: Experiment research on breaking capability of mechanical type HVDC circuit breaker. High Voltage Apparatus 56(11), 111–115 (2020) 2. Wen, J., Wu, R., Peng, C., et al.: Analysis of DC grid prospects in China. Proc. CSEE 32(13), 7–12 (2012). (in Chinese) 3. Belda, N.A., Smeets, R.P.P.: Test circuits for HVDC circuit breakers. IEEE Trans. Power Delivery 32(1), 285–293 (2017) 4. Green, S.: HVDC systems Gotland: the HVDC pioneer. Power Eng. Int. 12(7), 28–29 (2004) 5. Pauli, B., Mauthe, G., Ruoss, E., et al.: Development of a high current HVDC circuit breaker with fast fault clearing capability. IEEE Trans. Power Delivery 3(4), 2072–2080 (1988) 6. Ji, Y., Yuan, Z.C., Zhao, J.F., et al.: Hierarchical control strategy for MVDC distribution network under large disturbance. IET Gener. Transm. Distrib. 12(11), 2557–2565 (2018) 7. Song, Q., Zhao, B., Liu, W., et al.: An overview of research on smart DC distribution power network. Proc. CSEE 33(25), 9–19 (2013). (in Chinese) 8. Qu, L., Yu, Z., Huang, Y., Zeng, R.: Research on effect of circuit parameters on breaking characteristics of mechanical DC circuit breaker. Electr. Power Syst. Res. 179, 106075 (2020) 9. Zheng, Z.: Theory and application of high-speed DC vacuum switch based on active commutation circuit. Da Lian University of Technology (2013). (in Chinese) 10. Greenwood, A.N., Lee, T.H.: Theory and application of the commutation principle for HVDC circuit breakers. IEEE Trans. Power Appar. Syst. 91(4), 1570–1574 (1972) 11. Han, M., Zou, X., Zhang, G.: Technical Basis of Pulse Power. Tsinghua University Press (2010). (in Chinese) 12. Zhou, M., Xiang, W., Rao, H., et al.: Design and control of paralleled mechanical DC circuit breakers. Proc. CSEE 38(20), 5975–5982 (2018) 13. Zhang, Z., Li, X., Chen, M., et al.: Research on critical technical parameters of HVDC circuit breakers applied in nan’ao multi-terminal VSC-HVDC project. Power Syst. Technol. 41(8), 2417–2422 (2017). (in Chinese) 14. Tokoyoda, S., Sato, M., Kamei, K., et al.: High frequency interruption characteristics of VCB and its application to high voltage DC circuit breaker. In: 2015 3rd International Conference on Electric Power Equipment-Switching Technology, 25–28 October 2015, Busan, South Korea, pp. 117–121 (2015) 15. Li, X., Guo, Z., Fu, M., et al.: Current commutation characteristics and its influential factors for high-voltage direct current transfer switches. High Voltage Eng. 44(9), 2856–2864 (2018) 16. Ming, M., Jingyuan, Y., Xinyue, G., et al.: Topology of high voltage DC circuit breaker based on hybrid switching device. High Voltage Eng. 45(1), 31–38 (2019)
Structure Design and Electric Field Simulation of a Compact −150 kV/10 mA High Voltage Power Supply Xiaolong Lu(B) , Shangwen Chen, Zhiming Hu, Dapeng Xu, and Zeen Yao School of Nuclear Science and Technology, Engineering Research Center for Neutron Application, Ministry of Education, Lanzhou University, No. 222 Tianshui South Road, Lanzhou, Gansu, China [email protected]
Abstract. A three-dimension (3D) model of a compact Cockcroft-Walton (CW) high voltage (HV) power supply is developed for a compact neutron generator. First, parameters of the circuit of the CW HV power supply are given through analysis of the CW voltage multiplier circuit and based on the requirements of the output voltage of −150 kV and the output current of 10 mA. Second, the 3D structure of the compact HV power supply is set up, and its sizes are 543 (length) × 360 (width) × 445 (height) mm3 . Third, the distribution of the electric field of the power supply is simulated by the finite element method, and the field distribution is optimized to make it uniform relatively. The results of analysis and simulation show that the maximum of electric filed strength is less than the limit of the dielectric breakdown field strength, and the developed structure design of the compact power supply is cost-effective, electric field distribution reliable and structure compact. Keywords: Compact high voltage power supply · Structure design · Electric filed simulation
1 Introduction Neutron generators are a class of accelerator-based neutron source to generate neutrons, and have a merit of if there is no operation, then there is no radiation. Nowadays, the compact neutron generator is considered as a promising device for neutron oil logging, cancer therapy, radioisotope production, special nuclear materials detection, etc. It will be widely used in a variety of research, industrial, and medical applications [1, 2]. Recently, a compact neutron generator of the neutron yield of about 2 × 108 n/s has been developed at Lanzhou University [3]. A compact HV power supply supplies several hundreds of kV and several tens of mA power for the compact neutron generator. It is one of the most important components for the compact neutron generator to exploit its full potential because the reliability, volume and cost of the compact HV power supply are directly related to the those of the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 237–249, 2022. https://doi.org/10.1007/978-981-19-1528-4_24
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compact neutron generator. For HV power supply research, literatures pay more attention to issues such as circuit topology, voltage ripple and voltage drop, etc., however, reports of detail studies related to the geometry structure and inner electrostatic field of the HV power supply are seldom [4–7]. In fact, the structure and the inner electric field are very important to develop an operation reliability and cost-effective HV power supply, especially for developing a compact one, due to the structure is directly related to the strength and the distribution of the electric field. In this paper, the circuit design and analysis of a compact −150 kV/10 mA power supply are given, the 3D geometry structure model is built up, and the distribution of the potential and the electrostatic field are simulated by the finite element method.
2 Design of CW High Voltage Circuit and Consideration Figure 1 shows the circuit schematic of the compact HV power supply. A 380 V/50 Hz alternating current (AC) input a medium frequency power supply (MFPS), then, a 350 V/33 kHz AC output through rectification and inversion. A medium frequency high voltage step-up transformer boosts the 350 V/33 kHz to 22.5 kV/33 kHz. By a 4-stage Cockcroft-Walton (CW) voltage multiplier circuit, the 22.5 kV/33 kHz medium frequency power is rectified and multiplied to be a direct current (DC) voltage of −180 kV, and then to be delivered to HV instruments through a HV output port. A protection resistor is set up to avoiding the overcurrent to damage the power supply caused by load short circuit. In order to measure the value of the HV output voltage, the method of a microampere meter and a measuring resistor in series to read the current and calculate the output voltage are used. A milliampere meter is used to measure the total current that through the output current of the compact HV power supply. As Fig. 1 shows above, the designed CW voltage multiplier circuit takes a half-wave CW voltage multiplier circuit to produce a negative DC high voltage. The principle of the half-wave CW voltage multiplier can be described as follows [6, 7]. Assuming that there is no load, and the input voltage is presented as Us = Up sin(ωt + ϕ)
(1)
where, Up is the peak voltage of the input voltage; ω is the radian frequency; t is the time; and ϕ is the initial phase. During the positive half cycle of the input sine wave voltage, the auxiliary capacitor C1 is charged through diode D1 to a voltage of −Up . When the input sine wave voltage rises to the positive −Up during the next half cycle, the capacitor C2 is charged through diode D2 to a voltage of −2Up . the voltage at node 1 with respect to the reference 0 potential node can be expressed as U10 = − Up − Up sin(ωt + ϕ)
(2)
when U10 is zero, the capacitor C3 is charged to the potential −2Up through diode D3. The voltage at node 3 with respect to the reference 0 potential node can be expressed as U30 = − Up − Up sin(ωt + ϕ) − 2Up
(3)
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Fig. 1. Circuit configuration of the compact HV power supply
The next voltage variation of U30 from zero to −4Up will force the diode D4 to conduct, therefore C4 will also be charged to a voltage of −2Up In summary, the steadystate voltages at odd nodes with respect to reference 0 potential, can expressed as Uodd 0 = −(2n − 1)Up − Up sin(ωt + ϕ)
(4)
while the steady-state voltages at even nodes with respect to the reference 0 potential can be expressed as Ueven0 = −2nUp
(5)
where, n is the number of stages of the half-wave CW voltage multiplier. As formal (5) shows, the CW voltage multiplier will reach a maximum value of voltage of −2nUp . When the CW voltage multiplier circuit with a load, in the beginning, even (main) capacitors supply the load current, while odd (auxiliary) capacitors are floating. At the positive half cycle of the sine wave input voltage, as the even capacitors discharge to the load, the voltage at the node 8, that is u8 , is less than the voltage of node 7, that is u7 . So that D8 is conducted at first, all odd capacitors are discharged and all even capacitors are charged. While u7 = u8 , D8 is blocked, and D6 begins to be conducted. Odd capacitors C1, C3, C5 are discharged while even capacitors C2, C4, C6 are charged. Simultaneously, all even capacitors supply the load current, while C7 is floating. The circuit operation principle is similar to the analysis above at the condition of when D1 – D5, D7 is conducted, respectively. When the CW voltage multiplier circuit supplies a load current i, the main capacitor and the auxiliary capacitors will be charged and discharged, respectively. The output voltage of the CW voltage multiplier suffers from voltage drop and the voltage ripple appears at its output voltage. The ripple voltage δU and the drop voltage U can be expressed as formula (6) and formula (7), respectively [7, 8]. δU =
n(n + 1) Id 4 fC
(6)
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U =
Id 2 3 1 2 1 ( n + n − n) fC 3 2 6
(7)
where, Id is output current; f is frequency of the input voltage; C is capacitance of the main and auxiliary capacitors; and n is stage number. As Fig. 1 shows, Id is the sum of the load current Il and the measuring current Im . Therefore, the maximum value of the output voltage Umax and the mean value of the output voltage U are given by formula (8) and (9), respectively. Umax = −2nUp + U = −2nUp + ( U = −Umax + δU = −2nUp + (
4n3 + 3n2 − n Id ) 6 Cf
8n3 + 9n2 + n Id ) 12 Cf
(8) (9)
According to the formulas above, Umax and U are both related to the stage number n, the peak value of the input voltage Up , the capacitance of the main and auxiliary C, the frequency of the input voltage f , and the output current Id . In order to read the value of the HV output voltage, a HV measuring component composed of a micro-ampere meter and a HV measuring resistor is set up. According to the Ohm’s law, the measuring value of the HV output voltage Um can be calculated by Um = R2 Im
(10)
where, Im is the current value measured by the micro-ampere meter; R2 is the HV measuring resistor. The R2 should has a large resistance to ensure the measuring current is much less than the load current. For the HV power supply of an accelerator-based neutron generator, the phenomenon of electrical spark will appear almost essential caused by many reasons. Such as the reverse acceleration of secondary electrons in the accelerating tube, the decrease of the insulating ability caused by the sputtering of ions onto the surface of the insulating material, the energy stored in the capacitor, and the transformer is instantaneously released when the intermediate frequency power supply is shut down suddenly, etc. Therefore, a protection resistor is connected in series with the negative HV output port to suppress the overcurrent. In order to reduce the volume of the CW voltage multiplier, the number of the stages should be less, the peak value of the out voltage of the step-up transformer should be more, and the capacitance of the capacitors should be large. However, the more the peak value of the output voltage of the step-up transformer, the more volume and insulation requirements. The stage number is more less, the requirement of the withstand high voltage of the capacitors and the rectifier diodes are higher. The more capacitance of the capacitor, the larger volume and the more energy storage. According to the aim of developing the compact HV supply power for a compact neutron generator, that is to obtain an output voltage of 150 kV and current of 10 mA with characteristics of compact structure, reliable operation and cost-effective, the parameters of the CW voltage multiplier are designed based on the formulas above, and listed in Table 1.
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Table 1. Parameters for the CW voltage multiplier circuit Parameters
Values
Drive input frequency f
33 kHz
Drive input peak voltage Up
22.5 kV, AC
Maximum output current Id
10 mA
Number of stages n
4
Maximum output voltage −2nUp (no output current)
−180 kV, DC
Maximum output voltage Umax (load current of 10 mA)
−155.5 kV, DC
Voltage drop U
24.5 kV, DC
Voltage ripple δU
2.3 kV, DC
Measuring resistor R2
2000 M
Protection resistor R1
600 k
Rectifier diode
30 mA/45 kV
Main and auxiliary capacitor
666 pF/45 kV
3 Structure of the Compact HV Power Supply To build up a strong electric field without breakdown phenomenon in a compact space is a tricky problem. By means of structure analysis and electric field distribution simulation, the 3D structure model of the −150 kV/10 mA power supply is developed, as Fig. 2 shows. Seen from the Fig. 2, the compact −150 kV/10 mA power supply consist of a stepup transformer, a 4-stage CW voltage multiplier, a HV output measuring component, a HV output port, and a grounding shell. The medium frequency power supply will be installed on the upper side of the HV power supply and the medium frequency power will be inputted to the transformer through two connecting wires. The center of the core of the transformer distance from the center of the HV output port is 75 mm. The negative HV output port is installed on the right side of the grounding shell. and the distance between the center of the port and the bottom of the grounding shell is 90 mm. The CW voltage multiplier is installed in a Plexiglas box which placed near the left side of the grounding shell. The sizes of the compact power supply are 543 (length) × 360 (width) × 445 (height) mm3 . In order to optimize the potential distribution and the electric field distribution of the HV power supply, and to make their distribution relatively uniform, this work focuses on the structure of the CW voltage multiplier and the HV output port because both of them will have a high voltage of −180 kV. The structure of the CW voltage multiplier is shown in Fig. 3. It consist of main capacitors, auxiliary capacitors, and rectifier diodes, and they are all installed on the
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Fig. 2. The structure of the compact HV power supply
epoxy skeleton. Each main or auxiliary capacitor is composed of 6 capacitors, respectively, by connection style as shown in Fig. 4. Each capacitor with capacitance of 1000 pF and can withstand voltage of 15 kV. Therefore, the main and auxiliary capacitors with total capacitance of about 666 pF and can withstand high voltage of 45 kV, and they meet the requirements of the parameters of the CW voltage multiplier. Each capacitor with a length of 35 mm and a diameter of 18 mm. Each main and auxiliary capacitor with a height of about 72 mm. The main and auxiliary capacitor are composed by connection style as Fig. 4 shows, this style not only have an advantage of cost-effective, but also can save space by use both side of the epoxy board. Besides, in order to increase the creepage distance between adjacent capacitors, grooves are engraved on the epoxy board. another important electronic element is the HV rectifier, which is composed of 3 diodes connection in series, and installed on an epoxy board with a length of 130 mm. Each diode with length of 12 mm and diameter of 3 mm, can withstand voltage of 20 kV and the maximum of the average forward current of 50 mA. So, each HV rectifier can withstand high voltage of 60 kV and the maximum of the thermal value of about 0.005 J/, which can meet the parameters of the CW voltage multiplier. The HV measuring resistor and the protection resistor are installed on an epoxy board and placed at the middle between the main and auxiliary capacitor boards. The HV measuring resistor is composed of 25 resistors in series, each resistor has a resistance of 40 M and can withstand voltage of 10 kV, and the length and the diameter of each resistor are 75 mm and 12 mm, respectively. Therefore, the total resistance of the HV measuring resistor is 1000 M and can withstand high voltage of 250 kV. The protection
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resistor has a resistance of 600 k and be installed between the high-voltage terminal and the 5th equipotential ring. the equipotential rings are set up to make the potential distribution relatively uniform. Each equipotential ring is connected to the jacent main capacitor. The voltage on equipotential rings from 1th to 5th are −22.5 kV, −45 kV, − 90 kV, −135 kV, −180 kV, respectively. The structure of the HV output port is shown in Fig. 5. It mainly consist of a HV electrode, a movable metal connecting rod, a metal sealing & connecting flange, and an epoxy insulator. The total length of the HV output port and the epoxy insulator are 168 mm and 110 mm, respectively. In order to avoid breakdown along the surface, the skirt is set up to increase the creepage distance, and the total creepage distance is about 277 mm. The inner diameter of the epoxy insulator is 42 mm. The HV output cable will be inserted into the epoxy insulator to connect to the movable metal connecting rod, and the HV electrode will connect with the 5th equipotential ring use a copper wire with ceramic ring through the side of the Plexiglass box and the epoxy support board of the CW voltage multiplier. The triple junction zones between the metal sealing & connecting flange, the epoxy insulator and the transformer oil are the key zones to avoid to the electric breakdown happen, hence, the epoxy insulator is designed of a chamfer of 45°, and the metal sealing & connecting flange is designed of a fillet of radius of 3 mm to optimize the electric field distribution [9].
Fig. 3. The structure of the CW voltage multiplier
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Fig. 4. Layout of the composed main or auxiliary capacitors
Fig. 5. The structure of the HV output port
4 Electric Field Simulation of the Compact HV Power Supply In order to investigate the potential distribution and the electric field distribution inner the compact HV power supply, a physics model based on the structure of the compact −150 kV/10 mA power supply mentioned above is developed by means of the finite element method. Elements with little effect on the electric field distribution are omitted in the simulation model to save computing resources, such as capacitors, diodes, and resistors. The voltage on the equipotential rings from 1th to 5th are set as −22.5 kV, −45 kV, −90 kV, −135 kV, −180 kV, respectively. The voltage of the HV electrode of the HV output port is set as −180 kV. The grounding shell is set up voltage of 0 V. The compact HV power supply is filled full of No. 25 transformer oil, and its relativity permittivity is set up 2.2, the relativity permittivity of the epoxy, the plexiglass and the ceramic are set as 4, 3, 9, respectively [9]. The limits of the breakdown strength of the No. 25 transformer oil, the plexiglass and the epoxy are 3 × 107 V/m, 1.5 × 107 , 2 ×
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107 V/m, respectively [9, 10]. The limit of the surface electric field strength of the solid insulator is about 6 × 106 V/mm [9]. The simulation results as follows. Figure 6 shows the potential distribution of the compact HV power supply, and it illustrates that the potential distribution is relatively uniform inner the compact HV power supply because of the effects of the equipotential rings. Figure 7 shows the electric field distribution inner the compact HV power supply. It illustrates that there are two zones, zone A and B, where the electric field strength are distinctly stronger than the other areas. In zone A, the maximum field strength is about 6.5 × 106 V/m around the 5th equipotential ring, and it is much less than the limits of the breakdown strength of the transformer oil. In the zone B, the maximum filed strength is about 1.9 × 107 V/m around the electrode of the HV output port, and it is also less than the limits of the breakdown strength of the transformer oil.
Fig. 6. Map of the inner potential distribution the compact HV power supply
In order to investigate the surface electric field distribution along the Plexiglass surface, and the electric field strength in the transformer oil near the bottom of the grounding shell, the electric field strength distribution along line EF and GH (see Fig. 7) are simulated. The results are shown as Fig. 8 and Fig. 9, respectively. Figure 8 shows the electric field strength distribution along the line GH. It is shown that the distributions of the surface tangential and normal electric field strength are wave shaped due to the influence of 5 equipotential rings. The maximum of the surface tangential and normal electric field strength are about 2.5 × 106 V/m and 6 × 106 V/m, respectively. The maximum of the normal electric field strength is less than the limits value of the breakdown strength of the transformer oil and the Plexiglass, and the maximum of the tangential electric field strength is less than the surface flashover strength of the Plexiglass in the transformer oil. Figure 9 shows the electric field strength distribution along the line GH. It illustrates that the normal electric field near the bottom of the grounding shell (along the line EF) in the transformer oil is U shaped distribution, a peak value appears caused by the metal
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Fig. 7. Map of the inner electric field distribution of the compact HV power supply; the white arrow lines respect electric field lines
clip for fixing the epoxy support board, the maximum is about 1.35 × 106 V/m. The tangential electric field along the line EF increases slowly from point E to point F, a peak value also appears caused by the metal clip, the maximum value is about 2.5 × 105 V/m. The simulation results show that the tangential and normal electric field strengths in the transformer oil near the bottom of the grounding shell are both much less than the limits of the electric field strength of the transformer oil No. 25. For the HV output port, to know the surface electric field strengths at the interface between any two of the metal electrodes, the epoxy insulator, and the transformer oil are important to avoiding flashover happen. Therefore, the distribution map of surface electric field of the HV output port and the surface electric field strength along the broken line OP are investigated, respectively. The results are shown as Fig. 10 and Fig. 11, respectively. Figure 10 shows the distribution map of the surface electric field strength of the HV output port. It illustrates that the strengths of surface electric field are strong near the HV electrode, and are weak near the grounding shell. Figure 11 shows that the distribution of surface electric field strength of the HV output port along the line OP. It is shown that the surface electric field strength along the broken line OP decrease quickly. The maximum at the triplet junction point O is of about 5.5 × 106 V/m, then quickly decrease to a value of about 2 × 106 V/m at the second skirt surface of the HV output port. From the second skirt to the grounding shell, the surface electric field strengths decrease slowly from about 2 × 106 V/m to about 1 × 106 V/m. It illustrates that the length of the epoxy insulator of the HV output port can be shortened about half length. However, shorten epoxy insulator means longer copper wire with high voltage of −180 kV must ensure electric field safety to connection the HV electrode of the HV output port and the 5th equipotential ring. Therefore, the length of the epoxy insulator adopts long length design
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plan. The simulation results show that the surface electric field strength of the HV output port is less than the limits of the surface electric field strength of the epoxy insulator in the transformer oil No. 25, the design leaving sufficient safety margin to avoid flashover happen.
Fig. 8. Distribution of the surface electric field strength along the line EF of the compact HV power supply
Fig. 9. Distribution of the electric field strength along the line GH of the compact HV power supply
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Fig. 10. Distribution map of the surface electric field strength of the HV output port; the pink arrow lines respect the surface electric field line
Fig. 11. Distribution of the surface electric field strength of the HV output port along the broken line OP
5 Conclusion A 3D structure model of the compact negative HV power supply with output voltage of −150 kV and output current of 10 mA has been developed for a compact neutron generator. the 3D structure model as Fig. 2 shows, and its sizes are 543 (length) × 360 (width) × 445 (height) mm3 . The geometry structure of the CW voltage multiplier and the HV output port are carefully designed as Fig. 3 and Fig. 4 shows. The potential distribution and the electrostatic field distribution of the compact HV power supply are simulated, respectively. The simulation results show that the maximum electric field strength is about 1.9 × 107 V/m, and the maximum surface electric field strength is about 5.5 × 106 V/m. they are both less than the limits of electric field strength of the transformer oil and the limits of the surface electric field strength of the epoxy insulator,
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leaving sufficient electric field safety margin. The results provide a scientific reference for the structure design and electric optimization of a compact HV power supply. Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (Contract Nos. 12005085) and the National Key R&D Program of China (Contract Nos. 2019YFA0405401).
References 1. Carpenter, J.M.: The development of compact neutron sources. Nat. Rev. Phys. 1, 177–179 (2019) 2. Leung, K.N.: New compact neutron generator system for multiple applications. Nucl. Technol. 206(10), 1607–1614 (2020) 3. Huang, Z.W., Wang, J., Wei, Z., et al.: Development of a compact D-D neutron generator. J. Instrum. 13(01), P01013 (2018) 4. Zhao, G., Liu, X., Wu, C., et al.: 300 kV/6 mA integrated Cockcroft-Walton high voltage power supply for a compact neutron generator. Rev. Sci. Instrum. 91, 074704 (2020) 5. Lu, X., Chen, S., Zhang, Y., et al.: Development of a 400 kV 80 mA Cockcroft-Walton power supply and 12 kW isolation transformer systems for neutron generators. J. Instrum. 12(06), P06006 (2017) 6. Mao, S., Popovic, J., Ferreira, J.A., et al.: Diode reverse recovery process and reduction of a half-wave series Cockcroft-Walton voltage multiplier for high-frequency high-voltage generator applications. IEEE Trans. Power Electron. 34(2), 1492–1499 (2019) 7. Quraan, M., Zahran, A., Herzallah, A., et al.: Design and model of series-connected highvoltage DC multipliers. IEEE Trans. Power Electron. 35(7), 7160–7174 (2020) 8. He, Z.-F., Zhang, J.-L., Liu, Y.-H., et al.: Characteristics of a symmetrical Cockcroft-Walton power supply of 50 Hz 1.2 MV/50 mA. Rev. Sci. Instrum. 82(5), 055116 (2011) 9. ECSS Secretariat: Space Engineering High Voltage Engineering and Design Handbook. ESASP (2014) 10. Franklin, A.C., Franklin, D.P.: The J & P Transformer Book: A Practical Technology of the Power Transformer. Elsevier, Amsterdam (2016)
A DC Microgrid System Architecture and Control Strategy for Aerospace Applications Yinghua Dou1,2(B) , Tao Liu1,2 , Baolei Dong1,2 , Wei Xie1,2 , and Aiwei Yang1,2 1 State Key Laboratory of Space Power Technology, Shanghai, China
[email protected] 2 Shanghai Institute of Space Power-Sources, Shanghai, China
Abstract. Power conditioning unit is the key power supply unit in the spacecraft. The use of grid-connected power supply technology can realize the redundant protection of the power supply system of spacecraft. DC microgrid is an effective architecture that solves the interconnection of multiple DC power supply systems. Droop control is a commonly used control method in DC microgrid. In this paper, a new distributed DC microgrid architecture for aerospace is proposed, and the operation mode and control strategy of proposed DC microgrid system are described in detail. Next, the working principle of the grid-connected controller is introduced. According to the operating characteristics of the mentioned DC microgrid system, the operation status of three working modes of the grid-connected controller is analyzed and control strategy is also designed. At the same time, an adaptive droop control based on PCU bus voltage information is proposed, and the specific control method is explained. Afterwards, stability and simulation analysis are carried out according to the operation mode and control strategy of the microgrid system, and finally an experimental platform was built to verify the proposed design scheme and control strategy. Keywords: Power Conditioning Unit · DC microgrid · Adaptive droop control · Spacecraft power supply system
1 Introduction The Power Conditioning Unit (PCU), as an energy autonomous power supply system, has become the main power supply unit in spacecraft [1]. All the energy required in the spacecraft is supplied by PCU, and the redundancy and reliability of the PCUs can ensure the normal operation of the spacecraft. Grid-connected power supply can realize the redundant protection of the busbar layer of the power supply system. When a certain power system meets a failure, other power systems are allocated to the load of the failed power system through grid-connected power supply to ensure the normal operation of key loads and the normal implementation of aerospace tasks. Due to the intervention of the grid-connected power supply system, the faulty system can also be removed when a certain power system fails or fails to prevent the failure from spreading, and to ensure the safety operation of the entire system to the greatest extent. For the parallel © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 250–261, 2022. https://doi.org/10.1007/978-981-19-1528-4_25
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connection between multiple PCUs, the International Space Station [2–4] is equipped with two kinds of DC/DC converters--American to Russian Converter Unit (ARCU) and Russian-American Converter Unit (RACU) to realize the interconnection between the power system of the American cabin and the Russian cabin. Through RACU and ARCU, the connection between 120 V DC bus and 28 V DC bus is realized, and the power is transmitted to each other according to the system requirements. However, the output power of the DC converter is not adjustable, and the output voltage is a constant value in all operating modes. [5, 6] propose a power system grid-connected control technology based on 1553B bus communication, and the grid-connected converter adopts constant current and constant voltage mode. When the grid-connected power supply is connected, the grid connected controller is in constant current output mode, and the output power of the converter is adjusted by adjusting the output current. However, this method has the following drawbacks: 1) When the PCU is connected to the grid, the voltage of the PCU cannot be maintained, and the voltage fluctuation of the PCU is large during grid-connected mode; 2) Due to the control commands are all implemented via the bus, when the system communication meets faults, the normal operation of the entire energy system cannot be guaranteed; 3) In order to reduce the impact on the grid-connected bus, the output voltage of the grid connected controller needs to be adjusted to be equal to the grid connected bus before operating in the grid-connected bus. [7] proposes a high voltage grid-connected technology based on variable current and voltage limiting control strategy. In addition to the constant voltage and constant current control modes, the variable current and voltage limiting mode is added. The grid connected controller can adjust the output current according to the demand of load to realize the effective utilization of energy. However, this method has not solved the problem of voltage stability of PCUs when working in the grid-connected mode. As the load demand suddenly increases, it will cause a certain impact on the voltage of PCUs. As a micro-autonomous power supply network that effectively manages distributed units within a certain area, microgrid (MG) is an effective architectural form to solve the interconnection of multiple power supply system [8, 9]. Due to the wide application of AC power systems, most of the MG technologies are related to AC MGs. The AC MG has significant advantages in long-distance transmission compared with the DC MG. However, when the local load increases, the demand for long-distance transmission will decrease accordingly. At the same time, with the development of distributed power generation units (i.e., photovoltaic (PV) panels and fuel cells) and DC loads, DC MGs have become the main architecture in the power generation system. Compared with AC MG, the advantages of DC MG as follows [10–12]: 1) The DC MG uses DC transmission lines to connect distributed power generation units, energy storage units, and loads through DC/DC converters, eliminating the needs for inverter and rectification links, reducing costs and the number of energy conversions in the system, and improving the system operating performance, reliability, and efficiency; 2) The energy balance between the distributed power generation units and the loads can be achieved by maintaining the stability of the DC bus voltage, without considering the power management and frequency synchronization in the AC MG. According to the advantages above, DC MGs have been widely accepted for utility grids, electric aircraft, and electric ship [13, 14].
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The commonly used DC MG control strategies are divided into master-slave control and peer-to-peer control according to the different DC bus voltage control strategies [15, 16]. Compared with master-slave control, peer-to-peer control has high reliability and plug-and-play performance, which is convenient for system expansion. Droop control is an effective way to achieve peer-to-peer control. Because the droop control method does not require high-speed communication, it is more in line with the “distribution” characteristics of the DC MG. When there are multiple DC/DC converters operating in parallel in the DC MG, the droop control strategy can maintain the current distribution among the converters. In this paper, the adaptive droop method will be adopted for controlling the circulation current and the DC bus voltage of the parallel converters. Comparing with other currenting sharing control methods (i.e., average-current method) [17], the implement of droop control method without the need of communication lines and realizes the function of plug-and-play in DC MGs. This paper is organized as follows: In Sect. 2, a new distributed DC MG architecture is proposed, and the operation mode and control strategy of the MG system are described in detail; In Sect. 3, the operating principle of the grid-connected controller is introduced. The grid-connected controller adopts the phase-shifted full-bridge topology; next, according to the operating characteristics of the proposed DC MG system, an adaptive droop control based on PCU bus voltage information is proposed. In Sect. 4, stability and small signal analysis are performed. And the equivalent circuit model is established according to the control strategy of GCC proposed in Sect. 3. In Sect. 5, the simulation and experimental results verify the proposed DC MG operating mode and control strategy proposed in this article. Finally, the conclusions are given.
2 Proposed DC MG System and Operating Strategy Figure 1 shows the block diagram of the DC MG system proposed in this paper, which mainly includes solar systems, batteries, PCUs (including sequential switch shunting regulators (S3 Rs), battery charging regulators (BCRs), battery discharge regulators (BDRs) and main error amplifiers (MEAs)), grid-connected controllers (GCCs) and DC loads. Each PCU is connected to the same DC bus through GCC to realize the interconnection between multiple PCUs. Similar to AC MG, the operating mode of the DC MGs can be divided into two different modes: grid-connected and islanded mode. (1) Grid-connected mode: In this mode, solar systems, batteries and DC loads are connected to the grid via GCCs. And the main purpose of controlling the DC MG is to inject the power to the DC bus. When the energy produced by solar system meets the system load demand and battery charging, and the energy has more surplus: P = PPV − Pload + Pbat_charge (P > P1 )
(1)
The solar system works in the constant-voltage-current-limiting (CVCL) mode, and the PCU bus voltage is maintained stable through S3 R, and the positive GCC (PGCC) transmits energy to the common DC bus. If the common bus requires less energy, PGCC
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will work in the droop mode. The power is unbalanced due to the differences between converters and other parameters. Therefore, droop control is introduced into the control strategy to achieve reasonable power distribution of each unit; if the common bus requires more energy, PGCC will transmit energy to the common bus in current limiting mode to prevent the common bus voltage from rising too high. When the energy generated by solar system has less energy remaining after meeting the DC load and the charging demand of battery: P = PPV − Pload − Pbat_charge (P1 > P > 0)
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And PCU bus voltage is controlled by PGCC. At this time, the common bus is kept stable by other GCCs. When the energy generated by solar system is insufficient to meet the system load demand and battery is in an off-work state: P = PPV − Pload (P2 < P < 0)
(3)
The negative GCC (NGCC) obtains energy from the grid-connected bus, provides it to the load, and controls PCU bus voltage. When the solar system is in an off-work state, the battery maintains the stability of the PCU bus voltage through BDR, and NGCC works to provide energy for the DC load with battery. P = PPV − Pload + Pbat_discharge (P3 < P < P2 )
(4)
When the power supply capacity of the common bus is strong, to prevent the PCU bus voltage from rising too high, the converter works in output current-limiting mode; when the power supply capacity of the grid-connected bus is weak, NGCC works in droop mode to control the common bus voltage.
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(2) Islanded mode: When the MG works in islanded mode, GCC is in off-work state. The energy generated by solar system just meets the system load and the battery charging demand, the battery maintains the stability of PCU bus voltage through BCR. P = PPV − Pload − Pbat_charge = 0
(5)
3 Working Principle and Control Strategy of GCC For the DC MG system proposed in Sect. 2, the main purpose is to guarantee the reliable operation in each operating mode. Figure 2 shows the general block diagram of the proposed control system for GCC in DC MG. The proposed control system is effective in grid-connected mode and during the transitions from grid-connected mode to islanded mode. According to Fig. 2, GCC consists of two control loops. Outer voltage control loop and inner current control loop. The voltage control loop consists of four controllers and modules: DC bus voltage controller based on adaptive speed down control, output current limiting controller, PCU bus voltage controller and minimum circuit. And the inner loop uses a typical PI current controller to control the output current of the converter. iLf
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(1) Droop mode: When GCC works in droop mode, each GCC can be regarded as a voltage source. One of the main disadvantages of the traditional controller based on droop control is that the MG components are not utilized in an efficient way [12]. The main contribution of this article is to propose a new droop control method, which introduces the MEA signal
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of PCU into the outer voltage loop as a reference signal for the inner current loop. The MEA signal is given by ki0 (k0 uPCU − uref 0 ) uMEA = kp0 + (6) s where uPCU is the PCU output voltage; uref0 is the reference voltage of PCU bus; k 0 is the sampling ratio; k p0 and k i0 are parameters of MEA PI regulator, respectively. The sampled signal of PCU bus voltage k 0 uPCU will be amplified into a larger range via MEA to satisfy the control accuracy. For PGCC, the more energy generated by solar system will be transferred from PCU bus to DC bus as the MEA voltage rises. For NGCC, when the MEA voltage falls, the more energy will be transmitted from DC bus to PCU bus. Figure 3 shows the control principle of PGCC in droop mode. This article uses the adaptive droop control. After using the traditional droop control, the PGCC operating point will move from urate (voltage at no load condition) to OP1 under idc1 load condition and OP2 under idc2 load condition, respectively. After using the adaptive droop control, the operating point will shift from OP1 to OP1’and OP2 to OP2’. And the nominal voltage will change from urate to urate + u. According to Fig. 2, the adaptive droop controller generates the output current reference of GCC in droop mode. Considering the above description, the adaptive droop profile is constructed by uref = urate − rio ± u
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According to (7), the generated voltage reference (uref ) includes the nominal voltage (urate ), output sampling current(io ), droop gain(r), and the voltage adaptive adjustment signal. The voltage adaptive adjustment signal is given by u = ±k1 (uMEA − uMEA_ref ) where k 1 is the sampling ratio.
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(2) Output Current Limiting mode: When the output current of the GCC reaches the threshold, GCC works in the currentlimiting output mode. In this mode, the output sample current idc is compared with the given current signal ilim and sent to the PI regulator. Then the output signal of voltage PI regulator is used as the current given signal and passes through the limiter. After comparing with the output current of GCC, the current given signal is sent to the current PI regulator. (3) PCU Voltage Control mode: When the GCC works in voltage control mode, the PCU bus voltage is controlled by the GCC. The PCU reference voltage is compared with the current PCU bus voltage, and then the comparison result is adjusted by PI regulator to generate the MEA signal, which is compared with the preset MEA reference signal uMEA_ref1 as the input signal of the voltage PI regulator. Then the signal is sent to the current PI regulator and produce a phase-shifted drive signal. Figure 4 shows the way the controller generates a current reference value based on the MEA signal of PCU. According to the Fig. 4, the power generation is higher than the load as the DC bus voltage is higher than v3 . Therefore, additional energy is used to maintain the voltage stability of DC bus. On the other hand, as the MEA voltage is lower than v2 , the load of PCU is too heavy and the PCU needs to inject more power to maintain the DC bus voltage. There are two main advantages of the control strategy presented in Fig. 4. First, it provides better dynamic response for PCU to optimize the use of available energy. In the same time, it also limits the range of DC bus voltage and DC voltage variation. Therefore, GCC can operating in a more optimized way. In addition, the proposed method can adaptively obtain the desired voltage deviation range and accurate power allocation.
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4 Small Signal Modelling of GCC In this section, in order to ensure the dynamic characteristics of GCC, the stability of the proposed control method is analyzed [18–20].
Fig. 5. The block diagram of closed-loop control system of GCC
Figure 5 shows the small signal block diagram of GCC. Figure 5(a) is the smallsignal equivalent circuit of GCC. According to the Fig. 5(a), the loss of duty cycle and equivalent series resistance of Cf are considered in the small-signal equivalent circuit. Figure 5(b), Fig. 5(c) and Fig. 5(d) show the control block diagram of GCC in droop mode, output current limiting mode and control PCU voltage mode, respectively. When GCC works in the droop mode, the GCC can be regarded as voltage source. According to Fig. 5(b), the dynamics of the output voltage signal is given by
v o = Go v rate − Zo io + Gvv v in
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When GCC works in the output current limiting mode, the GCC can be regarded as current source. According to Fig. 5(c), the dynamics of the output current signal is given by
io = Gi_lim ilim −Y o vo
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When GCC works in the PCU voltage control mode, the GCC can be regarded as current load. According to Fig. 5(c), the dynamics of the DC Bus voltage signal is given by
v bus = Gvv v pcu − Zo io − GMEA v MEA_ref
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5 Simulation and Experimental Results The simulation and experiment will be conducted in this section to verify the effectiveness of proposed control system and operation strategy of GCC. The parameters of GCC used in the simulations and the experiments are given in Table 1.
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5.1 Simulation Results and Analysis of GCC
Table 1. Parameters of GCC for simulations and experiments Symbols
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In order to verify the proposed control strategy and stability analysis of GCC, a simulation model is built in SIMPLIS. It should be pointed that simulation model only contains one PCU and one GCC. And other PCUs, GCCs and DC bus are replaced by a DC Load in simulation model. 1. Case One (RL1 = 5 and Rdc = 25 ): In this case, GCC operating in the droop mode, and GCC is stable according to section IV. The control signal and output voltage simulation waveforms of GCC are shown in Fig. 6. According to the simulation results, it is obvious that the bus voltage ripple is controlled below 0.5%. 2. Case Two (RL1 = 5 and Rdc = 10 ): In this case, GCC operating in the output current limiting mode, and GCC is stable according to Sect. 4. The control signal and output voltage simulation waveforms of GCC are shown in Fig. 7. 5.2 Experimental Results and Analysis Based on the above simulation results and analysis, a proposed DC MG experiment platform is built. The experimental platform of proposed DC MG system is shown in Fig. 8.
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The experiment waveforms of DC bus voltage and the MEA signal of GCC in different operating mode are shown in the Fig. 9.
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In order to verify the transient performance of the GCC, the proposed DC MG system is set up. According to Fig. 10(b) (c), three solar array simulators (SASs) are used to simulate solar systems; three DC sources combined with programmable DC electronic loads are used to simulate batteries, and three programmable DC electronic loads are used to simulate PCU loads. Two cases are selected to verify the transient operation of the GCC.
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6 Conclusion In this paper, A new a new architecture and operating mode for aerospace DC MG system have been proposed, which is suitable for the scenario of multiple PCUs interconnected in aerospace. Next, a droop control method has been proposed to realize the efficient use of energy generated by solar systems and batteries. Simulation and experimental results have verified the stability of the proposed DC MG and control strategy. Therefore, this paper provides a good reference for the interconnection of multiple PCUs in aerospace.
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References 1. Mukind, R.P.: Spacecraft Power Systems, pp. 1023–1025. CRC Press, New York (2005) 2. Gietl, E.B., Gholdston, E.W., Cohen, F., Manners, B.A., Delventhal, R.A.: The architecture of the electric power system of the International Space Station and its application as a platform for power technology development. In: Collection of Technical Papers, 35th Intersociety Energy Conversion Engineering Conference and Exhibit (IECEC), vol. 2, pp. 855–864 (2000) 3. Aghabarari, E., Adams, C.J.: On the control and operation of the electrical power system for the International Space Station. In: IECEC 1996, Proceedings of the 31st Intersociety Energy Conversion Engineering Conference, vol. 1, pp. 160–165 (1996) 4. Zhang, D.P., Meng, X.H.: Research on parallel operation between power support systems of different spacecrafts. Spacecraft Eng. 5, 101–107 (2009). (in Chinese) 5. Zhou, X.S., Wang, B.B., Guo, X.F., et al.: Research on high space power bus interconnection control technology for spacecraft. Chin. Space Sci. Technol. 6, 59–66 (2018). (in Chinese) 6. Yu, L., Zheng, T.Q., Wan, C.A.: A current-variable voltage-limited method of spacecraft power utility grid connection. Trans. China Electrotech. Soc. 29, 226–231 (2014). (in Chinese) 7. Lasseter, R.H.: MicroGrids. In: Proceedings of the 2002 IEEE Power Engineering Society Winter Meeting, vol. 1, pp. 305–308 (2002) 8. Hatziargyriou, N., Asano, H., Iravani, R., Marnay, C.: Microgrids. IEEE Power Energy Mag. 5(4), 78–94 (2007) 9. Wang, F., Pei, Y., Boroyevich, D., Burgos, R., Ngo, K.: AC vs. DC distribution for off-shore power delivery. In: 2008 34th Annual Conference of IEEE Industrial Electronics, pp. 2113– 2118 (2008) 10. Chen, S., Liang, T., Hu, K.: Design, analysis, and implementation of solar power optimizer for DC distribution system. IEEE Trans. Power Electron. 28(4), 1764–1772 (2013) 11. Ciezki, J.G., Ashton, R.W.: Selection and stability issues associated with a navy shipboard DC zonal electric distribution system. IEEE Trans. Power Delivery 15(2), 665–669 (2000) 12. Hajebrahimi, H., et al.: A new energy management control method for energy storage systems in microgrids. IEEE Trans. Power Electron. 35, 11612–11624 (2020) 13. Gao, F., Bozhko, S., Costabeber, A., Asher, G., Wheeler, P.: Control design and voltage stability analysis of a droop-controlled electrical power system for more electric aircraft. IEEE Trans. Industr. Electron. 64(12), 9271–9281 (2017) 14. Werth, A., et al.: Peer-to-peer control system for DC microgrids. IEEE Trans. Smart Grid 9(4), 3667–3675 (2018) 15. Mazumder, S.K., Tahir, M., Acharya, K.: Master–slave current-sharing control of a parallel DC–DC converter system over an RF communication interface. IEEE Trans. Industr. Electron. 55(1), 59–66 (2008) 16. Wang, P., Lu, X., Yang, X., Wang, W., Xu, D.: An improved distributed secondary control method for DC microgrids with enhanced dynamic current sharing performance. IEEE Trans. Power Electron. 31(9), 6658–6673 (2016) 17. Panov, Y., Jovanovic, M.M.: Loop gain measurement of paralleled DC–DC converters with average-current-sharing control. IEEE Trans. Power Electron. 23(6), 2942–2948 (2008) 18. Vlatkovic, V., Sabate, J.A., Ridley, R.B., Lee, F.C., Cho, B.H.: Small-signal analysis of the phase-shifted PWM converter. IEEE Trans. Power Electron. 7(1), 128–135 (1992) 19. Schutten, M.J., Torrey, D.A.: Improved small-signal analysis for the phase-shifted PWM power converter. IEEE Trans. Power Electron. 18(2), 659–669 (2003) 20. Yadav, G.N.B., Narasamma, N.L.: An active soft switched phase-shifted full-bridge DC– DC converter: analysis, modeling, design, and implementation. IEEE Trans. Power Electron. 29(9), 4538–4550 (2014)
Research on Key Technology of Parallel Breaking for DC Circuit Breaker Wengang Xie(B) , Bo Liu, Guangrong Luo, Wei Li, Zhongjian Song, Zhicheng Zhang, and Gongyi Zhang Shandong Taikai High-Voltage Switchgear Co., Ltd., Tai’an, China [email protected]
Abstract. In order to meet the requirements of DC circuit breakers with higher current carrying and breaking capacity of power system, and to solve the problems of limited breaking capacity of single power electronic devices and de-saturation when conducting large current, a parallel breaking technology based on coupling negative voltage hybrid DC circuit breakers is proposed. This paper studies the influence of factors such as power electronic device driver performance and circuit inductance on parallel breaking of DC circuit breaker, and puts forward the corresponding improvement scheme, which provides reference for the overall structure and parameter design of DC circuit breaker. The results show that: the current and voltage stress of power electronic devices can be effectively reduced by using laminated busbar, selecting devices with similar on-state voltage drop as far as possible during device screening, and designing symmetrical structure at the same time, as well as reducing the dissimilarity of the device shutdown. Keywords: DC circuit breaker · Parameter design · Breaking capability
1 Introduction Compared with AC power grid, DC power grid based on flexible DC system shows obvious advantages in large capacity power transmission, distributed energy access, reactive power support of AC system and other aspects, which is an important development direction of future power grid [1–5]. In DC power network, it is necessary to use the DC circuit breaker with large current breaking capability in a few milliseconds to quickly remove the fault equipment or line, so as to ensure the stable operation of the non-fault part of the DC system and improve the reliability of the system [6–10]. Therefore, DC circuit breaker is the key technology to develop DC power grid. According to the breaking principles, DC circuit breakers can be divided into three types: the mechanical, the solid-state and the hybrid [11–13]. Their topologies are all composed of three branches: the main branch, the transfer branch and the energy absorption branch. In normal operation, the main branch carries the current. In case of short circuit fault, the transfer branch creates the current zero point to make the main branch current pass through zero, and finally the energy absorption branch consumes the residual energy of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 262–271, 2022. https://doi.org/10.1007/978-981-19-1528-4_26
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With the improvement of system voltage level and transmission capacity, the rated current and short-circuit current amplitude of transmission lines are increasing, which requires DC circuit breakers to have higher current carrying and breaking capacity. At present, the breaking capacity of a single power electronic device is limited, and desaturation will occur when conducting large current, which will result in rapid increase of conduction voltage drop, power increase and damage. Multiple parallel connections are needed to meet the requirements in the design, but at the same time, some new problems will be introduced, such as current sharing, device over-current and overvoltage caused by loop stray inductance, asynchronous triggering of electronic devices and so on. Finally, this paper introduces the 25 kA high-current parallel breaking test used in Zhangbei flexible direct current transmission project.
2 Structure and Working Principle 2.1 Overall Topology and Principle The topology of a low-loss hybrid DC circuit breaker based on coupled negative-voltage principle is shown in Fig. 1. During normal operation, the fast mechanical switch conducts current. In case of of a short-circuit fault in the circuit, the power electronic valve unit of the transfer branch is triggered and the opening command is sent to the fast mechanical switch.
Fig. 1. Topology of the coupled negative voltage hybrid DC circuit breaker
When the contact moves to a certain distance, the thyristor in the coupled negativevoltage loop is conducted. The capacitor of the coupled negative-voltage circuit and the primary side inductance of the transformer oscillate to generate a high-frequency current. The secondary side of the transformer couples a negative voltage to the loop formed by the main branch and the transfer branch, so that the overall on-state voltage of the transfer branch is lower than the arc voltage of the fast mechanical switch, and the current is transferred from the main branch to the transfer branch. The current of the main branch crosses zero and the arc is extinguished to complete the current transfer process.
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When the mechanical switch contacts move to the distance enough to withstand the transient recovery overvoltage after breaking, the power electronic switch in the transfer branch valve unit is turned off, and the current is transferred to the energy absorption branch. Finally, the energy is dissipated by the arrester until the current decays [14]. During this process, the main branch and the transfer branch need to withstand the system overvoltage limited by the arrester. 2.2 Topology and Principle of Power Electronic Switch The transfer branch in Fig. 1 is composed of multiple groups of power electronic valve units in series. The topology of each valve unit is shown in Fig. 2, including buffer resistance R, buffer capacitance C, arrester MOV, diodes D1–D4 (meeting the requirements of bidirectional flow) and power electronic switches IEGT1–IEGT2.
Fig. 2. Parallel breaking topology of valve unit in transfer branch
IEGT and IGBT are both voltage-controlled devices, which can be controlled comprehensively from the load side and the gate side with simple drive circuit and flexible control method. However, compared with IGBT, IEGT’s fault characteristics are short circuit, and its breaking current is larger, which is more suitable for DC interruption. Therefore, IEGT is selected as the power electronic device for DC circuit breakers in this paper.
3 Influence of Stray Inductance on Parallel Breaking When the power electronic switch turns off the fault current, the rapidly changing current will produce an overvoltage spike under the action of loop stray inductance, which will increase the voltage stress and power consumption of the device, causing high-frequency noise, radiation pollution and other problems at the same time, and the device could be even broken down in serious cases [15]. Therefore, in the design of the value unit structure, we must pay attention to the control of stray inductance.
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Because the power electronic switch adopts two IEGT devices in parallel, currentsharing effect is affected by stray inductance and resistance of the branch, and the fault current passing through the branch with smaller impedance is larger. The device will stand voltage stress after breaking, and its magnitude is mainly affected by turn-off speed, loop inductance and other factors. This paper mainly analyses the influence of loop stray inductance on the device parallel breaking characteristics. 3.1 Parallel Busbar Scheme The parallel bus scheme as shown in Fig. 3 is initially adopted in the structure design of the unit topology in Fig. 2, and the overall structure is asymmetric.
Fig. 3. The parallel busbar scheme.
Fig. 4. Principle diagram of the breaking test
In order to verify its breaking performance, the breaking test platform as shown in Fig. 4 is built. Before the test, K0 and K1 are in the opening state. During the test: firstly, K0 is closed, the charging source U charges the capacitor bank C to the specified voltage, and K0 is opened after the charging is completed. Then the IEGT in the value unit is trigged to turn on. The capacitor bank C discharges to the test object. When the current rises to the peak value, it is interrupted by the value unit. After the interruption, K1 is closed, and the residual energy in the test circuit and the buffer capacitor will be released.
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Fig. 5. Breaking results of initial structure scheme
The test results are shown in Fig. 5, where VE is the total voltage at both ends of IEGT, and iE1 and iE2 represent the current passing through IEGT1 and IEGT2 respectively. As can be seen from Fig. 5(a) and Fig. 5(b), due to the overall structural asymmetry of the valve unit, the turn-off speed and turn-off time of the two devices are inconsistent (20 µs difference). This is because the asymmetry of the structure will lead to different E voltage of the two devices in parallel, which will lead to inconsistent gate voltage and ultimately affect the device’s breaking. This can easily lead to device damage. It can be seen from Fig. 5(c) that there are two spikes in the overvoltage of the electronic device during breaking. The peak voltage point A (4.1 kV) is the peak voltage caused by the loop inductance when IEGT turns off, and the current is transferred from the IEGT branch to the RC branch. The peak voltage point B is the residual voltage value limited by the arrester. If the loop inductance is large, peak A may exceed peak B and damage IEGT. Since the peak value B is determined by the technical parameters of the arrester, it is enough to select appropriate parameters. To reduce peak A, the stray inductance of the loop must be reduced a certain level.
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3.2 Laminated Busbar Scheme In order to reduce the stray inductance in the loop, the module structure is optimized. The parallel busbar scheme is changed to the laminated busbar, and the overall structure of the loop is optimized into a symmetric structure, as shown in Fig. 6. Through calculation, the value of stray inductance in the loop can be reduced to 224 nH.
Fig. 6. Optimized structural scheme
The breaking results of the optimized structure are shown in Fig. 7. It can be seen that the first peak of overvoltage A is 2.6 kV, which is significantly reduced and lower than the second peak of overvoltage B, greatly reducing the voltage stress of the device.
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Through the above analysis, it can be seen that in the design of parallel breaking of power electronic devices, the overall structure of the valve unit should be designed symmetrically. The driver performance should be as consistent as possible to reduce the switching circuit of the two devices. The loop stray inductance should be minimized to reduce the turn-off over-voltage of the device.
4 Influence of Driving Performance on Parallel Breaking Characteristic Due to the difference of the driving performance and the device itself, the two devices will not be turned off at the same time, and the device turned off later will bear a larger impulse current. Therefore, the parallel breaking of power electronic devices requires higher driving performance. In order to study the influence of the driving performance on the parallel breaking characteristic, this section carries out a high current breaking test based on the power electronic switch with PI core, the test results are shown in Table 1. Among them, iall is the peak of the breaking current, unp is the negative peak of the driving voltage, uce-1 and uce-2 represent peak voltage between CE of 1# and 2#device respectively, di/dt is the changing rate of the current, and ip-1 and ip-2 represent peak breaking current of device 1# and 2# respectively. Table 1. Effect of driving performance on interruption characteristics iall /A
unp /V
uce-1 /V
uce-2 /V
3041
−13.5
3057
3077
5124
−13.0
3352
6473
−25.7
7698
−28.2
9900 12355
di/dt A/us
ip-1 /A
ip-2 /A
582
1514
1520
3390
1575
2542
2677
3450
3463
1483
3297
3199
3572
3555
1934
3873
3800
−28.9
3915
3996
2593
5013
4915
−28.2
3989
3996
3025
6300
6140
It can be seen from Table 1 that, from the overall trend, the higher the negative peak of the device driving voltage, the greater the peak of the breaking current, the higher the voltage uce of the device, and the higher the changing rate of breaking current. Therefore, from this perspective, increasing the negative peak of the device driving voltage can improve the current breaking level of the device. But at the same time, the voltage and current stress of the device will increase. In Table 1, the inconsistencies of uce , changing rate and amplitude of the breaking current are caused by the asymmetric structure design of the parallel bus. Therefore, in the design, the driving capacitance, driving resistance and other parameters can be adjusted to make the turn-off of the two devices as consistent as possible and reduce the turn-off asynchrony of devices.
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5 Prototype Development and Application The rated breaking current of ±500 kV hybrid HVDC breaker applied in Zhangbei DC power grid demonstration project is 25 kA, and IEGT is selected as the main breaking device of power electronic switch.
(a) Overall structure (b) Optimized power electronic valve units Fig. 8. Structure chart of 500 kV hybrid HVDC circuit breaker prototype.
The overall structure of the 500 kV hybrid high-voltage DC circuit breaker is shown in Fig. 8(a), which is mainly composed of the fast mechanical switch module, the valve module, the coupled negative-voltage module, the shielding cover, the supporting structure, and the lightning arrester. The components are assembled and connected with other primary equipment and secondary control system of the DC transmission system through tube bus and optical fibers. The power electronic valve string adopts the optimized parallel structure scheme, as shown in Fig. 8(b). The breaking waveforms of the 500 kV hybrid DC circuit breaker are shown in Fig. 9. Among them, TRIGGER is the opening trigger signal of the circuit breaker, IALL is the total current of the test circuit, VALL is the total voltage between the terminals of the circuit breaker, ICB and ICE are the current of the main branch and transfer branch respectively. From the waveform, it can be seen that the circuit breaker can realize reliable breaking of 25 kA fault current within 3 ms.
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Fig. 9. Breaking results of 500 kV hybrid HVDC circuit breaker
6 Conclusion 1) Based on a coupled negative-voltage hybrid DC circuit breaker, the main factors affecting the parallel breaking characteristics of power electronic switches are analyzed, including loop stray inductance and driver performance.
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2) Compared with the parallel busbar, the laminated busbar scheme can reduce the loop stray inductance and the turn-off voltage stress. In addition, multiple valve units will be used in series when the rated voltage is high. In order to ensure the reliability of the series, the devices with short-circuit characteristic after failure should be selected. 3) In order to reduce the current and voltage stress of the power electronic devices in parallel breaking, the devices with similar on-state voltage drop shall be selected as much as possible and the symmetrical structure design should be considered at the same time. Appropriate parameters of driving capacitance and driving resistance shall be selected to make the driving performance as consistent as possible and reduce the turn-off asynchrony of devices. 4) The parallel breaking technology used in the 500 kV hybrid high-voltage DC circuit breaker prototype of Zhangbei flexible DC transmission project is analyzed, which provides a reference for the development of subsequent DC circuit breaker products.
References 1. Baran, M.E., Mahajan, N.R.: DC distribution for industrial systems: opportunities and challenges. IEEE Trans. Ind. Appl. 39(6), 1596–1601 (2002) 2. Yi, R., Zhao, Z., Yuan, L.: Busbar optimization design for high power converters. Trans. China Electrotech. Soc. 31(8), 11–18 (2016) 3. Zhang, X., Yu, Z., Huang, Y., et al.: Principle and development of 500kV hybrid DC circuit breaker based on coupled negative voltage commutation. J. Global Energy Interconnection 1(04), 413–422 (2018) 4. Zhu, T., Yu, Z., Zeng, R., et al.: Transient model and operation characteristics researches of hybrid DC circuit breaker. Proc. CSEE 36(1), 18–30 (2016) 5. Song, Q., Zhao, B., Liu, W., et al.: An overview of research on smart DC distribution power network. Proc. CSEE 33(25), 9–19 (2013) 6. Dong, Y., Luo, H., Yang, H., et al.: Engineering design for structure and bus bar of 1.2 MVA hybrid clamped five-level converter module. Trans. China Electrotech. Soc. 31(8), 11–18 (2016) 7. Gu, T., Cheng, S., Guo, Q., et al.: IGBT half-bridge module with low parasitic inductance. J. Mech. Electr. Eng. 31(4), 527–531 (2014) 8. Wen, W., Huang, Y., Lü, G., et al.: Current commutation method in hybrid circuit breaker. High Voltage Eng. 42(12), 4005–4012 (2016) 9. Li, G., Ruan, J., Liu, C., et al.: Analysis for frequency characteristic of stray inductance in multilayer laminated busbar. High Voltage Eng. 45(7), 2101–2107 (2019) 10. Junjia, H.E.: Research on key technologies of high voltage DC circuit breaker. High Voltage Eng. 45(8), 2353–2361 (2019) 11. Pauli, B., Mauthe, G., Ruoss, E., et al.: Development of a high current HVDC circuit breaker with fast fault clearing capability. IEEE Trans. Power Delivery 3(4), 2072–2080 (1988) 12. Ning, H., Sun, X., Yang, Y.: A high power IGBT drive protection method based on dic /dt feedback control. Trans. China Electrotech. Soc. 30(5), 33–41 (2015) 13. Zhao, Z., Zhang, H., Yuan, L., et al.: Failure mechanism and protection strategy of high voltage three-level inverter based on IGCT. Trans. China Electrotech. Soc. 21(5), 1–6 (2006) 14. Sheng, W.W., Colino, R.P.: Power Electronic Modules. CRC Press, Boca Raton (2005) 15. Callavik, M., Blomberg, A., Hfner, J., et al.: The hybrid HVDC breaker: an innovation breakthrough enabling reliable HVDC grid. ABB Grid System, Zurich, Switzerland, Technical paper, November 2012
Adaptive Field Weakening Control Algorithm of Surface-Mounted Permanent Magnet Synchronous Motors with Low Carrier Ratio Chao Wu(B) , Xiangdong Sun, and Jianyuan Wang Department of Power Electronics and Motors, Xi’an University of Technology, No. 5 South Jinhua Road, Xi’an 710048, Shaanxi, China [email protected]
Abstract. The field weakening control algorithm based on the output voltage regulation of the surface-mounted permanent magnet synchronous motors (SPMSM) has some drawbacks, such as poor dynamic performance and low load capability, especially in the field weakening region with low carrier ratio. In this paper, an adaptive field weakening algorithm is proposed to improve the dynamic loading and unloading performance as the carrier ratio becomes low. In this algorithm, a pre-regulation branch of excitation current is added, which is realized by the qaxis voltage control loop. The q-axis reference voltage changes adaptively with the carrier ratio, so that the system with low carrier ratio will enter the field weakening region ahead of the traditional field weakening algorithm, which is conducive to fast torque response and better load performance. Finally, the effectiveness of the algorithm is verified by the experiments. Keywords: Field weakening · Low carrier ratio · SPMSM
1 Introduction Recently, the control of permanent magnet synchronous motors (PMSMs) has been a popular research topic. It is well known that the switching frequency of the motor driver gradually decreases with the increase of the motor power. Moreover, some high-power applications also need fast torque response in the field weakening control. In recent years, scholars have done a lot of research on the field weakening control, which can be divided into three categories: feedforward control methods, feedback control methods and mixed control methods [1]. Feedforward control methods are usually based on the PMSM model [2, 3], and have good performance and fast response, but computational complexity increases greatly when the carrier ratio is low. Feedback control methods are normally based on output voltage regulation [4–6], and have the merits of the robust and simplicity, but the torque ripple increases and the response speed gets worse as the carrier ratio decreases. The mixed control methods [7, 8] combine the advantages of the two methods, but computational burden becomes heavier. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 272–279, 2022. https://doi.org/10.1007/978-981-19-1528-4_27
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A simple, effective, robust field weakening algorithm of SPMSM is pursued in this paper, especially under the condition of low carrier ratio. Based on the field weakening algorithm of the output voltage regulation (Hereinafter referred to as traditional field weakening control), an adaptive field weakening algorithm with the carrier ratio is proposed. The algorithm is realized by adding a control loop of the q-axis voltage uq , which is adaptively with the carrier ratio. So, SPMSM can enter the field weakening region earlier as the carrier ratio decreases. During the loading and unloading process, the current loop is changed from the traditional dual-axis control to an approximate single-axis control. By building an experimental platform, the experimental results verify that the adaptive field weakening algorithm can improve the load capacity and response speed at low carrier ratio in the field weakening region.
2 Traditional Field Weakening Algorithm of SPMSM Figure 1 is the speed sensorless vector control block diagram of SPMSM with the traditional field weakening control. Where, PI regulators with feed-forward decoupling control are used in the current loop control and the flux observer is an adaptive compensation flux observer [9]. The field weakening algorithm is mainly studied in this paper. LPF
+
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Fig. 1. Speed sensorless vector control block diagram of SPMSM with the traditional field weakening control
In d-q synchronous rotating reference frame, the model of SPMSM is expressed: ⎧ di ⎪ ⎨ ud = Rs id + Ld dtd − Pp ωm Lq iq diq (1) uq = Rs iq + Lq + Pp ωm (Ld id + ψPM ) ⎪ ⎩ T = 3 P [ψ idt + (L − L )i i ] e q d q d 2 p PM q where, Rs is the stator resistance, Ld is the d-axis inductance, Lq is the q-axis inductance, Pp is pole pairs, id is the d-axis current,iq is the q-axis current, ud is the daxis voltage,uq is the q-axis voltage, ψPM is the permanent magnetic flux, ωm is the mechanical angular speed, Te is the electromagnetic torque.
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The dq-axis voltages udq and currents idq are constrained by the DC-bus voltage Udc and maximum current imax , respectively, they are expressed by (2). ⎧ √ ⎨ Udc / 3 ≥ u2 + u2 q d (2) ⎩ imax ≥ i2 + i2 q d When a motor runs at the high-speed region, the voltage drop on the stator resistance can be neglected as it contributes weakly to the dq-axis voltage. Therefore, the constraint condition of id , iq and Udc can be obtained. √ Udc / 3 ≥ Pp ωm (Lq iq )2 + (Ld id + ψPM )2 (3) It can be seen from (3) that when ωm is greater than a certain speed, it has to make id be negative to satisfy the constraint condition. That is the basic idea of the field weakening control. The traditional field weakening algorithm based on output voltage regulation is shown in Fig. 1, the excitation current id∗ is got by a voltage PI regulator and the low-pass filter (LPF). During the loading, the control loop forms a dynamic udq dual-axis regulation. The whole process of the loading is greatly dependent on the control loop bandwidth. As the carrier ratio decreases, the time of the regulation process becomes longer, and the loading performance becomes worse. Thus, the focus of this paper is on how to improve the torque response of the field weakening algorithm at low carrier ratio.
3 Adaptive Field Weakening Algorithm of SPMSM Once the load is increased, the torque response is greatly improved by only single regulation of ud if the motor can enter the field weakening state in advance. Hence, a uq control loop with an adaptive constraint condition uq∗ is introduced into the traditional algorithm. The algorithm description is represented by (4). √ ⎧ ∗ ⎨ uq = η(Fratio ) · Udc / 3 (4) F = F /F ⎩ ∗ratio ∗ c ∗ o id = id 1 + id 2 where, Fc is the carrier frequency, Fo is the operating frequency. Fratio is the carrier ratio, η(Fratio ) is the adaptive function of Fratio , and η(Fratio ) is calculated by (5). ⎧ ⎨ ηmin . Fratio < Fratio_ min η(Fratio ) = 1 − (1 − ηmin )ρ. Fratio_ min ≤ Fratio ≤ Fratio_ max (5) ⎩ 1. Fratio > Fratio_ max Figure 2 is the block diagram of the adaptive field weakening control. Where, id∗ is divided into two components: id∗ 1 and id∗ 2 . id∗ 1 is controlled by uq and uq∗ , and id∗ 1 is constrained by uq∗ . id∗ 2 is regulated by the traditional voltage loop, and id∗ 2 is restricted by Udc .
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In (6), (7) and Fig. 2, some variables, such as ηmin , ρ, Fratio_ min , Fratio_ max , parameters of PI1/PI2 regulators and LPF, need to be defined and designed respectively. The variable: ηmin . The minimum value of η(Fratio ) is expressed: ηmin =
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(6)
where, iq_ max is the maximum output torque current of the driver, it is generally set as 1–2 times the rated current of the motor. id∗ _ min is the lower limit of id∗ , which is normally less than the rated current of the motor. λ is a coefficient for the regulation of the real system and ideal system. The variable:ρ. In (5), ρ is an important factor, it implies the bandwidth. Generally, the system bandwidth is decided by some factors such as the regulator parameters, carrier ratio, and control loop structure. It is a complex factor and it has a great relationship with the carrier ratio. For simplicity, ρ is calculated by linearizing the bandwidth of the system with the carrier ratio, shown as (7). ρ=
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where, Fratio_ min and Fratio_ max indicate the upper and lower threshold of the carrier ratio Fratio . The parameters of the proportional and integral parameters of PI1/PI2 regulators and LPF. The uq control loop and the Udc control loop are basically same due to the same structure. Furtherly, the proportional and integral parameters of PI1 and PI2 regulators are basically same as well. The LPF can reduce the transient noise into the current loop and improve the stability of the current loop. Thereby, the field weakening control block diagram can be obtained from Fig. 2, as shown in Fig. 3. In Fig. 3, kp_v and ki_v are the proportional and integral coefficients of PI1 or PI2 regulators respectively, ωc is the cut-off frequency, kp_c and ki_c are the proportional and integral coefficients of PI regulator in the current loop respectively, KPWM is the modulation ratio, Ts is the sampling period, and KR = 1/Rs , time constant TL = Ls /Rs , where, L s is the stator inductance. After simplification, the closed-loop transfer function of the current loop is expressed by (12). Gcurrent_close (s) =
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It can be known from (8) that the closed-loop transfer function is a typical secondorder system. According to the optimal design of the second-order system, the closedloop transfer function of the current loop with KPWM = 1 is obtained. Gcurrent_close (s) =
1 2Ts s + 1
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Therefore, the system open-loop transfer function can be expressed by (10). Gopen (s) = ki_v
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ωc 1 s + ωc 2Ts s + 1
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It is seen from (10) that the open-loop transfer function is a third-order system. Under the condition of 2Ts = kp_v /ki_v , the system is reduced to the second-order system, which is represented by (11). Gopen (s) =
ki_v ωc s s + ωc
(11)
It is known from (11) that both ki_v and ωc are positive, and the poles of the transfer function are always in the left half plane of s plane. Therefore, according to the Routh criterion, the system is always stable. Equation (11) also shows that the system is a bandpass filter. According to the second-order characteristics of the system, the following expression can be obtained. ki_v = ωn /2ξ (12) ωc = 2ξ ωn where, ωn is the natural frequency, ξ is the damping factor. Considering that the fluctuation frequency of the DC voltage is 6 times of the fundamental frequency of the input three-phase AC power supply, the natural frequency ωn is finally set as 314 rad/s and the damping factor ξ = 2 is selected. In a word, according to the above analysis and parameter design, the system is still stable by adding the q-axis voltage control loop.
4 Experimental Verification and Analysis In order to verify the performance of the algorithm with low carrier ratio, the hardware platform with the control core of Xe164HM72F80L is built. The PWM carrier frequency is 2 kHz. The motor parameters of the SPMSM are shown in Table 1.
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Table 1. Motor parameters Rated power
3 kW
Rated current
8.9 A
Back EMF coefficient
102.4V/1000 RPM
Rated speed/frequency
2000 RPM/133.33 Hz
Pole number
8
Stator resistor/inductance
1.6 /9.15 mH
Figure 4 is the experimental setup, which consists of the tested motor and the loading motor with a coaxial connection. Since the BEMF of TECO is smaller than the rated AC supply, a transformer is used to adjust the input voltage. In these tests, the DC voltage is set around 410 V.
Motor control prototype
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Auto transformer
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Fig. 4. Experimental setup
The parameters in the control system are listed as follows. The time constant of the LPF of the flux frequency is 2 ms. PI regulator of the speed control loop: kp = 0.5, ki = 100. PI regulator of current control loop: kp_c = 4.6, ki_c = 17.53. PI regulator of the field weakening control: kp_v = 0.078, ki_v = 78.5. The time constant of the low pass filter is 5 ms. id∗ is limited to 95% of the rated current: −8.45 A. The maximum torque current is 1.5 times the rated motor current, λ = 0.9, so ηmin = 0.63. Fratio_ max = 100 and Fratio_ min = 15 are set. For short, the traditional field weakening algorithm is abbreviated as T-FWA. The adaptive field weakening algorithm is abbreviated as A-FWA. The system with no load operates at 180 Hz, then suddenly increases the load to 21 Nm. The carrier ratio is 11.1. It can be clearly seen from the loading waveform in Fig. 5 and Fig. 6 that, the response speed of the A-FWA is far faster than that of the T-FWA with low carrier ratio. The comparison of the dq-axis voltage and current waveforms also verifies the previous analysis.
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5 Experimental Verification and Analysis An adaptive field weakening control algorithm is proposed to solve the performance degradation problem of the traditional field weakening algorithm at low carrier ratio. The adaptive carrier ratio function related to the q-axis voltage is established, and the field weakening region is entered in advance at low carrier ratio. Thereby, the current control loop is changed from the dual-axis regulation to the single-axis regulation. The experimental results verify that the dynamic response speed and load capacity have been significantly improved.
References 1. Bolognani, S., Calligaro, S., Petrella, R.: Adaptive flux-weakening controller for interior permanent magnet synchronous motor drives. IEEE J. Emerg. Sel. Top Power Electron. 2(2), 236–248 (2014) 2. Chen, K., Sun, Y., Liu, B.: Interior permanent magnet aynchronous motor linear fieldweakening control. IEEE Trans. Energy Convers. 31(1), 159–164 (2016) 3. Pan, C.-T., Sue, S.-M.: A linear maximum torque per ampere control for ipmsm drives over full-speed range. IEEE Trans. Energy Convers. 20(2), 359–366 (2005) 4. Bolognani, S., Calligaro, S., Petrella, R., Pogni, F.: Flux-weakening in IPM motor drives: Comparison of state-of-art algorithms and a novel proposal for controller design. In: Proceedings of the 2011 14th European Conference on Power Electronics and Applications, Birmingham, U.K., pp. 1–11 (2011) 5. Kim, J.-M., Sul, S.-K.: Speed control of interior permanent magnet synchronous motor drive for the flux weakening operation. IEEE Trans. Ind. Appl. 33(1), 43–48 (1997) 6. Kwon, T.S., Sul, S.-K.: Novel antiwindup of a current regulator of a surface-mounted permanent-magnet motor for flux-weakening control. IEEE Trans. Ind. Appl. 42(5), 1293–1300 (2006) 7. Kwon, T.-S., Choi, G.-Y., Kwak, M.-S., Sul, S.-K.: Novel flux-weakening control of an IPMSM for quasi-six-step operation. IEEE Trans. Ind. Appl. 44(6), 1722–1731 (2008) 8. Gallegos-Lopez, G., Gunawan, F.S., Walters, J.E.: Optimum torque control of permanentmagnet AC machines in the field-weakened region. IEEE Trans. Ind. Appl. 41(4), 1020–1028 (2005) 9. Wu, C., Sun, X.D., Wang, J.Y.: A rotor flux observer of permanent magnet synchronous motors with adaptive flux compensation. IEEE Trans. Energy Convers. 34(4), 2106–2117 (2019)
A High-Precision Method for High-Power Electric Heating Element Insulation Inspection Tiejun Zeng(B) , Jianhui Liu, Song Yu, and Jiaqi Yang School of Electrical Engineering, University of South China, Hengyang 421001, China [email protected]
Abstract. The problem of high-precision insulation detection in the neutral point ungrounded system has not been well solved, especially in the case of non-faulty branch insulation resistance is not infinite, the traditional insulation testing device with the total insulation resistance to replacing the fault branch insulation resistance method error is very large. To solve this problem, this paper designs an injection source circuit that can be switched between the neutral point of the transformer and the output line of a phase. The actual total insulation resistance of the system can be calculated using the dual-frequency method when the injection source is switched to a phase-out, and detailed analysis of each branch before the fault industrial frequency zero-sequence current relationship and then find out each branch to ground resistance. After the insulation fault, the insulation resistance of the faulty branch is calculated from the value of the resistance to the ground of the non-faulty branch before the fault. This method eliminates the error caused by replacing the faulty branch insulation resistance with the total insulation resistance when the non-faulty branch insulation resistance is non-infinite. Simulation models are built-in Matlab Simulink to verify the method. The simulation results show that the method has high accuracy and a wide range compared with the traditional insulation resistance calculation method. Keywords: Branch insulation detection · IT system · Single-phase ground fault · High accuracy · Wide range
1 Introduction Nuclear reactor regulator electric heater using IT system (according to the national standard GB4776-1984 “electrical safety nomenclature”, is the neutral point of the power supply is not grounded or grounded by high impedance grounding and the metal shell of the equipment grounding system) power supply, due to the engineering environment necessary for high temperature, high current, heating losses lead to frequent replacement of regulator heating tube and serious damage. Given the high percentage of replacement during maintenance, special location (voltage regulator) and special size (slender) and other factors caused by time-consuming and laborious, operation and maintenance engineers and technicians need to accurately and quickly determine the corresponding line © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 280–289, 2022. https://doi.org/10.1007/978-981-19-1528-4_28
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position of the faulty heating tube [1]. To make an accurate determination of the faulty branch, a high-precision insulation inspection of the IT system is desirable. The main methods that have been proposed to detect the insulation state in realtime are the dual-frequency injection method [2], the single-frequency injection method [3], and some algorithms optimized on these two methods. The traditional injection method can directly calculate the insulation resistance of the faulty branch in principle, but due to the difficulty of detecting the injection source signal of the faulty branch, the injection method is often implemented in practical installations by collecting the injection source signal of the injected branch, and after calculating and processing, the total current and voltage to ground of the injected source signal flowing through the IT system are obtained, and finally, the total insulation resistance of the IT system is calculated according to the principle. The total insulation resistance of the IT system is calculated based on the principle. This method, which uses the total insulation resistance as an approximate substitute for the fault branch insulation resistance to judge the fault branch, produces little error when the non-faulty branch insulation resistance of the IT system is approximately infinite and can make a good judgment of the system fault. However, when the non-faulty branch insulation resistance is not infinite [5], this approximation error is very large, which may cause the misjudgment of the fault. A high-precision insulation resistance calculation method is proposed to address the problems of the appealed traditional insulation detection method in the actual implementation of the device. The method is designed with an injection source circuit that can be switched on the neutral point of the transformer and a phase-out, and when the injection source is switched to a phase-out, the total insulation resistance and the total capacitance to the ground are calculated using the improved dual-frequency injection method [9], and an analysis of the relationship between the Industrial frequency zero-sequence current after the device is connected to a phase of the system is made, and the insulation resistance calculation method is derived, which can realize high-precision faulty branch insulation resistance calculation [10], and at the same time can This method can realize the high accuracy of the insulation resistance calculation of the faulty branch and the real-time detection of the insulation resistance of the non-faulty branch [5], which is difficult to realize by the traditional method.
2 Frequency Zero Sequence Current Analysis Before and After the Fault On the basis of calculating the total insulation resistance, in order to calculate the accurate insulation resistance of the faulty branch [8], so the analysis of the zero-sequence current before and after the fault of the IT system after the insulation detection device is connected, due to the injection source signal of each branch is difficult to detect, so the larger signal quantity of the industrial frequency zero-sequence current before and after the fault will be used. Using the improved dual-frequency injection method [1] to calculate the total insulation resistance of the IT system whose schematic diagram is shown in Fig. 1, when no single-phase ground fault occurs due to the device is connected from a phase of the IT system [4], which is equivalent to the introduction of the grounding point from the
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phase at this time all three branches will generate zero-sequence current. each branch zero-sequence current and the device to form a circuit [6], at this time, because the device injection circuit to join the current-limiting resistance, smaller current-limiting resistance and Will did not cause the grid leakage current protection device action, the device can be connected to the grid safe operation [7].
Ia1 Ib1 Ic1
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The zero sequence current generated by the three load branches flows through the earth from the device when a single-phase ground fault does not occur, and the zerosequence circuit diagram of the power grid before the fault is shown in Fig. 2. I1
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1
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Fig. 2. The corresponding industrial frequency zero-sequence circuit diagram when no ground fault occurs
When a single-phase ground fault occurs, the fault branch becomes a zero-sequence source, all non-fault branch of the zero-sequence current, through the earth into the grounding point, respectively, as well as injected into the branch, the fault after the zero-sequence circuit diagram road shown in Fig. 3 below.
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I1
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Fig. 3. Single-phase ground resistance for Rf fault when the industrial frequency zero-sequence circuit diagram
Analysis of the zero-sequence current relationship after the fault shown in Fig. 3 zerosequence current flows from the two grounding points, respectively. application the KCL law we knew that I1 + I2 = I3 + Ir , and total insulation resistance Rt = R1 //R2 //R3 cannot be correlated for calculation. Analysis of Fig. 2 before the fault frequency zerosequence current relationship can be seen, when no ground fault occurs injection source branch is equivalent to the zero-sequence source, branch 1, 2, 3 according to the branch impedance for shunt Ir = I1 + I2 + I3 , can use leakage current transformer can detect the fault before each branch frequency zero sequence current, by the three road zerosequence current parallel shunt relationship, plus the total insulation resistance has been calculated before the fault, and each branch to the ground to ground voltage and insulation resistance zero-sequence current relationship it is possible to calculate the insulation resistance of each branch before the failure.
3 Principle of High Precision Insulation Resistance Calculation According to the analysis of the relationship between the Zero sequence current of industrial frequency before and after the fault of the IT system after the previous access to the insulation detection device, the industrial frequency zero-sequence current relationship before the fault is used to calculate the insulation resistance of each branch circuit. When considering only the industrial frequency signal alone combined with Fig. 1, Fig. 2 can be obtained before the failure of the IT system equivalent circuit shown in Fig. 4. In Fig. 4, the injection branch is the signal source, and each load branch satisfies the parallel shunt relationship.
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Rt and Ct can be found by the improved dual-frequency method of injection proposed in the literature [1], The calculation method of branch insulation resistance in the literature [1] is extended to calculate the total insulation resistance as follows: when the branches are connected in parallel, the Eq. 1, If in the literature [1] can be replaced by Ir (injecting the effective value of the branch current), and the measured insulation resistance of each branch is the parallel value Rt as shown in Eq. 1 below, and the equation of the total capacitance to ground can be obtained as shown in Eq. 2. √ Uf 1 Uf 2 k 2 − 1 Rt = 2 U2 − I2 U2 k 2 Ir1 r2 f 1 f2 k = f 2/f 1 • URS Ir = Rs • • • • Ir2 Ur1 − Ir1 Ur2 Ct = 2π Ur1 Ur2 (k − 1)f1 k = f 2/f 1
(1)
(2)
Using the improved dual-frequency method injection method to find the total insulation resistance total capacitance to ground corresponds to the insulation resistance of each branch in Fig. 4, capacitance relationship as shown in Eqs. 3, 4. Rt = R1 //R2 //R3
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Then the following two equations can be obtained based on the equal relationship between the voltage of each branch to the ground in Fig. 4. •
•
I1 ∗(R1 //jwC1 ) = I2 ∗(R2 //jwC2 )
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The two equations are phase quantities, and there are 4 equations using the real and imaginary parts of the expansion, plus Eqs. 3 and 4, there are 6 equations, at this time there are R1, R2, R3, C1, C2, C3 a total of 6 unknowns, in principle can be solved. With the combination of Eq. 3, Eq. 4, Eq. 5, Eq. 6, you can get the impedance conductance of each branch before the fault is calculated as follows. Y1 = Y2 = Y3 = Yt =
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(7) (8) (9) (10)
The impedance conductance of each branch before the failure is obtained, the insulation resistance of each branch before the failure can be calculated as follows. R1 =
1 Re[Y1 ]
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1 Re[Y3 ]
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When a single-phase ground fault occurs, assuming a single-phase ground fault occurs in branch 3, the insulation resistance of the non-faulted branch remains the same as the calculated insulation resistance before the fault plus the calculated total insulation resistance Rt , The insulation resistance of the faulty branch 3 is obtained with the following formula: R3 =
Rt R1 R2 R1 R2 − Rt R2 − Rt R1
(14)
The calculated insulation resistance is not an approximate substitution of the total insulation resistance, but an accurate calculation of the faulty branch insulation resistance. Therefore, the calculated value is accurate and precise even if the insulation resistance of the non-faulty branch is not infinite.
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4 Simulation Analysis In Matlab Simulink to build the simulation model with the IT system equivalent diagram shown in Fig. 1, the parameters of the cable line refer to the model in literature 1. Each cable is considered to be 1 km long and the insulation degradation point is placed at 95% of a cable line to consider severe cases. To simulate the non-infinite insulation resistance of our branch circuits, the “Three-Phase parallel load” module is connected to each branch circuit, and the bottom left corner of the simulation model is the injection source branch circuit where we calculate the total insulation resistance using the modified dualfrequency method, and the injection source is chosen to be injected into the grid from phase C. The total simulation time is set to 100 s, and a ground fault occurs at 50 s. The simulation model is shown in Fig. 5.
Fig. 5. Simulation model of branch insulation resistance calculation
The calculation process of branch insulation resistance is as follows: first use the improved double-frequency injection method to calculate the total insulation resistance, with the total capacitance to the ground; 50 s before the simulation due to the absence of single-phase ground fault using Eq. 7 to Eq. 14 to derive the formula for the branch insulation resistance before the fault, 50 s after keeping the branch 1, 2 insulation resistance unchanged, using Eq. 14 to calculate the fault branch insulation resistance. When the grounding resistance is set to 3 K, the simulation results of the insulation resistance of the branch before and after the fault are shown below. From the simulation data, it can be calculated that the parallel resistance of each branch insulation resistance calculated by the previous branch insulation resistance calculation method in the first 50 s without fault is about 64841 , which is almost equal to the total insulation resistance calculated by our improved dual-frequency method, which can fully prove the correctness of our proposed insulation resistance calculation method. When the total insulation resistance, as well as the non-faulty branch insulation resistance, is used to invert the fault branch insulation resistance after a fault occurs,
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Fig. 6. Simulation results of branch insulation resistance calculation
the accuracy is higher compared to the total insulation analogous to the fault resistance. The calculated fault branch insulation resistance of 2988 is compared with the total insulation resistance of 2893 calculated by the dual-frequency method, which further proves the accuracy of our method in calculating the fault branch insulation resistance in the case of non-faulty branch insulation resistance not being infinite. We change the setting of the resistance value of the fault grounding module to compare with the fault branch insulation resistance value calculated by our proposed method to obtain the error size of the fault branch insulation resistance calculated by us in the case of different grounding resistance values as shown in Table 1. The accuracy of the calculation of the non-faulted branch insulation resistance value cannot be obtained because the line module that can directly set the resistance to the ground cannot be found in Simulink [6]. Table 1. Measured results of the simulation test under different fault branch grounding resistance values access Grounding module access The insulation resistance Error insulation resistance value value of the faulty branch is (k) calculated by simulation (k) 3
2.988
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From the data in the table can be seen in our insulation resistance calculation method, the calculation range within 150 K error is within 20%, when a single-phase ground fault
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occurs in general grounding resistance is very small, our insulation resistance calculation method in the grounding resistance is very small calculation accuracy.
5 Conclusions In response to the traditional method of judging the faulty branch by approximating the total insulation resistance instead of the faulty branch insulation resistance, which generates a large error when the insulation resistance of the non-faulty branch of the IT system is not infinite, based on the calculation of the total insulation resistance of the IT system, a detailed analysis of the change of the industrial frequency zero-sequence current before and after the fault is connected to the insulation detection device is proposed, and a method of using the industrial frequency zero-sequence current before the fault to The method of calculating the insulation resistance of each branch circuit is proposed to eliminate the error of the appeal by the accurate calculation method. The paper makes a detailed derivation of the insulation resistance calculation principle and simulates it in Matlab Simulink. The simulation results show that the method can accurately calculate each branch, and the calculation range is around 200 K. Acknowledgment. Supported by 1 Research on On-line Insulation Monitoring Technology of Electric Heater in Nuclear Reactor Regulator, Key Project of Education Department of Hunan Province (180SJZ005), hengyang, 421000, china. 2 the Project “Research on Environmental Qualification Technology of Electrical Instrument Equipment” of China Nuclear Power Research and Design Institute, ChengDu City, 610041, China.
References 1. Hu, Y., et al.: Design of insulation monitoring device based on improved dual-frequency injection method. Instr. Techn. Sens. 2020(07), 47–51 (2020) 2. Zhang, J., Xu, G., Qi, L., et al.: Application of dual frequency method in insulation fault location of floating ground AC power grid. Power Autom. Eq 23(2), 83–86 (2003) 3. Wang, Y., Zhang, Z., Yin, X., et al.: Principle and simulation analysis of single frequency insulation monitoring. Mar. Power Technol. 27(5), 277–281 (2007) 4. Pang, B., Zhu, B., Wei, X., Wang, S., Li, R.: On-line monitoring method for long-distance power cable insulation. IEEE Trans. Dielectr. Electr. Insul. 23(1), 70–76 (2016) 5. Zhang, W., Zhu, Y.T., Yang, B.T., Liu, Y.N.: Study on DC component method for hotline XLPE cable diagnosis. In: IEEE International Conference on Symposium on Electrical Insulation, Pittsburgh, PA, pp. 95–98 (1994) 6. Fan, C., Li, K.K., Chan, W.L., Yu, W., Zhang, Z.: Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines. Int. J. Electr. Power Energy Syst. 29(6), 497–503 (2007) 7. Cui, T., Dong, X., Bo, Z., Juszczyk, A.: Hilbert-transform-based transient/intermittent earth fault detection in noneffectively grounded distribution systems. IEEE Trans. Power Delivery 26(1), 143–151 (2011) 8. Liu, Z., Guan, G., Shi, C.: Theoretical Analysis and Simulation of a Novel Method on Insulation On-line Monitoring of Cross-linked XLPE Electrical Cable at 220kV[C]. In: IEEE International Conference on Automation & Logistics. IEEE (2007)
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9. Etemadi, A.H., Sanaye-Pasand, M.: High-impedance fault detection using combination of multi-layer perceptron neural networks based on multi-resolution morphological gradient features of current waveform. IET Gener. Transm. Distrib. 5(5), 588–595 (2011) 10. Carcia, M., Montanes, A., Halabi, N.E.: High resistive zero-crossing instant fault detection and location scheme based on wavelet analysis. Electr. Power Syst. Res. 31(92), 138–139 (2012)
Dynamic Hierarchical Collaborative Optimal Scheduling in Energy Internet Based on Cooperative Game Yongjie Zhong1,2(B) , Yuping Li1,2 , Wei Zhang1,2 , Bing Hu1,2 , Yinian Qi1,2 , and Dong Chen1,2 1 Guodian Nanjing Automation Co., Ltd., Nanjing 210032, China
[email protected] 2 Nanjing SAC Power Grid Automation Co., Ltd., Nanjing 211153, China
Abstract. Under the background of the interaction between park and regional energy Internet, firstly, the basic framework of interaction between typical the park and the regional energy Internet is introduced. Secondly, the day-ahead energy efficiency optimization scheduling model of the lower level park energy Internet model, and the day-ahead economic optimization scheduling model of upper level regional energy Internet are proposed, and the energy efficiency and economic multi-objective collaborative optimization scheduling problem are mapped into the cooperative game strategy. Then, it expounds the effect and function of outer cooperative game and inner cooperative game. Finally, the rationality and superiority of the hierarchical collaborative optimization model and method based on cooperative game are verified by example analysis. Keywords: Energy Internet · Cooperative game · Economy energy efficiency
1 Introduction Since the 21st century, energy has been the foundation of national development [1– 3]. How to ensure the sustainability, flexibility and steady economic growth of energy utilization and supply while improving energy efficiency level, promoting energy conservation and emission reduction has become the focus of common concern in today’s society [1, 3–6]. The research on modeling and collaborative optimization of energy Internet can provide support for the key technologies such as planning and construction, optimizing scheduling and operation management of multi-heterogeneous energy systems, and help to play the potential of multi-energy complementary advantages, enhance the potential of coordination and allocation for resources, improve the efficiency of comprehensive energy utilization and achieve sustainable development of energy [7–10]. To sum up, it is necessary to further put forward hierarchical cooperative optimization model and method based on cooperative game according to application requirements of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 290–297, 2022. https://doi.org/10.1007/978-981-19-1528-4_29
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different engineering scenarios, and establish a clear and perfect cooperative optimization scheduling system for interaction between the park and regional energy Internet. Based on the relationship of cooperative game and the information interaction principle of energy management system, the interactive hierarchical collaborative optimization framework, upper and lower models, optimal scheduling mode and solution process are proposed, and the hierarchical collaborative optimal scheduling strategy based on cooperative game is proposed and verified by simulation.
2 Hierarchical Architecture of Typical Park and Regional Level Energy Internet Energy Internet has obvious hierarchical structure in geography, scheduling, management and other aspects [2, 5]. The interactive hierarchical structure of typical park and regional energy Internet is shown in Fig. 1: the lower level park energy Internet mainly geared to the needs of the end user side [5–7, 9], typical park energy economic development zone, comprehensive engineering application entities such as emerging business district, new and high technology development area, industrial demonstration zone, large entertainment center, etc., the lower level park energy Internet, various types of energy conversion equipment collaborative work with each other, make the kinds of energy coupling, transformation and interaction with the optimal way [4, 6].
Fig. 1. Framework of interaction between typical the park and regional energy Internet.
The heat load, electricity load or gas load of lower level park energy Internet is mainly the terminal load, such as the heat load, electricity load or gas load used in industrial area, office area, commercial area, residential area, etc. The heat load, electricity load or gas load of the upper level regional energy Internet is mainly distributed energy stations or energy hubs, such as distribution heat, gas or electricity stations. The upper level
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regional energy Internet is usually geographically far apart from the lower level park energy Internet, which belongs to different interest subjects. The lower level park energy Internet is user-oriented and focuses on the overall energy efficiency of the park, so it is a typical consumer. Obviously, there is a relationship of competition and conflict between the upper level regional energy Internet and the lower level park energy Internet, which is also known as game relationship. Certain game strategies can be used to make the upper level regional energy Internet and the lower level park energy Internet obtain the best dispatching mode.
3 Hierarchical Collaborative Optimal Scheduling Model of Park and Regional Level Energy Internet 3.1 Energy Efficiency Optimization and Scheduling of Lower Level Park Energy Internet Energy efficiency is one of the key issues that need to be paid attention to in the park energy Internet optimal scheduling operation [9, 10]. Different from primary energy utilization rate or comprehensive energy utilization rate, energy efficiency measures benefit of system from the perspective of energy quantity [5, 8–10], while primary energy utilization rate or comprehensive energy utilization rate measures the benefit of the system based on the difference in energy quality, that is, the difference in quality. In the general efficiency optimization scheduling related research, the component factors of input power are not considered, that is, the proportion of new energy generation, coal-fired unit generation and gas-fired unit generation in the power purchase. However, in actual engineering scenario application, the input value, especially the component of power purchase, will affect the size of input power [2, 5, 6]. The optimal scheduling of day-ahead energy efficiency refers to the optimal scheduling of energy efficiency. The purpose of optimal scheduling of energy efficiency is to obtain the maximum energy efficiency, which is defined as the ratio of the output energy value to the input energy value. The optimal scheduling model of day-ahead lower level park energy Internet efficiency of is as follows: EXcout + EXhout + EXeout + EXgout EX out = in + EX in + EX in + EX in EX in EXgrid re g bb ⎧ NT ϑcam,t ⎪ ⎪ EXcout = Ltc t ⎪ ⎪ ϑcref,t −1 ⎪ t=1 ⎪
⎪ ⎪ NT ⎪ ϑham,t ⎪ out = ⎪ 1 − Lth t EX ⎨ h ref,t
max fexη =
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NT t t ⎪ in = t + vcoal + vgas P t ⎪ EX v ⎪ re grid grid t ηcoal ηgas ⎪ ⎪ t=1 ⎪ ⎪ ⎪ NT ⎪ ⎪ t + P t t in ⎪ Pwt ⎨ EXre = pv t=1
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NT ⎪ ⎪ in = t t ⎪ EXbb ζbb Fbb ⎪ ⎪ ⎪ t=1 ⎪ ⎪ NT ⎪ ⎪ in ⎪ ζg Fgt t ⎩ EXg = t=1
Where EX , L, t, NT , ϑ, v, ζ and η are energy values of different types of energy, load, optimize the scheduling simulation step size, optimal scheduling period, temperature, penetration rate of different primary energy sources in purchased electricity, energy factors and the power generation efficiency, respectively. 3.2 Economic Cost Optimization and Scheduling of Upper Level Regional Energy Internet The economic cost is mainly reflected in the consumption cost of different types of energy. The economic optimization dispatching cost mainly includes the penalty cost of abandoning wind, penalty cost of abandoning light, cost of consuming natural gas, cost of fuel consumed by cogeneration units and cost of fuel consumed by thermal power units. Therefore, the day-ahead economic optimization dispatching model of upper level regional energy Internet is as follows: ⎛ ⎞ NT u,cos t ⎝ = fre + fGW + fTPGU + fCHP ⎠t (4) min foper t=1
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u, cost , fre , fGW , fCHP and fTPGU are respectively economic cost of operation, the Where foper economic cost function of punishment for abandoning new energy, the economic cost function of gas source output, the economic cost function of cogeneration unit operation, and the economic cost function of thermal power unit.
4 Hierarchical Cooperative Optimal Scheduling Based on Cooperative Game 4.1 Mathematical Description of Multi Objective Optimization Problems ⎧ min F(O) = min f1 (O), f2 (O), · · · , fp (O) ⎪ ⎪ ⎨ s.t. gi (O) < 0, i = 1, 2, · · · , ki ⎪ hj (O) = 0, j = 1, 2, ⎪ · · · , lj ⎩ O ≤ O ≤ O, O = o1 , o2 , · · · , oq
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Where F(O) is objective function with a total of p. O, O and O are respectively the optimization variable, upper limit of optimization variable, and lower limit of optimization variable, with a total of oq . gi (O) represents the i-th inequality constraint with a total of ki . hj (O) is the j-th equality constraint with a total of lj . 4.2 Solving Multi Objective Optimization Problems Based on Cooperative Game Method A game problem usually involves the following factors: game participants, game participants’ benefits, game participants’ strategies. Each objective is corresponding to each participant in the game, each objective optimization variable is corresponding to the strategy set controlled by each participant, each target value is corresponding to the benefit of each participant, and the constraint condition of the optimization problem restricts the strategy value of each participant. The cooperative game model based on above is as follows: p fi (O) − fi (O) (6) F(O) = ∗) f (O) − f (O i i i=1 Where fi (O) represents the i-th objective function. f i (O) is the least ideal value of fi (O). fi (O∗ ) denotes the optimal value of fi (O).
5 Case Study 5.1 System Description The typical park and regional energy Internet interactive hierarchical architecture as shown in Fig. 1 is taken as the simulation example in this paper. The typical day in winter is taken as a simulation cycle, that is, 24 h, and the simulation step is 1 h. There is no cooling load demand in the typical day in winter, and the ground source heat pump operates in heating mode. Therefore, the energy flow path in lower level park energy Internet is not enabled. 5.2 Results and Analyses energy efficiency of energy Internet in the lower level of the park under different operating modes changes over time, as shown in Fig. 2. In Fig. 2, energy efficiency optimal scheduling refers to the integration of the lower Park and the upper regional energy Internet, and the optimization objective is energy efficiency optimization, that is, the lower efficiency optimal scheduling. At this time, the proportion of power generation by different types of energy purchased is also variable, and the lower Park and the upper regional energy Internet are coupled with each other through the data interaction of tie line; Economic optimization dispatching refers to the integration of energy Internet in the lower level park and the upper level area. The optimization goal is economic
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optimization, that is, the economic dispatching in the upper level. The other cases are the same as energy efficiency optimization dispatching. According to the results of Fig. 2, the efficiency of single economic optimal dispatching operation mode is relatively low in the overall optimal dispatching cycle. The efficiency of the other three operation modes, especially during 1:00–7:00 and 19:00– 24:00, is very close. It can be seen that the efficiency changes of different operation modes in the optimal dispatching cycle have both similarities and differences.
Fig. 2. Dynamic changes of energy efficiency under different operating modes.
Fig. 3. Proportion change of different types of energy generation in power purchase.
Under different optimal dispatching, the proportion of different types of energy generation in the power purchase of the integrated energy system in the lower park is shown in Fig. 3. In the general efficiency optimal dispatching related research, the component factors of input energy are not considered, that is, the proportion of new energy generation, coal-fired unit generation, gas-fired unit generation, etc. in the power purchase, however, the actual engineering field is not very good In landscape application, the input value, especially the composition of power purchase energy, will affect the input
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value. The different proportion of various components of power sources, the different efficiency of various types of generating units and the different taste of various types of energy will directly or indirectly affect the efficiency and the output of each power source of the upper regional integrated energy system. According to Fig. 3, the proportion of new energy generation under different optimized operation modes is close to and higher than that of coal-fired thermal power units. It can be seen that the capacity of absorbing new energy under different optimized operation modes is close, and different optimized operation modes mainly affect the proportion of coal-fired thermal power units and gas-fired units.
Fig. 4. Variation of power supply and thermal output of gas cogeneration.
According to Fig. 4, it can be seen that the electric and thermal output operation area of gas-fired cogeneration is a polygonal area, but the actual gas-fired cogeneration under different operation modes operates under the back pressure condition, and the whole operation mode is stable When the thermal power is adjusted to 11.97 MW in the cycle, the corresponding electric power can be adjusted to 81.07 MW. The electric power can be adjusted in a large range, which can provide appropriate space for new energy to be connected to the grid. According to the actual power output data of combined heat and power supply and heat under different operating modes, it can be found that the power and heat output of combined heat and power supply are both large during 1:00–8:00 and 21:00–24:00, which makes wind power not fully received during this period. The operation state of Gas-fired Cogeneration in Ref. [10] is close to that of the optimal compromise solution of the outer game, while the operation state of the optimal compromise solution of the inner game is obviously different from the above two cases.
6 Conclusion The research shows that the model and method comprehensively consider the interactive layered characteristics of the energy Internet, which is more in line with the actual
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site of engineering application, and is conducive to reducing the economic cost of optimal scheduling and improving energy efficiency. From the perspective of economy and energy efficiency, the multiple demands for optimal dispatching of energy Internet are considered to better meet the interest demands of different optimal dispatching modes. Considering the pursuit of parties at different levels of energy Internet based on cooperative game is conducive to balancing the conflicts and contradictions of interests of all parties.
References 1. Wu, K.: Research on the operation mode of new generation electric power system for the future Energy Internet. Proc. CSEE 39(4), 966–979 (2019). (in Chinese) 2. Ton, D.T.: A more resilient grid: the U.S. department of energy joins with stakeholders in an R&D Plan. IEEE Power Energy Mag. 13(3), 26–34 (2015) 3. Wang, D.: Review of key problems related to integrated energy distribution systems. CSEE J. Power Energy Syst. 4(2), 130–145 (2018) 4. Bui, V.: A multi-agent-based hierarchical energy management strategy for multi-micro grids considering adjustable power and demand response. IEEE Trans. Smart Grid 9(2), 1323–1333 (2018) 5. Zhong, Y.J.: Multi-scenario optimal dispatch of integrated community energy system with power-heating-gas-cooling subsystems. Autom. Electr. Power Syst. 43(12), 76–84 (2019). (in Chinese) 6. Wang, Z.: Self-healing resilient distribution systems based on sectionalization into micro grids. IEEE Trans. Power Syst. 30(6), 3139–3149 (2015) 7. Ding, Y.R.: Multi-objective optimal dispatch of electricity-gas-heat integrated energy system considering comprehensive energy efficiency. Autom. Electr. Power Syst. 45(2), 64–73 (2021). (in Chinese) 8. Yang, L.J.: Electric-gas coupled integrated energy fault recovery strategy based on bi-level optimization model. Power Syst. Technol. 44(11), 4264–4273 (2020). (in Chinese) 9. Manshadi, S.: Coordinated operation of electricity and natural gas systems: a convex relaxation approach. IEEE Trans. Smart Grid 10(1), 3342–3354 (2019) 10. Zhong, Y.J.: Hierarchical multi-objective fuzzy collaborative optimization of integrated energy system under off-design performance. Energies 12(1), 1–27 (2019)
Improving Transmittable Active Power Capability of VFT with Optimal Stator Reactive Power Reference Jiahao Lu1(B) , Yun Zeng2 , Jielong Chen1 , Xiangxuan Kong1 , and Sizhe Chen3 1 Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Foshan, China
[email protected]
2 Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou, China 3 Guangdong University of Technology, Guangzhou, China
Abstract. The variable frequency transformer (VFT) is a new type of flexible AC transmission system equipment to achieve the interconnections of asynchronous power grids, whose transmittable active power capability is one of the most important performance indicators. Decoupled active-reactive power control is reported in the existing literature, however, without considering the changes in magnitudes of stator reactive power and slip will decline in the active power transfer ability of VFT. This paper intends to improve the transmittable active power capability of VFT. Behaviors of the VFT system with a series converter are introduced. The active power characteristics of VFT under varying stator reactive power and slip are analyzed in detail. The expression of optimal stator reactive power reference is deduced. Hardware-in-loop experimental platform is established to verify the correctness of the proposed optimal stator reactive power reference. Experimental results show that the transmittable active power ability of VFT is effectively improved with the proposed optimal stator reactive power reference, and hence maximum power transmission of VFT can be realized. Keywords: Variable frequency transformer · Transmittable active power capability · Optimal stator reactive power reference
1 Introduction Large-scale grid interconnection is the inevitable trend of future grid development [1]. Asynchronous power grids cannot be interconnected directly, while need to be connected by asynchronous interconnection equipment, such as high voltage direct current (HVDC) converters [2, 3]. However, HVDC converters have inherent drawbacks such as commutation failure, complicated control, and weak overload capability [4, 5]. To overcome these drawbacks, a new type of flexible AC transmission system equipment, the variable frequency transformer (VFT), was firstly proposed by General Electric (GE) in 2003, whose functions include asynchronous interconnections, power transfer control, frequency adjustment, supply power to the weak grid, suppress low-frequency power oscillation [6–9]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 298–306, 2022. https://doi.org/10.1007/978-981-19-1528-4_30
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[10] addressed the issue of uncontrollable reactive power exchange of VFT for the first time, and detailed the changes in 1) magnitude of grid voltage, and 2) magnitude and direction of active power may lead to large reactive power exchange. Then, a series compensation scheme based on a series converter by regulating the magnitude of stator voltage is proposed to realize the decoupled power control of VFT. However, this paper does not consider the influence of magnitudes of reactive power and slip on the transmittable power of VFT. If slip is not equal to 1, the apparent powers of stator and rotor windings are unequal, and therefore reduce the transmittable active power of VFT. This paper intends to improve the transmittable active power capability of VFT, and organized as follows. Section 2 introduces the behaviors of the VFT system with a series converter. The maximum stator active power characteristic curves of VFT are obtained by detailed analysis in Sect. 3. Later, the expression of optimal stator reactive power reference is deduced in Sect. 4. Hardware-in-loop (HIL) experiments on a VFT are conducted to verify the correctness of proposed optimal stator reactive power reference in Sect. 5. Finally, a conclusion is drawn in Sect. 6.
2 Behaviors of VFT System with a Series Converter 2.1 VFT Configuration with a Series Converter VFT configuration with a series converter is proposed in [10], as depicted in Fig. 1. Stator side grid
Series Transformer
Ps Qs
VFT
Pr Qr
Rot or side grid
DFIM C
C Pdc L
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DC Mot or
C Shu nt Con ver ter
H-bridge Con ver ter
Fig. 1. VFT configuration with a series converter.
Compared with the traditional VFT configuration, a series converter and a series transformer are added in the configuration shown in Fig. 1, where the shunt converter, the H-bridge converter, and the series converter share the same DC-link. The shunt converter is responsible for maintaining the DC-link voltage constant. H-bridge converter controls the armature voltage of the DC motor to regulate its driven torque. The series converter aims to control the reactive power of VFT by regulating the magnitude of stator voltage. In this configuration, the stator voltage is the sum of the stator side grid voltage and injected voltage of the series converter.
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2.2 VFT Operation VFT is used to achieve the asynchronous interconnection between two separated power grids. The relative position between stator and rotor of VFT is changed by controlling. the driven torque of DC motor, and thereby the magnitude and direction of active power flow through VFT can be regulated. The slip of VFT s under stable operation can be calculated as s = frg /fsg
(1)
where f sg is the frequency of stator side grid, f rg is the frequency of rotor side grid. According to the active power balance relationship, one can obtain Pr = Ps + Pdc
(2)
where Ps is active power flowing into the stator windings, Pr is active power flowing out of the rotor windings and Pdc is active power handled by the H-bridge converter. [11] gives the relationship between Pdc and Ps as Pdc = (s − 1) · Ps
(3)
Pr = s · Ps
(4)
Substituting (3) into (2) yields
The instantaneous reactive power Qm absorbed by VFT during any operation is Qm = Qs + Qr
(5)
where Qs is reactive power flowing into the stator windings, Qr is reactive power flowing into the rotor windings.
3 Active Power Transfer in VFT System In order to demonstrate the changeable maximum transmittable active power of VFT, it is necessary to obtain the active power characteristics of VFT. The machine parameters of VFT are given in Table 1. The apparent power of stator windings S s must be smaller than the rated apparent power S N , and the maximum stator active power Ps,max is written as
Ps,max =
SN2 − Qs2
(6)
The apparent power of rotor windings S r must be smaller than the rated apparent power S N , and the maximum rotor active power Pr,max is written as (7) Pr,max = SN2 − Qr2
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Table 1. Machine parameters of VFT Rated apparent power
4150 VA
Rated voltage
400 V
Rated frequency
50 Hz
Magnetizing inductance
1.354 p.u
Stator resistance
0.01965 p.u
Stator inductance
0.0397 p.u
Rotor resistance referred to stator
0.01965 p.u
Rotor inductance referred to stator
0.0397 p.u
Pole pairs
2
Turns ratio
1: 1
When Pr is set as (7), the corresponding maximum stator active power Ps,max can be obtained by substituting (4) into (7) yields as Ps,max = SN2 − Qr2 /s (8)
Long-term overload operation is not allowed for VFT. To maintain the apparent power of stator and rotor windings smaller than the rated apparent power S N simultaneously, the maximum stator active power Ps,max should be set as the smaller value between (6) and (8) as
Ps,max = min{Ps,max , Ps,max } Substituting (5), (6), and (8) into (9) yields Ps,max = min{ SN2 − Qs2 , SN2 − (Qm − Qs )2 /s}
(9)
(10)
As S N and Qm are constants, it can be noted from (10) that Ps,max is a function of Qs and s. In the following two subsections, the quantitative analysis method is used to obtain the stator active power characteristics of VFT. 3.1 Magnitude of Stator Reactive Power In this case, the frequency of stator side grid fsg is set at 50 Hz, while the frequency of rotor side grid frg is set at 60 Hz, i.e., s = 1.2. The stator reactive power command Qs∗ is changing from 0 to 3500 Var to obtain the maximum stator active power characteristic curve in Fig. 2(a). It is worthy to note from Fig. 2(a) that, despite no changes in the slip s, the maximum stator active power Ps,max depends on the magnitude of stator reactive power Qs . When Qs is set as point A, Ps,max reaches the maximum. Whereas, the deviation of Qs from point A will lead to a decrease in values of Ps,max , and hence reduce the active power transmission capability of VFT. Therefore, the key to improving the transmittable active power capability of VFT is to deduce the expression of the optimal stator reactive power.
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Fig. 2. Maximum stator active power Ps,max characteristic curves: (a) with slip s = 1.2 under varying stator reactive power Qs ; (b) with stator reactive power Qs = 1600 Var under varying slip s.
3.2 Magnitude of Slip In order to differentiate this characteristic from the previous case, in this case, the slip s is varied between 0.8 and 1.2, while keeping the stator reactive power command Qs∗ steady at 1600 Var to obtain the maximum stator active power characteristic curve in Fig. 2(b). It is easy to identify from Fig. 2(b) that the maximum stator active power Ps,max is fixed when s ≤ 1, and the gradual increase in s lead to the gradual decrease in Ps,max . To sum up, the changed stator reactive power and slip will affect the VFT’s active power transmission capability.
4 Optimal Stator Reactive Power Reference for VFT Based on the analysis in Sect. 3, it can be inferred that one reason for changeable maximum stator active power Ps,max is the magnitude of stator reactive power Qs . The ideal solution for this issue is to have effective control over the stator reactive power Qs , which can be realized by controlling the output voltages of series converter uscabc according to [10]. The other reason for changeable maximum stator active power Ps,max is the magnitude of slip s. However, the frequencies of two interconnected power grids are fixed, and hence the slip s is usually certain. Therefore, the most effective way to achieve maximum power transmission of VFT is to deduce the expression of optimal stator reactive power reference. The apparent power of stator windings S s is calculated as Ss2 = Ps2 + Qs2
(11)
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The apparent power of rotor windings S r is calculated as Sr2 = Pr2 + Qr2
(12)
Utilizing the maximum capacity of VFT to achieve maximum power transmission as Ss = Sr = SN
(13)
Substituting (13) into (11) yields Ps2 = SN2 − Qs2
(14)
According to (11), (12), and (13) Ps2 + Qs2 = Pr2 + Qr2
(15)
When s = 1, it is easy to know from (4) that Ps = Pr , and one can obtain Qs = Qr = Qm /2
(16)
When s = 1, substituting (4), (5), and (14) into (15) yields 2 (1 − s2 )(SN2 − Qs2 ) = Qm − 2Qm Qs
(17)
If s > 1, (17) is recalculated as Qs = −Qm /(s2 − 1) +
2 /(s2 − 1) + [Q /(s2 − 1)]2 SN2 + Qm m
(18)
Else if s < 1, (17) is recalculated as 2 /(s2 − 1) + [Q /(s2 − 1)]2 Qs = −Qm /(s2 − 1) − SN2 + Qm m
(19)
As the decoupled active-reactive power control of series converter has been well investigated in [10], it will be not discussed in detail in this paper.
5 Hardware-in-Loop Experimental Studies In order to validate the correctness of the proposed optimal stator reactive power reference, HIL experiments using OPAL-RT® OP5600 digital real-time simulator, dSPACE® DS1103 controller, and KEYSIGHT® DSOX3104T oscilloscope are conducted, as shown in Fig. 3. HIL experimental results, including three cases (i.e., Case 1: the slip is smaller than 1; Case 2: the slip is equal to 1; Case 3: the slip is bigger than 1) are depicted in Fig. 4. In case 1, f s = 60 Hz, f r = 50 Hz, i.e., s = 0.833. For the conventional scheme, injected voltage of series converter uscabc and stator current isabc are shown in Fig. 4(a) and (b). It is shown that the amplitudes of S s , Qs , and Ps are 4150 VA, 1787 Var, and 3745 W, respectively, which are measured from the oscilloscope, as shown in Fig. 4(c).
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®
®
®
Fig. 3. Hardware-in-loop experimental platform
However, as shown in Fig. 4(d), the amplitudes of S r , Qr , and Pr are 3519 VA, 1789 Var, and 3031 W. At t = 250 ms, the stator reactive power command Qs∗ is set as 875 Var according to (19). For the proposed scheme, with the injected voltage of the series converter uscabc , the stator current isabc is changed. It can be seen from Fig. 4(c) that S s is kept at 4150 VA, Qs is decreased to 875 Var, while the transmittable active power of VFT Ps is increased from 3745 W to 4057 W. At the same time, S r is also 4150 VA, Qr is increased to 2562 Var, and Pr is 3264 W, as shown in Fig. 4(d). It is concluded that the proposed scheme can not only improve the transmittable active power of VFT, but also can avoid both the stator and rotor windings overloading simultaneously. In case 2, f s = f r = 50 Hz, i.e., s = 1. As shown in Fig. 4(e)–(h), the experimental results of the proposed scheme and conventional scheme are the same, which indicates the Eq. (16) is also effective in the case of s = 1. In case 3, f s = 50 Hz, f r = 60 Hz, i.e., s = 1.2. For the conventional scheme, injected voltage of series converter uscabc and stator current isabc are shown in Fig. 4(i) and (j). From Fig. 4(k), it is seen that S r is 4150 VA, Qr is 1796 Var, and the transmittable active power of VFT Pr is 3742 W. However, as shown in Fig. 4(l), S s is only 3681 VA, Qs is 1796 Var, and Ps is 3213 W. At t = 250 ms, the stator reactive power command Qs∗ is set as 2352 Var according to (18). For the proposed scheme, with the injected voltage of the series converter uscabc , the stator current isabc is changed. It can be seen from Fig. 4(k) that S r is kept at 4150 VA, Qr is decreased to 1205 Var, while the transmittable active power of VFT Pr is 3971 W. At the same time, S s is also 4150 VA, Qs is increased to 2352 Var, and Ps is 3419 W, as shown in Fig. 4(l). One can summarize that the proposed scheme is with better results of amplitude of the transmittable active power of VFT compared to the conventional one. Further to the evaluations of three cases, it can be concluded that the expression of optimal stator reactive power reference is correct, which enables to improve the transmittable active power capability of VFT, and meet the maximum power transmission of VFT.
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Case 1： fs = 60 Hz，fr = 50 Hz
Case 2： fs = 50 Hz，fr = 50 Hz
Case 3： fs = 50 Hz，fr = 60 Hz
uscabc：10 V /div
uscabc：10 V /div
uscabc：10 V /div
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isabc：2.5 A /div
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(h) T = 30 ms /div
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(l)
Fig. 4. HIL experimental results of three cases.
6 Conclusions This paper has addressed the issue of maximum power transmission of VFT. The sensitivity of changeable maximum stator active power to the magnitudes of 1) stator reactive power and 2) slip of VFT is discussed in detail. To achieve the maximum power transmission of VFT, the expression of optimal stator reactive power reference is deduced. Then, an HIL platform is built up to verify the correctness of the proposed optimal stator reactive power reference. In the cases of three different slips, HIL experimental results have well-validated that the transmittable active power capability of VFT can be improved with the proposed optimal stator reactive power reference. Acknowledgments. This work was supported by National Natural Science Foundation of China under Grant 51307025 and Staff Technological Innovation Project of Guangdong Power Grid Co., Ltd under Grant 030600KK52200172 and 030600KK52200179.
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References 1. Rifkin, J.: The Third Industrial Revolution: How Lateral Power is Transforming Energy, the Economy, and the World. Palgrave Macmillan, London (2011) 2. Guo, Y., Gao, H., Wu, Q.: A combined reliability model of VSC-HVDC connected offshore wind farms considering wind speed correlation. IEEE Trans. Sustain. Energy 8(4), 1637–1646 (2017) 3. Wen, Y., Chung, C., Ye, X.: Enhancing frequency stability of asynchronous grids interconnected with HVDC Links. IEEE Trans. Power Syst. 30(2), 1800–1810 (2018) 4. Xue, Y., Zhang, X., Yang, C.: Commutation failure elimination of LCC-HVDC systems using thyristor-based controllable capacitors. IEEE Trans. Power Delivery 33(3), 1448–1458 (2018) 5. Ruffing, P., Collath, N., Brantl, C., Schnettler, A.: DC fault control and high-speed switch design for an HVDC network protection based on fault-blocking converters. IEEE Trans. Power Delivery 34(1), 397–406 (2019) 6. Piwko, R., Larsen, E., Wegner, C.: Variable frequency transformer - a new alternative for asynchronous power transfer. In: Power Engineering Society Inaugural Conference & Exposition in Africa, pp. 393–398 (2005) 7. Pratico, E., Wegner, C., Larsen, E.: VFT operational overview - the laredo project. In: IEEE Power Engineering Society General Meeting, pp. 1–6 (2007) 8. Pratico, E., Wegner, C., Marken, P., Marczewski, J.: First multi-channel VFT application the linden project. In: Proceedings of IEEE PES T&D, pp. 1–7 (2010) 9. Wang, L., Chen, L.: Reduction of power fluctuations of a large-scale grid-connected offshore wind farm using a variable frequency transformer. IEEE Trans. Sustain. Energy 2, 226–234 (2011) 10. Ambati, B., Khadkikar, V.: Variable frequency transformer configuration for decoupled activereactive powers transfer control. IEEE Trans. Energy Convers. 31(3), 906–914 (2016) 11. Merkhouf, A., Doyon, P., Upadhyay, S.: Variable frequency transformer - concept and electromagnetic design evaluation. IEEE Trans. Energy Convers. 23(4), 989–996 (2008)
Simulation Research on Train LKJ Split Simulation Operation Equipment System Qingsheng Shi, Shuaishuai Liu(B) , and Qingsong Shang College of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450000, China [email protected]
Abstract. LKJ2000 train operation monitoring and recording device, the main function of real-time monitoring of the train operation, so as to effectively ensure the safety of the train. This paper adopts the combination of complete hardware system and software to study that the full-function LKJ split-type simulation running device can be used in the training system such as the locomotive analog control console, so as to realize the communication with the system control computer software, and at the same time realize the control control switch between ATP and LKJ. To realize the transmission of ATP color light; Implementation of the locomotive running speed, a current actual locomotive signal, signal type, the distance, operation and so on the real-time acquisition of information, and can be transmitted through the network to the training system of control, computer software for new locomotive professional teaching plan and the transformation of the attendants, pre-service, increases the driving, single driver value multiplied, etc. In the process of training, mastering the skill, maximum increase the safety of train. Keywords: LKJ2000 · Monitoring device · Simulation system · Hardware system
1 Introduction The LKJ2000 train operation monitoring and recording device has the main function of monitoring the operation of the train in time, thus effectively ensuring the safety of the train. It can prevent accidents, record train operation and crew operations and other conditions [1], and through the screen display, display the front line status, locomotive operation status and other information in graphics, curves, text, etc., Plays an extremely important role in ensuring the safe operation of trains. With the continuous increase in the operating speed of railway trains and the further increase in operating density, passenger and cargo transportation have increasingly higher requirements for the function, reliability and safety of driving safety equipment [2, 3]. Train operation monitoring devices have become an indispensable safety equipment for operating locomotives, and locomotive crews are required to master the basic structure and operating procedures of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 307–315, 2022. https://doi.org/10.1007/978-981-19-1528-4_31
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the monitoring devices. Therefore, major companies at home and abroad are studying the LKJ2000 simulation [4] equipment to make the new locomotive professional teaching plan and the process of crew transformation, pre-job, driving increase, Complete the training process such as driver duties, master various skills proficiently, and maximize train safety. At present, the railway industry has become more mature, but safety issues have always been of concern to the country and people. The original LKJ-93 and JK-2H models [5–7] can no longer meet the current railway situation. LKJ2000 absorbs these two advantages and is more prominent in functionality, safety and reliability, and has become an indispensable equipment in the railway industry. This article uses the host computer, LKJ2000 display and various major modules to connect, input commands through UDP, and complete a series of simulations.
2 Overview of LKJ2000 Analog Equipment The LKJ split-type simulation running device can be used in training systems such as locomotive simulation consoles to achieve communication with system control computer software, and can control operating conditions such as transmission of locomotive signals, handles, speed, diesel speed and pressure; implemented in ATP and LKJ [8] Switching of control rights; realize the sending of color lights under ATP; realize the real-time acquisition of the current actual operating speed of the locomotive, locomotive signal, signal type, distance, operation disclosure, etc., and can be transmitted to the control computer of the training system through the network Software: This solution greatly reduces the cost, achieves more and more reliable functions, and makes the design of training systems such as locomotive simulation consoles more convenient and lower in cost. The train LKJ split-type simulation running device host and LKJ2000 [9, 10] monitoring display can simulate trains in various scenes, better control the running status of the monitoring device, and can better record some data of train staff, such as: attendance, retiring, etc. Some events that need to be monitored can simulate the actual operation and on-site conditions. The LKJ analog device host adopts an integrated design, which combines the computer’s main board with the LKJ2000 portable recording board. The host part of the simulated operation equipment can simulate the monitoring device to send various control signals. The host part of the analog device can transmit signals through the network interface to achieve the effect of simulation. The host also adopts a standardized design and can be installed on a 19-inch standard cabinet, which is conducive to the application of secondary development. The mask buttons, IC card sockets, LCD screen display content and button operation methods displayed on the part of the LKJ display are exactly the same as those of the LKJ2000 screen display. The host of LKJ simulation operation equipment mainly includes: monitoring simulation and network control and other functional modules. The monitoring simulation has the same function as the train operation monitoring and recording device. It can accurately simulate the locomotive signal, locomotive handle, speed, diesel speed, pressure and other conditions required by the monitoring device, and realize the simulation operation of the monitoring device based on this. The network control part can control
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the operating host of the simulation device at the control end through the network, and process and control various signals. Various feedback signals required by the control terminal can also be realized through the network interface.
3 System Composition The design of the system mainly uses C# program design, design Windows window, realize the simulation system of LKJ2000, the flow chart of system design is shown in Fig. 1:
Program initialization
Display screen and buttons
Key message?
Button module
Voice messages?
Voice module
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End of processing
Fig. 1. System flow chart
LKJ analog equipment mainly includes: USB-CAN box. Firstly, this part needs the power supply part for power supply. Secondly, it needs to monitor the recording board and the information processing board. The two core boards are connected to the computer motherboard through the serial interface. The computer motherboard is finally connected to the LKJ receiving control terminal. At the same time, the PC, IC card voice version, LCD driver board, and LCD display required by the equipment are connected to USB-CAN to make the function more complete. Figure 2 shows the system principle structure diagram.
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Fig. 2. System principle structure diagram
3.1 LKJ2000 Display Simulation The display simulation is mainly divided into three parts, which are divided into display screen simulation, display button function, button and action sound simulation. The principle structure diagram of the simulation system is shown in Fig. 3:
Key simulation
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Sound simulation
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Show simulation
Fig. 3. Principle structure diagram of LKJ2000 simulation system
The contents displayed on the main interface are: the windows above the main interface have speed, speed limit, distance, signal mileage, and time in sequence; the window displayed on the right has the current color light type and the current monitoring mode; the middle window of the interface is mainly in the form of a curve To indicate the various statuses of the train; the following buttons can realize the monitoring of the train; click the “preparation 2” button to switch between ATP and LKJ control modes. In the ATP mode, the color light can be sent and displayed. The color light signals include: green 6, green 5, green 4, green 3 lights, green 2 lights, green lights, green yellow lights, green yellow 2 lights, yellow lights, yellow 2 lights, yellow 2 lights flashing, double yellow lights, double yellow lights flashing, red and yellow lights, red and yellow lights flashing, red lights, white lights and no code; click the “parameters” button on the main interface
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to run the system parameters Set up. The system parameter setting mainly includes three configuration windows of business parameters, automatic correlation parameters and image position adjustment. Communication protocol (UDP). The control terminal is the server terminal, the port number is 1985; the structure information of the protocol instrument{information-type = informatio-n instruction} such as: sending alert command is {1 = 20}; the interval between sending a command should be more than 0.2 s. Some of the following protocol command strings are not added with “{”, “}” before and after them, and should be added when sending commands. As shown in Table 1: Table 1. Instructions of the communication protocol Button
Send value
Set up
{1 = 17}
Alert
{1 = 20}
Ease
{1 = 0}
Unlock
{1 = 19}
Backward
{1 = 2}
Forward
{1 = 1}
Drive
{1 = 4}
Shunting
{1 = 3}
Automatic correction
{1 = 6}
Parking space
{1 = 5}
In and out
{1 = 8}
Access number
{1 = 7}
Inspection
{1 = 10}
Target
{1 = 9}
Left
{1 = 12}
Inquire
{1 = 11}
Down
{1 = 14}
Up
{1 = 13}
To the-right
{1 = 16}
Long press the up button 3S
{1 = 21}
Confirm
{1 = 18}
3.2 LKJ Receiving Control Terminal Operation The interface is mainly divided into three parts: locomotive operation information, control signal and virtual keyboard. The locomotive signal part mainly displays various operating information of the locomotive. When the locomotive operating information changes under normal working conditions, the information on the interface will also change in real time. The control signal area is for various control signals and mode switching buttons and the host IP input box. The virtual keyboard part is temporarily unavailable. The next step is to connect with the host. First, ensure that the host of the simulation running device and the host of the LKJ control receiving end are in the same network, and the two machines can communicate through the network. Enter the IP address of the host computer of the simulation running device in the IP configuration box of the LKJ receiving control terminal, and set the IP address of the LKJ receiving control terminal at the end of the configuration file of the simulation running device software.
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Please ensure that affiliations are as full and complete as possible and include the country. The addresses of the authors’ affiliations follow the list of authors and should also be indented 25 mm to match the abstract. If the authors are at different addresses, numbered superscripts should be used after each surname to reference an author to his/her address. The numbered superscripts should not be inserted using Word’s footnote command because this will place the reference in the wrong place—at the bottom of the page (or end of the document) rather than next to the address. Ensure that any numbered superscripts used to link author names and addresses start at 1 and continue on to the number of affiliations. Do not add any footnotes until all the author names are linked to the addresses. For example, to format.
4 Switching Between LKJ and Modes On the LKJ receiving control software interface, click the button, the button will display ATP, and the software is in ATP control mode. The difference between LKJ and ATP control mode interface is mainly the difference of color light interface. In LKJ control mode, the color light control interface is divided into operation interface and adjustment interface. In the LKJ control mode, click the run button in the control signal and display the run interface. Click the leveling button, the color light control interface will display the leveling signal interface. In ATP control mode or LKJ control mode, the output signal color light signal can be controlled. Click the corresponding color light button directly, and the color light information will be uploaded to the monitoring host. The monitoring display interface displays the color light information, and there is Corresponding voice prompts, and the same color light information will also be displayed on the interface of the LKJ receiving control terminal software. ATP/LKJ switch and (ATP/LKJ) color light command information is shown in Table 2: Table 2. ATP/LKJ switching and color light command information Green light
{9 = C3}
Red and yellow lights
{9 = CD}
Green 2 lights
{9 = C2}
Red and yellow flashing
{9 = CC}
Green 3 lights
{9 = C1}
White light
{9 = C0}
Green 4 lights
{9 = CF}
Uncoded
{9 = D3}
Green 5 lights
{9 = D0}
Switch ATP
{9 = 2}
Green 6 lights
{9 = C9}
Switch LKJ
{9 = 3}
Green and yellow light
{9 = C4}
Automatic correction
{9 = 4}
Yellow light
{9 = C6}
Get reveal
{9 = 5}
Yellow 2 lights
{9 = C8}
Power on the LKJ device
{9 = 1}
Double yellow flash
{9 = CA}
Power off the LKJ device
{9 = 0}
Double yellow lights
{9 = CB}
Isolation state
{9 = 6}
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5 Supervisory Simulation Operation The monitoring simulation - operation interface is divided into the following functional areas, as shown in Fig. 4: (1) “Color light” control the output color light signal and switch signal. Output signal: green light, yellow light, red and yellow light, double yellow light, green and yellow light, yellow 2 light, red light, white light, no code, SD1 (speed level 1), SD2 (speed level 2), SD3 (speed level 3), speed 0, speed 1, speed 2 and the adjustment of track signal system; (2) “Speed” adjust the speed control handle of the driver controller to adjust the speed value. The speed value and speed limit value can be displayed on the speed twoneedle meter; (3) “Pressure” adjust the electronic brake valve can adjust the size of each pressure, and can simply simulate the eight-step gate and five-step gate test; (4) “Virtual Keyboard” the virtual key mask of the monitoring device has the same function as the actual key. (5) “Electronic whiteboard” it is mainly used to outline the key points in the lecture. When clicking “Electronic whiteboard”, the display interface is static, and you can choose the color and thickness of strokes. (6) “Pause” when the “Pause” button is clicked, the monitoring screen will be paused.
Fig. 4. Monitoring simulation operation interface
The monitoring simulation - leveling interface is divided into the following functional areas: Output signals: parking, propulsion, starting, connection, humping, deceleration, ten cars, five cars, three car parking, fault, closed/decentralization, emergency stop, emergency stop no. 2, 1, emergency stop, emergency stop 3 4 5 emergency stop, emergency stop, emergency stop no. 7, no. 6, emergency stop, unlock the no. 1, 8 unlocked no. 2 and no. 3, unlocked 4, 5, unlocked, unlock the no. 7, no. 6, no. 8, no. Speed 0, speed 1, speed 2, as shown in Fig. 5:
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Fig. 5. Monitoring simulation - leveling interface
6 Conclusion Through the combination of complete hardware system and software, this paper studies that the full-function LKJ split simulation running device can be used in the training system such as the locomotive analog control console, so as to realize the control control switch between ATP and LKJ, as well as fault processing. At the same time, display the operation interface and adjustment interface, which can effectively complete the training process of locomotive professional teaching plan and steward transition, pre-job, increased driving, single driver on duty and so on. Acknowledgements. This research was financially supported by the Foundation of 2019 Training plan of young backbone teachers in universities of Henan Province and 2017 training plan of young backbone teachers in Henan University of Technology.
References 1. Pan, L.: Design and implementation of on-board voice recognition system for trains. Beijing Jiaotong University (2016) 2. Li, X., Qian, X.: Simulation research on LKJ2000 train operation monitoring system. Railway Comput. Appl. 24(05), 38–41 (2015) 3. Jiang, D.: Research on simulation and application of train monitoring system. Tongji University (2008) 4. Qu, X., Qian, X.: LKJ2000 simulation system. Railway Comput. Appl. 02, 51–53 (2008) 5. Wang, G.: Application and improvement of LKJ2000 monitoring device. China New Technol. New Prod. 11, 26–27 (2019) 6. Qiao, L.: The application and prospect of LKJ monitoring device. Electron. Technol. Softw. Eng. 15, 61–62 (2018) 7. Gong, Z.: LKJ2000 train operation monitoring technology analysis and new generation technology research. Shanghai Railway Sci. Technol. 03, 47–48 (2016) 8. Cui, S.: LKJ2000 train operation monitoring device idle function and spare channel development and utilization. Inner Mongolia Sci. Technol. Econ. 09, 79–80 (2016)
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9. Wang, W.: Overhaul and maintenance of the screen display of LKJ2000 train operation monitoring device. Gansu Sci. Technol. 32(03), 49–50+45 (2016) 10. Jiang, H.: Research on the plug-in function of LKJ2000 monitoring device. Times Agric. Mach. 42(11), 40+42 (2015)
Application of Pulse Width Modulation to Structure Optimization of Permanent Magnet Synchronous Linear Motor Song Huang1 , Shuhong Wang1(B) , Nana Duan1 , Weijie Xu2 , and Bowen Shang1 1 Faculty of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
[email protected] 2 State Grid Shaanxi Electric Power Research Institute, Xi’an 710100, China
Abstract. Aiming at the problem of electromagnetic thrust fluctuation of permanent magnet synchronous linear motor (PMLSM), a design method of the magnetic pole of PMLSM is proposed based on the pulse width modulation. The equal area control method is used to modulate the width of the permanent magnets. And a magnetic pole composed of permanent magnet arrays of PMLSM is proposed. Simulation analysis was carried out for both the conventional and the proposed PMLSM respectively. And the air gap magnetic field generated by the two types of magnetic pole was experimentally measured. Both simulation and experimental results show that the magnetic pole consists of permanent magnet arrays can significantly reduce the harmonic content of the air gap magnetic field, making the air gap magnetic field and the no-load back-EMF waveform closer to a sine wave, effectively reducing electromagnetic thrust fluctuations. Keywords: PMLSM · Pulse width modulation · Magnetic field analysis · Thrust fluctuation · Harmonic analysis
1 Introduction Permanent magnet synchronous linear motors (PMLSMs) have broad application prospects in the fields of industrial robots and automation [1]. However, the thrust fluctuation makes it difficult to apply to high-precision applications [2]. The air-gap magnetic field contains high-order harmonic performance which is one of the reasons for the electromagnetic thrust fluctuation of PMLSM [3]. The air-gap magnetic field generated by the conventional permanent magnet arrangement is a non-sinusoidal wave, which will generate harmonic magnetic field. Therefore, it is very necessary to optimize the structure of the secondary magnetic pole. Jing used segmented permanent magnet poles in surface-mounted permanent magnet motors to suppress motor torque ripple [4]. The full-arc permanent magnets replaced by segmented permanent magnets to improve the quality of back-EMF and torque of surface-mounted permanent magnet motors [5]. Permanent magnet arrays are applied in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 316–323, 2022. https://doi.org/10.1007/978-981-19-1528-4_32
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permanent magnet synchronous motors to improve the performance of permanent magnet synchronous motors [6]. And Halbach array poles are used as an auxiliary magnetic pole in a linear motor, which improved the thrust fluctuation of the motor [7]. This paper adopts a PMLSM magnetic pole structure designed based on the sinusoidal pulse width modulation (PWM) method to reduce the harmonic content of the air gap magnetic field and reduce the electromagnetic thrust fluctuation. The conventional magnetic poles construction of PMLSM consists of a single permanent magnet. The magnetic pole structure proposed in this paper is shown in Fig. 1. Each magnetic pole of this structure is composed of multiple permanent magnets, and the size of each permanent magnet is determined by the result of pulse width modulation. Comparison of the air-gap magnetic field distribution and no-load back-EMF waveforms are carried out, and it is verified that the permanent magnet array pole structure can improve the air-gap magnetic field and reduce the electromagnetic thrust fluctuation. 1
2
3
4
5
1 Primary iron yoke, 2 Winding, 3 and 4 permanent magnets, 5 Non-magnetic support material
Fig. 1. PMLSM structure using pulse width modulation technology
2 Permanent Magnet Width Modulation Method The application of PWM idea in linear motor requires a simple and feasible modulation method. In 1988, Wang Xueqin proposed equal area control method of PWM waveform, which is very suitable for engineering applications [8]. Therefore, this article use the equal-area method to modulate the magnetic poles composed of permanent magnets. A three-phase symmetrical current with a phase difference of 120° is connected to the primary of the PMLSM, and a sinusoidal traveling wave magnetic field is generated in the air gap. The magnetic field generated by the permanent magnet interacts with the traveling wave magnetic field to generate electromagnetic thrust. Assuming that the permanent magnet generates a sinusoidal magnetic field with 2τ as the period (τ is the pole pitch) and its amplitude is Bm , the expression for the sinusoidal magnetic field is B = Bm sin(wx)
(1)
Where w is the angular frequency, w = π/τ . In order to use the equal area method to modulate the width of permanent magnets, this article uses five permanent magnets to form a magnetic pole as an example. As shown in Fig. 2, the sinusoidal magnetic field is divided into several parts in the 0–π interval. The area of the rectangular area is equal to the area under the sine curve, namely xi+1 Bm sin(wx)dx (2) Br m = xi
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Where Br represents the remanence value of the permanent magnet, which is also the height of the rectangle in Fig. 4. m is the width of the rectangle, corresponding to the width of the permanent magnets. n is the number of rectangles, corresponding to the number of permanent magnets. As shown in Fig. 4 i = 0, 1, ..., n − 1, the sine wave in a half cycle is approximated by 5 rectangular pulses, and the width of each pulse can be determined according to the equal area modulation method. m1 , m2 , m3 , m4 , m5 represents the width of the rectangle, which is also the width of the permanent magnet; Bm represents the peak value of the target sinusoidal magnetic field; Br represents the remanence of the permanent magnet.
Fig. 2. Principle of equal area PWM method
The area enclosed by the sine curve and the x-axis while x belonging to 0–xθ can be denoted by xθ Bm (1 − cos wxθ ) Bm sin(wx)dx = (3) S= w 0 Where Br = Bm = 1.3T, Replace xθ by xj in Eq. (3) as follows: Sj = Where xj =
jπ 5 ,
Bm (1 − cos wxj ) w
(4)
j = 1, 2, ..., n. And the area of all five parts are given by
S1 = S1 , S2 = S2 − S1 , S3 = S3 − S2 , S4 = S4 − S3 , S5 = S5 − S4
(5)
The width of the permanent magnet can be calculated by (2) and (5). The width values of the five permanent magnets in one half period have been calculated. In the other half periods, the width values of the permanent magnets are the same, but the remanence direction is opposite.
3 Simulation Analysis 3.1 Magnetic Field Calculation A PMLSM with a rated thrust of 500 N and a rated speed of 3 m/s is taken as an example to calculate the magnetic field distribution. The number of slots is 12 and poles is 10, and the number of slots per pole per phase is 2/5. It is a fractional-slot motor with concentrated winding. The detailed parameters are as follows Shown in Table 1 [9].
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Table 1. Motor parameters Parameters
Value
Parameters
Value
Slot width
12.5 mm
Width of PM
9 mm
Number of slots
12
PM height of PM
21 mm
Tooth width
12.5 mm
Number of pole-pairs
5
Slot depth
57 mm
Pole pitch
30 mm
Winding tunes
50
Length of PM
49 mm
Air gap
1.2 mm
Remanence
1.3 T
According to Eq. (6), the widths of the permanent magnets in the 5 permanent magnets array pole are 1.8 mm, 4.7 mm, 5.9 mm, 4.7 mm, 1.8 mm, respectively. The finite element method is widely used in the multi-physics analysis of the motor. This paper uses the finite element method to analyze the magnetic field distribution and no-load back EMF of the conventional structure PMLSM and the PMLSM proposed in this paper. The waveform of the air gap magnetic field at a distance of 1 mm from the surface of the permanent magnet is shown in Fig. 3. The red dotted line represents the air gap magnetic field waveform of the conventional PMLSM. At the point of zero crossing, there is a straight line approaching to the horizontal level, and then it increases sharply to the maximum value. Although the air gap magnetic field generated by the proposed PMLSM has small fluctuations, the waveform is much closer to a sine wave.
Fig. 3. Air gap magnetic field waveforms of motors with different structures
3.2 Harmonic Analysis of No-Load Back Electromotive Force The air gap magnetic field is the interaction medium between the stator and the mover, and its distribution directly affects the performance of the motor. Therefore, optimizing the air gap magnetic field distribution is very important for the design of the motor. The air gap magnetic field waveform in Fig. 3 cannot fully reflect the characteristics of the air gap magnetic field. The no-load back-EMF of PMLSM is the most basic and important parameter, which has a direct impact on the thrust and its fluctuation, maximum speed and other indicators. So it is essential to analyze the no-load back EMF of PMLSM.
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In PMLSM, the three-phase windings are symmetrical, so only one of the three phases A, B, and C needs to be considered when calculating the no-load back EMF. The parameters of the motor during the simulation are the same as those in Table 1. The mover speed is 3 m/s, the number of winding turns is 50, and the simulation time is two cycles of 0.04 s. The no-load back EMF of phase A winding can be expressed as [10]: E=
d ψA d φA d ψA =v = Nvlk dt dx dx
(6)
Where ψA denotes the flus linkage of A-phase winding, N is the turns number of winding, v represents the speed of the mover, l is the width of PMLSM, k is the core stacking coefficient, φA is the magnetic flux of the A-phase winding. It can be seen from the formula (8) that the waveform of the no-load back EMF is determined by the differential of the magnetic flux to the position. Figure 4 shows the back-EMF waveforms of linear motors with different structures. Obviously, the no-load back-EMF of the PMLSM proposed in this paper is smoother and closer to the standard sine wave. Fourier decomposition is performed on the no-load back-EMF waveform, and the spectrum diagram is shown in Fig. 5. The result shows that the third harmonic is the main harmonic component. The third harmonic of the back EMF of the proposed PMLSM in this paper is 19.7% smaller than that of the conventional PMLSM. This is because the permanent magnet arrays can make the air gap magnetic field closer to the standard sine wave. Harmonic distortion rate is given by ∞ Bδi /Bδ1 (7) ε= i=2
Where Bδi represents the magnitude of ith harmonic. Taking the first 18 harmonic data, the harmonic distortion rate of linear motors with two structures is calculated by Eq. (7) as shown in Table 2.
Fig. 4. Waveforms of no-load back EMF of PMLSMs with different structures
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Fig. 5. Spectrograms of the no-load back EMF of PMLSMs with different structures Table 2. Two-structure linear motor no-load back-EMF harmonic distortion rate Harmonic distortion ratio (i = 1, 2, ···, 18)
Convention structure
PWM modulation structure
ε%
11.75%
9.67%
It can be concluded from Table 2 that the no-load back EMF of the PMLSM with magnetic poles formed by the 5 permanent magnet arrays proposed in this paper has a lower harmonic distortion rate. Therefore, it can be proved that the structure proposed in this paper improves the air gap magnetic field, making the air gap magnetic field closer to the sine wave, thereby reducing the electromagnetic thrust fluctuation and improving the performance of the PMLSM.
4 Experiment In order to verify that the magnetic pole structure proposed in this paper can improve the distribution and reduce the harmonic content of the air gap magnetic field, we measured the air gap magnetic field of PMLSMs. Here we only consider the magnetic field generate by permeant magnets, so this experiment constructed two types of secondary magnetic pole structure with two pole pitch lengths. The conventional magnetic pole structure is shown in Fig. 6. Taking 4 alternating magnetic poles for measurement can form a periodic waveform. Since the pole pitch is 30 mm, the total length of the 4 magnetic poles is 120 mm. Each magnetic pole is composed of a permanent magnet, and is arranged alternately according to N, S, N, S, and 4 permanent magnets form 4 magnetic poles within two pole pitch lengths. As shown in Fig. 7, the structure of a magnetic pole optimized by PWM method formed by an array of 5 permanent magnets. There are 4 magnetic poles arranged alternately according to N, S, N, S in the two pole pitch lengths, a total of 20 permanent magnets are used. All 5 permanent magnets in one magnetic pole have the same magnetization direction, and the thickness of each one is determined by the calculation result in Sect. 2. The pole pitch of the magnetic pole structure consists of permanent magnet
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array is the same as that of the conventional magnetic pole structure, and the total length of four magnetic poles is 120 mm. The Gauss meter used in the experiment is Cuihai Technology CH-3600 Gauss meter.
N
S
S
N
120mm
Fig. 6. Conventional magnetic pole structure
N
S
N
S
120mm
Fig. 7. Magnetic pole structure composed of five magnet arrays
The measurement results are depicted in Fig. 8, the red curve represents the air gap magnetic field distribution of conventional structure, which has a clear transition area that tends to be parallel to the time axis. Obviously, the black curve is much closer to a sin curve, which is the air gap magnetic field of the structure proposed in this paper.
Fig. 8. Comparison of the air gap magnetic field
Fig. 9. Harmonic content of air gap magnetic field
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Figure 9 shows the percentage of each harmonic of the air gap magnetic field waveform generated by two different structures of magnetic poles to the fundamental wave. The content of the third harmonic and the fifth harmonic in the air gap magnetic field waveform of the five permanent magnet array structure are 39.4% and 10.3% lower respectively than these of the conventional structure. And the harmonic distortion rate of this two types PMLSM is shown in Table 2. The air gap magnetic field harmonic distortion rate of the proposed PMLSM is only 30.67% of that value of the conventional one. Through this experiment, it can be concluded that the proposed magnetic pole structure can effectively improve the air gap magnetic field, and reduce the electromagnetic thrust fluctuation of the linear motor.
5 Conclusion From the simulation analysis and experimental results, the magnetic pole structure composed of the permanent magnet array proposed in this paper can effectively reduce the 3rd harmonic content in the no-load back EMF, make the air gap magnetic field distribution closer to the sinusoidal magnetic field. Of course, the magnetic pole structure proposed in this article is more complicated than the conventional magnetic pole structure, so the processing and installation process need to be improved. At the same time, the proposed magnetic pole structure increases the amount of permanent magnets and the magnetic flux leakage is larger than the conventional structure, these limits need to be improved for a wildly application. In general, the method proposed in this paper effectively improves the air gap magnetic field, suppresses electromagnetic thrust fluctuations, and provides new ideas for improving the operating performance of PMLSM.
References 1. Lu, Q., Shen, Y., Ye, Y.: Overview of the structure and research development of permanent magnet linear motors. Proc. Chin. Soc. Electr. Eng. 039(009), 2575–2588 (2019) 2. Cheng, Y.: Research on optimization design of thrust fluctuation of permanent magnet synchronous linear motor. Huazhong University of Science and Technology, Hubei (2011) 3. Wang, M., Jiao, L., Chen, Y., et al.: Research on the air gap magnetic field of low-speed permanent magnet linear synchronous motor. In: National Linear Motor Academic Annual Conference (2006) 4. Jing, L., Luo, Z., Qu, R., et al.: Investigation of a surface PM machine with segmentedeccentric magnet poles. IEEE Trans. Appl. Superconduct. 28(3), 1–5 (2018) 5. Chaithongsuk, S., Takorabet, N., Meibody-Tabar, F.: On the use of pulse width modulation method for the elimination of flux density harmonics in the air-gap of surface PM motors. IEEE Trans. Magn. Mag. 45(3), 1736–1739 (2009) 6. An, Y., Wen, H., Liu, G., Xue, L.: Magnetic field and starting performance of magnet arrayed PMSM. J. Shenyang Univ. Technol. 33(01), 14–19 (2011) 7. Fujitu, E., Tahara, S., Ogawa, K.: Thrust of 2-pole PM linear synchronous motor with Halbach array. In: Electrical Machines and Systems, ICEMS 2009 (2009) 8. Wang, X.: Equal area of PWM waveform. J. Chongqing Univ. (Nat. Sci. Ed.) 02, 85–93 (1988) 9. Huang, S.: Design and research of permanent magnet linear motor. Xidian University, Xi’an (2018) 10. Chen, D., Liu, Q., Wang, X., et al.: Hierarchical finite element analysis of no-load back EMF of permanent magnet synchronous linear motor. J. Tsinghua Univ. (Nat. Sci. Ed.) 44(002), 212–214 (2004)
High-Efficiency Drive for IPMSM Based on Finite Model Predictive Control Yunfei Zhang(B) and Rong Qi School of Automation, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, China [email protected]
Abstract. This paper presents a high-efficiency drive based on finite model predictive control (FMPC-HED) for the interior permanent magnet synchronous motor (IPMSM), aiming to reduce copper loss, iron loss, and switching loss. FMPC is selected as the controller due to its high robustness and transient performance. The equivalent circuit model (ECM) of IPMSM is used to analyze the power loss and reach the optimal current algorithm. Furthermore, the switching loss is introduced to the FMPC’s objective function to improve efficiency. The maximum voltage and current limitations have been considered in FMPC-HED to protect the drive system. The experimental results demonstrate that FMPC-HED has the ability to improve the IPMSM’s efficiency. As seen from the efficiency map, FMPC-HED can not only improve IPMSM’s efficiency but also extend its speed range with the same torque load. Hence, with the upper advantages, FMPC-HED is helpful for IPMSM’s application in the industry and daily life. Keywords: IPMSM · High-efficiency drive · Finite model predictive control (FMPC) · Copper loss · Iron loss · Switching loss
1 Introduction The interior permanent magnet synchronous motor (IPMSM) has some advantages: low starting current, low motor losses, high electrical torque, and high power density [1– 3]. Consequently, the IPMSM is widely applied in the industry and daily life, and it is essential and significant to reduce the power loss of the IPMSM drive system on account of global climate warming. In practice, the improved control strategy can reduce the copper, iron, and switching loss of the IPMSM drive system [4, 5]. One way to minimize the copper loss is to achieve the maximum torque per ampere (MTPA) control for IPMSM. Tianfu Sun et al. established two MTPA look-up tables, separately with the field orientated control and the d-axis flux control, to achieve MTPA control and limit the stator voltage [6]. Similarly, Tatsuki Inoue et al. proposed an MTPA scheme based on the direct torque control (DTC). This strategy utilized a look-up table to simplify the calculation from the required electric torque to the stator flux [7]. Besides, the maximum torque per voltage (MTPV) is a way to minimize the IPMSM’s iron loss. But the MTPV can’t be applied for the IPMSM drive due to its high flux-weakening © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 324–333, 2022. https://doi.org/10.1007/978-981-19-1528-4_33
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current [8, 9]. Some researchers utilized the equivalent iron loss resistance to minimize copper loss and iron loss [10]. Whereas, it is a complex work because both d-current and q-current can affect the power loss of the IPMSM drive system. The switching loss of the voltage source inverter (VSI) is often studied separately. Hyun-Sam Jung et al. proposed a PWM scheme, which calculated the minimum switching frequency to reduce the switching loss [11]. Differently from that, Yuchen Wang et al. utilized a predictive control to minimize switching loss [12]. Additionally, Matthias Preindl and Silverio Bolognani utilized the finite model predictive control (FMPC) to reduce copper loss [13]. However, this control strategy performed differently with different weighing coefficients in the cost function, which should be adjusted according to the importance and the performance. Besides, the finite control set also brought about a time delay for the VSI output. This paper proposes a high-efficiency drive based on finite model predictive control (FMPC-HED) for IPMSM, aiming to minimize copper loss, iron loss, and switching loss. The equivalent circuit model (ECM) is utilized to analyze the power loss. Besides, the maximum voltage and current limitations have been considered in FMPC-HED. What’s more, we have implemented experiments to verify FMPC-HED. With more than 150 test points, we obtained efficiency maps with FMPC-HED and MTPA. As seen from the efficiency maps, FMPC-HED can not only improve IPMSM’s efficiency but also extend its speed range with the same torque load.
2 IPMSM Drive Analysis 2.1 IPMSM Model The ECM of IPMSM is a usual way to analyze the power loss, which has the equivalent iron resistance (Rc ) and the stator resistance [14]. The equivalent circuit model of IPMSM in the dq-coordinate system is drawn in Fig. 1, and the mathematical model is given as: ⎧ ⎪ di ⎪ ⎪ ud = Rs id + Ld dtod − ωe Lq ioq ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ uq = Rs iq + Lq dioq + ωe ϕf + Ld iod dt ⎪ ⎪ ⎪ id = iod − ωe Lq ioq Rc ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ iq = ioq + ωe ϕf + Ld iod Rc
Fig. 1. ECM of IPMSM
(1)
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Rs is stator resistance; L d and L q are dq-inductances; With the parameters (K e , K h ), Rc ) can be defined as (2). Besides, according to the results from the finite-element tools, the upper parameters can be estimated well [15]. Rc =ωe (ωe Ke + Kh ) (2) Additionally, the electrical torque of IPMSM is defined as:
Te = p ϕf ioq + Liod ioq = p ϕf ioq + Ld − Lq iod ioq
(3)
The other main symbols: id and iq are stator dq-currents; ud and uq are dq-stator voltages; T s is sampling period; ωe is rotor electrical angular speed; ϕ f is the flux linkage; θ e is rotation angle; p is pole pairs. 2.2 Voltage Source Inverter (VSI) The VSI is an essential module of the IPMSM system, which directly drives the IPMSM. The topology of the drive system is presented in Fig. 2-(2). Udc is the direct voltage source; Sa , Sb , and Sc are gate commands for the upper switching elements (Sa , Sb , and Sc for the lower). In this paper, the gate commands of VSI are given by the finite model predictive control (FMPC). Additionally, the VSI’s model is defined as: ⎧ ⎨ U αβ = T abc Sabc (4) 2√ −1 −1 √ ⎩ T abc = U3dc 0 3− 3 Herein, U αβ = [uα , uβ ]T ; Sabc = [Sa , Sb , Sc ]T ; Sabc ∈ {0,1}3 . Therefore, the VSI can provide eight different voltage vectors (V 0 ~ V 7 ) for IPMSM, and the voltage space vector and the switching states of VSI are shown in Fig. 2-(2). In the modulation algorithm, the eight voltage vectors are the basal voltage vector.
Fig. 2. Topology of IPMSM drive and VSI
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2.3 Power Loss The definition of copper loss (Pcu ) is given as follows, samely as the iron loss (Piron ). And, the definition of Pcu and Piron can be utilized to be analyzed the efficiency of the IPMSM drive system.
⎧ 2 + i2 = R i2 ⎪ P i = R cu s s ⎪ q d ⎪
s ⎪ ⎨ 2 2 2 Piron = ωe ϕod + ϕoq Rc (5) ⎪ ⎪ ϕ = ϕ + L i ⎪ od f d od ⎪ ⎩ ϕoq = Lq ioq The switching loss model is helpful to analyze the voltage source inverter (VSI). The switching elements (e.g., IGBT) control the voltage output of VSI through the quick switch. But the switching loss could reduce the drive’s efficiency at the same time. The switching loss of one switching element, including turn-on loss (E on ) and turn-off loss (E off ), is defined as: tr Esw = Eon + Eoff =
tf ir ur dt +
0
if uf dt
(6)
0
Where t r is the turn-on crossover time; t f is the turn-off crossover time. From the power loss function, the rising current (ir ), fall current (if ), the rising voltage (ur ), and fall voltage (uf ), which equal U dc , are the essential factors.
Fig. 3. IPMSM drive system diagram
3 Drive System The diagram of the whole drive system is given in Fig. 3. An anti-windup PI (anti-PI) controller is introduced as the speed controller to carry out the experiment, and ωr is the speed reference. The FMPC-HED can produce the gate commands for VSI.
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3.1 High-Efficiency Strategy (HES) Improving IPMSM’s efficiency is a significant issue that can benefit its industrial applications and daily life. Improving efficiency means maximizing the output power (Pout ) to Pcu and Piron . With the power loss’s definition in Sect. 2, we can describe the optimization as: max : Pout = Te ωe p = ωe ϕf ioq + Liod ioq s.t. : Ploss = Pcu + Piron (7) With constrained optimization, we can get the primal HES function.
2 2 fHES0 =Rs ϕf iod + L iod − ioq
2 2 +ωe2 (Rs + Rc ) ϕf Lq ϕod + L ϕod R2c = 0 − ϕoq
(8)
As seen from Sect. 2, the equivalent currents (icd , icq ) have been considered to analyze Pcu and Piron . However, we should ignore icd and icq , which are very small, to simplify the control algorithm. Similarly, there is a big difference in value between Rc and Rs . Hence, we can obtain the following simplified formula with enough accuracy, and the simplified HES function is given as:
⎧ ⎪ fHES =Rs ϕf id + L id2 − iq2 ⎪ ⎪
⎪ ⎪ ⎪ ⎨ Rc = 0 + ωe2 ϕf Lq ϕd + L ϕd2 − ϕq2 (9) id ≤ 0 ⎪ ⎪ ⎪ ⎪ ϕ = ϕf + Ld id ⎪ ⎪ ⎩ d ϕq = Lq iq
3.2 FMPC-HED In this paper, FMPC is implemented to reduce copper loss, iron loss, and switching loss. Meanwhile, the maximum voltage and current limitations should be considered, determined by the system’s parameters. As seen from the upper subsection, Pcu and Piron can be minimized by HES. According to (6), we can get the discrete-time model of the switching loss. ⎧ ⎪ |fδ (Sx (k) − Sx (k − 1))· Udc ix (k) tr + tf Esw (k) = ⎪ ⎪ ⎨ x=a,b,c (10) ⎪ 1, x = 0 ⎪ ⎪ ⎩ fδ (x) = 0, x = 0 Hence, the control system can be described as an FMPC with multiple constraints: (11.a) min : f0 := (Te (k + i) − TeR2 + ηEsw (k + i)) i=1,...,n
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s.t. : h := fHES = 0
(11.b)
f1 := id2 + iq2 − Im2 ≤ 0
(11.c)
f2 := id ≤ 0
(11.d)
f3 := ud2 + uq2 − Um2 ≤ 0
(11.e)
Te (k) = p ϕf + Lid (k) iq (k)
(11.f)
x(k + 1) = Ax(k) + Bu(k) + C
(11.g)
cos(θe ) sin(θe ) u(k) = Vi , i = 0, . . . , 7 − sin(θe ) cos(θe )
(11.h)
f 0 is the objective function, which has two destinations: tracking the torque commands (TeR) and minimizing the switching loss. n is the horizon step; η is the multiplier, defined by the importance of E sw . (11.b) and (11.d) are the HES; (11.c) is the current limitation; (11.e) is the voltage limitation. Let’s define the Lagrangian function of the FMPC as: L(Sabc , λ,μ) := f0 (x) +
3
λi fi (x)
(12.a)
i=1
s.t. :
λi ≥ 0, i = 1, 2, 3
(12.b)
Obviously, there always is an optimal point J* (S* abc , λ* ). Hence, there is a small enough ε (ε ≥ 0). ∇L(λ, Sabc ) = ε, s.t. L(S∗abc , λ∗ ) = max min L(Sabc , λ) λ
Sabc
(13)
According to the steepest descent method, we can get: λ(k + 1) = λ(k) + ∇λ = λ(k) + ∇λ L + ε ≈ λ(k) + ∇λ L
(14)
4 Experiments The test platform is shown in Fig. 4. The parameters of IPMSM and its drive system are listed in Table 1. The control unit is selected as TM320F2812, and the IPM (PM50R) is chosen as the inverter. With the serial communication, the measured data is sent to the computer for further analysis.
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Value
Udc
150 V
Im
6A
Ld
9 mH
Lq
27.4 mH
Rs
0.83
Ke
4.67 × 10−4
Kh
1.05
ϕf
122 mWb
Ts
100 us
Fig. 4. Experimental platform. (1) Computer; (2) driver board; (2a) current sensor; (2b) voltage sensors; (3) IPMSM; (4) position sensor; (5) torque sensor; (6) servomotor; (7) power source; (8) servomotor driver
4.1 General Behavior The FMPC-HED utilizes HES to minimize Pcu and Piron , and brings in the switching loss cost to reduce the power loss of VSI. In this subsection, a general operating condition was given to the IPMSM drive system. MTPA, using the traditional modulation (SVM), is selected as the compared object. In the beginning, we gave a step speed command (5 krpm) with a torque load (0.6 Nm). Then, at 3 s, the speed reference ramped down to 2 krpm, and the torque load started to increase until 1.2 Nm. The experimental results are shown in Fig. 5. As shown in Fig. 5, FMPC-HED has a higher speed than MTPA (2–3 s), and the dynamic
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Fig. 5. General behavior
Fig. 6. Switching-gate signals (Sa) and phase-a current (ia)
response performance is almost the same in the low-speed region. Besides, we can see that the FMPC-HED’s dq-currents change with speed (especially 0–1.27 s). Figure 6 has shown the switching-gate signals (Sa ) and the phase-a current (ia) (left: FMPC-HED and right: MTPA with SVM). The switching frequency of MTPA is 10 kHz, and the gate signal changes in every control period. The switching loss of MTPA is 2.42 w (from 5.8 s to 6 s) and 4.44 w (from 8.8 s to 9 s). At the same time, the average switching frequency of FMPC-HED is only 5.04 kHz (from 5.8 s to 6 s) and 5.05 kHz (from 8.8 s to 9 s). Besides, the switching loss is 1.4 w (from 5.8 s to 6 s) and 2.24 w (from 8.8 s to 9 s). As can be seen, the FMPC-HED can effectively reduce the switching loss and the switching frequency. 4.2 Efficiency Map For further comparison, more than 150 tests with different operating conditions were implemented on the test platform. The efficiency contour map of MTPA and FMPCHED are presented in Fig. 7. As can be seen, the high-efficiency region of FMPC-HED is larger, and the efficiency of FMPC-HED is higher with the same operating condition. Besides, FMPC-HED has a more extensive speed range than MTPA. Therefore, IPMSM with FMPC-HED has not only a higher efficiency but also a more extensive speed range.
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Fig. 7. Efficiency contour map: FMPC-HED and MTPA
5 Conclusion This paper proposes a high-efficiency drive based on finite model predictive control (FMPC-HED), aiming to reduce the power loss of the IPMSM drive system. The optimal dq-current of HES is reached with the optimization algorithm. With the introduction of the swiching loss model, FMPC-HED obtains the ability to reduce the switching loss. Besides, the maximum current and voltage have been considered as the inequality constraints in FMPC-HED. As seen from the experimental results, the presented FMPCHED is feasible to drive IPMSM. What’s more, according to the efficiency map analysis, FMPC-HED has more advantages in efficiency and speed range.
References 1. Seo, J., Woo, D., Chung, T., Jung, H.: A study on loss characteristics of IPMSM for FCEV considering the rotating field. IEEE Trans. Magn. 46(8), 3213–3216 (2010) 2. Hoang, K.D., Zhu, Z.Q., Foster, M.: Online optimized stator flux reference approximation for maximum torque per ampere operation of interior permanent magnet machine drive under direct torque control. In: 6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012), pp. 1–6 (2012) 3. Dharmasena, S., Choi, S.: Model predictive control of five-phase permanent magnet assisted synchronous reluctance motor. In: 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 1885–1890 (2019) 4. Nguyen, Q.K., Roth-Stielow, J.: Analysis and modelling of the losses for the electrical drive system of an electric vehicle. In: 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1–6 (2014) 5. Cholewa, D., Mazgaj, W., Szular, Z., Woszczyna, B.: Reduction of switching losses in three-phase three-level voltage source inverters. In: 2018 14th Selected Issues of Electrical Engineering and Electronics (WZEE), pp. 1–4 (2018) 6. Sun, T., Wang, J., Jia, C., Peng, L.: Integration of FOC with DFVC for interior permanent magnet synchronous machine drives. IEEE Access 8, 97935–97945 (2020) 7. Inoue, T., Inoue, Y., Morimoto, S., Sanada, M.: Maximum torque per ampere control of a direct torque-controlled PMSM in a stator flux linkage synchronous frame. IEEE Trans. Ind. Appl. 52(3), 2360–2367 (2016) 8. Pellegrino, G., Armando, E., Guglielmi, P.: Direct-flux vector control of IPM motor drives in the maximum torque per voltage speed range. IEEE Trans. Industr. Electron. 59(10), 3780– 3788 (2012)
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9. Sarikhani, A., Mohammed, O.A.: Demagnetization control for reliable flux weakening control in PM synchronous machine. IEEE Trans. Energy Convers. 27(4), 1046–1055 (2012) 10. Uddin, M.N., Zou, H., Azevedo, F.: Online loss minimization based adaptive flux observer for direct torque and flux control of PMSM drive. In: 2014 IEEE Industry Application Society Annual Meeting, pp. 1–7 (2014) 11. Jung, H., Hwang, C., Kim, H., Sul, S., Hee-Won, A., Yoo, H.: Minimum torque ripple pulse width modulation with reduced switching frequency for medium-voltage motor drive. IEEE Trans. Ind. Appl. 54(4), 3315–3325 (2018) 12. Wang, Y., Li, H., Liu, R., Yang, L., Wang, X.: Modulated model-free predictive control with minimum switching losses for PMSM drive system. IEEE Access 8, 20942–20953 (2020) 13. Preindl, M., Bolognani, S.: Model predictive direct torque control with finite control set for PMSM drive systems, part 1: maximum torque per ampere operation. IEEE Trans. Industr. Inf. 9(4), 1912–1921 (2013) 14. Lee, B., Kwon, S., Sun, T., Hong, J., Lee, G., Hur, J.: Modeling of core loss resistance for d−q equivalent circuit analysis of IPMSM considering harmonic linkage flux. IEEE Trans. Magn. 47(5), 1066–1069 (2011) 15. Xie, W., Wang, X., Wang, F., Xu, W., Kennel, R., Gerling, D.: Dynamic loss minimization of finite control set-model predictive torque control for electric drive system. IEEE Trans. Power Electron. 31(1), 849–860 (2016)
Comparison of Two Drive Topologies for Ironless-Stator Permanent Magnet Motor Driven by Square Wave Haoyan Li1,2
, Haiping Xu2(B) , Xi Chen2 , Tao Guan2 , and Zengquan Yuan2
1 University of Chinese Academy of Sciences, Beijing, China 2 Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China
{lihaoyan19,hpxu,chenxi,guantao,yuanzengquan}@mail.iee.ac.cn
Abstract. Such natural advantages as high efficiency, light weight, high torque density, simplicity and controllability exist in ironless-stator permanent magnet brushless DC motor, which has been widely used in aerospace. However, low inductance caused by ironless-stator structure induces serious problems, like sharp current change rate, for instance, or even worse, current intermittent. Improved upon full-bridge, two types of topologies referring to dual-stage drive consists of Buck converter with half-bridge cascaded as well as single-stage drive of full-bridge and additional inductances in series without dramatically increasing switching frequency are used to drive ironless-stator motor. Some performances of topologies mentioned above such as maximum speed limit by topology, control regulation under different current and rotational speed conditions, calculation of power inductance value that is essential to stabilize armature current, current flowing in armature are compared. All those two drive schemes are further designed and manufactured to verify the feasibility. Calculation and comparison of current waveforms are presented. Keywords: Permanent magnet brushless DC motor · Ironless-stator · Buck converter with half bridge cascaded · Full bridge and additional inductances in series
1 Introduction Reaction/momentum wheels (RW/MW) are flywheels used to provide attitude control authority and stability on spacecraft, which does not require rockets or external applicators of torque. Considering the space application in high vacuum and the bearing support implement which has a precision processing, wind resistance and frictional resistance are diminished to a great extent, hence reaction/momentum wheels can operate under extreme low loss conditions. Reaction/momentum wheels whose output torque are on the order of mN·m typically consume very little energy in an order of magnitude of tens of watts. Thus the drive motor can be considered to work in no-load mode. Under this circumstance, the design of coreless motor has great advantages. While the cogging torque © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 334–344, 2022. https://doi.org/10.1007/978-981-19-1528-4_34
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ripple and reluctance torque ripple are eliminated, the flux harmonics caused by the stator groove and the eddy current loss and hysteresis loss of the core completely disappear [1, 2]. Besides, the development of composite materials also provides convenience for the processing and manufacturing of coreless motors. The permanent magnet (PM) brushless DC(BLDC) machine [3–5] has been widely used in various applications due to its reliability, efficiency, simplicity and controllability which guarantee a wide speed range and low power loss as a vital part of reaction/momentum wheels. Thus, brushless dc (BLDC) motors with ironless and slotless stator generally used in high-speed rotations are introduced. Armature of coreless motor has small inductance of several microhenries. Small electrical time constant gives rise to high current change rate and large torque ripple. Things get worse when phase current is intermittent using three-phase full-bridge topology if phase current and switching frequency of the drive are low to a certain value [6]. For this reason, the coreless motor cannot start to rotate properly. There are usually three ways to figure this out, such as increasing the switching frequency dramatically; adding additional inductances in the armature circuits; converting drive from single-stage to multi-stage. The switching frequency will be increased dramatically [7, 8] to maintain stator armature current in continuous mode, which will increase the loss and diminish the life of switching devices [6]. If additional inductances are added in the armature circuits, common control methods can be applied since coreless motors are equivalent to traditional motors. However, advantages that coreless motors bring such as quick response may be neglected. Larger equivalent phase inductances prolong the time of commutation in which time uncontrolled current circulation inside the motor exists if no other control algorithms are applied. Furthermore, the problems that traditional motors with large phase inductances have will emerge, for instance, torque degradation in high speed region. Last but not least, one additional power inductance in every single phase means extra quality, volume and cost from an engineering point of view. Cascaded circuit topologies are preferred in many articles when referring to coreless motors drive. The pre-stage DC/DC converter which is controlled in closed current loop is obliged to regulated a stable current, and realizes the PAM control of the post-stage inverter by adjusting the output voltage according to the motor speed. Current regulation and PWM chopping are decoupled, and torque ripple caused by PWM chopping which is extensively used in single-stage full-bridge drive can be eliminated extremely because the post-stage circuit is only used for commutation. In general, pre-stage regulating converter topologies are classified into two categories: single-output DC/DC converter including Buck converter [9–12], Boost Converter [13], Cuk converter [14, 15], Sepic converter [16, 17], etc. and multi-output DC/DC converter aiming at suppressing commutation torque ripple. The common three phase inverters are half-bridge and full-bridge. BLDCM driven by full-bridge with additional inductances in the armature circuits may be a solution to the problem of discontinuous current in coreless motor which has been confirmed in some articles. Volume and cost of half-bridge are diminished comparing with that of full-bridge since three switches are removed. However, the most fatal drawback of halfbridge is that current or voltage cannot be regulated because no current circulation path
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exists. As a consequence, pre-stage converter is indispensable to regulate the current or voltage. Back to engineering practicality, Buck converter with current flittering inductance and half-bridge cascaded which has simple structure and high reliability and full-bridge with inductances cascaded where there is no need to increase the switching frequency dramatically are preferred. In this paper, design and manufacture of these two types of topologies are implemented and driving performances are compared. Considering torque mode of reaction/momentum wheels, motor is controlled in current closed loop. Voltage bus input and motor in experiment are identical and both topologies are controlled in square wave. Pure analog circuits of two topologies mentioned above are fabricated because the high reliability. Texas Instruments UC1625 is used to control full-bridge, in which H_PWM-L_PWM control method integrated. In the following, Buck converter with half-bridge cascaded as well as full-bridge and additional inductances in series are substituted as B3 topology and B6 topology for convenience. B3 and B6 that appears as superscripts in equations are used to represent the parameters of half-bridge topology and the full-bridge topology respectively.
2 Configuration of BLDCM Control System Assuming that operating angle of every phase in every working interval is θe ∈ (−α, +α), thus the back EMF can be expressed as Eq. (1). ek (θe ) = Fk (θe ) · ωm ≈ keq ωm fk (θe ), θe = ωe t − α, ωm = 2π ·
n ·p 60
(1)
Where ek (θe ) is back EMF, Fk (θe ) is line-to-line back EMF shape function which is a continuous nonlinear function with period of 2π, keq is equivalent back EMF coefficient, fk (θe ) represents unit back EMF shape function, θ is the rotor position, ω is the rotor speed, the subscripts “e” and “m” denote the electrical value and mechanical value, respectively, the subscript “k” refers to a particular phase. In practice, the back EMF of the motor is the superposition of the fundamental wave and the high-order harmonics. For the convenience of calculation and illustration, the high-order harmonics of back potential waveform are ignored. It is assumed that the waveform function of the unit back potential is replaced by the fundamental cosine function, which is fk (θe ) = cos θe .In the following calculation, the asymmetry of the armature is ignored, that is, the resistance value of the three-phase armature is considered to be equal to the inductance value. The average coefficient kav and effective value coefficient keff of the unit back EMF waveform function in a working interval is described as Eq. (2). 1 α 2 1 α kav = f (θe )dθe , keff = f (θe )dθe (2) θe −α θe −α BLDCM driven by Buck converter with half-bridge cascaded operates in three beats working mode with operating angle θeB3 ∈ (− π3 , + π3 ). The back EMF in armature is the phase back EMF of motor as eB3 (θeB3 ) ≈ keq ωm cos θeB3 . BLDCM driven by full-bridge
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and additional inductances in series operates in six beats working mode with operating angle θeB6 ∈ (− π6 , + π6 ). Normalized relative to the former topology, the back EMF in √ armature is the line back EMF of motor is presented as eB6 (θeB6 ) ≈ 3keq ωm cos θeB6 . The BLDCM voltage equation of three phase armatures is presented as Eq. (3). ⎤ ⎡ ⎡ ⎤⎡ ⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Uao Rpa 0 0 ia Lpa 0 0 ia ea Uno ⎥ ⎢ ⎣ Ubo ⎦ = ⎣ 0 Rpb 0 ⎦⎣ ib ⎦ + p⎣ 0 Lpb 0 ⎦⎣ ib ⎦ + ⎣ eb ⎦ + ⎣ Uno ⎦ (3) ic ic ec Uno 0 0 Rpc 0 0 Lpc Uco Where Rpk, k=a,b,c are phase-armature resistances which is the sum of armature resistance, the resistance of phase inductance if there are additional and on-state resistance of switch; Lk, k=a,b,c are the phase-armature inductances including additional inductance in single-stage topology; Uao , Ubo , Uco are the phase-armature terminal-to-ground voltages; ia , ib , ic are the phase-armature currents; ea , eb , ec are the line-to-neutral back EMFs; Uno is the neutral point-to-ground voltage, which equals Buck converter output voltage in dual-stage topology; p is differential operator.
3 Comparison of Performances 3.1 Drive Topology Inherent Characteristics According to kT = kav · keq , ratio of torque coefficients of B3 topology and B6 topology k2
can be derived as kTB3 = 21 kTB6 .According to kE = keff keq , ratio of back EMF coefficients av of B3 topology and B6 topology can be derived as kEB3 = 0.5158kEB6 . The mechanical angular velocity at the ideal no-load point can be considered ωm0 = Udc kE , then the ratio of the highest speed value that can be realized between B3 topology and B6 topology when driving the same motor under the same DC bus in the theoretical case is
B3 ωm0 B6 ωm0
=
kEB6 kEB3
≈ 1.93. When the speed is normalized referring to the maximum
B3 = 1, ωB6 = 0.5158. speed of the half-bridge drive, ωm0 m0 At each moment in each beat, the three-beat BLDC is one-phase conducting, compared to the six-beat BLDC is two-phase conducting. Therefore, it is plain that the output torque of the motor driven by the half-bridge topology is only half of that driven by the full-bridge topology under ideal condition that the bus voltage and the motor remain unchanged. The bus voltage is “occupied” by the phase back EMF in the half-bridge drive, and “occupied” by the line back EMF in the full-bridge topology, so the maximum speed of the full-bridge drive motor is lower than that of the half-bridge drive motor. In engineering applications, for the applications of same torque or speed requirements, the motor design should be carried out after the drive topology design. It is necessary to choose different voltage levels of the bus input along with the power level of the drive circuit and component selection if a motor is applied in all occasions unchanged.
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3.2 Control Regulation B3 Topology. The duty cycle D of the Buck converter switch can be obtained according to the voltage equation in each beat of the half-bridge driving topology in Eq. (4). D=
kEB3 · ω+iR Udc
(4)
B6 Topology. The duty cycle of the full-bridge switch can be obtained according to the voltage equation in each beat of the full-bridge driving topology in Eq. (5) (Fig. 1). D=
Udc + kEB6 · ω+iR e 1 Udc + e = + = 2Udc 2 2Udc 2Udc
(a) B3 Topology
(5)
(b) B6 Topology
Fig. 1. Control regulation under different current and rotational speed conditions
Similar to the brushed DC motor, the torque mode BLDCM has hard mechanical characteristics and good stability when it adjusts speed stepless within the speed range allowed by the driving topology. Switch duty cycle of Buck converter and full-bridge are linearly regulated at different current or speed. The difference between these two topologies is that the duty cycle variation range of the full-bridge converter switch using H_PWM_L_PWM control mode is only half of that of the Buck converter switch.
3.3 Inductance B3 Topology. The cumulative charge on the capacitor certainly equals zero in one switching cycle when the converter stabilizes. Thus, the average current through the capacitor is zero if the capacitor leakage current is not considered. It is concluded that all the inductance current of the Buck converter flows into the armature of the motor, which means that whether the inductance current of Buck converter is continuous or not is consistent with the motor current. The average voltage of the output filter capacitor of Buck converter is stabilized to the terminal voltage of the motor without rapid change because the mechanical time constant of the motor is much larger than the electrical time constant of the converter.
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In the full speed range of the motor, the Buck converter works in the current mode. In order to ensure that the Buck converter inductance works in the current continuous mode, the inductance value can be obtained from the minimum output current value Io(min) in Eq. (6). Udc · (1 − Udc · (1 − D) · D L≥ = 2Io(min) · f
kEB3 ·ω+Io(min) R k B3 ·ω+I R ) · E Udco(min) Udc
2Io(min) · f
(6)
It can be seen from the above equation that the minimum inductance value is obtained when the duty cycle of the switch of the Buck converter is equal to 0.5. B6 Topology. The inductance and resistance of armature are neglected in the calculation of full-bridge topology since electrical time constant of the driving circuit is much larger than that of the motor armature. The inductance value can be obtained from the minimum output current value in Eq. (7) according to the voltage equation in current continuous mode (Fig. 2).
2 − 4 k B6 · ω + iR 2 2 − 4e2 Udc Udc E = L≥ 8 · Udc · Io(min) · f 8 · Udc · Io(min) · f
(a) Buck inductance
(7)
(b) Inductance cascaded in full-bridge
Fig. 2. Minimum inductance value to ensure continuous inductance current under different current and rotational speed conditions
As can be seen from the above equation, for the current type three-phase full-bridge driving topology, the series inductance can realize the normal operation of drive within the full speed range as long as the inductance current is continuous at low speed so that the motor can be started normally. As the speed increases, so is the motor’s back EMF, the voltage applied on the inductance decreases, and the rate of change of inductance current decreases. At this time, the inductance current is less likely to be interrupted compared with that at low speed.
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3.4 Current B3 Topology. The pre-stage Buck converter operates in current closed loop and the current feedback is sampled from the average motor current. Rotor speed can be considered to be constant in each beat because of the large moment of inertia of motor. Thus during the time of a beat the output voltage of Buck converter is assumed to be constant. The Kirchhoff voltage equation of equivalent circuit in θe ∈ (− π3 , + π3 ) is Eq. (8). di (8) dt According to the initial conditions i = 0(t = 0), analytical solution of phase current can be derived in Eq. (9).
Uno Uno f ωm cos(ωe t − α) + ωe τ sin(ωe t − α) f ωm P sin α − cos α − t i= − e τ − + R R 1 + P2 R R 1 + P2 (9) Uno = e + Ri + L
Where time constant is τ = RL , equivalent impedance ratio is P = relate to rotor speed.
ωe L R
= ωe τ which is
B6 Topology. Compared with half-bridge, working interval of full-bridge topology is divided into conduction region and commutation region which naturally exists and can not completely disappear. Precise time-domain equations cannot be given because the strong nonlinearity of single-stage full-bridge topology coupling commutation and voltage or current control. Current fluctuation caused by PWM chopping is inversely proportional to the speed, thus in high-speed region where the voltage applied to inductance which is the difference between bus voltage and back EMF, is very small, low rate of current change leads to small current ripple. At the maximum speed point, the line back EMF is close to the input voltage of the bus, the ripple caused by chopping is small or even negligible, and the duty cycle of the full-bridge switch is approximately 1, which is similar to the situation of the dual-stage drive topology with pre-stage voltage regulation, as can be seen in the Fig. 3 (d). The commutation region occupies a very low proportion in the operating angle of each beat, so the back EMF is approximately considered to remain constant during commutation. Take the commutation of upper switch for example. A phase is set as the outgoing phase and the flow continues through the lower tube, B phase is the incoming phase and C phase is the non-commutation phase. The A phase current drops from the initial reference value ia (0) = iL to zero, analytical solution of A phase current is derived as Eq. (10).
Uan − ea − RLpa t Uan − ea e a + ia = iL − (10) Rpa Rpa A phase current drop time that is commutation time can be derived in Eq. (11) by setting iA = 0, similarly, current analytical solutions of B phase and C phase are derived. tiafall = −
La −(Uan − ea ) ln Rpa Rpa iL − (Uan − ea )
(11)
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Based on the calculation above, a rough estimation of current at the point between commutation and conduction can be obtained by substituting A phase current drop time into the above equation on the premise that the inductance and resistance of threephase armature are approximately equal, and the back EMF is approximately treated as trapezoidal wave. At this time, ic + ib = 0, the commutation region ends and the two-phase conduction begins to enter the conduction region. In the conduction region, the full-bridge topology is analyzed in the same way as the half-bridge topology, and the voltage equation is the same. The difference lies in the opposite potential and line back potential are introduced in single-phase conduction and two-phase conduction, thus the mathematical derivation will not be repeated here. The trend of waveform in the latter was consistent whether the three-beat control or the six-beat control in conduction region, which is obvious in high-speed region when ripple caused by chopping is slight.
(a) Driven by Buck converter and halfbridge at 2krpm.
(b) Driven by full-bridge at 2krpm.
(c) Driven by Buck converter and halfbridge at 3krpm.
(d) Driven by full-bridge at 3krpm.
Fig. 3. Current waveforms of two topologies driven by 0.5A in different speed (current waveform is shown in purple, hall signal is shown in cyan, buck converter output voltage is shown in blue). Ripple caused by PWM chopping has substantial reduction which is shown in d figure. Current waveform trend are same for both topologies at the end of each beat without the consideration of chopping.
4 Conclusion In order to drive ironless-stator brushless DC motor used in reaction/momentum wheel, two types of topologies referring to dual-stage drive consists of Buck converter with halfbridge cascaded as well as single-stage drive of full-bridge and additional inductances
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in series are designed and manufactured in detail. Performances’ comparison of diverse topologies are presented through theoretical calculation and experiment results. • Theoretically, for a BLDCM driven by half-bridge and full-bridge separately, maximum speed of the former is twice that of the latter in the condition that bus voltage and motor are unchanged, while output torque in a certain reference value of former is around half that of the latter. To function normally in the whole working condition, adjustments should be made in either bus voltage or motor design in two types of drive. Drive topology design should be finished ahead of motor design for convenience. • For the dual-stage drive topology, the stepless speed regulation is realized by regulating the voltage of the front circuit, and the duty cycle changes from 0 to 1, while the duty of full-bridge topology controlled in H_PWM-L_PWM mode should be over 0.5 even when the motor just start to rotate which means that a small change in duty cycle results in a sharp change in velocity compared with dual-stage drive. • High value of inductance is needed in both topologies to suppress current ripple. Inductance value is calculated in this paper and verified by experiments. Buck converter inductance is taken at around medium motor speed when the duty cycle is 0.5 in different minimum current conditions. Additional inductance cascaded in fullbridge is taken at low speed, and the worst case is when the motor starts. The best value is that the inductance is just large enough to ensure the motor to start, or larger value of inductance will deteriorate of motor high-speed performance. Theoretically, Inductance value of Buck converter is around twice that of full-bridge. However, three inductances are need means more volume and cost. • The half-bridge topology has no continuous current path when switch is off, so there is a large voltage and current spike during turn-off, which deteriorates the current and torque performance. According to the time domain equation of current, the current waveform is saddle-shaped, and the ripple increases obviously with the speed rising. Due to the continuous flow path in the full-bridge topology, the total motor current in the commutation region will not start from zero like half-bridge circuit, and the turnoff phase follow current will continue to participate in the electromechanical energy conversion to make up for the lost torque. However, the turn-off phase follow current in the commutation region forms a path inside the motor, which cannot be reflected in the bus current. Of these two types of drive topologies, the stationarity of current and output torque driven by Buck converter with half-bridge cascaded is worse than that of full bridge and additional inductances in series under same operation condition, but the former is easier to implement with less cost.
Acknowledgments. The authors thank the Institute of Electrical Engineering, CAS (Y92S350101) for financial support.
References 1. Wang, R.J., Kamper, M.J., Westhuizen, K.V.d., Gieras, J.F.: Optimal design of a coreless stator axial flux permanent-magnet generator. IEEE Trans. Magn. 41(1), 55–64 (2005). https://doi. org/10.1109/TMAG.2004.840183. (in English)
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2. Ooshima, M., Kitazawa, S., Chiba, A., Fukao, T., Dorrell, D.G.: Design and analyses of a coreless-stator-type bearingless motor generator for clean energy generation and storage systems. IEEE Trans. Magn. 42(10), 3461–3463 (2006). https://doi.org/10.1109/TMAG.2006. 879071. (in English) 3. Yang, L., Zhao, J., Yang, L., Liu, X., Zhao, L.: Investigation of a stator-ironless brushless DC motor with non-ideal back-EMF. IEEE Access 7, 28044–28054 (2019). https://doi.org/ 10.1109/access.2019.2901632. (in English) 4. Zhou, X., Chen, X., Peng, C., Zhou, Y.: High Performance nonsalient sensorless BLDC motor control strategy from standstill to high speed. IEEE Trans. Industr. Inf. 14(10), 4365–4375 (2018). https://doi.org/10.1109/tii.2018.2794461. (in English) 5. Yang, L., Zhao, J., Liu, X., Haddad, A., Liang, J., Hu, H.: Effects of manufacturing imperfections on the circulating current in ironless brushless DC motors. IEEE Trans. Industr. Electron. 66(1), 338–348 (2019). https://doi.org/10.1109/tie.2018.2826474. (in English) 6. Liu, Y., Hu, J., Dong, S.: A torque ripple reduction method of small inductance brushless DC motor based on three-level DC converter. In: 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1669–1674. IEEE, Xi’an (2019). https://doi.org/ 10.1109/ICIEA.2019.8834385. (in English) 7. Zwyssig, C., Round, S.D., Kolar, J.W.: An ultrahigh-speed low power electrical drive system. IEEE Trans. Industr. Electron. 55(2), 577–585 (2008). https://doi.org/10.1109/tie.2007. 911950. (in English) 8. Valle, R.L., de Almeida, P.M., Ferreira, A.A., Barbosa, P.G.: Unipolar PWM predictive current-mode control of a variable-speed low inductance BLDC motor drive. IET Electr. Power Appl. 11(5), 688–696 (2017). https://doi.org/10.1049/iet-epa.2016.0421. (in English) 9. Mishra, P., Banerjee, A., Ghosh, M.: Implementation of DIGITAL PWM on buck-type CSI fed BLDC motor drive. In: 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE), pp. 1–6. IEEE, Shillong (2018). https://doi. org/10.1109/EPETSG.2018.8659247. (in English) 10. Xiaofeng, Z., Zhengyu, L.: A new BLDC motor drives method based on BUCK converter for torque ripple reduction. In: 2006 CES/IEEE 5th International Power Electronics and Motion Control Conference, pp. 1–4. IEEE, Shanghai (2006). https://doi.org/10.1109/IPEMC.2006. 4778134. (in English) 11. Shan, T., Wang, X., Sheng, T.: One-cycle control for buck inductor current based on BLDC control system. In: 2015 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 790–796. IEEE, Beijing (2015). https://doi.org/10.1109/ICMA.2015.7237586. (in English) 12. Li, W., Fang, J., Li, H., Tang, J.: Position sensorless control without phase shifter for highspeed BLDC motors with low inductance and nonideal back EMF. IEEE Trans. Power Electr. 31(2), 1354–1366 (2016). https://doi.org/10.1109/tpel.2015.2413593. (in English) 13. Wei, W., Wang, J.: Dynamic response enhancement and fault protection of boost converter-fed brushless DC motor in aerospace applications. Appl. Sci. 9(10), 2113 (2019). https://doi.org/ 10.3390/app9102113. (in English) 14. Chen, W., Liu, Y., Li, X., Shi, T., Xia, C.: A novel method of reducing commutation torque ripple for brushless DC motor based on Cuk converter. IEEE Trans. Power Electr. 32(7), 5497–5508 (2017). https://doi.org/10.1109/tpel.2016.2613126. (in English) 15. Khopkar, R., Madmi, S.M., Hajiaghajani, M.,Tohya, H.A.: A low-cost BLDC motor drive using buck-boost converter for residential and commercial applications. In: IEEE International Electric Machines and Drives Conference, IEMDC 2003, pp. 1251–1257. IEEE, Madison (2003). https://doi.org/10.1109/IEMDC.2003.1210400. (in English)
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16. Zhao, K., Yang, S., Zhang, Q.: A novel commutation torque ripple suppression method for position sensorless brushless DC motor based on SEPIC converter and phase error compensation. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), pp. 1–7. IEEE, Harbin (2019). https://doi.org/10.1109/ICEMS.2019.8921798. (in English) 17. Shi, T., Guo, Y., Song, P., Xia, C.: A new approach of minimizing commutation torque ripple for brushless DC motor based on DCDC converter. IEEE Trans. Industr. Electron. 57(10), 3483–3490 (2010). https://doi.org/10.1109/TIE.2009.2038335. (in English)
Research of Ultra-High Voltage DC Generator Based on Neural Network PID Hongda Zhang1 , Lingjie Xu1 , Xiao Chen1 , Peng Guo1 , Xunan Ding1 , and Xinghui Jiang2,3(B) 1 State Grid, Zhejiang Electric Power Co., Ltd. Marketing Service Center,
Hangzhou 311121, China 2 Huadian Institute of Electric Power Technology, Wenzheng College, Soochow University,
Suzhou, China [email protected] 3 Suzhou Huadian Electric Co., Ltd., Suzhou 215104, China
Abstract. In order to solve the problem of insufficient output voltage stability due to the use of ordinary PID controllers in traditional Ultra-high voltage DC generators, a PID control model based on improved BP neural network is proposed. Meanwhile, the BP neural network algorithm is improved by the optimization algorithm of adaptive learning rate to compensate for the shortage of easily falling into local minimum point and slow convergence speed. The simulation results show that the proposed model has a small overshoot, fast response speed and stable output, and the control performance is much better than that of ordinary PID controller. The test results show that all parameters and functions of the high voltage DC generator based on the proposed control model meet the requirements of national standards and have good stability. Keywords: Ultra high voltage · DC generator · BP neural network · Self-tune · PID
1 Introduction Ultra-high voltage DC generator is one of the main testing equipment for high voltage test. It is widely used in the leakage current and DC withstand voltage test of converter transformer, arrester, power capacitor, power cable and other devices. As for field testing, it is important to improve the stability of voltage output because of the large output power. Therefore, for the design of Ultra-high voltage DC generator, except for the special design of the power circuit, the control system is also required to have excellent control performance [1, 2]. At present, the design and development of Ultra-high voltage DC generator in engineering practice are mostly based on experience, which affects the stability of DC generator. The paper used a simple proportional control mode, but the regulation performance of the proportional control mode cannot meet the requirements when the output voltage and current are high, because of the voltage doubling rectifier circuit is a nonlinear © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 345–355, 2022. https://doi.org/10.1007/978-981-19-1528-4_35
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system with time delay. the regulation performance of the proportional control method cannot meet the requirements when the output voltage is high and the current is large. The paper [3–5] use PID control mode, but PID parameters are not easy to determine because of it is difficult to determine the mathematical model of the output loop of voltage doubling rectifier, and the PID parameter is fixed so that the robustness of the output voltage will get worse when the output current getting large. From the above view, in order to improve the output voltage stability of the Ultrahigh voltage DC generator and meet the needs of field engineering testing, this paper uses BP (Back Propagation) neural network to self-tune the PID parameters online. The optimized PID control results can be obtained without establishing the mathematical model of the controlled object. At the same time, for the standard BP neural network, it is easy to fall into the local minimum point and the convergence speed is slower [6, 7], the adaptive learning rate optimization algorithm is used to improve the designed control model. In addition, the control effects of the designed neural network PID controller and the common PID controller are compared through simulation analysis. Finally, based on the designed control model, an Ultra-high voltage DC generator prototype is developed for field test. The relevant test results verify the feasibility and effectiveness of the designed controller.
2 Control Model of Ultra-High Voltage DC Generator 2.1 Structure of Ultra-High Voltage DC Generator The principle structure of the Ultra-high voltage DC generator is shown in Fig. 1. The power circuit is mainly composed of three main parts: power frequency rectifier filter, full bridge inverter and voltage doubling rectifier circuit. The main power supply is input from the external three-phase AC power supply, and the pulsating DC voltage is formed after rectification and filtering by the three-phase bridge full control rectifier. Then, using the full bridge inverter, the obtained DC voltage is inverted to the medium frequency AC square wave voltage. Then the intermediate frequency AC voltage of 0–56 kV and 20 kHz is obtained through the intermediate frequency transformer. Finally, a 1200 kV, 10 mA stable DC high voltage is obtained by using the voltage doubling rectifier circuit
Fig. 1. Ultra-high voltage DC generator structure
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to further boost voltage. Considering the energy loss and capacity margin of each link of power circuit, the output power of intermediate frequency power supply is designed to be 20 kVA, the output power of DC high voltage is required to be 12 kW. The controller part monitored in real time for the output of voltage and current of the Ultra-high voltage DC circuit, and the monitoring results are taken as the input of the control model. By adjusting the thyristor conduction time in the three-phase bridge fully controlled rectifier and the driving waveform of the PWM in the full-bridge inverter part, the output voltage is stabilized. In addition, when the controller detects that the low or high voltage output circuit is short circuit or overload, it needs to automatically block all the control signal output immediately to protect the main circuit. 2.2 Control Model System The signal of detection feedback comes from the high voltage output end, and the control object is the three-phase bridge type fully controlled rectifier. Due to the traditional PID algorithm is mainly suitable for linear system, according to the precise model of controlled object is needed to determine the PID parameters, and by the intermediate frequency step-up transformer and voltage doubling rectifying circuit for booster circuit, composed of nonlinear time-delay systems, it is difficult to determine the precise mathematical model, so the traditional PID control algorithm cannot satisfy the requirement of high voltage dc generator [8, 9]. Therefore, to improved BP neural network of PID control model system is established, shown as Fig. 2.
Fig. 2. Control system model
In the control model system, the improved BP neural network is used to self-tune and optimize the control parameters of the PID regulator, which can make up for the deficiency of the standard PID algorithm in the nonlinear system and effectively improve the control performance.
3 Improved BP Neural Network 3.1 BP Neural Network BP network is a multi-layer feed forward network based on signal forward propagation and error back propagation given the learning goal, the neural network can imitate the learning process of human brain and automatically adjust the parameters of the neural
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network, thus forming a neural network architecture with certain judgment ability [10– 12]. There are three main elements of BP neural network, which are neuron change function, network topology, connection weight and learning algorithm. 3.1.1 Neuron Model Neuron is the basic unit of neural network, which can respond to input variables in different degrees. The model of a single neuron and its abstract mathematical model are shown in Fig. 3.
Fig. 3. Mathematical model of single neuron
If the cumulative value of the product of each neuron input and corresponding weight is greater than the excitation threshold of the neuron, the neuron will be activated, and then the output of the neuron will be obtained after a nonlinear activation function. xi ωij θj f (x)yj therefore, the input-output relationship of a single neuron can be expressed as: m (1) yj = f ωij xi − θj j=1
3.1.2 Network Topology Structure BP neural network is a neural network model with multi-layer structure, generally including input layer, hidden layer and output layer. Among them, there may be one or more hidden layers. There are 4 state variable nodes in the input layer of BP neural network model of Ultra-high voltage DC generator, which are the deviation between output and set value e (k), the first derivative e (k−1), the second derivative e (k−2) and the first derivative e (k−1) of the output of neural network. The output layer has three control nodes, which are the three parameters of the PID controller: the proportionality coefficient KP, integral coefficient KI, the differential coefficient KD. In general, three-layer neural network can approximate any mathematical model. Although increasing the number of layers can increase the accuracy of the neural network, it will increase the complexity of the network structure and reduce the convergence rate and stability. Therefore, the network performance is generally adjusted by changing the number of hidden layers. If there are too many nodes in the hidden layer, the convergence speed of the network will be reduced or even not converged. When the number of hidden layer nodes is too small, the generalization ability of the network is too weak and the
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Table 1. Convergence rate of the number of hidden layer nodes Number of hidden layer nodes
5
6
7
8
9
10
11
12
Convergence steps
925
862
755
505
412
358
302
256
Number of hidden layer nodes Convergence steps
13
14
15
16
17
18
19
20
289
325
356
406
459
498
545
596
error is too large. Therefore, an experiment was carried out to design the number of hidden layer nodes, and the results are shown in Table 1. It can be seen from Table 1 that when the number of hidden layer nodes is 12, the convergence rate of neural network is the fastest, so the structure of neural network is 4-12-3. 3.1.3 Learning Algorithm A part of the data set was randomly selected as the training sample and the corresponding expected output to calculate the input and output of each neuron in the hidden layer, and the difference between the expected output and the actual output was used to calculate the partial derivative of the error function to each neuron in the output layer, to correct the connection weights and calculate the global error. If the error does not meet the requirements, the next learning process will be entered until the error meets the requirements or the maximum learning times are reached. Then the learning process will be finished and the trained neural network will be obtained. Where, the error is defined as the mean square error. 3.2 Optimization of BP Neural Network Because the standard BP neural network has the shortage of slow convergence speed, the adaptive learning rate optimization algorithm is used to improve the BP neural network. The typical algorithms of adaptive learning rate include AdaGrad, RMSProp and ADAM etc. AdaGrad applied adaptive constraint to the learning rate, and used the past gradient to participate in the calculation, which improved the slow update rate of SGD to some extent. However, it needed to set a global learning rate in advance, and it was easy to jitter around the minimum value in the later training period. RMSProp is an improvement of AdaGrad. By changing the sum of gradient squares to an exponentially weighted moving average, it can effectively reduce the oscillation in non-convex Settings. Adam is a comprehensive improvement of the above two algorithms. On the basis of the above algorithm, the momentum term is added to make the parameter adjustment more stable. Therefore, in the specific design, Adam algorithm is selected to improve the trained BP neural network.
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4 Simulation Analysis Based on the above theories, an Ultra-high voltage DC generator based on BP neural network is designed, and a simulation model of the circuit is built in the MATLAB/Simulink environment, as shown in Fig. 4.
Fig. 4. Simulation model of Ultra-high voltage DC generator
In the figure, the module of nnpid is the BP neural network PID controller, which is composed of two parts: the traditional PID controller and the BP neural network controller written by S-function. BP neural network algorithm is set as 4-12-3 threelayer structure, and the input is four state variables, namely e (k), e (k−1), e (k−2) and u(k−1). The hidden layer has 12 neurons, and the output is three parameters of the PID controller, namely the proportionality coefficient KP, integral coefficient KI, the differential coefficient KD, which adaptively adjusts the PID parameters through the autonomous learning ability of the neural network. The voltage doubling circuit is encapsulated in the module of multi-voltage. The model is used in the symmetric level 9and 18 times voltage doubling circuit, to produce 1200 kV DC high voltage. Based on the paper, the traditional PID control algorithm and the improved BP neural network PID control algorithm are introduced to build the circuit model respectively, and the simulation is carried out. Figure 5 shows the output voltage waveform of the Ultra-high voltage DC generator obtained by the two control algorithms. It can be seen from the output of voltage waveform that the output of the neural network PID controller reaches a stable output voltage of 1200 kV within 0.1 s, and the output voltage is not overshoot. However, the traditional PID controller can only achieve stable output at about 0.2 s, and the output has 18% overshoot, which will put forward higher requirements for the voltage resistance level of Ultra-high voltage DC generator devices. The designed BP neural network PID controller can adjust the PID control parameters dynamically and get the best KP, K.I, K.D. So that improve the control performance of Ultra-high voltage DC generator.
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(a) Neural network PID control
(b) Traditional PID control Fig. 5. Output voltage waveform of the two control algorithms
In order to design the recovery ability of the neural network PID controller in the partial discharge of the sample, a pulse signal with a pulse width of 0.01 s was added as the disturbance at 0.5 s. The corresponding disturbance recovery curve is shown in Fig. 6.
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From the disturbance recovery curve, it can be seen that after the disturbance occurs, the system can restore stable output in about 0.03 s. It can be seen that the designed neural network PID controller has fast response speed and good anti-interference ability. The Ultra-high voltage DC generator has a good recovery ability when the test product breaks down and discharges.
Fig. 6. Disturbance response curve of neural network PID controller
5 Application Tests Based on the proposed method, an Ultra-high voltage DC generator prototype was developed, and a practical application test was carried out on the prototype. The test site is shown in Fig. 7.
Fig. 7. Prototype test site
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5.1 Main Test Parameters The main test parameters include voltage stability and ripple coefficient. The calculation formula of voltage stability is as follows: ⎧ ⎨ Umax −U0 × 100% U (2) Voltage stability = U −U0 ⎩ 0 min × 100% U0 Take the larger of the two test parameters. In the formula, U0 is the output rated voltage, Umax and Umin are respectively the maximum and minimum values measured at the test point. The calculation formula of ripple coefficient is as follows: Ripple coefficient =
Upp × 100% 2U0
(3)
In the formula, the Upp is the peak-to-peak value of the envelope of all ripple voltages (high frequency, medium frequency and low frequency), U0 is the output voltage rating of the test point. 5.2 Test Methods It the output position of the DC generator, it is connecting 1200 kV/10mA constant load. Power on the generator, press the start button after 5 min (preheating), start point by point test. Continue to test for 30 s and 3 min at each test point. The Agilent 34401A 61/2 digital multimeter is used to measure and record the voltage value. The measurement mode of the multimeter is set as automatic continuous measurement, and the maximum and minimum values of the measurement record are kept automatically. Tektronix TDS1002 digital oscilloscope was used to measure and record the waveform and ripple peak [13–15]. 5.3 Test Results Samples were taken every 3 min, and the test results were shown in Table 2. As can be seen from Table 2, the stability of all points within 30 s is less than 0.03%. The stability of each point was less than 0.05% within 3 min. At 1200 kV, the ripple coefficient is 0.081%. At the same time, the test results show that the adjustment rate of power supply voltage is less than 0.05%, and all the protection functions can work normally. The test results show that the voltage stability, ripple coefficient and other items meet the requirements of DL/T 848.1-2004.
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7esting voltage(U/V )
1XPEHU
200000
400000
600000
800000
1000000
1200000
9oltage(Um Voltag(Umi ax/V) n/V)
6tability%
Voltag(Uma Voltag(Umi x/V) n/V)
6ability%
1
200313.9
200284.6
0.015
200358
200320.7
2
200699.6
200672.4
0.014
200746.2
200706.4
0.019 0.02
3
200204.6
200175.6
0.015
200415.9
200364.1
0.026
1
400722.3
400680.6
0.01
400792.7
400720.3
0.018
2
401468.8
401426.7
0.011
401544.4
401467.5
0.019
3
400487.6
400436.7
0.013
400415.9
400339.4
0.019
1
600321.6
600156.6
0.028
600381.3
600247.8
0.022
2
601430.1
601326.1
0.017
601503.8
601371.9
0.022
3
600045.1
599898.5
0.024
601756.4
601558.4
0.033
1
800562.8
800454.8
0.014
800583.7
800449.3
0.017
2
802081.4
801972.9
0.018
802139.2
801968.4
0.021
3
801548.7
801384.5
0.021
800913.5
800603.8
0.039
1
999252
999043.8
0.021
999362.8
998938.9
0.042
2
1001224
1000943
0.028
1002070
1001604
0.047
3
1001846
1001577
0.027
1001452
1000989
0.046
1
1199985
1199543.8
0.036
1199362
1199038
0.027
2
1201194
1200943
0.021
1201870
1201504
0.031
3
1202046
1201877
0.014
1201862
1201529
0.028
6 Conclusion The traditional PID control method cannot effectively guarantee the stability of the output voltage, because of the Ultra-high voltage DC generator has the characteristics of large inertia and non-linearity. For this reason, a control model of Ultra-high voltage DC generator based on BP neural network is proposed. The improved BP neural network is used to self-tune the PID parameters online. The simulation results show that the control performance of the designed model is much better than that of the common PID controller. At the same time, the new developed Ultra-high voltage DC generator has a strong application value, for that the parameters and functions can meet the requirements of the relevant national standards.
References 1. Wang, H., Xiao, B., Zeng, L., et al.: Research on the cave exploration technology of overhead transmission line tower foundation. J. Phys. Conf. Ser. 1769(1), 012034 (10p.) (2021) 2. Ievoli, R., Palazzo, L., Ragozini, G.: On the use of passing network indicators to predict football outcomes. Knowl.-Based Syst. 222, 106997 (2021) 3. Guo, S., Niu, Z., Li, H., et al.: Diagnostic criterion of turn-to-turn overvoltage test in dry-type air-core reactor. High Volt. Eng. 44(3), 804–811 (2018). (in Chinese) 4. Altimania, M., Alzahrani, A., Ferdowsi, M., Shamsi, P.: Operation and analysis of nonisolated high-voltage-gain DC-DC boost converter with voltage multiplier in the DCM. In: Proceedings of IEEE Power Energy Conference at Illinois (PECI), pp. 1–6 (2019)
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5. Maalandish, M., Hosseini, S.H., Ghasemzadeh, S., Babaei, E., Jalilzadeh, T.: A novel multiphase high step-up DC/DC boost converter with lower losses on semiconductors. IEEE J. Emerg. Sel. Top. Power Electron. 7(1), 541–554 (2019) 6. Wang, Y., Su, H., Liu, J., et al.: Current Situation and Prospect of High Capacity Short Circuit Current Suppression and Interruption Technology. South China University of Technology Press, Guangzhou (2018).(in Chinese) 7. Wu, S., Meng, F., Zhao, W., et al.: Dynamic char-acteristic optimization for transmission mechanism of UHV circuit breaker. High Volt. Eng. 44(3), 727–732 (2018). (Chinese) 8. Yang, S.,Weng, Y., Li, X., et al.: Analysis and research on hidden dangers of uneven ice coating damage to UHV DC fittings. Hubei Electr. Power, 042(003), 5–8, 25 (2018). (in Chinese) 9. Yang, J.,Wang, Y., Zhang, W., et al.: Research on distribution and shielding of magnetic field of dry-type air-core smoothing reactor. High Volt. Apparatus 55(4), 133–140 (2019). (in Chinese) 10. Schlatter, S., Illenberger, P., Rosset, S.: Peta-pico-voltron: an open-source high voltage power supply. HardwareX4, Article No. 00039 (2018) 11. Chou, D., Lei, Y., Pilawa-Podgurski, R.C.N.: A zero-voltage-switching, physically flexible multilevel GaN DC–DC converter. IEEE Trans. Power Electron. 35(1), 1064–1073 (2020) 12. Ravi, V., Satpathy, S., Lakshminarasamma, N.: An energy-based analysisforhighvoltagelowpowerflybackconverterfeedingcapacitiveload. IEEE Trans. Power Electron. 35(1), 546–564 (2020) 13. Chivite-Zabalza, J., Trainer, D.R., Nicholls, J.C., Davidson, C.C.: Balancing algorithm for a self-powered high-voltages witch using series-connected IGBTs for HVDC applications. IEEE Trans. Power Electron. 34(9), 8481–8490 (2019) 14. Qi, W.,Chen, B., Dong, J., et al.: Spatial magnetic field distribution on interturn short circuit fault of Iron core reactor. Power Capacitor React. Power Compensation 40(1), 86–91 (2019). (in Chinese) 15. Yang, F., Sheng, G., Xu, Y., Hou, H., Qian, Y., Jiang, X.: Partial discharge pattern recognition of XLPE cables at DC voltage based on the compressed sensing theory. IEEE Trans. Dielectr. Electr. Insul. 24(5), 2977–2985 (2017)
High Frequency Single Phase Induction Motor Driver Based on Half Bridge Circuit and Soft Switch Control Yu Wang1 , Xupeng Fang1(B) , Ying Zang1 , and Dengjun Yan2 1 College of Electrical Engineering and Automation, Shandong University of Science and
Technology, Qingdao, China [email protected] 2 Stanley Black Decker, 701 E Joppa Road, Towson, MD 21286, USA
Abstract. The high frequency single phase induction motor has been hoped to replace PM DC motor or Universal motor in the lower power special appliance where the commutation spark will bring the risk of dust explosion or electromagnetic interference on wireless communication. In order to drive motor run in high speed, usually higher than 25000 rpm, a power electronic circuit is needed, i.e. variable frequency driver. This paper introduces the half bridge topology into the high frequency single phase induction motor driver, so that the motor can easily start and work at various speed. In the introduced driver, the over current protection has been realized, and the soft switch process has been analyzed to improve the circuit EMI performance. The validating work has been carried out through both the software simulation based on the equivalent motor model and experiment test in the laboratory. Both of them have shown that the proposed circuit performance can meet the industrial EMI requirement in the output distortion and power loss when the soft switch technology utilized. Keywords: High frequency single phase induction motor · Soft switch · Variable Frequency Driver · Electromagnetic interface · Industrial safety
1 Introduction Single Phase Induction Motor (SPIM) has been used extensively for lower power loads less than kWs, such as power tools and household appliances. Working with Variable Frequency Drivers (VFD), the motor can run at a speed higher than 25000 rpm, so that it would be utilized in more fields [1–3]. For example, in the situation where commutation spark is strictly limited to avoid dust explosion and electromagnetic noise, SPIM has won the competition against the universal motor and PMDC motor. Therefore, power tools manufacturers have recently displayed special interests in high frequency single phase induction motor [4, 5]. The classical driver for the SPIM has been based on the full bridge topology circuit model [6–10]. The half bridge drive circuit requires only 2 MOSFETs, and the full © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 356–362, 2022. https://doi.org/10.1007/978-981-19-1528-4_36
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bridge drive circuit requires not only 4 MOSFETs, but also two sets of driving pulses with opposite phases to control the two pairs of MOSFETs respectively. Therefore, compared with the half bridge drive circuit, the full bridge drive circuit is not only more complex, but also increases the cost of components. However, in order to output the same power, the input current of the half bridge drive circuit is twice that of the full bridge input current, so the half bridge drive circuit is more suitable for the use of low-power load [11]. In this paper, a driver being based on the half bridge topology is introduced, in which the number of MOSFETs has been decreased and the topology has become simpler. In the presented driver, the over current protection has been realized, and the soft switch process has been analyzed to improve the Electro-Magnetic Interference (EMI) performance of the circuit. The control strategy has been designed according to the motor starting process and steady state in order to realize the over current protection. The soft switch technology has been applied to decrease switch loss and noise, and the transient switch process has been analyzed in detail so that the switch dead time could be easily forecasted. Both of the computer simulation and lab test result have carried out to validate to the circuit practicability. The rest of this paper is described as follows: the background of driver circuit and control strategy and soft switch in Sect. 2, where the most important innovations are discussed. In Sect. 3, empirical research is conducted on driver circuit and control strategy and soft switch results are compared and analyzed. Section 4 summarizes the conclusions and makes recommendations for future work.
2 Background 2.1 Driver Circuit The diver circuit, shown in Fig. 1, is based on widely used half-bridge topology [12– 14], which is made up with only two switches, upper and lower switch, i.e. ‘totem-pole’ configuration. The two switches are turned on and off complementarily to each other. Although the voltage wave form on the mid-point is square, the motor is driven with the pseudo sinusoidal wave because of the capacitor C, thus the current through the motor armature will be AC instead of DC.
Fig. 1. SPIM driver circuit based on half bridge topology
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2.2 Control Strategy and Soft Switch 2.2.1 Vary Frequency and Power In order to limit the armature current, both of the two driving parameters, PWM frequency and duty cycle, should be adjusted according to the motor speed during the starting period. 1. PWM Frequency ∈ [20 800]Hz 2. Duty Cycle ∈ [10 99]percentage When motor speed is zero, the frequency is 20 Hz and the duty cycle 10%. When motor reach the rated speed, PWM frequency equals to the rate speed, and the duty cycle always is 99%. These two parameters are calculated with PID theory with over current protection put into consideration. 2.2.2 Soft Switch The soft switch technology [15–19] is an essential part to all kinds of power conversion circuit in order to reduce the switch loss and electromagnetic noise. In this paper, it has been analyzed in six phases 1–6, briefly shown in Figs. 2 and 3, and all results are in Table 1.
Phase 1
Phase 2
…….
Phase 6
Fig. 2. Half-bridge resonant inverter circuit working condition
Fig. 3. Main waveforms of half-bridge inverter circuit
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Table 1. Voltage and current transition during switching process Time
M1
UGS_M1
M2
UGS_M2
UA
UC
Phase 1
ON
1
OFF
0
VBUS
Ascend
Time t1
ZCS
1≥0
OFF
0
Phase 2
OFF
0
OFF
0
IM1
IM2
IC1
IC2
period or moment
Time t2
OFF
0
OFF
0
Phase 3
OFF
0
Freewheeling ZVS
0
Time t3
OFF
0
ON
0≥1
Phase 4
OFF
0
ON
1
Time t4
OFF
0
ZCS
1≥0
Phase 5
OFF
0
OFF
0
Time t5
OFF
0
OFF
0
Phase 6
Freewheeling
0
OFF
0
ZVS Time t6 (t0 )
ON
VBUS ≥ 0 V
−0.7 V
0.2 V
Imotor
0
0
0
0
0
Imotor /2
−Imotor /2
0
0
0
0
0
0
0
−Imotor
0
0
0
Top
0
0
0
Descend
0
−Imotor
0
0
0
0
0
0
0.2V ≥ VBUS
VBUS +
0
0
Imotor /2
−Imotor /2
0
0
0
0
Imotor
0
0
0
0
0
0
0
0.2 V 0≥1
OFF
0
Bottom
3 Results The software simulation circuit is shown in Fig. 1. The pulse signal waveforms, 450 Hz, on the MOSFET M1/2 are shown in Fig. 4 with 2% dead time, the voltage waveform at the middle point in Fig. 5, and the motor terminal voltage and current in Fig. 6.
Fig. 4. Pulse wave on the MOSFET
The experiment results are shown in the Figs. 7 and 8. Figure 7(a) displays the gate wave of MOSFET where dead time is so tight that the high frequency oscillate phenomenon is observed, which is eliminated in Fig. 7(b) by changing the dead time. The voltage wave on the motor terminals is shown in Fig. 8, which is quasi-sinusoidal.
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Fig. 5. Voltage wave on the middle point of the bridge arm
Fig. 6. Waveform of voltage (red) and current (green) of driven motor
Compared with full bridge drive circuit, half bridge drive circuit not only has simple circuit structure, low economic cost, but also improves the anti-balance ability of the drive circuit. Through simulation and experiment, we can see from the waveform based on soft switching technology control strategy, can greatly reduce switching loss and reduce switching noise problem, and also protect the overcurrent problem of the drive circuit.
(a) hard switching
(b) soft switching
Fig. 7. Switch signal wave on MOSFET
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Fig. 8. Voltage on the motor terminals
4 Conclusion To drive a motor at high speeds, usually higher than 25,000 rpm, a power electronic circuit, i.e., a variable frequency drive, is required. In this paper, a half-bridge topology is introduced into a high-frequency single-phase asynchronous motor drive, thus enabling the motor to start and operate easily at various speeds. Being compared to the full bridge driver, the half bridge driver has the following advantages: less components, less power loss, and simpler topology. Working with the proposed driver, two PWM parameters, frequency and duty, are calculated according to the motor speed and load to start the motor smoothly without the armature current excelling the limit. The soft switch technology has been utilized to improve the switch loss and EMI performance. Both of the software simulation and laboratory test have validated the circuit applicability. In the field of Variable Frequency Drivers (VFD) for power tools and household appliances, the half bridge drive circuit and the control strategy of soft switch can not only simplify the circuit and save the cost, but also greatly improve the protection ability of the circuit.
References 1. Song, Y., Blaabjerg, F.: Single-Phase Induction Motor and AC Drives, pp. 237–264. Academic Press, Cambridge (2018) 2. Darbali-Zamora, R., Merced-Cirino, D.A., Díaz-Castillo, A.J., et al.: Single phase induction motor alternate start-up and speed control method for renewable energy applications, pp. 743– 748. IEEE (2014) 3. Latt, A.Z., Win, N.N.: Variable speed drive of single phase induction motor using frequency control method, pp. 30–34. IEEE (2009) 4. Lettenmaier, T.A., Novotny, D.W., Lipo, T.A.: Single-phase induction motor with an electronically controlled capacitor. IEEE Trans. Ind. Appl. 27 (1991) 5. Bathunya, A.S., Khopkar, R., Kexin, W.: Single phase induction motor drives - a literature survey. In: IEMDC, pp. 911–916 (2001) 6. Gajare, A.M., Bhasme, N.R.: Control of Air flow rate of single phase induction motor for blower application using V/F method. IJESET 6, 102–112 (2013) 7. Pooja, S., Rupali, B., Pooja, K., Purvee, J.: Speed control of induction motor by using variable frequency drive. Int. IJERA 4, 35–37 (2014) 8. Chaitanya, N.J., Asstt, V.H.: Speed control of single phase induction motor using MicroController. In: ICIAC (2014)
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9. Waswani, R., Pawar, A., Deore, M., et al.: Induction motor fault detection, protection and speed control using Arduino, pp. 1–5. IEEE (2017) 10. Kumar, A.A., Bindu, G.R.: Energy efficient drive system for domestic and agriculture applications: a comparative study of SPIM and SRM drives, pp. 389–394. IEEE (2018) 11. Bhargava, R., Shrivastava, A.: Cascaded H-bridge multilevel inverter using micro-controller for single phase induction motor. Int. J. Emerg. Technol. 101–108 (2012) 12. Manuel, A., Francisco, F.L., Didier, B., Almadidi, A.D.: Design of a soft-switching asymmetrical half-bridge converter as second stage of an LED driver for street lighting application. IEEE TPEL 27(3), 1608–1621 (2012) 13. Wu, T.F., Yu, T.H.: Analysis and design of a high power factor, single-stage electronic dimming ballast. IEEE Ind. Appl. 34(3), 606–615 (1998) 14. Wu, T.F., Yu, T.H.: An electronic dimming ballast with bifrequency and fuzzy logic control. IEEE Ind. Appl. 36(5), 1308–1317 (2000) 15. San, L.O.: Design strategy for a 3-phase variable frequency drive (VFD). Senior Project, California Polytechnic State University (2011) 16. Jaroslav, D., Juraj, O.: High-frequency soft-switching DC-DC converters for voltage and current DC power sources. Acta Polytech Hung. 4(2), 29–46 (2007) 17. Smith, K.M., Smedley, K.M.: Properties and synthesis of passive lossless soft-switching PWM converters. IEEE TPEL 14(5), 890–899 (1997) 18. Maksimovic, D., Cuk, C.: General properties and synthesis of PWM DC-to-DC converters. IEEE PESC Conf Rec. 2, 515–525 (1989) 19. Sullivan, C.R., Sanders, S.R.: Soft-switched square-wave half-bridge DC-DC converter. IEEE Trans. AES 33(2), 456–463 (1997)
Research and Application of Partial Discharge Inspection Instrument for Converter Transformer Based on High Frequency Coupling Method Xiu Zhou(B) , Qingping Zhang, Yan Luo, Yunlong Ma, Xiuguang Li, Jin Bai, Lu Tian, and Yuhua Xu State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China [email protected] Abstract. Converter transformer is one of the important operation equipment in converter station. Live detection can effectively detect the equipment status, find the equipment defects in time and eliminate the hidden dangers, which is of great value to avoid the occurrence of malignant accidents. In this paper, by studying the characteristics of high-frequency interference signal in converter station, a highsensitivity high-frequency sensor is designed, and the high-frequency coupling method is used to suppress the interference. The field test shows that the instrument can suppress the field interference, obtain the real partial discharge signal, improve the accuracy of test data, effectively find the potential insulation fault of converter transformer, prevent the occurrence of malignant accidents. Keywords: High frequency coupling method · Partial discharge · Interference suppression · Inspection instrument
1 Introduction As an important part of power grid, the operation state of converter transformer determines the safety and reliability of the whole HVDC system. The most basic index to measure the performance of converter transformer is its insulation performance, especially the high-end converter transformer. Because of its high voltage level, large capacity, and its own technical characteristics and complexity, it is necessary to carry out on-site partial discharge live detection, so as to detect its insulation performance and find the latent defects in the operation process in time, Determine the cause and severity of insulation defects and faults [1–3]. When the PD live detection is carried out on site, all converter stations are live. The PD test of converter transformer should fully consider various interference factors on site. The background interference component of PD test is very complex. The measurement signal is buried in the interference signal due to the pulsating interference generated by valve hall, so it is difficult to observe and detect PD signal. Therefore, the development of on-site partial discharge inspection instrument is of great significance to the insulation diagnosis and safe operation of converter transformer [4, 5]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 363–374, 2022. https://doi.org/10.1007/978-981-19-1528-4_37
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2 High Frequency Partial Discharge Detection Principle of Converter High frequency partial discharge detection technology uses the principle of pulse current to detect the partial discharge of high voltage electrical equipment, and the frequency of detection signal is generally 3 MHz–30 MHz. Taking the converter transformer as an example, there is distributed capacitance between the core and the high voltage winding, and there is distributed capacitance between the core and the low voltage winding. If there is a fault inside the converter transformer winding, the fault point will radiate electromagnetic wave outward to form high-frequency pulse current [6, 7], The highfrequency pulse signal generated by the discharge forms a loop through the coupling channel and the core ground wire. The high frequency sensor is installed on the core ground wire, which can couple the high frequency partial discharge signal. The detection principle is shown in Fig. 1.
Fig. 1. Principle of high frequency partial discharge detection
As shown in Fig. 1, the high frequency partial discharge detection system mainly includes: high frequency current transformer, high frequency detection equipment, reliable grounding point, etc.
3 Design of High Frequency Pulse Current Sensor 3.1 Equivalent Circuit of High Frequency Sensor The general effective detection frequency band of high-frequency partial discharge sensor is generally 3 MHz–30 MHz. The equivalent circuit of high-frequency sensor is shown in Fig. 2. I(t) is the high-frequency pulse current coupled by the sensor, M is the mutual inductance between the measured conductor and the coil of high-frequency current transformer, LS, RS and CS are the self inductance, equivalent resistance and equivalent stray capacitance of the coil of high-frequency transformer respectively, R is the load integral resistance, u0(t) is the output voltage signal of high frequency current sensor [8].
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Fig. 2. Equivalent circuit diagram of partial discharge detection for high frequency current sensor
In the case of rated load, ignoring stray capacitance C S , the transfer function of the system can be calculated as follows: H (S) =
M Uo (S) R ≈ R= I (S) LS N
(1)
where N is the number of winding turns of the coil. It can be seen from (1) that the sensitivity of the high frequency sensor is inversely proportional to the number of winding turns N and directly proportional to the load resistance R. But in practical application, the influence of stray capacitance CS must be considered in high frequency transmission characteristics. H (S) =
MS Uo (S) = I (S) LS CS S 2 + ( LRS + RS CS )S +
RS R
+1
(2)
where M is the mutual inductance of the sensor. From (2), it can be seen that the sensitivity of the high frequency sensor is directly proportional to the mutual inductance of the sensor, and has a relationship with LS, RS and CS. According to the analysis of resonant circuit, the expression of lower cut-off frequency is shown in (3), and the expression of upper cut-off frequency is shown in (4). f1 =
R + RS R + RS ≈ 2π(LS + RRS CS ) 2π LS
(3)
LS + RRS CS 1 ≈ 2π LS RCS 2π RCS
(4)
f2 =
According to (3), the lower cut-off frequency is inversely proportional to L S and directly proportional to the sum of R and RS (4) The lower cut-off frequency is inversely proportional to the product of C S and R. It is known from (2)–(4) that the characteristics of high frequency sensor with wide frequency band and high sensitivity are directly related to the magnetic core material, turns and other parameters of high frequency sensor.
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B. Parameter design of high frequency sensor The high frequency current sensor with open design can be opened and closed at any time, which is convenient for installation and disassembly during test, and also suitable for long-term use. The performance parameters of high frequency sensor are effective bandwidth: 3 MHz–100 MHz, Conversion characteristics: more than 5 mV/mA, Matching impedance: 50 Coaxial cable. Choosing the core with high permeability can effectively improve the sensitivity and band width of high frequency sensor, and reasonable turn number design can also improve the band range and sensitivity. The design plan of high frequency sensor is shown in Fig. 3, (1) output regulating coil, (2) shielded aluminum shell, (3) output interface, (4) magnet, (5) magnetic core, (6) calibration interface, (7) coupling coil. The shield aluminum shell plays the role of shielding interference signal. The high-strength magnet is used to make the upper and lower parts connect quickly. The coupling coil and output regulating coil control the turn to turn ratio of the transformer, and BNC terminal is used for the output interface, which is convenient for connection.
Fig. 3. Plane drawing of high frequency sensor design
In order to obtain wider frequency band and higher sensitivity, different core materials and turns ratio are selected for testing. Take turns ratio is 5, select magnetic cores of different materials, and test the magnetic cores with ferrite core, iron silicon aluminum core, molybdenum permalloy core and iron nickel alloy core respectively. The contrast diagram of amplitude frequency characteristic test is shown in Fig. 4, and the test sensitivity is shown in Table 1. Taking Mo permalloy core as an example, the comparison of amplitude frequency characteristics test for changing different turns ratio is shown in Table 2. Through the above data comparison and analysis, the transmission impedance, sensitivity and amplitude frequency characteristics of high frequency sensor with turn ratio of 5 and magnetic core material of molybdenum permalloy are the best.
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(a) Amplitude frequency curve of high frequency sensor with ferrite core
(b) Amplitude frequency curve of high frequency sensor with Fe Si Al magnetic core
(c) Amplitude frequency curve of high frequency sensor for molybdenum permalloy
(d) Amplitude frequency curve of high frequency sensor with iron nickel alloy core Fig. 4. Comparison of amplitude frequency curves of high frequency sensors with different magnetic core materials
Table 1. Comparison table of test data of different core materials Core material
Transmission impedance
Sensitivity
Frequency characteristic
Ferrite
3.48 mV/mA
3.2 pC
The result of 6 dB bandwidth is 39 MHz, and the corresponding frequency band is 1 MHz–40 MHz (continued)
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Core material
Transmission impedance
Sensitivity
Frequency characteristic
Fe-Si-Al
7.28 mV/mA
1.2 pC
The result of 6 dB bandwidth is 20 MHz, and the corresponding frequency band is 1 MHz–21 MHz
Molybdenum permalloy
9.88 mV/mA
0.5 pC
The bandwidth of 6 dB is 99 MHz, and the corresponding frequency band is 1 MHz–100 MHz
Ferronickel alloy
8.88 mV/mA
1.0 pC
The result of 6 dB bandwidth is 96 MHz, and the corresponding frequency band is 4 MHz–100 MHz
Table 2. Comparison table of test data of different turns Number of turns
Transmission impedance
Sensitivity
Frequency characteristic
1 turn
4.48 mV/mA
7.2 pC
The result of 6 dB bandwidth is 76 MHz, and the corresponding frequency band is 4 MHz–80 MHz
3 turn
6.28 mV/mA
2.2 pC
The result of 6 dB bandwidth is 84 MHz, and the corresponding frequency band is 2 MHz–86 MHz
5 turn
9.18 mV/mA
0.6 pC
The result of 6 dB bandwidth is 99 MHz, and the corresponding frequency band is 1 MHz–100 MHz
7 turn
6.08 mV/mA
2.6 pC
The bandwidth of 6 dB is 80 MHz, and the corresponding frequency band is 8 MHz–88 MHz
9 turn
5.08 mV/mA
3.6 pC
The result of 6 dB bandwidth is 70 MHz, and the corresponding frequency band is 8 MHz–78 MHz (continued)
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Table 2. (continued) Number of turns
Transmission impedance
Sensitivity
Frequency characteristic
11 turn
4.18 mV/mA
6.8 pC
The result of 6 dB bandwidth is 66 MHz, and the corresponding frequency band is 16 MHz–72 MHz
15 turn
3.88 mV/mA
7.5 pC
The result of 6 dB bandwidth is 65 MHz, and the corresponding frequency band is 17 MHz–72 MHz
4 Design of Field Interference Suppression Circuit The interference of high frequency partial discharge detection of field converter transformer mainly comes from electromagnetic noise interference generated by converter valve, which is the main disturbance source in DC high voltage converter station [9, 10]. The pulsating interference signal radiates to the PD measurement system through the earth and space, which leads to the excessive background amplitude of PD measurement, and the PD pulse is covered by 12 pulsating interference signal, so the real PD signal can not be extracted, which causes the PD measurement error. After field measurement, the interference signal is 12 pulse signals with a time interval of 1.67 ms, and the amplitude of the interference signal is relatively large, resulting in too large measurement background to measure high frequency partial discharge [11, 12]. The principle of interference suppression circuit for on-site high-frequency partial discharge detection is shown in Fig. 5. The power frequency extraction module extracts the effective power frequency signal. After filtering and amplifying, the power frequency signal is input into the phase-locked synchronization unit, which shapes the power frequency current to obtain the zero phase trigger signal. At the same time, according to the characteristics of the pulsating interference signal, the phase-locked synchronization unit generates the zero phase trigger signal. The synchronization signal is divided into 12 pulses with a time interval of 1.67 ms. The phase lock synchronization unit locks the 12 pulses. The 12 pulse phase lock signal is input to the CPU processing unit, and the high frequency signal is obtained by the high frequency extraction module. The obtained high frequency signal includes pulsating interference signal and internal insulation discharge signal of high voltage electrical equipment. The high-frequency signal is amplified by the conditioning module to improve the sensitivity of high-frequency signal detection. The amplified high-frequency signal is input to the CPU processing unit. The CPU processing unit obtains the high-frequency signal and interference 12 pulse phase signal at the same time. The CPU processing unit first converts the high-frequency signal into digital signal. The digital high-frequency signal is interpolated by the interference 12 pulse phase signal, and the interference signal in the high-frequency signal is eliminated by interpolation to obtain the real partial discharge signal.
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12 pulsation interference generating unit
CPU
High frequency extraction module
Channel conditioning module
Fig. 5. Schematic diagram of interference suppression circuit for field high frequency partial discharge detection
The circuit diagram of 12 pulsation interference generating unit is shown in Fig. 6. R1, R2 and C1 constitute the power frequency current extraction circuit. The filter cutoff frequency of the circuit is 100 Hz, so that the frequency signal above 100 Hz can be filtered and the power frequency signal of 50 Hz can be retained. The power frequency signal of 50 Hz is amplified 200 times by U1. The model of U1 is ad301, and U1 is a low noise and high gain operational amplifier. To ensure the effective detection of power frequency signal, the amplified power frequency signal is modulated into pulse signal through U2. The model of U2 is lm311, and the frequency response speed of lm311 chip is kHz. The pulse signal passes through U3, and U3 divides the pulse signal into 12 equal time intervals. The pulse time interval is 1.67 ms. The model of U3 is CD4069, The 12 pulse interference signal is transmitted to the CPU processing unit.
Fig. 6. Analog 12 pulse jamming circuit
5 Design and Development of High Frequency Inspection Instrument High frequency partial discharge inspection instrument adopts an integrated portable structure, which is composed of high frequency sensor, detection host and power frequency phase sensor. The design principle of the detection host is shown in Fig. 7. The high frequency detection host consists of CPU processing unit, channel conditioning unit, synchronization and power supply module and LCD module. CPU processing unit processor chip adopts stm32h753 [11], chip is based on cortex ®- M7 products, 480 MHz frequency to achieve the best performance, built-in TFT-LCD, JPEG codec,
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Ethernet. The channel conditioning unit includes the front protection circuit, isolation amplifier circuit, combined filter circuit and amplifier driver circuit. The synchronous and power module includes lightning protection module, interference synchronous pulse circuit, synchronous trigger circuit and power management circuit. In isolation amplifier circuit, the first stage amplifier selects 50 times amplification, the intermediate filter network attenuation is 2 times, the second stage amplifier is set to 100 times, 10 times, 1 times, and the three-stage fine-tuning amplifier drive makes the overall amplification factor controlled at about 1000 times. It is composed of 5 gears (x1, X10, X100, x100x10000) with attenuation network, and the overall dynamic range can reach 100dB. The combined filter circuit sets the filter network as low-end filter band 1 MHz, 2 MHz and 5 MHz. The high-end filtering frequency band is 10 MHz, 20 MHz and 30 MHz. The CPU processing unit controls the gain of the channel conditioning module and the filter frequency band of the isolation amplifier circuit. The synchronous conditioning signal ensures the synchronous acquisition of high-frequency analog signal. The interference pulse circuit signal and the channel conditioning high-frequency pulse signal are processed by the CPU to obtain the real high-frequency partial discharge signal. The amplitude and waveform of high-frequency partial discharge are displayed on the liquid crystal, Convenient for users to query [12, 13].
Fig. 7. Schematic diagram of high frequency detection host
6 Field Application In a ± When detecting high frequency partial discharge of 800 kV converter station, the main interference waveform of converter station is shown in Fig. 8 (a). The 12 pulse
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interference phase is relatively fixed, the amplitude of pulse is different, and the phase interval of each interference is basically the same and the pulse width is basically the same. The background signal is about 400 pc before interference elimination. From Fig. 8 (b) and (c), it can be seen that when the instrument turns on the interference elimination function, it can eliminate 12 pulsation interference signals, retain the real high frequency discharge signal in the converter flow, improve the accuracy of the on-site rheological detection, and the application effect is good in the field [14–16].
a
b
c
Interference waveform of converter station
Waveform of high frequency measurement signal before elimination
Waveform of high frequency measurement signal after elimination Fig. 8. Field high frequency measurement signal waveform
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7 Conclusions In view of the influence of converter transformer field interference signal on PD live detection, this paper designs and implements a PD live inspection instrument for converter transformer based on high frequency coupling method, and obtains the following conclusions through field verification. (1) 12 pulsation interference is the main interference source in converter station, the phase of pulsation interference signal is relatively fixed, the width of single pulsation is basically the same, and the pulse interval is equal. (2) The transmission impedance, sensitivity and amplitude frequency characteristics of high frequency partial discharge sensor with turn ratio of 5 and magnetic core of molybdenum permalloy are the best. (3) The high frequency detection host consists of CPU processing unit, channel conditioning unit, synchronization and power supply module and LCD module. Each part is interrelated and interacts with each other. (4) In this paper, the high frequency coupling interpolation method is used to eliminate the interference in the on-site PD live inspection instrument of converter transformer, which can effectively suppress the interference, retain the real PD signal, improve the sensitivity and accuracy of PD live inspection, so as to find the latent defects in time and prevent the occurrence of malignant accidents.
References 1. Li, J., Han, X., Liu, Z., et al.: Review on partial discharge measurement technology of electrical equipment. High Volt. Eng. 41(8), 2583–2601 (2015). (in Chinese) 2. Wang, X.W., Zhong, X.Y.: Review on partial discharge detection methods for electrical equipment. J. Shenyang Inst. Eng. (2017) 3. Li, P., Gu, C., Chen, D., et al.: Development of technologies in ±1 500 kV UHV DC transmission research. High Volt. Eng. 43(10), 6–15 (2017). (in Chinese) 4. Shi, X., Meng, J., Liang, H., et al.: Quality and efficiency evaluation system of distribution network fault repair. Electric Power Eng. Technol. 37(5), 143–147 (2018). (in Chinese) 5. Pang, L., Jing, Z., Chen, X., et al.: Study of fault repair control mode for distribution network based on automation and information under big-maintenance system. In: International Conference and Exhibition on Electricity Distribution (2013) 6. Xiong, Q., Zhu, L., Ji, S., et al.: Review on partial discharge of oil-paper insulation under DC voltage and compound voltage. Insul. Mater. 50(1), 1–7 (2017). (in Chinese) 7. Sha, Y., Zhou, Y., et al. Partial discharge characteristics in oil-paper insulation under combined AC-DC voltage. IEEE Trans. Dielectr. Electr. Insul. (2014) 8. Wang, S., Ye, Z., Mei, B.: Application status of online monitoring and live detection technologies of transmission and distribution equipment in electric network. High Volt. Apparatus 47(4), 84–90 (2011). (in Chinese) 9. Qi, B., Wei, Z., Li, C., et al.: Discharge characteristics of the typical defects in oil-paper insulation under AC-DC compound voltage. High Volt. Eng. 41(2), 639–646 (2015). (in Chinese) 10. Power transformers—Part 3: Insulation levels,dielectrictests and external clearances in air:GB/T 1094.3—2017[S] (2017). (in Chinese)
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11. Liu, H., Yang, S.: STM32 library development guidelines based on STM32F103, pp. 173–195. China Machine Press, Beijing (2017). (in Chinese) 12. Li, T., Wang, X., Zheng, C., et al.: Investigation on the placement effect of UHF sensor and propagation characteristics of PD-induced electro-magnetic wave in GIS based on FDTD method. IEEE Trans. Dielectr. Electr. Insul. 21(3), 015–1025 (2014) 13. Dan, M., Hao, J., Liao, R., et al.: Different motion and bridging characteristics of fiber particles in mineral oil and natural ester under DC voltage. Power Syst. Technol. 42(2), 665–672 (2018). (in Chinese) 14. Leblanc, P., Paillat, T., Morin, G., et al.: Behavior of the charge accumulation at the pressboard/oil interface under DC external electric field stress. IEEE Trans. Dielectr. Electr. Insul. 22(5), 2537–2542 (2015) 15. Li, S., Li, Q., Liu, H., et al.: PD pulse waveform characteristics and PD mechanism in oilpressboard insulation with needle-plate model under positive DC voltage. Proc. CSEE 38(20), 6173–6187 (2018). (in Chinese) 16. Li, Q., Li, S., Si, W., et al.: Analysis of the key problem about insulation condition assessment of oil-paper in power trans-formers based on partial discharge. High Volt. Eng. 43(08), 132– 139 (2017). (in Chinese)
Research on Mobile Unit of Refrigerated Truck Based on STM32 Ying Zhang(B) , Shuchen Liu, and Ningning Ren Shandong Institute of Commerce and Technology, Jinan 250103, Shandong, China [email protected]
Abstract. With the rapid development of cold chain logistics, the rate of refrigerated transportation of related goods continues to increase. The number of refrigerated trucks has also increased significantly, and the monitoring of refrigerated trucks is becoming mlengcangche ore and more important. Based on the analysis of the parking and driving conditions of refrigerated vehicles, this paper designs a mobile unit for refrigerated vehicles suitable for the current situation. It can not only meet the monitoring of refrigerated cars, but also realize the monitoring of the battery status of refrigerated cars and the automatic call for help after an accident. It can stop the loss in time, which has certain practical significance. Keywords: Refrigerated truck · Vehicle mobile unit · STM32 · Monitor
1 Introduction With the requirements of related policies, the rate of refrigerated transportation of aquatic products, meat, fruits and vegetables has been continuously increased, coupled with the continuous investment in cold chain logistics hardware facilities, the number of refrigerated trucks has increased significantly. Up to now, there are approximately 20000 refrigerated trucks in my country [1], and the number is still growing rapidly. According to research, the use of refrigerated transportation for food can reduce the damage rate to less than 10%, so it is particularly important to monitor the status of refrigerated vehicles. In fact, many companies and research institutions are committed to the monitoring and data collection of cold storage, ignoring the monitoring of some key data of the vehicle itself. For example, when the battery voltage is too low, the vehicle cannot start and the refrigeration system cannot work normally, which will also cause refrigerated food damage. Furthermore, in order to maximize profits, some cold chain transportation companies maximize the operation of refrigerated trucks and neglect maintenance [2]. The overall performance of the vehicles is poor, which increases the risk of driving accidents. Once an accident occurs, it cannot be dealt with in time [3]. In view of the current market situation and demand, this paper studies a mobile unit for refrigerated trucks, which monitors the battery voltage in real time while monitoring the temperature of the cold storage, adds automatic call for help after a collision, and enriches the safety monitoring of refrigerated trucks Features. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 375–383, 2022. https://doi.org/10.1007/978-981-19-1528-4_38
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2 Overall Design This vehicle-mounted module realizes functions such as temperature monitoring of the refrigerated vehicle cold storage, electrical measurement of the battery voltage of the refrigerated vehicle, and automatic call for help after a refrigerated vehicle collision. The monitored data information is analyzed and processed by the central processing unit MCU, and sent to the owner of the cargo (abnormal temperature of the cold storage), the owner (abnormal battery voltage), and the rescuer (an accident) through the communication module to give an alarm. The design process is shown in Fig. 1.
Fig. 1. Block diagram of overall design
2.1 Communication Method There are two data communication methods for refrigerated trucks, short message method or other wireless communication networks such as 4G, zigbee etc. [4]. The wireless communication network requires a server or a base station, which is costly. The short message communication method has small data transmission, low cost, convenient implementation and simple deployment. This module only sends information when the data is abnormal, the transmission frequency is very low, and the information content is small, which is more in line with the communication method required by this system [5]. The communication method is shown in Fig. 2.
Fig. 2. SMS communication path
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2.2 Implementation STM32 has the characteristics of high performance, low cost, low power consumption, higher main frequency, richer peripherals and resources, etc., combined with module functions, taking into account the cost and power consumption, the STM32 series is used as the on-board module MCU.
3 Hardware Design According to the functions implemented by the module, the hardware part includes a central processing unit MCU, a refrigerated compartment temperature monitoring module, a refrigerated vehicle battery voltage monitoring module, a refrigerated vehicle accident monitoring module, a GSM module and a power supply circuit. 3.1 MCU Module The STM32 series MCU is an application MCU with a Cortex-M3 core developed by STMicroelectronics. Because of its strong operability and high performance, it is extremely popular in the MCU market. STM32 includes three series: enhanced, basic, and interconnected. STM32F103RB is an enhanced MCU with low power consumption, only 35 mA, and a maximum operating frequency of 72 MHz. In addition, STM32F103RB is rich in resources, with 256 KB of FLASH, 3 SPI interfaces, and 5 Serial port, 3 12-bit ADCs, 48 general IO interfaces, etc. Since the system needs to control multiple modules and requires more interfaces, in order to control the peripheral circuits stably and effectively, STM32F103RB is used as the system MCU. 3.2 Refrigerator Compartment Temperature Monitoring Module The temperature monitoring module is used to read the temperature in the cold storage compartment to determine whether the cold storage temperature is normal. Temperature sensor module adopts DS18B20 temperature measurement module.
Fig. 3. DS18B20 temperature sensor module
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The DS18B20 temperature measurement module is a single-bus digital temperature sensor produced by DALLAS in the United States, with a temperature measurement range of −50° to +200°, with a built-in memory, and the data will not disappear after power failure. DS18B20 and MCU are connected through a universal I/O interface, and data acquisition and monitoring are performed by the ADC port of the MCU (Fig. 3). 3.3 Refrigerator Battery Voltage Monitoring Module Some refrigerated trucks will cause rapid aging of electrical appliances or functional components during long-term high-intensity operation, which will cause the selfdischarge or leakage of the battery. After serious, the vehicle cannot start normally, especially in winter. This phenomenon will be more obvious. The normal operation and quality of refrigerated goods have adverse effects. In order to solve the above problems, the system is equipped with a battery monitoring unit to monitor the voltage of the battery in real time. Refrigerated vehicles often use 24 V batteries. When the voltage is lower than 22.6 V, it is under voltage and needs to be charged in time. The battery also affects the normal start of the vehicle. When the voltage is lower than this voltage, a voltage warning is given. After the battery voltage is divided by the resistor set in the battery monitoring unit, it is connected to the ADC port of the MCU, and the MCU monitors the battery voltage. 3.4 Refrigerated Truck Collision Monitoring Module The configuration and technical standards of different brands of refrigerated vehicles are not uniform. The use of trigger signals such as airbags as the signal of a vehicle collision accident has a greater impact on the original vehicle circuit. The system has a separate collision sensor to monitor whether the vehicle has an accident, call for help. The collision sensor adopts SW-420. As shown in Fig. 4, the inside of SW-420 is a resistance strain gauge. When the collision reaches a certain level, the resistance value of the resistance strain gauge changes. When the threshold value is exceeded, an alarm will be realized. SW-420 is connected with MCU through GPIO and works in interrupt mode.
Fig. 4. Shock sensor module SW-420
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3.5 GSM Communication Module The GSM communication module is connected to the MCU via UART. The MCU controls the GSM module to send the refrigerated truck cold storage temperature abnormality and battery voltage abnormality to the cargo owner and vehicle owner in the form of short messages. When the refrigerated truck crashes, it will actively call the rescue party to realize automatic call for help. Taking into account factors such as price, size, performance, etc., the module adopts the SIM900A module of SIMCOM, as shown in Fig. 5. The SIM900A [6] module adopts standard AT commands to control the working mode, and realizes the sending of short messages and voice calls.
Fig. 5. SIM900A module
4 Software Design 4.1 Main Program Design In order to reduce power consumption, the disabled mode and timing RTC wake-up mode are set. After the wake-up is triggered, a function monitoring is performed. At the same time, the alarm function has also been set for repeated alarms [7]. If the owner does not deal with the message after the message is sent, the message will be repeated and the alarm will be repeated at an interval of 1 h. The main program flow is shown in Fig. 6.
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Fig. 6. Main program flow chart
4.2 Temperature Monitoring Program for Refrigerated Compartment The module enters the refrigerated truck temperature monitoring program after the RTC is awakened or interrupted, and the average measurement value is taken by ADC counting multiple samples to determine whether the temperature of the refrigerated truck is normal (Fig. 7).
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Fig. 7. Temperature monitoring program
4.3 Design of Monitoring Program for Refrigerated Truck Battery The battery voltage monitoring program start working after RTC wake-up or interrupt wake-up. The system uses ADC to count multiple samples to get the average measurement value to determine whether the refrigerated truck voltage is normal (Fig. 8).
Fig. 8. Battery voltage monitoring program
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4.4 Collision Monitoring Program Design The collision monitoring program and the main program are initialized at the same time, and the program starts to execute when the interruption enters. When the refrigerated truck crashes, the vibration module will trigger the interruption (Fig. 9).
Fig. 9. The collision monitoring program
4.5 Communication Subprogram Design STM32F103 controls the SIM900A module to read the owner’s mobile phone number and send text messages through AT commands [8]. In order to reduce system power consumption, when the text messages are sent, the SIM900 should be set to sleep mode in time (Fig. 10).
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Fig. 10. Communication subprogram design
5 Conclusion In order to monitor the safety of refrigerated trucks, the on-board mobile unit studied in this paper monitors the temperature of the cold storage, while also realizing the function of monitoring the battery voltage of the refrigerated truck and automatically calling for help in a timely manner when an accident occurs in the refrigerated truck, filling the gap in the market; taking into account the market at the same time, Cost and other factors, the software and hardware of the on-board module have been studied, which provides a basis for further development and application.
References 1. Zhang, J.: Research on the status quo and future development of my country’s cold chain logistics. Guangxi Qual. Supervision Guide (11), 85–86 (2018). (in Chinese) 2. Bai, E.: Research on Food Cold Chain Information Monitoring System Based on Internet of Things. Zhejiang University, Zhejiang (2017). (in Chinese) 3. Ruiz-Garcia, L., Rodríguez-Bermejo, J.:Monitoring the intermodal, refrigerated transport of fruit using sensor networks. Span. J. Agric. Res. 5(2), 142–156 (2007) 4. Zhang, Z., Chen, J.: Design of cold chain monitoring system based on the internet of things. Electron. Des. Eng. (2018). (in Chinese) 5. Zhu, Y.: Design and development of vehicle monitoring system based on GPS/GPRS/RFID. Nanjing University of Aeronautics and Astronautics, Nanjing (2014). (in Chinese) 6. SIMCOM: SIM900 Data Sheet. SIMCOM Integrated Products (2012) 7. Zhang, Z.: Design of cold chain monitoring system based on the internet of things. Electron. Des. Eng. (2018). (in Chinese) 8. Zhang, J.: Design on wireless SO2sensor node base on CC2530 for monitoring table grape logistics. J. Food Agric. Environ. 115–117 (2013)
Design Method for Power Unit of Power Conversion System Based on SiC MOSFET Ning Xie(B) , Yanjun Zhao, Wei Zhao, Jingpeng Yue, Wei Wang, and Chiye Zhang Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou, China [email protected]
Abstract. With the rapid development of renewable energy, power conversion systems play a vital role in the new energy grid. In this paper, aiming at the power unit of power conversion system based on SiC MOSFET, which includes 10 kV high voltage AC module and 750 V low voltage DC module, the low inductance design and heat dissipation design methods of the power unit are emphatically studied, and the overall design scheme of the power unit is proposed. By optimizing the structure of laminated bus, the stray inductance of high voltage AC module and low voltage DC module is reduced to 794 µH and 235 µH respectively, which effectively reduces the turn-off overvoltage of power unit. Through the thermal simulation analysis, the heat dissipation scheme of forced air cooling is established, and the required radiator and fan are designed, so that the maximum temperature of the device in the operation process would not exceed 50 °C. Finally, the prototype of the power unit is built and the towing experiment is carried out, which verifies the effectiveness of the optimization design of the laminated bus structure and the heat dissipation design scheme of the power unit. Keywords: Power unit of power conversion system · Laminated busbar · Stray inductance · Heat dissipation design
1 Introduction As the interface between the energy storage system and the micro-grid, the power conversion system can realize the transmission and transformation of electric energy. It has the functions of peak clipping and valley filling, load control, emergency power supply, off-grid switching, and isolated island operation. Under the trend of new energy power generation, it is the key development equipment of the future power system. With the development of power conversion system to large capacity and modularization, it generally adopts bipolar structure based on DC/AC converter and DC/DC buck converter [1]. In the selection of power devices, compared with Si IGBT, Si MOSFET has the characteristics of higher switching frequency, lower switching loss and higher operating junction temperature [2]. However, due to the limitation of the characteristics of Si materials, Si devices are close to the upper limit of development, and SiC devices will become the new direction of device development [3]. Compared with Si material, it has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 384–395, 2022. https://doi.org/10.1007/978-981-19-1528-4_39
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obvious advantages in energy loss, calorific value, use frequency and current density. It has smaller volume at the same power level and is more suitable for application at high frequency. This paper systematically studies the design method of power conversion system power unit based on the new generation power device SiC MOSFET, which has strong theoretical and practical characteristics, and is especially suitable for the application of new energy power generation. Firstly, the circuit principle of the power unit is briefly introduced. Secondly, the low inductance design is realized by using the laminated bus bar. Then the heat dissipation design of the power unit is carried out by forced air cooling, and the simulation calculation is carried out based on the COMSOL Multiphysics software, and the applicable scheme is given. Finally, an experimental prototype is built to verify the effectiveness of the power unit design scheme.
2 Circuit Principle of Power Unit The topology of the high-frequency isolated large-capacity power conversion system is shown in Fig. 1. The converter adopts modular cascade design, which can realize higher voltage level by connecting the same power units in series, has more flexible structure and is convenient to expand capacity. Limited by the withstand voltage level of SiC MOSFET [4], a topology structure in which the high voltage side of several power units is connected in series and the low voltage side is connected in parallel is adopted to further expand the capacity and form a low voltage and high current DC port. Table 1. Related parameters of power unit Items 750 V low voltage module
Parameters SiC MOSFET model
CAS300M12BM2
Rated output power
66.7 kW
Rated output voltage
700 V
Rated through current
95.3 A
10 kV high voltage
SiC MOSFET model
CAS120M12BM2
Module
Rated output power
23.8 kW
Rated output voltage
700 V
Rated current
34 A
Rated frequency
50 Hz
Cooling mode
Forced air cooling
Overall parameters
The SiC power unit designed in this paper includes 10 kV high voltage AC module and 750 V low voltage DC module, both of which are based on isolated H-bridge topology, as shown in Fig. 2, and the relevant parameters are shown in Table 1.
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A
Low-Voltage DC Side
Energy storege battery
B
Phase B
C
Phase C
Fig. 1. Topology diagram of power conversion system
3 Key Structure Design of Power Unit 3.1 Overall Design Scheme The design scheme of the power unit for power conversion system based on SiC MOSFET is as follows, and the corresponding component layout is shown in Fig. 2. Compartment 1 Compartment 2 drive circuit
metal shell
absorption capacitor laminated busbar air-cooled radiator
high-frequency transformer blocking capacitor
SiC power device
Partitions for isolation and forming compartments
(a) High voltage AC power unit Compartment 1 metal shell
Partitions for isolation and Compartment 2 forming compartments
SiC power device
high-frequency reactance
air-cooled radiator laminated busbar
absorption capacitor
blocking capacitor
(b) Low voltage DC power unit Fig. 2. Component layout of power cell
The power unit consists of modular SiC power devices, high frequency transformers, absorption capacitors, DC blocking capacitors, laminated bus bars, air-cooled radiators
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and metal shell. The metal shell is designed for different compartments, and the compartments are interconnected through the air ducts of the air-cooled radiator for convection heat exchange; In compartment 1, the SiC power device is arranged on the surface of the air-cooled radiator, the absorption capacitor is arranged on one side of the air-cooled radiator, and is connected with the SiC power device through a laminated bus bar. The driving circuit and the control circuit of the SiC power device are fixed on the metal casing, and power is taken from a switching power supply connected with the absorption capacitor to realize high-level energy taking. In compartment 2, the DC blocking capacitor is connected between the module and the high-frequency transformer and respectively fixed on the metal casing. The output of the high-frequency transformer adopts a knife-shaped contact structure. The AC side interface copper bar is placed in front of the power unit, passes through the current sensor and is fixed on the front side panel. The high frequency side interface is the secondary output of the high frequency transformer, which is a knife contact with a through-wall bushing structure. The laminated bus bar connects the positive and negative terminals of the absorbing capacitor to the measuring terminal located on the front panel to facilitate the measurement of the capacitor voltage. 3.2 Low Inductance Design Method 3.2.1 Analysis of Stray Inductance in Converter Loop Taking the commutation process when the low voltage sides S22 and S23 are turned off as an example, the phenomenon of power device turn-off voltage spike is illustrated. In Loop A and Loop B shown in Fig. 3, S22 and S23 are turning off. In the commutation process, the current iS through the power switch tube gradually decreases, while the current iD through the reverse diode is increasing. Rapid current changes act on parasitic inductance flowing through paths and devices, causing them to induce high-frequency voltages and form commutation circuits [4]. The voltage induced on the commutation circuit and the DC bus voltage superposition act on the power devices S22 and S23 together, resulting in excessive du/dt, i.e. voltage turn-off spikes, posing a threat to the insulation of the power switch tube. This phenomenon especially occurs when the distributed inductance is large, the load current is large, and the current drop time of the power switch tube is short. Reducing parasitic inductance is an effective method to eliminate voltage turn-off spikes.
S 21
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D21 S23
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Fig. 3. Commutation circuit
Fig. 4. Size and current path diagram of laminated busbar
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The laminated bus bar uses proximity effect and skin effect to make the radiation magnetic field generated by signal current and mirror phase current cancel each other out [5] and minimize the current loop area, thus effectively reducing stray inductance [6]. Taking the laminated bus bar shown in Fig. 4 as an example, the structural dimensions of the laminated bus bar are: length L, width W, copper plate thickness T, spacing D, and its equivalent inductance is: L = L + M 2l μ0 l ln + 0.5 = 2π t+w w2 μ0 l 2l d d2 −1 w − ln + 0.5 − 2 tan − 0.5 1 − 2 ln 1 + 2 2π d w d w d
(1)
where μ0 is air permeability, L is its self-inductance, and M is its mutual inductance. When there is only one copper bar with a length of L, a width of W and a thickness of T, its inductance is L. Comparing the inductance values of one copper bar and two copper bars, it can be found that the parasitic inductance decreases due to the existence of another copper bar with opposite and parallel current directions. Besides reducing the closed cross-sectional area of the current loop to reduce parasitic electricity, the electrical connection between the laminated bus bar and the capacitor can also improve the integration level of the power unit, and can be used as a structural component. It is convenient to install, and has good heat dissipation, insulation performance, and higher reliability [7, 8]. 3.2.2 Design Scheme of Laminated Bus bar According to the different structural layout of power modules, laminated bus bars have various topologies [9, 10]. Considering the stray inductance balance problem of the commutation loop, the laminated bus bar adopted in this paper is a symmetrical structure, which is composed of two-level positive and negative copper bar conductors through the laminated structure, and insulating materials are superimposed between the conductors for hot pressing treatment. The model is shown in Fig. 5.
negative busbar positive busbar
Fig. 5. Structure of the stacked bus bar
positive and negative terminal of DC capacitor
+
-
positive and negative terminal of SiC MOSFET
+
Fig. 6. Stacked busbar model
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The design of laminated bus bars should also consider the following factors: (1) Structural dimensions. Equation (1) shows that reducing the length L of the copper bar and the distance D between the two copper bars and increasing the width W can reduce the loop distributed inductance value to a certain extent. (2) Location of components and connection terminals. By adjusting the placement position of components and the position of connection terminals, the current path is changed to reduce the loop area, thus reducing the parasitic inductance value. Taking the capacitor as an example, the principle is to ensure that the conduction paths of the positive and negative poles of the capacitor are basically equal, and at the same time to minimize the coverage area of the conduction circuits of the positive and negative terminals of the capacitor and the power device, as shown in Fig. 6. (3) Multiple capacitors are connected in parallel. Multi-capacitor parallel connection increases stray inductance branches and improves consistency, which can meet the requirements of overlapping upper and lower loop paths as much as possible and magnetic field cancellation to reduce loop inductance. However, with the increase of the number of absorption capacitors, the effective amplitude of the inductor decreases, so considering comprehensively, four absorption capacitor structures are selected. The three-dimensional structure model of the stacked bus bar on which the capacitor bank and the power device are installed is shown in Fig. 7. Tj ( MOS )
SiC MOSFETs Tj ( MOS )
Tj ( MOS )
Tj ( MOS )
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absorption capacitor
Fig. 7. Power module structure model
Tc Ts
Rths-a
Ta
Fig. 8. Analysis of equivalent thermal resistance
The stray inductance of stacked bus is extracted by simulation. The stray inductance of stacked bus of high voltage AC module is L t_H = 734 nH, and the stray inductance of stacked bus of low voltage DC module is L t_L = 175 nH. According to the device data sheet and Reference [11] given by the manufacturer, the high-frequency parasitic inductance of a CAS300M12BM2 SiC MOSFET is about 15 nH, a CAS120M12BM2 SiC MOSFET is about 15 nH, and a diode is about 15 nH. In the high-voltage AC module and the low-voltage DC module, as shown in Fig. 4, the stray inductance of the converter circuit is: Ls_ h = Lt_ h + 2Lstray + 2Lsak = 794 nH
(2)
Ls_ l = Lt_ l + 2Lstray + 2Lsak = 235 nH
(3)
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3.3 Heat Dissipation Design Method 3.3.1 Thermal Loss Analysis of Power Devices The heat dissipation problem of power devices is related to the service life of power equipment and whether it can operate safely and reliably [12]. Especially for largecapacity high-frequency device SiC MOSFET, reasonable heat dissipation design must be adopted to ensure its operation within the allowable temperature range. The basic parameters of the heat source are shown in Table 2. Since the power module shell will have a gap surface when directly placed on the radiator, a very thin layer of heatconducting silicone grease can be coated during the assembly process, and the thermal conductivity of the heat-conducting silicone grease is 1 W/(m·k). The equivalent thermal resistance analysis of the power device module arranged on the radiator is shown in Fig. 8. Table 2. Basic parameters of heat source Project
Parameters
Heat source type
SiC MOSFET
Rated current/A
120
On-state loss/W
≤240
Quantity
4
Contact gap
Thermal conductive silicone
Where Ta is the ambient temperature, Tj (MOS) is the SiC MOSFET junction temperature, Tc is the power device module housing temperature, and Ts is the heat sink surface temperature. Rthj-cMOS is the thermal resistance from the die to the shell of SiC MOSFET, Rthc-S is the thermal resistance from the shell to the radiator. The above parameters can be obtained from the data sheet provided by the manufacturer. Rths-a is the thermal resistance from the radiator to the air, which can be calculated by adding up the heat transfer thermal resistance of the radiator itself and the heat transfer thermal resistance between the radiator and the air. As can be seen from Fig. 8, Rthj-cMOS is connected in series with Rthc-s , then different bridge arm thermal resistances are connected in parallel, and then connected in series with Rths-a to form a complete power unit thermal resistance. After comprehensively considering power density, cost, environment and other factors [13], this paper chooses forced air cooling as the heat dissipation method. 3.3.2 Radiator Design The size layout of the radiator should be matched with the laminated bus bar and device placement [14]. If the power devices of each H-bridge module are respectively equipped with radiators, the structure is not compact, and the laminated bus bar is large, which will generate greater parasitic inductance, and the heat dissipation effect for the coupling part in the middle of the module is general, so the power devices in the H-bridge module share one radiator.
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Table 3. Surface emissivity of aluminum alloy Surface treatment methods of aluminum alloy
1 µm band
1.6 µm band
8–14 µm band
Oxidation
N.R
0.4
0.3
Rough surface
0.2–0.8
0.2–0.6
0.1–0.3
Smooth surface
0.1–0.2
0.02–0.1
N.R
The material, process and fin parameters of radiator are also important factors affecting heat dissipation. The radiator in this paper is made of aluminum alloy, which has the characteristics of light weight and good heat dissipation. Different treatment methods of the material surface will affect the surface emissivity. As shown in Table 3, the rougher the material surface, the greater the surface emissivity, and the worse the thermal conductivity, which is not conducive to heat dissipation. Therefore, smooth aluminum alloy is preferred. Increasing the heat dissipation area is conducive to reducing the thermal resistance, so increasing the number of fins can improve the heat dissipation efficiency. On the other hand, too many fins will lead to larger radiator size, which is not conducive to the miniaturization and light design of power units. Combined with the device layout size, the basic parameters of the heat sink are finally determined as shown in Table 4. Table 4. Basic parameters of radiator Project
Overall dimension/mm
Substrate thickness/mm
Number of fins
Fin thickness/mm
Surface treatment
Material
Heat dissipation method
Parameters
120 × 90 × 400
28
15
1
Smooth surface
Aluminum alloy
Forced air cooling
The total thermal resistance Rth_h = 0.7944 °C/W of forced air-cooled radiator in high-voltage AC module and Rth_l = 0.7087 °C/W of radiator in low-voltage module. At full load, the loss of SiC MOSFET is about 150 W, and the maximum allowable thermal resistance of power devices is calculated to be 0.83 °C/W according to the formula provided in document [12], which indicates that the radiator selection meets the heat dissipation requirements.
4 Experimental Verification The prototype of 10 kV high-voltage AC module and 750 V low-voltage DC module are built, and the power module is tested by back-to-back circuit. The actual assembly diagram of the high-voltage AC power unit is shown in Fig. 9 (a), the back-to-back test circuit is shown in Fig. 9 (b), and the capacitor charging voltage is 720 V; The actual assembly diagram of the low-voltage DC power unit is shown in Fig. 9 (c), the back-to-back test circuit is shown in Fig. 9 (d), and the capacitor charging voltage is 720 V.
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high-frequency transformer
absorption capacitors
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(a) High voltage AC power unit SiC MOSFET
driver
absorption capacitors
(b) High-voltage AC module test circuit
blocking capacitor
high-frequency reactance
(c) Low voltage DC power unit
(d) Low-voltage DC module test circuit
Fig. 9. Schematic diagram of power module and back-to-back test
For the high-voltage AC power unit, the back-to-back test is carried out between the two H-bridges of the high-voltage AC single module. By controlling the phase difference of the H-bridge output voltages of the two modules, the magnitude and direction of the current are adjusted until full load. The principle circuit of back-to-back test is shown in Fig. 10 (a). The low-voltage DC power unit is tested between the two H-bridges of the two modules. Its principle is the same as that of the high-voltage power unit, as shown in Fig. 10 (b). Module1
Module1 MF1
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Fig. 10. Schematic diagram of power module towing
Figure 11 is a back-to-back test waveform of a high-voltage AC module. The effective value UH1 of the H1 bridge in the high-voltage AC module is 709.55 V, and the peak of the turn-off voltage is less than 733 V; The voltage rms value UH2 of H2 bridge is 692.73 V, and the peak of turn-off voltage is less than 813 V; The rms value of the current is 40 A. The above analysis shows that the stack design scheme proposed in this paper effectively reduces stray inductance and improves the ability of the module to suppress overvoltage.
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Fig. 11. Voltage and current waveform of high voltage AC module
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Fig. 12. Temperature rise results of high voltage AC module back-to-back test
Under the experimental environment where the ambient temperature is 10 °C, the effective value of current is 40 A, and the charger works at 720 V/0.7 A, the temperature of each power device is measured every half hour by using the thermal imager, and Fig. 12 is obtained. After 3.5 h, the temperature rise gradually stabilizes, and the highest temperature of each power device does not exceed 90 °C, among which the temperature of SiC power device does not exceed 40 °C, which shows that the heat dissipation effect is obvious and meets the design requirements. Similarly, Fig. 13 is a back-to-back test waveform of a low-voltage DC module; The effective value UHF of the voltage of module 1 in the low-voltage DC module is 700.00 V, and the peak of the turn-off voltage is less than 753 V; The voltage rms value ULVDC of module 2 is 723.43 V, and the peak of turn-off voltage is less than 776 V; The rms value of the current is 130 A. It can also be seen that the laminated bus bar has a better application effect.
Fig. 13. Voltage and current waveform of low voltage DC module
Fig. 14. Temperature rise results of low voltage DC module
Under the experimental environment where the ambient temperature is 10 °C, the effective value of current is 130 A, and the charger works at 720 V/2.4 A, the temperature of each power device is measured every half hour by a thermal imager, and Fig. 14 is obtained. After 2.5 h, the temperature rise gradually stabilizes, and the highest temperature of each power device does not exceed 70 °C, among which the temperature of SiC power device does not exceed 50 °C, which shows that the heat dissipation effect is obvious and meets the design requirements.
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5 Conclusion In this paper, a power unit of power conversion system based on SiC MOSFET is designed, including 10 kV high voltage AC module and 750 V low voltage DC module, which is suitable for the integrated and modular development of power conversion system. The design focuses on the low inductance and heat dissipation of the power unit, and the following conclusions are obtained: (1) The power unit consists of modular SiC power devices, high-frequency transformers, absorption capacitors, DC blocking capacitors, laminated bus bars, air-cooled radiators and metal shell. The structure is symmetrical, the disassembly, assembly and maintenance are convenient, and the capacity is convenient to further expand. (2) The application of laminated bus bar can improve the switching characteristics of the device, effectively reduce the stray inductance of the commutation circuit, and make the overall structure of the power unit compact. Besides, it can effectively improve the integration level of the power unit and has good electromagnetic compatibility characteristics. (3) Forced air cooling heat dissipation design is adopted, and appropriate fans and radiators are selected to ensure heat dissipation requirements and prolong the service life of devices. The fan control strategy can also ensure the voltage sharing among all modules in the power cabinet during the uncontrolled rectification charging phase.
Acknowledgments. This work is partially supported by the China Southern Power Grid (GDKJXM20193412).
References 1. Gu, L.: Summary of research on single-stage high-frequency isolated three-phase bidirectional AC/DC converter. Proc. CSEE, 1–16 (2021). https://doi.org/10.13334/j.0258-8013. pcsee.201524 2. Roscoe, N.M., Zhong, Y., Finney, S.J.: Comparing SiC MOSFET IGBT and Si MOSFET in LV distribution inverters. In: Industrial Electronics Society IECON 2015-41st Annual Conference of the IEEE (2015) 3. Wei, T., Li, W., Yang, Y., Yu, S., He, Z.: Design of 10kV SiC flexible DC power conversion unit. Guangdong Electric Power 33(08), 27–35 (2020) 4. Li, Z., Gao, F., Zhao, C., et al.: Research summary of power electronic transformer technology. Proc. CSEE 38(5), 1274–1289 (2018) 5. Moongilan, D.: Skin-effect modeling of image plane techniques for radiated emissions from PCB traces [C]. In: IEEE International Symposium on Electromagnetic Compatibility, Austin, TX, USA (1997) 6. Yi, R., Zhao, Z.: Study on IGCT turn-off characteristics in large-capacity converter affected by stray inductance. Proc. CSEE 27(31), 115–120 (2007) 7. Gao, Q.: Research on H-bridge inverter power unit using laminated bus. Dalian University of Technology (2015)
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8. Niu, Y.: Research on low inductance design and heat dissipation of inverter main circuit for electric bus. China Jiliang University (2015) 9. Chen, M., Mao, B., Chen, Y., Wang, X.: Application of low inductance busbar technology in IGBT converter. High Power Converter Technol. 06, 14–17 (2012) 10. Du, L.: Research on power module structure of intermittent pulse high current power converter. China Ship Research Institute (2018) 11. Caponet, M.C., Profumo, F., De Doncker, R.W., Tenconi, A.: Low stray induction bus bar design and construction for good EMC performance in power electronic circuit. IEEE Trans. Power Electron. 17(2), 225–231 (2002) 12. Zhong, H.: Development of power unit in air-cooled high-power high-voltage frequency converter. Shanghai Jiaotong University (2012) 13. Zheng, R.: Development and application of medium voltage test platform for SiC MOSFET power module. Zhejiang University (2019) 14. Lu, Z.: Research on full-bridge combined DC/DC conversion technology with dual-mode control. Zhejiang University (2017)
Impedance-Based Synchronization of Active Rectifier in Inductive Power Transfer Systems Guodong Zhu and Dawei Gao(B) State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China [email protected]
Abstract. Active rectification is a common option for efficiency improvement and load impedance regulation in inductive power transfer systems. One of the technical challenges in active rectification is phase synchronization, i.e., to maintain a certain phase difference between the PWM driving signals of the inverter and the rectifier. In this work, the rectifier input impedance, which is calculated from the AC current and AC voltage, is used as the control objective during phase synchronization. When the impedance angle matches the target value, phase synchronization of the rectifier is automatically fulfilled. A PI-controller-based phase synchronization algorithm is introduced, and the PI coefficients are manually optimized. The advantages over cycle-by-cycle hardware-triggered phase synchronization methods are discussed. Experimental results demonstrate the good performance of the proposed phase synchronization method. Keywords: AC impedance · Active rectifier · Inductive power transfer · Phase synchronization · Wireless power transfer
1 Introduction Recent research on inductive power transfer (IPT) is increasingly focused on improving the system performance, after the basic operating principles and optimization techniques have been thoroughly studied. Among the concerns in system design, energy efficiency and system controllability remain important ones. On the receiver (RX) side, active rectifiers are commonly adopted to reduce the forward voltage drop on the power switches and increase the degree of freedom. For instance, the two-stage load regulation scheme consisting of a passive rectifier and a DC/DC converter [1, 2] can be replaced by a single-stage scheme which contains an active rectifier only [3–6]. Active rectification is commonly applied in maximum efficiency tracking [5, 7] and bi-directional power transfer [3, 4, 8]. However, active rectification has its own challenges in that extra PWM driving signals are needed. Because the inverter and the rectifier are driven by separate MCUs, phase synchronization is indispensable. Zero-crossings (ZCs) of the rectifier input current (irec ) are usually employed as the hardware synchronization signal of the PWM module [5, 6, 8–10]. Besides, phase-locked © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 396–404, 2022. https://doi.org/10.1007/978-981-19-1528-4_40
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loop (PLL) is adopted to regulate the phase difference between irec and the PWM signals. Despite having a simple hardware structure, this scheme is susceptible to noises, as is analyzed in [11] and proved by an example therein. The example reveals that the PWM signal may fail to toggle when the ZC of irec is advanced due to frequency dithering. In some references, an extra phase-sensing coil is adopted on the RX side to acquire the phase of the primary coil current [4, 5]. The induced voltage in the extra coil and irec are combined to yield the phase difference between the coil currents. Obviously, the extra coil increases system complexity. All these schemes are based on hardware synchronization signals that are directly connected to the PWM module and synchronization is executed cycle-by-cycle. The scheme proposed in [11] combines the PLL method with the proposed “chained trigger” method, i.e., multiple signals are arranged in a specific order. From front to end, the signals are: irec , PWM1, PWM2 and PWM3. They are connected in such a way that the phase of each signal (except irec ) is synchronized when the one preceding it encounters a ZC. The rectifier driving signals are based on PWM2 and PWM3 and almost immune to the dithering in the ZC of irec due to one restriction: the instant at which PWM2 toggles is kept away from the ZC of irec by a certain margin. However, the restriction means that the phase of PWM signals cannot be completely freely regulated. Besides, the hardware circuitry is relatively complex. There are two deficiencies common to cycle-by-cycle hardware-triggered synchronization methods: (1) the phase accuracy of the PWM signals is affected by the noises in the ZC of irec and such an error cannot be eliminated because synchronization is executed every switching cycle and (2) without Zrec feedback, the actual phase difference between the rectifier input voltage and irec cannot be accurately controlled due to adverse factors such as dead bands. To avoid the shortcomings associated with cycle-by-cycle hardware-triggered synchronization, this work aims to develop a simple synchronization method based on directly regulating the rectifier input impedance angle ( Zrec ). The main technical features are as follows: (1) Phase synchronization is based on the direct control of Zrec to guarantee the accuracy of Zrec . (2) The ZCs of irec are not directly connected to the synchronization pin to reset the CTR registers, hence noises in the ZCs can be effectively filtered. (3) The PWM control registers are changed in the PI-controller-based phase synchronization algorithm (PSA), which is executed at a relatively low frequency (designated as fps ), instead of cycle-by-cycle. Between two neighboring PSA executions, the PWM control signals are unchanged and thus immune to noises. The rest of this paper is arranged as follows. Section 2 introduces the mathematical model of the IPT system. Section 3 analyzes the active rectifier control strategy. Section 4 demonstrates the experimental results under both static and dynamic conditions. Section 5 gives the conclusions.
2 Mathematical Model of IPT System 2.1 Circuit Model With the coils compensated using the LCC topology, the simplified circuit diagram of a typical IPT system is shown in Fig. 1 (a). The transmitter (TX) and receiver (RX)
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coils (L1 and L2 ) are coupled by the mutual inductance M. The LCC compensation networks are composed of Lf 1 , Lf 2 , C1 , C2 , Cf 1 and Cf 2 . U inv is the inverter output voltage and U rec is the rectifier input voltage. The input impedance of the rectifier is designated as Zrec . The rectifier conduction angle (θrec ) and the rectifier phase angle (αrec ) are the control variables for obtaining the target Zrec . A graphical illustration of these two parameters is given in Fig. 1 (b). In the following analysis, finv and frec refer to the switching frequency of the inverter and the rectifier, respectively. According to the superimposition principle, I rec contains two components, one contributed by U inv and the other by U rec : I rec = I rec1 + I rec2 = αU inv + βU rec
(1)
Fig. 1. Simplified circuit diagram of an IPT system and the main control variables of the rectifier. (a) Circuit diagram. (b) Definition of control variables (in this example, αrec > 0).
where the coefficients α and β are related to finv and frec as well as the circuit parameters. I rec1 is derived when U rec is short-circuited, and I rec2 is similarly derived. In the time domain, the two components are formulated as irec1 (t) = |I rec1 |(t) · sin(ωrec t + ωt)
(2-a)
irec2 (t) = |I rec2 |(t) · sin(ωrec t)
(2-b)
where ωrec = 2π frec and ω = 2π (finv − frec ). Replacing the equations into (1) yields irec (t) = |I rec1 |(t) sin(ωrec t) cos(ωt) + |I rec1 |(t) cos(ωrec t) sin(ωt) + |I rec2 |(t) sin(ωrec t)
(3)
Even when |I rec1 | and |I rec2 | are constant, the amplitude of I rec is time-varying due to the first and second terms in (3). When |finv − frec | is significantly smaller than finv , cos(ωt) and sin(ωt) vary very slowly with time, hence I rec can be simplified as a phasor with a slowly varying amplitude. 2.2 Instantaneous Rectifier Input Impedance The instantaneous frequency-domain rectifier input impedance is defined as Zrec =
|U rec |(t) j(ωrec t) ·e |I rec |(t)
(4)
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where t is the time difference by which the zero-crossing of U rec leads that of I rec . t is affected by |I rec1 |, |I rec2 |, ωrec and ω. Besides, t is time-varying when |finv − frec | = 0. Only when finv = frec and the rectifier driving signals have correct phase angles can Zrec be stabilized at the target value. Because the inverter and the rectifier are driven by separate MCUs, the inequality between finv and frec is unavoidable. The phase of the PWM signals should be adjusted to maintain Zrec at the target value. |finv − frec | is typically smaller than finv by several orders of magnitude, hence the adjustment, i.e., PSA, can be executed at a low frequency. Zrec is selected as the control target in this work for two reasons: (1) A stable Zrec implies that the active rectifier has been successfully synchronized to the inverter, i.e., the |finv − frec | error is compensated. Compared to other quantities, Zrec is easy to measure. (2) It is apparent that Zrec is an important factor that affects system performance, e.g., power transfer efficiency, hence the ability to maintain it at a suitable value is of practical interest.
3 Active Rectifier Control The control of Zrec is simple: when |Zrec | is below the target value, θrec should be increased, and vice versa. On the other hand, αrec should strictly follow the target Zrec . The amplitude and phase of U rec and I rec are acquired using simplified versions of the design introduced in [12], which output a DC voltage signal that corresponds to |U rec | or |I rec | and a digital signal that contains the phase information. Changes in U rec or I rec are immediately detected due to the low time constant of the sense circuits. 3.1 Introduction of the PWM Generation Scheme TMS320F28335 is adopted in this work. The eCAP module measures Zrec based on the ZCs of U rec and I rec and triggers an interrupt at a low frequency. PSA is executed in the interrupt functions following the operating principle illustrated in Fig. 2 (a). The phase of the PWM output signal is regulated by either the CMP register or the PHS register. The “tooth missing” phenomenon in Fig. 2 (b) will be discussed in Sect. 3.3.
Fig. 2. Operating principle of the PWM module. (a) Normal operation. (b) “Tooth missing” caused by the negative zero-crossing of CMPB.
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In the full-bridge active rectifier, both half-bridges are driven by 50%-duty-ratio PWM signals with a phase difference determined by θrec . CMPA and CMPB are updated in accordance with Zrec . When Zrec exceeds the target value, the CMPs are increased so that the PWM output and the resultant U rec are delayed, and vice versa. The CMP registers of the two half-bridges are the same, and θrec is adjusted via the phase register of the second half-bridge (PHS2). 3.2 Impedance-Angle-Based Phase Synchronization Algorithm The algorithm illustrated in Fig. 3 is adopted for phase synchronization. Using a PI controller, the CMPs are adjusted based on the difference between Zrec and its target value (designated as Zrec ). Once Zrec exceeds a certain threshold, the CMPs are changed to minimize it. Meanwhile, PRD remains constant as it is determined by frec only. Using this approach, not only is the clock frequency error compensated, but the target Zrec is also reached. Because |frec − finv | finv , Zrec accumulates slowly and can be easily compensated.
Fig. 3. Phase synchronization algorithm (PSA).
In the PI controller, the input is Zrec and the output is a float-type CMPA increment value (CMPA_INC). When |CMPA_INC| exceeds 1, the increment to CMPA and CMPB becomes effective. 3.3 “Tooth Missing” Phenomenon In this work, the “shadow mode” instead of the “immediate load mode” is selected for the PWM module. Specifically, the active registers of each PWM channel are updated upon CTR = 0. The CMPs are between 0 and PRD, hence zero-crossings are inevitable for them. When the CMPs jump to a value near PRD after they are decreased to zero, the PWM output may fail to toggle, which is referred to as “tooth missing” in this work. An example is given in Fig. 2 (b). When CTR reaches 0 and CMPB ≈ 0, the PWM output is just about to toggle. However, because CMPB has been updated to a value close to PRD (CMPB_new≈PRD), the condition for the output to be pulled low is lost and the output remains high during the following switching cycle. Besides, another type of tooth missing is caused by the updating of PHS2. One can infer from Fig. 2 that when CMPA = PHS2 but PHS2 is increased and updated at t = 0, the PWM output may fail to be pulled high because CTR is forced to equal PHS2,
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which is greater than CMPA, before the condition CTR = CMPA occurs. The result is that PWM2 remains low for the entire switching cycle that follows. The consequence of tooth missing is severe oscillations in irec . The root cause of tooth missing is that the PWM output transitions are disrupted by the updating of PWM control registers at an inappropriate time, hence the possibility of tooth missing can be reduced by separating these two actions, e.g., by inserting a time delay between them. Obviously, the PWM control registers being varied by excessively large step sizes results in more frequent occurrence of tooth missing, hence the variations in PWM control variables should be minimized. With sufficiently low PI coefficients and a small fps , the changes in CMPs are smooth and tooth missing rarely occurs. Besides, by restricting CMPA_INC to be no larger than 1 and modifying the CMPCTL [LOADAMODE] bit so that the active registers are updated upon both CTR = PRD and CTR = 0, tooth missing can theoretically be avoided. In this work, however, these measures are not taken. The purpose is to intentionally retain the possibility of tooth missing, as tooth missing is a good indicator of system smoothness.
4 Experimental Results The experiments are carried out on an electric vehicle wireless charger prototype. The DC load resistance is 25 . The main circuit parameters are given in Table 1. Table 1. Parameters of the prototype. Parameter
Value (µH )
Parameter
Value (µH )
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Value (nF)
Lf 1
31.3
L2
191.2
C1
15.6
L1
254.8
M
42
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113
Lf 2
31.1
Cf 1
113
C2
21.8
4.1 Steady-State System Smoothness The experimental results of static performance are presented in Fig. 4. PSA is executed in the eCAP-triggered interrupts at a fps of 2.7 kHz. When the PI coefficients are small, both Zrec and |Zrec | are very stable and tooth missing rarely occurs. The waveforms are as smooth as those obtained in passive rectification mode. When I or both P and I are increased tenfold, the frequency of tooth missing is significantly increased, as is evidenced by the ripples in |Zrec |. When P alone is increased tenfold, the impact on tooth missing is much weaker and the system still operates smoothly. Therefore, to expedite dynamic response, the P coefficient can be increased to a suitable level while the I coefficient should be much lower. The final choice of P and I is 0.18 and 0.02, respectively. Experimental results demonstrate that when fps is improved under the restriction that the PI coefficients are inversely proportional to fps , the static performance is almost unaffected.
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4.2 Dynamic Performance Step responses of αrec and θrec are tested to examine the dynamic performance of the proposed PSA. αrec and θrec are controlled by CMPs and PHS2, respectively. CMPs are regulated in the eCAP-triggered interrupts while PHS2 is regulated in the timer-triggered interrupts. The frequency of both interrupts is 2.7 kHz and 1.0 kHz, respectively, hence the burden on the MCU is light. Both the target αrec and the target θrec are varied at a gradient of ±4 °/ms.
Fig. 4. Impact of PI coefficients on steady-state system smoothness. The output power is 900W. αrec = −10◦ and θrec = 180◦ . (a) Zrec and |Zrec | waveforms under low PI coefficients. (b) |Zrec | waveforms under different PI coefficients.
The test results in Fig. 5 demonstrate the transition times, the frequency of tooth missing and the ability of the system to operate smoothly. Using the coefficients “P = 0.18, I = 0.02”, the MCU has no difficulty in keeping up with the gradient. Approximately 35 ms and 47 ms are needed to change θrec and αrec by 30°, respectively. It is observed during the tests that both the dynamic response speed and the occurrence frequency of tooth missing increase monotonically with the gradients. Therefore, the choice of the
Fig. 5. Dynamic performance. (a) Step response of θrec between 120 and 150°. (b) Step response of αrec between 0 and −30°.
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gradients depends on whether dynamic response speed is of the highest priority. If system smoothness is an important concern, then moderate gradients should be adopted. The response speed and system smoothness reflected in Fig. 5 is quite acceptable for most static inductive battery chargers as both the mutual inductance and the target charging current vary slowly with time. 4.3 Discussions Both the static and dynamic performance of the proposed phase synchronization method is good. PSA is executed at a relatively low frequency, hence the burden on the MCU is light. In this work, Zrec is measured at a frequency of 4fps , hence another benefit of decreasing fps is that the majority of noises in irec , which may cause phase errors in the PWM signals, are unnoticed by the MCU. By contrast, the noises in irec cause inaccurate timings of the PWM signals in cycle-by-cycle hardware-triggered synchronization methods, which may further destabilize the irec waveform, thereby resulting in severe oscillations in irec .
5 Conclusion An impedance-based indirect phase synchronization method for the active rectifier in IPT systems is proposed. The operating principle of phase synchronization method is discussed. Based simply on a PI controller that is executed at a low frequency, the proposed method directly controls the phase angle of the rectifier input impedance. When the impedance angle equals the target value, phase synchronization is automatically fulfilled. The PI coefficients are manually optimized and yield good static and dynamic performance. Advantages of the proposed method are discussed. Acknowledgement. This work was supported by Beijing Natural Science Foundation (No. 3212030).
References 1. Fu, M., Ma, C., Zhu, X.: A cascaded boost-buck converter for high-efficiency wireless power transfer systems. IEEE Trans. Industr. Inf. 10(3), 1972–1980 (2014) 2. Kato, M., Imura, T., Hori, Y.: Study on maximize efficiency by secondary side control using DC-DC converter in wireless power transfer via magnetic resonant coupling. In: 2013 World Electric Vehicle Symposium and Exhibition (EVS27), pp. 1–5 (2013) 3. Madawala, U.K., Thrimawithana, D.J.: A Bidirectional inductive power interface for electric vehicles in V2G systems. IEEE Trans. Industr. Electron. 58(10), 4789–4796 (2011) 4. Thrimawithana, D.J., Madawala, U.K., Neath, M.: A synchronization technique for bidirectional IPT systems. IEEE Trans. Industr. Electron. 60(1), 301–309 (2013) 5. Mai, R., Liu, Y., Li, Y., Yue, P., Cao, G., He, Z.: An active-rectifier-based maximum efficiency tracking method using an additional measurement coil for wireless power transfer. IEEE Trans. Power Electron. 33(1), 716–728 (2018)
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6. Ozalevli, E., et al.: A cost-effective adaptive rectifier for low power loosely coupled wireless power transfer systems. IEEE Trans. Circuits Syst. I Regul. Pap. 65(7), 2318–2329 (2018) 7. Liu, Y., Madawala, U.K., Mai, R., He, Z.: An optimal multivariable control strategy for inductive power transfer systems to improve efficiency. IEEE Trans. Power Electron. 35(9), 8998–9010 (2020) 8. Jia, S., Chen, C., Liu, P., Duan, S.: A digital phase synchronization method for bidirectional inductive power transfer. IEEE Trans. Industr. Electron. 67(8), 6450–6460 (2020) 9. Cheng, L., Ki, W., Lu, Y., Yim, T.: Adaptive on/off delay-compensated active rectifiers for wireless power transfer systems. IEEE J. Solid-State Circuits 51(3), 712–723 (2016) 10. Park, Y.-J., et al.: A design of inductive coupling wireless power receiver with high efficiency active rectifier and multi feedback LDO regulator. In: 2016 IEEE Wireless Power Transfer Conference (WPTC), pp. 1–4 (2016) 11. Jiang, Y., et al.: Phase-locked loop combined with chained trigger mode used for impedance matching in wireless high power transfer. IEEE Trans. Power Electron. 35(4), 4272–4285 (2020) 12. Zhu, G., Gao, D.: High-frequency voltage and current sense circuits for inductive power transfer systems. IEEE Trans. Power Electron. 35(11), 11352–11362 (2020)
Arc Simulations of a 252 kV Self-blast Circuit Breaker for Different Currents Considering Gas Control Valves Zhijun Wang(B) , Jianying Zhong, Shengwu Tan, Yongqi Yao, Hao Zhang, and Yinghui Chai Pinggao Group Co., Ltd, Pingdingshan, China [email protected]
Abstract. Gas valves are the key control components for the reuse of arc energy in high voltage self-blast circuit breaker. In this paper, the formulas of check valve and relief valve are derived considering the spring force, gas pressure load, inertial force and contact force applied. Then the arc model of a 252 kV self-blast circuit breaker is established, simulated and verified by pressure test. The comparison results show that the simulation has a good agreement with measurement, and the maximum relative error was less than 10%. The load simulation results show that for both 75 kA and 25 kA working conditions, the peak value of expansion chamber pressure lags behind the current by about 0.0018 s. In addition, the pressure rise of expansion chamber is jointly dominated by the compression effect of piston and arc heating. The simulation of gas valves can give useful information for the design of the interrupter and mechanical driver. Keywords: Simulation · Self-blast circuit breaker · Expansion chamber · Valve
1 Introduction The self-blast circuit breaker (CB) has significant advantages over puffer CB on size and cost due to the efficient use of arc energy to increase the pressure of expansion chamber. Gas control valves are the key components for pressure establishment of the expansion and compression under high or low current working conditions. As an important design tool, arc simulation has made significant progress. Limited by computational power and CFD software technology, arc simulation mainly focused on the nozzle using highly simplified geometry [1–5]. Based on commercial software PHOENICS, JD Yan et al. developed a more detailed model taking account of radiation transport, arc radiation induced nozzle ablation, turbulence enhanced momentum, energy transport and the moving parts of the breaker [6]. Then This model was used to optimize nozzle expansion angle of an ultra-high voltage SF6 circuit breaker [7]. Wang et al. developed a method for piston reaction force calculation [8]. However, there are few researches focused on the modeling of control valves and their influence on the breaking performance of self-blast circuit breaker. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 405–413, 2022. https://doi.org/10.1007/978-981-19-1528-4_41
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In this paper, the formulas of check valve and relief valve will be derived. Then the arc model of a 252 kV self-blast circuit breaker will be established and verified by test. The characteristics of pressure and temperature of 25 kA and 75 kA short current will be analyzed.
2 Modelling Considering the symmetry of geometry and load, the arc model can be simplified into a two-dimensional axisymmetric model. The simplified geometry and computational domain of a 252 kV self-blast circuit breaker is shown as in Fig. 1. The main components include compression chamber, expansion chamber, main nozzle, auxiliary nozzle, moving contact, relief valve and check valve, etc. The motion of check valve is controlled by its surface pressure. Compared with check valve, the motion mechanism of relief valve is more complicated as the implementation of spring force and contact force.
Pressure sensor
Pressure-Outlet Spring Compression chamber
Pressure-Outlet
Auxiliary nozzle Expansion chamber
Main nozzle
Relief valve Piston
Check valve Asymmitry axis
Moving contact Upstream of nozzle
Nozzle throat
Fig. 1. Simplified geometry and computational domain of a 252 kV circuit breaker
2.1 Arc Model Taking account of Ohmic heating, radiation loss, Lorenz force, nozzle ablation, transport of polytetrafluoroethylene (PTFE) species and turbulence, the general conservation equations can be described as the following form: − ∂(φ) → + ∇ · ρφ V − ∇ · φ ∇φ = Sφ · ∂t
(1)
− → where ρ is the density, V is the velocity vector. The variable to be solved φ, the source term Sφ , and the diffusion coefficient Γφ are listed in Table 1, and their meanings are described in literature [1]. The thermodynamic and transport properties of SF6 -PTFE gas mixture are given in [6]. Modified NEC radiation model of Zhang et al. [7] and modified Prandtl Mixing Length model of Yan et al. [8] are used. According to the analysis above, the velocity formulas of the check valve need to be derived. As the radial motion of check valve is limited, we only need to consider the z-component of movement.
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Table 1. Terms of governing equations Equation
φ
Γφ
Sφ
Continuity
1
0
0
Z-momentum
w
μl + μt
− ∂P ∂z + Jr Bθ + viscous terms
r-momentum
v
μl + μt
− ∂P ∂r + Jz Bθ + viscous terms
Enthalpy
h
(kl + kt )/cp
σ E 2 − q + dP dt + viscous dissipation
PTFE mass concentration
Cm
ρ(Dl + Dt )
0
2.2 Load Analysis of Check Valve As shown in Fig. 1, the z-coordinate of the left and right boundary of check valve are set as z0_check and z1_check , respectively. The load of check valve can be written as: FP_check = PZ dSZ (2) where Pz and Sz are the infinitesimals of pressure and area on the check valve surface. So, the velocity increment of (N + 1)-timestep can be written as: uN +1 =
FP_check N +1 t N +1 mcheck
(3)
where FP_check N +1 , t N +1 are the load, timestep of (N + 1)-timestep, respectively. mcheck is the mass of check valve, which is set to be 0.04 kg. The position and velocity of check valve at N-timestep and (N + 1)-timestep are set to be zcheck N , zcheck N +1 , ucheck N , and ucheck N +1 , respectively. The velocity of (N + 1)-timestep can be written as: ⎧ ⎪ uN +1 ≤ 0, uN +1 = 0 N ⎪ ⎪ ifzcheck = z0_check , ⎪ ⎪ > 0, uN +1 = uN +1 u ⎨ N +1 (4) uN +1 ≤ 0, uN +1 = uN +1 ⎪ ifz check N = z1_check , ⎪ ⎪ uN +1 > 0, uN +1 = 0 ⎪ ⎪ ⎩ ifz N 0 ⎨ kT < fT F(isT ) | (30) → kT < −| f ⎪ F(i ⎪ sT ) ⎩ kT < − T , if isT < 0 F(isT ) Combing (29) and (30), the observer stability condition can be summarized as: kM , kT < min(−|
fM F(isM )
| , −|
fT F(isT )
|)
(31)
Because F(isM ) and F(isT ) are between −1 and 1, theoretically, even by making the magnitudes of k M and k T extremely large, the observer cannot always keep stable, especially when isM and isT are near zero. But in the real applications, fluctuations can be witnessed in the current estimation errors around the sliding surfaces with pretty small variations. Define the smallest tolerance in engineering is required to be as η, η = min(|isM |, |isT |)
(32)
Then, the minimum switching function value is: min F =
em·η − e−m·η em·η + e−m·η
(33)
Hence, the observer gain can be set as: kM = kT = min(−|
fT fM | , −| |) min F min F
(34)
In theory, there exist k M and k T making the observer stable as long as the disturbance values are not exaggeratively large (where the system is unstable). During the control process, although isM and isT are possible to be less than the pre-set value of η, the proposed SMO can re-converge again once their values increase. Hence, although the proposed observer is asymptotically stable, the disturbances can be estimated.
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After estimating the disturbances caused by the parameter mismatch using the proposed SMO, the disturbances can be directly incorporated the FCS-MPCC predicting plant model for compensation. By applying Euler implementation to (18) and (19), the predicting model used in practice is: k+1 = isM
Tuk Ls0 − TRs0 k k isM + T ωe isT + sM +TfMk Ls0 Ls0
k+1 k isT = −T ωe isM +
Tuk Ls0 − TRs0 k isT + sT + TfTk Ls0 Ls0
(35)
(36)
where fMk and fTk are the disturbances at t k observed by the SMO.
4 HIL Testing Results To verify the effectiveness of the proposed SM disturbance observer and the SMObased FCS-MPCC, HIL experiments were carried out on an IM drive prototype whose parameters are presented in Table 1. Considering that the disturbances principally affect the static errors during control, the steady-state control performance is mainly discussed in this section, though the dynamics are given. The HIL testing results were obtained from an RT Lab–based control board (see Fig. 4). Table 1. Key parameters of the IM prototype Drive parameters
Value
Stator-winding inductance L s /H
0.12
Stator-winding resistance Rs /
0.065
Mutual inductance L m /H
0.075
Rotor-winding inductance L r /H
0.095
Rotor-winding resistance Rr /
0.05
Friction coefficient F
0.003
Rated load T L_rated /Nm
4.5
Rated speed ωr_rated /rad/s
110
By comparing the predicting plant model (35) and (36) with the comprehensive ones (6) and (7), without considering the disturbance terms, the rotor parameters (rotor inductance and resistance) are removed totally when the mutual effect is ignored. On this ground the rotor parameters can be treated to be one hundred percent (100%) mismatched in terms of an FCS-MPCC algorithm. As for the stator inductance and resistance, they are inclined to encounter mismatch problems during control as well. For the sake of comprehensiveness, the following typical case is analyzed in this part: the measured stator inductance and resistance are 90% lower than the actual ones.
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Fig. 4. HIL testing setup for verifying the proposed FCS-MPCC method
4.1 FCS-MPCC Performance Without Disturbance Observer Figure 5 shows the dynamic speed and torque performance characteristics of the FCSMPCC controller without using any disturbance observers. According to the dynamics in Fig. 5, the HIL setup can be described as follows. Between 0 and 3 s, the machine speeds up from 0 to 40 rad/s, after which the reference speed is set as 80 rad/s. Between 6 s and 12 s, the machine is expected to be controlled to remain 110 rad/s, and at 12 s, the motor decelerates to 80 rad/s. Finally, from 15 s, the speed reference is set to 40 rad/s. In terms of the external load, before and after 12 s, it is set to 0 and 4.5 Nm, respectively. Before leaving Fig. 5, it should be noticed that firstly, the machine has fast dynamics regardless of acceleration and deceleration. Specifically, the rise time for the acceleration processes (0–40 rad/s, 40–80 rad/s and 80–110 rad/s) is nearly 1.85 s, 0.65 s and 0.45 s, respectively. The deceleration is a little faster than the acceleration process. In detail, the settling time is about 0.3 s. Figure 6 depicts the steady-state performance of the FCS- MPCC at the speed of 40 rad/s under no-load (between 2.5 s and 3 s) and rated load (between 17 s and 17.5 s) conditions. In the no-load conditions, the speed can remain at 42 rad/s, so the static error rate (SER) is 5%. As far as the rotor flux is concerned, the SER is 2.4%. Comparatively, the speed and flux SER is 3% and 8.2%, respectively. This is interesting that the static error for speed decreases, but it increases for the flux after the external load is imposed on the rotor shaft. This happens mainly because that the flux is more closely related to the load (current) than the speed. Because currents are not the final control targets, we just give the current ripples of the isM and isT . Comparatively speaking, the M, T-axis current ripples (CR) under load (3.1 A and 1.3 A) are slightly higher than those under no load (2.4 A and 1.25 A).
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Ψf_ref
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0.07 10 0 -10 10 0 -10
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(a)
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35
45 40 35
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ωr=42
45 40
Current (A)
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Fig. 5. Speed and torque dynamic performance of machine
0.1 0.09 0.08 10 0 -10 10 0 -10
ωr=41.2 ωr_ref
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Ψr=0.092 Ψf_ref
SER=8.2%
isM
CR=3.1
isT
CR=1.3
17 17.1 17.2 17.3 17.4 17.5 time (s)
(b)
Fig. 6. Steady-state performance at 40 rad/s with rotor parameter 100% and stator parameter 90%-lower mismatch. (a) No-load condition; (b) rated load condition
The steady-state performance of the system when the machine runs at 80 rad/s is illustrated in Fig. 7. Compared to Fig. 6, the magnitude of the speed static error increases to 3.2 rad/s and 1.8 rad/s under no-load and load conditions. This complies with the conclusion in Sect. 2. In terms of the rotor flux, the STR witnesses a dramatical growth regardless of the load conditions. isM and isT are very similar to those in the situation that the machine speed is 40 rad/s, and they are 2.2 A and 1.28 A when the machine rotates without external load, 3.0 A and 1.42 A with rated load imposed on the shaft. Figure 8 demonstrates the control performance over higher speed range (11 rad/s), the magnitudes of the static errors experience an upward trend in comparison with the low-speed cases, while the currents nearly remain at the similar level. Overall, the traditional FCS-MPCC that ignores the mutual coupling has marked static errors over the full-speed range.
Flux (Wb) Current (A)
SER=4%
Ψf_ref
0.09 0.08
Ψr=0.08
0.07 10 0 -10 10 0 -10
isM
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ωr_ref
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ωr=83.2
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SER=2.3%
Ψr=0.095 Ψf_ref
0.08 10 0 -10 10 0 -10
SER=11.7%
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CR=3.0
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(a)
(b)
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Ψf_ref
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Ψr=0.079 isM
8.5
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CR=1.3 isT 8.6 8.7 8.8 8.9 9.0 time (s)
(a)
Speed (rad/s)
ωr=113.5
115 110 105
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115 110
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Fig. 7. Steady-state performance at 80 rad/s with rotor parameter 100% and stator parameter 90%-lower mismatch. (a) No-load condition; (b) rated load condition
0.09 0.08 0.07 10 0 -10 10 0 -10
ωr=112.5 ωr_ref
SER=3%
Ψf_ref Ψr=0.078 SER=8.2% isM
CR=3.3
isT
CR=1.48
11 11.1 11.2 11.3 11.4 11.5 time (s)
(b)
Fig. 8. Steady-state performance at 110 rad/s with rotor parameter 100% and stator parameter 90%-lower mismatch. (a) No-load condition; (b) rated load condition
4.2 FCS-MPCC Performance with Disturbance Observer Figure 9 (a) and (b) show the estimated disturbances using the proposed SMO when the motor operates at the speed of 40 rad/s under no load and load, respectively. At the moment, the observed disturbances have not been incorporated into the FCS-MPCC implementations. It can be seen when the machine runs without load, the M-axis disturbance (around −5) is much smaller than the T-axis one (−31). However, in the load condition, the M-axis witnesses a sharp increase to about −180, while the T-axis disturbance is about −210. Although the estimated values are pretty large, when they are substituted into (35) and (36) for prediction, they need to multiply by the sampling period
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0 -15
fT
-30 2.6
2.5
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2.9 3.0
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(0.001 s). Therefore, the estimated values are reasonable. Overall, these results represent that the disturbances caused by rotor and stator parameter mismatch is closely related to the load states. -150 -180 -210
fT fM
17 17.1 17.2 17.3 17.4 17.5 time (s)
(a)
(b)
Fig. 9. Steady-state estimated disturbances using the proposed SMO at 40 rad/s. (a) No-loadcondition disturbances; (b) rated-load-condition disturbances
ωr=39.8 SER=0.5%
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Ψf_ref
0.09 0.08
Ψr=0.0845
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(a)
Speed (rad/s)
ωr_ref
45 40 35
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45 40
Current (A)
Speed (rad/s)
When integrating the SMO into FCS-MPCC control structure, the system performance characteristics at 40 rad/s are shown in Fig. 10. Compared to the results in Fig. 6, the speed and flux static errors have been dramatically reduced regardless of the working conditions. In detail, the SER for speed and rotor flux is 0.5% and 0.057% respectively under no load, and it is 0.75% and 0 respectively in the load conditions. Overall, the proposed observer is proven to enhance the control performance of the IM drive system. Apart from the static errors, the current ripples witness an obvious decrease as well.
0.1 0.09 0.08 10 0 -10 10 0 -10
ωr=40.3 ωr_ref
SER=0.75%
Ψr=0.085 Ψf_ref
SER=0
isM
CR=0.5
isT
CR=0.5
17 17.1 17.2 17.3 17.4 17.5 time (s)
(b)
Fig. 10. Steady-state performance when incorporating the proposed SMO at 40 rad/s. (a) No-load condition. (b) Rated load condition
Figure 11 (a) and (b) show the estimated disturbances at the speed of 80 rad/s and Fig. 11 (c) and (d) depict the control performance after compensation. Obviously, the disturbances are much larger than those in Fig. 9, so it is further proven that the
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Flux (Wb)
4.9 5.0
ωr=80.1 ωr_ref
SER=0.12%
Ψf_ref
0.09 0.08
Ψr=0.085
0.07 10 0 -10 10 0 -10
isM
4.5
SER=0
CR=0.55
CR=0.6 isT 4.6 4.7 4.8 4.9 5.0 time (s) (c)
Disturbances
4.7 4.8 time (s) (a)
0 -300
fM fT
-600 14 14.1 14.2 14.3 14.4 14.5 time (s) (b)
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4.6
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fT
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disturbances are highly related to the machine speed. Similar to the results in Fig. 10, the speed and flux static errors are nearly zero. In terms of the M, T-axis currents, the ripples are about 0.5 A, which is truly small. Therefore, the steady-state control performance is proven to be enhanced markedly when adopting the proposed observer. In Fig. 12, the no-load-condition and rated-load-condition disturbances and the steady-state control performance at the speed of 110 rad/s are demonstrated. The disturbances become larger than the lower speed cases. In detail, the M-axis disturbance is about 50, while the T-axis disturbance is −780 under no load, but they become around −275 and −1180 when the external load is imposed on the rotor shaft. After the SMO is adopted for disturbance observation and compensation, the speed and flux static errors become very small (SER for speed and flux is 0.4% and 0 respectively under no load).
0.1 0.09 0.08 10 0 -10 10 0 -10
ωr=80.1 ωr_ref Ψr=0.085 Ψf_ref
SER=0.12%
SER=0
isM
CR=0.5
isT
CR=0.45
14 14.1 14.2 14.3 14.4 14.5 time (s) (d)
Fig. 11. Steady-state performance of the machine when incorporating the proposed SMO at 80 rad/s. (a) No-load-condition disturbances; (b) Rated-load-condition disturbances; (c) No-load-condition performance; (d) Rated-load-condition performance
800 0
fM
-800 8.5
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0 -600
fT
-1200
8.7 8.8 time (s)
8.9 9.0
11 11.1 11.2 11.3 11.4 11.5 time (s)
SER=0.4%
8.5
Ψr=0.085
SER=0
isM
CR=0.35
CR=0.46 isT 8.6 8.7 8.8 8.9 9.0 time (s)
(c)
Speed (rad/s)
ωr_ref Ψf_ref
0.09 0.08 0.07 10 0 -10 10 0 -10
ωr=110.5
Flux (Wb)
105
(b)
Current (A)
Current (A)
Flux (Wb)
Speed (rad/s)
(a) 115 110
115 110 105
0.09 0.08 0.07 10 0 -10 10 0 -10
ωr=110 ωr_ref
SER=0
Ψf_ref Ψr=0.0853 SER=0.4%
isM
CR=0.55
isT
CR=0.6
11 11.1 11.2 11.3 11.4 11.5 time (s)
(d)
Fig. 12. Steady-state performance of the machine when incorporating the proposed SMO at 110 rad/s. (a) No-load-condition disturbances; (b) Rated-load-condition disturbances; (c) No-load-condition performance; (d) Rated-load-condition performance
5 Conclusion In order to improve the control performance of the FCS-MPCC methods in the IM applications, this paper proposes a new SMO to diagnose and compensate the real-time disturbances caused by parameter mismatch. The main novelties and contributions of this paper are as follows: (1) Based on the predicting model established for IMs, the M, T-axis disturbances caused by parameter mismatch are analyzed theoretically for the traditional FCSMPCC strategy. It is found that, apart from the parameter deviations, the disturbances are also closely related to the machine working states, such as speed and currents. (2) For the purpose of eliminating the impacts of the disturbances, a SMO based on hyperbolic function is developed. It deserves to be mentioned that the hyperbolic function is pretty novel, and the stability analysis procedures introduced in this paper for this kind of SMO is new. (3) After incorporating the SM disturbance observer into the FCS-MPCC control implementations, the speed and flux static errors are significantly reduced. Meanwhile, the current ripples witness a visible decrease. These prove that the proposed FCS-MPCC strategy is effective in the IM applications.
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Acknowledgment. This work was supported in part by the Science and Technology Commission Shanghai Municipality (No. 17511102302).
References 1. Xu, W., Elmorshedy, M.F., Liu, Y., et al.: Finite-set model predictive control based thrust maximization of linear induction motors used in linear metros. IEEE Trans. Veh. Technol 68(6), 5443–5458 (2019) 2. He, L., Wang, F., Ke, D.: FPGA-based sliding-mode predictive control for PMSM speed regulation system using an adaptive ultralocal model. IEEE Trans. Power Electron. 36(5), 5784–5793 (2021) 3. Niu, F., et al.: Model predictive current control with adaptive-adjusting timescales for PMSMs. CES Trans. Electr. Mach. Syst. 5(2), 108–117 (2021) 4. Gong, C., Hu, Y., Ni, K., et al.: SM load torque observer-based FCS-MPDSC with single prediction horizon for high dynamics of surface-mounted PMSM. IEEE Trans. Power Electron. 35(1), 20–24 (2020) 5. Jun, E.-S., Park, S.Y., Kwak, S.: Model predictive current control method with improved performances for three-phase voltage source inverters. Electronics 8(6), 625 (2019) 6. Gao, J., Gong, C., Li, W., et al.: Novel compensation strategy for calculation delay of finite control set model predictive current control in PMSM. IEEE Trans. Ind. Electron. 67(7), 5816–5819 (2020) 7. Yang, S., Ding, D., Li, X., et al.: A novel online parameter estimation method for indirect field oriented induction motor drives. IEEE Trans. Energy Convers. 32(4), 1562–1573 (2017) 8. Liu, J., Gong, C., Han, Z., et al.: IPMSM model predictive control in flux-weakening operation using an improved algorithm. IEEE Tran. Ind. Electron. 65(12), 9378–9387 (2018) 9. Gonzalez-Prieto, I., Duran, M.J., Aciego, J.J., et al.: Model predictive control of six-phase induction motor drives using virtual voltage vectors. IEEE Trans. Ind. Electron. 65(1), 27–37 (2018) 10. Gonçalves, P., Cruz, S., Mendes, A.: Finite control set model predictive control of six-phase asymmetrical machines—an overview. Energies 12(24), 4693 (2019) 11. Wang, W., Fan, Y., Chen, S., et al.: Finite control set model predictive current control of a five-phase PMSM with virtual voltage vectors and adaptive control set. CES Trans. Electron. Mach. Syst. 2(1), 136–141 (2018) 12. Wang, X., Zhang, Y., Yang, H., et al.: A robust predictive current control of induction motor drives. In: 2020 IEEE Energy Conversion Congress and Exposition (ECCE), Detroit, MI, USA, pp. 5136–5140 (2020) 13. Xia, C., Wang, Y., Shi, T.: Implementation of finite-state model predictive control for commutation torque ripple minimization of permanent-magnet brushless DC motor. IEEE Tran. Ind. Electron. 60(3), 896–905 (2013) 14. Bernard, P., Andrieu, V.: Luenberger observers for nonautonomous nonlinear systems. IEEE Trans. Autom. Control 64(1), 270–281 (2019) 15. Yan, L., Wang, F., Dou, M., et al.: Active disturbance-rejection-based speed control in model predictive control for induction machines. IEEE Trans. Ind. Electron. 67(4), 2574–2584 (2020) 16. Shao, M., Deng, Y., Li, H., et al.: Sliding mode observer-based parameter identification and disturbance compensation for optimizing the mode predictive control of PMSM. Energies 12(10), 1857 (2019)
432
Y. Han et al.
17. Chen, H., Qu, J., Liu, B., et al.: A robust predictive current control for PMSM based on extended state observer. In: Proceedings of the 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, pp. 1698–1703 (2015) 18. Gong, C., Hu, Y., Chen, G., et al.: A DC-bus capacitor discharge strategy for PMSM drive system with large inertia and small system safe current in EVs. IEEE Trans. Ind. Inform. 15(8), 4709–4718 (2019) 19. Zha, F., Sheng, W., Guo, W., et al.: Dynamic parameter identification of a lower extremity exoskeleton using RLS-PSO. Appl. Sci. 9(2), 324 (2019) 20. Tang, J., Yang, Y., Blaabjerg, F., et al.: Parameter identification of inverter-fed induction motors: a review. Energies 11(9), 2194 (2018) 21. Wu, X., Fu, X., Lin, M., et al.: Offline inductance identification of IPMSM with sequence-pulse injection. IEEE Trans. Ind. Inform. 15(11), 6127–6135 (2019) 22. Gong, C., Hu, Y., Gao, J., et al.: An improved delay-suppressed sliding-mode observer for sensorless vector-controlled PMSM. IEEE Tran. Ind. Electron. 67(7), 5913–5923 (2020) 23. Wang, B., Dong, Z., Yu, Y., et al.: Static-errorless deadbeat predictive current control using second-order sliding-mode disturbance observer for induction machine drives. IEEE Trans. Power Electron. 33(3), 2395–2403 (2018) 24. Ding, X., Cheng, J., Zhao, Z., et al.: A high precision and high efficiency PMSM driver based on power amplifiers and RTSPSs. IEEE Trans. Power Electron. 36(9), 10470–10480 (2021)
Direct Power Control of Dual-Active-Bridge Three-Phase Shift Modulation Featuring Multi-order Harmonic Current Minimization Ziwei Liu, Zhaolong Sun, Baolong Liu(B) , and Zhixin Li College of Electrical Engineering, Naval University of Engineering, Wuhan 430030, Hubei, China [email protected]
Abstract. Dual-active-bridge (DAB) converter is the vital part of the hybrid energy storage system. The efficiency could be seriously affected for DAB due to the large current pulsations caused by the step changes in input power and the multiple harmonics generated by non-linear loads. To improve efficiency, a direct power control (DPC) of DAB three-phase shift modulation featuring multiorder harmonic current minimization method is proposed in this study. The multiorder reactive-current suppression (MRS) strategy is analyzed to reduce the harmonic current. Furthermore, DPC is used to improve the transient response for the input voltage and power fluctuations. The simulation results have verified the effectiveness of the proposed scheme. Keywords: Dual-active-bridge (DAB) · Frequnency based analysis (FDA) · Direct power control (DPC)
1 Introduction Dual-active-bridge (DAB) is a typical topological structure in the isolated bidirectional DC-DC converter. It has been widely used in electric vehicles DC distribution networks distributed energy, and other fields mainly due to its advantages of bidirectional energy flow, small switching stress, electrical isolation, high power density, and easy to realize soft-switching [1–3]. At present, the DAB literature mainly focuses on the development of a modulation strategy suitable for a wide range of high power. These methods mainly include time domain analysis (TDA) [4] and frequency domain analysis (FDA) [5]. TDA has the advantages of intuitiveness and accuracy. The literature [6–8] studied different modulation methods of DAB converters, which can realize zero voltage switching (ZVS) under all the operation range. The research under TDA divides the conversion period into different periods, but the determination of each period requires a large amount of online and offline calculations. Compared with TDA, FDA is independent of the different conditions, and the model complexity is significantly reduced. In [9], FDA is applied to DAB, which significantly simplifies the modeling process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 433–442, 2022. https://doi.org/10.1007/978-981-19-1528-4_43
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Phase shift modulation (PSM) is commonly used modulation method for DAB because it is easy to implement and control. The main disadvantage of traditional PSM is that the reactive power component of the fundamental wave is large, the efficiency is low and the ZVS range is limited when DAB is under the light load condition.To optimize the fundamental component, the fundamental duty modulation (FDM) is proposed in the literature [7]. Compared with PSM, FDM can effectively reduce the fundamental current. However, the FDM harmonic current, especially the third harmonic component, has a large increase. Then literature [9] proposed a multi-frequency reactive current suppression (MRS) strategy to decrease the harmonic current, but the theoretical analysis of the MRS strategy under DAB isn’t be analyzed in detail. To achieve fast dynamic response, advanced control strategies such as sliding-modecontrol, model-predictive-control have been proposed to make the dynamic process smooth. However, most of them are too complex to be implemented in practice. Hence, In [10], a direct power control(DPC) based on single-phase-shift (SPS) control is proposed. The method is simple in structure and fast in response, but the SPS control strategy lacks flexibility and cannot deal with fast dynamic response and high conversion efficiency simultaneously. According to the analysis mentioned above, a DPC of DAB three-phase shift modulation featuring multi-order harmonic current minimization method is proposed to improve the efficiency and dynamic response simultaneously. Firstly, the MRS strategy with DAB under the TPS control is discussed to reduce the harmonic current. Then an overall description of the DPC scheme is proposed to improve the dynamic response. Finally, the simulation result to verify the effectiveness of the proposed method is presented.
2 DAB Analysis of MRS Modulation Strategy The DAB DC/DC converter topology is shown in Fig. 1.(a), where Vin and Vout represent the input voltage and output voltage, v1 and v2 are the primary and secondary H-bridge output voltages, and iL is the inductor current. The duty ratio signal synthesis according to the switching sequences is shown in Fig. 1.(b). The control variables of v1 and v2 are primary side duty ratio d1 and secondary side duty ratio d2 , phase shift angle between the primary side and secondary side is α. The DAB modulation can be achieved through three control variables d1 , d2 and α. To decrease multi-order harmonic current, [9] proposed a multi-frequency reactive current suppression (MRS) strategy. The primary side duty ratio is expressed in (1), the secondary side duty ratio is expressed in (2). d1 ) arcsin vinvosin(π cos(α) d1 = (1) π d2 = Md1
(2)
in where M = KV Vout . To realize the MRS modulation strategy in the controller, the expressions of three control variables must be obtained. Combining (1) and (2) can be represented as:
sin(π Md1 )cos(α) = Msin(π d1 )
(3)
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S P1
SS1
SP2
+
vin
+ v 1
Cin SP3
−
SS 2
+
fs
Lr
+ v 2
−
High frequency transformer
SP4
Primary side
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−
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SS 4
vout −
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(a) DAB DC/DC converter topology. Ts S P1S P 2 SP3SP 4
v1
d1Ts 0.5d1Ts
S S 1S S 2 SS 3SS 4
d 2Ts
v2 0.5d 2Ts
α Ts 2π
(b) DAB working waveform Fig. 1. (a) DAB DC/DC converter topology. (b) DAB working waveform.
It is not easy to obtain an explicit solution (3). Here, we use Newton’s iteration method to solve Eq. (3) and set the function f (d ) as: f (d ) = sin(π Md1 )cos(α) − Msin(π d1 )
(4)
The derivative of the function f (d1 ) is f (d ) = π Mcos(π Md )cos(α) − M π cos(π d )
(5)
Figure 2 shows the relationship between d1 and α at M = 0.25. When d1 < 0.5, the relationship between d1 and α is close to linear, and an approximate linear method can M be used to simplify the relationship between d1 and α, set Mx = arccos sin(0.5π M) . And combine (2) to obtain d1 , d2 and α as follows:
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Fig. 2. Relation of d1 and α at M = 0.25
where My = arccos(M ).
d1 =
d2 =
⎧ ⎪ ⎨
1 2Mx α
α ≤ Mx
⎪ ⎩ 0.5 M < α x ⎧ ⎪ ⎨ M α α ≤ My 2Mx
(6)
⎪ ⎩ 0.5 M < α y
Figure 3 shows the DAB waveforms under MRS. The DAB model is solved by a piecewise linear calculation method. According to the assumption that there is overlap in the zero-level area of the square wave voltage, the working mode can be divided into two cases, as shown in Fig. 3(a) and (b). 0.5Ts
0.5Ts
v2
v2 0.5 2Ts
0.5d 2TS Ts 2π /
v1
α
v1 0.5 1Ts
0.5d1TS
Ts 2π /
d
α
d
iL
iL
t
t0
t1
t 2 t3 t 4
(a)
t5
t6
t
t0 t1
t2
t3 t 4 t5
t6
t 7 t8
(b)
Fig. 3. (a) The square wave voltage zero level region overlaps in case I, (b) the square wave voltage zero level area without overlapping in case II.
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In the case of I, the current can be described as follows: ⎧ ⎪ ⎪ iL (t0 ) + UL1 (t − t0 ), t0 < t < t1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 2 ⎨ iL (t1 ) + U1 −nU (t − t1 ), t1 < t < t2 L iL (t) = ⎪ ⎪ 2 ⎪ iL (t2 ) + −nU ⎪ L (t − t2 ), t2 < t < t3 ⎪ ⎪ ⎪ ⎪ ⎩ 0, t3 < t < t4
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(7)
The half-wave is symmetric in the steady-state period of iL , so the relationship of iL (t0 ) and iL (t4 ) can be derived as: iL (t4 ) = −iL (t0 )
(8)
Combing (7) and (8), iL (t0 ) can be expressed as: iL (t0 ) = 0
(9)
The transmission power can be expressed as: P=
1 Ts
Ts
v1 (t)iL (t)dt =
0
Ui Ts (Ui d1 2 − Uo (0.5d1 + 0.5d2 − TPh )2 ) L
α where TPh = 2π . In the case of II, the current can be described as follows: ⎧ ⎪ ⎪ iL (t0 ) + U1 +nU 2 (t − t0 ), t0 < t < t1 ⎪ L ⎪ ⎪ ⎪ ⎪ ⎪ U ⎨ iL (t1 ) + 1 (t − t0 ), t1 < t < t2 L iL (t) = ⎪ ⎪ 2 ⎪ iL (t2 ) + U1 −nU (t − t2 ), t2 < t < t3 ⎪ L ⎪ ⎪ ⎪ ⎪ ⎩ −nU 2 L (t − t2 ), t3 < t < t4
(10)
(11)
Combing (9) and (11), iL (t0 ) can be expressed as: iL (t0 ) =
−((d1 Ui − (1 − d1 − 2TPh )Uo )Ts 2L
(12)
The transmission power can be expressed as: 1 P= Ts
Ts 0
v1 (t)iL (t)dt =
Ts Ui Uo (2(1 − 2TPh )TPh − (d1 − 0.5)2 − (d1 − 0.5)2 ) 2L (13)
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Fig. 4. Curves of the power and duty ratios varied with α.
3 Direct Power Control of DAB Combining (6), (10), and (13), the curves of the power and duty ratios varied with α in TPS control are given in Fig. 4. According to the different case and whether d1 and d2 are saturated, the calculation of power can be divided into four stages. Specifically expressed as [0, α1 ], [α1 , α2 ], [α2 , α3 ], [α3 , α4 ]. According to (6), (10), and (13), α1 , α2 and α3 can be calculated as: α1 =
0.5 1 4Mx
+
M 4Mx
+
1 2π
, α2 = Mx , α3 =
M 4Mx
(14)
Combing (10), (13) and (14), P can be obtained as: ⎧ 2 ⎪ d1 d2 1 2 ⎪ V (M d − + − )α 2 , 0 < α < α1 s 1 ⎪ 2 2 2π ⎪ ⎪
1 ⎪ 1 ⎪ ⎨ 2V s (− 2π + (1 + M )d1 2 α 2 + π + d1 + d2 α − 0.5), α1 < α < α2
2 P= 2 1 1 +d2 +d ⎪ 1 2 ⎪ π 2V (− + ( π1 2 2 ) − 0.25), α2 < α < α3 ⎪ s 2 + d2 α − 1 2 π ⎪ +d2 +d2 ⎪ ⎪ π2 ⎪ π 2 α α ⎩ 2Vs 1 − π π , α3 < α < α4 (15) where Vs = Ui U2Lo Ts . Then, the α is obtained as ⎧ 2 d d 1 ⎪ P/2(M d1 2 − 21 + 22 − 2π )Vs ⎪ ⎪ ⎪ , 0 < P < P1 ⎪ π ⎪ ⎪ √ 2 ⎪ ⎪ ⎨ C1_b − C1_b −P/Vs −0.5 , P1 < P < P2 πC α= √ 21_a ⎪ ⎪ ⎪ C2_b − C2_b −P/Vs −0.25 , P < P < P ⎪ 2 3 ⎪ π C2_a ⎪ ⎪ ⎪ ⎪ ⎩ 1 1 2 − 4 − P/Vs , P3 < P
(16)
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d +d2 + π1 d2 + π1 where C1_a = π12 + d1 2 + d2 2 , C1_b = 1 2C1_a , C2_a = π12 + d2 2 , C2_b = 2C . 2_a Combining (6) and (16), the duty cycle can be adjusted by changing the phase shift angle, and the power P can be directly adjusted. The implementation steps of the DPC scheme are as follows (Fig. 5):
Fig. 5. Block diagram of the proposed DPC scheme
4 Simulation Results To verify the effectiveness of MRS, the simulation results of different times of reactive current under PSM, FDM, and MRS have been shown in Fig. 6. Obviously, MRS has a better suppression effect on reactive current under different voltages.
Fig. 6. Reactive current at different order under P = 1 kW at (a) Vin = 200 V. (b) Vin = 250 V. (c) Vin = 300 V. (d) Vin = 350 V.
Actually, the reactive current is just one part of the RMS current. To exam the effect of the suppression of the RMS current by the MRS strategy, the RMS current at different power ratings input voltages and under the PSM, FDM, and MRS strategies are shown in Fig. 7. As shown in Fig. 7, compared with PSM and FDM, the RMS current under MRS is the smallest at light load. When the power increases, the active current increases.
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Fig. 7. DAB RMS current at different modulation strategies at (a) Vin = 200 V. (b) Vin = 250 V. (c) Vin = 300 V. (d) Vin = 350 V.
That means that the reactive current part becomes smaller, and the suppression effect of the multi-frequency reactive current is less effective, but it still has a better suppression effect than PSM and FDM. When the power decreases, its active current becomes smaller, which means that the proportion of reactive current in RMS current increases, so the effect of MRS in reducing RMS current is more prominent. Figure 8(a) and (b). show the transient simulation results of the DAB. In Fig. 8(a), the input voltage steps from 250V to 350 V at 0.015 s, it takes 5 ms for the power and iL to reach the desired value. In Fig. 8(b), the power steps from 4 kW to 5.5 kW at 0.015 s, it takes 5 ms for iL to reach the desired value. It can be seen that the proposed direct power control can achieve a fast transient response for the input voltage and power fluctuations. However, in the process of transition, the change of the input voltage and the phase-shift ratio will cause the transient dc-bias current due to the transient voltage-time imbalance of the transformer.
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(a) Input voltage step from 250 V to 350 V
(b) Load power step from 1kW to 3kW Fig. 8. Comparative simulation results of the DAB converter at (a) Input voltage step from 250 V to 350 V. (b) Load power step from 1 kW to 3 kW.
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5 Conclusion A high efficiency method under FDA for DAB is proposes in this paper. To minimize the fundamental and multiple harmonics, the MRS method is analyzed to reduce the harmonic current by adjusting the phase shift angle and duty cycle. MRS improves the efficiency of DAB in a wide range of voltage and power. Then the DPC method is used to achieve the fast transient response for the input voltage and power fluctuations. The effectiveness of the method has been verified due to the simulation results. However the phase-shift ratio adjustment will cause the transient dc-bias current, it needs to be improved in the future.
References 1. Lu, Z., et al.: High-probability neurotransmitter release sites represent an energy-efficient design. Curr. Biol. 26(19), 2562–2571 (2016) 2. Inoue, S., Akagi, H.: A bidirectional isolated DC–DC converter as a core circuit of the nextgeneration medium-voltage power conversion system. IEEE Trans. Power Electron. 22(2), 535–542 (2007) 3. Costinett, D., Maksimovic, D., Zane, R.: Design and control for high efficiency in high stepdown dual active bridge converters operating at high switching frequency. IEEE Trans. Power Electron. 28(8), 3931–3940 (2013) 4. Zhao, B., et al.: Overview of dual-active-bridge isolated bidirectional DC–DC converter for high-frequency-link power-conversion system. IEEE Trans. Power Electron. 29(8), 4091– 4106 (2014) 5. Choi, W., Rho, K.M., Cho, B.H.: Fundamental duty modulation of dual-active-bridge converter for wide-range operation. IEEE Trans. Power Electron. 31(6), 4048–4064 (2016) 6. Dinh, N.D., et al.: Observer-based nonlinear control for frequency modulated dual-activebridge converter. In: 2016 IEEE Energy Conversion Congress and Exposition (ECCE) (2016) 7. Filba-Martinez, A., et al.: Operating principle and performance optimization of a three-level NPC dual-active-bridge DC–DC converter. IEEE Trans. Industr. Electron. 63(2), 678–690 (2016) 8. Li, X., Li, Y.-F.: An optimized phase-shift modulation for fast transient response in a dualactive-bridge converter. IEEE Trans. Power Electron. 29(6), 2661–2665 (2014) 9. Liu, T., et al.: Design and implementation of high efficiency control scheme of dual active bridge based 10 kV/1 MW solid state transformer for PV application. IEEE Trans. Power Electron. 34(5), 4223–4238 (2019) 10. Li, K., et al.: Sliding-mode-based direct power control of dual-active-bridge DC-DC converters. In: 2019 IEEE Applied Power Electronics Conference and Exposition (APEC) (2019)
Effect of Oxygen Concentration on the Current-Carrying Friction and Wear Performance of C/Cu Contact Pairs Ziran Ni(B) , Zhijiang He, Hong Wang, Guoqiang Gao, Zefeng Yang, and Wenfu Wei Southwest Jiaotong University, Chengdu, China [email protected]
Abstract. As a typical carbon/metal contact pair, the pantogrid system is the only way of energy transfer, and its performance in service is closely related to the ambient atmosphere. Oxygen, as an active gas, participates in the physical and chemical reactions of the interface in the process of carbon/copper contact secondary flow friction, and then affects the tribological properties of the contact pair. With the extension of China’s railways to high-altitude areas [1–3], the oxygen content is different from that in plain areas, and the performance of the pantograph catenary contact pair is deteriorated during operation. In this paper, the oxygen environment at different altitudes was simulated by controlling the oxygen concentration quantitatively. By changing the oxygen concentration in the atmosphere chamber, the effects of different oxygen concentrations on the friction coefficient, contact resistance and wear of contact pair materials are studied by using the sliding reciprocating current carrying friction test rig and the environmental atmosphere chamber. The results show that with the increase of oxygen concentration, the friction coefficient and wear amount of the material show a trend of decreasing first and then increasing. It indicates that too large or too small oxygen content will lead to deterioration of tribological properties of materials. In addition, with the increase of oxygen concentration, the contact resistance increases first and then decreases, which is related to the oxidation process of the friction interface. The research content of this paper has a certain reference significance for the safe and stable operation of pantograph and the analysis of abnormal wear in different altitude areas. Keywords: Oxygen concentration · Current-carrying friction and wear · C/Cu contact pairs
1 Introduction Because of its excellent self-lubrication, stability and high electrical conductivity, carbon materials are often paired with metal materials and widely used in rail transit, aerospace and electric power fields. With the rapid expansion of electrified railways in China, trains will face more changeable environment when they run in different regions [4– 6]; Among them, high altitude area is a common working area. Studies show that the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 443–450, 2022. https://doi.org/10.1007/978-981-19-1528-4_44
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service status of sliding conductive pairs in high altitude area will be directly affected by oxygen concentration, which will directly affect their service life [7–11]. At present, a large number of achievements have been made in the research on the tribological properties of carbon/copper contact pairs in the field of pure machinery. According to literature [12], under the condition of no current carrier, the wear rate of carbon materials in nitrogen is lower than that in air. The study in literature [13] shows that oxygen can play a lubrication role in the friction process of carbon materials. The study in literature [14] shows that with the increase of oxygen concentration, different surface films are formed on the metal surface, resulting in changes in the wear mechanism, from adhesion wear to adhesion wear and oxidation wear and then to oxidation wear. There are few studies on the effect of oxygen on the friction and wear properties of the carbon/copper contact pair under the intervention of current. Literature [15] shows that the wear rate of C/C composites in air environment is higher than that in nitrogen environment under the intervention of current. The influence of oxygen concentration change on the friction and wear properties of carbon/copper contact with the secondary stream has not been studied in the above literatures, and the mechanism of the influence of oxygen on the friction of carbon/copper contact with the secondary stream is still unclear. In this paper, a selfmade sliding reciprocating current-carrying friction test rig and an ambient atmosphere chamber were used to simulate the oxygen environment in different altitudes by changing the oxygen concentration in the chamber. At the same time, the effects of different oxygen concentration on the friction coefficient, contact resistance and wear of contact pair materials were studied. The research results can provide theoretical guidance for the operation, maintenance and maintenance of the sliding current-carrying friction of carbon/metal contact pairs in different oxygen content areas.
2 Experimental Details 2.1 Experimental Equipment Sliding reciprocating current-carrying friction test bed [16] is shown in Fig. 1. The test bed is mainly composed of current-carrying friction device, DC power supply, air compressor and data acquisition system. The copper contact wire is fixed between the two supports through the fixture and the insulator, and the carbon slider is fixed under the contact line through the fixture. The Angle between the support column and the slide rail makes the friction process of carbon slider simulate the Z-shaped motion of carbon slider in actual pantograph. The carbon slider is in contact with the contact line and moves in a reciprocating linear motion driven by a linear track. Two force sensors are installed under the fixture to measure tangential and normal loads. The cylinder below the sensor can provide 20 N–200 N continuously adjustable load, and the power supply can provide 0–200 A DC current. The data acquisition system can collect tangential and normal load data in real time. The physical drawing of the ambient atmosphere cabin is shown in Fig. 2. The ambient atmosphere cabin is mainly composed of the main cabin and the high-power vacuum pump. The cabin’s inner tank size is 1.8 m * 1.3 m * 2.4 m, and the cabin is equipped with a 300 KW power distribution device and a signal output channel with 8 standard output heads. The cabin pressure is vacuum −1 standard atmospheric pressure
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Fig. 1. Structure drawing of sliding reciprocating current-carrying friction test bed.
adjustable, the pressure adjustment accuracy of 1%–5%; The interior of the cabin body is corrosion-resistant, which can be filled with various inert gases, corrosive gases and water vapor. It can simulate complex environmental conditions in different areas.
Fig. 2. Structure drawing of multifunctional environment and atmosphere tank.
3 Experimental Methods and Parameters The current carrying friction pair is pure copper contact wire and pure carbon slide plate used in railway operation. The mass fraction of copper in the contact line is 99.99%, the mass fraction of pure carbon slide block is 99.5%, and the size is 100 mm * 35 mm * 25 mm. The test conditions are shown in Table 1. Before the test began, the surface of the contact line was polished with 300# and 2000# sandpaper to eliminate the surface oxide film, and then the test stand was pushed into the ambient atmosphere chamber to adjust the oxygen concentration in the ambient atmosphere chamber to reach the preset value. After the start of the test, the carbon slider and the contact line were pre-ground without current for 5 min, and then the test current was connected. The carbon sliders before and after the test were weighed by an electronic
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balance with an accuracy of 0.001 g. The wear amount of the carbon sliders was the weight before the test minus the weight after the test. The data acquisition card collected real-time data of voltage, current, tangential force and normal force in the test process to obtain the contact resistance and friction coefficient. In order to reduce the error, the average value of the three test results under the same condition was taken. Table 1. Current carrying friction test parameters. Parameter
Conditions
Oxygen concentrations(%)
15, 16, 17, 18, 19, 20
Current I(A)
100
Load Fn (N)
90
Sliding speed v(m/s)
1.5
Sliding time t(min)
45
4 Results and Discussion 4.1 Effect of Oxygen Concentration on Wear Carbon block under different oxygen concentrations of wear is shown in Fig. 3, can be seen from the diagram, when oxygen concentration increases from 15% to 17%, the wear of carbon slider decreases sharply. With the increase of oxygen concentration, the carbon slider wear also increases. When the oxygen concentration increases to 19%, the wear trend of carbon slider tends to be flat. It can be seen from the test data that the change of oxygen concentration will affect the wear amount of the material. The addition of current increases the friction surface temperature significantly, catalyzes the oxidation reaction on the surface of the material, and amplifies the influence of oxygen concentration change on the wear rate of carbon materials. In low oxygen environment, due to the low oxygen concentration, the rate of oxidation reaction on the surface of the material is relatively slow, and the oxide generated by the reaction of the contact line material and oxygen can not be covered on the carbon matrix in time to form an oxidation film with lubrication protection, which increases the wear of the material. With the increase of oxygen concentration, the formation rate of copper oxide is accelerated, and the surface of carbon material is attached with an oxide film which can slow down the wear of carbon material, and the wear amount will decrease. But as the oxygen concentration continued to increase, the accelerating oxidation reaction rate and thickness of oxide film on the surface of the carbon material increases, when the oxide film reaches a certain thickness, because of differences between the thermal expansion coefficient of oxide and matrix and the effect of friction, in formation of crack and oxide layer falls off, make direct contact with the metal contact wire carbon material. At this point, the wear rate will gradually increase and then plateau.
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Fig. 3. Curve of wear with oxygen concentration.
4.2 Effect of Oxygen Concentration on Coefficient of Friction The friction coefficient of the carbon slider under different oxygen concentrations is shown in Fig. 4. As can be seen from the figure, the friction coefficient of the material decreases first, then increases and finally becomes flat with the increase of oxygen concentration. When oxygen concentration is 17%, the friction coefficient is the smallest. The main reasons for the above phenomena are as follows: the addition of current increases the temperature of the friction surface; When the oxygen concentration is low, the speed of oxygen-containing compounds formed on the contact surface is slow, and the carbon material is in direct contact with the metal contact line, so that the friction coefficient is at a high level. With the increase of oxygen concentration, the formation rate of oxide becomes faster, and a stable oxide film forms on the friction surface. The contact state changes from carbon/metal contact to carbon/metal oxide contact, and the friction coefficient decreases. When the oxygen concentration reaches 17%, the contact state returns to the carbon/metal contact due to the thickening and shedding of the oxide film attached to the surface of the carbon material, which leads to the increase of the friction coefficient and then tends to be stable.
Fig. 4. Curve of friction coefficient with oxygen concentration.
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4.3 Effect of Oxygen Concentration on Contact Resistance The contact resistance of carbon slider at different oxygen concentrations is shown in Fig. 5. It can be seen from the figure that, with the increase of oxygen concentration, the contact resistance increases first and then decreases, and reaches the maximum when the oxygen concentration is 17%. According to the analysis, in the low oxygen environment, the oxide formation is less, at this time the contact resistance is carbon/metal contact, so the contact resistance is low. With the increase of oxygen concentration, oxidation reaction speed is accelerated, and the products of oxidation reaction of contact wire copper material adhere to the surface of carbon material in the friction process. The oxide formed on the friction surface and the surface wear products form a surface layer with extremely uneven conductivity, which makes the contact state of the material worse and the contact resistance increase. When the oxygen concentration increases to 17%, the thickness of the oxide film increases and becomes brittle and falls off, and the contact surface forms carbon/metal contact again, and the contact resistance gradually decreases.
Fig. 5. Curve of contact resistance with oxygen concentration.
After the test, the surface of carbon slider is shown in Fig. 6. From left to right, the surface morphology of carbon slider under 15%, 16%, 17%, 18%, 19% and 20% oxygen concentration is shown. It can be seen from the figure that with the increase of oxygen concentration, the oxides attached to the surface of carbon materials first increase and then decrease. It is proved that the temperature of the contact surface greatly increases the oxidation reaction rate of the material with the participation of current, and the change of oxygen concentration affects the adhesion degree of metal oxides on the surface of carbon material, thus affecting the wear amount, friction coefficient and contact resistance of carbon material in the process of current-carrying friction.
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Fig. 6. Wear surface morphology of carbon sliders with different oxygen concentrations.
5 Conclusion In this paper, the influence of oxygen concentration on the friction and wear properties of carbon/copper contact secondary flow was studied by using sliding reciprocating currentcarrying friction test rig and ambient atmosphere chamber. The following conclusions can be drawn from the above test results: 1. With the increase of oxygen concentration, the wear rate and friction coefficient of contact pair materials show a trend of first decreasing, then increasing and finally reaching a stable trend. 2. In the process of oxygen concentration change, the wear amount of carbon material has a low wear range, and the lowest oxygen concentration is 17%. 3. With the increase of oxygen concentration, the contact resistance increases first and then decreases. The change of oxygen concentration will directly affect the contact resistance and further affect the quality of the contact pair.
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References 1. Cui, G., Cui, X.: Review and prospect of railway history research in China. J. Southwest Jiaotong Univ. (Soc. Sci. Ed.) 17(05), 8–21 (2016). (in Chinese) 2. Hong, H.-S.: The role of atmospheres and lubricants in the oxidational wear of metals. Tribolo. Int. 35(11), 725–729 (2002) 3. Wang, X.: Acceleration of Sichuan-Tibet railway construction. Well-Off 04, 52–53 (2016). (in Chinese) 4. Zhang, H., Meng, L., Dai, X.: China railway (05), 12–16+26 (2019). (in Chinese) 5. Chen, G., et al.: Impacts of climate change on major projects in China. Adv. Clim. Chang. Res. 11(05), 337–342 (2015). (in Chinese) 6. Wang, G., Chen, J., Sun, J.: Research status and development trend of electric locomotive pantograph slide. Mater. Rev. 01, 18–20 (2003). (in Chinese) 7. Ozsarac, U., Aslanlar, S.: Wear behaviour investigation of wheel/rail interface in water lubrication and dry friction. Industr. Lubr. Tribol. 60(2) (2008) 8. Energy; Researchers from Southwest Jiaotong University Discuss Findings in Energy (Characteristics of the Sliding Electric Contact of Pantograph/Contact Wire Systems in Electric Railways). Energy Weekly News (2018) 9. Lancaster, J.K.: A review of the influence of environmental humidity and water on friction, lubrication and wear. Tribol. Int. 23(6) (1990) 10. Jiang, Y., Song, X., Li, D.: Discussion on fault treatment and routine maintenance of engine carbon brush. Shandong Coal Sci. Technol. 01, 65–66 (2009). (in Chinese) 11. Wang, S.: Preliminary discussion on dust wear of the brush of the starter generator of Dongfeng type 4 locomotive. Diesel Locomotive (05), 38–43 (1987). (in Chinese) 12. Mao, P., Yue, B., Guo, R., Yi, M., Xu, H., Ge, Y.: Friction and wear properties of carbon/carbon composites under nitrogen and air conditions. Powder Metall. Mater. Sci. Eng. 17(02), 166– 171 (2012). (in Chinese) 13. Kazuhisa, M.: Considerations in vacuum tribology (adhesion, friction, wear, and solid lubrication in vacuum). Tribol. Int. 32(11) (1999) 14. Du, S., Zhang, Y., Shang, G., Chen, Y.: Friction and wear properties of CrNiMo steel at high temperature in different oxygen concentration atmospheres. Lubr. Eng. 35(08), 12–14+3 (2010). (in Chinese) 15. Zhang, H.: Effect of ambient atmosphere on arc generation and frictional and wear performance of C/C composites. Henan Univ. Sci. Technol. (2014). (in Chinese) 16. He, D.H., Manory, R., Sinkis, H.: A sliding wear tester for overhead wires and current collectors in light rail systems. Wear 239(1) (2000)
Direct Torque Control of Squirrel Cage Motor Based on Sector Optimization Xiaolong Wang, Zhenpeng Luo(B) , Siqing Zhang, and Shuyu Wang Inner Mongolia Key Laboratory of Electromechanical Control, Inner Mongolia University of Technology, Hohhot 010080, Inner Mongolia, China [email protected]
Abstract. Aiming at the problems of large torque ripple, slow magnetic linkage response and stator current distortion in the squirrel cage motor when the traditional six-sector direct torque control method is applied to the squirrel cage motor, a squirrel cage motor based on sector optimization is proposed. First, a twentyfour sector direct torque control method is designed, and then a three-bit hysteresis flux linkage controller is introduced, and the twenty-four sectors are optimized again according to the different effects of the voltage vector on the flux linkage and torque at different positions up to thirty sectors. This scheme can solve the problem of ignoring the flux linkage requirement in the traditional vector table when Sgn(T e ) = 0 in the 1 + 4n(n = 0, …, 5) sector, and divide this sector into the central angle of Two small sectors of 7.5° to reduce the flux linkage error of this sector due to torque demand when Sgn(Ψ s ) = 0; in the 3 + 4n(n = 0, …, 5) sector, Solve the problem of unbalanced flux linkage and slow torque response caused by voltage vector selection. The simulation results show that compared with the traditional six-sector DTC, the sector-optimized DTC can significantly improve torque ripple, speed up the flux linkage response, and make the stator current closer to a sine wave. Improved the performance of the traditional squirrel cage motor DTC system. Keywords: Direct torque control (DTC) · Thirty sector voltage vector select tables · Torque ripple · Flux response
1 Introduction With the improvement of the performance requirements of asynchronous motors, the high-performance AC variable frequency speed regulation technology based on the dynamic mathematical model of the asynchronous motor has developed rapidly. Highperformance asynchronous motor control mainly includes vector control based on the rotor flux orientation and indirect control of the electromagnetic torque by controlling the current, and direct torque control (DTC), which directly controls the electromagnetic torque and flux by controlling the voltage space vector.) [1, 2]. Compared with vector control, the decoupling of the DTC system is not complete. A voltage space vector will change both the stator flux and the electromagnetic torque. This control method has the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 451–461, 2022. https://doi.org/10.1007/978-981-19-1528-4_45
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advantages of insensitive to motor parameters, fast torque response, strong robustness and simple algorithm. However, the defects of the traditional six-sector DTC voltage vector selection table have brought about shortcomings such as large torque ripple and poor low-speed control performance [3–8]. At present, the methods to improve the performance of direct torque control include increasing the number of effective voltage vectors by using a three-level inverter [9]; introducing the DTC control of the duty cycle to determine the effect of the effective voltage space vector in each sampling period Time [10]; use torque and flux linkage sliding mode controller and space vector modulation DTC control [11] to fix the switching frequency; refine the sector to 12 or 18 DTC control [12–14] and other methods. In this paper, on the basis of the traditional six-sector DTC, the flux linkage control is also set to three-bit hysteresis, the original 6 sectors are optimized into 24 sectors, and the sector where the voltage vector is located is optimized, and finally 30 sectors the voltage vector is selected more carefully in the area, so that the selection of the voltage vector will not cause the imbalance of the flux linkage, and the torque control is more accurate. The simulation experiment results show that this method increases the response speed of the flux linkage, reduces the pulsation of the flux linkage and torque, and makes the stator three-phase current closer to a sine wave.
2 Basic Theory of Direct Torque Control 2.1 The Effect of Voltage Space Vector on Flux Linkage and Torque In order to facilitate understanding, the effect of the stator voltage space vector on the stator flux and torque is analyzed in the dq coordinate system oriented according to the stator flux linkage. The stator flux equation in the dq coordinate system can be simplified to: dΨsd dΨs dt = dt = −Rs isd + usd (1) dΨsq dt = −Rs isq − ω1 Ψs + usq = 0 The torque equation is: Te = np
Lm |Ψs ||Ψr | sin θ σ Ls Lr
(2)
where: Ψ s and Ψ r are the stator and rotor flux linkage space vectors; L m is the stator and rotor mutual inductance; np is the number of pole pairs; L s and L r are the stator and rotor mutual inductances respectively; σ is the flux leakage coefficient; θ is the angle between the stator flux linkage and the rotor flux linkage, that is, the flux angle, as shown in the following formula (3) θ = (ω1 − ω)t
(3)
In the formula: ω1 is the rotation angular velocity of the stator flux linkage, and ω is the rotation angular velocity of the rotor flux linkage.
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When the motor speed is high, the influence of the stator resistance voltage drop can be ignored, and the formula (1) shows that (4) Ψs ≈ usd dt That is, the d-axis component usd of the space voltage vector determines the increase or decrease of the amplitude of the stator flux linkage. Ignoring the stator voltage drop, it can be obtained by formula (1)(2)(3) usq Lm |Ψs ||Ψr | sin ( Te = np − ω)t (5) σ Ls Lr Ψs where: ω is the rotation speed of the rotor. Among them, the q-axis component usq of the space voltage vector determines the rotational angular velocity ω1 of the stator flux linkage vector, thereby changing the size of the magnetic flux angle θ to achieve the purpose of controlling the electromagnetic torque T e of the motor. 2.2 Basic Principles of Direct Torque Control In actual operation, in order to make full use of the iron core, it is generally necessary to maintain the flux linkage amplitude |Ψ s | to a constant value, that is, to ensure that the track of the flux linkage vector is a circle. Therefore, the electromagnetic torque of the motor can be controlled by controlling the magnetic flux angle θ. The control process of direct torque control is: the stator voltage and current are transformed by 3/2 to obtain the voltage and current in the static coordinate system. In the static coordinate system, the voltage and current are obtained by the flux linkage observer to obtain the amplitude of the stator flux vector |Ψ s | and the sector in which it is located, the torque T e is obtained through the torque observer. Compare the set value of the flux linkage with the |Ψ s | obtained by the flux linkage observer and send it to the two-position hysteresis controller to obtain the increase or decrease signal of the flux linkage. After the speed is passed through the controller, the torque set value Te∗ and T e are obtained. The comparison is sent to the three-position hysteresis controller to obtain
M sa s b sc Sgn ∆Ψ s )
Sgn(∆Te)
two hysteresis
Ψs
isa isb isc usausbusc φ
∆Ψ s
three hysteresis
∆Te Te*
Ψ s*
3s/2s isα isβ
3s/2s usα usβ
Calculation of flux linkage and torque
Ψs
Te
Te
ω*
PI controller
ω
Fig. 1. Schematic diagram of direct torque control
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the torque increase or decrease signal, and the voltage vector is selected according to the torque signal, the flux linkage signal and the location of the sector. The system control block diagram is as shown in Fig. 1.
3 Optimized 30-Sector Voltage Vector Table In order to solve the problem that the traditional six-sector DTC voltage vector affects the flux linkage and the vector in the traditional table gives priority to the torque and sacrifices the flux linkage response speed, a 24-sector direct torque control is proposed, with ϕ = −7.5° + 15° * m(m = 1, …, 24) is the boundary to divide the flux linkage circle into 24 sectors. When ϕ is located near the traditional six-sector boundary (ϕ = 30° + 60° * n, n = 0, …, 5), the imbalance of the flux linkage can be solved; the effective voltage vector is located at (ϕ = 60° * n, n = 0, …, 5) solves the problem of ignoring the demand of the flux linkage in the traditional vector table when Sgn(T e ) = 0. The division of the flux circle is shown in Fig. 2 below.
Fig. 2. Twenty-four sector flux circle
Literature [15, 16] pointed out that the traditional six-sector stator flux can be regarded as unchanged within the wide range of the hysteresis loop. Due to the large mechanical inertia, the rotor flux is also considered unchanged. The formula (3) is considered to affect the torque. There is only the flux angle, which makes the selection of the voltage vector in the six sectors incorrect in some cases. Therefore, in order to meet the tolerance range of the flux linkage and further reduce the pulsation of the flux linkage, this paper adds an extra hysteresis band in the control of the flux linkage, so that the flux linkage controller is also a three-position hysteresis loop like the torque. In this way, in addition to increasing or decreasing the state of the flux linkage, there is an additional state where the flux linkage remains unchanged, which can better control the flux linkage to be circular. The three-position hysteresis controller is shown in Fig. 3 below.
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Fig. 3. Three-position hysteresis controller
Where Sgn(Ψ s ) = 1, increase the flux linkage; Sgn(Ψ s ) = 0, the flux linkage remains unchanged; Sgn(Ψ s ) = −1, decrease the flux linkage. Three-position hysteresis flux control has three more control states than two-position hysteresis flux control, namely Sgn(Ψ s ) = 0, Sgn(T e ) = ±1,0. In this way, the system can be further optimized. For example, in the traditional six-sector DTC, the flux linkage has only two states of increase or decrease, but now a three-position hysteresis controller is introduced, and the voltage vector that enables the torque to respond quickly can be selected when the flux linkage is unchanged. As shown in Fig. 2, when ϕ ∈ (22.5°, 37.5°), if the three-position hysteresis of the flux linkage is added, the voltage vector U 2 can be selected to achieve a rapid increase in torque when the flux linkage does not need to change, instead of using voltage vector U 6 under the control of two-position flux linkage hysteresis. In the same way, when the stator flux rotates to the sector (3 + 4n, n = 1, …, 5) where the vertical line of each voltage vector is located, one can be selected which has a large impact on the torque and a small impact on the flux linkage to increase the torque response speed. At the same time, the introduction of three-bit hysteresis, in the sector where the effective voltage vector is located (1 + 4n, n = 0, …, 5), the state is Sgn(Ψ s ) = 0, Sgn(T e ) = ±1, The twenty-four sectors can be further optimized. Taking the first sector ϕ ∈ (−7.5°, 7.5°), the flux linkage is unchanged and the torque is increased as an example. At this time, selecting the voltage vectors U 6 and U 2 can increase the torque. The normal and tangential components of U 6 and U 2 in the flux linkage are decomposed and discretized: 2 n Udc Ts cos(60◦ − ϕ) (6) Ψs (U 6 ) = 3 2 n Udc Ts cos(120◦ − ϕ) (7) Ψs (U 2 ) = 3 2 1 Udc (8) θ (U 6 ) = Ts sin(60◦ − ϕ) |Ψs | 3 2 1 Udc θ (U 2 ) = (9) Ts sin(120◦ − ϕ) |Ψs | 3
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When ϕ ∈ (−7.5°,0°), |sn (U 6 )| ≤ |sn (U 2 )|, θ (U 6 ) ≥ θ (U 2 ), the U 6 vector responds to the torque demand faster than the U 2 vector, and generates less impact. When ϕ ∈ (0°,7.5°), |sn (U 6 )| ≥ |sn (U 2 )|, θ (U 6 ) ≤ θ (U 2 ), the U 2 vector responds to the torque demand faster than the U 6 vector, and produces more flux less impact. Therefore, the U 6 vector is selected in ϕ ∈ (−7.5°,0°), and the U 2 vector is selected in ϕ ∈ (0°,7.5°). In the same way, it can be obtained that on both sides of the dividing line (ϕ = 60° * n) where each voltage vector is located, this sector is divided into two small sectors with a central angle of 7.5°, so that this sector is in Sgn(Ψ s ) = 0, there is a fast torque response and a small flux linkage effect. In order to solve the problem that when Sgn(T e ) = 0 occurs in 1 + 4n(n = 0, …, 5) sectors, the traditional vector table ignores the demand of flux linkage; and solves the problem of 3 + 4n(n = 0, …, 5) Current distortion and slow torque response caused by the imbalance of the flux linkage caused by the selection of the voltage vector in the sector. This paper designs the DTC voltage vector selection based on sector optimization as shown in Table 1. Table 1. DTC voltage vector selection table based on sector optimization (a) Voltage vector selection table for sectors 1 to 10 S gn (ΔΨ s ) 1
0
−1
S gn (T e )
Sector 1
2
3
4
5
6
7
8
9
10
1
U6
U6
U6
U6
U2
U2
U2
U2
U2
U3
0
U4
U4
U7
U0
U0
U6
U6
U0
U7
U7
−1
U5
U5
U5
U4
U4
U4
U4
U4
U6
U6
1
U6
U2
U2
U2
U2
U2
U3
U3
U3
U3
0
U0
U0
U0
U0
U0
U7
U7
U7
U7
U7
−1
U1
U5
U5
U5
U5
U5
U4
U4
U4
U4
1
U2
U2
U2
U3
U3
U3
U3
U3
U1
U1
0
U3
U3
U0
U0
U7
U1
U1
U7
U7
U0
−1
U1
U1
U1
U1
U5
U5
U5
U5
U5
U4
(b) Voltage vector selection table for sectors 2 to 20 S gn (ΔΨ s ) 1
0
S gn (T e )
Sector 11
12
13
14
15
16
17
18
19
20
1
U3
U3
U3
U3
U1
U1
U1
U1
U1
U1
0
U2
U2
U7
U0
U0
U3
U3
U0
U7
U7
−1
U6
U6
U6
U2
U2
U2
U2
U2
U3
U3
1
U3
U1
U1
U1
U1
U1
U5
U5
U5
U5
0
U0
U0
U0
U0
U0
U7
U7
U7
U7
U7
−1
U4
U6
U6
U6
U6
U6
U2
U2
U2 U2 (continued)
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Table 1. (continued) (b) Voltage vector selection table for sectors 2 to 20 S gn (ΔΨ s ) −1
S gn (T e )
Sector 11
12
13
14
15
16
17
18
19
20
1
U1
U1
U1
U5
U5
U5
U5
U5
U4
U4
0
U5
U5
U0
U0
U7
U4
U4
U7
U7
U0
−1
U4
U4
U4
U4
U6
U6
U6
U6
U6
U2
(c) Voltage vector selection table for sectors 3 to 30 S gn (ΔΨ s ) 1
0
−1
S gn (T e )
Sector 21
22
23
24
25
26
27
28
29
30
1
U5
U5
U5
U5
U4
U4
U4
U4
U4
U6
0
U1
U1
U7
U0
U0
U5
U5
U0
U7
U7
−1
U3
U3
U3
U1
U1
U1
U1
U1
U5
U5
1
U5
U4
U4
U4
U4
U4
U6
U6
U6
U6
0
U0
U0
U0
U0
U0
U7
U7
U7
U7
U7
−1
U2
U3
U3
U3
U3
U3
U1
U1
U1
U1
1
U4
U4
U4
U6
U6
U6
U6
U6
U2
U2
0
U6
U6
U0
U0
U7
U2
U2
U7
U7
U0
−1
U2
U2
U2
U2
U3
U3
U3
U3
U3
U1
4 Simulation Analysis In order to verify that the squirrel cage motor DTC based on sector optimization improves the performance of the motor, the above control strategy is simulated and compared with the traditional six-sector direct torque control. The used squirrel cage motor parameters and simulation parameters are shown in Table 2 below. Table 2. Squirrel cage motor parameters and simulation parameters Motor parameters
Numerical value
Simulation parameters
Rated power Pn /kW
2
The sampling period T s /s 10−6
Numerical value
Stator resistance Rs /
0.435
Flux link amplitude Ψ s /Wb
1.2
Stator leakage inductance L s /mH
2
Torque hysteresis lower limit c1
0.5
Rotor resistance Rr /
0.816
Torque hysteresis upper limit c2
1 (continued)
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Motor parameters
Numerical value
Simulation parameters
Numerical value
Rotor leakage inductance L r /mH
2
Flux hysteresis lower limit c1
0.001
Magnetizing inductance L m /mH
69
Flux hysteresis upper limit c2
0.022
Moment of inertia J/(kg·m2 )
0.09
The following is the traditional six-sector and sector-based optimized squirrel-cage motor DTC under the same conditions, the stator flux trajectory at 1000 r/min; local flux and torque waveform; overall flux torque response curve; steady state The current is compared. As shown in Fig. 4, when the flux linkage vector of the six sectors reaches the set value, it takes about 8 voltage vector selections, while the flux linkage vector based on sector optimization needs to go through about 5 voltage vector selections to reach the set value. Therefore, it can be shown that the sector-based optimized DTC can indeed make the flux linkage increase rapidly. In addition, the track of the stator flux is closer to a circle.
Fig. 4. Flux trajectory of two control methods
As shown in Fig. 5(a), when the six-sector DTC momentarily produces a large pulsation of the flux linkage at t1 , the torque at this moment will also have a large pulsation, about 6N·m. In Fig. 5(b), due to the adoption of a three-position hysteresis and a sector-based optimized voltage vector meter, the flux linkage pulsation is reduced, and the maximum torque pulsation is only 2 N·m. Therefore, it can be seen that the direct torque control based on sector optimization reduces both flux ripple and torque ripple.
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Fig. 5. Two control methods, local flux linkage and torque waveform
The flux linkage and torque response curves of the traditional six-sector DTC and the sector-based optimized DTC are shown in Fig. 6 below. Compared with the traditional six-sector DTC, the optimized DTC has the same flux amplitude at startup and load changes. Will exceed the tolerance of the hysteresis controller; the torque ripple is also significantly smaller.
Fig. 6. Two control methods flux torque response waveform
Iabc/A
As shown in Figs. 7 and 8, compared with the stator three-phase current in the traditional six-sector DTC control, the DTC stator current distortion rate based on sector optimization is reduced and closer to a sine wave.
t/s
Fig. 7. Traditional six-sector DTC stator three-phase current steady-state waveform
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Iabc/A
460
t/s
Fig. 8. Sector optimization DTC stator three-phase current steady-state waveform
5 Conclusion In this paper, in some cases, the traditional six-sector DTC has large torque ripple, slow flux linkage response, and stator current distortion, etc., and solves the above problems by optimizing the traditional six-sector to twenty-four sectors, which is: (1) The angle ϕ between the stator flux linkage and the α axis is located near the traditional six-sector boundary line (ϕ = 30° + 60° * n). The voltage vector has a large impact on the flux linkage amplitude, which will lead to current distortion; (2) Near ϕ = 60° * n in the traditional six sectors, when Sgn(T e ) = 0, the traditional vector table ignores the problem of flux linkage requirements. In this paper, by increasing the number of bits of the magnetic link hysteresis controller, the number of selectable voltage vectors is increased in some specific sectors, and then the twenty-four sectors are re-optimized to 30 sectors, which realizes the optimization of torque and flux linkage. The control performance of flux linkage, torque, and stator current is compared and simulated for the traditional six-sector and the DTC control method based on sector optimization. The simulation results show that using the sector-based optimization method proposed in this paper, the flux demand can be quickly met, the torque ripple is reduced, and the stator current is closer to a sine wave, which improves the performance of the direct torque control system of the squirrel cage motor.
References 1. Ban, F., Lian, G., Gu, G., et al.: Research on single vector decoupling model predictive torque control strategy for PMSM. Adv. Technol. Electr. Eng. Energy 39(12), 44–51 (2020). (in Chinese) 2. Xue, C., Song, W.S., Feng, X.Y.: Finite control-set model predictive current control of fivephase permanent-magnet synchronous machine based on virtual voltage vectors. IET Electr. Power Appl. 11(5), 836–846 (2017) 3. Ruan, J., Yang, M., Zhou, H.: Torque ripple minimization for switched reluctance motor based on DITC. Electr. Eng. 20(04), 37–41 (2019). (in Chinese) 4. Lin, Z., Tang, N., Xiao, Q.: Direct torque control of synchronous reluctance motor based on variable magnetic chain amplitude. Electr. Eng. 20(4), 1–6 (2019). (in Chinese) 5. Cheng, Q., Chen, L., Cheng, Y., et al.: Direct torque control of three-level direct matrix converter-fed PMSM based on dynamic torque hysteresis. Proc. CSEE 039(005), 1488–1498 (2019). (in Chinese) 6. Niu, F., Wang, B., Babel, A.S., et al.: Comparative evaluation of direct torque control strategies for permanent magnet synchronous machines. IEEE Trans. Power Electron. 31(2), 1408–1424 (2016)
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7. Zhou, H.W., Zhou, C., Tao, W.G., et al.: Virtual-stator-flux-based direct torque control of five-phase fault-tolerant permanent-magnet motor with open-circuit fault. IEEE Trans. Power Electron. 35(5), 5007–5017 (2020) 8. Ullah, Z., Lee, S.T., Hur, J.: A torque angle-based fault detection and identification technique for IPMSM. IEEE Trans. Ind. Appl. 56(1), 170–182 (2020) 9. Wang, S., Cheng, X.: Three-level direct torque control system for the brushless DC motor. Electr. Autom. 41(04), 68–69 (2019). (in Chinese) 10. Lv, S., Lin, H., Li, B., et al.: Improved model predictive direct torque control for permanent magnet synchronous motor. Electr. Mach. Control 24(07), 102–111 (2020). (in Chinese) 11. Li, W., Wu, A., Dong, N.: Variable structure direct torque control for BLDCM based on optimized sliding mode observer. Adv. Technol. Electr. Eng. Energy 36(01), 25–29 (2017). (in Chinese) 12. Wang, D., Wang, R., Lai, C.: Improvement and simulation of traditional induction motor direct torque control. Electr. Drive 48(03), 9–12 (2018). (in Chinese) 13. Ai, X., Wang, W., Wang, H.: 12-sector direct torque control of interior permanent magnet synchronous motor. Acta Energiae Solaris Sinica 41(01), 325–332 (2020). (in Chinese) 14. Huang, S., Zhou, L., Zhen, J., et al.: Space vector pulse width modulation strategy for sixphase voltage source inverter in full modulation range based on sector subdivision method. Trans. China Electrotech. Soc. 34(24), 5070–5083 (2019). (in Chinese) 15. Hu, C., Fu, B., Zhao, G.: Research on direct torque control of open winding permanent magnet synchronous motor. Electr. Drive 50(08), 8–14 (2020). (in Chinese) 16. Zou, B.W., Guo, Y.G., Xiao, X., et al.: Performance improvement of matrix converter direct torque control system. Energies 13(12) (2020)
Characteristic Analysis of Fast Vacuum Switch Based on Electromagnetic Repulsion and Permanent Magnet Holding Mechanism Anying Cao1 , Yao Liu1 , Jianfu Chen3 , Xu Cheng1 , Wei Li2 , Zhongjian Song3(B) , Guanxin Qiu1 , and Huaihao Cheng1 1 Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd., Zhuhai, China 2 Shandong Taikai DC Technology Co. Ltd., Tai’an, China 3 Shandong Taikai High-Voltage Switchgear Co. Ltd., Tai’an, China
[email protected]
Abstract. To solve the problem of high demand for the fast vacuum switch in DC power grid, a fast vacuum switch based on electromagnetic repulsion with permanent magnet retaining mechanism is proposed. The retaining force of permanent magnet mechanism and the multi-physical field of electromagnetic repulsion with permanent magnet retainer mechanism in the opening process are simulated using finite element method. The influences of the permanent magnet thickness and the internal thickness of static iron core on the retaining force of permanent magnet retainer mechanism are obtained, and the main factors influencing the opening process are mastered. The correctness of the simulation results is verified by the opening test of the prototype, which is of guiding significance for the subsequent optimization design of the mechanism. Keywords: Electromagnetic repulsion · Permanent magnet retaining mechanism · Fast vacuum switch · Multi-physical fields
1 Introduction In recent years, the electromagnetic repulsion mechanism based on the principle of electromagnetic induction eddy current has developed rapidly [1–5]. Compared with the traditional operating mechanism, its structure is simple, the opening speed is fast, the mechanical delay time is short, and the initial response speed is fast. It is especially suitable for high-speed opening of the driving contact of the arc extinguishing chamber. With the improvement of the current capacity of the DC circuit breaker under the fixed voltage level, the electric repulsion of the loop generated by the interaction of the current flowing between the contact and the contact arm will increase, and long-term stable holding force is required. At present, the advantages of permanent magnet holding mechanism in this application are very obvious, its permanent magnetic force will hardly disappear, and its life span is as high as 100,000 times [6]. It has the advantages © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 462–473, 2022. https://doi.org/10.1007/978-981-19-1528-4_46
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of large holding force and stability. The combination of the two mechanisms makes the large-capacity fast mechanical switch performance more superior. This paper is based on the finite element method to simulate and calculate the holding force of the permanent magnet holding mechanism. The single-variable method is used to simulate and calculate the Z-axis electromagnetic force by changing the thickness of the permanent magnet and the internal thickness of the static iron core respectively, which points out the direction for the subsequent structural design optimization of the permanent magnet holding mechanism. The finite element method is used to simulate the electromagnetic field, thermal field and displacement field of the electromagnetic repulsion-permanent magnet holding mechanism during the opening action process, and the influence of the variation on the opening action process of the mechanism is analyzed. Finally, the simulation results are compared with the prototype test data to verify the correctness of the simulation results, which can be used as a reference for the subsequent optimization of the product structure design of the electromagnetic repulsion-permanent magnet holding mechanism.
2 Working Principle of Electromagnetic Repulsion and Permanent Magnetic Holding Mechanism The overall schematic diagram of the fast vacuum switchgear based on electro-magnetic repulsion and permanent magnetic holding mechanism is shown in Fig. 1, mainly composed of vacuum interrupter, pull rod, permanent magnet holding mechanism, and electromagnetic repulsion mechanism. The permanent magnetic attraction is used to maintain the closing position. The electromagnetic repulsion is used to achieve opening action and opening buffer. The permanent magnet holding mechanism is shown in Fig. 2. It is mainly composed of permanent magnet, moving iron core, static iron core, opening spring and contact pressure spring. The electromagnetic repulsion mechanism is shown in Fig. 3, which is mainly composed of the opening coil, the opening buffer coil, and the repulsion plate. The drive control loop of the electromagnetic repulsion mechanism is shown in Fig. 4. C1 and C2 are the pulse capacitors used in opening action and opening buffer respectively, R is the equivalent resistance of the circuit and the coil, and L0 is the equivalent inductance of the circuit except the coil, D is freewheeling diode, K is the drive thyristor. The operating principle of the opening process of the electromagnetic repulsion mechanism is: When receiving the opening operation command, K will be turned on after receiving the opening trigger signal, and C1 is discharged to the opening coil. A millisecond-level pulse high current is generated by opening coil, which induces a large eddy current on the surface of the metal disk, and its direction is opposite to the direction of the opening coil current. Under the action of electromagnetic repulsive force, a downward force instantly is generated by the metal disc. When the downward electromagnetic repulsive force is greater than the upward closing holding force of the permanent magnet mechanism, the metal disc drives the connecting rod and the valve core to start opening. After a certain period of time, the capacitor C2 discharges to the buffering coil and the mechanism enters the opening buffer stage.
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Fig. 1. Overall schematic diagram of fast vacuum switchgear
Fig. 2. Schematic diagram of permanent magnet holding mechanism
Fig. 3. Schematic diagram of electromagnetic repulsion mechanism
Fig. 4. Drive control circuit of electromagnetic repulsion mechanism
3 Simulation Analysis of the Permanent Magnet Retaining Actuator The geometric model of the permanent magnet retaining mechanism in the fast vacuum switchgear is shown in Fig. 5. White is static iron core, black is moving iron core, their materials are all soft iron, and the green part is the permanent magnet. The green part is a permanent magnet, whose material is NdFeB N54.
Fig. 5. Dimension diagram of permanent magnet holding mechanism
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3.1 Simulation Calculation
Fig. 6. Schematic diagram of simulation model
In order to analyze the steady-state characteristics of the equipment, an axisymmetric simulation model was established based on the actual model of the permanent magnet retaining mechanism, as shown in Fig. 6. The materials of the blue part are NdFeB N54, soft iron and air. The material parameters are shown in Table 1. Table 1. Material properties required for simulation Attributes
NdFeB N54
Soft iron
Air
Conductivity
1/1.4 [S/m]
1.12e7[S/m]
0
Relative permittivity
1
1
1
Permeability
1.05
–
–
Residual flux density mode
1.47[T]
–
–
BH curve
–
Figure 7
–
Relative permeability
–
–
1
Fig. 7. BH curve of soft iron
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3.2 Simulation Results The magnetic field density modulus (T) and magnetic flux density vector dia-grams obtained by simulation as shown in Fig. 8. It can be seen that the magnetic leakage in the magnetic circuit of the mechanism is relatively small. The magnetic field force (N) and the vector diagram of the magnetic field force in the vertical direction obtained by simulation as shown in Fig. 9. It can be seen that the suction force in the steady state and the vertical state is 12100N, which fully meets the closing current of 88 kA and the fault current of 63 kA. The pressure on the main contact and arc contact under the fault current of 63 kA is required to be 7000N ≤ Farc ≤ 16708N [7].
Fig. 8. Simulated magnetic field density mode Fig. 9. The vertical magnetic force (n) and (T) and flux density vector diagram magnetic force vector obtained by simulation.
3.3 Influence Analysis of Different Structures of Permanent Magnet Mechanism on Electromagnetic Force (Suction) of Z Axis 3.3.1 Influence of Permanent Magnet Thickness on the Z-axis Electromagnetic Force Based on original mechanism, the permanent magnets of different thicknesses, namely 1 mm, 3 mm, 6 mm, 9 mm, 12 mm, 15 mm, 16 mm, 19 mm and 21 mm are simulated in this paper. The simulation results are shown in Fig. 10. It can be seen from Fig. 10 that with the thickening of the permanent magnet, the z-axis electromagnetic force first increases and then decreases, and the change is larger in the range of 1 mm–9 mm, and smaller after more than 9 mm. 3.3.2 Motion Characteristic Analysis of Fast Vacuum Switchgear Based on Electromagnetic Repulsion Permanent Magnet Holding Mechanism When the thickness of the permanent magnet is 24 mm, the inner diameter thickness of the static iron core is increased by 15 mm, and the z-axis electromagnetic force is increased to 13900N. The simulated Z-axis electromagnetic force is shown in Fig. 11, and the magnetic field distribution comparison is shown in Figs. 12a and 12b.
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Fig. 10. Results of z-axis electromagnetic force with different permanent magnet thickness
Fig. 11. Z-axis electromagnetic force with 15 mm inner diameter of static iron core added
Fig. 12. Magnetic field distribution of different thickness permanent magnets
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In summary, in the circular permanent magnet operating mechanism, increasing the thickness of the permanent magnet when the thickness is small is more obvious for the increase in the Z-axis electromagnetic force. When the thickness increases to a certain extent, it will reduce the cross-sectional area of the magnetic circuit, and finally weaken the Z-axis electromagnetic force. At the same time, when the thickness of the permanent magnet is the same, increasing the cross-sectional area of the static iron core also has a significant effect on increasing the magnetic flux.
4 Simulation Analysis of Motion Characteristics Based on Electromagnetic Repulsion-Permanent Magnet Retaining Mechanism In order to study the opening motion characteristics of the fast vacuum switch device based on the electromagnetic repulsion permanent magnet holding mechanism and improve its design theory, the finite element method was used to simulate and analyze the variation and interaction of electromagnetic field, thermal field and displacement field in the action process of the mechanism. 4.1 Simulation Model Due to the symmetry of this mechanism, an axisymmetric model is used for equivalent simulation analysis in this article. The geometric model is shown in Fig. 13. In order to simplify the model, the link between the electromagnetic repulsion mechanism and the permanent magnet holding mechanism is omitted, and its motion is only integrated in the simulation setup. Since this article only studies the initial opening of the switchgear, the opening buffer coil is omitted. According to the design requirements of the prototype, the material of the repulsion plate in the simulation is aluminium alloy, the radius is 90 mm, the thickness is 15 mm, the cross-sectional area of the opening coil is 7 mm2 , the inner diameter of the coil is 25 mm, the outer diameter is 107 mm, and the number of turns is 55; the value of the capacitor in the drive circuit is 2.5 mF, the initial value of the capacitor is 1300 V. Refer to Fig. 5 for the dimensions and related parameters of the permanent magnet holding mechanism. According to the overall design requirements of the fast vacuum switchgear, the total mass of the moving parts (including the moving parts of the interrupter, the moving iron core, the connecting guide rod, and the repulsion plate) is 12.5 kg, and the rated distance is 12 mm.
Fig. 13. Simulation model of permanent magnet retaining mechanism
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4.2 Mathematical Model (1) Electromagnetic field equation ∇ ×h=J
(1)
B=∇ ×V
(2)
J = σE + σV × B
(3)
E=
∂A ∂t
(4)
The constitutive relationship of its field is: B = μH
D = εE
(5)
In the equation: σ is electrical conductivity, μ is magnetic permeability, and ε is dielectric constant. (2) Heat transfer equation ρcp
∂T − ∇ · k∇T = Q ∂t 1 Q = σ E 2 2
(6) (7)
In the equation: ρ is the density, Cp is the specific heat at constant pressure, T is the temperature, k is the thermal conductivity, and Q is the induction heat source. (3) Motion equation of repulsion mechanism Fem − Fg ∂v = ∂t mp
(8)
∂u =v ∂t
(9)
In the equation: v is the speed of the moving part; F em is the sum of the electric repulsion force received by the repulsion plate and the suction force received by the moving iron core; F g is the gravity of the entire moving part; mp is the total mass of the entire moving part; u is the displacement of the moving part from time zero.
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4.3 Comparison Between Simulation and Experiment Results
Fig. 14. Distribution diagram of magnetic flux density vector and mode
The magnetic field distribution changes during the movement of the mechanism obtained by simulation are shown in Fig. 14. The initial opening time of the mechanism is t = 0.5 s. At this time, the thyristor in the opening circuit is turned on, and the current is put into the opening coil. With the change of time, the magnetic field in the electromagnetic repulsion mechanism increases rapidly, and the electric repulsion force on the repulsion disk increases rapidly. Overcoming the retention force of the permanent magnet mechanism, the moving parts move downward rapidly. The curve of the change of the current of the opening coil and the sum of the magnetic force on the repulsion disk and the moving core is shown in Fig. 15. It can be seen that the permanent magnetic force at the initial moment of opening is about 10000N. With the rapid increase of the opening current, the force on the moving parts in the Z direction changes from positive to negative, and changes with the changing trend of the opening current. The speed and displacement curves of the moving parts during the movement of the mechanism obtained by simulation are shown in Fig. 16. It can be seen that when the electromagnetic repulsion force overcomes the permanent magnet retention force, the moving parts start to move, and the time interval is only 0.6 ms, so the reaction speed is very fast. Under this parameter, the 2 ms stroke can reach 3.8 mm, and the average speed can reach 1.9 m/s, which can meet the requirements of high current fast mechanical switch.
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Fig. 15. The curve of the opening coil current Fig. 16. Velocity and displacement curves of and the magnetic field force during the moving parts during mechanism movement movement of the mechanism
Based on the simulation, a prototype of fast vacuum switch device with electromagnetic repulsion permanent magnet holding mechanism is designed, as shown in Fig. 17; the measured opening characteristics are shown in Fig. 18. The comparison of stroke parameters in the initial process of mechanism opening is shown in Table 2. It can be concluded that the structure obtained by the simulation and the prototype test has a good consistency. Since the friction of the mechanism is not considered in the simulation, there is a certain difference. In summary, this simulation calculation has a very good guiding significance for the design and optimization of this type of mechanism.
Fig. 17. Prototype drawing of fast vacuum switch
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2.00E0
3.00E3
1.00E0
2.50E3
0.00E0
2.00E3
-1.00E0
1.50E3
-2.00E0
1.00E3
-3.00E0
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-4.00E0
0.00E0
-5.00E0
-5.00E2
1 362 723 1084 1445 1806 2167 2528 2889 3250 3611 3972 4333 4694 5055 5416 5777 6138 6499
current/A
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stroke/mm
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Fig. 18. Opening characteristic curve of fast vacuum switch
Table 2. Comparison of simulation and test parameters Related parameters
Simulation
Test
Stroke at the time 0.3 ms
0.00 mm
0.00 mm
Stroke at the time 0.6 ms
0.12 mm
0.07 mm
Stroke at the time 0.9 ms
0.45 mm
0.42 mm
Stroke at the time 1.2 ms
1.48 mm
1.37 mm
Stroke at the time 1.5 ms
2.31 mm
2.19 mm
Stroke at the time 1.8 ms
3.32 mm
3.14 mm
Stroke at the time 2.1 ms
4.39 mm
4.18 mm
5 Conclusion The opening characteristics of the mechanism with electromagnetic repulsion permanent magnet are simulated and analyzed by finite element method. A set of prototype is designed for test and verification, and the following conclusions are drawn: (1) The purpose of this paper is to obtain the preliminary design parameters of the fast switch based on the electromagnetic repulsion and permanent magnet holding mechanism. By simplifying the simulation model reasonably and optimizing the simulation ideas, the design of the mechanism effectively in order to maximize the movement process close to the actual product is analyzed. (2) The simulation results show that the permanent magnet holding force can be counteracted by the electromagnetic repulsion force received by the repulsion disk in a
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very short time. The opening reaction time of the switch will not be affected too much by larger permanent magnet holding force. Therefore, the mechanism can’t only provide the holding force required by the high current switch in the closing state, but also effectively open the switch in a very short time. (3) Under the same conditions, the correctness of the simulation is verified by comparing the opening characteristics of the simulation and the prototype test, which is conducive to the structure design of the later products. It lays a foundation for the parameter selection and debugging of the high current fast vacuum switchgear based on the mechanism, and greatly shortens the product development cycle.
References 1. Yi, Z., Xianggen, Y.: Comparative research on HVDC and UHV power transmission. High Voltage Eng. 27(004), 44–46 (2001) 2. Sayed, A.H.E., Kenaar, R.W.P.K., Atmadji, A.M.S.: Modeling the opening mode of a fast acting electrodynamic circuit-breaker drive. In: Proceedings of the Universities Power Engineering Conference, Leicester, UK, pp. 169–173 (1999) 3. Ahn, K.Y., Kim, S.H.: Modeling and analysis of a high-speed circuit breaker mechanism with a spring-actuated cam. In: Proceedings of the Institute of Mechanical Engineers 2001(C), pp. 663–672 (2001) 4. Bin, S.: Optimization design and dynamic characteristics analysis of fast electromagnetic mechanisms. Shandong University (2013) 5. Qingmin, L., Weidong, L., Guozheng, X., et al.: Development of high-voltage fast transfer switch. High Voltage Apparatus 2003 39(6), 6–7 (2003) 6. Yukimori, K., Kenichi, K., Hiroyuki, S., et al.: Development of the high speed switch and its application. In: Conference Record of IAS Annual Meeting (IEEE Industry Applications Society) (1998) 7. Li, Y.: The development of large current vacuum circuit breaker and research on the key technologies. Shenyang University of Technology (2010)
Discussion on Mechanism of the Gas Medium on Self-breakdown Probability of High-Power Gas Switch Xianfei Liu and Xuandong Liu(B) State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China [email protected]
Abstract. The gas switch is an important component of any pulse power facility. In this paper, the self-breakdown voltage of multi-gap multi-channel gas switch (MMGS) with corona needles filled with different gas medium are studied. The experiment results show that the self-breakdown probability of MMGS is obviously reduced by adding a small amount of strong electronegative gas like SF6 or C4 F7 N into N2 . Then we discuss self-breakdown process of MMGS with uniform voltage distribution between gaps, which can be divided into an initial electron generation stage, electron development stage, and streamer stage. We compare the effects of different gas media on the self-breakdown process. The mixture of nitrogen and trace strong electro-negative gases such as SF6 and C4 F7 N is shown to suppress discharge development in the first and second stages of the self-breakdown process. The mechanism at work in this process is defined accordingly. The results presented here may provide sound guidance for reducing the self-breakdown probability in high-power gas switches according to the switch gas medium. Keywords: Gas switch · Gas medium · Self-breakdown
1 Introduction The switch is an important component in pulse power facilities such as linear transformer drives and X-ray sources [1, 2]. It is triggered in a specific timing sequence to generate high-power pulses which control the overall stability of the facility. The high-power gas switch has grown increasingly common as pulse power facilities grow increasingly higher-powered. The gas switch must be trigged stably and reliably while functioning over a sufficiently long service life, which necessitates mitigating the self-breakdown problem. Self-breakdown is a common occurrence in the switches designed by Kinetech, L3, HCEI, Xi’an Jiaotong University, Northwest Institute of Nuclear Technology and other international engineering teams [3]. The self-breakdown probability of the switch designed by Sandia and L-3 is even close to 1%. The multi gap multi-channel gas © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 474–486, 2022. https://doi.org/10.1007/978-981-19-1528-4_47
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switch (MMGS) proposed by the researchers in HCEI has good working performance, The experiments showed that its self-breakdown probability has been reduced to 10–3 , which still falls far below the engineering application requirements of 10–6 [4]. There is a noteworthy distinction between self-breakdown and insulation damage effects. The main cause of insulation damage (e.g., surface flashover) is an unreasonable switch insulation structure design. Insulation damage is irreversible and accumulative, making the switch more likely to discharge along a previously sliding discharge channel and thus exacerbating the damage. Self-breakdown (also referred to as “pre-fire”) is an abnormal discharge of the gas switch that occurs when the switch is charging or waiting to be triggered. Self-breakdown is a random occurrence due to the inherent properties of gas discharge. At present, it is not considered to be accumulative; the switch may work normally for a long time after a self-breakdown occurs. Many researchers have attempted to remedy these technical problems. According to the previous research, there are three types of pre-fire that may occur in the MMGS, as shown in Table 1. First, as the voltage of the switch gaps is distributed through the surface resistance of the switch chamber, the resistance along the surface of the chamber is uneven; there may be over-high voltage in one gap that induces a breakdown of the switch. Kim et al. attempted to resolve this by equipping an MMGS with corona needles which provide a corona discharge current through the switch to balance the voltage of each gap [5]. The Northwest Institute of Nuclear Technology in China used mounting resistors and capacitors in parallel with switch gaps [6]. The other two types of pre-fire involve the even distribution of voltage between the switch gaps where the switch still breaks down at low voltage. The second type is caused by free particles in the switch chamber formed by electrode erosion. The third is caused by pits and burrs on the electrode surface that are also attributable to electrode erosion. These pits/burrs or particles enhance the electric field and partial discharge, inducing breakdown in the gas gap [7]. Both of these pre-fire types are related to electrode erosion, so the measures used to mitigate them generally centers on erosion-resistant electrode materials. This approach only suppresses the electron emission, however, which does not completely eliminate the self-breakdown problem. Table 1. Main causes of gas switch pre-fires. Pre-fire type
Main cause
I
Uneven voltage distribution
II
Free particles in switch chamber formed by electrode erosion
III
Pits and burrs on electrode surface due to electrode erosion
Although there has been some research on the self-breakdown of MMGS and some solutions have been proposed, there is still no effective measures for pre-fire caused by electrode erosion and researchers have not given any possible measures to suppress the self-breakdown of the switch from the perspective of gas medium of the switch. Previous research has shown that the gas medium has a great influence on the corona
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characteristics of MMGS with corona needles, but there is no adequate study and explanation of influence and mechanism of gas medium on self-breakdown probability of the switch, which is of great significance to the selection of switch gas medium and the stable operation of the switch. In the present study, we focus on the self-breakdown process of MMGS with uniform voltage distribution between gaps. The self-breakdown voltage of single switch gap with different needle length are studied. Then the self-breakdown probability of MMGS filled with different gas medium are calculated through Weibull distribution. Furthermore, simulation studies have been carried out to help explain the role of corona needle of the switch. Finally, combining with the experiment results, the mechanism of the gas medium on self-breakdown probability of high-power gas switch is proposed. Our goals are to understand the suppression mechanism of the gas medium on self-breakdown in the high-power gas switch and to resolve the self-breakdown problem from the source.
2 Experiment Setup 2.1 Structure of MMGS The structure of MMGS with corona needles is shown in Fig. 1. The switch has six gaps and the length of each gap is 5 mm. The middle electrode is 20 mm. The corona needles with diameter of 0.9 mm and length of 6 mm are screwed at the center of negative electrode of each single gap. The tip of the needle is 2 mm lower than the annular electrode flat surface. The electrodes and needles are all made of stainless steel. The insulator of the switch is made of PMMA.
Fig. 1. Configuration of MMGS with corona needles
2.2 Test Platform of Self-breakdown Voltage The test stand of MMGS with corona needles is shown in Fig. 2. The capacitors are charged by ±120kV DC high voltage source automatically via the 20M resistor until the switch breaks down. The load resistance is a water resistor. For the experiment of single switch gap, MMGS is replaced by the single switch gap with corona needle.
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Fig. 2. Test stand of the self-breakdown voltage of MMGS with corona needles
3 Experimental Results 3.1 Self-breakdown Voltage of Single Switch Gap Figure 3 presents the self-breakdown voltage of single switch gap with the needle length from 4 mm to 12 mm under 1 atm dry air. Three kinds of corona needle with diameter 0.7 mm, 0.9 mm and 1.3 mm were used. The radius curvature of the needle tip is 29 µm, 74 µm and 217 µm. The experiment results shows that the application of the corona needle does not reduce the breakdown voltage of the gap.
Fig. 3. Self-breakdown voltage of single switch gap with different needle length
Note that the arc still develops between annular electrodes, not starts from the tip of the needle when the signal switch gap with the needle length from 4 mm to 12 mm breaks down. But when the needle length increases to 15 mm, the arc will develop from the tip of the needle to the plate of anode. 3.2 Self-breakdown Probability of MMGS In our previous work [8], we observed a significant difference in the self-breakdown probability of MMGS with corona needles under different gas media even when the
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switch electrodes were all fabricated from ordinary stainless steel. The switch was filled with 0.1–0.3 MPa of various gas media and its self-breakdown voltage was adjusted to about 200 kV, then it was fired in self-breakdown mode for 100 shots. The results showed that self-breakdown occurs at low voltage when the MMGS is filled with N2 but no such low-voltage breakdown occurs under SF6 /N2 and C4 F7 N/N2 conditions, as shown in Fig. 4. The dispersion of the self-breakdown voltage when the switch was filled with these two gas mixtures was also lower than with three other gas media. The volume fraction of SF6 or C4 F7 N in the gas mixture was 1%, 3%, and 5%.
(a) N2
(b) C4F7N/N2
Fig. 4. Self-breakdown voltage of MMGS with corona needles filled with N2 and C4 F7 N/N2
According to IEEE Std 930–2004, Weibull distribution can be used in the fitting of the distribution of electrical breakdown data. Therefore, we also adopted Weibull distribution to fit the self-breakdown voltage distribution and then calculate the self-breakdown probability of MMGS filled with different gas medium. The probability density function of Weibull distribution and Weibull cumulative distribution function are shown in Eq. (1) and Eq. (2), b u b−1 exp{−( au )b } u ≥ 0 f (u, a, b) = a a (1) 0 u0, β = 1− Ld Ld
(8)
And then: π
dξ = α cos θ − 2β sin 2θ > 0, θ ∈ 0, dθ 2 π
ξ =α−β >0 2 π ξ > |ξ (0)| 2
(9) (10) (11)
It can be concluded that the torque is sensitive to frame deviation, especially entering into deep flux-weakening. Paper [19] proposes a dynamic compensation strategy to compensate the phase and amplitude of the output voltage. 2.3 Calculation of Motor Losses The composition and mechanism of motor losses are complex. The calculation of motor losses will affect the power analysis, which in turn affects the accuracy of the estimated torque. Copper Loss Fundamental copper loss is mainly affected by temperature, which can be obtained by (12), (13): (12)
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(13)
where Tbase , T , μ, Rbase , Rdc are room temperature, actual temperature, constant coefficient, DC resistance at room temperature and actual temperature respectively. However, after injecting high-frequency alternating current, additional resistance will be generated due to the skin effect and proximity effect, which causes additional copper loss. Due to many non-ideal factors during the production and offline operation of stator, it is difficult to accurately calculate the high-frequency copper loss. Iron Loss Iron loss accounts for a large part of the total losses with complicated mechanism, which cannot be ignored at high frequency especially. It can be divided into three parts: hysteresis loss, eddy loss, and additional loss. Caused by the main flux, hysteresis loss and eddy loss can be roughly calculated by (14):
K h (14) + Ke PFe_base = 1.5wr2 λ2d + λ2q wr where Kh , Ke are hysteresis and eddy coefficient respectively. Under a sine-distributed magnetic field, the iron loss can be roughly calculated by (15) [20] considering the additional loss. α 2 1.5 + Kc f 2 Bm + Ke f 1.5 Bm PFe = Kh fBm
(15)
where Kc , f , Bm , α are the additional loss coefficient, frequency, magnetic density amplitude, and constant coefficient respectively. The coefficients involved are approximate indicators provided by the manufacturers. In addition, stamping, lamination, heating and other operations in the process of manufacturing cannot be ignored, which are difficult to be quantified. Besides, part of the active power is converted into stray loss and mechanical loss, which are also difficult to be accurately calculated by mathematical models.
3 Torque Estimation and Multiple Closed-Loop Control The current closed-loop control cannot suppress the interference of the frame deviation and the change of the motor losses on the torque accuracy. In order to solve the above problems, this paper performs power analysis based on calibration experiments, and proposes a new torque estimation method. Then a multiple closed-loop control strategy is designed to realize the correction of the preset current angle and amplitude. 3.1 Torque Estimation The active power injected by the inverter into the motor can be calculated by (16) (16) Pinv = 1.5 id ud + iq uq
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Part of the active power is converted into mechanical power to produce output torque, and the rest is consumed by various motor losses, which satisfies (17) Pinv = Pm + Ploss
(17)
Ploss = PCu_base + PFe_base + Pstr + Pfw
(18)
where motor losses satisfy (18)
Paper [14] estimates the output torque by calculating the mechanical power: T = 9550Pm /n
(19)
It involves the calculation of various motor loss, which cannot be obtained accurately because of many non-ideal factors. We recorded the output mechanical power, active power, and fundamental copper loss of PMSM under various working conditions through calibration experiments, and introduces the parameter Pic that satisfies (20) Pic = Pinv − PCu_base = Pm + PFe_base + Pstr + Pfw
(20)
Analyzing the data, the non-linear fitting relationship between power parameter and output mechanical power can be obtained, and a look-up table model can be established to realize the accurate observation of torque. Although the active power and fundamental copper loss can be affected by the temperature fluctuation, their calculation only depends on the actual detected electric vector and temperature signal, so the influence of temperature has been taken into consideration. Therefore, this method not only avoids the complex and inaccurate calculation of motor losses, but also is a feasible method to avoid the influence of temperature on torque estimation. For (14), due to the inaccuracy of Ke and Kh , it is difficult to get the accurate result of iron loss. However, under the same working conditions, the iron loss is mainly affected by the flux linkage. The consistency of the iron loss during mass production can be ensured by specific adjustment of the output voltage of each working condition. This action makes the calibration between Pic and Pm of a certain motor have a strong universality. 3.2 Multi-closed-Loop Control Strategy The actual output torque will be affected by the deviation of frame, temperature fluctuation and inconsistency of motor products, which cannot be solved by the current closed-loop. This paper proposes a multi-closed-loop control strategy for the torque, and its structure is shown in Fig. 3.
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Fig. 3. Power-voltage-current closed loop control strategy
The adjustment process of current after introducing frame deviation and rising temperature can be seen in Fig. 4.
Fig. 4. Current adjustment after introducing frame deviation and rising temperature
Hypothetically, the current command obtained by optimized trajectory in the ideal dq axis is I1 corresponding the ideal output torque T1 and the voltage limit curve. I1 will change to I2 if there is certain frame deviation, as shown in Fig. 4(a). As a result, the actual torque is reduced to T2 and the current no longer reaches the inverter voltage limit at the speed. The voltage closed loop detects the deviation between the given voltage us_cali and the feedback voltage us . And the PI controller outputs the compensation θ for the initial current angle, until the current is readjusted to I1 . Under the same working condition, eliminating the effect of frame deviation, the voltage limit curve will shrink due to the increase of motor temperature, as shown in Fig. 4(b). I1 can be adjusted to I2 by voltage closed loop to meet the voltage limit again, but actual torque will be reduced to T2 . The power closed loop detects the deviation between the given parameter Pic_ref and the feedback Pic . And the PI controller outputs the compensation Is for the initial current amplitude, until the current reaches to I3 .
4 Simulation To verify the torque estimation method and multi-closed loop control strategy in Matlab/Simulink. In speed mode, the motor’s given speed is 100 rad/s, number of pole pairs
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is 4, d-axis inductance is 0.0002H, q-axis inductance is 0.0004H. Initial stator resistance is 0.02 and initial magnet flux linkage is 0.04962web. PMSM’s output torque is stable at 41.8 N. Sampling the current and rotor position with delay in Fig. 5(a), raising the stator resistance to 0.03 and then reducing the permanent magnet flux to 0.04web in Fig. 5(b), the simulation results show that the torque estimation method and control strategy in this paper can effectively suppress the interference of the above factors to ensure the accuracy.
Fig. 5. Output torque after introducing frame deviation and raising temperature
Due to delayed sampling, actual current angle increases and then output torque decreases. Keeping the current amplitude and reducing the current angle, as shown in Fig. 6, the accuracy of torque is maintained under the adjustment of voltage closed-loop. When temperature rising, stator resistance increases, permanent magnet flux linkage decreases and then output torque goes down. The power closed loop adjusts the current amplitude, meanwhile, the current angle is adjusted by voltage closed loop, as shown in Fig. 7.
Fig. 6. Actual current angle and amplitude after introducing frame deviation
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Fig. 7. Actual current angle and amplitude after raising temperature
5 Experiment In order to verify the strategy proposed in this paper, a PMSM control system based on NXP MPC5744P was developed. The motor parameters are shown in Table 1. The experimental devices are shown in Fig. 8.
Fig. 8. Photo of bench test
Under the current closed-loop control, the effect of frame deviation and temperature rise on the output torque accuracy is verified. The results are shown in Fig. 9. With the working condition of given speed of 6000 rpm and torque of 100 N, when the frame deviates by 3°, the output torque obviously deviates from the value when the frame is accurate. In addition, the temperature rise of the motor caused by long-time work also reduces the actual torque to a certain extent. Therefore, the current closed-loop control cannot meet the demand for high-precision requirement. In order to verify that the control strategy can effectively eliminate the influence of the above factors on the accuracy, experiments of raising motor temperature and modifying the preset current command are performed under the same working condition. As shown in Fig. 10, after running for a while under the state of power open-loop, the motor’s temperature gradually increases, as a result, the actual torque decreases slowly. Power closed-loop is switched in the program at 150 s, and a deviation between the given power parameter and the actual feedback power parameter is detected. Subsequently, the PI controller outputs the compensation of the given current amplitude and corrects the current angle under the control of the voltage inner loop until the voltage constraint of the working condition is satisfied. The results show that the final output torque is effectively adjusted.
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Values
Number of pole pairs p
4
DC resistance of stator Rs /m
17.4
Magnet flux linkage λPM /web
0.07875
d-axis inductance Ld /uH
123.03
q-axis inductance Lq /uH
246.30
DC linkage voltage Udc /V
360
Rated current Isn /A
119
Rated voltage Vn /V
316
Rated speed N /rpm
7000
Peak torque Tpeak /N · m
158
Fig. 9. Influence of frame deviation and temperature rise
Fig. 10. Experiment of motor temperature rise
Under the same working condition, a certain deviation is introduced for the initial given current amplitude and angle. It can characterize the deviation of the frame and the consistency problem caused by mass production to a certain extent. As shown in
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Fig. 11, the given torque is 100 N. When the power closed-loop is not working, the actual torque deviates by 6 N. After cutting into the power closed-loop, the actual output torque stabilizes at around 100 N.
Fig. 11. Experiment of modifying the preset current
6 Conclusion The accuracy of the actual output torque of PMSM is affected by many non-ideal factors such as the deviation of frame, the fluctuation of the motor loss, and the parameter inconsistency during the mass production. In this paper, a high-accuracy torque estimation method is proposed, which can cope with the fluctuation of temperature. And a multiclosed-loop control strategy is designed to realize the adjustment of the preset current amplitude and angle, so as to ensure the accuracy of the actual control of the output torque. Both simulation and experimental results verify the effectiveness and feasibility of this control strategy.
References 1. Yao Yingbei, L., Yesheng, J.F., et al.: Electric vehicle development trends and electricity demand forecast in East China. Power Syst. Prot. Control 49(04), 141–145 (2021). (in Chinese) 2. Tiantian, Q., Yaping, L., Xiaorui, G., et al.: Calculation of electric vehicle charging power and evaluation of demand response potential based on spatial and temporal activity model. Power Syst. Protect. Control 46(23), 127–134 (2018). (in Chinese) 3. Lingling, M., Jun, Y., Cong, F., et al.: Review on impact of electric car charging and discharging on power grid. Power Syst. Prot. Control 41(03), 140–148 (2013). (in Chinese) 4. Yongbin, Z., Zhen, L.: Summarization for flux-weakening performance of permanent magnet synchronous motors applied in electric vehicle. Electr. Eng. (10), 1–7+67 (2015). (in Chinese) 5. Jahns, T.M., Kliman, G.B., Neumann, T.W.: Interior permanent-magnet synchronous motors for adjustable-speed drives. IEEE Trans. Ind. Appl. IA-22(4), 738–747 (1986) 6. Jahns, T.M.: Flux-weakening regime operation of an interior permanent-magnet synchronous motor drive. IEEE Trans. Ind. Appl. IA-23(4), 681–689 (1987)
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7. Morimoto, S., Takeda, Y., Hirasa, T., Taniguchi, K.: Expansion of operating limits for permanent magnet motor by current vector control considering inverter capacity. IEEE Trans. Ind. Appl. 26(5), 866–871 (1990) 8. Yucheng, B., Xiaoqi, T., Gongping, W.: Speed control of flux weakening on interior permanent magnet synch-ronous motors. Trans. China Electrotech. Soc. 26(09), 54–59 (2011). (in Chinese) 9. Hu, D., Xu, L.: Characterizing the torque lookup table of an IPM machine for automotive application. In: 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 31 August–3 September 2014, pp. 1–6 (2014) 10. Zhang, B., Wen, X., Gong, X., Guo, J.: A wide range speed of PMSM constant voltage generation PWM rectifier systems research. In: 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 31 August–3 September 2014, pp. 1–6 (2014) 11. Lei, Z., Xuhui, W., Feng, Z., Liang, K., Baocang, Z.: Deep field-weakening control of PMSMs for both motion and generation operation. In: 2011 International Conference on Electrical Machines and Systems, 20–23 August 2011, pp. 1–5 (2011) 12. Taherzadeh, M., Hamida, M.A., Ghanes, M., Koteich, M.: A new torque observation technique for a PMSM considering unknown magnetic conditions. IEEE Trans. Ind. Electron. 68(3), 1961–1971 (2021) 13. Se-Kyo, C., Hyun-Soo, K., Chang-Gyun, K., Myung-Joong, Y.: A new instantaneous torque control of PM synchronous motor for high-performance direct-drive applications. IEEE Trans. Power Electron. 13(3), 388–400 (1998) 14. Huang, W., Zhang, Y., Zhang, X., Sun, G.: Accurate torque control of interior permanent magnet synchronous machine. IEEE Trans. Energy Convers. 29(1), 29–37 (2014) 15. Zhigang, Z., Jia, L., Ying, G., et al.: Investigation on the improved loss model of magnetic materials under non-sinusoidal excitation environment. Trans. China Electrotech. Soc. 34(13), 2693–2699 (2019). (in Chinese) 16. Xiaojun, Z., Can, C., Lin, L., et al.: Analysis of the hysteresis and loss characteristics in the laminated core by fixed-point harmonic-balanced method. Trans. China Electrotech. Soc. 29(07), 10–18 (2014). (in Chinese) 17. Zhigang, Z., Man, X., Xinjian, H.: Magnetic losses characteristics of ferromagnetic materials based on improvement loss separation model. Trans. China Electrotech. Soc. 1–9 (2021). (in Chinese) 18. Lee, J.H., Sul, S.K.: Compensation of nonlinearity of inverter through estimation of dead time effect. In: 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 29 November–2 December 2020, pp. 572–577 (2020) 19. Lv, W., Huang, K., Wu, H., Xiaoling, M.O., Shen, M.: A dynamic compensation method for time delay effects of high-speed PMSM sensorless digital drive system. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), 11–14 August 2019, pp. 1–5 (2019) 20. Bertotti, G.: General properties of power losses in soft ferromagnetic materials. IEEE Trans. Mag. 24(1), 621–630 (1988)
The Introduction of Dissociation Term in Numerical Simulation of Trichel Pulses in Air Mengting Han(B) , Ziqing Guo, Qizheng Ye, and XiaoFei Nie School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China [email protected]
Abstract. In this paper, a 2D axisymmetric model of corona discharge is established to simulate the Trichel pulses in the needle-plate gap at atmospheric pressure. We take into account the negative ions dissociation process in our model. The addition of dissociation term helps to balance with the attachment term and keeps the density of negative ions in a reasonable range. Then we obtain the discharge parameters such as the movement of charged particles and the distribution of electric field in Trichel pulses. The physical process of the rising edge, the falling edge and the extinction of the pulses is discussed in detail. In addition, the variation of amplitude and frequency of discharge pulse versus applied voltage is also presented. Our study on Trichel pulses helps to further understand the physical mechanism of corona discharge and provide theoretical guidance for the design of insulating medium. Keywords: Trichel pulses · Numerical simulation · Dissociation term · Corona discharge · Needle-plate short gap
1 Introduction Corona discharge is a typical form of discharge in extremely inhomogeneous field. During the operation of electrical equipment, corona discharge is easy to occur in the geometric structure with small curvature radius [1]. When corona discharge occurs, the gap can still withstand the voltage and maintain the insulating property. However, the existence of corona discharge causes huge power loss, chemical corrosion and aging of electrical equipment [2, 3]. The repeated disappearance and appearance of discharge pulses will form high-frequency electromagnetic wave, leading to problems such as radio interference and noise interference [4, 5]. In recent years, more and more attention has been paid to corona discharge. In 1938, Trichel found the regular current pulses in the negative corona discharge and named it Trichel pulses [6]. Different from the positive corona discharge, the shape of Trichel pulses is similar, the amplitude and frequency of the Trichel pulse are regular. Therefore, the research on Trichel pulse has become a focus of the researches on corona discharge. There are many researches on the formation mechanism of Trichel pulse, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 535–546, 2022. https://doi.org/10.1007/978-981-19-1528-4_53
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variation of amplitude and frequency under the influence of discharge parameters [7– 9] and external condition [10-14] such as geometric characteristics, swarm parameters, pressure, humidity, temperature, etc. Numerical simulation is one of the important methods to study Trichel pulses. The microscopic parameters, including the movement of charged particles and the distribution of electric field can be obtained through numerical simulation [15–19], which are difficult to measure in experiments. However, the microsecond time scale of Trichel pulses has higher requirements for the accuracy and convergence of model. In the previous numerical simulations, if the density of negative ions left by the last discharge tends to be too high, the space electric field generated by the negative ions will reverse the electric field near the tip. This is not consistent with the real process and also suppresses the convergence of the model. This paper presents a numerical investigation of Trichel pulses in the atmosphere. In Sect. 2, the details of the numerical model are described. In Sect. 3, we simplify the dissociation reaction and select a reasonable dissociation coefficient. The addition of dissociation term helps to balance with the attachment term and keeps the density of negative ions in a reasonable range. In Sect. 4, we present the movement of charged particles and distribution of electric field in a single current pulse. And the variation of amplitude and frequency of Trichel pulses with the applied voltage are obtained. The simulation results are consistent with the experiment. The Sect. 5 is the summary of this paper.
2 The Model 2.1 Governing Equations The transport process of non-thermal plasma is usually described by a set of classical fluid equations and the coupled Poisson equation of electrostatic field. The fluid equations consist of particle time-varying term, drift-diffusion term and source term. The governing equations in this paper are shown in Eq. (1). − − − → → → ∂Ne W − ηN W = S + ∇ · N − D ∇N + αN W e e e e e e e e − βep Ne Np + kd NO2 Nn ph ∂t ∂Np αNe We − βep Ne Np − βnp Nn Np ∂t + ∇ · Np Wp − Dp ∇Np = Sph + − → ∂Nn ∂t + ∇ · Nn Wn − Dn ∇Nn = ηNe We − βnp Nn Np − kd NO2 Nn ∇ · (−ε0 εr ∇u) = e Np − Ne − Nn (1) e, where, N e , N p and N n are the density of electrons, positive ions and negative ions, W W p, W n and De, Dp, Dn are the drift velocities and diffusion coefficients, α, η, β are ionization coefficient, attachment coefficient, recombination coefficients for electronion and ion-ion respectively, t is time, e is electronic charge,ε0εr is the permittivity of air, u is the electric potential, S ph is the photoionization term, and k d is the dissociation coefficient, which will be discussed at length in Sect. 3.
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2.2 Swarm Parameters The swarm parameters in this paper are slightly modified based on the parameters proposed by Kang [20], as shown in Table 1. Table 1. swarm Parameters, E in (V/m) Parameter
Value
Unit
α
3500*exp(−1.66*105 /E)
1/cm
η
15*exp(−2.5*104 /E)
1/cm
We
−6060*E0.75
cm/s
Wp
2.43*E
cm/s
Wn
−2.70*E
cm/s
βep
2*10–7
cm3 /s
βnp
2*10–7
cm3 /s
De
1800
cm2 /s
Dp
0.028
cm2 /s
Dn
0.043
cm2 /s
As for the photoionization term, the classical integral model proposed by Zheleznyak is widely used [21], as shown in Eq. (2). Pq ωNe ve 1 (2) f (|r − r1 |P) Sph = d 3 r1 |r − r1 | 4π P + Pq where, r1 and r are source coordinates and field coordinates respectively, P is atmospheric pressure, Pq is attenuation pressure of excited nitrogen atoms, a constant between ω is → r1 P represents the 0.06 and 0.12, which is taken as 0.1 in this paper, and f r − − photoionization absorption coefficient. The photoionization can be calculated by Eddington approximation method. After the transformation to the cylindrical coordinate system, Eq. (3) is obtained as follows. Pq rm rm e−k1 Po 2 r− e−k2 P0 2 r Sph = ωVr Ne ve (Vr = dr) (3) P + Pq 2 0 r log kk21 Referring to the relevant literature [22], the photoionization within the sphere 0.02 cm away from the source is effective, and V r is calculated to be 63.78. 2.3 Geometric Dimensions and Initial Conditions The air gap length is 6 mm and the computational domain is 5 cm * 5 cm. The shape of the tip is controlled by the hyperbolic equation [23], and the radius of curvature is set to 35 um, as shown in Fig. 1.
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Fig. 1. Simulation geometry
Table 2. Boundary conditionals
u Ne Np Nn
Boundary1
Boundary2
Boundary3
u=0 e ne − De ∇ne = 0) (n · W
∂u = 0 ∂r
u = Us
n · (−De ∇ne ) = 0 n · −Dp ∇np = 0
(n · (−De ∇ne ) = 0) p np − Dp ∇np = 0) (n · W
n · (−Dn ∇nn ) = 0
(n · (−Dn ∇nn ) = 0)
(n · −Dp ∇np = 0) n nn − Dn ∇nn = 0) (n · W
The boundary conditions are shown in Table 2. γ is the secondary electron emission coefficient, which is set to 0.01. U s is the applied voltage, which is set as 2.9 kV. Set the same distribution of electrons and positive ions as initial particles, as shown in Eq. (4). (r)2 (z − z0)2 (4) Ne− 0 = Np− 0 = Nmax exp − 2 − 2σr 2σz2 where, Nmax = 1010 cm−3 , σ r = σ z = 62.5 um.
3 Dissociation Term 3.1 Dissociation Reaction R1:e + O2 → O− + O R2:e + O2 + M → O2− + M
R3:O− + O → e + O2 ∗ R4:O2− + M → O2− + M → e + O2 + M R5:O3− + M → e + Products
(5)
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As shown in Eq. (5), the common electron attachment includes separation attachment R1 and three-body attachment R2. R3 and R4 are dissociation reactions corresponding to the separation attachment and the three-body attachment respectively [24]. In addition, there is a heavy particle dissociation reaction R5, as shown in Eq. (5). The dissociation is the process in which the negative ions lose electrons and turn into the neutral particles. The dissociation reaction is the back reaction of the electron attachment. Ponomarev [26] believe that the rapid charge transfer reactions will make O2− replace − O rapidly, so R3 can be ignored to some extent. And they find that the reaction rate of M = N2 is one or two orders of magnitude lower than that of M = O2 in R4. So the case of M = N2 in R4 can be ignored. In addition, R5 is considered to have an important influence only when the electric field is lower than the corona onset electric field [27]. When the electric field is higher than the corona onset electric field, R5 can be ignored because the density of O3− particles is too low. To sum up, the dissociation process in air can be simplified to Eq. (6). O2− + O2 → e + 2O2
(6)
3.2 Dissociation Coefficient Dissociation reaction and attachment reaction can establish the balance of negative ion density to a certain extent, which will have an important influence on conductivity and plasma properties [25]. In the previous numerical simulation, if the process of negative ions generated by the discharge pulse cannot achieve balance, the density of negative ions tends to be too high. And then the space electric field generated by the negative ions reverses the electric field near the needle tip, causing the negative ions to move towards the tip and even be absorbed by the tip. This is not consistent with the real process and also reduces the convergence of the model. Therefore, it is necessary to introduce a negative ion dissociation process which can balance with the attachment reaction. Scholars have done a lot of research on the dissociation process of negative ions in air. At present, there are mainly two methods to deal with the dissociation coefficient. One is to give the empirical formula of dissociation coefficient changing with the electric field [25], as shown in Fig. 2. The other is to set dissociation coefficient as a constant independent of the electric field [28]. The dissociation coefficient is often set in the range of 10–15 –10−10 cm3 /s. In this paper, we adopt the fitting curve of the result of Ponomarev. Because this fitting curve only contains the result of E/N > 50 Td, we set dissociation coefficient as 5e14 cm3 /s when E/N < 50 Td, as shown in Eq. (7). when E/N < 50Td , kd = 5e−14 when E/N ≥ 50Td , kd =
1.1e−10 exp
2.5 450 − 150+E/N
(7)
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Fig. 2. Dissociation reaction rate [25]
4 Simulation Result 4.1 Single Pulse Discharge As shown in Eq. (8), the cathode flux method is used to calculate the discharge current [29]. This method is based on the rate when the charged particles pass through the cathode and the integral region is the cathode surface. − → − → − → (8) I= sign(i) · ee Ne We + sign(i) · ep Np Wp + sign(i) · en Nn Wn · d S
Discharge current (mA)
The discharge current is shown in Fig. 3. Each discharge pulse contains three stages. The rising edge of pulse corresponds to discharge development stage and the falling edge of pulse corresponds to discharge suppression stage and the pulse extinction corresponds to discharge extinction stage. In this section, the physical process of the rising edge, the falling edge and the extinction of the pulses is discussed in detail.
Rising edge
Falling edge
Pulse extinction
Time (us)
Fig. 3. Discharge current when the applied voltage is 2.9 kV.
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Discharge Development Stage: As shown in Fig. 4, the discharge is during the rising stage when t = 0.03 us. At the initial time, positive ions and electrons are identically distributed. Then electrons move rapidly to the plate, during which collision ionization occurs and electrons near the tip begin to increase rapidly. The discharge is accelerated to nanosecond time scale. Meanwhile, a large number of positive ions are generated, which enhances the electric field near the tip. During this discharge development stage, the positive ions absorbed by the tip are less than those generated by ionization, so the density of positive ions keeps increasing. The negative ions are generated due to electron attachment. However, their inhibitory effect on the electric field near tip is not obvious because of the existence of positive ions.
(a) electrons
(b) positive ions
(c) negative ions
(d) electric field
Fig. 4. Distributions of electrons, positive ions, negative ions and electric field when t = 0.03 us.
(a) electrons
(b) positive ions
(c) negative ions
(d) electric field
Fig. 5. Distributions of electrons, positive ions, negative ions and electric field when t = 0.25 us.
Discharge Suppression Stage: As shown in Fig. 5, the discharge is at the peak of the pulse when t = 0.25 us. The discharge current reaches the peak when the positive ions reach the peak. Then the positive ions decrease because the positive ions absorbed by the tip are more than those generated by ionization. Therefore, the discharge enters the falling edge stage, as shown in Fig. 6. The decrease of positive ions leads to a significant decrease in the electric field near the tip. The collision ionization near the tip is suppressed and a lot of electrons form negative ions by attachment during the migration, which leads to the decrease
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(a) electrons
(b) positive ions
(c) negative ions
(d) electric field
Fig. 6. Distributions of electrons, positive ions, negative ions and electric field when t = 0.35 us.
(a) electrons
(b) positive ions
(c) negative ions
(d) electric field
Fig. 7. Distributions of electrons, positive ions, negative ions and electric field when t = 9us.
of electron density during the migration towards the plate. The shape of the electron is also expanding due to the transverse electric field and diffusion. Negative ions move slowly towards the plate and their inhibition effect on the electric field near the tip begins to appear. The electric field near the tip decreases obviously, which is lower than the initial electric field at t = 0 s. The density of each particle decreases gradually, collision ionization stops basically, and the discharge will enter the discharge pulse extinction stage. Discharge Extinction Stage: As shown in Fig. 7, the discharge is during the pulse extinction when t = 9 us. After the last current pulse, the negative ions will remain in the gap for a short time due to its slow drift velocities and long migration distance. During this stage, the negative ions are much more than that other particles. The electric field near the tip is suppressed by the space field generated by negative ions and collision ionization stops basically. After a few microseconds of negative ion migration, negative ions move away from the tip and the density decreases due to the dissociation term, both of which lead to the enhancement of the electric field near the tip. When the electric field is restored to corona onset electric field, the current pulse will not appear immediately. Because the electron density near the tip keeps at a low level. Electrons begin to grow due to secondary electron emission and collision ionization. When the electron density increases, more electrons can be generated by collision ionization and the next discharge pulse is coming.
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4.2 Multiple Pulse Discharge As shown in Fig. 3, the amplitude of the first pulse is about 0.156 mA, and that of the subsequent pulses is about 0.032 mA. The amplitude of the first pulse is higher than that of the subsequent pulse, because there is no residual charge in the gap when the first pulse develops. The residual negative ions of last discharge suppress the development of subsequent discharge pulses. After several pulses, the residual negative ions of last discharge remain almost the same and the subsequent current pulse tends to be stable. This section focuses on amplitude and frequency of subsequent current pulse. Figure 8 shows the variation of current pulse amplitude and frequency with applied voltage. When the voltage is between 2.82 kV and 2.9 kV, the pulse amplitude increases with the increase of applied voltage. When the voltage is between 2.9 kV and 3.2 kV, the pulse amplitude decreases with the increase of voltage. The applied voltage mainly affects the discharge pulse through the collisional ionization. With the increase of applied voltage, the collision ionization is enhanced, and electrons, positive ions, negative ions and electric field also increase. Therefore, the discharge current amplitude increases with the increase of voltage. However, the inhibition effect of negative ions on the next discharge increases with the increase of negative ions, which leads to the decrease of current pulse amplitude. The current pulse frequency increases from 113 kHz to 524 kHz with the increase of voltage. The frequency growth is nonlinear. When the voltage is low, the frequency increases slowly, but when the voltage is high, the frequency increases rapidly. Because the negative ions increase with the increase of the applied voltage, and the time required for the process of negative ions migration also increases. Figure 9 shows amplitude and frequency of the oscilloscope pulse current in previous experiments [30]. The variation of current amplitude and frequency in the experiment is consistent with the simulation, proving the feasibility and accuracy of the simulation model.
Frequency(kHz)
Amplitude(mA)
Amplitude Frequency
Applied voltage(kV)
Fig. 8. Amplitude and frequency versus applied voltage in the simulation.
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Frequency(kHz)
Amplitude(mA)
Amplitude Frequency
Applied voltage(kV)
Fig. 9. Amplitude and frequency versus applied voltage in experiment.
Discharge current (mA)
In addition, if the voltage increases further, the Trichel pulses turns to pulseless discharge. Figure 10 shows the discharge current when the applied voltage is 3.2 kV. The minimum current between pulses is higher than zero, which means that the discharge has begun to develop before the extinction of last discharge pulse. This is at the critical state of Trichel pulses and pulseless discharge.
Time (us)
Fig. 10. Discharge current when the applied voltage is 3.2 kV.
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5 Conclusion (1) We take into account the negative ions dissociation process in our model. The addition of dissociation term helps to balance with the attachment term and keeps the density of negative ions in a reasonable range. This not only makes the simulation closer to the actual discharge process, but also improves the convergence of the model. (2) The distortion effect of positive and negative ions on the electric field is reflected in different stages. In the discharge development stage, the density of positive ions is high, which enhances the electric field near the tip and affects the spatiotemporal evolution of other charged particles. In the discharge extinction stage, the inhibition effect of negative ions on the electric field dominates. With the migration and dissociation of negative ions, the electric field recovers gradually and prepares for the next pulse. (3) The amplitude of Trichel pulses varies in two directions versus the applied voltage. The current amplitude first increases and then decreases when the applied voltage increases. While the frequency of Trichel pulses increases monotonously with the increase of voltage. These are consistant with our previous experiments. When the voltage increases further, the Trichel pulses turns to pulseless discharge.
Acknowledgments. This paper is supported by National Natural Science Foundation of China (52077090). The first author would like to thank Prof. Ye and Dr. Guo for their continuous support.
References 1. Yang, J.: Gas discharge. Science and Technology Press, Beijing (1983).(in Chinese) 2. Raizer, Y., Braun, C.: Gas discharge physics. Appl. Opt. 31, 2400–2401 (1991) 3. Tang, J., Liu, F., Zhang, X.: Partial discharge recognition through an analysis of SF6 decomposition products part 1 decomposition characteristics of SF6 under four different partial discharges. IEEE Trans. Dielectr. Electr. Insul. 19(1), 29–36 (2012) 4. Zhu, D., Yan, Z.: High Voltage Insulation. Tsinghua University Press, Beijing (1992).(in Chinese) 5. Zhang, H., Pang, Q., Chen, X.: The characteristics of high-voltage corona and its detection. Electr. Meas. Instrum. (02), 6–8+31(2006) 6. Trichel, G.: The mechanism of the negative point to plane corona near onset. Phys. Rev. 54(12), 1078–1084 (1938) 7. Li, Z., Zhang, B., He, J.: Specific characteristics of negative corona currents generated in short point-plane gap. Phys. Plasmas 20(9), 093507 (2013) 8. Liu, M., Tang, J., Pan, C.: Development processes of positive and negative dc corona under needle-plate electrode in the air. High Voltage Eng. 42(04), 1018–1027 (2016) 9. Zhang, S., Zhang, B., He, J.: Comparison of direct current and 50 Hz alternating current microscopic corona characteristics on conductors. Phys. Plasmas 21(6), 063503 (2014) 10. Lu, B., Sun, H., Wu, Q.: Characteristics of Trichel pulse parameters in negative corona discharge. IEEE Trans. Plasma Sci. 47(4), 1935–1943 (2017)
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11. Shirai, N., Ichinose, K., Uchida, S.: Influence of liquid temperature on the characteristics of an atmospheric dc glow discharge using a liquid electrode with a miniature helium flow. Plasma Sources Sci. Technol. 20(3), 034013 (2011) 12. Bian, X., Hui, J., Huang, H.: Variation of the characteristics of negative DC corona streamer pulse with air pressure and humidity. Proc. CSEE. 30(10), 134–142 (2010) 13. Li, Z., Zhang, B., He, J.: Influence of gap spacing on the characteristics of Trichel pulse generated in point-to-plane discharge gaps. Phys. Plasmas 21(1), 012113 (2014) 14. Xu, P., Zhang, B., He, J.: Influence of humidity on the characteristics of negative corona discharge in air. Phys. Plasmas 22(9), 093514 (2015) 15. Georghiou, G., Papadakis, A., Morrow, R.: Numerical modelling of atmospheric pressure gas discharges leading to plasma production. J. Phys. D Appl. Phys. 38(20), R303 (2005) 16. Dordizadeh, P., Adamiak, K., Castle, G.: Numerical investigation of the formation of Trichel pulses in a needle-plane geometry. J. Phys. D Appl. Phys. 48, 415203 (2015) 17. Tran, T., Golosnoy, I., Lewin, P.: Numerical modelling of negative discharges in air with experimental validation. J. Phys. D Appl. Phys. 44(1), 015203 (2010) 18. She, C., Li, K., Ni, S.: Transition mechanism of negative DC corona modes in atmospheric air: from Trichel pulses to pulseless glow. Plasma Sources Sci. Technol. 28(5), 055017 (2018) 19. Woo, S.K., Jin, M.P., Yongho, K.: Numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics. IEEE Trans. Plasma Sci. 31(4), 504–510 (2003) 20. Liu, L., Becerra, M.: Application of the position-state separation method to simulate streamer discharges in arbitrary geometries. IEEE Trans. Plasma Sci. 45, 594–602 (2017) 21. Zhang, F., Zeng, R., Yang, X.: Numerical simulation of streamer discharge photoionization process at atmospheric pressure. Chin. J. Electr. Eng. 29(04), 110–116 (2009). (in Chinese) 22. Lama, W.L., Gallo, C.F.: Systematic study of the electrical characteristics of the ‘Trichel’ current pulses from negative needle-to-plane coronas. J. Appl. Phys. 45(1), 103–113 (1974) 23. Pancheshnyi, S.: Effective ionization rate in nitrogen–oxygen mixtures. J. Phys. D Appl. Phys. 46(15), 155201 (2013) 24. Aleksandrov, N.L., Anokhin, E.M.: Electron detachment from O2-ions in oxygen: the effect of vibrational excitation and the effect of electric field. J. Phys. B Atomic Molec. Opt. Phys. 44(11), 115202 (2011) 25. Ponomarev, A.A., Aleksandrov, N.L.: Monte Carlo simulation of electron detachment properties for O2 -ions in oxygen and oxygen: nitrogen mixtures. Plasma Sources Sci. Technol. 24(3), 035001 (2015) 26. Pancheshnyi, S.: Effective ionization rate in nitrogen–oxygen mixtures. J. Phys. D Appl. Phys. 46(15), 155201 (2013) 27. Ardelyan, N.V., Bychkov, V.L., Kosmachevskii, K.V.: On electron attachment and detachment processes in dry air at low and moderate constant electric field. IEEE Trans. Plasma Sci. 45(12), 3118–3124 (2017) 28. Liu, L., Becerra, M.: Gas heating dynamics during leader inception in long air gaps at atmospheric pressure. J. Phys. D Appl. Phys. 50(34), 345202 (2017) 29. Chen, W., Chen, J.: A simulation test method for positive upward leader. Proc. CSEE. 32(10), 22–31 (2012) 30. Guo, Z., Ye, Q., Wang, Y., Han, M.: Study of the development of negative dc corona discharges on the basis of visible digital images. IEEE Trans. Plasma Sci.. 48(7), 2509–2514 (2020)
A High Sensitivity Sensor for Reconstruction of Conductivity Distribution in Region of Interest Zhiwei Tian1 , Yanyan Shi1,2(B) , Feng Fu2 , Yuehui Wu1 , Zhen Gao1 , and Yajun Lou1 1 College of Electronic and Electrical Engineering, Henan Normal University,
Xinxiang 453007, China [email protected] 2 Faculty of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, China
Abstract. Electrical impedance tomography (EIT) is potential in industrial and biomedical applications. It offers an alternative for visualizing conductivity distribution. However, due to soft-field effect of sensitive field and ill-posedness of reconstruction, it is rather difficult to obtain high imaging quality. Different from EIT with traditional uniform arrangement of electrodes, this work proposes a novel high sensitivity sensor with offset electrode arrangement. It is supposed to enhance sensitivity in region of interest of a head-shaped region. Sparse L1 regularization is introduced to address the ill-posed problem. Imaging of inclusions with high conductivity against background is simulated. Simultaneous imaging of inclusions with high conductivity and low conductivity is also performed. Compared with traditional uniform electrode arrangement, the results show that reconstruction quality is enhanced as sensitivity in ROI is improved under the offset arrangement of electrodes. Keywords: electrical impedance tomography · Electrode arrangement · Reconstruction
1 Introduction Electrical impedance tomography (EIT) is an emerging visualization technology [1– 3]. It is used for reconstructing conductivity distribution in the measured region. EIT establishes a sensitive field by injecting current into a pair of electrodes and voltage is measured from remaining neighboring electrodes. Then the visualization of conductivity can be realized by processing these measured voltages. Because of non-invasion, nonradiation and portability, EIT is preferred in a number of industrial and biomedical applications [4, 5]. It is common knowledge that the number of voltages measured from the electrodes is far less than the number of pixels in the area to be reconstructed. So reconstruction in EIT is an ill-posed problem. Besides, the sensitive field of EIT is soft-field. In order to address above problems, a variety of method has been proposed and high-quality reconstruction © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 547–554, 2022. https://doi.org/10.1007/978-981-19-1528-4_54
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is obtained. In Ref [6], seven typical data preprocessing methods for EIT are reviewed. The reconstructed image acquired by these methods is compared by simulation. The results show that data preprocessing is helpful for improving image quality. In addition, by adding hard-field modulation, soft-field effect in EIT is effectively reduced. In Ref [7], an ultrasound modulated EIT is presented. However, there are some limitations in the above methods. If less information is included in the measured voltage, those methods would not be satisfactory. For example, the information contained in the measured voltage is counteracted when conductivity of inclusions changes in opposite against the background. The ultrasound modulated EIT is complicated in hardware design. Note that sensitivity sensor in most studies is uniformly distributed around detected region. In some applications such as in biomedical area, the location of disease can be identified in advance [8, 9]. The prior knowledge provides an important guideline for optimization of sensor configuration. In this work, a novel sensor arrangement with high sensitivity in region of interest (ROI) is proposed. In addition, reconstruction of conductivity distribution in a head-shaped region is performed with sparse L1 regularization method.
2 Mathematical Model of EIT According to known injection current and conductivity distribution, forward problem of EIT is to calculate boundary voltage on measuring electrodes [10]. Complete electrode model [11] is used to describe the forward problem: ∇ · (σ (x)∇φ(x)) = 0, x ∈ where σ is conductivity and φ is electrical potential, is measured region. Boundary conditions are expressed by ⎧ L ⎪ ∂φ(x) ⎪ ⎪ σ (x) = 0, x ∈ ∂\ el ⎪ ⎪ ⎪ ∂n ⎪ ⎪ l=1 ⎨ ∂φ(x) = ψl , x ∈ el u(x) + zl σ (x) ⎪ ⎪ ∂n ⎪ ⎪ ⎪ ⎪ ∂φ(x) ⎪ ⎪ dS = Il ⎩ σ (x) ∂n el
(1)
(2)
where L is the number of electrodes, zl , ψ l and I l respectively denote contact impedance, electric potential and injected current on the electrode el . Besides, charge conservation and potential reference level are fulfilled as L l=1
Il = 0 and
L
ψl = 0
(3)
l=1
Compared with analytical method, finite element method (FEM) is more superior when solving complicated region [12, 13]. By discretization, forward problem can be rewritten as U = Aσ
(4)
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where U denotes variation of boundary voltage, A is sensitivity matrix which indicates sensitivity of boundary voltage to conductivity variation, σ is conductivity perturbation. From (4), it is found that conductivity distribution can be estimated if sensitivity matrix and boundary voltage are known which is defined as inverse problem [14, 15]. As mentioned in Sect. 1, image reconstruction in EIT is an ill-posed problem [16, 17]. In order to solve this problem, L1 regularization method is introduced as
λ (5) gˆ = arg min Ag − b22 + g1 2 g where gˆ is the estimated conductivity, g and b respectively represent σ and U in (4), λ is regularization parameter used to balance the fidelity term Ag − b22 and the regularization term g1 .
3 Sensor Design In this work, reconstruction of conductivity distribution in ROI of a head-shaped region is studied. Figure 1 shows the model established in Comsol 5.4 which is meshed into triangular elements. As shown in Fig. 1, ROI is located in the upper region which is separated into 1 , 2 and 3 . A finite number of boundary voltages can be measured from adjacent electrode pair when an alternating current is injected into a pair of opposite electrodes [18].
Fig. 1. A model with ROI denoted. Green part is 1 , cyan part is 2 , red part is 3 .
Four different arrangements of electrodes are investigated as shown in Fig. 2. In Fig. 2(a), sixteen electrodes are equidistantly positioned around the model which is commonly used. Figure 2(b) shows asymmetric arrangement of electrodes with fifteen electrodes placed around the upper boundary and one electrode positioned at the lower boundary. A symmetric arrangement of electrodes is illustrated in Fig. 2(c). Two groups of eight electrodes are respectively placed around the boundary of ROI and its symmetric lower position. Figure 2(d) gives the offset arrangement of electrodes with nine electrodes positioned around the boundary of the ROI and other seven electrodes equipped equidistantly around the rest boundary. The sensitivity distribution is also shown in Fig. 2.
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It is seen that sensitivity is uniformly distributed throughout whole sensing domain for uniform arrangement. Comparatively, under other three arrangements, sensitivity distribution is more concentrated in ROI.
Fig. 2. Four different arrangements of electrodes and associated sensitive filed distribution. (a) uniform, (b) asymmetric, (c) symmetric, (d) offset arrangement.
For quantitative comparison, relative sensitivity (RS) is defined as RS =
S i , i = 1, 2, 3 S
(6)
where S i is sensitivity in the ith region and S is sensitivity of the whole area. Relative sensitivity under four electrode arrangements is in Table 1. For uniform arrangement, RS values in the three regions are respectively the lowest. Comparatively, RS values under the symmetrical arrangement are much higher in the three regions. As sensitive filed is mainly distributed in ROI and its symmetrical position, RS values are lower than that for asymmetrical and offset arrangements. RS values in 2 and 3 under offset arrangement are a bit higher while RS value in 1 is a little lower than asymmetrical arrangement. Note that inclusion cannot be accurately reconstructed under the asymmetrical arrangement. Therefore, offset arrangement of electrodes is selected. Table 1. Comparison of relative sensitivity values Arrangement
Uniform
Asymmetric
Symmetric
Offset
1
0.0299
0.0933
0.0523
0.0664
2
0.0725
0.1889
0.1371
0.1917
3
0.0350
0.0757
0.0645
0.0763
Region
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Blur radius (BR) is introduced to evaluate the artifact around the reconstructed inclusion which is defined as At (7) BR= A where At and A are respectively the area of target and whole region.
4 Results and Discussions To avoid inverse crime, square mesh is adopted in image reconstruction [19]. The reconstruction is realized by programming in Matlab 2016a. 4.1 Imaging of an Inclusion with High Conductivity Imaging of an inclusion with high conductivity of 0.8 S/m is simulated, as shown in Fig. 3. It is worth noting that high conductivity here is relative to the background conductivity. Reconstruction under offset arrangement is compared with results under uniform arrangement. The circular or ellipse inclusion is separately positioned in 1 with the center at x = −90 mm and y = 190 mm, 2 at x = 0 mm and y = 190 mm, and 3 x = 90 mm and y = 190 mm. The radius of the circular inclusion is 15 mm. For the ellipse inclusion, the semi-major axis is 30 mm and the semi-minor axis is 15 mm. As shown in Fig. 3, the inclusion is not accurately recovered and artifact is observed in the background under uniform arrangement. In comparison, the inclusion with different shapes and at different positions can be well reconstructed when electrodes are in the offset arrangement. Besides, clearer background is observed. Even for inclusion positioned in 1 and 3 , reconstruction with higher quality is obtained.
Fig. 3. Simulated imaging of an inclusion with high conductivity.
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Under two arrangements of electrodes, blur radius is compared in Fig. 4. Except for case 5, BR values under offset arrangement are much lower for other cases. BR values of case 5 under two arrangements are almost identical. However, obvious artifact is observed in the background of reconstruction.
Fig. 4. Comparison of BR values for different cases.
4.2 Imaging of Inclusions with High Conductivity and Low Conductivity Generally, reconstruction quality worsens when inclusions with high conductivity and low conductivity simultaneously appear. In the study, an additional inclusion with low conductivity of 0.06 S/m is considered. Figure 5 shows performance of the proposed sensor configuration in simultaneous image reconstruction of inclusions with high conductivity and low conductivity. Six cases are investigated. It is observed from Fig. 5 that the reconstructed images obtained under the offset arrangement are more competitive than the results under the traditional uniform arrangement. Both of inclusions with high conductivity and low conductivity in ROI are accurately reconstructed.
Fig. 5. Simulated imaging of inclusions with high conductivity accompanied by low conductivity for six cases.
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Figure 6 compares the blur radius values. From Fig. 6, it can be found that BR values under the offset arrangement of electrodes are much lower than that under the uniform arrangement for the six cases. The result suggests that inclusions with high conductivity and low conductivity can be more accurately reconstructed when the electrodes are in offset arrangement.
Fig. 6. Comparison of BR values in simultaneous reconstruction.
5 Conclusion In this paper, a high sensitivity sensor with offset electrode arrangement is proposed. Compared with traditional uniform electrode arrangement, sensitive field is more distributed in ROI. L1 regularization method is used for image reconstruction. Numerical simulation is conducted to verify performance of the proposed sensor configuration in reconstructing conductivity distribution. The results indicate that inclusions in ROI can be more accurately reconstructed for an inclusion with high conductivity or inclusions with high and low conductivity. In addition, quantitative evaluation is also compared with blur radius value which further demonstrates the performance of the proposed method in the reconstruction. This work mainly provides an alternative for sensor design when EIT is required to detect conductivity distribution in ROI. Acknowledgement. This work is supported in part by National Natural Science Foundation of China under Grant 61903127 and 51837011, in part by Postdoctoral Research Foundation of China under Grant 2020M673664, and in part by Scientific and Technological Innovation Program for Universities in Henan Province of China under Grant 21HASTIT018.
References 1. Zhang, K., et al.: Supervised descent learning for thoracic electrical impedance tomography. IEEE Trans. Biomed. Eng. 64(4), 1360–1369 (2021) 2. Ko, Y., Cheng, K.: U-Net-based approach for automatic lung segmentation in electrical impedance tomography. Physiol. Meas. 42(2), 025002 (2021) 3. Zhang, Y., et al.: A proportional genetic algorithm for image reconstruction of static electrical impedance tomography. IEEE Sensors J. 20(24), 15026–15033 (2020)
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4. Polansky, J., Wang, M.: Proper orthogonal decomposition as a technique for identifying two-phase flow pattern based on electrical impedance tomography. Flow Meas. Instrum. 53, 126–132 (2017) 5. Yang, L., et al.: Lung regions identified with CT improve the value of global inhomogeneity index measured with electrical impedance tomography. Quant. Imaging Med. Surg. 11(4), 1209–1219 (2021) 6. Wang, Z., Yue, S., Wang, H., Wang, Y.: Data preprocessing methods for electrical impedance tomography: a review. Physiol. Meas. 41(9), 09TR02 (2020) 7. Song, X., Xu, Y., Dong, F., Witte, R.S.: An instrumental electrode configuration for 3-D ultrasound modulated electrical impedance tomography. IEEE Sensors J. 17(24), 8206–8214 (2017) 8. Saood, A., Hatem, L.: COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med. Imaging 21(1), 19 (2021) 9. Song, S., Qiu, J., Lu, W.: Predicting disease severity in children with combined attention deficit hyperactivity disorder using quantitative features from structural MRI of amygdaloid and hippocampal subfields. J. Neural Eng. 18(4), 046013 (2021) 10. Taghizadeh, L., et al.: Bayesian inversion for electrical-impedance tomography in medical imaging using the nonlinear Poisson-Boltzmann equation. Comput. Meth. Appl. Mech. Engine 365, 112959 (2020) 11. Ma, E.: Integral formulation of the complete electrode model of electrical impedance tomography. Inverse Probl. Imaging 14(2), 385–398 (2020) 12. Wagner, J., Gschossmann, S., Schagerl, M.: On the capability of measuring actual strain values with electrical impedance tomography using planar silkscreen printed elastoresistive sensors. IEEE Sensors J. 21(5), 5798–5808 (2021) 13. McDermott, B., et al.: Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis. Physiol. Measur. 41(7), 075010 (2020) 14. Mueller, J.L., Siltanen, S.: The D-bar method for electrical impedance tomographydemystified. Inverse Probl. 36(9), 093001 (2020) 15. Huska, M., et al.: Spatially-adaptive variational reconstructions for linear inverse electrical impedance tomography. J. Sci. Comput. 84(3), 46 (2020) 16. Santos, T., et al.: Introduction of sample based prior into the D-Bar method through a schur complement property. IEEE Trans. Med. Imag. 39(12), 4085–4093 (2020) 17. Li, J., et al.: Adaptive Lp regularization for electrical impedance tomography. IEEE Sensors J. 19(24), 12297–12305 (2019) 18. Shi, X., et al.: High-precision electrical impedance tomography data acquisition system for brain imaging. IEEE Sensors J. 18(14), 5974–5984 (2018) 19. Xu, F., Deshpande, M.: Iterative nonlinear Tikhonov algorithm with constraints for electromagnetic tomography. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 5(3), 707–716 (2012)
Fault Location of T-type Line with Double-Circuit Line on the Same Tower with Asymmetrical Parameters Zhongan Yu, Junjun Wu(B) , and Da Deng School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou City, Jiangxi Province, China [email protected]
Abstract. In order to realize the fault location of the T-type line with asymmetrical parameters on the same tower double-circuit line, an intelligent optimization algorithm fault location scheme is proposed. Through the analysis of the electrical characteristics of the T-shaped line, based on the distributed parameter model and the principle that the positive sequence fundamental frequency voltage components are equal to the fault point from both ends of the line after decoupling, a ranging equation containing multi-dimensional complex hyperbolic functions is constructed. The random weighted particle swarm algorithm is further introduced to optimize the equation, and the new fault line selection criterion is combined to realize the precise location of the line fault point. A large amount of experimental simulation data shows that the ranging result is not affected by factors such as fault type, transition resistance and system operation mode, and has the advantages of no need to distinguish false roots and easy implementation of programming. Keywords: Double-circuit lines on the same tower · Asymmetric parameters · T-shaped transmission lines · Fault location · RWPSO
1 Introduction With the accelerated development of smart grids, many double-circuit lines are transformed from the original single-circuit lines. The double-circuit line on the same tower is interconnected with the original single-circuit line, which is easy to form a T-shaped line with asymmetrical parameters for the double-circuit line on the same tower. When the structure of the line fails, the fault location can be quickly and accurately located, which will be of great significance to the restoration of safe power supply and the reduction of economic losses. Transmission grid fault location methods are mainly divided into traveling wave method [1] and fault analysis method [2]. The traveling wave method uses the transient traveling wave transmission properties after the fault to find the distance, but it has wave head identification and complex technology. Disadvantages: The failure analysis method © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 555–563, 2022. https://doi.org/10.1007/978-981-19-1528-4_55
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does not require high equipment, and the cost is low. It is widely used in actual engineering fault location. At this stage, most of the existing T-shaped transmission line fault analysis studies are based on the symmetrical parameters. In practice, the line parameters are not completely symmetrical, and the applicability is greatly reduced. Compared with ordinary double-circuit lines, the structure of the T-shaped line with asymmetrical double-circuit lines on the same tower with parameters is more complicated, so there are relatively more issues to consider, mainly including that the distance measurement results are affected by the size of the transition resistance [3], It is necessary to search for fault points in the whole line [4] Distributed parameter distance measurement method to solve the existence of false roots [5], etc. In order to further improve the above situation, literature [6] uses the positive sequence current difference at both ends of the faulted line to construct the ranging function for the distance measurement of the double-circuit T-shaped line, but this method is limited to the line with symmetrical parameters and the selection of the fault line is complicated. In view of the above analysis, By introducing random weighted particle swarm algorithm to optimize and solve the analytical expressions, fault location and fault line selection can be realized at the same time, and the ranging results have relatively good results. High precision.
2 Analysis of Different Line Characteristics 2.1 T-shaped Transmission Line with Asymmetrical Parameters on the Same Tower Double-Circuit Line The T-type line model of the double-circuit line on the same tower with asymmetric parameters is shown in Fig. 1. This type of line is composed of two parts, namely the double-circuit line part on the same tower with asymmetric parameters and the single-circuit line part. M
N
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Fig. 1. T-shaped line model of double-circuit line on the same tower with asymmetrical parameters
2.2 Principle of Fault Location for Double Circuit Asymmetrical Line As shown in Fig. 2, the asymmetric parameters are the same tower double circuit model, Suppose the total length of the double circuit line is L; the parameters of the I and II
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circuits are symmetrical and evenly transposed, but the parameters of the two circuits are not equal to each other. That is, the self-impedance of loop I is Zl1 , and the impedance between phases is Zm1 ; the self-impedance of loop II is Zl2 , and the impedance between phases is Zm2 ; the impedance between loops I and II is Zp . m
n f
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Fig. 2. Model of two-circuit lines on same tower with asymmetry parameters
According to the new six-sequence component method [7],When a fault occurs inside the double circuit line, the corresponding distributed parameter model is shown in Fig. 3, where the total length of the line is l; Z1 and Y1 are the positive sequence impedance and admittance per unit length of the line. m Im
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Um
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Fig. 3. Distribution parameter model of transmission line
According to Fig. 3 and the long-line equation, for any point F on the I loop of the double-circuit line with asymmetric parameters on the same tower, set the distance between this point and the M terminal as d 1 , then the voltage at point F can be expressed as: ˙ U˙ ImF = U˙ Im1 cosh(γ I1 d1 ) − ZIc1 IIm1 sinh(γI1 d1 ) (1) U˙ InF = U˙ In1 cosh γI1 (l − d1 ) − ZIc1 I˙In1 sinh γI1 (l − d1 ) U˙ ImF and are the positive-sequence component voltage at point F derived from the m and n terminals of the I loop, respectively, U˙ Im1 and I˙Im1 , U˙ In1 and I˙In1 are the positivesequence fundamental frequency components at the m and n terminals of the I loop, respectively. In the same way, the voltage at F at any point d 2 from the M terminal on the II loop can be expressed as: ˙ U˙ ImF = U˙ Im1 cosh(γ I1 d1 ) − ZIc1 IIm1 sinh(γI1 d1 ) (2) U˙ InF = U˙ In1 cosh γI1 (l − d1 ) − ZIc1 I˙In1 sinh γI1 (l − d1 )
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U˙ IImF and U˙ IInF are respectively the positive sequence component voltage at point F on the II loop;U˙ IIm1 and I˙IIm1 ,U˙ IIn1 and I˙IIn1 are the positive sequence fundamental frequency components of the m and n terminals of the II loop, respectively. If there are fault points at d1 and d2, the positive sequence fundamental frequency component can be replaced with the positive sequence fault component into the calculation, and the influence of the load current can be eliminated by this, and the ranging accuracy can be improved. Since the voltages of the loops are equal at the fault point, there are: jδ ˙ U˙ Im1 cosh(γ I1 d1 ) − ZIc1 IIm1 sinh(γ11d1 ) e = (3) U˙ InF cosh γI1 (l − d1 ) − ZIc1 I˙In1 sinh γI1 (l − d1 ) jδ ˙ U˙ IIm1 cosh(γ II1 d1 ) − ZIIc1 IIIm1 sinh(γII1d1 ) e (4) U˙ IInF cosh γII1 (l − d1 ) − ZIIc1 I˙IIn1 sinh γII1 (l − d1 ) In Eqs. (3) and (4), U˙ ij1 , I˙ij1 (i = I, II, j = m, n)are the positive sequence fault components at both ends of each loop; δ is the asynchronous angle of data sampling at both ends. In order to eliminate the out-of-synchronization angle δ, formulas (3) and (4) are combined as the quotient, as follows: U˙ ImF1 U˙ InF1 = U˙ IInF1 U˙ IImF1
(5)
Among them, U˙ ijF1 (i = I, II; j = m n) are the fault point voltages derived from the corresponding positive sequence fault components at both ends of each loop. According to the above formula, the ranging equation can be constructed: F(d1 , d2 ) = U˙ ImF1 U˙ IInF1 − U˙ InF1 U˙ IImF1 = 0
(6)
From the above derivation process, it can be seen that F 1 (d 1 , d 2 ) only uses the independent positive sequence components between the loops, and also contains the fault information of the two loops of the asymmetric parameter, which can be achieved by solving Eq. (6) Fault location for two circuits.
3 Fault Location of Transmission Line 3.1 Introduction of RWPSO Algorithm Model RWPSO (random weight particle swarm optimization) algorithm is an intelligent evolutionary computing technology based on the improvement of initial particle swarm algorithm. The speed and position update formula is: k k Vidk = ωVidk + c1 r1 (Pid − Xidk ) + c2 r2 (Pgd − Xidk )
(7)
Xidk + 1 = Xidk + Vidk + 1
(8)
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Among them, ω is the inertia weight; c1 is the cognitive learning factor, c2 is the social learning factor; k is the number of iterations; r 1 and r 2 are random numbers in the interval; Pk id is the current optimal position of the particle; Pk gd is the current optimal position of the group. The above algorithms often have local optimal defects. In order to maintain the diversity of the algorithm population, the improved PSO algorithm is used here. The inertial weight is the key to the improved PSO algorithm. After the inertial weight is increased, the false roots will be effectively avoided in the iterative optimization process. Jumping out of the local optimum, the formula after increasing the inertia weight will become: ω = μ + σ ∗ N (0.1) (9) μ = μmin + (μmax − μmin )rand (0, 1) Based on the above analysis, the improved RWPSO algorithm is easy to implement in programming and has a more powerful global optimization capability for solving practical engineering problems. 3.2 Optimal Solution of Ranging Equation According to the optimization idea of the RWPSO algorithm, the fault location equation of the T-type line with asymmetrical parameters on the same tower double-circuit line can be transformed into a multi-dimensional variable function of the minimum value optimization problem. First of all, According to formula (3) and (4), the variables d 1 and d 2 are normalized to variables x 1 and x 2 on the interval [0,1], and d 3 and d 4 are normalized to the interval [0,1] according to literature [8] On the variables x 3 and x 4 , the following equations are established: ⎧ f1 (x1 , x2 ) = U˙ Im1 cosh(γI1 lx1 ) − ZIc1 I˙Im1 sinh(γ lx ) ⎪ 11 1 ⎪ ⎪ ⎪ ⎪ γII1 l(1 − x2 ) − ZIIc1 I˙IIn1 sinh γII1 l(1 − x2 ) ∗ U˙ IIn1 cosh ⎪ ⎪ ⎪ ˙ ˙ ⎪ f2 (x1 , x2 ) = ⎪ ⎪ UIIm1 cosh(γ II1 lx2 ) − ZIIc1 IIIm1 sinh(γII1 lx 2 ) ⎨ ˙ ˙In1 sinh γI1 l(1 − x1 ) cosh γ l(1 − x ) − Z I ∗ U In1 I1 1 Ic1 (10) f3 (x3 ) = U˙ NP cosh(γNP lx3 ) − ZNP I˙NP sinh(γNP lx3 ) ⎪ ⎪ ⎪ ⎪ ˙ ˙ ⎪ ⎪ ⎪ f4 (x3 ) = UNP cosh γNP l(1 − x3 ) − ZNP INP sinh γNP l(1 − x3 ) ⎪ ⎪ ˙ ˙ f5 (x4 ) = UNQ coshγNQ lx4 − ZNQ ⎪ ⎪ INQ sinh γNQ lx4 ⎩ f6 (x4 ) = U˙ NQ cosh γNQ l(1 − x4 ) − ZNQ I˙NQ sinh γNQ l(1 − x4 ) Construct a multi-dimensional variable function: ⎧ ⎨ G(x1 , x2 ) = |a − c + j(b − d )| G(x3 ) = |e − g + j(f − h)| ⎩ G(x4 ) = |k − m + j(l − n)| 1 ,x2 ) G(x3 ) G(x4 ) + + H (x1 , x2 , x3 , x4 ) = G(x G(x ) G(x4 ) G(x1 ,x2 ) 3
min[H (x1 , x2 , x3 , x4 )] → 0 x1 , x2 , x3 , x4 ∈ [0, 1]
(11)
(12)
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When the line fails, the data collected by the protection devices on each line is preprocessed, and then the fundamental frequency component of the positive sequence fault is extracted according to the ranging equation, and the objective function H(x 1 ,x 2 ,x 3 x 4 ) is assigned to the adaptive According to the degree function, the algorithm performs adaptive iterative optimization based on the operation logic to quickly and accurately find the fault distances d 1 , d 2 , d 3 , and d 4 . 3.3 Ranging Result Analysis and Line Selection Criterion According to the derivation of the distance finding equation H(d 1 , d 2 , d 3 , d 4 ), and the optimization solution process above, when the double-circuit line fails and only a singleline fault occurs inside the double-circuit line, the fault distance of the non-faulty line can be considered as 0 or Length of line l; When a cross-line fault occurs, the fault distance of the two circuits should be approximately equal, and the non-faulty line can be considered as 0 or the full length of the line l. When the T-connected branch fails, the fault distance of the non-faulty line can be considered as 0 or the full length of the line l: Based on the above analysis, take the single-line failure of the double-circuit line as an example. A single-line failure occurs on the double-circuit line, and the other lines have no failure. When G(x 1 , x 2 , x 3 , x 4 ) → 0 and the output solution x 1 , x 2 , x 3 , x 4 satisfies: x1 = d x =d or 1 (13) x2 = l x2 = 0 x3 = 0 x =l or 3 (14) x4 = l x4 = 0 Then it can be judged that the I loop of the MN section of the double-circuit line is faulty, and the fault distance is d; at this time, the distance measurement result of other lines is 0 or the length of the line is l, and it can be judged that the other lines are not faulty. Same for other faults.
4 Simulation 4.1 Line Parameters and Error Calculation In order to verify the correctness of the proposed method, this paper uses PSCAD/EMTDC to build a system simulation model as shown in Fig. 1. The threeterminal voltage level is 500 kV, the MN section is 300 km long, the NP section is 100 km long, and the NQ section is 150 km long (Table 1). 4.2 Simulation Results Under Different Transition Resistances Table 2 lists the results of distance measurement and line selection when the transition resistance is 0.01 and 100 , the fault distance of the double circuit line is 100 km, the fault distance of the T branch line is 50 km, and different typical faults occur. (I and II represent I and II loops, G represents grounding, the same below).
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Table 1. Line parameters Parameter type
MN II Loop
NP
NQ
Self-impedance (/km) 0.119981 + j0.664752
MN I Loop
0.126983 + j0.661216
0.119981 + j0.664752
0.119981 + j0.664752
0.085648 + j0.245939
0.092287 + j0.242481
0.085648 + j0.245939
0.085648 + j0.245939
Phase impedance (/km) Self-admittance(S/km)
j0.256835E-05
j0.259199E–05
j0.256835E–05
j0.256835E–05
Interphase admittance (S/km)
−j0.175743E–06
−j0.160684E–06
−j0.175743E-06
−j0.175743E–06
Line impedance (/km)
0.088948 + j0.260051
0.088948 + j0.260051
–
–
−j0.290442E–06
–
–
Line admittance (S/km) −j0.290442E–06
Table 2. Simulation results of fault with different transition resistances Fault branch
Fault type
Transition resistance/
Fault distance/km d1
MN
IAG
IIBCG
IABCG
IAIIAG
IABIIBCG
NP
NPBG
NPABCG
NQ
NQBCG
d2
Maximum relative error/% d3
d4
0.01
99.72
0
100
150
0.10
100
99.87
0
100
0
0.04
0.01
0
99.72
100
150
0.09
100
0
99.78
100
150
0.07
0.01
99.63
0
100
150
0.12
100
99.81
0
100
0
0.06
0.01
99.78
99.72
0
150
0.09
100
101.22
101.16
100
150
0.41
0.01
99.96
99.93
100
150
0.27
100
99.93
99.87
0
150
0.04
0.01
0
0
49.86
150
0.14
100
300
300
50.2
0
0.02
0.01
0
0
49.63
150
0.37
100
300
300
49.98
0
0.02
0.01
0
0
150
50.49
0.33
100
0
0
150
50.07
0.05
4.3 Comparison of Fitness Curves of Different Algorithms Figure 4 shows the comparison of representative fitness curves obtained by using the PSO algorithm, RWPSO algorithm, and WDPSO algorithm [9] to solve the problem of IAIIBCG failure at 100km of the double circuit line as an example. The remaining parameters of each algorithm are consistent. Experiments show that the convergence speed of the three algorithms is RWPSO > PSO > WDPSO, and the convergence accuracy is RWPSO≈WDPSO > PSO. while RWPSO has achieved better results in convergence speed and solution accuracy, and the solution performance has been significantly improved.
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500
Fitness
400 300
200 100 0 0
20
40
60
Evolutionary algebra
Fig. 4. Comparison of fitness curves of different algorithms
5 Conclusion In this paper, the theoretical analysis is carried out by building a line model that is more suitable for the actual project, using the six-sequence component method to completely decouple, and by building a distributed parameter model, further analysis, building a multi-dimensional variable function, and cleverly transforming the ranging problem into the search for the equation solution. Optimized to achieve the final simplified solution.
References 1. Yu, Z., Liang, X., Ding, W.: Unified traveling wave ranging scheme for asymmetrical parameter double-circuit and four-circuit lines on the same tower. In: 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), pp. 1–6 (2021). https://doi.org/10.1109/CIEEC5 0170.2021.9510205 2. Abdulkhader, A.: Impact of designed line parameters on distance protection - investigation on a 110 kV double circuit transmission line fault. In: 2021 IEEE PES/IAS PowerAfrica, pp. 1–5 (2021). https://doi.org/10.1109/PowerAfrica52236.2021.9543223 3. Li, Z., Zhang, Z., Xiao, Y., Li, Z.: Analysis of the influence of transition resistance on different impedance characteristics. Hubei Electric Power 44(04), 28–34 (2020). (in Chinese) 4. Cai, D., Zhang, J.: New fault-location algorithm for series-compensated double-circuit transmission line. IEEE Access 8, 210685–210694 (2020). https://doi.org/10.1109/ACCESS.2020. 3039877 5. Chen, X., Zhang, X., Wu, X., Zhang, C., Zhang, L., Wang, T.: Asynchronous fault location algorithm for dual-terminal transmission lines based on the straight-line distance of poles and towers. Power Syst. Technol. 45(04), 1581–1587 (2021). (in Chinese) 6. Zhang, S., Li, Y., Chen, X.: A new fault location algorithm for double-circuit T-type transmission lines on the same tower based on positive sequence current difference. Proc. Chin. Soc. Electr. Eng. 38(05), 1488–1495 (2018). (in Chinese) 7. Chumarov, S.G., Fedorova, A.V.: Undefined, 2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), pp. 1–4 (2020). https://doi.org/10. 1109/REEPE49198.2020.9059183
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8. Fan, C., Shu, Q.: The application of sequence component method in power system. J. Electr. Electron. Educ. 32(05), 32–36+46 (2010). (in Chinese) 9. Li, Y., Chen, Q.: Research on particle swarm optimization algorithm based on nonlinear decrease of inertial weight. J. Shaanxi Univ. Sci. Technol. 38(03), 166–171 (2020). (in Chinese)
Fault Location of Distribution Network Based on Stacked Autoencoder XinTong Li1 , LiWen Qin1 , YongZan Li2(B) , Xin Yang3 , and XiaoYong Yu1 1 Electric Power Research Institute of Guangxi Power Grid Co. Ltd., Guangxi, China 2 Shenzhen Shenbao Electric Instrument Co. Ltd., Shenzhen, China
[email protected] 3 Guilin Power Supply Bureau of Guangxi Power Grid Co. Ltd., Guilin, China
Abstract. The topology of the distribution network is complex, with many branches.Users have increasingly higher requirements for power supply reliability. Once a fault occurs, how to quickly and accurately locate the fault point and reduce the power outage time is an important aspect of ensuring the safe operation of the power system and providing power supply reliability. With the continuous improvement of the monitoring level of the distribution network, the fault location based on the traveling wave characteristics of the distribution network has gradually become a current research hotspot. Based on the analysis of the propagation characteristics of the traveling wave in the distribution network and the attenuation characteristics of the traveling wave in the distribution line, this paper uses the Stacked Autoencoder (SAE) model to fit the difference between the attenuation characteristics of the traveling wave and the location of the fault point. The simulation results show that the scheme is less affected by the transition resistance, the initial phase angle of the fault, and the fault type, and the positioning accuracy is high. It can accurately locate the fault points on the line with high accuracy. Keyword: Distribution network · Traveling wave · Stack autoencoder
1 Introduction The distribution network has the characteristics of numerous feeders, wide distribution of switchgear, small power supply range, and hybrid power supply of overhead lines and cable lines. With the access of large-scale distributed energy sources, the distribution network structure has become more complex. Therefore, when the distribution network fails, the traditional method is difficult to meet the fault location requirements [1]. In the distribution network, single-phase grounding faults account for more than 80% of all faults. After a single-phase grounding fault, the system can operate with a fault for some time. A serious threat to the insulation of the line and the normal operation of the equipment, with the development of the smart grid, finding the fault branch and the specific location of the fault has become more and more important [2]. This year, many scholars researched the fault location of the distribution network, and they have also proposed a large number of location methods. It can be roughly divided into three © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 564–572, 2022. https://doi.org/10.1007/978-981-19-1528-4_56
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categories, impedance method, traveling wave method, and wide-area communication method. Reference [3] proposes a positive sequence component ranking algorithm based on a distributed parameter line model. This method uses two-terminal asynchronous voltage and current data. The fault location of the distribution network without branch lines is realized. The simulation calculation results show that the method is not affected by inaccurate line parameters, simple calculation, no need to solve the derivative, easy to program, and no need to identify false roots. The sampling data can be asynchronous, which avoids the requirement of using GPS technology in the distribution network and reduces the cost of use. However, the fault location of the distribution network with branches cannot be realized. Reference [4] proposed a fault section location method based on zero-mode current traveling wave phase. This method is based on the phasefrequency characteristics of the transmission line. It first obtains the zero-mode current recording data of the faulty terminal after aligning the wave heads, and then analyzes the zero-mode current traveling wave phase difference of the adjacent terminals through the cross wavelet transform. The zero-mode current traveling wave of adjacent terminals with opposite phases can be judged to be located on both sides of the fault point. This method can only locate the faulty section and cannot meet the high positioning accuracy. At present, domestic and foreign scholars also combine traditional methods with artificial intelligence. Reference [5] proposed a multi-layer perceptron feedforward neural network and backpropagation algorithm to train, test, and evaluate the intelligent positioning process. The mean square error (MSE) algorithm is used to evaluate the performance of the fault locator. However, the positioning model does not consider the branch model of the distribution network. This paper proposes a distribution network fault location based on stacked autoencoders, which extracts the attenuation characteristics of traveling waves in transmission lines, and then uses the data for model training. The distribution network fault location process is divided into three steps: 1) Use stacked autoencoders to extract features and reduce dimensions of fault data; 2) Use models to locate faults; 3) Use double-ended fault measurement distances to judge fault locations, and complete the fault location of the distribution network.
2 Stack Autoencoder As an extension and development of neural networks, deep learning develops shallow neural networks into deep neural networks including four typical models: stacked autoencoder, long and short-term memory network, deep belief network, and convolutional neural network. As a typical deep learning model, stack auto-encoder is composed of multiple auto-encoders. With weights to connect adjacent units and a greedy algorithm for training layer by layer, a deep network is easy to reach the local optimum and the training is difficult [6]. AE (Auto-Encoder) belongs to a feature extraction algorithm, including encoder and decoder [7]. It is also the basic unit of the stacked autoencoder. It is an unsupervised learning method. It is divided into two parts, the encoder, and the decoder, as shown in Fig. 1. The encoder compresses high-dimensional data to low-dimensional data, and the decoder decompresses the data and restores it to the original data.
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Fig. 1. Structure of AE
The data set can be denoted as Xi = (xi1 , xi2 , . . . . . . xim ), where, i = 1, 2, . . . . . . n is the number of samples, j = 1, 2, . . . . . . m represents the number of features of the sample. The non-linear functions in the encoder network sf and the decoder network sg are sigmoid functions. The feature vector or representation is represented by εi . Basic AE minimizes the reconstruction error L(·), which is the difference between the input data x and the reconstructed data x˜ . The parameter set is represented by θ = {W , b, W , d }, which means that the parameters in the encoder and decoder networks are learned at the same time. The formula of AE and loss function is shown in the following formula: ε = sf (Wx + b)
(1)
x˜ = sg (W ε + d )
(2)
Loss(θ ) = L(x, x˜ )
(3)
For training an autoencoder model, it was expressed as a three-layer neural network. We used the original data as a hypothetical target output to build a supervisory error to train the entire network. After the training is over, the output layer can be removed, since only the transformation from x to ε are needed [8]. The autoencoder can map high-dimensional data to low-dimensional data, reduce the amount of data, and achieve the effect of data feature extraction. The proposed idea uses the greedy learning algorithm to make each layer of the network reach the local optimum, and then trains the entire network as a whole, which reaches the overall optimum [9]. This method worsen the training process of the traditional deep network, and it is easy to fall into the problem of local optimization. The next step is to obtained the feature expression ε, use ε as the original information to train a new autoencoder and get a new feature table. This is the so-called stack autoencoder. Stacked means stacking layer by layer, which is a bit like “stack”. After stacking multiple autoencoders, the system looks like Fig. 2:
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Fig. 2. The structure of the stack autoencoder
3 Scheme of Fault Location Based on Stack Autoencoder 3.1 Data Collection Compared with the transmission grid, the distribution network has more complicated branches and multiple lines, so the fault traveling wave process is complicated. This article is based on a 1-branch branch distribution network. As shown in Fig. 3 below, the traveling wave measurement is performed at both ends of the line, and the sampling frequency of the traveling wave device is 2 MHz.
Fig. 3. Distribution network wiring diagram
The distribution network can be divided into 6 lines C, D, E, F, G, H. Each line has the same length and is 2 km. At a frequency of 50Hz, each line has the same parameters and the positive sequence impedance matrix is ⎡
⎤ 0.12 + 0.66j 0.086 + 0.25j 0.086 + 0.25j ⎣ 0.086 + 0.25j 0.12 + 0.66j 0.086 + 0.25j ⎦/km 0.086 + 0.25j 0.086 + 0.25j 0.12 + 0.66j
3.2 Data Characteristics A low-resistance fault occurs every 100m on each line, and the fault resistance is set to 2, 4, 6, 8, and 10. The line mode waveforms of the fault voltage data on different fault lines are shown in Fig. 4. The figure shows the traveling wave waveforms measured at both ends of the line C, D, E, F, and G when a fault occurs at 1 km of the line, and the waveform is output. It can be seen that the fault waveform will change with the location of the fault. The farther the fault occurs from the measurement point, the more the traveling wave attenuates and the lower the measured traveling wave amplitude. On the contrary, the closer the fault occurs to the measurement point, the less the traveling wave attenuation, and the higher the amplitude of the traveling wave measured.
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Fig. 4. The characteristics of fault traveling wave faults at different fault locations
Set the fault at the same position on the E line, and set the fault resistance to 2, 4, 6, 8, 10. The fault waveform is as shown in Fig. 5. The larger the fault resistance, the more the traveling wave measurement consumes the resistance. The smaller the amplitude of the measured traveling-wave. Conversely, the smaller the fault resistance is, the less the traveling wave measurement consumes on the resistance, and the greater the amplitude of the measured traveling-wave;
Fig. 5. Traveling wave fault characteristics of different fault resistances
3.3 Network Construction According to the training method of the stack autoencoder, this article abandons the traditional shallow multi-level training, but chooses one-time deep training. This training method is concise and convenient. The network structure is designed as Fig. 6 The automatic encoder is divided into two small models: encoder and decoder. The encoder processes these vectors through a hidden dense layer with 4 neurons decreasing ([64, 16, 8,4]), and the activation function of each layer of dense uses sigmoid. When
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Fig. 6. The structure of stack autoencoder
the data flows into the network, it will be formed to filter the data according to the weight, leaving the key information of the data; the decompressor uses a dense with an increasing number of 4 neurons ([4, 8, 16, 200]). The data is sorted by layers, and finally reconstructed into a vector with a dimension of 200, so the output of the decoder has the same shape as the input of the encoder, and the data reconstruction is completed. When the data flow into the decoder, the data will be supplemented with information according to the weight. 3.4 Selection of Loss Function In the training of the SAE model, there are two processes, namely the compression process and the decompression process. The final output data feature number is the same as the input data feature number to achieve the effect of data reconstruction. The loss function is: 1 n m 2 yij − yij (4) loss = i=1 j=1 n·m 3.5 Fine-Tuning of the Model Network fine-tuning is the modification of the network model parameters of the stacked autoencoder. Supervised learning is performed on the modified network so that it has the ranging ability. When the gradient descent method is used to train the network, the mean square error loss function is used, which can reflect the error between the positioning result and the real result. 1 n 2 loss = yi − yi (5) i=1 n After the stack autoencoder training is completed, the data is compressed and decompressed. For training simplicity, the model parameters of the compression part of the autoencoder are extracted as shown in Fig. 6, and used as the input of following network. Finally, this part is trained with labeled data to make the model complete with ranging ability.
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3.6 Model Prediction and Fault Node Judgment After the fault distance measurement is being completed, the fault location needs to be judged in the distribution network fault location, which is divided into two aspects: 1) determine the line in which the fault occurs; 2) the distance between the fault location and both ends. For the study of multi-branch distribution networks, this paper uses the zero-sequence voltage to judge, from the point of the fault to the zero-sequence voltage at both ends, the magnitude of the zero-sequence voltage decreases, and the nodes near the fault can be judged by comparison. By adding the ranging results, it can be judged whether the fault occurred on the trunk line or the branch line. As shown in the figure below, when the sum of the ranging distances is greater than the length of the trunk line, it indicates that the fault has occurred on the branch road. On the contrary, it happened on the trunk line (Fig. 7).
Fig. 7. Principle diagram of fault location judgment
Fig. 8. The process of Fault location
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The overall algorithm flow chart is shown in the figure below. The algorithm flow is mainly divided into three parts: 1) data collection and neural network model training; 2) using the model to predict the distance to failure. 3) judgement of the exact fault location to find it occurs on trunk line or branch line (Fig. 8).
4 Result Analysis In the PSCAD/EMTDC environment, build the distribution network model and data collection according to the above network structure and parameters. Under the Python/TensorFlow framework, the neural network model is built, the learning rate is set to 0.002, the activation function is ‘sigmod’, and the number of iterations is set 5000. The optimizer selects ‘adam’. And complete the training model and model verification. Six pieces of fault data on the lines in the distribution network system are selected and numbered. The data are sent into the models at both ends for prediction. The prediction results are compared with the real ones in Table 1: Table 1. Table captions should be placed above the tables. Fault data number
Real distance 1 (km)
Bus 1 forecast (km)
Real distance 2 (km)
Bus 1 forecast (km)
Sum of fault distance (km)
The location of fault occurred
1
1
1.0631
9
8.9954
10.0585
Trunk line
2
3
2.9846
7
6.9579
9.9403
Trunk line
3
5
4.9583
5
4.8602
9.8185
Trunk line
4
7
6.9373
3
3.0131
9.9504
Trunk line
5
8.8
8.7022
1.2
1.2120
9.9142
Trunk line
6
4.8
4.7975
6.8
6.7159
11.5134
Branch line
Average error
0.0472
0.0493
With the prediction results in Table 1, the average prediction error of the prediction model does not exceed 100 m, which fully meets the needs of the actual project. According to the sum of the predicted results of bus 1 and bus 2, the sum of the predicted values of the line double-ended model of 1, 2, 3, 4, and 5 are: 10,585, 9.9403, 9.8185, 9.9504, 9.9142. The distance from the fault to the two ends can be obtained: 1, 2, 3, 4, and 5 faults occurred on the C, D, E, F, and G lines respectively; the sum of the predicted values of the 6 double-ended model is: 11.5134. Therefore, it is judged that the 1, 2, 3, 4, and 5 lines are the main lines, and the 6 faults occur in the branch roads, and the distance from the node is 1.5134 km.
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5 Conclusion The experimental results show that the fault location method based on traveling waves and neural networks can solve the complicated problem of the process of the traveling wave in a distribution network. When a line fails, a faulty traveling wave signal will be generated. This article extracts the attenuation information of traveling waves in the transmission process, which is input to the stacked autoencoder for learning and positioning, and the SAE neural network learns the difference between the fault characteristics and the fault distance. The non-linear and complex relationship between the two can achieve the effect of fault location. This paper combines traveling wave and neural network to realize fault location. The precise location of the fault can be judged by integrating the model ranging. Acknowledgements. This research is supported by the Science and Technology Project of Gangxi Power Grid Corporation (Project No.: GXKJXM20190607).
References 1. Zichao, M., Wenjuan, D., Haifeng, W.: Distribution network fault location based on transfer learning deep convolutional neural network. South. Power Grid Technol. 13(07), 25–33 (2019) 2. Rui, L., Zheng, J., Chonglin, W., Jianhua, L.: Research on fault location of distribution network based on traveling wave time-frequency composite analysis. Proc. Chin. Soc. Electr. Eng. 33(28), 130–136+20 (2013) 3. Lingxiao, Y., Huiping, N., Wei, Z., et al.: Research on fault location of distribution network based on double-terminal impedance method. Ind. Mine Autom. 5, 30–32 (2008) 4. Yinliang, L., Dehui, Z.: Distribution network fault section location method based on zero-mode current traveling wave. Electr. Autom. 41(2), 73–75,82 (2019). https://doi.org/10.3969/j.issn. 1000-3886.2019.02.022 5. Elnozahy, A., Sayed, K., Bahyeldin, M.: Artificial neural network based fault classification and location for transmission lines. In: 2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE), pp. 140–144 (2019). https://doi.org/10.1109/CPERE45374.2019.8980173 6. Luo, G., Yao, C., Liu, Y., Tan, Y., He, J., Wang, K.: Stacked auto-encoder based fault location in VSC-HVDC. IEEE Access 6, 33216–33224 (2018). https://doi.org/10.1109/ACCESS.2018. 2848841 7. Kexu, C., Chunhua, X., Ling, L., Tao, H.: Power load identification based on stack noise reduction self-encoding network [J/OL]. China Test: 1–6 [2021–06–06]. http://kns.cnki.net/ kcms/detail/51.1714.TB.20210602.1534.002.html 8. Wen, L., Gao, L., Li, X.: A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans. Syst. Man Cybern. Syst. 49(1), 136–144 (2019). https://doi.org/10. 1109/TSMC.2017.2754287 9. Yingjie, T.: Research on single-phase ground fault location method of distribution network based on stacked autoencoder. Beijing Jiaotong University (2020)
Prediction of Failure Probability of Overhead Lines in Distribution Network Based on Historical Statistics and Meteorological Monitoring Data LiWen Qin1 , XiaoYong Yu1 , WenLin Liu2(B) , HaiTao Gui3 , and LiFang Wu1 1 Electric Power Research Institute of Guangxi Power Grid Co. Ltd., Guangxi, China 2 Shenzhen Shenbao Electric Instrument Co. Ltd., Shenzhen, China
[email protected] 3 Guilin Power Supply Bureau, Guangxi Power Grid Co. Ltd., Guilin, China
Abstract. As the core component of the distribution network, overhead lines are the key to ensuring the safe and reliable operation of the power system. However, under the influence of internal aging effects and external environmental effects, it will cause different failure effects on overhead lines. Compared with the traditional failure probability prediction of overhead lines, the real-time failure probability calculation requires the time-varying failure probability value of the overhead line. This paper proposes a time-varying failure probability calculation method for overhead lines in distribution networks based on the combination of historical statistics and meteorological monitoring. First, a calculation method for the internal failure rate of overhead lines based on exponential function fitting is proposed. Then, a technique based on SAE neural network training weather monitoring data to fit the external failure rate is offered. Finally, the Fokker Planck equation is used to calculate the overhead line’s overall time-varying failure probability value. Validation of calculation examples shows that the results calculated in this paper can effectively reflect the failure probability of overhead lines under different operating times and conditions. Keywords: Overhead lines · SAE neural network · Failure probability
1 Introduction The safe and stable operation of distribution lines is the basis for ensuring the reliability of the power system, the calculation of the probability of failure of distribution lines has attracted more and more attention at home and abroad. Overhead line is an essential part of the distribution network, and its safe and stable operation plays a vital role in the reliability of power grid operation [1, 6]. Therefore, it is necessary to predict the failure probability of overhead lines. Under normal circumstances, the overhead transmission line is usually affected by aging loss or health status and external factors—for example, weather, environment, and geographical © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 573–584, 2022. https://doi.org/10.1007/978-981-19-1528-4_57
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location conditions [2]. Domestic and foreign scholars have conducted a lot of research on the calculation of time-varying failure probability value. Researchers usually divide the status into two steps. The first step is to establish a prediction model that changes with equipment operating condition information. The second step is to use the equipment failure rate obtained in the previous step according to the failure characteristics of the equipment to select an appropriate random process model to describe the equipment in the future, and then solve the real-time failure probability of the equipment. Regarding equipment failure rate research, the primary methods include: (1) The probability distribution function is fitted to historical data to obtain the failure rate. Literature [3] uses the Fourier function, Gaussian function, and Weibull function to analyze the line fault in a specific place in the south. The historical data of the rate is fitted month by month, and the parameters are adjusted. This method can flexibly adjust the probability distribution of the fitted curve according to the sample data, but the generality is poor. (3) Fit the relationship between the equipment health index and the failure rate based on the function. Literature [5] uses an exponential function to fit the relationship between the health index and the internal failure rate. This method reflects the failure rate of the equipment in real-time and has universal applicability in different equipment and different regions. This paper also uses this method to fit the distribution network. The current technical route for calculating the probability of failure through the realtime failure rate is roughly divided into two types [6]. The first is to establish a model of the random process of electrical equipment and calculate the real-time failure probability of critical electrical equipment through the calculated failure rate and the Markov process. The other is to solve the real-time failure elementary probability of equipment based on a mathematical distribution function and obtain the real-time failure probability of electrical equipment through the definition of conditional failure probability. The Markov model calculates the probability of failure by studying the conversion relationship between the initial possibility of the different states of the event and these states and then obtaining each state’s development trend at the time. Through demonstration, the transfer rate from the running state to the fault state in the Markov model is taken as the equipment failure rate [4]. By substituting the transfer rate and state probability into the Fokker Planck equation, the real-time failure probability of the equipment can be obtained. In terms of overhead lines, literature [7] established a transmission line and insulator state evaluation model by analyzing the causes of transmission line faults and using the Markov process. Literature [8] demonstrated that the time-varying failure rate function expressions of exposed equipment whose overhead lines fail due to changes in external environmental conditions could be analytically given based on the prediction of external environmental conditions. The conditional probability model of the mathematical distribution function, in solving the equipment failure rate, often describes the probability distribution function of the failure rate and decodes the parameters. From the conditional probability definition of failure probability, we can see: T +t Pf (T ) =
T ∞ T
f (t)dt
f (t)dt
=
F(T + t) − F(T ) 1 − F(T )
(1)
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Substituting the probability distribution function of the failure rate, the real-time failure probability of the equipment can be obtained. It is generally believed that the aging failure of equipment obeys the Weibull distribution. In reference [9], the temperature-dependent aged failure model of the overhead line is described, and the Weibull distribution curve describes the aging failure process. The senior failure model of the overhead line is obtained by considering the influence of temperature. According to the distribution law of Weibull distribution, the failure rate is used to get the failure probability value. Similar to the statistical method for calculating the failure rate of crucial equipment, the mathematical distribution function only uses the calculated failure rate to calculate and solve the failure probability of the equipment. The data requirements are high, and the external environmental impact is not fully considered. Moreover, the conditional probability method is mainly used to calculate the probability of failure monthly in the distribution network. It is suitable for the calculation of the likelihood of failure in a long time interval. For time intervals of hours or days, the error is significant. This paper considers the different causes of overhead line faults and divides the defects caused by internal and external considerations. Considering the factors that cause overhead line faults due to aging as internal fault factors, the faults are external faults considering different weather conditions. Dividing the operating status of overhead lines in the distribution network. 1.1 Classification of Fault Types for Overhead Lines in Distribution Networks The components of overhead transmission lines are diverse and complex. They are erected on the ground to operate, and the operating environment is changeable. Faults are caused by external weather conditions such as gale, lightning, pollution, ice, snow, etc., and the overhead line itself, such as aging. Therefore, the overhead line status can be divided into normal operation status, fault status caused by internal aging factors, and external fault status caused by external meteorological factors. Note that the overhead line is in state two due to external faults, in state one due to the aging of its line, and in state 0 when being regular operation. The overall state transition diagram of the overhead line is shown in Fig. 1 below. Because some accidental failure factors in the distribution network, such as car crashes, bird damage, etc., are unpredictable, they are not considered.
Fig. 1. Overhead line status division
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The failure rate of overhead line operation is generally recorded as λ. It is a function of time t, so it is also registered λ(t). In the field of electrical engineering reliability research, the failure rate has different meanings. According to classic foreign literature, the purpose of failure rate is: λ=
The number of failures Total operating time
(2)
The failure rate represented by the above formula means the frequency of failure of the equipment per unit time. The failure rate of the failure caused by the aging factors of the overhead line itself is expressed λ1 . The failure rate of the external loss of the overhead line caused by external weather and other factors is expressed λ2 . Similarly, when the overhead line is being repaired, the internal and external repair rates are recorded as μ1 and μ2 . The meaning is the reciprocal of the repair time. 1.2 The Meaning of the Internal Fault of the Overhead Line of the Distribution Network According to the distribution network data collected by the Ministry of Work Safety in a particular area, the main reasons for the failure of overhead lines due to their factors are insulation deterioration caused by temperature, poor quality of the product itself, and product defects caused by operation for a certain period. According to the Grid standards, it can be known that critical distribution network equipment such as overhead line utilization guidelines are scored. The current operating status of overhead lines can be assessed according to the health index obtained from regular scores. 1.3 The Meaning of the External Fault of the Overhead Line of the Distribution Network Overhead lines operate outdoors, and their failures are mainly due to weather factors. For example, the line collapses are usually due to windy weather, direct lightning failure and induced overvoltage failure caused by lightning weather, insulation damage caused by wildfire conditions, and line flashover caused by icing conditions. These failure factors are all related to the external environment of the overhead line operation. The results of the condition inspection cannot express them, so consider the relationship between the failure rate caused by the external meteorological factors and the external operating environment separately.
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2 Dividing the Operating Status of Overhead Lines in the Distribution Network 2.1 Calculation of the Internal Failure Rate of Overhead Lines Based on Exponential Function Fitting According to the Grid standards, the status evaluation of overhead lines is divided into eight components: towers, wires, and insulators, iron, and fittings, pull wires, channels, and grounding devices and accessories. The content of the status evaluation of the overhead line includes insulation performance, temperature, mechanical characteristics, appearance, load condition, grounding resistance, and electrical distance. According to the evaluation guidelines, the distribution network evaluation content is scored to obtain the health index equipment. The passage proposed a calculation method based on the health index fitting the real-time failure rate of the equipment to reflect the health status of the equipment in real-time. Overhead lines usually use unfavorable exponential functions to provide the relationship between their health index and failure rate. The fitting formula is shown in the following Eq. (4): λ = KeCH
(4)
Where:λ is the failure rate; K is the proportional coefficient; C is the curvature coefficient; H is the health index. Thus, the fitting curve of the equipment health index and the failure rate is obtained. 2.2 Calculation of External Failure Rate Based on Stacked Autoencoder Network The autoencoder comprises a three-layer network. The dimension reduction and feature extraction of data are realized through the internal coding and decoding process. It is an unsupervised learning network model. When an autoencoder has multiple hidden layers, it is called a stacked autoencoder. Its network model structure is shown in Fig. 2 below. Adding a hidden layer helps the autoencoder learn more complex codes and can better reconstruct the training data. Still, because it is unsupervised learning, it will not understand helpful data representations.
Fig. 2. Structure diagram of a stacked autoencoder
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When a large data set needs to be processed, but most of the data is unlabeled, one method is to train a stacked autoencoder with the existing data and then use the labeled data to teach it. The process is shown in Fig. 3 below.
Fig. 3. Unsupervised pre-training process using autoencoder
For example, the passage chooses the historical fault statistics data of the distribution network in a particular area. The meteorological factors affecting the external faults of the distribution network are selected as the input. By filtering the spots caused by the influence of climate, we will calculate the average number of sites for each line as the value of the failure rate in a day as the output. Then use this to train the neural network and return to the relationship between meteorological conditions and the failure rate.
3 Establishment of a Fault Prediction Model for Overhead Lines in a Distribution Network 3.1 Fokker-Planck Equation Theory The Fokker-Planck equation is also known as the Markov equation of state. A set of differential equations is used to obtain the instantaneous probability of being in a specific state at a particular moment in the entire process. It refers to a set of continuous parameter spaces, a Markov process with discrete state space. The fault-repair state can be regarded as a discrete state and time-continuous Markov process for overhead lines, so the FokkerPlanck equation can be used. The Fokker-Planck equation is derived from the ChepmanKolmogorov (C-K equation), and the form of the equation is as follows: P (t) = P(t)B
(5)
Formula (5), P (t) the derivative of P(t) concerning time, represents the derivation of state probability concerning time, P(t) represents the state probability of system components [P0 (t), P1 (t)......Pn (t)], and B is the transition rate matrix, a square matrix with non-negative elements. The sum of the ingredients in each row is 0. ⎤ ⎡ −λ1 − λ2 μ1 μ2 (6) B=⎣ λ1 −μ1 0 ⎦ 0 −μ2 λ2
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It should be pointed out that the failure rate is the frequency of failures within a certain period, and the failure probability refers to the likelihood that the equipment may fail at a particular moment. Therefore, the essential difference between the two is that the time corresponding to the failure rate and the failure probability is different. 3.2 Fokker-Planck Equation Theory The overhead line operates in the normal state initially P0 (0) = 1, which can be solved by solving Eq. (5). The probability of the standard operating state P0 (t) is: P0 (t) = P0 + Kμ1 eμ1 t + Kμ2 eμ2 t
(7)
Among them, The meanings of P0 , μ1 , μ2 , Kμ1 , Kμ2 are: P0 =
μ1 μ2 = α1 α2
1 1 λ1 λ2
+
1 1 λ1 λ2 1 1 λ1 μ2
+
1 1 λ2 μ1
(8)
Kμ1 =
(α1 + μ1 )(α1 + μ2 ) α1 (α1 − α2 )
(9)
Kμ2 =
(α2 + μ1 )(α2 + μ2 ) α2 (α2 − α1 )
(10)
Among them α1 and α2 are the roots of the quadratic algebraic equation in Formula (7). s2 + bs + c = 0
(11)
b = λ1 + λ2 + μ1 + μ2
(12)
c = λ1 μ2 + λ2 μ1 + μ1 μ2
(13)
The probability of failure is: P(t) = 1 − P0 (t)
(14)
4 Example Analysis 4.1 Calculation Example of External Failure Rate Based on SAE Algorithm For example, taking the distribution network data of a certain place, the statistical data is shown in Table 1.
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Date
1st day
2nd day
3rd day
Average wind speed
5.53
5.39
3.59
Average temperature
−4.97
−6.81
−3.96
Maximum temperature
−3.52
−5.8
−1.05
Lowest temperature
−7.52
−7.87
−6.41
Relative humidity
0.691
0.594
0.804
Solar irradiance
7.71
8.16
8.79
precipitation
0
0
0
Air pressure
914.35
924.98
921.62
Is there any thunder
0
0
0
Mean failure rate
0.0289855
0.01449275
0.028986
1 line somewhere
1
0
0
2 line somewhere
0
0
0
3 line somewhere
0
0
0
4 line somewhere
0
0
0
The loss function obtained by bringing into the neural network training is shown in Fig. 4.
Fig. 4. Trend curve of the loss function
It can be seen that as the number of iterations increases, the loss function value gradually decreases to close to zero. Then, 30 sets of weather-related input data are collected and brought into the trained SAE neural network model. Thecomparison between the predicted value and the actual value can be achieved as shown in Fig. 5. The blue curve in the figure is the value of the external failure rate predicted by the neural network, and the orange curve is the actual value of the internal failure rate. The
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Fig. 5. Comparison of neural network predicted failure rate value and actual value
external fault factors of overhead lines are related to climate variables and are affected by accidental factors such as collisions and car accidents. In addition, the amount of climate monitoring in a region is sometimes inaccurate due to the influence of sudden changes in weather and other factors. Therefore, as shown in the figure, it can be seen that the neural network can more accurately predict the relationship between the external failure rate and the climate. 4.2 Fitting Calculation Example of Health Index and Internal Failure Rate The following Table 2 shows the relationship between the health index score and the failure rate during the operation and maintenance period of part of the feeder in a specific distribution network. The relationship between the failure rate and the health index is fitted by formula 4. Table 2. The relationship between the operational health index and the failure rate in a specific area Health index
Failure rate Health index
Failure rate Health index
Failure rate Health index
Failure rate
94
9.29E−06
89
1.85E−05
90
9.51E−06
92
7.07E−06
84
3.04E−05
86
2.87E−05
99
1.88E−07
87
2.11E−05
96
8.70E−06
93
3.20E−06
94
5.84E−06
89
1.19E−05
94
7.16E−06
94
7.16E−06
98
9.42E−07
91
3.02E−06
94
5.28E−06
91
4.71E−06
83
4.70E−05
92
3.77E−06
93
5.65E−06
98
3.77E−07
97
3.77E−07
94
6.60E−06
92
8.86E−06
92
8.71E−06
93
3.96E−06
95
5.65E−06
94
6.71E−06
98
3.77E−07
93
3.20E−06
91
4.71E−06
94
6.35E−06
93
2.07E−06
92
3.39E−06
The fitting results are shown in Fig. 6.
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Fig. 6. Comparison of neural network predicted failure rate value and actual value
4.3 Overhead Line Real-Time Failure Probability Value It is known that the value of the external failure rate obtained by the neural network training of the environmental monitoring in a specific place is: λ2 = 2.899 × 10−2 , and the value of the internal failure rate obtained by fitting the equipment health index is: λ1 = 7.02 × 10−6 , The repair rate of distribution network repair is: μ1 = 5.95×10−3 , μ2 = 2.98×10−3 . Therefore, the real-time failure probability of the overhead line can be obtained, as shown in Fig. 7.
Fig. 7. The curve of Overhead Line Failure Probability vs. Time
It can be seen that as the number of working days increases, the failure probability value of overhead line operation gradually increases. When the operating time is close to two months, the failure probability of the overhead line of the distribution network gradually increases to a relatively large value. Therefore, When it is detected that the operation risk value of the overhead line is relatively high, timely measures shall be taken to repair it to extend its service life and avoid causing significant losses to the power grid.
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5 Conclusion This article introduces the failure prediction process of the overhead lines of crucial equipment in the distribution network. First, the method of exponential curve fitting is used to fit the health index of the overhead line and the internal failure rate caused by its factors. Then the trained stack self-encoder network is used to input the monitoring data of the current operating environment to obtain the value of external failure rate caused by meteorological factors. Finally, the Fokker Planck equation is introduced to solve the failure probability curve with time in the current operating environment. The test results show that this method has the following advantages: it considers different influencing factors that affect the operation of overhead lines and considers more comprehensively. In addition, it also feels the influence of historical statistical data and current monitoring data, and the angle of consideration is relatively complete. However, due to the limitation of the statistical data and the uncertainty of the weather monitoring results in different regions, the neural network prediction results have specific errors. When there are more accurate monitoring quantities in the future, the accuracy of the prediction results will be improved. Acknowledgments. This project is supported by the Science and Technology Project of Guangxi Power Grid Corporation (Project No. :GXKJXM20190607).
References 1. He, K., Hu, L.: Reliability study for distribution network considering adverse weather. In: 2019 6th International Conference on Information Science and Control Engineering (ICISCE) (2019) 2. Di, F.D., Yu, S.Z., Chuangxin,T.G., Yufeng, F.C., Jinhui, F.Z., Hui, S.L.: Failure probability model of transmission lines for risk assessment. Power Syst. Prot. Control 45(07), 69–76 (2017). (in Chinese) 3. Jian, F.W., Xiaofu, S.X., Zhe, T.L., Yun, F.L., Shijie, F.W.: Time distribution of weather-related transmission line failure and its fitting. Electric Power Autom. Equip. 36(03), 109–114+123 (2016). (in Chinese) 4. Tenemaza, C., Ortega, E.: State of art, reliability in electrical distribution systems based on Markov stochastic model. IEEE Lat. Am. Trans. 14(2), 799–804 (2016) 5. Changkai, F.S., Xin, S.N., Zhitao, T.S.: Quantitative Risk Assessment of Distribution Network Based on Real-time Health Index of Equipment, High Voltage Engineering (2018). (in Chinese) 6. Fang, J., Lin, X., Wang, H., et al.: Calculation method of outage probability of distribution network based on real-time failure rate of equipment. In: 2019 9th International Conference on Power and Energy Systems (ICPES) (2019) 7. Xishan, F.W., Lei, S.L., Rikun, J.: Operating risk assessment for ‘transmission and line insulators using Markov model. High Voltage Eng. 2011 37(8), 1952–1960 (2011). (in Chinese) 8. Konal, M., Oz, I., Uzunoglu, C.P., et al.: Electrical distribution network’s failure analysis based on weather conditions, pp. 269–272 (2018)
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9. Zhang, J., Yao, L., Jifan, L.V., et al.: Life prediction and verification of energy meter based on multi-stress influence Weibull distribution model. In: 2020 21st International Conference on Electronic Packaging Technology (ICEPT) (2020) 10. Fariza, V., Zulkarnain, Z., Surjandari, I.: Comparing artificial neural network and failure distribution methods for maintenance scheduling: a case study of wooden door industry. In: 2018 5th International Conference on Information Science and Control Engineering (ICISCE) (2018)
Research on Image Segmentation of Power Line Based on Encoder-Decoder Network Haotian Sun1,2(B) , Zhijian Fang1,2 , Zhiguo Wei1,2 , and Fei Xie1,2 1 School of Automation, China University of Geosciences, Wuhan 430074, China
[email protected] 2 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex
Systems, Wuhan 430074, China
Abstract. Power line network is the main carrier of the power system, and the smooth patrol inspection plays a crucial role in the fault prevention of the power line network. With the emergence of the patrol inspection UAV, how to detect the target of the power line through the picture and carry out accurate positioning has become a major research focus of the intelligent power system. A method for detecting and locating power lines based on an encoding lightweight decoder network (EDN-light) is proposed in this paper. It provides a foundation for the research and development of intelligent inspection of power line network, such as detection and identification, fault location, and guarantee of operation safety, and puts forward a new solution to the fault location diagnosis of power line using image. By EDN-light network, it focuses on detecting high level target and eliminating irrelevant information such as background, the convolutional layer of the encoder is used for extracting features, and the deconvolutional layer of the decoder for reconstructing images. The EDN-light network can improve the detection accuracy, and generate high-quality segmentation object suggestions. Use Turkish Electricity Transmission Corporation’s aviation power line network dataset and self-made dataset to train the model to improve the generalization ability of the model. Experiments have found that due to the fully convolutional network design, it can carry out multi-scale input, For power line image segmentation and detection, the accuracy is better, and the evaluation indicators of the proposed models have increased. And due to the design of the lightweight decoder, Without retraining the network, the learned model can be extended to unknown linear category targets without repeated training. Keywords: EDN-light · Fully convolutional network · Image segmentation · Power line location detection
1 Introduction With the upgrading of energy industry and the development of artificial intelligence, the position and role of technological innovation and efficiency improvement in the traditional power industry are becoming increasingly prominent. Automation and intelligence © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 585–595, 2022. https://doi.org/10.1007/978-981-19-1528-4_58
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have become the only way for the survival and development of power enterprises. And the concept of power computer vision (PowerCV) has also been proposed. PowerCV is an electric power artificial intelligence technology that uses artificial intelligence and image processing methods to solve visual problems in all aspects of the power system. At present, PowerCV is currently in its infancy, and there are only some scattered, simple research, and due to the characteristics of power facilities, some difficult problems such as complex image background, many interference factors, difficult to judge similar targets, and difficult to express defects that need to be solved urgently. So it is very necessary to study PowerCV [1]. The main carrier of power is transmission network, the safe operation of transmission network is the key to a national power safety. But a type of linear shape objects represented by power lines is still one of the difficulties in the field of PowerCV. Usually such objects have no texture or sparse texture, thin structure, difficult to judge similar targets, complex background interference, and other features that are difficult to learn, How to accurately extract the contour features of such objects is a major research hotspot in the field of PowerCV [2]. The purpose of image segmentation through contour detection is to find a collection of pixels with sharp brightness changes in the power line image, extract the target and the perceptible significant edge, that is, the junction between the power line and the background. To simplify the image information and retain the main points of the image Irrelevant details are ignored, and different marks are used to indicate the power line information carried in the image. In deep learning and computer vision, image segmentation is an extremely important image analysis method. It is a technique and process that divides an image into a number of specific areas with unique properties and proposes objects of interest. In the processing of computer vision tasks such as classification, location detection, segmentation, deep reasoning, etc., contour information is the most obvious and most useful image feature. Contour information often shows object segmentation, It provides a realistic basis for the subsequent image processing research of power line detection and identification, fault location, and operation safety [3]. Therefore, if the segmentation information in the power line image can be accurately measured and positioned, it also means that the actual power line can be positioned and measured, which is of great significance for the intelligent line patrol of the power line network [4]. A lot of previous work only focused on edge detection, but edge detection contains a large number of background edges, which hinders the use of edge (contour) and other knowledge as prior information for subsequent detection, positioning, segmentation, depth estimation and other conventional tasks.Most image segmentation detection algorithms have improved the level of abstraction a lot, but lack of cognition at the object level, resulting in the inability to reach the level of human perception [5]. Compared with previous image segmentation detection algorithms, the algorithm proposed in this paper focuses more on detecting high-level object segmentation and extracting the segmentation features of high-level objects.
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2 PowerLine Image Segmentation Network In the research of power line network segmentation detection, due to the non-texture and thin structure of the power line target, the detection effect using local information is not good. Pixel dense prediction based on global information has high feasibility for the segmentation and detection of power lines. The encoder-decoder network (END) represented by the full convolutional network can better use semantic information, reduce the complexity of the algorithm and increase the calculation speed, and the effect is better. The full convolutional network has no fully connected layer, saving The fully connected layer consumes a lot of computing time and parameters, and can process input images of any size, with high flexibility [6]. 2.1 EDN-Light This article uses the VGG19 network as the encoder network. VGG19 contains 19 hidden layers (16 convolutional layers and 3 fully connected layers) [7]. In the target recognition and classification of large-scale data, the VGG19 neural network model is widely used, which proves that increasing the depth of the network can affect the final performance of the network to a certain extent. The use of stacked small convolution kernels is betterthan the use of large convolution kernels. VGG19 continuously uses multiple small convolution kernels of size 3 × 3 to perform convolution operations in the shallow network. This operation imitates the feature extraction process of the large convolution kernel, and while achieving better results, it can also effectively reduce the weight parameters of the convolutional neural network. From an overall point of view, the prediction time and parameters have beenreduced (Fig. 1).
Fig. 1. The schematic diagram of VGG19.
The encoder-decoder network (EDN) can restore the output to the same size as the input image, but cannot generate very accurate segmentation marks. The combination of unpooling layer and deconvolutional layer helps to generate better label positioning. The commonly used symmetric structure introduces a complex decoder network, which makes it difficult to train with limited samples. To solve this problem, this article uses a lightweight asymmetric decoder (light-decoder) to restore the image size, and improve training speed. Among them, the convolutional network of the encoder is used to extract features, and the deconvolutional network of the decoder is used to reconstruct images.
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In view of the image segmentation label problem, by referring to the methods of full convolutional network and semantic segmentation, this paper regards the image segmentation detection problem as an image binary labeling problem, where: “0” represents the non-target, namely the background, and “1” represents the target, namely the power line [8]. 2.2 Network Architecture The convolutional network of the encoder removes the last two fully connected layers (FC-4096, FC-1000), and converts the first fully connected layer “FC-4096” into a convolutional layer, named Conv6, The encoder VGG19 network contains 17 convolutional layers and 5 maximum pooling layers. The network structure is shown in Fig. 2. Change the fully connected layer to the convolutional layer to construct a fully convolutional network based on vgg19, because the only difference between the convolutional layer and the fully connected layer is that the neurons and the input of the convolutional layer are locally connected, and different neurons in the same channel share weights. Both the convolutional layer and the fully connected layer perform a dot multiplication operation, and their function forms are the same, so the convolutional layer can be converted into the corresponding fully connected layer, and the fully connected layer can also be converted into the corresponding convolution layer, Therefore, the calculation method after conversion into a convolutional layer remains unchanged. This structure can extract multi-level and multi-scale feature information from the image, and can realize the training and post-prediction of images of any size and size, and due to the removal of fully connected layers, the amount of parameters is greatly reduced, which improves the operating efficiency of the network.
Fig. 2. The schematic diagram of Encoder and Decoder network (EDN-light).
The decoder consists of unpooling, deconvolutional layer, activation function and dropout. By recording the position of the largest pooling selected by the pooling operation, in the unpooling operation, each activation is returned to the original position before pooling, and the output is an enlarged but sparse activation map, Using multiple learnable deconvolutional layers, perform operations similar to convolution to densify the sparse graph obtained by the unpooling layer. The unpooling layer captures the spatial information of the instance image. Therefore, it can effectively reconstruct the detailed structure of the object with finer resolution. On the other hand, the deconvolutional layer can learn to capture the shape of a specific class, and has good feature
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feedback and mapping capabilities (mapping the high-latitude features of the final result to specific original graphics pixels), making it possible to face unknown types of targets. The proposed model does not require repeated training. The decoder can amplify the feature map through the maximum unpooling layer. The Deconv6 of the decoder is designed for dimensionality reduction, and the output dimension 4096 is reduced to 512 through the 1 × 1 convolution kernel, so that the unpooling layer can be used to scale up the feature map in the next deconvolutional layer. The number of channels of each unpooling layer of the decoder is designed to be the same as the maximum pooling layer corresponding to its encoder. Except for Deconv6, all decoder convolutional layers use 5 × 5 kernels. In addition to the Sigmoid activation function used for the output Pred, the ReLU activation function is added after all deconvolutional layers, and a loss function is added after the ReLU layer. The initial image can be an input of any size. The size is automatically adjusted to (224, 224, 3) through the network, and then the size is reduced to 224-112-56-28-14-7-1 through the maximum pooling down-sampling and Conv6 convolutional layer. The VGG19 encoder network is used to extract features and fix the parameters. After passing through the Conv5 convolutional layer, a feature map that maintains its high-dimensional features is formed. The final feature used in the lightweight decoder is 7 × 7 × 512. Then through the unpooling operation, deconvolutional layer, and finally restored to the Y segmentation graph, this graph is used to calculate the error with the segmentation graph, where the label graph includes the real graph and the boundary graph, and the input graph is only the real graph. The label image is a real image, and each pixel is 0. END-light input and output are shown in Table 1. Table 1. END-light input and output table. Input Layer name Conv1
Layer
[conv 3 − 64]× 2
Output
Layer
size
name
224 × 224
Deconv6
Max pooling Conv2
[conv 3 −128]× 2
Conv3
⎡conv 3 − 256⎤ ⎢ ⎥×2 ⎣conv 3 − 256⎦
Conv4
⎡conv 3 − 512⎤ ⎢ ⎥×2 ⎣conv 3 − 512⎦
Conv5
⎡conv 3 − 512⎤ ⎢ ⎥×2 ⎣conv 3 − 512⎦
Conv6
4096-d FC
112 × 112
size
14 × 14
Deconv5 Un pooling
56 × 56
28 × 28
Deconv4 Decoder
Max pooling 28 × 28
Un pooling 56 × 56
Deconv3
Max pooling
Un pooling 14 × 14
112 × 112
Deconv2
Max pooling pred
Output
Un pooling
Max pooling Encoder
Layer
Un pooling Deconv1 , Sigmoid
224 × 224
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3 Network Training 3.1 Dataset Turkish Electricity Transmission Corporation’s Aviation Power Line Network Dataset This article uses the aerial image data set generated by the Turkish Electricity Transmission Corporation (TEIAS) [9]. The imaging system installed on the helicopter is used to capture VL and infrared video from the air. The video resolution is infrared 576 × 325, VL 1920 × 1080. Manually select the examples that can represent the existence of the power line network, and adjust their size to 128 × 128. The data set consists of 2000 positive examples and 4000 negative examples. The images were taken from 21 different geographic locations in Turkey. Because the background, lighting and weather conditions are different, it can reflect a variety of environments. Self-made Dataset By using cameras to photograph power lines in Wuhan Hannan District, Jiangxia District, Caidian District and other places to make a dataset, the dataset forms include single line, multi-line, coil, sag, etc. And through the background, time, and weather, reflect different environments. The original size of the picture is 720 × 720, and it is manually cropped to 224 × 224 size for model training. The data set has a total of 600 images, which is expanded three times by the image augmentation method, and a total of 1800 positive images are obtained. Due to the limitation of the structural characteristics of the power line network itself, it is very difficult to collect high-quality segmentation annotations. In order to find the basic facts of high-fidelity segmentation for training, we need to align the annotated segmentation with the real image boundary. Therefore, the data set used to train the segmentation detection network is very limited and small in scale. 3.2 Network Training Parameters During training, the encoder parameters (VGG19) are fixed and only the decoder parameters are optimized. The Activation Function. In the EDN-light for power line identification studied in this paper, in order to make the neural network model more non-linear, an activation function is connected after each layer of convolution. The ReLU function is used as the non-linear activation function, because the ReLU function can not only effectively reduce the density of the network output data, but also speed up the reduction of the loss value in the network and shorten the time required for network training [10]. The mathematical expression of the ReLU function is as follows: f (x) = max(0, x)
(1)
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The Loss Function. In order to evaluate the difference between the power line marked in the image and the predicted power line, the pixel-wise loss that works well in the neural network is selected as the loss function [11]. The loss value obtained by comparing the predicted result of the neural network with the artificially labeled result can be used to iteratively update the model parameters in the reverse direction, and to evaluate the performance of the image segmentation of the power line. The mathematical expression of training loss is as follows: Loss = −y log yˆ − (1 − y) log(1 − yˆ ) (2) Optimization Algorithm of Learning Rate. In this paper, Adam algorithm is used to iteratively update the weight parameters. The weight parameter update through sample training can make the weight parameter obtained closer to the ideal value, which can minimize the loss function. Compared with other algorithms, the Adam optimization algorithm converges in the correct direction at a more accurate speed through deviation correction [12]. The specific update process can be expressed by the following formula: mt+1 = αmt + (1 − α)xt vt+1 = βmt + (1 − β)(xt )2 mt+1 xt+1 = −lr √ vt+1 + ε
(3)
Among them, α = 0.9 and β = 0.999, representing the default exponential decay rate of the first and second order moment estimation of the Adam optimization algorithm, ε is used to keep the value stable during the optimization process, and the default value ε = 10−8 lr is selected to represent the training process Learning rate, the exponential moving average of the optimization algorithm update gradient is mt , and the average gradient is vt .
3.3 Training Process By manually labeling the TEIAS dataset of the Turkish Electricity Transmission Corporation using the labelme tool, the power line image segmentation data set was created, The training set and the test set are divided at a ratio of 9:1 to train the model. The power line image segmentation data set is shown in Fig. 3.
Fig. 3. The power line image segmentation dataset.
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3.4 Evaluation Index In the detection and segmentation of power lines, the real essence is a two-class problem: with power lines and without power lines. In the image segmentation, the positive samples and negative samples are divided according to the presence or absence of power lines. This article uses Accuracy, Loss, mIoU (average intersection ratio), and mPA (average pixel accuracy) as evaluation indicators [13]. Accuracy. The accuracy rate mainly reflects the ratio of the number of samples of the prediction pair to the total number of samples. It is defined by the combination of the prediction result and the real situation. The calculation formula is as follows. Accuracy = TP + TN /(TP + TN + FP + FN )
(4)
mIoU. In the power line segmentation, the mIoU value is an important index to measure the accuracy of image segmentation. mIoU can be interpreted as the average intersection ratio, that is, the IoU value (true value/predicted value) is calculated on each category. The calculation formula is as follows. Figure 4 can intuitively represent the mathematical meaning of the IoU value. mIoU =
k pii 1 k k 1+k j=0 pij + j=0 pji − pii
(5)
i=0
Fig. 4. IoU
MPA. MPA is an image segmentation standard that calculates the proportion of correctly classified pixels in each class, and then averages all classes. The calculation formula is as follows. mPA =
k pii 1 k 1+k j=0 pij i=0
(6)
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4 Results In the above-mentioned environment, this article trains the weight parameters of the network on the 900 original image data sets and 900 artificially labeled data sets, and records and stores the relevant parameters and results in the network training process. Finally, The relevant output data is evaluated according to the performance index. At the same time, the evaluation is based on the segmentation results of the power line image by the encoder-decoder structure network (EDN-light). The image segmentation result of the power line is shown in Fig. 5.
Fig. 5. Image segmentation result of the power line.
In this paper, when the initial learning rate is le-5, the training indicators of the training set and the validation set are analyzed for results. The results of each indicator do not represent all data points. The specific training results are as follows: As the number of training iterations increases, the accuracy and loss are shown in Fig. 6(a). The overall trend of convergence after an increase is shown. The Accuracy is concentrated between 0.9–0.95, and the overall performance is stable with little fluctuation; the Loss starts from the beginning The value of 0.57 drops to the final 0.04, and the overall convergence indicates that the model is feasible and the effect is good. Figure 6(b), Figure 6(c) shows the mIoU and mPA of each round of verification of the verification set, and the mIoU and mPA of the segmentation of the power line and the background image, respectively. Through the performance index, it can be found that the mPA for background segmentation can reach more than 99%, and the mIoU is also more than 96%. The maximum mPA and maximum mIoU for the power line target reached 92.5% and 82.7%, respectively, and the average mPA and mIoU were both around 90.5%. Through experiments, it is found that the model used has better image segmentation accuracy for power lines, can segment power lines and background more accurately, has strong feature extraction capabilities for small pixel targets, and can accurately segment high-level targets.However, due to the influence of multi-line, coil and other forms of power lines, the evaluation of power line division is slightly lost. The reason is that when processing the dataset, the background between the lines is uniformly processed
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Fig. 6. Performance assessment (a) Loss and Accuracy (b) mIoU and mPA (c) Evaluation of image segmentation performance of power line
as the inside of the power line, and the background between the lines is not refined. This problem can be solved by refining the dataset.
5 Conclusion PowerCV is one of the hotspots in the field of power intelligence in recent years, but there are still few researches on the power line network, which is one of the most basic units. With the continuous development of deep learning, the method of training neural networks with a large number of samples as data sets has begun to play its huge and unique role. The identification, detection, positioning, and depth estimation of the power line network provide help for the subsequent research and development of power intelligence such as intelligent line inspection, fault detection, and line network planning. Based on the full convolutional neural network that performs well in image segmentation, this paper improves on the basis of semantic information, and proposes a encoder-decoder structure network composed of a good-performance vgg19 and a lightweight decoder to detect high-level linear targets. The power line image is a data set, which performs tasks such as segmentation, identification, detection, classification and analysis of the power line image. The proposal of this method provides a proposal for practical engineering applications in the intelligentization of power systems, and is of great significance to the safe construction of power construction.
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References 1. Zhao, Z., Fan, X., Xu, G., et al.: Aggregating deep convolutional feature maps for insulator detection in infrared images. IEEE Access 5, 21831–21839 (2017) 2. Pavlatos, C., Vita, V., Dimopoulos, A.C., et al.: Transmission lines’ fault detection using syntactic pattern recognition. Energy Syst. 10(2), 299–320 (2019) 3. Oberweger, M., Wendel, A., Bischof, H.: Visual recognition and fault detection for power line insulators. In: 19th Computer Vision Winter Workshop 2014, pp. 1–8 (2014) 4. Wang, H., Meng, F.: Research on power equipment recognition method based on image processing. EURASIP J. Image Video Process. 2019(1), 1–11 (2019) 5. Zheng, W., Li, C., Cui, X., et al.: A multi-objective parallel detection algorithm for images of power transmission line corridors. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, pp. 432–436 (2020) 6. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015) 7. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) 8. He, S., Yang, D., Li, W., et al.: Detection and fault diagnosis of power transmission line in infrared image. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, pp. 431–435 (2015) 9. Yetgin, Ö.E., Benligiray, B., Gerek, Ö.N.: Power line recognition from aerial images with deep learning. IEEE Trans. Aerosp. Electron. Syst. 55(5), 2241–2252 (2018) 10. Eckle, K., Schmidt-Hieber, J.: A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Netw. 110, 232–242 (2019) 11. Mosinska, A., Marquez-Neila, P., Kozi´nski, M., et al.: Beyond the pixel-wise loss for topologyaware delineation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3136–3145 (2018) 12. Mota, J.F.C., Xavier, J.M.F., Aguiar, P.M.Q., et al.: D-ADMM: a communication-efficient distributed algorithm for separable optimization. IEEE Trans. Sign. Process. 61(10), 2718– 2723 (2013) 13. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., et al.: A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857 (2017)
Design of Low-Cost Micro-PMU Based on e-IpDFT Shiying Zheng1 , Shuai Liang1 , Zelong Yu1 , Zaiji Yuan1 , Zhigang Cao1 , Renjie Ding1 , and Shuguang Li2(B) 1 Tongliao Power Supply Company of State Grid East Inner Mongolia Electric Power Company
Limited, Tongliao, China 2 Northeast Electric Power University, Jilin, China
[email protected]
Abstract. As new sources and loads such as distributed energy, electric vehicles are connected to the distribution network, their random and intermittent characteristics increase the difficulty of dynamic measurement of the distribution network. Constrained by the requirements of distribution network application scenarios, installation environment and communication conditions, the requirements of high precision, low cost and easy networking for synchronous measurement unit are proposed. Based on the study of distribution network signal characteristics, this paper designs a low-cost Micro-PMU, which can achieve high-precision estimation of distribution network signal. This device adopts the improved enhancedinterpolated-discrete Fourier transform (e-IpDFT) synchronization measurement algorithm, designs the data acquisition unit, data processing unit and communication unit, and builds the experimental device. By testing the steady state signal of the distribution network, the results show that the designed device can meet the application requirements of the distribution network. Keywords: Distribution network · Micro-PMU · Low-cost · High precision
1 Introduction With the deepening interaction between energy and environment, especially the ‘carbon peak’ and ‘carbon neutralization’ goals proposed, countries around the world have increased investment in distributed clean energy power generation, electric vehicles and demand side response.In the future, with the increasing penetration of these sources and loads which have these characteristics such as random and intermittent, the trend of power electronicization in distribution network (DN) is obvious and the operation mode is increasingly changing [1]. It will present new forms such as multi-interaction, bidirectional power flow, potential microgrid and unplanned islanding. The complexity of electrical quantity increases, the accurate measurement is difficult, and the single measurement information is insufficient [2]. The operation and control of distribution network are facing serious challenges, and new real-time measurement devices are urgently © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 596–603, 2022. https://doi.org/10.1007/978-981-19-1528-4_59
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needed.Based on the demand for the visual and controllable of distribution network, and learning from the synchronous vector measurement technology in the transmission network, it has become an important means to solve this demand to develop micro phase measurement unit (Micro-PMU) suitable for the operation environment of DN system [3]. Differ from the transmission network, the complex electrical quantity of the DN system, the more branch lines, and the diverse load changes bring difficulties to the rapid and accurate measurement, mainly including: 1) High accuracy of phase measurement: short distribution network lines, The voltage phase difference between the two bus is in the order of 0.1°. 2) Harmonics and interharmonics seriously affected: power electronic equipment introduces a lot of noise interference. 3) The dynamic change of the signal characteristics rich: the DN system is connected with the user, and is significantly affected by the intermittent and fluctuating sources and loads. 4) Measurement device cost requirements: There are many buses in the DN system, which requires the installation of measurement devices. In view of the above problems, domestic and foreign scholars study on micro-PMU. At present, the micro-PMU installed in the distribution network for practical application is typical of the FNET [4] system established by the University of Tennessee in the United States, the BTrDB system based on micro-PMU established by the University of California at Berkeley [5], the light wide area monitoring system based on light PMU [6] jointly established by Shandong University, and the commercial PMU developed by North China Electric Power University [7]. These PMU designs for distribution network are more or less inadequate in terms of cost, dynamic corresponding performance, scope of application, message rate, etc. The PMU technology suitable for distribution network demand is still in its infancy, which has great research value and application prospect. As a link closely related to users, the voltage and current of distribution network fluctuate frequently. The influence of inter-harmonics, harmonics, DC interference and random noise on fundamental phase estimation has been analyzed, and a feasible measurement method is proposed [8]. At present, there are many studies on synchronous phase algorithm. According to the different emphases of the algorithm on measurement accuracy and response performance, it can be divided into steady-state algorithm, dynamic algorithm and intelligent algorithm. Steady-state algorithms include: discrete Fourier transform [10] and its improved algorithm, finite impulse response/infinite impulse response filter method and least square method [11]. Dynamic algorithms include Kalman filter, Taylor series transform and wavelet transform. Intelligent algorithm includes support vector machine, neural network, etc. Considering the practical application requirements of distribution network, how to design a high-precision synchronous measurement method and a low-cost measurement device that meet the requirements of complex distribution network environment needs to be emphatically considered. Therefore, this paper studies the synchronization measurement method and practical device of PMU, and designs a micro-PMU device based on e-IpDFT. Firstly, the steady-state model of the measurement signal is constructed. Further, the e-IpDFT measurement signal estimation algorithm framework is designed by integrating the signal spectrum analysis method and correction strategy. Then, the
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algorithm software is designed based on hardware platform. Finally, the test system is built in the laboratory.
2 Micro-PMU Synchronous Measurement Method 2.1 The DN Signal Model In the steady state, it is generally believed that the amplitude, phase and frequency of the signal are constant in the sampling calculation data window. The main factors affecting the estimation accuracy of synchronous measurement are inter-harmonics, harmonics, DC interference and random noise. Therefore, the signal in the steady state can be expressed as: Ai cos 2π ∗ fi∗ nTs + θi x(n) = A0 cos 2π ∗ f ∗ nTs + θ + (1) + Ah cos 2π ∗ fh∗ nTs + θh + R(nTs ) + wnoise Where A0 indicates the amplitude of the fundamental wave; f is the actual frequency value; θ represents the phase; i, h represents the inter-harmonics, harmonics; Represents DC component. T s is the sampling detection, and its value is 1/f s . f s represents the sampling frequency of the device. N represents the sampling points. 2.2 Design of Synchronous Measurement Method When designing the algorithm, it is necessary to consider not only the accuracy and response time of the algorithm, but also the complexity of the algorithm. Considering that the micro-PMU requires low cost, the computing power of the data processing unit should be fully considered when designing the algorithm. The paper is based on the document [12] and the design of the synchronous measurement algorithm. Collect the input signal x(n), add a window to the signal, and perform DFT calculation, as shown in Eq. (2): X(k)
1 N −1 ω(n)x(n)WNkn k ∈ [0, N − 1] n=0 B
(2)
N −1 where B n=0 ω(n) is the sum of the window math; WN e−j2π/N is the rotation factor. It can be seen from Reference [13] that due to the spectral leakage after windowing, the main spectral line will affect the adjacent spectral lines. Therefore, the main spectral line and the adjacent two spectral lines are used to calculate the correction value. The calculation is based on Eq. (3): δ = 2ε ·
|X (nm + ε)| − |X (nm − ε)| |X (nm − ε)| + 2|X (nm )| + |X (nm + ε)|
(3)
Where ε = ±1 depends on the nm + 1 and nm –1 Amplitude. nm represents the large amplitude spectrum line number. According to Eq. (3), the correction value of
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measurement signal estimation can be obtained. After the correction value is obtained, the spectral line amplitude, frequency and phase calculated by DFT need to be corrected for the first time, and the correction expression is: ⎧ fnm = (nm + δ)f ⎪
⎨
π δ
2
Anm = |X (nm )| ∗ sin(π (4) δ) ∗ δ − 1 ⎪ ⎩ ψnm = X (nm ) − π δ After correction by Eq. (4), the signal estimation results are close to the real value, but the error is relatively large. It can be seen from Reference [12] that the spectral line of the signal is composed of positive and negative spectral line components, so X(k) can be expressed as: X (n) = Xo+ (n) + Xo− (n)
(5)
For Eq. (5), according to Fourier transform, the positive and negative spectral line components can be expressed as: + Xo = An ∗ ej∗θn W (n−f /N ) (6) Xo− = An ∗ e−j∗θn W (n+f /N ) where the expression of function W is:
sin π (n − 1) n ∗ −jπ (n−1)(N −1)/N WH = −0.25 e ∗ + ... sin π (n − 1)/N
sin π n sin π (n + 1) ∗ −jπ n(N −1)/N ∗ −jπ (n+1)(N −1)/N − 0.25 e ∗ ∗ + 0.5 e sin π n/N sin π (n + 1)/N (7) On the basis of Eq. (5), it is generally considered that the spectral line energy is mainly concentrated on the positive spectral line component, and the influence of the negative spectral line component is eliminated. Combined with Eq. (6) and Eq. (7), the Eq. (3) is modified to calculate the new correction value:
X (nm + ε) − X − (nm + ε) − X (nm − ε) − X − (nm − ε)
δ = 2ε ·
X (nm − ε) − X − (nm − ε) + 2 X (nm ) − X − (nm ) + X (nm + ε) − X − (nm + ε)
According to Eqs. (2)–(7), the estimation algorithm of synchronous measurement signal in the steady state can be obtained. In order to further obtain more accurate spectral line estimation, the iterative operation of Eqs. (2)–(7) is carried out, and the difference between the last two iterations is the smallest. The algorithm flow chart is shown in Fig. 1:
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Waiting for the completion of data acquisition Amplitude, Frequency, Phase Correction by Eg. (4)
Data windowed filtering
A
DFT calculation of fundamental wave k k+1 k-1
k
-A
k-1
k 0, ε is an extremely small positive constant, w(0) = −x(0), η(0) = 0.
3 Tracking Performance Analysis of Differential Observer In order to analyze the fast-tracking performance of the fast-tracking differential observer described in Eq. (3), the differential tracking error ef = η − η is defined. From η = f (θ, t) = x˙ and Eq. (2), the differential tracking error system could be obtained as following. e˙ f = η˙ − η˙ = η˙ − α/ε2 x˙ − η
= − α/ε2 η − η + η˙ = − α/ε2 ef + f˙ (θ, t)
(4)
The Eq. (4) could be simplified to Eq. (5). ε2 e˙ f = −αef + ε2 f˙ (θ, t)
(5)
It can be seen that the differential tracking error system described in Eq. (5) is a fastchanging singular linear system with input f˙ (θ, t) and output ef when ε is extremely small. Due to the particularity of the function f (θ, t), in order to analyze the fast convergence characteristics of the Eq. (5), the linear system superposition principle is adopted. According to Hypothesis 1, Hypothesis 2 and Hypothesis 3, the equivalent representation of f˙ (θ, t) can be obtained, which is f˙ (θ, t) = f˙1 (θ, t) + f˙2 (θ, t), f˙1 (θ, t) is a bounded function, f˙2 (θ, t) is composed by a series of discrete time impulse functions, which is illustrated in Eq. (6). f˙2 (θ, t) =
N
mi δ (ti )
(6)
i=1
Where, mi = mi − mi , δ (ti ) is the unit impulse function at time ti . The detailed description of Eq. (6) is shown in Fig. 1.
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Fig. 1. Equivalent representation of the function f˙ (θ, t) in Eq. (5)
Assuming the initial differential tracking error in Eq. (5) ef (t0 ) = e0 , according to the linear system superposition principle, the differential tracking error Eq. (5) can be equivalently described as the sum of the following two subsystems. ε2 e˙ 1 = −αe1 + ε2 f˙1 (θ, t)(e1 (t0 ) = e0 )
(7)
ε2 e˙ 2 = −αe2 + ε2 f˙2 (θ, t)
(8)
The subsystem described in Eq. (7) can be seen as a dynamic system with initial state e0 and input f˙1 (θ, t). The subsystem described in Eq. (8) can be seen as a dynamic system with an initial state 0 under the effects of series impulse f˙2 (θ, t). Thus, the fast convergence of the differential tracking error system described in Eq. (5) can be obtained by analyzing the subsystem described in Eq. (7) and the subsystem described in Eq. (8) respectively. Theorem 1: It could be indicated that there exists an extremely small positive constant ε in the fast-tracking differential observer described in Eq. (3). When ε < ε , the fast-tracking differential observer could track the differential signal f (θ, t) of system described in Eq. (1) rapidly. Proof: According to the linear system superposition principle, the fast convergence of the differential tracking error system described in Eq. (5) can be obtained by analyzing the subsystem described in Eq. (7) and the subsystem described in Eq. (8) respectively. Therefore, two steps are implemented to analyze the fast convergence of the system described in Eq. (5). Step 1: Fast Convergence of Subsystem Described in Eq. (7) For the subsystem described in Eq. (7), define the continuous positive definite Lyapunov function W (e1 ) = e12 , and derivate W (e1 ), the follows Eq. (9) could be acquired that √ √ ˙ = − 2α e12 + 2e1 f˙1 ≤ − 2α W + 2k0 W ≤ − α W − α W − 2k0 W (9) W ε2 ε2 ε2 ε2
Let k0 = sup f˙1 , c1 = 4k02 /α 2 . When W (e1 (t)) ≥ c1 ε4 , the Eq. (9) becomes ˙ ≤ − α/ε2 W (10) W
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Let t0 be the initial time of the system in Eq. (1), W (e1 (t0 )) = c2 , c2 is a positive constant. When c2 ≥ c1 ε4 , there exists the constant τε , which could be described as following (11) τε = − ε2 /α ln c1 ε4 /c2 W (e1 (t)) satisfies
W (e1 (t)) ≤ c2 e
−
α (t−t0 ) ε2
t(t0 , t0 + τε ]
(12) ≤ c1 ε4 . When W (e1 (t)) = c1 ε4 , the Eq. (10) is still gained. and W e1 t0 + τε Therefore, the Eq. (13) could be obtained. (13) W (e) < c1 ε4 t > t0 + τε
From Eq. (13), It can be seen that when t > t0 + τε , W (e1 ) is O(ε). Furthermore, according to the definition of the Lyapunov function W (e1 ), when t ≥ t0 + τε , |e1 | satisfies Eq. (14)
|e| < c3 ε2
(14)
Where |e| is O(ε),c3 = c1 . Meanwhile, τε is O(ε) in Eq. (11). Therefore, there
exists an extremely small positive constant ε in the subsystem described in Eq. (7), e1 will converge to O(ε) rapidly when ε < ε . Step 2: Fast Convergence of Subsystem Described in Eq. (8) Considering the subsystem described in Eq. (8), the transfer function e1 (s)/f˙2 (s) can be expresses as the following (15) e1 (s)/f˙2 (s) = −ε2 / ε2 s + α At present, the time domain descriptions of the impulse response for e2 at time t ∈ {ti }N i=1 could be described as the Eq. (16).
e2ti (t) = −ε2 mi e−
α/ε2 t
(t ≥ ti )
(16) Here, e2ti (t) is an exponential decay function with initial value ε2 mi at time ti , ε2 mi is O(ε). Since α/ε2 is extremely large when ε is very small, e2ti (t) has a rapid convergence speed. Meanwhile, according to the linear system superposition principle, under the effects of series impulse functions f˙1 (θ, t), the time domain description of e2 (t) in Eq. (8) can be equaled to the algebraic sum of the impulse response e2ti (t) at each time t ∈ {ti }N i=1 . e2ti (t) can be described as following e2 (t) =
N i=1
e2ti (t)
(17)
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2 For each time t ∈ {ti }N i=1 , ε mi is O(ε). Thus, e2 (t) is O(ε) as well. Furthermore, it can be obtained that the subsystem described in Eq. (8) has extremely small positive constant ε , so that when ε < ε , e2 is O(ε). According to the linear system superposition principle, the time domain description of ef in the differential tracking error system (5) can be obtained as ef (t) = e1 (t)+e2 (t). Let ε∗ = min ε , ε
, by the analysis of steps 1, 2 above and the O(ε) characteristics
of e2 (t) itself, it can be obtained that, when ε < ε∗ and t ≥ t0 + τε , the fast tracking differentiator (3) tracks the differential signal f (θ, t) of the system (1) in a short time. End of Proof. In addition, according to the fast-tracking differential observer described in Eq. (3), the transfer function η(s)/x(s) can be described as the following. η(s)/x(s) = αs/ ε2 s + α (18)
When ε is extremely small, the transfer function described in Eq. (18) can be expressed approximately as the following
η(s)/x(s) ≈ s
(19)
Thus, when ε is extremely small, η can be approximated as the differential signal f (θ, t) of Eq. (1).
4 Simulation Results In this section, numerical simulations of the fast-tracking differential observer described in Eq. (3) have been conducted. The differential term f (θ, t) in Eq. (1) is selected as following time functions: 1) f1 (t) = 3sin(2t), 2) f2 (t) = 5e−0.1t sin(4t), 3) f3 (t) = 1.5 2sign(sin(3t)), 2 4) f4 (t) = −2sign(sin(t)) · |sin(t)| , 5) f5 (t) = ln(2 + |sin(t)|), 6) −2t t + t + 1 . The system state initial value is selected as x(0) = 0 and the f6 (t) = e parameters of the fast-tracking differential observer are selected to be α = 8. Simulation results for ε = 0.5 and ε = 0.05 are shown in Fig. 2 and Fig. 3, respectively. In Figs. 2 and 3, the figure (a), (c), (e), (g), (i) and (k) show the time-varying curve of f1 (t), . . ., f6 (t) and its tracking curve of f 1 (t), . . ., f 6 (t) respectively, the figure (b), (d), (f), (h), (j) and (l) show the tracking error curve respectively. From Fig. 2 and Fig. 3, It can be seen that the fast-tracking differential observer described in Eq. (3) not only has superior fast differential tracking ability, but also the smaller of parameter ε, the better the fast-tracking performance of the fast-tracking differential observer could be achieved.
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Fig. 2. Simulation results of the fast-tracking differential observer (ε = 0.5)
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Fig. 3. Simulation Results of the Fast-Tracking differential observer (ε = 0.05)
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5 Conclusion This paper designs a new fast-tracking differential observer. The theoretical analysis results indicated the fast-tracking differential observer tracks the differential signal rapidly. The simulation results proved the fast-tracking performance of this observer. Acknowledgments. This research work is financially supported by the National Natural Science Foundation of China (grants no. U1760205).
References 1. Han, J.Q.: Active Disturbance Rejection Control Technique - The Technique for Estimating and Compensating the Uncertainties. National Defense Industry Press, Beijing (2009). (in Chinese) 2. Shi, Y.L., Hou, C.Z.: Design of improved nonlinear tracking differentiator. Control Decision 23(6), 647–650 (2008). (in Chinese) 3. Kong, Y., Tian, D., Xiu, J., Che, X.: Nonlinear tracking differentiator based on feedforward and arctangent function. In: 2020 39th Chinese Control Conference (CCC), pp. 2968–2973 (2020) 4. Deng, P., Long, Z., Dai, C.: An improved nonlinear tracking differentiator and its application. In: 2018 13th World Congress on Intelligent Control and Automation (WCICA), pp. 1037–1041 (2018) 5. Xie, Y., Zhang, H., She, L., Xiao, G., Zhai, C., Pan, T.-C.: Design and implementation of an efficient tracking differentiator. IEEE Access 7, 101941–101949 (2019) 6. Zong, X., Chen, Z., Zheng, J., Cheng, X.: Design of a rapid tangent sigmoid function tracking differentiator. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 15–19 (2020) 7. Alvarez-Ramirez, J., Suarez, R., Morales, A.: Cascade control for a class of uncertain nonlinear systems: a backstepping approach. Chem. Eng. Sci. 55(16), 3209–3221 (2000) 8. Peng, J.L., Sun, X.X., Dong, W.H., et al.: Design of a terse discrete high-speed trackingdifferentiator without chattering. In: IEEE International Conference on Intelligent Computing & Intelligent Systems. IEEE (2009). (in Chinese)
Identification of Transient Power Quality Disturbances Based on S-transform Feature Extraction and Random Forest Classification Qinqin Wang and Wang Guo(B) School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China [email protected]
Abstract. Aiming at the problem of cross coupling, complexity and difficulty in classification and identification of transient power quality composite disturbances, an accurate identification method based on S-transform feature extraction and random forest classification is proposed. Firstly, the complex time-frequency matrix of the disturbance signals is extracted by S-transform, and the corresponding 11 characteristic curves are obtained, and the feature set of the disturbance signals is constructed from five aspects as the input of the random forest classifier. Then the random forest classification model is constructed. 70% of the original data is taken as the training set and 30% as the test set. The important features are selected in the original feature set, and the number of base classifiers is determined to optimize the random forest model and train the model. The test data verifies the accuracy of the model. Simulation results in different noise environments verify the accuracy and robustness of the method. Keywords: Power quality · Transient compound disturbance · S transform · Random forest
1 Introduction With the high penetration of renewable energy with random, intermittent and fluctuating output characteristics, and the use of a large number of nonlinear and impact loads, the power quality problem is becoming more and more complex [1]. Among them, the duration of power quality transient disturbance is short and the variation range of electrical indicators is narrow, which is not easy to detect and interferes with the operation of sensitive equipment. The cross coupling of power quality transient disturbance makes the detection and identification of power quality transient disturbance more difficult [2]. In order to improve power quality and carry out targeted governance, it is the premise to extract effective features and accurate classification and recognition from the massive power quality disturbance signal data [3]. In the feature extraction stage, the commonly used time-frequency analysis methods include short-time Fourier transform (STFT), wavelet transform (WT), Hilbert Huang transform (HHT) and S transform. The window © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 656–665, 2022. https://doi.org/10.1007/978-981-19-1528-4_66
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function of STFT is fixed, so the time resolution and frequency resolution cannot be taken into account [4–8]. The size of WT window is variable, but it depends on the selection of wavelet basis function and is easily affected by noise. HHT is composed of EMD decomposition and Hilbert transform, which has the disadvantages of endpoint effect and mode aliasing [9]. S-transform uses Gauss window function, the window width is inversely proportional to the frequency, and transforms with the change of frequency, so it has strong noise immunity and can generate a lot of time-frequency information for feature extraction of power quality disturbances [10]. Classifiers based on machine learning mainly include decision tree (DT), support vector machine (SVM), artificial neural network (ANN), extreme learning machine (ELM) and random forest (RF) [11, 12]. RF is an integrated learning model, its basic units are decision trees. In the process of classifier construction, random samplings are used to reduce the risk of over fitting. It can deal with data samples with high-dimensional characteristics, and has strong generalization ability, good classification effect and obvious comprehensive advantages. In this paper, the advantages of S-transform and random forest are used for feature extraction and classification of transient power quality composite disturbances. Firstly, according to the mathematical model of transient power quality disturbance, the normal signal and 22 kinds of data sets of disturbance signals are generated by simulation. After S-transform of disturbance signals, the time-frequency matrix is obtained and the characteristic curves are extracted to construct disturbance characteristic quantities, which are used as the inputs of random forest classifier to generate training set and test set, and the transient power quality composite disturbances are classified and identified in different SNR environments.
2 Feature Extraction Based on S-transform 2.1 S-transform S-transform is a time-frequency signal processing method, which is the inheritance and development of STFT and WT. For nonstationary random signals h(t), the standard S-transform is as follows [13]: +∞ h(t)w(t − τ )e−i2π ft dt (1) S(τ, f ) = −∞
Where, τ is the translation factor, which controls the position of the window function on the time axis; f corresponding signal frequency; i is an imaginary unit; w(t − τ ) is a Gaussian window function: (t−τ )2 f 2 |f | w(t − τ ) = √ e− 2 2π
(2)
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In the Gaussian window function, 1/f is the scale factor. The scale of Gaussian window varies with the frequency. The smaller the frequency value is, the wider the window is, the higher the frequency resolution is, and the lower the corresponding time resolution is; The larger the frequency value is, the narrower the window is, the higher the time resolution is, and the lower the corresponding frequency resolution is. Substituting Eq. (2) into Eq. (1), the total variable form of S-transform is obtained: +∞ (τ −t)2 f 2 |f | h(t) √ e− 2 e−i2π ft dt (3) S(τ, f ) = 2π −∞ S-transform overcomes the shortcomings of STFT window time width fixed and difficulty in selecting the basis function of WT. It can adaptively adjust the window width according to the frequency transformation, and does not need to select the window function, and can provide intuitive time-frequency characteristic information. 2.2 Feature Extraction According to the mathematical model of power quality transient disturbance, the normal signal C0, five single transient disturbances (Voltage Sag C2, voltage interruption C3, transient pulse C4, transient oscillation C5),nine double compound transient disturbances (C1 + C2, C1 + C3, C1 + C4, C1 + C5, C2 + C1, C2 + C3, C2 + C4, C2 + C5, C4 + C5) and 8 kinds of triple compound transient disturbances (C1 + C2 + C3, C1 + C2 + C4, C1 + C2 + C5, C1 + C4 + C5, C2 + C1 + C3, C2 + C1 + C4, C2 + C1 + C5, C2 + C4 + C5) are constructed separately. There are 23 types in total. S-transform is carried out for each kind of signal, and the result is a two-dimensional complex time-frequency matrix with clear physical meaning. The row corresponds to the frequency sampling point, and the column corresponds to the time sampling point. The elements in the matrix are complex, and the phase and amplitude information can be obtained. Taking the modulus of the complex frequency domain matrix obtained by S-transform, 11 kinds of characteristic curves of the signal are extracted. They are time domain characteristic curves: fundamental frequency amplitude curve T1, maximum value curve T2, minimum value curve T3, average value curve T4, standard deviation curve T5 and root mean square value curve T6; Frequency domain characteristic curves: maximum curve T7, minimum curve T8, average curve T9, standard deviation curve T10 and root mean square curve T11. Due to space limitation, this paper takes C2 + C4 composite transient disturbance as an example.
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As shown in Fig. 1, the original sampling signal of C2 + C4 type transient composite disturbance is constructed according to the mathematical model. The fundamental frequency is 50 Hz, the sampling points are 1280, the sampling frequency is 6400 Hz, and the sampling period is 10 periods (0.02 s). Voltage sag disturbance is applied between 0.0455 s–0.1320 s (corresponding to sampling points 291–849), and transient pulse disturbance is applied between 0.1055 s–0.1065 s (corresponding to sampling points 675–682). Through S-transform, the three-dimensional diagram as shown in Fig. 1 is obtained, which is the intuitive expression of complex time-frequency matrix of S-transform result (Fig. 2).
Fig. 2. Three dimensional graph of S-transform matrix.
In Fig. 1, the transformation of T1 amplitude of characteristic curve reflects the amplitude sag of fundamental frequency component. The characteristic curve T2 reflects the change of the principal component of the signal amplitude, which is a concave curve, indicating the occurrence of voltage sag. In the characteristic curve T3–T6, the time corresponding to the peak can be clearly seen, and the start and end time of the two disturbances are recorded at the sampling points. It can be seen from the characteristic curve T7–T11 that the disturbance signal contains only one frequency principal component (excluding harmonics) (Fig. 3). According to the above 11 characteristic curves, the disturbance feature set is constructed from the difference between the maximum value and the minimum value, the sum of the maximum value and the minimum value, the average value, the standard deviation, and the root mean square value. A total of 55 disturbance features are obtained, which are labeled F1-F55.
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3 Transient Power Quality Disturbances Identification 3.1 Theory of Random Forest Classifier Random forest classifier is a kind of ensemble learning model which belongs to supervised learning. It uses decision tree as basic unit, and uses bootstrap resampling technology to sample randomly from input data set, then extracts n bootstrap data sets and selects
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random features. And each decision tree learns and classifies independently. Finally, the classification result is obtained by the majority voting method. 3.2 Importance Selection of Features Feature importance calculation is an embedded function of RF. In order to reduce the number of features in the classification model, reduce the redundancy of features and improve the classification accuracy, the 55 dimension features of the data sample set are selected. Gini index can be used to evaluate the importance of features in random forest. Gini index, also known as Gini impure, is used to reflect the chaos of the system. The smaller the Gini index of the feature, the higher the purity, and the better the classification effect. It also shows that the feature has a greater contribution to the classification. When generating the decision tree, Gini index is 0, which means that a certain category has reached the classification completion. The Gini index is calculated as follows [14]: Gini(D) = 1 −
k |Ck | 2 ( ) |D|
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Fig. 4. Feature importance ranking chart.
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This paper uses Gini index to evaluate the importance of the extracted 55 Viterbi characteristics and rank them. The results are shown in Fig. 1:
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The GBDT algorithm embedded in the random forest is used to train the classifier, and the corresponding relationship between the number of features and the accuracy of the test set is obtained, as shown in Fig. 1. It can be seen that when the number of features reaches 31, the accuracy of the test set reaches 97.91%, which is the highest. Therefore, the first 31 features are selected to form the importance feature set T* {F4, F12, F9, F15, F35, F14, F1, F20, F19, F3, F42, F47, F30, F23, F50, F6, F10, F52, F39, F45, F17, F25, F36, F48, F37, F2, F29, F34, F43, F53, F28} (Fig. 6). 3.3 Selection of the Number of Classifier Units In addition to the number of feature, there is another important parameter in the random forest model—the number of classifier units (the number of decision trees generated). When the number of decision trees reaches a certain value, the random forest model will achieve the best prediction effect. At the same time, the calculation time is considered to select the optimal number of base classifiers which make the classification effect the best and the calculation time the shortest. The upper limit of the number of base classifiers is set to 500, and the accuracy of classification and prediction of random forest model is calculated from 0. After repeated tests, the final number of base classifiers is 110, and the prediction accuracy of the model reaches 97.33%, as shown in Fig. 1:
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Fig. 6. Selection of the number of classifier units.
4 Simulation and Analysis In this paper, the classification and identification of transient power quality is mainly realized through the following steps [15–18]: a) According to IEEE power quality standard and mathematical model of transient power quality disturbance, the parameters and time interval of disturbance signal are set as random values within the specified range, and normal signal and 22 kinds of transient power quality disturbance are generated, 300 groups of each type and 6900 groups in total.
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b) All the disturbance signals are transformed by S-transform to obtain the corresponding complex time-frequency matrix, and the 11 dimensional characteristic curve of the signal is extracted. The 55 dimensional disturbance feature set is constructed from five aspects. c) The above 55 × 6900 dimension data set is used as the original data set of random forest classifier input, and the corresponding classification labels are set for each 300 groups of data in order in column 56. d) Two thirds (4600 groups) of the total number of feature sets are selected as the training set, and the remaining 2300 groups are selected as the test set to construct the random forest classifier model. e) According to the principle that the smaller Gini index is, the more important the feature is, the importance of the original feature set is sorted and filtered. f) Considering the prediction accuracy of the model and the time of the algorithm, the optimal number of classifier units is determined. g) All the base classifiers vote, and the tags displayed by the classification results are used to identify and verify the transient power quality disturbances. The experimental software is Matlab 2016a and Python. Due to the actual operation of the power system, affected by the objective environment, the actual data often contains noise. In order to simulate the real situation and test the anti-interference ability of RF in the process of classification, Gaussian white noise is added to the original signal to test the accuracy of RF recognition in noise free and signal-to-noise environments of SNR = 20 dB, 30 dB, 50 dB. Table 1. Prediction accuracy of RF classification under different noises. Disturbance types
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It can be seen from Table 1 that in the environment of ordinary noise (signal-to-noise ratio is not less than 30 dB), the accuracy is as high as 97%, and in the environment of extreme noise (signal-to-noise ratio is 20 dB), the accuracy can also reach 96%. This shows the method has good anti-interference ability and accurate recognition ability.
5 Concluding Remarks For normal signal and 22 kinds of power quality transient disturbance events, the simulation model is established, and the S-transform feature extraction and random forest classifier are used to identify the disturbance. Experiments show that the method has the advantages of high classification accuracy, stable performance and strong anti-noise ability, and has high practical value. However, there is still room for improvement in this paper, for example, how to further improve the time-frequency resolution accuracy of transient power quality disturbance by S-transform and the problem of super parameter adaptive optimization of random forest classifier model.
References 1. Wu, Z.X., Yang, A., Zhu, L.J.: Power quality disturbance recognition based on a recurrent neural network. Power Sys. Prot. Control 48(18), 88–94 (2020). (in Chinese) 2. Zheng, W., Lin, R.Q., Wang, J.: Power quality disturbance classification based on GAF and a convolutional neural network. Power Sys. Prot. Control 49(11), 97–104 (2021). (in Chinese) 3. Xu, L.W., Li, K.C., Luo, Y.: Classification of complex power quality disturbances based on incomplete S-transform and gradient boosting decision tree. Power Sys. Prot. Control 47(6), 24–31 (2019). (in Chinese)
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4. Wang, R.M., Wang, H.Y., Zhang. Y.N.: Composite power quality disturbance recognition based on segmented modified S-transform and random forest. Power Sys. Prot. Control 48(7), 19–28 (2020). (in Chinese) 5. Chen, H.W., Wang, Y.Y., Tang, X.F.: A new fault line selection method for distribution network system based on transient energy and direction of S-transformation. Power Sys. Prot. Control 46(14), 71–78 (2018). (in Chinese) 6. Zhang, W.M., Gao, D., Zhang, H.J.: Sparse fast fourier transform analysis of transformer vibration signal. Electr. Eng. 03, 59–63 (2017). (in Chinese) 7. Zhou, Z.H.: Analysis of excitation current in DC-biased transformer by wavelet transform. Electr. Eng. 21(6), 69–72 (2020). (in Chinese) 8. Cao, L.Z., Liu, J.F., Zheng, X.W.: Classification and recognition of power quality multidisturbance based on EEMD-HHT. Electr. Eng. 04, 66–70 (2017). (in Chinese) 9. Tang, Q., Qiu, W., Zhou, Y.C.: Classification of complex power quality disturbances using optimized s-transform and kernel SVM. IEEE Trans. Ind. Electron. 67(11), 9715–9723 (2020) 10. Kumar, R., Singh, B., Shahani, D.T.: Recognition of power-quality disturbances using Stransform-based ANN classifier and rule-based decision tree. IEEE Trans. Ind. Appl. 51(2), 1249–1257 (2015) 11. Qu, H.Z., Liu, H., Li, X.M.: Recognition of multiple power quality disturbances using multilabel random forest. Power Sys. Prot. Control 45(11), 1–7 (2017). (in Chinese) 12. Sun, Z.P., Cui, Q., Zhang, Z.l.: The application of multiclass support vector machine in power transformer fault diagnosis. Electr. Eng. 20(10), 25–28 (2019). (in Chinese) 13. Yang, J.F., Jiang, S., Shi, G.G.: Classification of composite power quality disturbances based on piecewise-modified S transform. Power Sys. Prot. Control 47(9), 64–71 (2019). (in Chinese) 14. Wang, T., Sun, Z.P., Cui, Q.: Research on fault diagnosis of power transformer based on classification decision tree algorithm. Electr. Eng. 20(11), 16–19 (2019). (in Chinese) 15. Mahela, O.P., Shaik, A.G., Gupta, N.: A critical review of detection and classification of power quality events. Renew. Sust. Energ. Rev. 41, 495–505 (2015) 16. Wang, J., Xu, Z., Che, Y.: Power quality disturbance classification based on compressed sensing and deep convolution neural networks. IEEE Access. 7, 78336–78346 (2019) 17. Khokhar, S., Zin, A.A.B.M., Mokhtar, A.S.B., Pesaran, M.: A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renew. Sust. Energ. Rev. 51, 1650–1663 (2015) 18. Deokar, S.A., Waghmare, L.M.: Integrated DWT–FFT approach for detection and classification of power quality disturbances. Int. J. Electr. Power Energy Syst. 61, 594–605 (2014)
Dynamic Potential Analysis of Dyneema Ropes Above Transmission Line Based on Three-Dimensional Simulated Charge Method Xuehuan Wang, Nana Duan(B) , and Shuhong Wang School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China [email protected], [email protected], [email protected]
Abstract. With the proposal of new infrastructure concepts, the demand for smart and digital grid construction is gradually increasing. In order to meet specific simulation needs in the power system, it is necessary to design a dedicated simulation program. In order to study the algorithm selection of a specific simulation program, this paper uses the three-dimensional simulation charge method (3DCSM) to analyze the potential distribution on the dyneema ropes above the transmission line, and compares the calculation results with the calculation results of the finite element method (FEM). Keywords: 3DCSM · Dyneema ropes · Potential
1 Introduction Due to the limitations of land resources and line corridors, the current transmission line construction will face crossover situation. Therefore, in order to ensure the construction period and construction safety, it is necessary to develop a special simulation system which is easy to operate and convenient to formulate the construction scheme, so as to provide theoretical data for the construction of transmission lines. At present, the calculation methods of electric field distribution around transmission lines are mainly FEM [1, 2], moment method [3, 4], CSM [5–7], etc. The above methods can obtain relatively accurate results in the process of electric field analysis of transmission lines. However, in practical applications, in order to ensure the construction period, it is often hoped that the calculation accuracy should be ensured and the calculation time should be as short as possible. Relatively speaking, the 3DCSM has the characteristics of simple principle, less unknown quantity and fast solving speed, which is suitable for compiling special calculation program. Based on the 3DCSM, this paper establishes a calculation model for overhead lines considering the sag of the conductor, and analyzes the potential distribution of the dyneema ropes above the transmission line during the movement. At the same time, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 666–674, 2022. https://doi.org/10.1007/978-981-19-1528-4_67
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simulation results are compared with the finite element simulation results. The results show that the calculation method can not only ensure the accuracy, but also save the calculation time.
2 Calculation Principle 2.1 Calculation Principle of 3DCSM The basic idea of the CSM is to use a set of virtual charge simulation method to equivalently replace the continuous distribution of charges on the electrode surface outside the solved field, and calculate the electric field with the potential or electric field strength formula of these simulated charges [8]. The calculation flow chart is shown in Fig. 1.
Fig. 1. Flow chart of CSM.
In order to analyze the potential around the three-dimensional transmission line, this paper adopts the simulated line charge method, and considers that the charge in each unit is linearly distributed [6, 9, 10]. The schematic diagram of a single line charge is shown in Fig. 2. Suppose the length of the linear charge unit is L, P1 (x 1 , y1 , z1 ), P2 (x 2 , y2 , z2 ) be the coordinates at both ends, and convert it to standard coordinates as lu L mu Y (u) = y1 + L nu Z(u) = z1 + L X (u) = x1 +
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Suppose the charge density at both ends of the linear charge is τ (0) = τ 1 , τ (L) = τ 2 , then τ (u) = τ1 +
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where, a and b are undetermined constant. The potential produced by the line charge at any point Q (x, y, z) is L τ (u) 1 du (5) ϕ= 4π ε0 0 D where, D is the distance from the origin to the field, let u = Lt, then D = (x1 + lt − x)2 + (y1 + mt − y)2 + (z1 + nt − z)2 Substitute (4), (6) into (5) may be simplified to 1 L At + B ϕ= dt √ 2 4π ε0 0 Et + Ft + G
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ϕ i (i = 1,2, ˑ ˑ ˑ , N), and the charge density at both ends of the line charge is τ i (i = 1, 2, ˑ ˑ ˑ , N), then the matrix equation can be established [12]. [λ][τ ] = [ϕ]
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According to formula (9), the value of the analog line charge is obtained, and finally the potential value at any point can be obtained according to the superposition theorem. 2.2 Catenary Equation In order to make the calculation model close to the actual project, the numerical simulation in this paper uses a three-dimensional catenary line model, as shown in Fig. 3.
Fig. 3. Diagram of unequal height catenary
The catenary equation at unequal heights is [13] ⎡ ⎤
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3 3DCSM Calculation Example 3.1 Calculation Example Parameters In this paper, the 500 kV voltage grade transmission line is calculated. The conductor phase spacing is 13.72 m, the conductor type is LGJ-400/35, the four-split conductor, and the height of the conductor to the ground is 17.2 m [14, 15]. The simple schematic diagram of the three-dimensional transmission line is shown in Fig. 4.
Fig. 4. Diagram of 3DCSM calculation model
The parameters of ABC three-phase voltage are 500 √ uA = √ 2 cos 2π f + 0◦ 3 500 √ uB = √ 2 cos 2π f − 120◦ 3 500 √ uC = √ 2 cos 2π f + 120◦ 3 where, f is voltage frequency, 50 Hz.
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3.2 Calculation Example Results The coordinates of the starting point of the dyneema ropes is (0, 0, 30), moving along the y-axis. The velocity is 1 m/s, the simulation time is 0.1 s, and the simulation step length is 0.005 s. The simulation result is: the calculation time is 1 s, and the potential of each point on the dyneema ropes at t = 0.1 s is shown in Table 1. Table 1. 3DCSM calculation result at 0.1 s. Coordinate
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(0, 0.08, 30)
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(0, 0.09, 30)
28265.6
(0, 0.1, 30)
28251.0
4 FEM Calculation Example 4.1 Calculation Example Model In order to verify the correctness of the calculation of the 3DCSM results, in the finite element simulation software, the simulation model with the same set of parameters and the meshing graph are shown in Fig. 5.
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Fig. 5. Simulation model and meshing graph
4.2 Calculation Example Results In the finite element simulation software, the simulation conditions are the same as the 3DCSM, the calculation time is 750 s, and the simulation results are shown in Table 2. Table 2. 3DCSM calculation result at 0.1 s. Coordinate
FEM/V
(0, 0, 30)
27811.9
(0, 0.01, 30)
27801.5
(0, 0.02, 30)
27991.3
(0, 0.03, 30)
27781.4
(0, 0.04, 30)
27771.7
(0, 0.05, 30)
27762.3
(0, 0.06, 30)
27753.1
(0, 0.07, 30)
27744.1
(0, 0.08, 30)
27735.3
(0, 0.09, 30)
27726.8
(0, 0.1, 30)
27718.5
5 Analysis and Conclusion The comparison results of the 3DCSM and the FEM are shown in Table 3. It can be seen from Table 3 that the maximum error between them is 2.10%, and the calculation of the same model, the simulation charge method takes a relatively short time.
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Table 3. FEM calculation result at 0.1 s. Coordinate
FEM/V
3DCSM/V
Error/%
(0, 0, 30)
27811.9
28395.8
2.1
(0, 0.01, 30)
27801.5
28381.5
2.1
(0, 0.02, 30)
27991.3
28367.1
1.3
(0, 0.03, 30)
27781.4
28352.7
2.1
(0, 0.04, 30)
27771.7
28338.3
2.0
(0, 0.05, 30)
27762.3
28323.8
2.0
(0, 0.06, 30)
27753.1
28309.3
2.0
(0, 0.07, 30)
27744.1
28294.8
2.0
(0, 0.08, 30)
27735.3
28280.2
2.0
(0, 0.09, 30)
27726.8
28265.6
1.9
(0, 0.1, 30)
27718.5
28251.0
1.9
In summary, when compiling the special simulation software for power analysis, if the accuracy of the calculation results is not high, the calculation time can be effectively saved by using the 3DCSM. The research conclusion has a certain reference value for the design of special programs.
References 1. Hu, Y., Weng, X.: The research of live-working on Sanxia 500 kV double transmission line. High Voltage Eng. 27(1), 57–58 (2001). (in Chinese) 2. Hu, Y., Nie, D.Z., Wang, L.N.: Calculation of the induced voltage and safety working condition on 500Kv compact double circuit transmission line. In: High Voltage Eng. 27(6), 31–33 (2001) 3. Zhang, X.W.: Research on radio interference prediction of EHVDC flexible DC converter station. North China Electric Power University (2018). (in Chinese) 4. Tang, B., Jiang, H.T., Sun. R., et al.: Calculation of power frequency electric field for AC parallel transmission lines in the same corridor based on MOM. High Voltage Apparatus 54(12), 104–109 (2018). (in Chinese) 5. Peng, Y., Ruan, J.J.: Calculation of three-dimensional harmonic electric field around ultra high voltage overhead line based on the charge simulation method. High Voltage Eng. 32(12), 69–77 (2006). (in Chinese) 6. Zhang, Q.H., Zan, H.Y., Zhang, M.H.: The calculation method of UHVDC transmission line’s surface electric field intensity based on the charge simulation method. J. Qingyuan Polytech. 8(1), 56–59 (2015). (in Chinese) 7. Zhang, Y.F., Wang, R.Z., Pu, S.B.: Analysis of potential distribution around lightning rod or wire using charge stimulation method. J. Microwaves 31(1), 55–60 (2015). (in Chinese) 8. Ni, G.Z.: Principles of Engineering Electromagnetic Field. Higher Education Press, Beijing (2002). (in Chinese) 9. Wang, Q.D., Luo, Y., Yang, F., Chu, X.: Three-dimensional simulation of electric field around building under EHV transmission lines. High Voltage Apparatus 49(10), 1–6 (2013). (in Chinese)
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10. Lee, B.Y., Park, J.K., Myung, S.H., et al.: An effective modelling method to analyze electric field around transmission lines and substations using a generalized finite line charge. IEEE Trans. Power Delivery 12(3), 1143–1150 (1997) 11. Wang, R., Tian, J., Wu, F., et al.: PSO/GA combined with charge simulation method for the electric field under transmission lines in 3D calculation model. Electronics 8(10), 1–18 (2019) 12. He, J.H., He, K., Cui, L.F.: Charge-simulation-based electric field analysis and electrical tree propagation model with defects in 10 kV XLPE cable joint. Energies 12(23), 1–22 (2019) 13. Meng, S.M.: Overhead Transmission Line Design. China Electric Power Press (2007). (in Chinese) 14. Wang, L.Z., Lu, H., Li, W.: Research on the ground electric field of 500 kV transmission line considering meteorological factors. Bull. Sci. Technol. 34(12), 182–191 (2018). (in Chinese) 15. Wang, L.Z.: Study on influence of meteorological factors on the ground electrical field strength of transmission lines based on matlab. North China Electric Power University (2018). (in Chinese)
Research on Control of Grid-Forming Converters Based on Virtual Oscillator Control Lei Huang1,2(B) 1 Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin, China 2 School of Automation and Electrical Engineering, Tianjin University of Technology and
Education, Tianjin, China [email protected]
Abstract. In view of the issues such as frequency synchronization, load sharing and transient frequency regulation capability of gird forming renewable power converters in power system, a nonlinear virtual oscillator control (VOC) is presented. Based on Van der Pol oscillator, VOC controller which is composed of rotation, synchronization and magnitude regulation components is illustrated and its relationship with droop control is analyzed. VOC control strategy requires only local measurements and set-points of pre-specified power flow to realize communication-less decentralized control of grid-connected power converters and guarantees robust stability. To verify the feasibility, the proposed method is verified using a model comprising multiple VOC controlled converters. Results show that VOC converters are capable of dynamic synchronization, frequency stability and real-time droop characteristics regulation with different line impedance. Keywords: Grid forming converter · Virtual oscillator control · Droop control · Voltage source converter
1 Introduction With the increase of penetration rate of renewable energy systems, the power grid is facing big challenge. Conventional synchronous generator system and control can realize stability and synchronization between multiple systems, while the power electronics system interfaced power grid lacks rotational inertia and synchronization ability. The majority of powers electronics converters interfaced to power system are controlled in grid-following mode [1, 2]. Under this mode, the converter unit is designed to output desired power command, with phase-lock-loop unit to synchronize with the grid. While with higher power electronics converter penetration, the grid-forming mode operation to form the grid voltage and provide grid frequency support is inevitable. Grid-forming control mode is a promising alternative to operate the inverter-based grid for the capability of tackling sudden load changes which will cause power sharing problem with grid-following control. Unlike traditional synchronous generator based power system, the power electronics converters do not possess the inherent inertia and damping. Grid-forming converters must © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 675–682, 2022. https://doi.org/10.1007/978-981-19-1528-4_68
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provide and regulate the reference for voltage and frequency, with load-sharing, drooping capability [3]. Droop control methods that are set to mimic the speed droop control of a synchronous generator have been studied. However, droop control is developed based on steady-state equations and its dynamic performance is not acceptable in certain applications. A plethora of control strategies is originated by emulating the dynamics of a synchronous generator called virtual synchronous generator (VSG) [4, 5]. Equivalence of virtual synchronous machines and frequency-droops for converter-based micro-grids has been verified [6]. Grid-forming converters are dispatchable voltage sources and do not rely on infinite grid for synchronization, they can operate without a dedicated phase-lock-loop while maintaining the grid voltage, frequency and synchronization. In this paper, a virtual oscillator control (VOC) method is proposed to realize the power sharing, voltage following and synchronization of grid-forming converters. The VOC method is inherently a nonlinear control method originated from Liénard-type oscillators which can globally synchronize paralleled converter-based power system [7, 8]. This method uses only the local measurement and frequency and voltage reference to realize grid-forming operation with global stability, and the intrinsic droop characteristic of the VOC control is illustrated in this paper. The proposed method is verified using MATLAB/Simulink.
2 Principle of Virtual Oscillator Control 2.1 Basic Principle of Nonlinear Oscillator It has been proved that Van der Pol type nonlinear oscillator can realize synchronization of parallel converters while maintaining almost global stabilization [9]. Figure 1 shows the principle of Van der Pol oscillator comprising of a negative resistor, current controlled current source, inductor and capacitor. The inductor and capacitor form the LC oscillation with ω = L C .
Fig. 1. Block diagram representation of a Van der Pol virtual oscillator controller.
The characteristics of the system shown in Fig. 1 can be expressed as ⎧ di vj L ⎪ ⎪ ⎨ L dt = k v dv ⎪ v3 j ⎪ ⎩c = −μ 2 + σ vj − kv iL − ki kv ij dt kv
(1)
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where ki and kv are the coefficient of the input current and output voltage respectively, sub j denotes the jth branch. The oscillator can be realized by software and by proper selection of parameters, thus the oscillator can output sinusoidal voltage with pre-defined frequency and realize global stability. 2.2 Decentralized VOC Control Based on Van der Pol oscillator, this paper proposes a virtual oscillator control(VOC) which can not only realize synchronization but also meet the objectives such as converge to prescribed magnitude of voltage, active and reactive power. This VOC method is decentralized control for it uses only the local measurements. The desired dynamics can be realized via decentralized control as d vk = ω0 Jvk + c1 · eθ,k (v) + c2 · ev,k (vk ) dt
(2)
0 −1 where J = . 1 0 In the above equation, the first term of the right side makes sure the voltage is rotating at ω0 , the second term is synchronization through physics and the third term performs local amplitude regulation. The whole system is almost globally asymptotically stable with respect to a limit cycle corresponding to a pre-specified solution of the AC power-flow equations at a synchronization frequency. The second term in Eq. (2) is 1 p∗ q∗ (3) c1 · eθ,k (v) = ηR(δ)( ∗2 ∗ ∗ vk − io,k ) q p v where η is the parameter to design, p∗ , q∗ and v∗ are pre-scribed magnitude value of active, reactive power and voltage, io,k is the measurement of k-th current under α − β coordinate, δ = arctan ωr0klk is the line parameter, and. cos(δ) − sin(δ) R(δ) = . sin(δ) cos(δ) The second term is viewed as phase error and this error vanishes when the voltage and power equal to the given values. The third term in Eq. (2) is c2 · ev,k (vk ) = ηα(v∗2 − vk 2 )
(4)
where vk is voltage measurement of the k-th branch and α is the parameter to design. 2.3 System Structure Using VOC The system diagram of grid-forming converter using VOC is shown in Fig. 2.
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Fig. 2. Schematic of gird forming distributed control system based on VOC.
3 Droop Property of VOC Control Conventional P-f and Q-V droop is applicable for power sharing among sources in highly inductive circumstances. The relationships of active power-frequency droop and reactive power-voltage droop in conventional droop control are d θ = ω0 + kp (pk∗ − pk ) dt
(5)
d vk = vk − vk + kq (qk∗ − qk ) (6) dt where kp and kq are droop coefficients. To compare with the conventional droop, assume that line impedance is pure inductive, ie. δ = π/2. Rearrange Eq. (2) in polar coordinate, we have p∗ pk d θk = ω0 + η( ∗k2 − ) (7) dt vk vk 2 q∗ 1 d qk vk = η( ∗k2 − (8) )vk + ηα(1 − ∗2 vk 2 )vk 2 dt vk vk vk
when system is stable, we have vk = v∗ , thus we can derive the following equations d η θk ≈ ω0 + ∗2 (pk∗ − pk ) dt vk
(9)
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η d vk ≈ ∗2 (qk∗ − qk ) + ηα(vk∗ − vk ) dt vk
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(10)
Compare Eqs. (9) and (10) with (5) and (6), we can see that VOC resembles droop controller. Equations (7) and (8) are nonlinear and can guarantee fast response when dynamic occurs [10].
4 Case Study Simulations To verify the proposed VOC of a grid-forming power system, the IEEE 9-bus test system based on Simulink is presented in Fig. 3. The system is assumed to be symmetric and balanced RLC impedance load. The Parameters in use are shown in Table 1. Table 1. Parameters used for the case study simulation. Parameter
Value
Parameter
Value
Base power
100 MVA LV/MV transformer capacity
Base voltage
230 kV
LV/MV transformer primary side 1 kV voltage
1.6 MVA
Base frequency
314 rad/s
LV/MV secondary side voltage
MV/HV transformer
210 MVA LV/MV transformer r1 = r2
13.8 kV 0.0072 pu
MV/HV transformer primary side 13.8 kV voltage
LV/MV transformer l1 = l2
0.019 pu
MV/HV secondary side voltage
230 kV
LV/MV transformer rm, lm
348,156 pu
MV/HV transformer r1 = r2
0.0028 pu VOC parameter η
0.023
MV/HV transformer l1 = l2
0.08 pu
VOC parameter α
0.066
MV/HV transformer rm = l m
500 pu
Node 5, 7, 9 constant load
0.75 pu
Node 7 transient load
0.75 pu
In order to study and compare the performance, the same strategy is applied for the three converters. Each grid-forming converter in Fig. 3 consists of the system illustrated in Fig. 2. These converters are identical 500 kVA converters.
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Converter 2
4
9
8
MV/HV
2 HV/MV
DC AC 6
Converter1 AC DC
7 Converter 3 DC AC
5 HV/MV 3
Fig. 3. IEEE 9-bus system for simulation.
The synchronization behavior of the VOC is tested under load disturbance Frequency of the system with three VOC converters after a 0.75 pu load increase. Figure 4 shows the synchronizing of three converters, from which we can see that the converters reach frequency synchronization at a very fast time-scale and synchronize to each other after load disturbance.
Fig. 4. Frequency of the system with three VOC converters after a 0.75 pu load increase.
Figure 5 shows droop performance of the VOC. It is observed that VOC exhibit similar performance with droop control behaviour, even under load disturbance dynamics.
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Fig. 5. Load sharing characteristics when x/r = 50.
5 Conclusions This paper discussed VOC control and verified its realization in grid-forming converter application. VOC control can realize dynamic synchronization and droop control under dynamic condition. The strategy need only local measurement and specified given values to achieve multi-machine coordination without communication. Acknowledgments. Project supported by Scientific Research Project of Tianjin Education Commission (JWK1617).
References 1. Rocabert, J., Luna, A., Blaabjerg, F., et al.: Control of power converters in ac microgrids. IEEE Trans. Power Electron. 27(11), 4734–4749 (2012) 2. Foglia, G.M., Frosio, L., Iacchetti, M.F., et al.: Control loops design in a grid supporting mode inverter connected to a microgrid. In: Proceedings of 17th European Conference on Power Electronics and Applications (EPE’15 ECCE-Europe), Geneva, Switzerland, pp. 1–10 (2015) 3. Kroposki, B., Johnson, B., Zhang, Y., et al.: Achieving a 100% renewable grid: Operating electric power systems with extremely high levels of variable renewable energy. IEEE Power Energy Mag. 15(2), 61–73 (2017) 4. Zhong, Q.-C., Nguyen, P.-L., Ma, Z., Sheng, W.: Self-synchronized synchronverters: Inverters without a dedicated synchronization unit. IEEE Trans. Power Electron. 29(2), 617–630 (2014) 5. Suul, J.A., D’Arco, S., Guidi, G.: Virtual synchronous machine-based control of a singlephase bi-directional battery charger for providing vehicle-to-grid services. IEEE Trans. Ind. Appl. 52(4), 3234–3244 (2016) 6. D’Arco, S., Suul, J.A.: Equivalence of virtual synchronous machines and frequency-droops for converter-based microgrids. IEEE Trans. Smart Grid 5(1), 394–395 (2014) 7. Johnson, B.B., Dhople, S.V., Hamadeh, A.O., et al.: Synchronization of parallel single-phase inverters with virtual oscillator control. IEEE Trans. Power Electron. 29(11), 6124–6138 (2014) 8. Johnson, B.B., Sinha, M., Ainsworth, N.G., et al.: Synthesizing virtual oscillators to control islanded inverters. IEEE Trans. Power Electron. 31(8), 6002–6015 (2016)
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9. Tôrres, L.A.B., Hespanha, J.P., et al.: Power supplies dynamical synchronization without communication. In: Proceedings of IEEE Power and Energy Society General Meeting, San Diego, USA, pp. 1–6 (2012) 10. Johnson, B., Rodriguez, M., Sinha, M., et al.: Comparison of virtual oscillator and droop control. In: Proceedings of 2017 IEEE 18th Workshop on Control and Modeling for Power Electronics (COMPEL), Stanford, USA, pp. 1–6 (2017)
Motion Artifact Removal Based on Stationary Wavelet Transform and Adaptive Filtering for Wearable ECG Monitoring Zhengyi Xu(B) , Yifeng Wang, Xingchen Tian, Xinlei Zheng, and Jiangtao Li Xi’an Jiaotong University, Xianning West 28, Shaanxi 710049, China [email protected]
Abstract. Non-contact ECG monitoring is an important method to realize longterm ECG monitoring, which is of great significance for cardiovascular diseases. However, during the monitoring process, motion artifacts will decrease the measured signal quality. This paper aims to solve the influence of motion artifacts and broaden the use scenarios of non-contact ECG monitoring. In this paper, a hybrid algorithm is proposed, which combines the stationery wavelet transform and adaptive filtering to greatly improve the adaptability of the algorithm to different kinds of motion artifacts. In order to evaluate the effectiveness of the motion artifact removal algorithm, we apply it to a variety of motion artifact interfered ECG signals collected by a self-designed non-contact ECG monitoring system. The results show that the proposed hybrid algorithm can significantly restrain the interference of motion artifacts, thus improving the signal quality. It also shows high adaptability to different types of motion artifacts. Keywords: Non-contact ECG measurement · Capacitive coupling · Motion artifact removal · Stationary wavelet transform · Adaptive filtering
1 Introduction 1.1 Background Cardiovascular disease has become the No.1 killer, threatening the health of Chinese residents. According to the “China Cardiovascular Disease Report 2019” released by China Cardiovascular Disease Center, 330 million people are suffering from cardiovascular disease, accounting for more than 20% of the national population [1]. Among them, the morbidity and mortality of the elderly are the highest. Therefore, with the deepening of the aging degree, the social health pressure brought by cardiovascular diseases will gradually increase. Electrocardiogram (ECG) is a series of curves reflecting the changes of electrophysiological activities produced by each cardiac cycle [2]. It is one of the important methods to examine the heart health state. Current mainstream ECG monitoring employs gel electrodes which have shortcomings for it is a direct contact with skin, such as poor comfort © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 683–693, 2022. https://doi.org/10.1007/978-981-19-1528-4_69
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(easy to cause allergies and restricted activities), not suitable for long-term monitoring (conducting gel will be air-dried and peeled off). In recent years, capacitive coupling non-contact ECG monitoring has attracted wide attention because it does not directly contact with the skin, so that it can meet the requirement of long-term and distributed monitoring. The schematic diagram of capacitive coupling non-contact ECG monitoring is shown in the Fig. 1. Through the design of PCB structure, a coupling capacitor is formed between the copper-coated layer (electrode layer) in PCB and the skin, to extract ECG signals. The coupling effect depends on many factors, such as electrode area, distance from electrode to skin, electrode input impedance, etc. Through the previous tests [3,4], the values of the corresponding parameters were determined, and the capacitive coupling non-contact ECG monitoring vest with input impedance up to T level was designed, as shown in Fig. 2, which is capable of high quality ECG monitoring in different use scenarios. REF Cg
Cs R2
Clothing
Skin
vi
vs Electrode
Shield
Solder Shield REF Mask
+
C1 Rbias REF
C2 OA1
R3
+ OA2 -
R4
Vin
REF
PCB
Fig. 1. Schematic diagram of capacitive coupling non-contact ECG monitoring
Fig. 2. Capacitive coupling non-contact ECG monitoring vest
1.2 Artifact and Noise in ECG Monitoring The amplitude of ECG signal is small (uV ~ mV) and the main frequency band is 0.2– 100 Hz. In addition, the generation mechanism of ECG signal and the internal system of human body are very complex, leading to a lot of interference during ECG monitoring [5]. Interference is mainly divided into four categories: powerline interference, baseline wandering, electromyogram (EMG) interference and motion artifact [6]. Table 1 shows the typical frequency and amplitude of these four artifact and noise. Powerline interference is generated by spatial electromagnetic field coupling on the surface of human body. Baseline wandering is caused by respiration and adds a low-frequency interference to the ECG signal. EMG interference is caused by muscle contraction. Muscle contractions produce high-frequency electrical signals with small values that are superimposed as tiny ripples that blur and distort the ECG signal. Motion artifact is caused by the movement of the human body. The relative position between the electrode and the human body changes after the motion, resulting in the change of the coupling interface distribution and the irregular fluctuation of the baseline. Among them, powerline interference, baseline wandering and EMG interference can be well filtered through the combination of high-pass and low-pass filters, while the
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Table 1. Frequency and amplitude of artifact and noise Artifact/Noise
Frequency (Hz)
Amplitude (of ECG VPP)
Powerline interference
50
50%–200%
Baseline wandering
0.05–2
15%
EMG interference
5–1000
10%
Motion artifact
1–10
100%–500%
Table 2. Evaluation indexes change Sample
Corr
MSE
MAE
PRD
1
After
0.983
516.475
15.672
15.613
Before
0.655
15815.309
102.568
86.398
2
After
0.997
184.524
9.383
12.587
Before
0.680
15815.309
102.568
116.534
motion artifact often shows different artifact waveforms due to the variety of motion types and the difference between measured individuals. Motion artifacts will affect the quality of measured ECG signals and influence the information of each band. Recorded ECG signal with and without motion artifacts is shown in the Fig. 3(b).
Fig. 3. (a) Recorded clean ECG signal (b) Recorded ECG signal with left hand lift motion artifact.
Commonly used signal processing methods include regression method, adaptive filtering method, wavelet transform method and so on. Regression method is to perform
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regression fitting on artifact, and then deduct from original signal, but it is easy to produce overestimation, also has the problem of two-way cross interference. The adaptive filtering method uses the adaptive algorithm to remove the artifact interference, but the reference signal needs to be given. The wavelet transform method is to carry on the multi-scale transform to the original signal, and then carry on the threshold filter to the wavelet coefficient of each layer, but the determination of the threshold value of each layer is quite complicated. Therefore, the existing motion artifact removal methods have poor adaptive ability and can only target on some specific motion artifacts. In this paper, we combine stationery wavelet transform with adaptive filtering, then we propose a hybrid algorithm for motion artifact removal method. This hybrid algorithm does not need to set the artifact reference signal and has good adaptability to complex human body movement.
2 Algorithm 2.1 Stationary Wavelet Transform Stationary wavelet transform no longer conducts downward sampling during each decomposition, so that the length of high-frequency detail coefficient and low-frequency approximation coefficient after transformation can be equal to the length of the original signal, and most valuable information of the original signal can be preserved [7]. In the decomposition process of stationary wavelet transform, orthogonal wavelet bases of different scales are used to decompose signals into different frequency bands to obtain the high frequency detail coefficient and low frequency approximation coefficient of the corresponding frequency band, in which the high frequency detail coefficient represents the transient phenomenon and the low frequency approximation coefficient represents the fundamental frequency component [8]. The corresponding decomposition formula of g(t) ∈ L2 (R) is: g(t) =
I i=1
cij φij (t) +
J I
dij ψij (t)
j φij (t) = 2− 2 φ 2−jt − i ci,j+1 =
M m=1
(1)
i=1 j=1
H (m − 2j)cij , di,j+1 =
M
(2)
G(m − 2j)cij
(3)
m=1
In the formula (2), φij (t) represents the scale function. In the formula (3), m represents the ordinal value of the current sampling window; M represents the total number of sampling points in the sampling window; i represents the decomposition degree of the wavelet function; j represents the discrete degree of the wavelet function; ci,j+1 and di,j+1 represent the approximation coefficient and the fine-segment coefficient; H (j) and G(j) represent the low-pass filter and high-pass filter of layer j.The corresponding wavelet decomposition process is shown in Fig. 4.
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Fig. 4. Graph of stationary wavelet decomposition
2.2 Adaptive Filtering Adaptive filter is a digital filter which can automatically adjust the performance of digital signal processing according to the input signal. The block diagram of adaptive filtering is shown in Fig. 5.
Fig. 5. Block diagram of adaptive filtering
The input signal x(n) = [x(n), x(n − 1), . . . , x(n − M + 1)]T enters the m-order T FIR filter, the filter coefficient Wn = w0 (n), w1 (n), . . . , wM −1 (n) convolves with the input signal to get the output signal dˆ (n) = Wn ∗ x(n), and compares with the expected signal to get the error signal e(n) = d (n) − dˆ (n). The error signal is updated through the update algorithm, and the filter coefficient is updated, which is Wn+1 = Wn + Wn . Different adaptive filters are formed according to different update algorithms. Update algorithms mainly used are divided into Least Mean Square (LMS) and Recursive Least Square (RLS). LMS sets the convolution of the error signal e(n) with the input signal x(n) multiplied by a coefficient μ as the Wn . Update algorithm of LMS is shown in formula (4) Wn+1 = Wn + μe(n) ∗ x(n)
(4)
RLS builds a judgment function, which introduces the concept of forgetting factor, so that the closer the error e(n) is to n, the greater the effect it has. The gain vector is obtained by taking the partial derivative of this judgment function. Jn =
n
λn−i e2 (i)
(5)
i=1
Wn = Wn−1 + k(n) ∗ x(n)
(6)
P(n − 1) ∗ x(n) , λ + x(n)T P(n − 1) ∗ x(n) 1 P(n − 1) − k(n)x(n)T P(n − 1) P(n) = λ
(7)
k(n) =
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Formula (5) shows the judgment function. Formula (6) shows the update algorithm of RLS, where k(n) is the gain vector, shown in formula (7). P(n) is the covariance matrix. When compared with each other, LMS has lower convergence speed and weaker anti-jamming. While RLS has higher startup speed, faster convergence speed and better filtering effect. 2.3 Proposed Hybrid Algorithm The hardware system has the three-level filter composed of high-pass filter, notch filter and low-pass filter, which can play a good role in inhibiting the powerline interference, baseline wandering and EMG interference. However, during the actual testing process, it was found that the PCB board and connecting lines would be coupled with the spatial electric field, resulting in a large ripple. As a result, preprocessing is an essential part. Proposed hybrid algorithm mainly includes four parts: preprocessing, QRS waveform extraction, SWT threshold filtering and adaptive filtering ARLSF. Flow diagram is shown in Fig. 6.
Fig. 6. Flow diagram of hybrid algorithm
A. Preprocessing Preprocessing has three steps, gaussian smoothing, high-pass & low-pass filter and median filter. Preprocessing part is meant to remove impulse interference, high frequency ripple and powerline interface. B. QRS waveform extraction The artifact interference caused by the change of motion state usually has a large amplitude fluctuation range, so it is necessary to extract the QRS band with the largest amplitude in the ECG, to avoid information loss. ECG signals present a certain periodicity and the energy contained in QRS band is the largest in each cardiac cycle, so the range of QRS band can be determined based on the segmented waveform energy. Firstly, calculate E(n), which represents the energy of the ECG signal at the window of the set length. Then, divide E(n) into segments of equal length and calculate the maximum value of each segment Emax (m). Calculation criterion F as formular (8). F = median(Emax) − c ∗ 1.4826 ∗ mad (Emax), c ≥ 2
(8)
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The corresponding sequence position of E(n) ≥ F is recorded as QRS. C. SWT threshold filtering Since the length of the data needs to be divided into 2M during the stationery wavelet transform, where M is the order of the stationery wavelet transform. Set M = 7 and choose “Haar” wavelet [9]. The main features of QRS complex are mainly observed in the wavelet coefficients from d 3 to d 7 , while the main features of motion artifacts are mainly observed in the wavelet coefficients from d 7 to d 8 . After the length conversion, the wavelet coefficients of each layer corresponding to the QRS band are set to zero, and the soft threshold method is used to denoise each layer to get the artifact signal. ECG signal is divided into several segments of equal length. Calculate the maximum statmax and minimum statmin of the coefficient series of each layer on each segment. The upper and lower thresholds of each coefficient series are calculated as formular (9)(10). Smax = median(statmax ) + mad (statmax )
(9)
Smin = median(statmin ) + mad (statmin )
(10)
D. adaptive filtering ARLSF The motion artifact signal of stationary wavelet transform is taken as the reference signal to carry out adaptive filtering processing, and the processing result is superimposed with the QRS waveform to obtain the final signal. 3rd RLS algorithm is adopted.
3 Measurement and Results In order to verify the effectiveness of the proposed algorithm, we use ECG data from Physionet.org to create an artificial ECG signal interfered by motion artifacts. ‘MITBIH Noise Stress Test Database’ is taken as the motion artifact signal and ‘MIT-BIH Arrhythmia Database’ as the pure ECG signal [10]. The effectiveness of the proposed hybrid algorithm is evaluated by comparing the changes of several evaluation indexes, including: correlation coefficient (Corr), mean square error (MSE), mean absolute error (MAE) and percent root mean square difference (PRD) [11]. Figure 7 and Fig. 8 show two samples, including original signal, motion artifact interfered signal and processed signal. Our proposed hybrid algorithm can remove artifacts. Table 2 shows evaluation indexes change of these two samples before and after processing. After being processed, Corr increases while MSE, MAE and PRD decreases, which means the signal quality has been greatly improved. Furthermore, we verify the effectiveness of the proposed hybrid algorithm for the acquired ECG signals by wearable devices [12]. ECG signals were recorded using our self-designed non-contact ECG monitoring system as shown in Fig. 9. Put on the designed ECG vest and adjust the velcro so that the electrodes can fit tightly against the body. ECG signals are coupled through the high input impedance capacitance coupling non-contact ECG electrode. Then these signals will be processed including differential
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Fig. 7. Sample 1
Fig. 8. Sample 2
amplification and three-stage filtering. The boost gain factors is designed as 1000 times. The upper limit frequency of the filter passband is 100 Hz, the lower limit frequency is 0.1 Hz, and the notch frequency is 50 Hz. Finally, processed signals are converted into digital signals and transmitted to PC GUI via Bluetooth (the baud rate is set as 115200).
Fig. 9. ECG acquisition process with non-contact ECG monitoring system
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Motion artifact interfered ECG signals are acquired by performing 6 motion common in daily life, including: (a) right-arm lifting, (b) left-arm lifting, (c) electrode shifting, (d) walking, (e) jumping, (f) standing up and sitting down. Each of these movements is performed for 10 s. Figure 10 shows the recorded motion artifact interfered ECG signals while performing these movements. All of these ECG signals are greatly interfered by motion artifacts compared with the pure signals in Fig. 3(a). Figure 11 shows the processed signals by our proposed hybrid algorithm. The removed artifacts are shown in Fig. 12. The proposed hybrid algorithm can sinificantly rstrain the interference of motion artifacts, thus improving the signal quality. It also shows high adaptability to different types of motion artifacts.
Fig. 10. Recorded ECG signals. (a) right-arm lifting, (b) left-arm lifting, (c) electrode shifting, (d) walking, (e) jumping, (f) standing up and sitting down
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Fig. 11. Processed ECG signals
Fig. 12. Removed artifacts (a) right-arm lifting, (b) left-arm lifting, (c) electrode shifting, (d) walking, (e) jumping, (f) standing up and sitting down
4 Conclusion Capacitive coupling non-contact ECG monitoring is an important means to realize longterm monitoring of ECG signals, which is of great significance to monitor the health status of patients with cardiovascular diseases. However, during the process of noncontact ECG monitoring, it will be affected by motion artifacts, which will decrease the measured ECG waveform quality. This paper aims to solve the influence of motion artifacts and broaden the use scenarios of non-contact ECG monitoring. In this paper, a hybrid algorithm is proposed, which combines the stationery wavelet transform and adaptive filtering to greatly improve the adaptability of the algorithm to different kinds of motion artifacts. In order to evaluate the effectiveness of the motion artifact removal algorithm, we apply it to a variety of motion artifact interfered ECG signals collected by a self-designed non-contact ECG monitoring system, calculate the change of the evaluation indexes between the ECG signals before and after processing, and analyze the ECG signals after removing the motion artifact. The results show that the proposed hybrid algorithm can significantly restrain the interference of motion artifacts, thus improving the signal quality. It also shows high adaptability to different types of motion artifacts. Future work will be focused on how to dynamically implant the algorithm into the self-designed non-contact ECG monitoring system to realize dynamic removal of motion artifacts during the monitoring process.
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References 1. National Center for Cardiovascular Diseases, China.: Report on cardiovascular diseases in China 2019. Beijing: Encyclopedia of China Publishing House (2020) 2. Harland, C.J., Clark, T.D., Prance, R.J.: Electric potential probes - new directions in the remote sensing of the human body. Meas. Sci. Technol. 13(2), 163–169 (2002) 3. Wang, Y., Xu, Z., Liu, S., Dai, Z., Li, J.: Noncontact ECG measuring system based on capacitive PCB electrodes. In: Ma, W., Rong, M., Yang, F., Liu, W., Wang, S., Li, G. (eds.) The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering. LNEE, vol. 742, pp. 607–615. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6606-0_55 4. Xu, Z., Wang, Y., Zheng, X., Ali, R.S., Li, J.: Modelling research on the mechanism of noncontact ECG measurement based on capacitive coupling. In: Ma, W., Rong, M., Yang, F., Liu, W., Wang, S., Li, G. (eds.) The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering. LNEE, vol. 742, pp. 785–794. Springer, Singapore (2021). https://doi.org/10. 1007/978-981-33-6606-0_72 5. Mason, J.W., Hancock, E.W., Gettes, L.S.: Recommendations for the standardization and interpretation of the electrocardiogram. part ii: electrocardiography diagnostic statement list. a scientific statement from the american heart association electrocardiography and arrhythmias committee, council o. Heart Rhythm 4(3), 413–419 (2007) 6. Pawar, T., Anantakrishnan, N.S., Chaudhuri, S., Duttagupta, S.P.: Impact of ambulation in wearable-ecg. Ann. Biomed. Eng. 36(9), 1547–1557 (2008) 7. Nason, G.: The stationary wavelet transform and some statistical applications. Lecture Notes in Statistics 103, Wavelet and Statistics (1995) 8. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(4) (1989) 9. Strasser, F., Muma, M., Zoubir, A.M.: Motion artifact removal in ECG signals using multiresolution thresholding. Signal Processing Conference. IEEE (2012) 10. Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215-e220 [Circulation Electronic Pages. http://circ.ahajournals.org/content/101/23/e215.full. 2000 (June 13) 11. M. Jasim, Abdulhamed & Abd, Hamam & Abdul-Jabbar, Jassim. (2020). COMPLEXITY REDUCTION OF SLANTLET TRANSFORM STRUCTURE BASED ON THE MULTIPLIERLESS REALIZATION. https://doi.org/10.13140/RG.2.2.18094.13127 12. Berwal, D., Vandana, C.R., Dewan, S., Jiji, C.V., Baghini, M.S.: Motion artifact removal in ambulatory ecg signal for heart rate variability analysis. IEEE Sens. J. 19(24), 12432–12442 (2019)
Exploration of Macroscopic Characterization for Low-Voltage AC Arc State Zhi-ang Huang1,2,3 , Xin Zheng1,2,3(B) , and Xiaojie Shan1,2,3 1 College of Electrical Engineering and Automotion, Fuzhou University, Fuzhou 350108,
Fujian Province, China [email protected] 2 Fujian Key Laboratory of New Energy Generation and Power Conversion, Fuzhou University, Fuzhou 350108, Fujian Province, China 3 Fujian Province University Engineering Research Center of Smart Distribution Grid Equipment, Fuzhou 350108, China
Abstract. As far as low voltage AC arc is concerned, there is a close relationship between its micro state and macro state. Previous studies have shown that there is a correlation between the characteristic parameters of wavelet energy spectrum of arc voltage and arc burning trend, but there is a lack of theoretical elaboration and verification. In this paper, from the perspective of spectral analysis, an arc generation platform with spectral measurement function is built to realize the real-time measurement of plasma spectral line information in the arc gap. And the electron temperature and density of the arc are calculated by the Boltzmann diagram method and Stark broadening method, and their data trajectories in the whole arcing process are recorded. The comparison between the data trajectory and the wavelet energy time spectrum of arc voltage shows that the wavelet energy spectrum eigenvalue is feasible in theory as the macroscopic characterization parameter of arc state. Finally, the wavelet energy time spectrum of the new intelligent contactor at different breaking speeds is extracted and analyzed to verify the practicability of this macro characterization parameters. Keywords: AC Arc · Wavelet energy spectrum · Macroscopic characterization · Spectrum analysis · Electron density
1 Introduction In order to reduce the harm of low-voltage AC arc, many scholars build arc models to study its characteristics. In reference [1], aiming at the problem that the arc signal is nonstationary and difficult to identify, the arc characteristic parameters are extracted by using artificial neural network and empirical mode decomposition. However, the generation and combustion of the arc involve the transition of dielectric states between insulation and conductivity, and simple numerical analysis has limited guidance for the dynamic characteristics of the arc. To solve this problem, the literature [2, 3] uses the magneto hydro dynamic model to build a 3D mathematical model of arc plasma which realizes © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 694–700, 2022. https://doi.org/10.1007/978-981-19-1528-4_70
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the whole process simulation of arc starting, combustion and extinguishing. At present, many achievements have been made on the arc dynamic characteristics of low-voltage AC switch, mainly focusing on simulation and offline analysis. Due to the measurement of arc dynamic characteristics with certain limitations in the actual operation process, it is difficult to realize real-time detection and adjustment. In order to solve this problem, Zheng Xin and others found the correlation between the characteristic value of the highscale wavelet energy spectrum of the AC arc voltage and the arc combustion trend and discussed the possibility of the macro characterization parameter of the arc [4, 5], but due to the failure of deep analysis of its microscopic characteristics, the experimental phenomenon lacks theoretical explanation. In order to further study the physical significance of the eigenvalues of the arc voltage high-scale wavelet energy spectrum in arc state characterization, it is necessary to analyze the microscopic process of the arc plasma. The most commonly used diagnostic techniques of electric arc plasma are probe method, laser Thomson scattering method and spectroscopic method [6, 7]. In the studies on arc plasma, spectroscopic diagnostic technology gradually occupies an irreplaceable position and is mainly used in the field of high-voltage switching in recent years. Based on the preliminary research, the AC arc spectrum detection system is built to measure the plasma spectral line information in the arc gap, to record the electron density data of the whole combustion process and then to compare it with the arc voltage wavelet energy spectrum, so as to discuss the physical significance of the wavelet energy spectrum as the macro characterization parameter and its applicability is verified.
2 Design of AC Arc Spectral Measurement System 2.1 Construction of the Spectral Measurement System In order to measure the dynamic characteristics of AC arc, The spectral detection system is built on the basis of the AC arc characteristic analysis system designed in the literature [8], as shown in Fig. 1(a). The spectral information is generated by Ocean FX high-speed spectrometer, which has the wavelength detection range of 200–1000 nm and its integral time can be configured between 10 us–10 s as well as the scanning speed can reach 4500 times per second.
(a) Schematic diagram
(b) Physical diagram
Fig. 1. The low-voltage AC arc spectroscopy measurement system
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Figure 1(b) shows a physical diagram of the spectral measurement system. During the experiment, the dynamic and static contacts are separated to produce the arc, and the optical fiber probe collects the arc spectral data and transmits it to the spectrometer through the optical fiber, and then to the computer for data processing. 2.2 Processing of the Spectroscopic Measurement Data This paper selects Boltzmann diagram method as the calculation method of the electronic temperature [9]. The Boltzmann diagram method formula as follows: ln(
0.625 Iλ )=− E+C gA Te
(1)
In the formula: I is the relative strength of the measured spectral line; E is the excitation potential of the two spectral lines; g is a statistical weight; A is the spontaneous transition probability at the corresponding energy level; λ is the wavelength of the corresponding spectral line. Stark broadening method is the most effective method to detecting electron density when the plasma electron density is large enough [10]. The Stark broadening formula as follows: 1/4 1/6 −1/2 ωne 1 + 1.75 × ne α 1 − 0.068ne Te (2) λω = 1016 In the formula: λw is the half-height width of the peak of the atomic line; ne is an electron density; Te is the electron temperature of the plasma. The values of broadening parameter α and collide half width ω can be obtained from literature [11, 12]. 2.3 Spectral Measurements Over a Continuous Time Domain In order to discuss the physical significance of the wavelet energy time spectrum as the macroscopic characterization parameter, its electron density should be calculated. But the electron density at some point in the arc combustion is not enough to explain its physical significance, so it is necessary to collect the arc spectrum continuously. So the spectrometer is set to work on the continuous acquisition mode, which can collect plasma spectra on the continuous time domain and then to calculate the data track of electron temperature and electron density. The relative light intensities corresponding to different arcing time and different wavelengths are listed in Table 1.
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Table 1. The Intensity and electron temperature and density of the different combustion arc moment Arc time/ms
Intensity 383.3347 nm
Electron temperature/K
Electron density/m−3
467.666 nm
19
1133.36
184.0302
8031
8.54705 × 1020
345
3840.23
1175.3675
7838
8.209 × 1020
495
13150.3
4724.0405
7823
8.18 × 1020
625
18686.06
3162.5516
7870
8.26392 × 1020
949
11006.96
2757.629
7741
7.4801 × 1020
1109
9002.82
1259.8933
7708
7.43297 × 1020
1178
297.64
48.9032
7415
6.51156 × 1020
By fitting the Boltzmann curve [6], the electron temperature can be obtain. By substituting Te, α, ω and the measured λw into formula (2), the electron density ne is obtained. The obtained electron temperature and electron density are shown in Table 1. It can be seen that by setting the working mode of the high-speed spectrometer, we can obtain the spectral information on the continuous time domain. And the data of the electron temperature and electron density that changes over time can be obtained by calculating, which lays the foundation for further research.
3 Low-Voltage AC Arc Spectroscopy Analysis Through the AC arc characteristic analysis system [8] and the embedded wavelet energy spectrum transformation program in it, the arc voltage waveform and its wavelet energy spectrum waveform can be obtained. Meanwhile, the data track of the arc gap ne is obtained through the aforementioned spectral measurement system. Then the arc voltage waveform, arc ne and wavelet energy time spectrum can be comparative analyzed. However, due to the large acquisition interval in time domain, it is not enough to reflect the evolution process of ne in different time points. Therefore, the acquisition interval is reduced to 5–6 ms for further experimental analysis. The experimental conditions are as follow: 125 V, 5A, cos ϕ = 0.75, 0.1 mm/s for the electrode velocity, and 0.02 mm for the arc gap. A group of experimental data are shown in Fig. 2. By substituting the parameters of arc spectral lines in the figure into Eq. (2), the trajectory of ne changing with time can be obtained. At the same time, the arc voltage waveform corresponding to this group of data is transformed by high-scale wavelet energy spectrum, and the wavelet energy spectrum waveform is obtained. Both of them and arc voltage waveform are shown in Fig. 3. In Fig. 3, t1 -t3 is stable arcing stage and t3 –t4 is unstable arcing stage. The arc ne gradually increases from t1 , and the characteristic value of the wavelet energy spectrum E4 is small. With the electrode breaking, the arc is elongated, and the ne suddenly decreases as well as the E4 value increases. Then the arc enters the steady arcing phase,
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intensity/a.u.
50000 400
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30000 250 200
20000
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Fig. 2. Continuous spectral diagram of the electric arc combustion process
and ne reaches 1 × 1021 , E4 value is about 3000. From t3 moment, ne reach above 2.5 × 1021 , the arc enters unstable arcing stage and E4 value increased to about 25000. Finally, at t4 moment ne decreases, arc extinguished and E4 value reached the maximum of about 30000. At the same time, compared the arc voltage waveform with the wavelet energy spectrum, it can be found that in a very short time after arc voltage crossing zero, the eigenvalue of wavelet energy spectrum is larger. Because there is competition between dielectric recovery strength and voltage recovery strength during the moment after arc voltage crossing zero. So the size of the wavelet energy spectrum may be related to the competing results. Combined with Fig. 3, the analysis is as follows: 200
AC arc voltage waveform
150 100 Arc voltage/V
50 0 -50
-100 -150 -200
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30 25 20 15 10 5 0
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The value of E4
30000 25000 20000 15000 10000 5000 0
t1 t2
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Fig. 3. Arc voltage waveform, electron density change waveform and wavelet energy spectrum
At the moment of t1 , the electrode began to separate and the ne gradually increases. With the separation of the contacts, the electronic average free travel becomes longer, so the ne decreases. But with the combustion of the arc, = ujf -uhf < 0 (ujf is dielectric recovery strength, uhf is voltage recovery strength), the ne gradually rises to about 1021 . At the t2 − t3 stage, as the electrode stops moving, the combustion of the arc becomes stable and intense At this time, the is still less than zero, but the is relatively stable at this time. Due to the current crossing zero, the ne decreases, but the increase of the electron temperature leads to the upward trend for the maximum of the ne . During this period, E4 remains at about 3000. Finally, during t3 –t4 , the uhf is large, but with the increase of the ujf , the value gradually increases. When = 0, the maximum of ne reaches the peak, and then begins to decrease until the arc extinguish. At t4 moment, the ujf is much higher than the uhf , and the E4 value reaches the maximum, which also means that the arc is extinguished.
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By comparing the wavelet energy spectrum of arc voltage and the change process of electron density, the following conclusions can be drawn: The size of the wavelet energy spectrum eigenvalue of the arc voltage can be used to represent the competition result between ujf and uhf during arcing. The greater the ujf is, the greater the voltage during the arc breakdown, and on the macro circuit, the characteristic value of the wavelet energy spectrum of the arc voltage is larger.
4 Application of the Macroscopic Characterization Parameters In this section, based on the measured data of the new intelligent contactor [13], E4 is extracted and analyzed when the arc is extinguished under the condition of different breaking speed, which further verifies the feasibility of the wavelet energy spectrum eigenvalue as the arc state characterization parameter. Different breaking velocities are obtained by using contact spring with different stiffness coefficients. The experimental conditions are as follow: 220 V with power frequency, 630 A, 0.45 for load power factor. The stiffness coefficients of contact spring are 14460 N/m and 21969 N/m separately, and the E4 values are listed in Table 2. Table 2. The E4 value for the different contact breaking velocities Contact spring stiffness coefficients N/m
E4 Group 1
Group 2
Group 3
Group 4
14460
37200
34720
35076
36240
21969
190873
197737
190400
187608
As shown in Table 2, the E4 value with stiffness coefficient of 14,460 N/m remains about 30,000; the E4 value with stiffness coefficient of 21,969 N/m is stable magnitude 200,000, much higher than that of 14,460 N/m. That means the ujf is greater when E4 value is bigger, and it is easier to arc extinguish under the same conditions. The measured results in the literature [13] agree with the conclusions obtained in this paper.
5 Conclusion This paper establishes an arc generation platform with spectral measurement function, introduces spectral measurement to LV AC arc characteristic analysis, realizes continuous real-time measurement of plasma spectral line information in LV AC arc gap, and expounds the physical significance of wavelet energy spectrum parameters in the characterization of arc state, and verifies the feasibility in practical application.
References 1. Liu, Y., Guo, F., Li, L., et al.: A kind of series fault arc mathematical mode. Trans. China Electrotech. Soc. 34(14), 2901–2912 (2019). (in Chinese)
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2. Rong, M., Li, M., et al.: 3-D MHD modeling of internal fault arc in a closed container. IEEE Trans. Power Delivery 32(3), 1220–1227 (2017) 3. Rau, S.H., Zhang, Z., Lee, W.J.: 3-D magnetohydrodynamic modeling of dc arc in power system. IEEE Trans. 52(6), 4549–4555 (2016) 4. Zheng, X., Xu, Z.: An exploration on the best breaking areas of AC contactors. Trans. China Electrotech. Soc. 31(7), 198–206 (2016). (in Chinese) 5. Zheng, X., Shan, X.: Characteristic analysis and application research of low voltage AC Arc voltage waveform at the current zero. Trans. China Electrotech. Soc. 35(22), 4717–4725 (2020). (in Chinese) 6. Jiang, X., Wu, H., Wang, H., et al.: Spectral characteristics of AC Arc plasma over an ice surface. High Voltage Eng. 45(8), 2596–2602 (2019). (in Chinese) 7. Lin, X., Li, X., Xu, J., et al.: Research on numerical computation of SF6 breakdown voltages and spectral experiment in uniform electric fields. Proc. CSEE 36(1), 301–3009 (2016). (in Chinese) 8. Xu, H., Zheng, X.: Design of AC Arc analysis system based on LabVIEW. Electr. Energy Manage. Technol. 23, 30–34 (2018). (in Chinese) 9. Qiu, D.: Atomic Spectroscopic Analysis. Fudan University Press, Shang Hai (2002). (in Chinese) 10. Tu, X., Lu, S., Yan, J., et al.: Diagnosis of atmospheric DC argon plasma. Spectroscopy Spectral Anal. 26(10), 1785–1789 (2006). (in Chinese) 11. Griem, H.: Principles of Plasma Spectroscopy. Cambridge University Press, Cambridge (1997) 12. Bengoechea, J., Aguilera, A., Aragon, C.: Application of laser-induced plasma spectroscopy to the measurement of stark broadening parameters. Spectrochimica Acta PartB: Atomic Spectroscopy 61(1), 69–80 (2006) 13. Zhuang, J., Xu, Z.: Demagnetization control module with energy storage/release mode design for contactor. Proc. CSEE 38(21), 6470–6480 (2018). (in Chinese)
Cluster Division in Wind Farm Based on DTW and KL-GMM Fengrui Liu1(B) , Xiaojing Li2 , Xiaorui Hu1 , Shuxuan Li1 , Yunlian Liu1 , and Qiang Shi1 1 Department of Electrical Engineering, Northeast Electric Power University,
No. 169 Chang Chun Lu, Chuan Ying District, Jilin, China [email protected] 2 State Grid Jilin Electric Power Co. LTD, No. 169 Chang Chun Lu, Chuan Ying District, Jilin, China
Abstract. In order to improve the contour precision of large-scale wind farms and widen the adaptability of various working conditions, this paper proposed a twostage framework which calculates the clustering indexes of generators inside the wind farm based on dynamic time warping (DTW) and divides generator groups on the basis of Gaussian Mixture Model (GMM). Specifically, it first calculate the DTW distances of the active power time series between generators as the clustering index. Then it improves GMM by proposing a soft target distribution and utilizing the Kullback-Leibler (KL) divergence to implement adaptive and unsupervised generator clustering. Finally, it employs Matlab®/Simulink® to structure a simulated model. In this way, a grid-connection wind farm could be constructed. The results verify the effectiveness and considerable application value of the proposed equating approach by setting a fault three-phase short-circuit. Keywords: Dynamic time warping · Gaussian mixture model · Cluster division · Wind farm equivalence
1 Introduction Under the challenges of resource shortages and environmental pollution in the world, the scale of wind farms is gradually increasing [1, 2]. Therefore, how to establish a wind farm equivalent model that accurately represents the operating characteristics has a significant impact in analyzing the safe and stable operation of the system [3, 4]. At present, research on wind farm clustering mainly focuses on clustering indexes and clustering methods. As for clustering indexes, [5] regards the rotor speed as the clustering index, but the physical quantity at a fixed time point cannot represent the change of the fan’s operating state; [6] and [7] exploit the data of a certain period of time to group wind turbines, so the results are suitable for the selected period, without considering the impact of faults on grouping; Considering the impact of faults on groups, [8] selects pitch angles under both steady states and fault states. The above-mentioned documents all © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 701–707, 2022. https://doi.org/10.1007/978-981-19-1528-4_71
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select mechanical indicators as the clustering indicators, but under the electromechanical transient time scale, the inertial time constant of the mechanical system is relatively large, leading them not appropriate to only adopt mechanical characteristics. [9] and [10] opt the multivariate monitoring indicators of the doubly-fed induction-generators (DFIG) as grouping indexes, where there is redundant and strongly correlated data; [2] selects the mechanical and electrical features of DFIG as clustering indexes, and applies principal component analysis (PCA) to mitigate the above-mentioned interference. Yet, the principal components obtained by PCA are not produced by the actual system, leading to limitations for practical engineering applications. For example, if a user has prior knowledge and knows some data features, the user cannot intervene in the process, the efficiency and effect may be worse than expected. Hence, different perspectives must be taken into consideration when choosing grouping indexes. When the mechanical and electrical indicators are selected simultaneously, the effectiveness of clustering results will be improved. However, the subjective selection of clustering indicators may lead to insufficiently mining the information of power units represented by clustering data. Plus, the variable relationships and the data redundancy also have a significant influence on the groupings. On the one hand, the variables with strong correlation increase the workload of index acquisition and aggregation classification. On the other hand, it will cause that the clustering results mainly represent these variables, while ignoring the influence of other factors. Such a strong correlation between variables and redundant information will lead to the tendency of the cluster division results and relatively low equivalent accuracy. Clustering methods have been a research hotspot in the domain of machine learning recently. There are many studies concentrate on fast clustering with significant advantages in dealing with massive data. Popular clustering algorithms are k-means [11], fuzzy c-means [12], spectral graph clustering [13], etc., in which k-means is applied the most frequently. However, k-means is sensitive to initial centroids and noise, which may cause an overall offset. Plus, it requires pre-setting the k value, which has strong limitations. [14] proposed a power aggregation algorithm based on k-means to increase the aggregation accuracy by reducing the effects of outliers. But it still can’t deal with time series, because it can not capture temporal dependencies while requiring fixed points in space as the initial centroids and regarding each sampling point as a discrete dimensionality. Though many breakthroughs have been made in these field, wind farm equivalencespecific approaches still require more attention since the generally-designed methods cannot totally fit in this practical sense. In summary, k-means has a fast processing speed, but it is sensitive to dataset shapes, noisy samples and starting centroids. Moreover, in the wind farm, the different wind speeds will cause turbines to have different dynamic response times [15]. To tackle the aforementioned challenges, this paper proposes a two-stage approach to compute clustering indexes based on DTW and divide generator groups by modifying GMM on the basis of KL divergence to cluster generators inside a wind farm. The main innovations are summarized as follows: (1) The dynamic response curves from a single DFIG model in a fixed wind farm may be different under different wind speeds, and it will be more different considering
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different electrical distances, failure positions or typologies. But the antecedent of adapting conservative similarity measurements in grouping algorithms is the correspondence of temporal points, so they can not cluster DFIG monitoring data effectively. DTW algorithm is novelly introduced by generating a path for integrating, thus, the above problems are solved through transforming time series. It is more reliable to adopt the Euclidean distance of the integration paths as the similarity index between wind turbines, and it can effectively solve the problem of missing values in practical data of wind farms, which improves the accuracy of the model. (2) As for the unsupervised problem of generator clustering inside a wind farm, this paper derives a novel approach of optimizing the clustering parameters by mining the intrinsic principles of statistics, which leverage the gradient descent method and takes the KL divergence as the loss function. Generally, KL divergence can be only adopted into supervised learning. Plus, traditional clustering algorithms can not map the input features to the output labels so that the gradient can not be obtained. Compared with traditional methods, this approach can compute the clustering centroids and the optimal number of groups more accurately. An extensive experimental study was conducted for comprehensively prove the practical value of this model. The proposed algorithm was implemented by Python with packages including sklearn, pandas, and numpy. The simulated 16-DFIG wind farm was constructed on Matlab®/Simulink®. The three-phase short-circuit fault was set at a coordinate. The validity and accuracy of the presented framework are tested and verified by comparing the differences between the response curves belonging to the original and the equivalence model. The equivalent model is inferred following dividing the generator groups inside the wind farm by implementing the method stated in this paper.
2 Calculate Clustering Indexes Based on DTW and KL-GMM This section introduces how to apply DTW to consolidate timing alignment paths. Compared with previously popular similarity computation methods, DTW benefits the model considerably in respect of unaligned temporal points. Such phenomenon is caused by the response time differences of wind generators. 2.1 Error Analysis of Calculating Similarities Between Fans Based on Traditional Euclidean Distance Define two sequences P and Q with sizes m and n, respectively. When m = n, the Euclidean distance should be calculated as (1) shows. m (1) D(E) = (pi − qi )2 i=1
Different wind turbines in the recovery characteristics mismatch after the failure occurs. Nevertheless, the Euclidean distance requires temporal alignments of response dynamics of various wind energy generators, resulting in huge computation errors. Intuitively, the clustering result is unauthentic, since the detailed dynamic behaviors of the
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wind farm can not be reflected. To verify the above theory, this paper leverages Matlab®/Simulink® to build a wind farm. The simulated model composes 30 DFIGs with 1.5e3 kW rated power.
Fig. 1. Wind farm simulation figure
We set the terminal voltage as 575v. The local voltage increased to 25 kV where a generator and a transformer are wired together in a unit wiring method. Overhead wires link each of the field’s six transformers, which are then transmitted to the 25 kV/220 kV substation and the external power grid. The fault started at 12 s and lasted one second, which was set on the parallel point, cleared at 12.1 s. The active power values of the wind turbines dropped at the time when the failure started. The instantaneous condition soon terminated following a hundredths-of-asecond transient and a steady state was soon achieved. Corresponding values could then be obtained. After cleaning the failure, the turbine’s active power experienced violent ups and downs within a fixed period of time. The active power was then restored to a normal functioning condition at a particular slope following the transient. Hence, dominant dynamical analytics for wind turbines principally concerns two aspects: 1) the temporary steady state during the fault’s survival; and 2) the restoring procedure once the fault has been cleaned. The two points are assigned top priority in analyzing elector-mechanical transient at the system hierarchy. Following a fault clearing, wind turbines working at varying wind speed levels behave differently. In terms of dynamic active power, the magistrate patterns are explained as follows. 1) They have different starting power when launching recovering. 2) All wind turbines would come back to the same way they are supposed to be at a fixed efficiency, in despite of the varying working wind speeds. Yet, low-speed wind condition allows the turbines to reach the stable status much more quickly, while high-speed wind condition makes it more difficult and time-consuming to recover. Such diverse conditions bring barriers to align different response curves. At 12.5 s, the 2-nd turbine reached a stable status, while the 1-st fan was keeping changing. Traditionally, manually dividing the transient and steady-state periods is
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required because the similarity degrees are different. On the one hand, such method break an entire working pipeline. On the other hands, manually labeling is time-consuming and error-prone. Therefore, automated period alignment is necessary. 2.2 DTW: Calculating the Similarity of Fans The wind speed inside a fixed region can be varying due to the effect of geographical features, wake flow, and so on, not mention the local differences of each power unit. Even for the same type of wind turbine, its dynamic response time is also different. Since the interior collection networking topology of the wind park would affect the real-world fault received by a certain wind fan, the corresponding response dynamics of different unites of would be dissimilar. Let us present a practical case. If a failure occurs at the exit, there will be low-voltage ride-through situations occur on several fans, leaving others unchanging. Under such complicated conditions, the monitoring operation indicators of disparate units are unaligned from the temporal perspective. Though the DBSCAN can be combined with traditional distance measurements like cosine distance, the representations of response dynamics would be fully mined or leveraged. Thus, DTW is applied to align temporal sequences. DTW is an important algorithm to snap two sequences with different lengths [16]. It lengthens the shorter temporal sequence while tailoring the longer one. The processed is accelerate by pruning and setting reasonable optimization constraints. Thus, the optimal path of integrating the two samples would be obtained. Then the existing distance measurements could be utilized to generated the minimal warp path distance, as the inverse degree of similarity between two units. 2.3 Divied Wind Generator Groups Based on KL-GMM The most common clustering algorithm is k-means, requiring a predefined number of groups, and the clustering result is “hard”, which means a single sample can be only assigned to one group and its possibility of belong to another group is always zero. So the algorithm is sensitive to initial centroids and evaluating the clustering performance is difficult, relying on the evaluation of downstream tasks. Additionally, an all-pervading challenge of clustering techniques is how to define the optimal number of groups. Therefore, this paper proposes a Probabilistic data-driven clustering approach, which calculates the KL divergence between the actual distribution and current clustering distribution and minimize the distance by modifying the initial centroids and fine-tuning the parameters of the encoder. Through the grid search method, the group number can be decided automatically by choosing the parameter combination proposal with the minimum KL loss.
3 Analysis of Cases This paper utilizes MATLAB®/Simulink® for model development. The wind park contained 30 DFIGs and the details are depicted in the Fig. 1. All of the rated power were set to be 1.5e3 kW. The wind speed distribution is displayed as follows. At the 12th second,
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there was a three-phase short-circuit failure at the exit, and it sustained for 100 ms. The data from 11 s to 17 s was collected, and the step length is 50 us. Taking the samples processed through a traditional k-means algorithm as the control group, we adopted the capacity weighting method, the equal input wind energy method, and the principle of constant loss to identify the equivalent parameters of the wind park. Table 1 presents the experimental equivalent results by comparing k-means and the proposed approach. The results demonstrate that the proposed framework can be utilized to cluster generators inside the wind farm and improve equivalence effectiveness significantly in three aspects: 1) mining the 1) voltage, 2) active power, and 3) reactive power deviations. Table 1. Comparison of dynamic equivalent deviation Algorithms
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4 Conclusion This paper proposed an end-to-end framework aiming to clustering wind generators by leveraging a improved GMM method based on KL divergence and the clustering indexes can be automatically computed via the DTW method. Based on the collected wind turbine operating data, the generator groups could be divided inside a wind farm. By analyzing the equivalent results, the following conclusions could be summarized. (1) DTW takes into account that the dynamic response time of the same type of DFIGs inside a fixed wind farm is varying when experiencing various wind speeds. It improves the accuracy of clustering indexes for wind generator groups. (2) The improved GMM, which leverages KL divergence as the loss function and updates the clustering parameters along the gradient descent direction, can realize unsupervised and adaptive clustering in the wind farm, whose high clustering accuracy can be beneficial to improve the equivalent effect.
References 1. Peng, W., Zenyuan, Z., Qi, H., et al.: Improved wind farm aggregated modeling method for large-scale power system stability studies. IEEE Trans. Power Syst. 33(6), 6332–6342 (2018) 2. Xia, Yu.: Research on Equivalent Modeling of Large Wind Farm. Hefei University of Technology (2019)
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3. Wei, H., Zhang, X.: Equivalent modeling of large scale wind farm based on feature analysis. Power Syst. Technol. 37(08), 2271–2277 (2013) 4. Han, J., Miao, S., Li, L., Yang, W., Li, Y.: Division of aircraft groups in wind field and comprehensive optimization of equivalent wind field parameters based on multi-view transfer learning. Proc. CSEE 40(15), 4866–4881 (2020) 5. Mi, Z., Su, X., Yang, Q., et al.: Multi-machine characterization method of wind farm dynamic equivalent model. Trans. China Electrotech. Soc. 25(5), 162–169 (2010) 6. Cao, N., Yu, Q.: Grouping method of wind Turbines in grid-connected wind farm under wind speed fluctuation. Autom. Electric Power Syst. 36(02), 42–46 (2012) 7. Zhang, X., Li, L., Hu, X., et al.: Dynamic equivalence of wind farm based on wind turbine output time series data clustering. Power Syst. Technol. 39(10), 2787–2793 (2015) 8. Mi, Z., Su, X., Yu, Y., Wang, Y., Wu, T.: Dynamic equivalent model of double-fed wind farm. Autom. Electric Power Syst. 34(17), 72–77 (2010) 9. Zou, J., Peng, C., Xu, H., et al.: A fuzzy clustering algorithm-based dynamic equivalent modeling method for wind farm With DFIG. IEEE Trans. Energy Convers. 30(4), 1–9 (2015) 10. Chen, S., Wang, C., Shen, H., Gao, N., Zhu, L., Lan, H.: Dynamic equivalence of wind farm based on clustering algorithm. Proc. CSEE 32(04), 11–19+24 (2012) 11. Yang, M., Dong, J.: Study on wind power fluctuation characteristics based on mixed distribution model. Proc. CSEE 36(S1), 69–78 (2016) 12. Zou, J., Peng, C., Xu, H., et al.: A fuzzy clustering algorithm-based dynamic equivalent modeling method for wind farm with DFIG. IEEE Trans. Energy Conversion 30(4), 1329– 1337 (2015) 13. Wang, H., Liu, D., Wang, J.: Prediction of ultra-short-term wind speed based on spectral clustering and optimized extreme learning machine. Power Syst. Technol. 39(05), 1307–1314 (2015) 14. Lin, L., Pan, W., Zhang, L., et al.: Wind farm cluster division based on immune outlier data and sensitive initial center K-means algorithm. Proc. CSEE 36(20), 5461–5468 (2016) 15. Chao, P., Li, W., Jin, X., Qi, J., Chang, X.: Practical equivalent method for doubly-fed wind farm based on active power response. Proc. CSEE 38(06), 1639–1646+1900 (2018) 16. Sharma, A., Sundaram, S.: A novel online signature verification system based on GMM features in a DTW framework. IEEE Trans. Inf. Forensics Secur. 12(3), 705–718 (2017). https://doi.org/10.1109/TIFS.2016.2632063
An Integrated Boost Micro-inverter for PV Generation System Xuefeng Hu, Zikang Long(B) , Chenjin Fei, Zhenhai Yu, and Kunshu Mu College of Electrical and Information Engineering, Anhui University of Technology, Xiang Shan Town, Yu Shan District, Maanshan, China [email protected]
Abstract. This paper proposed an integrated boost micro-inverter (IBMI) that adapts MOSFETS without reverse recovery problem of their body diodes. Only two active switches are required in the proposed IBMI to realize boosting voltage and dc-ac conversion synchronously, which greatly simplifies the circuit structure and reduces the system cost. At the same time, the shoot-through problem of active switches may be avoided effectively enhancing the system reliability. Owing to eliminating dead time, the quality of output ac waveform and system efficiency are also improved. In addition, the leakage current can be greatly suppressed due to the inherent structural characteristics of the IBMI, so the IBMI is suitable for photovoltaic (PV) power generation system. The operating principles and pulse-width modulation strategy are shown to validate the proposed concepts, and parameter design guidelines are carried out. The validity and performance of IBMI are verified by experiment on a laboratory prototype. Keywords: Boost inverter · PV generation system · Transformer-less · Leakage current
1 Introduction Nowadays, the single-phase inverters have been extensively gained in many industry applications, Photovoltaic (PV) power systems, for example. The PV inverters can converter the energy given by a PV array and it is delivered into the ac load or mains on grid [1, 2]. Usually, the low frequency transformer is used as galvanic isolation for safety concerns of PV power system. However, this type transformer is big and heavy, and makes the system bulky. On the dc side, the high frequency transformer is often used to realize galvanic isolation, which was smaller than the low frequency transformer. However, the system will become more complex due to the cascaded dc-dc converter, and additional power processing also affects the efficiency of PV system. Therefore, various different types of transformer-less dc-ac converter topologies have been hot research topics for achieving high efficiency, high reliability, low cost and small volume for PV power systems [3, 4, 5]. It is known to all that, for transformer-less inverters, the full bridge inverter is a good choice to realize dc-ac power conversion. However, a traditional full bridge inverter © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 708–715, 2022. https://doi.org/10.1007/978-981-19-1528-4_72
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can be regarded as buck converter, in which the peak of output ac voltage is smaller than input voltage. In the grid-connected photovoltaic power energy system, it is usually good way to connect many PV plants in series to obtain adequate DC voltage. Whereas, the series connection of the PV panels will produce the knotty shadow problem [6, 7]. Many single stage inverter topologies with step up have attracted the attention of some scholars to solve above problems lately [8, 9]. In the references [10, 11], the authors added a coupled inductor in a full bridge inverter to attain voltage boosting. From the experimental results, it has been realized that allows operation from low input voltage to high output voltage. Nevertheless, the modulation is a little complex, and the problem of leakage current remains unresolved. A transformer-less integrated boost inverter is studied for the photovoltaic generation system in this article. This structure is very simple and it can be derived from a unidirectional boost dc-dc unit and an inversed boost switching cell, as shown in Fig. 1. The presented inverter topology has the following characteristics: 1) It can realize synchronous boosting voltage and dc-ac conversion, besides it has simple structure and fewer power devices advantages; 2) Due to the inherent structure of the proposed converter, the leakage current can be suppressed greatly, improving reliability and electromagnetic interference of the inverter; 3) The filter inductor current freewheels through external diodes. So, there is no reverse recovery problems by selecting independent diodes; 4) The shoot-through issues in a leg phase has been solved, enhancing system reliability. Moreover, there is no need to add delay time between driving signals of switches, reducing the THD in the output waveforms.
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2 Working Principle of Micro-inverter 2.1 Structure and Operating Principles The circuit diagram of the inverter is showned as Fig. 1. In the dotted green frame, a boost converter is used including an input source, the input inductor L in , the switch S1 and diode D1 . The D0 is blocking diode of solar cell. At the same time, the switch S 1 and diode D1 are multiplexed as a leg of the proposed SSBI. An auxiliary leg of the SSBI is
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shown in the pink dotted frame, which is an inversed boost switching cell. Therefore, the proposed single-phase boost micro-inverter uses fewer components, reducing the cost and control complexity. To facilitate analysis, this article assumps that: 1) The elements are all ideally; 2) The voltage on capacitors (V C1 , V C2 ) can be regard as steadily for capacitors are large enough; 3) The input inductor L in operates in DCM when the current flows through it. Figure 2(a) represents the gate signal generation of SSBI. The driving signals of switches S 1 and S 2 are obtained by comparing the modulated signal with the carrier signal in a cycle of line frequency. Switch S 1 is turned on, While modulating signal is greater than the carrier signal, and the gate signal of S 2 is complementary to that of S 1.
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The waveform of the current of input inductor L in during one switching period T S is shown as Fig. 2(b). It is clearly iLin operates in DCM. As their working principles are entirely consistent, the operation of the inverter is divided in three modes in each half cycle. Each operating mode can be represented by the equivalent circuit in Fig. 3(a)–(c). V in is input dc voltage source. The current through L in , L 1 and L 2 is denoted by iLin , iL1 and iL2 . V C1 and V C2 are respective voltage across C 1 and C 2 . The driving signals of switches S 1 and S 2 are complementary. Mode I [t 0 − t 1 Fig. 3(a)]: During this interval, Switch S 1 , diode D0 and D2 are turned on while switch S 2 and diode D1 are turned off. The inductor L in is charged by the input source V in through switch S 1 , so iLin increases linearly. At the same time, iL1 is increased linearly as well, and the capacitor C 2 delivers the stored energy through a loop C 2 -load-L 1 -S 1 . While, the iL2 is decreased. This mode ends at t = t 1 when the switch S 1 is turned off. The current iLin can be expressed as: iLin (t) =
Vin (t − t0 ) + iLin (t0 ) Lin
(1)
Mode II [t 1 − t 2 Fig. 3(b)]: The switch S 1 is turned off and switch S 2 is turned on. Diode D0 and D1 are forward biased and diode D2 is reverse biased. Figure 4(b) shows
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the current flowing path. V in and L in are deliver energy to capacitors C 1 and C 2 , also to the load. iL2 is increased linearly in this way. Thus, iLin is decreased. This mode ends at t = t 2 . The follow Eq. (2) can be written as: iLin (t) =
Vin − VC1 − VC2 (t − t1 ) + iLin (t1 ) Lin
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Mode III [t 2 − t 3 Fig. 3(c)]: During this interval, the switches S 1 , S 2 and diodes D1 , D2 are maintains the condition in mode II. D0 is turned off when iLin reduced to zero. Capacitor C 1 delivers energy through switch S 2 to the load. iL2 is increased. This mode ends at t = t 3 . iL1 can be expressed as: iLin (t) =0
(3)
2.2 Voltage Gain (G) Neglecting the loss of all components. What can we get is as follow: √ Vin Iin = (Vom / 2)2 /RO
(4)
Where V C1 = V C2 = V C , V om is the peak value of the output voltage. From Fig. 4(a), the current iLin through the inductor L in reaches a maximum I LP at t = t 1 , and then the current iLin is dropped to 0 at t 2 . The average value I Lin through L in is deduced as follow. ILin =
(D1 + D2 )ILP 2
Where I LP is the peak current of inductor L in , I LP = V in D1 T S /L in .
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The modulating strategy is shown in Fig. 4(b). From the similarity of triangles, one can obtain the formula: 1+m sin wt = 2
ton 2 T 2
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(6)
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The relationships of voltage gain G versus modulation index m and inductor L in is shown in Fig. 5 while f S = 20 kHz, RO = 50 . As we can see, the G increases when the m increases or the L in decreases.
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Fig. 5. Diagram of voltage gain G and L in , m
3 Experimental Results A hardware prototype has been fabricated and tested to verify the analysis and advantages of the topology. The experimental results are as follows:
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Figure 6(a) and (b) shows the waveforms of the capacitors voltage V C1 /V C2 , output voltage V o , input inductor current iLin and output voltage V o respectively. one can see that iLin is worked in DCM. The voltage ripple on each capacitor is varying with the line frequency oscillation. In addition, the quality of output wave is good because the two capacitors are alternately charged and discharged. iS120A/div
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Fig. 7. Experimental results. (a) iS1 and V S1 . (b) iS2 and V S2 .
The waveforms of the voltage and current stress of switches S 1 and S 2 can be seen clearly in Fig. 7. V S1 equals V S2 , which is the sum of V C1 and V C2 . Due multiplexing of each active device, iS1 can be obtained by adding the iLin and io , corresponding to theoretical analysis. Figure 8(a) demonstrates the current waveforms iL1 and iL2 of filter inductors, and the output current io . From this figure, the two filter inductors are alternately operated, and the experimental results are agree with the analysis. The common mode voltage and current of the proposed inverter are given in Fig. 8(b). From the Fig, it is obviously that the common mode voltage only pulsates at low frequency. So, the inverter has the characteristics of low leakage current and improved security.
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Fig. 9. Dynamic experiments for io and V O .
Figure 9 demonstrate the dynamic waveforms. The inverter can quickly return to a stable working state while the load is changed, which proofs good dynamic performance of proposed IBMI. By using a power quality analyzer HIOKI3197, the measured THD of inverter is about 2.1%, meeting the requirements of grid connection, which also proves that this inverter has good practical significance.
4 Conclusion A transform-less single stage integrated boost micro-inverter (IBMI) base on multiplexing power devices was proposed in this paper. The configuration is compact and uses lesser components compared with conventional step up inverters, which greatly simplifies the circuit structure, reduces the system cost and improves the power density. In the IBMI, the loss of reverse-recovery issues of body diodes has been reducing due to the freewheeling current diodes can be selected independently. It has no shoot-through risk, enhancing the reliability, and dead-time between the PWM signals is not required, improving the output waveform quality. In addition, the leakage current can be greatly suppressed to low level. A 250 W micro-inverter was tested successfully and effectively.
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References 1. Ciobotaru, M., Teodorescu, R., Blaabjerg, F.: Control of single-stage single-phase PV inverter. In: 2005 European Conference on Power Electronics and Applications 16(3), 20–26 (2005) 2. Guo, X.: A novel CH5 inverter for single-phase transformerless photovoltaic system applications. IEEE Trans. Circuits Syst. II: Express Briefs 64(10), 1197–1201 (2017) 3. Caceres, R.O., Barbi, I.: A boost DC-AC converter: analysis, design, and experimentation. IEEE Trans. Power Electron. 14(1), 134–141 (1999) 4. Yu, T., Yang, B., Kan, J., Fei, X.: Improved dual boost inverter with half cycle modulation. IEEE Trans. Power Electron. PP(99), 1 (2016) 5. Ribeiro, H., Pinto, A., Borges, B.: Single-stage DC-AC converter for photovoltaic systems. In: Energy Conversion Congress & Exposition (2010) 6. Khan, A.A., Cha, H., Akbar, F., Kisu, K., Lai, J.S.: Dual buck-boost inverter. In: Applied Power Electronics Conference & Exposition (2017) 7. Khan, A.A., Cha, H.: Dual-buck structured high reliability and high efficiency single-stage buck-boost inverters. IEEE Trans. Ind. Electron. PP(99), 1 (2017) 8. Ardashir, J.F., Sabahi, M., Hosseini, S.H., Blaabjerg, F., Babaei, E., Gharehpetian, G.B.: A single-phase transformerless inverter with charge pump circuit concept for grid-tied PV applications," IEEE Trans. Ind. Electron. PP(99), 1 (2017) 9. Rajeev, M., Agarwal, V.: Analysis and control of a novel transformer-less microinverter for PV-grid interface. IEEE J. Photovoltaics 8(4), 1110–1118 (2018) 10. Abramovitz, A., Zhao, B., Smedley, K.M.: High-gain single-stage boosting inverter for photovoltaic applications. IEEE Trans. Power Electron. 31(5), 3550–3558 (2016) 11. Nag, S.S., Mishra, S.: A coupled inductor based high boost inverter with sub-unity turns-ratio range. IEEE Trans. Power Electron. 31(11), 7534–7543 (2016)
Parameter Identification and Co-simulation Verification of Dynamic Inductance in Electromagnetic Switch Liang Meng1 , Jierong Zhuang1,2,3(B) , and Zhihong Xu1,2,3 1 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116,
Fujian, China [email protected] 2 Fujian Key Laboratory of New Energy Generation and Power Conversion, Fuzhou University, Fuzhou 350116, Fujian, China 3 Fujian Province University Engineering Research Center of Smart Distribution Grid Equipment, Fuzhou University, Fuzhou 350116, Fujian, China
Abstract. Dynamic inductance is an important parameter that reflects the dynamic characteristics of the electromagnetic switch during the movement process. Its accurate and rapid identification is of great significance to the optimization and control of the electromagnetic switch. In the actual operation process of electromagnetic switch, the dynamic inductance of electromagnetic mechanism is affected by the coil current and the working air gap, and it changes nonlinearly, which makes it difficult to identify. For this reason, this paper proposes a dynamic inductance identification model based on the discrete differential equation. The coil voltage and current are used as input of the model, which considers the online self-correction method of coil resistance. It realizes the identification of dynamic inductance of electromagnetic switch under PWM closed-loop control. At the same time, a three-dimensional finite element co-simulation model of the electromagnetic switch considering the collision bounce is established, and the dynamic inductance is obtained by the method of electromagnetic-mechanical coupling. The calculation results of the inductance identification model are compared with the co-simulation model, and the reliability and accuracy of the identification model are verified. It lays a foundation for further intelligent control of electromagnetic switch. Keywords: Dynamic inductance · Identification model · Self-correction · Co-simulation
1 Introduction Electromagnetic switch is widely used in the automatic control of electrical equipment, which is mainly used to make and break the circuit. Electromagnetic switch adopts intelligent control, which can effectively improve the dynamic performance of the switch, and this method is widely recognized by academia and industry [1]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 716–723, 2022. https://doi.org/10.1007/978-981-19-1528-4_73
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At present, scholars have carried out a lot of research on intelligent control of electromagnetic switch. Qin et al. [2] combined PWM with fuzzy control technology, and the displacement of iron core is taken as the feedback to adjust the coil voltage, so that the permanent magnet mechanism of circuit breaker can move along with the given standard displacement curve. Liu et al. [3] proposed an intelligent contactor with vlotage feedback, which can make the coil voltage stable near the reference value during the closing process. The closed-loop chopping technology was introduced into the intelligent control of AC contactor [4]. The coil current was used as the feedback to realize the closed-loop DC starting of AC contactor. At present, the control strategy is mostly based on the above mechanical or electromagnetic parameters as feedback. However, the acquisition of mechanical parameters is difficult and costly. Although the acquisition of electromagnetic parameters is relatively easy, it is difficult to reflect the movement state of electromagnetic mechanism. As an important parameter which can reflect the dynamic characteristics of electromagnetic switch, inductance is affected by coil current and working air gap. It is the link between mechanical and electromagnetic parameters. At present, there are few researches on inductance identification and the intelligent control with inductance as the feedback quantity. But there are many experiences in other fields that can be used for reference. In the field of motor, scholars have used high-frequency voltage injection [5], rotating voltage injection [6], least square method [7], model reference adaptive [8] to identify the inductance of motor on-line and formulate relevant control strategies. Thus, this article takes an AC contactor under current closed-loop control as the research object, discretizes the dynamic differential equation of its electromagnetic mechanism, and only uses the coil voltage and current as the input to build a dynamic inductance identification model. At the same time, considering that the coil resistance will change with the temperature and aging degree, an online self-correction method of coil resistance is proposed to effectively reduce the influence of coil resistance error on the inductance identification model. Finally, a three-dimensional finite element co-simulation model considering collision bounce is established. The model uses the electromagnetic-mechanical coupling method to obtain the dynamic inductance. After verifying the accuracy of the co-simulation model with experimental current and displacement data, the calculation results of the inductance identification model are compared with it. The inductance identification model lays the foundation for further intelligent control of electromagnetic switch.
2 Dynamic Inductance Identification Model 2.1 Current Closed Loop Control The driving topology of the coil under current closed-loop control is shown in Fig. 1. U 1 is the AC and DC universal input voltage. D1 , D2 , D3 , D4 are diodes to form rectifier circuit. U c is the rectified DC bus voltage, C 1 is the filter capacitor. D5 and D6 are fast recovery diodes. S1 and S2 are power electronic switches, chopping the voltage after rectification and filtering. L and r are inductance and resisitance of the contactor coil, u represents the coil voltage, and i represents the coil current.
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The principle of current closed-loop control is to change the voltage at both ends of contactor coil by adjusting the conduction state of S1 and S2 , and finally make the coil current dynamically stabilize near the preset reference value. The driving topology has three states: excitation, freewheeling and fast demagnetization.
Fig. 1. Driving topology of the coil
2.2 Inductance Mathematical Model The voltage balancing equation followed by the electromagnetic mechanism of the contactor in the actual movement process is as follows: u(t) = i(t)r +
d Ψ (t) dt
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where Ψ (t) is flux linkage. The flux linkage can be expressed as the product of inductance and current. Since the coil voltage and current collected in the actual control process are discrete in the time domain, the backward differentiation formula is used to discretize Eq. (1) as: uk = rik +
Lk ik − Lk−1 ik−1
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where represents the sampling period, the subscript k represents the current moment, k-1 represents the previous moment, and L is inductance. The identification result of dynamic inductance can be obtained by Eq. (2): Lk =
(uk − rik ) + Lk−1 ik−1 ik
(3)
According to Eq. (3), the model only needs coil voltage u, coil current i, and coil resistance r. It can realize the identification of dynamic inductance without additional sensors and complicated methods. At the same time, the calculation efficiency of the model is high, which meets the real-time requirements of the electromagnetic switch real-time control strategy.
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2.3 Coil Resistance Self-correction In the actual operation of contactor, the coil heating and aging will affect the coil resistance in varying degrees, resulting in its value has deviated from the nominal resistance value. According to the analysis of Eq. (3), the accuracy of coil resistance will affect the dynamic inductance identification result. Therefore, this paper proposes an online self-correction method of coil resistance, which uses the experimental coil voltage and current to complete the calculation and correction of coil resistance. For the AC contactor under the current closed-loop control, high-frequency square wave voltage is applied at both ends of the coil, which generates moving anti-potential in the closing process, so it is difficult to realize on-line resistance calculation. However, in the keeping stage of the contactor, the moving core remains stationary, so it can be considered that the flux linkage of the electromagnetic mechanism hardly changes with time. In this case, the coil resistance can be obtained directly from Ohm’s law. Therefore, when the contactor enters the keeping stage and enters the steady state, the hysteresis comparison tracking control strategy is used to maintain the dynamic stability of the coil current at the reference current iref . At this time, the coil voltage is a high-frequency positive and negative square wave, and any period is selected to calculate the sampling average value u+ , u− and duty cycle d of the positive and negative voltage. Finally, the coil resistance used for correction is obtained as: r=
u+ d + u− (1 − d ) u = i iref
(4)
3 Co-simulation Model of Electromagnetic-Mechanical Field 3.1 Research Object This paper takes an AC contactor as the research object, and its structure is shown in Fig. 2. The electromagnetic suction is generated by electrifying the coil, which makes the moving core overcome the spring force to drive the moving holder and the moving contact to move. After the moving contact is closed, the moving core overcomes the
Fig. 2. Structure diagram of contactor
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reaction spring and the contact spring to continue moving, so that the moving contact is reliably attracted. When the coil is powered off, the moving core and moving contact return to the initial position under the action of spring force. 3.2 Dynamic Equation of Motion The moving core is mainly affected by electromagnetic and spring force in actual work. Let yx be the stroke of contactor, yk the distance between the moving and static contact, y the displacement of moving core. The movement meets the following equations: Fx −2(f1 +k1 y) dv 0 < y < yk dt = m1 (5) Fx −2(f1 +k1 y)−3f2 −3k2 (y−yk ) dv = yk < y < yx dt m2 where v is the velocity of moving core, and m1 and m2 are the mass of moving parts and total mass of moving core and holder, respectively. F x is the electromagnetic suction. The preload of reaction spring is f 1 and its stiffness is k 1 . f 2 is the preload of contact spring, and k 2 is the stiffness coefficient of contact spring. There is also a contact force f n when the moving core collides with the static core. fn = kn δ n + cn δ n δ˙
(6)
where k n and cn are the contact stiffness coefficient and damping coefficient between the static core and moving core, respectively. δ is the contact penetration depth of the moving core and static core and n is the exponent of force. 3.3 Multiphysics Coupling Calculation The bounce of iron core and contact generally exists in the actual operation of contactor. At present, electromagnetic-mechanical coupling mostly adopts the method of loading discrete and static current and displacement data tables into the mechanical dynamics equation to solve [9]. Because the loading data of this method is static, it can not reflect the influence of the mechanical movement on its current, flux and other electromagnetic parameters. And the data table solving process is tedious and time-consuming. When the structure of the contactor changes, the static table needs to be made again, which is inefficient. In this paper, the multi-field co-simulation technology is used to establish the electromagnetic-mechanical coupling three-dimensional co-simulation model of electromagnetic switch. The model realizes the dynamic and continuous solution of electromagnetic and dynamic parameters in the closing process, and simulates the collision bounce of the iron core and contacts. The calculation process of the co-simulation model is shown in Fig. 3. Firstly, the finite element model of electromagnetic mechanism is established by electromagnetic finite element simulation software to realize the real-time calculation of its electromagnetic suction and other dynamic characteristics. Secondly, the multi-body dynamics analysis software is used to establish the multi-body dynamics model of the contact and the electromagnetic system. Then the data of electromagnetic suction and spring reaction are transmitted in real time by the visual software interactive platform. Finally,
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a complete electromagnetic switch multi-field co-simulation model is formed to simulate the dynamic process of each moving part of the electromagnetic switch during the closing process.
Fig. 3. Electromagnetic-mechanical field coupling calculation process
4 Experimental Verification and Analysis 4.1 Experimental Verification of Co-simulation Model In order to verify the accuracy of the above-mentioned electromagnetic-mechanical field co-simulation model, the AC contactor shown in Fig. 2 is used as the control object for experimental analysis. During the experiment, the contactor adopts the intelligent control module. The starting current reference value of the closed-loop control is set to 4 A, and the experimental waveforms are acquired as shown in Fig. 4.
Fig. 4. Experimental waveform of dynamic parameters
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The experimental coil voltage is used as the coil excitation of the co-simulation model, and the coil current and displacement are obtained by simulation. The results of coupling calculation are compared with the experiment, as shown in Fig. 5. Through waveform comparison, it can be found that the coil current and displacement calculated by the co-simulation model are basically consistent with the experimental waveform. However, there are local differences in the keeping stage. Since the inductor keeps dynamic stability in the keeping stage, the error of current and displacement will not affect the accuracy of inductor identification.
Fig. 5. Co-simulation and experimental waveform comparison
4.2 Verification of Inductance Identification Model The nominal value of the prototype coil resistance is 41 . In order to analyze the influence of the coil resistance on the inductance, the coil resistance in the model is artificially set to 35 . When the contactor turns to the keeping stage and the coil current enters the steady state, the model uses Eq. (4) to correct the coil resistance. The corrected coil resistance is 41.45 , which is close to the nominal value. The inductance comparison results before and after coil resistance correction are shown in Fig. 6:
Fig. 6. Comparison of inductance before and after resistance correction
From the comparison results in Fig. 6, it can be seen that the inductance after coil resistance correction is closer to the co-simulation results, which shows the effectiveness of the resistance self correction method.
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5 Conclusion In this paper, a dynamic inductance identification model based on discrete differential equation is proposed, and a three-dimensional co-simulation model of contactor is built for verification. The dynamic inductance identification of contactor under current closedloop control is realized. Considering the changes of resistance caused by coil heating and aging, this paper deduces the coil resistance self-correction method. At the same time, the principle of co-simulation model is given. The accuracy of the co-simulation model is verified by comparing the current and displacement calculation results with the experimental data. On this basis, the inductance data is extracted. Finally, the results of the two methods are compared to verify the accuracy of the inductance identification model. Acknowledgments. Project Supported by Fujian Provincial Natural Science Foundation of China (2020J05134); Educational Research Project for Young and Middle-aged Teachers of Fujian Provincial Department of Education (JAT190038); Research Start-up Project of Fuzhou University (GXRC-19050); The Key Laboratory of Special Machine and High Voltage Apparatus (Shenyang University of Technology), Ministry of Education under Grant KFKT202002.
References 1. Fang, S., Chen, Y., Lin, H.: A self-adaptive control for phase-controlled electromagnetic contactor using weighted moving average filter. IEEE Trans. Industr. Electron. 68(9), 8963–8972 (2021). https://doi.org/10.1109/TIE.2020.3016268 2. Qin, T., Dong, E., Chen, Y., et al.: Path tracking control of vacuum circuit breaker. Proc. CSEE 34(33), 5983–5990 (2014). (in Chinese) 3. Liu, Y., Chen, D., Niu, C., et al.: Analysis and simulation of dynamic behavior and contact bounce for an intelligent contactor with feedback mechanism. Proc. CSEE 30, 20–25 (2007). (in Chinese) 4. Tang, L., Qu, H., Xu, Z.: Research on double closed-loop control strategy of contactors based on flux linkage observers. IEEE Trans. Industr. Electron. 69(3), 2769–2779 (2022). https://doi. org/10.1109/TIE.2021.3070509 5. Tao, Z., Chen, C.: On-line identification method for dq inductance of permanent magnet synchronous. Electric Drive Autom. 40(02), 33–36 (2018). (in Chinese) 6. Gabriel, F., De Belie, F., Neyt, X., et al.: High-frequency issues using rotating voltage injections intended for position self-sensing. IEEE Trans. Industr. Electron. 60(12), 5447–5457 (2013). https://doi.org/10.1109/TIE.2012.2230604 7. Lin, J., Chen, T.: PMSM parameters identification based on improved RLS method. J. Hefei Univ. Technol. (Natural Science) 42(07): 876–880+934 (2019). (in Chinese) 8. Tang, Y., Xu, W., Liu, Y., et al.: Dynamic performance enhancement method based on improved model reference adaptive system for SPMSM sensorless drives. IEEE Access 9, 135012– 135023 (2021). https://doi.org/10.1109/ACCESS.2021.3116761 9. He, X., Xu, Z.: Virtual prototyping technology of AC contactor. Trans. China Electrotechnical Soc. 31(14), 148–155 (2016). (in Chinese)
Review on Applications of Artificial Intelligence in Relay Protection Ming Dai1 , Guomin Luo2(B) , Zhenlin Wang3 , and Qihui Chen1 1 Beijing Smartchip Semiconductor Technology Company Limited, Beijing, China 2 Beijing Jiaotong University, Beijing, China
[email protected] 3 Beijing SmartChip Microelectronics Technology Company Limited, Beijing, China
Abstract. With the continuous development of power grid sources, networks and loads, the emergence of distributed power sources and new types of loads has brought new challenges to the traditional power system relay protection. Combining artificial intelligence technologies, relay protection technology has made great developments. In this paper, the development of power grid from three aspects are firstly introduced: sources, networks and loads. Then impacts of power grid development on relay protection are discussed. Finally, the application of artificial intelligence technologies in relay protection is introduced in details. Keywords: Relay protection · Artificial intelligence
1 Introduction With rapid developments in different areas, there emerges new status of power grid, for example, the AC-DC hybrid networks appear; the grid-connected capacity of clean energy continues to grow; and more and more power electronic apparatus are adopted. For the power generation, the application of distributed energy on the power side has effectively alleviated the energy crisis. Voltage source converters (VSC)-HVDC can realize the independent control of active and reactive power, transmit power to the passive network, and connect the electric energy generated by small and scattered renewable energy sources more economically. For the power transmission, large grids are interconnected. DC grids and active distribution networks are widely used. For the power loads, a series of new loads such as DC loads and controllable loads appear. With the development of sources, networks and loads, the random and nonlinear characteristics of power system are enhanced, and the fault features become more complicate, which brings new problems to relay protection. For example, the interconnection of distributed energy reduces the accuracy of setting calculation; the wide application of DC links in large power grid interconnections produce setting methods that are different from those of AC systems; and the huge data information increases the difficulty of fault diagnosis. Artificial intelligence (AI) technology has many advantages in feature extraction, identification, big data processing and so on. It can make outstanding performance in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 724–733, 2022. https://doi.org/10.1007/978-981-19-1528-4_74
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modern power system relay protection with abundant information, chaotic fault features and high performance requirements. It can be used for online protection value settings, fault detections, disturbance and fault identification, fault diagnosis, and fault location, etc. This paper firstly discusses the new form of power grid development, then analyzes some problems of relay protection under the new form of power grid, and finally focuses on the application of AI in relay protection.
2 Development of Power Grids 2.1 The Developments at Source Side Distributed generation (DG) has a variety of classification methods according to different classification methods. According to the installed capacity of the power supply, distributed power supply can be divided into three categories: small ones (less than 100 KW), medium ones (between 100 KW and 1 MW) and large ones (greater than 1 MW). According to the interconnecting forms, DGs can be divided into: AC-DC-AC connected distributed DGs and DC-AC connected DGs. In accordance with the energy type, DGs can be divided into wind power, solar power, tidal power generation, fuel cell power generation, small hydro power generation, the micro gas turbine power generation, biomass power generation [1]. The advantages of DGs are less investment and short construction time. Chinese government prefers to interconnect DGs into the power grids, and thus more and more distributed power generation will be connected. Wind power farm DC grid-connection is a hot research topic in recent years. Through DC grid-connection, wind power farm can improve the power factor, and then transport electric energy with the highest efficiency. It has low coupling with the power grid and can realize independent control of active power and reactive power [2, 3]. 2.2 The Developments at Network Side As the key to build global energy interconnection, power grid interconnection helps to optimize the allocation of all kinds of energy resources in a wide range of the world more conveniently. China has basically built a smart power grid with ultra-high voltage network as the backbone network and multi-level grid coordination. At the same time, China is also actively carrying out power grid interconnection and power exchange with Russia, Kazakhstan, Laos and other countries. China has elevated the development of renewable energy to an important position, and gradually realized the strategic adjustment of the energy structure. The construction of a nationwide DC transmission network will not only make more effective use of renewable energy, but also actively promote the construction of a stronger smart grid. The South Australia HVDC Demonstration Project which was constructed by China Southern Power Grid Corporation was successfully put into operation in December 2013. It is the first flexible MTDC project based on VSC-HVDC in the world [4]. Subsequently, the State Grid Corporation of China built a five-terminal flexible DC demonstration project in Zhoushan with a voltage grade of ±200 kV and put it into operation. In February
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2018, the construction of Zhangbei Flexible DC Project started, and the voltage level of VSC reached ±500 kV, which is the highest voltage level of flexible DC project in the world. Active distribution network is a development mode of smart distribution network in the future. It has active management of distributed power supply, energy storage equipment and customer bi-directional load model. It is a distribution system with flexible topology [5, 6]. From 2010 to 2014, the European Union has also successively carried out many demonstration projects, including the ADINE demonstration project, DISPOWER project and DER LAB project. The EU Fifth Framework Project, the EU Sixth Framework Project and the EU Seventh Framework Project also contain many demonstration projects related to active distribution network technologies. 2.3 The Developments at Load Side In recent years, more and more new kinds of loads begin to appear, such as electric vehicles. In contrast to AC loads, DC loads appear. DC load is the general term of the power load supplied by DC, such as electric vehicles, variable frequency air conditioners and mobile phones, etc. Also, the controllable load is proposed under the circumstance of smart grid. Controllable load, also known as flexible load, is a kind of power load with interrupting ability or translation ability, such as washing machines, air conditioners, dishwashers, etc. There is also industrial power adjustable equipment and electric vehicles using charging piles for charging, etc. DC load and controllable load are not completely separated. For example, electric vehicles belong to both DC load and controllable load [7, 8].
3 Relay Protection Problems Due to the Developments of Power Grid Relay protection device is an integral part of power system. When a fault or disturbance occurs in a part of the power system due to natural, man-made or equipment failure, relay protection devices should quickly isolate the fault part to ensure the stability of the power system, to maximize the non-fault part of the power grid, and to continue reliable power supply. When more and more large power grids are interconnected via DC links, the fault features of hybrid DC networks will appear. The setting calculation of protection will change, which is different from that of AC networks. The power flow calculation changes when the distributed power supply is connected to the power grid. First of all, the calculation model is changed. There are several different interface forms and calculation models when distributed power is connected to the grid. Among them, the calculation method of power flow in distribution network based on sensitivity compensation has become the basic model to study many grid-connection problems of distributed power supply. It can be divided into synchronous generator interface, asynchronous generator interface and power electronic converter interface, as shown in Table 1. Secondly, the applicable calculation method is changed. The research on power flow calculation of distribution network with distributed power supply mainly focuses on establishing more
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accurate distributed power generation model and improving the existing power flow calculation method [9]. Table 1. Distributed power supply capacity and its interface to the power grid Power generation forms
Typical capacity range/W
Common interface with power grid
Solar photovoltaic
1.0–1.0 × 105
The DC/AC converter
Wind
1.0–1.0 × 105
Asynchronous generator
Micro-gas turbine
1.0 × 104 –1.0 × 105
AC/AC converter
A fuel cell
1.0 × 104–1.0 × 106
The DC/AC converter
The connection of DC system changes the network topology and power supply structure. The failure analysis method and the electric parameters is different from the traditional power grid, and is more likely to produce chain fault. The fault characteristics change from linear distribution to nonlinear distribution in the time space domain. Rapid control of DC system changed the failure characteristics of AC devices, and the fault characteristics of traditional LCC-based DC networks and flexible VSC-based DC networks are quite different. The hybrid grid fault characteristic changes impact the composition of the relay protection [10]. After the relay protection equipment is put into operation, a large amount of data is generated, including equipment factory data, test data, production and maintenance data, online real-time data, operation data, etc. Massive data provides a more comprehensive basis for online checking of setting, online monitoring and analysis, setting calculation, fault diagnosis, etc. At the same time, it puts forward higher requirements to obtain effective information and extract features from multi-source heterogeneous data.
4 Application of AI in Relay Protection 4.1 Relay Protection Setting and Online Checking (1) Setting Value The coordination of power system relay protection setting values should comprehensively consider the working range and performance of protection components, the voltage level of the system, the sensitivity and selectivity of protection actions, and the cooperation between different protection components. And the setting plan will change with system operations. This makes the setting calculation more complicated, and the workload of repeated calculation is very large and takes a long time. The optimal scheme may not be obtained. In Literature [11], the expert experience, logical reasoning and object-oriented method in the AI expert system are combined with the numerical calculation function in the traditional setting calculation. AI reasoning programs are limited to construct
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forward, backward, and bidirectional reasoning from a static knowledge base. Objectoriented technology can well compensate for the low efficiency and flexibility of reasoning caused by this problem. The application of expert system and object oriented method can help to realize the concrete model of relay protection setting calculation. Literature [12] gives a detailed description of the scheme of realizing the whole process automation of setting calculation, including the system structure, functional division, data platform. ActiveX Scripting technology is used to realize the realization of custom setting calculation in the secondary development of prefectural and county integrated setting calculation. Based on the technology, various models of protective device templates, setting calculation principle and setting calculation can be modified by the custom. This method is flexible, quick and easy to extend and maintain. An example of grid setting in LiShui district of ZheJiang province is given, which shows that this scheme can improve the efficiency of setting calculation and the level of intelligent equipment, and reduce the workload of setting personnel and the possibility of manual mistakes. In Literature [13], the fuzzy theory is introduced to deal with the uncertainty of some coefficients in the value setting. In the setting calculation, there are quite a few coefficients (such as reliability coefficient) with uncertainty, and there are also quite a few stipulations about the value of setting parameters with fuzzy concept. The fuzzy theory is introduced to optimize part of the coefficients in the setting. An algorithm model is proposed to deal with the uncertainty of the setting coefficients, which avoids the arbitrariness of determining the parameter values only by experts’ experience. This method enlarges the setting calculation ability of the expert system and improves the accuracy, reliability and practicability of the setting results. (2) Online Checking Online checking for the relay protection setting system is used to verify the performance of the relay protection setting value during operation. To determine whether the protection value meets the requirements of selectivity and sensitivity under the current operating state, it is necessary to consider the topology structure, operating state factors. Due to the complex and changeable structure of power grid, the fixed off-line setting mode leads to large amount of data, difficult analysis and time-consuming calculation for online checking. Literature [14] applied the genetic algorithm to the relay protection setting calculation process, and verified it by an example. The protection fixed value and time fixed value of the distance protection section are calculated at the same time, and the set values which could meet the requirements were obtained. It can be seen that the genetic algorithm can adjust the fitness function to get the protection values of different requirements, while the conventional protection cannot get the protection values of different requirements. In Literature [15], an optimal power grid partition strategy based on GN splitting algorithm is proposed. This strategy effectively uses the “maximum number of edge interfaces” to realize the initial partition of the complex grid. The optimal number of partitions is determined by calculating the comparative weighted modularity. Then, the quantitative measurement standard of checking computation time is defined, which is used as the optimal objective function to carry out boundary node migration and achieve the optimal partition. It effectively overcomes the defects of the traditional methods,
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for example, the difficulty in measuring the rationality of the partition results and the inability to determine the optimal partition results. This method ensures that the partition results have higher parallel checking efficiency and acceleration ratio. A hybrid parallel strategy for online checking of setting values based on multicore cluster is also proposed in literature [15]. Through parallelism analysis, reasonable division of computing tasks and parallel algorithm design, this strategy realizes the parallelism of online checking process level and thread level. 4.2 New Principle of Protection (1) Disturbance Identification HVDC system has long transmission distance and wide distribution of lines, which is easily affected by climate conditions. The lightning damage becomes the most important interference. The non-fault lightning strike impact contains a great high frequency interference easy to introduce mistakes of the protection devices. Traditional relay protection methods need to select appropriate features according to the expression ability of features, for example, distinguish disturbance from fault by threshold method. But, due to the difficulty of feature extraction and high identification error, the reliability of disturbance identification is low. The artificial neural network has the ability of self-learning. The neural network is trained by using the simulation data, and the fault features can be extracted objectively and reliably by unsupervised learning. Literature [16] deeply discusses the model characteristics of BP neural network and LVQ neural network and their learning and training process. Neural network can simulate the state and parameters of the power line when fault occurs, and then give corresponding responses. In the actual operation, if similar mistakes occur in the training, a correct response can be made in time. For unknown signals, it can also make the optimal response according to the existing knowledge, which is less dependent on the actual error signals and has a strong expansibility. When the system is disturbed, the electromagnetic torque will change, and then the balance is destroyed, resulting in a change of the generator speed. The system frequency changes and oscillates. Literature [17] establishes a suitable oscillation model according to the actual situation, and studies the protection action in the oscillation process. The improved current mutation is selected as one of the input characteristic parameters of the oscillatory discriminant subnetwork of neural network. After calculation, the threshold to distinguish oscillations from faults is obtained. This value is not calculated manually, but given by a neural network to achieve the maximum accurate transient disturbance identification. A large sample of BP network training and border fitting classification are needed for training this neural network. (2) Adaptive Protection Adaptive protection is the protection which can adjust the protection characteristics or setting values according to the change of power system operation mode and fault state. When the power system is connected to the distribution network or the photovoltaic power station is connected to the grid, the setting of the adaptive distance protection will be affected. Adaptive distance protection is a method based on mathematical model, which can protect the power grid by calculating the relevant parameters. The concrete
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method is to define the inclination angle α, obtain the short-circuit impedance Zd by vector graph, and then determine the setting value of the protection impedance according to the adaptive distance protection action criterion. The protection principle is shown in Formula (1). |Zm − Zm | = |Zd | ≤ |Zset |
(1)
Literature [18] studied how to use SVM to realize real-time and accurate adaptive protection. A neural network is used to help identify faults by taking all kinds of measurable electrical quantities in the fault circuit. The error caused by one or two discriminant parameters of distance protection action can be effectively avoided. Reliability and selectivity of the range protection action are greatly improved. Literature on [19] studied an adaptive strategy of setting value of transmission line current protection based on online fault identification. The action mechanism of threestage current protection is discussed. The PV system output active power and the effect of transition resistance are considered. The calculation formula of current protection adaptive setting value is constructed and the corresponding current protection adaptive criterion is established. It is used to solve the misoperation problems of transmission line current protection which may be caused by PV system connecting to distribution network. In Literature [20], the influence of photovoltaic power station access on adaptive protection is analyzed based on the establishment of the grid fault analysis method including photovoltaic power station. Based on the analysis of the relationship between the positive sequence voltage and the positive sequence current at the protection site when the fault occurs, an adaptive setting formula suitable for photovoltaic output under random conditions is proposed. An adaptive protection scheme is proposed to reflect the fault by the positive sequence measurement value of the protection and operation to remove the fault in time. 4.3 Fault Location and Diagnosis (1) Line fault location Traveling wave-based fault location is the main method for transmission lines. Travelling wave detection technology is a combination of new mathematical analysis method, data acquisition device and digital signal processing method to realize fault location. The methods include mathematical morphological method, wavelet transform method and fault analysis method based on distribution parameters of transmission lines. The improvement direction based on AI mainly includes three aspects. The first one is based on BP neural network. Some internal feedback channels are added to increase the learning ability of neural network. The second one is to use algorithms to optimize the location algorithms, such as particle swarm optimization, cloud genetic algorithm, L-M algorithm, quasi Newton algorithm, etc. The third one is to improve the initial weight and initial threshold of BP neural network. In Literature [21], a multi-sensor fault detection for photovoltaic array is put forward based on the improved BP neural network. Based on the BP neural network, the internal feedback channel and deviation unit are added to improve the structure of the neural network, as well as the input and output.
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An improved BP neural network algorithm based on particle swarm optimization is proposed in literature [22]. PSO algorithm and BP neural network are combined to form PSO-BP algorithm, which can not only use PSO algorithm to adjust the global characteristic network parameters, but also use BP algorithm to adjust and optimize the local characteristic parameters. Literature [23] took BP neural network as a tool to study the regular relationship between eigenvalue and distance. Firstly, a single amplitude-distance BP neural network is established, and the acquired data is used as the sample data to train the BP neural network. As only one kind of features cannot describe the whole input in details, the actual output error of the trained network is very large. Then, a BP neural network with four eigenvalues as four inputs and distance value as one output is established. Through repeated training with small errors, the BP network achieves the effect of distance identification to a certain extent. (2) Fault diagnosis After the failure of the power network, the system can measure the changes of the voltage, branch current, power and other electrical quantities of each node of the power network. The action signal of the protection device, and the action information of the relevant circuit breaker started by the protection device can be got. At present, the fault diagnosis systems studied at home and abroad are based on protection information and switch action information. Most of the developed intelligent diagnostic systems rely on SCADA of the power grid dispatching center to provide complete switching and protection information. Transmission network fault diagnosis mainly uses the logic relation of fault equipment, switch, relay protection action and operator’s experience to infer the possible fault type. This process is difficult to be described by traditional mathematical model. AI technology is widely used in this field because it is good at simulating the process of human dealing with problems, easy to take into account human experience and has certain learning ability. In Literatures [24, 25], Bayesian network method is used in fault diagnosis. A Bayesian network fault diagnosis methods integrates with a priori information and posterior information. It can effectively avoid the subjective bias that comes from using only a priori information and the blind searching and calculation in the absence of sample information. Also, it can avoid the effects of noise from using only posterior information. The problem of fault diagnosis is transformed into a decision problem with uncertain information, and the problem of uncertainty of fault information is solved better. A distributed diagnosis model is established by combining with the characteristics of power system. Literature [26] proposed a fault diagnosis method based on RBF neural network. According to the simulation model of a VSC-HVDC system, the currents from the rectifier side and AC side of the converter station are collected. The root mean square value of each current was used to establish the fault dictionary, and the fuzzy numbering data was determined. Combined with a RBF neural network, the feasibility of the fault diagnosis method was verified. Literature [27] put forward a fault diagnosis model of transmission network based on DS evidence theory. This method has a good ability of information fusion and can
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synthesize the fault information from all directions to get the final judgment. Also, the fault diagnosis model of transmission network based on mutual information network is proposed. Mutual information network is an effective feature selection and data classification method. It can select the input attributes associated with the target attributes, eliminate the redundant features, control the structure of the network, improve the processing speed of the algorithm, and use the connection rules between the end node and the target node to extract the knowledge, which can be used for future classification decisions and pattern recognition.
5 Conclusions and Prospects AI technology is based on unsupervised learning algorithms. Those algorithms have good ability to fit complex functions and strong feature expression ability. It is suitable for solving big data, strong uncertainty and other complex problems, so that it has a better and better performance in data-driven research and application fields. Under the new situation, the model of power grid system is changeable, the fault mechanism is complex, and the fault features are difficult to be extracted. AI technology can solve the dilemma faced by traditional relay protection. With the improvement of the sampling accuracy of relay protection, the function of fault analysis based on transient signal is applied. At the same time, with the improvement of CPU processing performance, complex machine learning can also be applied in relay protection. The development of technology will better promote the research and application process of the new generation of AI in the field of relay protection.
References 1. Kakran, S., Chanana, S.: Smart operations of smart grids integrated with distributed generation: a review. Renew. Sustain. Energy Rev. 81(1), 524–535 (2018) 2. Beheshtaein, S., Cuzner, R.M., et al.: DC Microgrid Protection: IEEE Journal of Emerging and Selected Topics in Power Electronics (Early Access), 1–1(2019) 3. Husain, N., Smu, A.: On integration of wind power into existing grids via modular multilevel converter based HVDC systems. Int. J. Renewable Energy Res. 10(3),1060–1070 (2020) 4. Yang, L., Li, X., Xu, S., et al.: The integrated system design scheme of nan’ao VSC-MTDC demonstration project. Southern Power Syst. Technol. 9(1), 63–67 (2015). (in Chinese) 5. Wang, J., Hu, Z., Xie, S.: Expansion planning model of multi-energy system with the integration of active distribution network. Appl. Energy 253(1) (2019) 6. Li, Y., Feng, B., Li, G., et al.: Optimal distributed generation planning in active distribution networks considering integration of energy storage. Appl. Energy 210(15), 1073–1081(2018) 7. Murari, K., Padhy, N.P.: A network-topology-based approach for the load-flow solution of AC–DC distribution system with distributed generations. IEEE Trans. Industr. Inf. 15(3), 1508–1520 (2019) 8. Zhang, D., Li, J., Hui, D.: Coordinated control for voltage regulation of distribution network voltage regulation by distributed energy storage systems. Protection Control Modern Power Syst. 3 (2018) 9. Razavi, S.-E., Rahimi, E., et al.: Impact of distributed generation on protection and voltage regulation of distribution systems: a review. Renew. Sustain. Energy Rev. 105, 157–167 (2019)
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10. Mirsaeidi, S., Dong, X.: An Integrated control and protection scheme to inhibit blackouts caused by cascading fault in large-scale hybrid AC/DC power grids. IEEE Trans. Power Electron. 34(8), 7278–7291 (2018) 11. Zhu, H.: Research on expert system of relay protection setting calculation software. Shandong university (2006). (in Chinese) 12. Yu, S.: Application Study of County Integrated Intelligent Relay Protection Setting Calculation System. North China Electric Power University (2017). (in Chinese) 13. Qin, B.: An expert system for 110kV grid relay protection setting calculation. Guangxi university (2002). (in Chinese) 14. Ren, L.: Research of On-line Check System of Relay Protection Setting Value. North China Electric Power University (2012). (in Chinese) 15. Liu, G.: The Research on Fast Algorithm of Online Checking System of Protection Settings. North China Electric Power University (2014). (in Chinese) 16. Qin, B.: Transformer excitation inrush current based on neural network Identification method of the research. Hunan University (2013). (in Chinese) 17. Wang, J.: Study of Distance Protection Scheme of Transmission Line Based on Neural Network And DSP. Zhejiang University (2006). (in Chinese) 18. Wang, Y.: Research on the application of artificial neural network in the distance relay Protection. North China Electric Power University (2015). (in Chinese) 19. Meng, L.: Research on Adaptive Strategy for Overcurrent Protection of Distribution Grid Including Photovoltaic Power Stations. Xinjiang university (2016). (in Chinese) 20. Liang, X.: Research on Adaptive Protection of Distribution Network with Photovoltaic Power Station Considering LVRT Control Strategy. North China Electric Power University (2017). (in Chinese) 21. Li, Y.: Research on fault detection and location method of photovoltaic array multi-sensor based on improved BP neural network. Xi’an University of Technology (2018). (in Chinese) 22. Wang, L.: Fault Classification and Location of Transmission Line Based on BP Neural Network Improved by PSO. Northeast Petroleum University (2014). (in Chinese) 23. Feng, W.: The research on location method of submarine cable fault. Northeastern University (2015). (in Chinese) 24. Tai, S.: The Fault Diagnosis of Relay Protection Based on Bayesian Network. Xidian University (2010). (in Chinese) 25. Wu, X.: Research on Power System Fault Diagnosis Based on Improved Bayesian Network Method. Zhejiang university (2005). (in Chinese) 26. Yu, H.: Research on Fault Location and Diagnosis Technologyof VSC-HVDC. Lanzhou University of Technology (2016). (in Chinese) 27. Zhang, Y.: Power system fault diagnosis based on DS evidence theory and mutual information network. Southwest Jiaotong University (2007). (in Chinese)
Voltage Adaptive Dynamic Partition Method Considering Reactive Power Margin Rui Zhang1(B) , Kecheng Liu2 , Xiaoming Li1 , Jianhu Guo3 , Zhe Wang3 , and Jifeng Liang1 1 State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, Hebei, China
[email protected]
2 Dunshi Magnetic Energy Technology Co., Ltd., Shijiazhuang 050000, Hebei , China 3 State Grid Hebei Electric Power, Xingtai Power Branch, Xingtai 054000, Hebei , China
Abstract. In order to better realize the grid reactive voltage division, this paper adopts a new method to realize it. First, the initial partition is formed. Then, considering the degree of reactive power balance in the area, a new feature is constructed as a measure to merge the areas to form the optimal partition, so as to determine the number of partitions. The controllability and observability of nodes in the area and the anti-interference of nodes outside the area are used as evaluation indicators to determine the selection plan of the dominant node. Keywords: Full-dimensional sensitivity · Dynamic partition · Community structure · Reactive power balance · Dominant node
1 Introduction The hierarchical and zoned control of reactive power and voltage is an important means for great power to realize the stable operation of the power system and the reasonable and effective dispatch of electric energy. The three-level voltage control mode proposed by EDF in the 1970s has been widely used in many countries [1, 2]. As an important section of the three-level voltage level, and two-level voltage control mode is based on the characteristics of reactive power balance among different regions of the power grid. Based on the basic principle of “strong coupling within the region and weak coupling outside the region”, the large power is divided into sub regions that can be controlled independently. At the same time, the dominant node which can reflect the voltage level of the region is selected in each sub region. By monitoring and controlling the voltage level of the leading node, the voltage of the whole power grid can be adjusted rapidly and dynamically [3, 4]. At present, the traditional method of power grid partition is to define the electrical distance between nodes, and to achieve regional partition by various clustering algorithms. In reference [2], the energy sensitivity matrix is constructed to define the electrical distance, and the fuzzy clustering algorithm is used to divide the voltage control area. Literature [5] divides the regions by clustering, and establishes the objective function © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 734–741, 2022. https://doi.org/10.1007/978-981-19-1528-4_75
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based on the characteristics of dominant nodes to solve the dominant nodes in each region. In reference [6, 7], the partition scheme is obtained by spectral clustering. Considering the reactive power reserve and reactive power balance in the region, some nodes need to be manually adjusted in the later verification process. In reference [8, 9], PV nodes are relaxed to PQ nodes, and then central nodes are determined. Finally, cloud clustering algorithm is used to realize soft partition of power grid. No matter what kind of clustering algorithm is used, the number of grid divisions should be determined in advance, or a certain parameter threshold should be given to decide the number of final divisions. In recent years, it has become a new research trend to use complex network theory to apply power network. As an important branch of complex network theory, community structure has good applicability in power grid zoning. According to the community structure theory, reference [10] constructs a new modular function for partition, and considers the controllability and observability index of nodes in the region when selecting the leading node, but does not consider the influence of the leading node on other regions.
2 Full Dimension Voltage and Reactive Power Sensitivity In the process of power system partitioning, there are two types of nodes in the power grid, one is load nodes, and the other is control nodes. Load nodes can be equivalent to PQ nodes. Control nodes are the reactive power supply nodes that provides reactive power support for load node, as PV nodes. Generator node with AVR is generally PV nodes in power flow equation. Through the inverse matrix of Jacobian matrix in electric current calculation, the voltage and power responsiveness matrices of PQ nodes can be obtained. Through processing, the original AC power flow equation is obtained, and then the equation is linearized to obtain. ∂P ∂P θ θ P ∂θ ∂U = J (1) = ∂Q ∂Q U U Q ∂θ ∂U P and Q are the deviation matrix of active and reactive power injected into the node, θ and U are node voltage phase angle and amplitude change matrix, J is Jacobian matrix. Reverse Eq. (1) to get: P P θ SPθ SQθ −1 P =S (2) = =J SPU SQU Q Q Q U In the formula: the sensitivity factors SPU and SQU are the voltage amplitude changes of the node injected unit active and reactive power respectively, SPθ and SQθ are the changes of the phase angle of the node injected unit active and reactive. In Eq. (2), it can be seen that the relationship between the voltage change of the grid node U and the sequence of active and reactive power changes P and Q can be expressed as: U = SPU P + SQU Q
(3)
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The voltage of node i is not only affected by its own active and reactive power changes, but also affected by the injection of other nodes Pj and Qj , expressed as: Ui = Ui0 +
N j=1
SPU ,ij Pj +
N
SQU ,ij Qj
(4)
j=1
Where: Ui0 is the stable voltage, SPU ,ij is the member of SPU respectively, SQU ,ij is the elements of SQU respectively. The sensitivity factors SPU reflect the magnitude of the influence of active power on the nodes voltage. The sensitivity factors SQU reflect the magnitude of the influence of reactive power on the nodes voltage. PV nodes have electrical coupling relationship with other load nodes, which has the ability to regulate the voltage of load nodes in the region. Increasing the dimension of PV node into the sensitivity matrix can better reflect the coupling characteristics between nodes in the whole network. For n node system, nodes 1 to m are PQ nodes, and nodes (m + 1) to (n − 1) are PV nodes. Due to the small resistance in the transmission network, the influence of active power on voltage is generally ignored. According to the linearized equation of tidal current, the sensitivity equation of PQ nodes is obtained as follows: U/Q = SQU = Tm×m
(5)
According to the reactive power sensitivity equation proposed above, all reactive power source nodes in the system are solved recursively one by one, which can reflect the changes of other nodes. Set one of the observation power supplies, and the rest are PV nodes, and the expanded voltage and reactive sensitivity matrix T can be obtained as follows: ⎡ ⎤ t11 · · · t1t t1(t+1) ⎢ .. . . ⎥ . .. ⎢ ⎥ . .. . (6) T = ⎢ . ⎥ ⎣ tt1 · · · ttt tt(t+1) ⎦ t(t+1)1 · · · t(t+1)t t(t+1)(t+1)
The established matrix T includes (t + 1) × (t + 1) elements to represent various voltage reactive sensitivity. First, set one of the nodes as the observation power source, obtain the reactive power sensitivity to other nodes, and obtain the T matrix of this node. Then set the second node as the observation power source, repeat the above process, so as to obtain the T matrix of all nodes, and finally get the augmented sensitivity matrix S. The system power flow before and after the node change is very small, and the t-order matrix in the upper left corner of the T matrix corresponding to each power source can be approximately equal to the T matrix. Mt×(n−t−1) Tt×t (7) S= N(n−t−1)×t Y(n−t−1)×(n−t−1) It is more reasonable than the existing two-level voltage control partitioning algorithm in automatic voltage control (AVC), which only partitions the load nodes and adds the power nodes to the geographically similar partition.
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3 Modular Function Index Considering Reactive Power Margin As an important link in a complex power grid, community structure is that nodes with similar characteristics and functions are closely connected to form a specific structure in the changing process of the system. Therefore, the concept of modular function is proposed and add to weight networks to quantify the community structure quality of power grids and get the final result.
ki kj 1 Aij − δ(i, j) (8) θ= 2M 2M i j Aij (9) ki = j
⎛ ⎞ M =⎝ Aij ⎠/2 i
(10)
j
Where: Aij is the expression of weight node. When they are connected, Aij = 1. In the remaining cases, it is equal to 0. ki is the merge of the weights of all edges connected with node i, if node i and node j are in the same partition, then δ(i, j) = 1, otherwise δ(i, j) = 0. If in a complex network, the sum of the weight of the connected edges in the community is greater than the sum of the weight of the connected edges when the community is randomly connected, the modularity will increase. When the modularity reaches the maximum, the best network partition is obtained, and the accuracy of the community structure is also the highest. As an artificial complex network, power system shows obvious union construction characteristics, it can be obtained by community structure principle. According to the method proposed above, the expression of node weight is given below, which can reflect the coupling degree relationship SQU ,ij + SQU ,ji (11) 2 In view of the importance of voltage sensitivity, this paper uses it as one of the indicators for evaluating grid divisions. Voltage sensitivity can reflect the impedance and geographic attributes between nodes, and thus can reflect the connectivity and coupling of grid divisions. The lack of reactive power reserve in each area of power grid will cause voltage instability in the system, and even voltage collapse, which can not be ignored in the process of zoning. In practice, the reactive power reserve changes with the operating conditions, so the improved modular function considering reactive power reserve is constructed, and the dynamic partition is carried out to meet the actual demand. To achieve reactive power balance in the area, the maximum output of reactive power source in each area should be greater than the total reactive load. The reactive power balance index is defined to measure the relationship between reactive power output and reactive load. QLi QGi − 1 QLi > QGi (12) λi = 0 QLi ≤ QGi Aij =
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Where: QLi is the total reactive load in the region, which changes with the change of operation mode. Considering the scenario of fast dynamic partition of power grid, QGi refers to the maximum output of reactive power source in the region, excluding the output provided by reactive power compensation device. The closer λi is to 0, the more balanced the reactive power relationship is. Considering the influence of regional reactive power balance degree on partition, a new modular function is constructed by introducing reactive power balance degree index into modular function θ : θ = θ − ω
N
λi /N
(13)
i=1
Where: ω is the weight coefficient of reactive power balance degree, and N is the number of regions in the current partition state.
4 Simulation and Analysis 4.1 Computing Method In this paper, partition method is divided into initial partition and partition merging. The improved modular value of the voltage partition is obtained, and the improved modular function is obtained by pairwise combination of the initial partition. If the value of the modulus function is higher than the value before the combination, the objective function with the largest value of the established function is selected. Subsequent iterations of the above process are carried out to maximize the function, and the grid voltage division is stopped at this time. At this time, the grid division is optimal. According to the sensitivity theory proposed in this paper, the computational complexity of the initial partition is greatly reduced, and the efficiency of the grid voltage partition is increased. The improved modularity indicators can merge regions to ensure the coupling of nodes in the region, to ensure that the reactive power balance of each region meets the operation requirements, and to automatically and quickly determine the number of regions according to the current operation state of the system. 4.2 Case Simulation Analysis and Results This paper uses a typical distribution network model to verify the correctness of the proposed method. Among them, 32 nodes are balance nodes, nodes 1 to 29 are PQ nodes, the other nodes are PV nodes. According to the partition method proposed in this paper, the change of modular function index in the process of partition is obtained (Fig. 1).
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Fig. 1. IEEE-39 bus power grid system
The final partition scheme and the selection of leading nodes are shown in Table 1. Table 1. IEEE-39 node system partition scheme Partition number Power node Load node 1
31, 32, 39
1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
2
30, 37
2, 25
3
38
3, 17, 18, 26, 27, 28, 29
4
35, 36
16, 21, 22, 23, 24
5
33, 34
19, 20
After the network partition is completed, the partition quality is evaluated. The sensitivity index εα and reactive margin index σb of reactive power source voltage control are calculated. A group of operation data is randomly selected, and the results of this paper are compared with the partition results of reference [10], which also divides the 39 bus system into five voltage control regions. The index comparison of the two zoning schemes is shown in Table 2 and Table 3. It can be seen from Table 2 that the reactive power source control sensitivity index of each region in this paper is generally higher than that of the results in reference [10], and the average reactive power source control sensitivity is higher, which proves that the partition results obtained by the proposed partition method in this paper show that the reactive power source has relatively good control ability to the nodes in each region. It can be seen from Table 3 that the reactive power margin index of the three regions in reference [10] reaches 92.2%, and the regional operation is close to full load, which is not conducive to the normal operation of the
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power grid in case of system disturbance. Compared with the average value of reactive power margin index of the two schemes, the average reactive power margin index of the whole network obtained in this paper is smaller, that is, the regional reactive power margin of the whole network is larger, and the result is better. Table 2. Comparison of reactive power voltage control sensitivity index εα Region
Results of this paper
Results of literature [10]
1
0.0242
0.0207
2
0.0275
0.0203
3
0.0382
0.0208
4
0.0250
0.0457
5
0.0317
0.0317
Average value
0.0293
0.0278
Table 3. Comparison of reactive power margin index σb Region
Results of this paper
Results of literature [10]
1
0.7973
0.6407
2
0.1538
0.4124
3
0.5980
0.9220
4
0.2587
0.2625
5
0.2470
0.2470
Average value
0.4109
0.4981
5 Conclusion In this paper, a dynamic partition method is proposed to track the change of power grid operation mode in real time and divide power supply area correctly. This method is an adaptive dynamic partition method without human intervention. Based on the improved modular function which considers the reactive power balance in the region, the region division of power grid can be automatically updated according to the current operation condition of power grid.
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References 1. Sun, H.B., Guo, Q.L., Qi, J.J., et al.: Review of challenges and research opportunities for voltage control in smart grids. IEEE Trans. Power Syst. 34(4), 2790–2801 (2019) 2. Bian, Y., Gui, H., Bie, Z.: Optimal DG allocation considering reconfiguration and microgrid zoning. Smart Power 48(7), 8–15 (2020). (in Chinese) 3. Zhao, C., Zhao, J., Wu, C., et al.: Power grid partitioning based on functional community structure. IEEE Access 99, 152624–152634 (2019) 4. Lin, S., Wu, J., Mo, C., et al.: Dynamic parition and optimization method for reactive power of distribution networks with distributed generation based on second-order cone-programming. Power Syst. Technol. 42(1), 238–246 (2018). (in Chinese) 5. Lou, X., Ma, G., Guo, C., et al.: System and framework design of risk coordination control for whole operation process of power system. Autom. Electric Power Syst. 44(5), 161–170 (2020). (in Chinese) 6. Cui, W., Yan, W., Wei-Jen, L., et al.: Optimal pi-lot-bus selection and network partitioning algorithm considering zonal reactive power balance. Power Syst. Technol. 41(1), 164–170 (2017). (in Chinese) 7. Chen, Z., Xie, Z., Zhang, Q.: Community detection based on local topological information and its application in power grid. Neurocomputing 170, 384–392 (2014) 8. Ge, H.C., Guo, Q.L., Wang, B., et al.: Multivariate statistical analysis-based power-gridpartitioning method. IET Gener. Transm. Distrib. 10(4), 1023–1031 (2016) 9. Cheng, Yu., Hang, N.: Influence of inverter-interfaced distributed generator with low-voltage ride-through capability on short circuit current of distribution network. Electric Power Autom. Equipment 35(8), 45–52 (2015). (in Chinese) 10. Pan, G., Wang, X., Peng, X., et al.: Improved network partition and pilot node selection method for reactive power/voltage control based on community detection algorithm. Power Syst. Protection Control 41(22), 32–37 (2013). (in Chinese)
Optimal Allocation of Capacity for Vehicle Charging Stations with Wind-PV Microgrid Zhongan Yu, Da Deng(B) , and Junjun Wu School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou City, Jiangxi Province, China [email protected]
Abstract. The proposal of “carbon hit peak emissions and carbon neutrality”, pointed out the direction for my country’s energy development, this paper proposes a capacity optimization strategy that integrates wind and solar storage microgrid systems for electric vehicle charging stations. First, the capacity optimization model and charging load model of the wind and solar storage microgrid system are analyzed. According to the charging time and user behavior, the Monte Carlo method is used to simulate the charging load curve of electric vehicles. HOMER software and the NSGA-II algorithm is used to obtain the optimal system capacity optimization configuration. Finally, the life of wind and solar storage components is used as a sensitive variable to analyze its impact on system economy and renewable energy utilization rate. Keywords: Wind and solar storage microgrid · Electric vehicle charging station · Capacity optimization · HOMER · NSGA-II · Sensitivity analysis
1 Introduction To achieve the goal of “carbon peak and carbon neutrality”, combining local renewable energy with charging piles to form a microgrid system and consuming renewable energy on-site is an effective way to solve the problem. There have been many research studying the planning of renewable energy electric vehicle charging stations. research [1] introduced the structure of the wind and solar hybrid energy vehicle charging station and the role of each component, and designed and optimized the charging station through the HOMER software. In the research [2], the charging load of electric vehicles is calculated and analyzed, and the load calculation of different types of electric vehicles under different charging methods is discussed. The research [3] proposes a method to optimize the capacity of the photovoltaic storage charging station considering the demand response and the utilization rate of new energy. The main innovation of this paper is the establishment of a microgrid system for electric vehicle charging stations that integrates wind-solar power supply; Monte Carlo method that considers user charging is used to simulate electric vehicle charging load; the number and type of each component are used as variables, and HOMER and genetic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 742–751, 2022. https://doi.org/10.1007/978-981-19-1528-4_76
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algorithm are used. The combined multi-level optimization algorithm is solved; taking the specific data of Ganzhou charging station as a reference, the feasibility and accuracy of the optimization plan are evaluated from the three aspects of system economy, system power shortage rate, and component life sensitivity.
2 Car Charging Station Microgrid System The focus of this paper is to establish a car charging station based on the wind and solar storage microgrid system as shown in Fig. 1 below, which is mainly composed of photovoltaic power generation systems, wind power generation systems, energy storage systems, charging piles, and control systems.
Fig. 1. System structure diagram.
2.1 Mathematical Model of System PV system output power, ambient temperature is determined by the intensity of the light radiation [8], which is expressed as: PPV (t) = GC (t)
1 + k(TC (t) − TSTC ) PSTC NPV GSTC
(1)
In the formula, PPV (t) represents the output power at the working point of the photovoltaic array at time t; GC (t) represents the solar irradiance at the working point at time t; TC (t) is the battery surface temperature at time; k is the power temperature coefficient; GSTC , TSTC and PSTC are the solar irradiance, the surface temperature of the battery and the output power of the photovoltaic array under the standard rated conditions given by the manufacturer, respectively. Typically, GSTC = 1 KW/m2 , TSTC = 25 ◦ C.
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The expression for wind power generation is as follows [4]: ⎧ v ≤ vci or v ≥ vco ⎪ ⎨ 0,3 3 v −vci PWG = v3 −v3 Pr , vci ≤ v ≤ vcr ⎪ ⎩ cr ci Pr vcr ≤ v ≤ vco
(2)
In the formula, Pr is the rated power; vcr is the rated wind speed; vci is the cut-in wind speed; vco is the cut-out wind speed. The expression of the battery is as follows [5]: ⎧ ⎨ EBS (t − 1)(1 − σBS ) + ηsb PSUP (t) − PCV (t) ; charge ηinv EBS (t) = ⎩ EBS (t − 1)(1 − σBS ) − PCV (t) − PSUP (t) ; discharge ηinv (3) PSUP (t) = PPV (t) + PWG (t); In the formula, ηinv , ηsb are the efficiency of the inverter and the charging efficiency of the BS respectively; EBS (t), EBS (t − 1) are the energy storage of BS at t and t–1 respectively; σBS is the self-leakage rate of the battery; PCV (t) a is the load power of the charging pile at time t; PSUP (t) is the sum of the power supply of PVGS and WTGS at time t. 2.2 Mathematical Model of Charging Load The main factors of electric vehicle charging load are affected by the initial charging time and electric vehicle charging behavior. According to the research [6], the charging time of electric vehicles connected to the grid and the charging time of leaving the grid obey the normal distribution. Its probability density function is as follows: 1 (t0 − μs )2 fs (t0 ) = √ exp − (4) 2σs2 σs 2π In the formula, t0 is the time when the vehicle is connected to the grid. μs , σs are the expectation and standard deviation of the initial charging time of the electric vehicle, respectively. Typically, μs = 17.1, σs = 3.25. According to Electric vehicle initial charging time, the initial charging state (SOC0 ) can be obtained together with vehicle mileage Sd , and then get the user’s charging time( Tcar ).
W1 SOC0 = SOC1 − SCdcar (5) 0 )Ccar Tcar = (SOC1 −SOC ηPn,i In the formula, Ccar is the battery capacity of electric vehicles, W1 is the electric energy required for driving 1 km. Pn,i is the charging power of n car at time i, η is the charging efficiency.
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At present, electric vehicles are mainly divided into two types of charging modes: conventional slow charging and fast charging. Research [7, 8] gives the proportion of domestic electric vehicle users choosing fast charging mode, and the approximate probability is 0.252. To determine the user’s charging mode, this article assumes a random number Rcar , which satisfies a uniform distribution in U(0,1): PQC , Rcar > 0.252 (6) Pn,i = PNC , Rcar ≤ 0.252 In the formula, PQC is fast charging power, PNC is the conventional slow charge charging power. Optimal Configuration Model of Microgrid System. 2.3 Objective Function The design and optimization objectives of this paper are as follows: ➀Minimum total system investment and operating cost. ➁Highest reliability of system power supply The total investment cost CDG of each distributed generator is composed of initial investment cost CI , system maintenance and operation cost COM , and system replacement cost CSR , as shown below: CDG = CI + COM + CSR
(7)
CI = Npv Cpv + Nwt Cwt + Nbs Cbs fo
(8)
In the formula, Cpv , Cwt , Cbs are the unit prices of photovoltaic panels, wind turbines, storage batteries, and battery management systems, respectively; Npv , Nwt , Nbs are the number of photovoltaic panels, wind turbines, and batteries.fo is the depreciation factor,which is defined as: fo =
r(1 + r)m (1 + r)m − 1
In the formula, r is the depreciation rate; m is the system age. The maintenance and operation costs of the DG unit(COM ):
COM = Com.pv tpv + Com.wt twt + Com.bs tbs + CB CB = Cb ∫T0 PG (t)dt
(9)
(10)
In the formula, Com.pv , Com.wt , Com.bs are the maintenance and operation costs of photovoltaic panels, wind turbines, storage batteries, and battery management systems per unit time. tpv , twt , tbs are the operating hours of photovoltaic panels, wind turbines, storage batteries, and battery management systems, respectively. CB is the total electricity cost for purchasing electricity from the grid. Cb is the unit price for purchasing grid electric energy, which is billed by time period. It is 0.8 yuan/KWh from 6:00–22:00 every day, and 0.35 yuan/KWh during the remaining time. At present, my country’s power grid does not advocate the reverse sale
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of electricity from the microgrid system to the grid, so this article only considers the one-way purchase of electricity from the microgrid. The replacement cost of the DG unit is: CSR = Csr.pv + Csr.wt + Csr.bs
(11)
In the formula, Csr.pv , Csr.wt , Csr.bs are the replacement costs of photovoltaic panels, wind turbines, storage batteries, and battery management systems. Due to the randomness and volatility of wind energy and solar energy in the microgrid system, the system’s power generation and power supply will be less than the load demand at certain moments, so this paper uses the Loss Of Power Supply Probability (LPSP) to reflect the reliability of the system power supply. It is defined as the ratio of the load demand power that the system cannot meet to the total load demand power: T
fLPSP =
[PCV (t) − ηinv .(PBS (t) + PPV (t) + PWT (t))]
t=1 T
(12) PCV (t)
t=1
In the formula, PBS (t), PPV (t), PWT (t) are the power of battery, photovoltaic, and wind power at time t, respectively; T is the total operating time of the system. It can be seen that the smaller the load power shortage rate, the higher the system stability. 2.4 Restrictions According to the energy exchange strategy, at any time period.When the energy storage system is in a charging state: PPV (t) + PWT (t) = PCV (t) + PBS (t)
(13)
When the energy storage system is in a discharge state: PCV (t) = PPV (t) + PWT (t) + PBS (t) + PG (t)
(14)
In the actual community environment, subject to the constraints of floor space and load upper limit, the number of configurations of photovoltaic cells, wind turbines, and energy storage batteries needs to be planned according to the load power demand to plan an appropriate upper limit to narrow the search space for the optimal solution. ⎧ ⎨ 0 ≤ NPV ≤ NPV. max (15) 0 ≤ NWT ≤ NWT. max ⎩ 0 ≤ NBS ≤ NBS. max The entire energy storage system is dynamically changing, and the power change range of the entire energy storage system is as follows: E ≤ EBS (t) ≤ EBS. max BS. min Pc.min ≤ PBS (t) ≤ Pc.max Pdisc.min ≤ PBS (t) ≤ Pdisc.max
(16)
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After considering the discharge depth of the battery (cDOD ), the power of the energy storage system (EBS (t))needs to further meet the constraints: (1 − cDOD ) NBS WBS ≤ EBS (t) ≤ α NBS WBS
(17)
In the formula, α is the capacity retention rate of the battery. As the system continues to cycle, the value of α will gradually decrease.
3 Analysis 3.1 Simulation Calculation of Electric Vehicle Charging Load This paper takes Ganzhou City, China (25°49 N, 114°56 E) as the research area, and queries NASA for the entire year of 2020 sunlight data and wind speed data at a height of 10 m above the ground in this area from NASA [9]. After discretizing it, the data shown in the figure below is obtained: Set the Monte Carlo method to simulate 1000 times to calculate the total charging load of the electric vehicle. The calculation unit is days, and the step length is accurate to the minute, which is 1440 min. The CV charging power as shown in the Fig. 2 below.
Fig. 2. Electric vehicle total load curve.
There is a significant peak-to-valley difference between the charging load of electric vehicles at night and during the day, and the charging load curve at night is relatively flat. This is because users who use fast charging with high power and short charging time tend to focus on peak hours during the day, while more users choose regular fast charging at night.
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3.2 Multi-level Optimization This paper optimizes the selection of the configuration scheme that meets the constraints on the HOMER platform as the initial population of NSGA-II optimization, which provides a correct and reasonable direction for the secondary optimization of NSGA-II, saves solution time and improves the correctness of the understanding set. After the Pareto solution set is obtained in the secondary optimization, the HOMER platform is used for sensitivity analysis. The rated capacity of a single photovoltaic panel used in this paper is 1 KW, and the rated capacity of a single wind turbine is 10 KW. The nominal capacity of a single battery is 2.36 KWh. Related system configuration model parameters are shown in Table 1 below. Perform simulation calculations in the HOMER software, combined with the constraint Eqs. (13) –(17), and get the preliminary optimization results of HOMER capacity as shown in Fig. 3 (limited by space, only partial results are given):
Fig. 3. Summary of HOMER optimization results
Table 1. System configuration model parameters Type
Initial cost /(/kW)
Replacement cost/(/kW)
Operation cost /(/kW)
Life /year
WT
Eocycle EO10
3000
2400
30
20
PV
Peimar SG300MBF
2000
1600
10
25
Battery
BAE PVV 420
150
80
1
10
Converter
Studer Xtender XTS 120
300
300
3
15
Import the HOMER optimized data into matlab as the initial population, set the number of iterations G to 1000, and the last-generation population set is the Pareto solution set as shown in Fig. 4:
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Fig. 4. Pareto sets
It can be seen that the utilization rate of new energy and economy restrict each other. The reason is that if the system is to increase the utilization rate of new energy on the basis of meeting the charging load (that is, reduce the system power shortage rate), more PV and WT and BS, this will lead to an increase in the cost of the entire system. Select the representative scheme of Pareto solution set as shown in Table 2 below. Through the comparative analysis of the results in Table 2, it can be seen that the load shortage rates of scheme 1, 2, and 3 are similar; the load shortage rate of scheme 4 is obviously insufficient; scheme 5 has the lowest load shortage rate, but the economy is obviously insufficient; scheme 3 and Schemes 1 and 2 increase the number of BSs and converters when the number of PVs is similar to the number of WTs. Although the load shortage rate is reduced, the initial investment cost and operating investment cost are greatly increased. In summary, considering economy and new energy utilization rate comprehensively, options 1 and 2 are better than other options. Analyze schemes 1 and 2 separately. Scheme 2 increases the number of WTs and BSs, reduces the number of PVs, and increases the cost, but in exchange for the reduction of the load shortage rate. In practical applications, due to the large footprint of photovoltaic solar panels, users can prioritize the solution that suits their local needs according to their own needs. Table 2. Capacities of distributed generations proposal
PV/KW
WT/SET
Battery/SET
Convert/KW
CI (106
COM
)
LPSP/%
/ (105 )
1
534
189
716
750
5.3
2.58
16.5%
2
522
195
734
750
5.44
2.53
16.1%
3
526
180
1055
1000
5.6
2.65
15.8%
4
315
176
595
750
5.53
3.10
23.2%
5
791
237
1337
1000
5.77
2.18
7.3%
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3.3 Sensitivity Analysis The sensitivity analysis function provided by HOMER software can further improve the solution. Taking Scheme 2 in Table 2 as a representative scheme, Fig. 6 below is the relationship curve between the lifespan of WT and PV and the total net present value (TNPC) and the utilization rate of new energy under different life conditions of BS. The abscissa is the PV life, and the ordinate is the WT life. Figure 5(a)–(c) shows the relationship between BS’s net present value in 3 years, 5 years, and 8 years respectively; Fig. 6(d)–(f) shows the relationship diagram of new energy utilization rate.
Fig. 5. Relationship curve of the net present value of BS
Fig. 6. The relationship curve of new energy utilization value of BS
Analyzing Fig. 5(a) and Fig. 6(d), when the BS life is 3 years, the value of the net present value is inversely proportional to the PV and WT life (the same applies to Fig. 5(b) and (c)); new energy The utilization rate is proportional to the WT life and PV life (the same applies to Fig. 6 (e) and (f)). This is because if the life of PV and WT is short, the cost of replacement and maintenance will be higher. At the same time, in order to meet the demand of the load and improve the stability of the system, the system will purchase more electricity from the grid, and the utilization rate of new energy will decrease.. Comparing and analyzing Fig. 5 (a), (b), (c), when the life of PV and WT are constant, increasing the life of BS within a certain range can effectively reduce the net present value of the system and improve the economy of the system, but After a certain level, increasing the BS life has almost no effect on the net present value. This is because BSs with short lifespans tend to have small capacities. Under the condition that a certain new
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energy utilization rate is met, more numbers need to be placed, and the cost and loss brought by this will also increase. Carry out life sensitivity analysis on system components, and users can further optimize the capacity configuration results before the actual engineering application is implemented.
4 Conclusion In this paper, a capacity optimization model is established for electric vehicle charging stations, combined with specific regional climate conditions, using HOMER software and NSGA-II algorithm for secondary optimization solutions, which can be used for wind-light storage microgrid electric Provide reference for the construction of car charging station. (1) Monte Carlo simulation of electric vehicle load based on charging time and user behavior eliminates the error caused by subjectively setting the probability density of electric vehicle SOC, and improves the reliability and accuracy of charging stations. (2) Through analysis, it can be known that combined with specific climate data, the secondary optimization model makes the solution more rapid and accurate. (3) Conduct sensitivity analysis on the life of system modules, and evaluate the impact of device life on system economy and new energy utilization. Satisfy the balance between environmental protection and economy of electric vehicle charging stations, in line with future development trends.
References 1. Ekren, O., Hakan, C.C., Güvel, Ç.B.: Sizing of a solar-wind hybrid electric vehicle charging station by using HOMER software. J. Clean. Product. 279, 123615 (2021) 2. Luo, Z., Hu, Z., Song, Y., et al.: Electric vehicle charging load calculation method. Autom. Electr. Power Syst. 35(14), 36–42 (2011). (in Chinese) 3. Ma, X., Li, Y., Wang, H., et al.: Research on charging pile demand based on stochastic simulation of electric vehicle travel. Trans. China Electrotech. Soc. 32(S2) (2017) 4. Almutairi, K., Hosseini, D.S.S., Hosseini, D.S.J., et al.: A thorough investigation for development of hydrogen projects from wind energy: a case study. Int. J. Hydrogen Energy 46(36), 18795—18815 (2021) 5. Qi, Y., Jianhua, Z., Zifa, L., et al.: Multi-objective optimization design of wind-solar hybrid power supply system. Autom. Electr. Power Syst. 33(17), 86–90 (2009). (in Chinese) 6. Zhang, D., Chen, J., Liu, Y.: Characteristics and development countermeasures of private motor vehicle transportation in Beijing. Dalian, Liaoning, China: 20068. (in Chinese) 7. Tian, M.: China’s New Energy Vehicle Big Data Research Report (2020) is officially released. Product Safe. Recall. (5), 49 (2020) 8. Xiangning, X., Jianfeng, W., Shun, T., et al.: Research and suggestions on some key issues in the planning of electric vehicle charging infrastructure. Trans. China Electrotech. Soc. 29(08), 1–10 (2014) 9. The Prediction Of Worldwide Energy Resources (POWER) Project [DB/OL] .https://power. larc.nasa.gov/data-access-viewer . 18–04–2021
Effect of Thermo-oxidative Ageing on Physicochemical and Electrical Properties of Liquid Silicone Rubber Mengqi Wang1 , Jiachen Yu1 , Hao Liu1 , Jiacai Li1 , Wei Shen2 , and Shengtao Li1(B) 1 State Key Laboratory of Electrical Insulation and Power Equipment, Jiaotong University,
Xi’an, China [email protected] 2 Shaanxi Electric Power Corporation State Grid Shaanxi Electric Power Research Institute, Xi’an, China
Abstract. Liquid silicone rubber (LSR) used for the umbrella skirt of composite insulator will be aged under thermal oxygen caused by its self-heating, which will affect the safe operation of transmission line. In order to explore the effect and mechanism of thermo-oxidative ageing on LSR, the LSR samples were aged under 150 °C.The cross-section morphology and microstructure of LSR with different ageing time were compared. Besides, the macroscopic performance including mechanical properties, DC breakdown field strength and high field conductivity of LSR were tested. In addition, terahertz time domain spectroscopy (THz-TDS) was used to obtain the dielectric parameters in terahertz frequency domain. It was found that nano-SiO2 particles precipitated on cross-section of LSR during ageing, and the number of particles increased. The content of silyl methyl in LSR decreased, and the main chain of polysiloxane was oxidized and decomposed, resulting in the increase of polar-chain-ends of LSR and the decrease of orderliness. Due to the change of molecular structure and the precipitation of nano-SiO2 , the DC breakdown field strength increased and the conductivity decreased as well as the dielectric constant and dielectric loss increased in terahertz frequency domain. In this work, the thermo-oxidative ageing mechanism of LSR was studied from the molecular structure level, and THz-TDS was innovatively employed to investigate the ageing mechanism. Keywords: Thermo-oxidative aging · Liquid silicone rubber · Microstructure · Mechanical properties · DC breakdown field strength · High field conductivity · Terahertz time domain spectroscopy (THz-TDS)
1 Introduction Composite insulator is mainly composed of glass fiber reinforced resin sleeve and its outer silicone rubber sheath umbrella skirt. It is light and easy to install and transport, and also has excellent mechanical and electrical properties, so it gradually replaces © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 752–763, 2022. https://doi.org/10.1007/978-981-19-1528-4_77
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ceramic insulator and is widely used in power system [1]. Liquid silicone rubber (LSR) has the advantages of light weight, good pollution resistance, explosion-proof and high temperature resistance, so LSR is commonly used for some composite insulator sheds in Japan, South Africa and Europe [2]. Compared with high temperature vulcanized silicone rubber (HTV-SIR), LSR also has the advantages of good fluidity and fast vulcanization speed [3]. However, as silicone rubber is organic polymer for external insulation, it will deteriorate under the influence of ultraviolet, strong electric field, operation heating and other factors under long-term operation. As a result, its mechanical and electrical properties will be reduced, which will seriously affect the normal operation of local power system [4]. Therefore, it is particularly important to explore the ageing mechanism of LSR, which can provide a theoretical basis for exploring new non-destructive ageing monitoring methods. Many scholars have carried out a lot of research on the ageing characteristics of LSR. Yan Zhenhua used scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis and other experimental methods to analyze the physical and chemical properties of silicone rubber and found that the microstructure and element change of silicone rubber can reflect the signs and degree of ageing [5]. Fei Yijun conducted in-depth research on the electrical tree ageing angle of LSR, and explored the influence law of temperature on electrical tree. With the decrease of initial voltage of electrical tree and the acceleration of ageing speed, it was a new angle to study the ageing mechanism [6]. Chen Guoqing carried out accelerated thermal ageing of LSR at 120 °C, and studied its dielectric and mechanical properties. The results showed that with the increase of ageing time, the resistivity increased, the dielectric constant decreased, the breakdown field strength increased first and then decreased, the tensile strength and elongation at break decreased significantly, but the hardness increased. The average free path, ion mobility and other microscopic mechanisms explained the performance changes [7]. Lin Ying analyzed the micro and macro properties of LSR with different ageing time by FTIR, XPS, heat resistance and electromechanical properties. The results showed that the difference of ageing mechanism between LSR and HTV-SIR was mainly caused by cross-linking structure, flame retardant ATH and content of hydrogen containing silicone oil, which provided support for the ageing theory of LSR [8]. There is little research on the internal structure changes of LSR after ageing. As a new characterization method, THz-TDS can make up for the physical and chemical information of 0.1–10THz band between broadband dielectric spectrum (10–2 –107 Hz ) and FTIR (>1015 Hz) [9–11]. It is significant for the study of high frequency polarization and relaxation characteristics of materials and more perfect structural analysis. The traditional broadband dielectric spectroscopy can characterize the interface and steering polarization, and FTIR can characterize the chemical bond scale. The application of terahertz time domain spectroscopy can not only realize the analysis of “macro-mesomicro” spatial structure and relaxation characteristics in different scales, but also study the microstructure characteristics in deeper layers of materials due to its non-destructive and laser transmittance. T. Kaneko carried out different accelerated ageing tests on silicone rubber, tested their THz-TDS, and analyzed the refractive index and absorption index, indicating that THz-TDS can be used for SIR ageing detection and ageing state
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evaluation [12]. However, the dielectric parameters in terahertz frequency domain was not studied. Based on this issue, the cross-section morphology of LSR after brittle fracture was observed by SEM and FTIR was used to analyze the change of functional group content of LSR at different ageing time, as well as DC breakdown test system and high field conductivity test system were carried out to test the DC breakdown field strength and conductivity to further explore the internal structural changes. THz- TDS was used to get the dielectric constant and dielectric loss of LSR in terahertz frequency domain before and after ageing to analyze the polarization of ions, small molecular chains and molecular groups.
2 Experiment 2.1 Sample Preparation The LSR used in this work was provided by Chenguang Chemical Co., Ltd. The sample was 100 mm × 100 mm × 1 mm square gray flake LSR sample. The ageing box was a standard electric ageing box. According to GB/T3512-2001 “Hot Air Ageing and Heat Resistance Test for Vulcanized Rubber or Thermoplastic Rubber”, LSR samples were naturally suspended in a 150 °C thermal ageing oven for thermo-oxidative ageing test. Three groups of ageing samples were set up, the time interval was 20 days, and the unaged sample was the blank group. 2.2 Experiments The SEM was used to observe 20 kX and 50 kX cross section morphology of LSR. The equipment was Gemini SEM 500 field emission scanning electron microscope produced by Shanghai Carl Zeiss Management Co., Ltd. The acceleration voltage of the equipment was 0.02–30 kV, continuously adjustable, and the magnification was 20–2000 kX. The Fourier transform infrared spectrometer (IR presitge-21, Shimadzu International Trading (Shanghai) co. Ltd.) used the attenuated total reflection mode. The LSR was characterized in the wavenumber range of 500–4500 cm−1 , the scanning times was 32, and the resolution was 4 cm−1 . It can be used to characterize the structure of organic polymer materials with the depth of several microns to tens of microns. The mechanical strength can be regarded as the macroscopic reflection of the change of molecular chain structure during the ageing process. The main parameters of mechanical strength were elongation at break and tensile strength, reflecting the change of molecular chain structure during the ageing process. In this paper, JJ-T5K tensile testing machine was used to test. Cut the sheet sample into standard dumbbell shaped sample. The tensile rate was 200 mm/min, and each sample was tested for five times and the average was taken. The computer controlled HJC-100 kV high voltage breakdown test system was used to study the DC breakdown characteristics of LSR. The test medium was insulating oil. The electrode used in this system was ball-ball electrode with a diameter of 25 mm. In this paper, we set the boost rate of 2.0 kV/s and the test temperature of 50 °C. Recorded the accurate sample thickness during the test.
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The DC conductivity measurement system was used to test the conductivity of LSR before and after ageing. The high voltage power supply was SL 600 W 40 kV DC high voltage power supply produced by Spellman company. The galvanometer was a 6517B high impedance meter with a current range of 1 fA to 20 mA. The electrode system was a three electrode system, including high voltage electrode, measuring electrode and protective electrode. At the same time, the electrode device was protected by shielding device to avoid external electromagnetic interference and other factors. The test temperature was 50 °C and the electric field intensity was 10 kV/mm. In this paper, the CIP-TDS terahertz time domain spectroscopy system produced by Beijing Daheng technology company was used to test the THz-TDS of LSR before and after ageing. The test system included: LEO-50Ti sapphire femtosecond laser, terahertz time domain spectrometer, EPS301 electric translation stage (time delay control), DC bias controller, SR830 lock-in amplifier (data acquisition) and LabVIEW software control system. The test method was transmission method, light path schematic diagram is shown in Fig. 2.
Fig.1. Schematic diagram of THz-TDS
3 Effect of Thermal Oxidative Ageing on Microstructure Properties 3.1 Molecular Structure (FTIR) It can be seen from Fig. 2.(a) and (b) that with the increase of ageing time, the silicon methyl Si-CH3 showed a downward trend and the Si-(CH3 )2 first increased but decreased then. From Fig. 2.(c), the main chain Si-O of polysiloxane initially rose and then declined. The C-H bond also increased initially and then decreased from Fig. 2.(d).
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Fig. 2. FTIR results of LSR with different ageing time (a) -CH3 in Si-(CH3 )2 (b) -CH3 in Si-CH3 in (c) Si-O (d) C-H
3.2 Cross-Section Morphology Figure 3 and Fig. 4 showed the cross-section morphologies of 20 kX and 50 kX of LSR with different ageing time. It can be clearly seen from Fig. 3 and Fig. 4 that with the increase of ageing time, the number of white spherical particles in the brittle section of. LSR increased obviously. This was because under the high temperature of accelerated thermo-oxidative ageing, the Si-C bond was easy to break and new free radicals were generated, which further oxidized and led to the formation of SiO2 particles. Therefore, these white granular substances were SiO2 [13]. It can be seen from Fig. 2 that at the initial stage of thermal ageing, the content of Si-O bond increased first. That meant that further cross-linking occurred at the initial stage, that was, the number of Si(–O)3 structure increased, and it was not easy to fracture under the action of thermal oxygen, which inhibited the further loss of SiO2 [8]. Under the action of the two factors, with the increase of thermo-oxidative ageing time, the content of granular substances on the surface and cross-section of the composites increased.
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Fig. 3. 20kX cross-section morphology of LSR with different ageing time (a) not ageing (b) ageing for 20 days (c) ageing for 40 days (d) ageing for 60 days.
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Fig. 4. 50kX cross-section morphology of LSR with different ageing time (a) not ageing (b) ageing for 20 days (c) ageing for 40 days (d) ageing for 60 days.
4 Effect of Thermal Oxidative Ageing on Macroscopic Performance 4.1 Mechanical Properties
Fig. 5. (a) Elongation at break and (b) Tensile strength of LSR with different ageing time
It can be seen from Fig. 5 that the elongation at break and tensile strength of LSR gradually decreased with the increase of ageing time. After ageing for 60 days, the elongation at break decreased by about 24%, and the tensile strength decreased by about 17%. The decrease of mechanical strength reflects the destruction of internal structure of LSR materials. 4.2 DC Breakdown Field Strength The DC breakdown field strength of the sample is determined by Weibull distribution and average value and standard deviation. The relationship between the two parameter
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Fig. 6. Weibull distribution of DC breakdown field strength
Weibull distribution and the DC breakdown field strength follows the formula [14]: Eb β (1) P(Eb ; α, β) = 1 − exp −( ) α Where: E b -breakdown field strength/kV·mm−1 ; α-The scale parameter/kV·mm−1 , is usually the breakdown field strength with a breakdown probability of 63.2%; β-The shape parameter, reflects the dispersion of breakdown data. The Weibull distribution of DC breakdown field strength of LSR in Fig. 6 shows that with the increase of ageing time, the DC breakdown field strength of LSR firstly increased during the first 40 days, but decreased then. The Table 1 showed that the shape parameters β of Weibull distribution of unaged LSR and LSR aged for different days were 25.48, 15.56, 19.63, 16.24 respectively, all greater than 10, so the data reliability was ensured. After ageing, the shape parameter of Weibull distribution of LSR breakdown field strength decreased. Table 1. Parameters of Weibull distribution of LSR at different ageing time. Ageing time
α (kV/mm)
β
0 days
62.816
25.481
20 days
63.141
15.562
40 days
64.730
19.630
60 days
63.211
16.239
4.3 High Field DC Conductivity LSR is not an ideal insulating material, and leakage current will flow through it under external electric field, which is the conductivity of dielectric. In this paper, the DC
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conductivity of LSR is measured by the method of voltage current measurement. In order to reduce the measurement error, the current data measured at the 11th minute is taken as the conductance current of LSR under the test condition. The DC conductivity is calculated by Eq. (2). σ =
Id US
(2)
Where: I-the median current in the 11th minute; D-the thickness of the sample; U-the applied voltage during measurement; S-the area of the measuring pole.
Fig. 7. Conductivity of LSR aged for different time
From Fig. 7, the conductivity of LSR was between 5.0 × 10–15 and 2.0 × 10–14 S·m–14 under the test conditions of 50 °C and 10 kV/mm. The conductivity of LSR first rose and then declined with the increase of thermo-oxidative ageing time. 4.4 Terahertz Time Domain Spectroscopy There are obvious absorption peaks for water in the terahertz frequency domain [15]. In order to avoid the absorption of terahertz signal by water, it is necessary to continuously inject dry nitrogen into the equipment cavity during the test to keep the humidity at about 3% and keep the test temperature at about 22 °C. The THz-TDS of nitrogen (Reference) and sample were obtained by normal incidence of terahertz wave. The frequency domain reference signal E ref (ω) and sample signal E s (ω) were obtained by fast Fourier transform. The transfer function of terahertz wave is as follows: H (ω) =
4ns (ω) Es (ω) κs (ω)ωd [ns (ω) − 1]ωd = · exp{− −j } 2 Eref (ω) c c [ns (ω) + 1]
(3)
Where: H(ω)-transfer function of terahertz wave; ns (ω)-refractive index; κ s (ω) Is extinction rate; D-thickness of the sample; c-propagation velocity of terahertz wave.
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The refractive index ns (ω) and extinction rate κ s (ω) can be obtained from the amplitude and phase information of THz wave transfer function: cϕ(ω) ns (ω) = 1 + ωd c 4ns (ω) κs (ω) = ln − ln ρ(ω) ωd (ns (ω) + 1)2
(4) (5)
Through the generalized Maxwell relation ε* (ω) = [n* (ω)]2 and n* (ω) = ns (ω) + jκ s (ω), the real part and imaginary part of the dielectric constant of the sample are as follows: ε (ω) = [ns (ω)]2 − [κs (ω)]2
(6)
ε (ω) = 2ns (ω)κs (ω)
(7)
Figure 8(a) below shows the terahertz time domain spectra of the reference signal and LSR with different ageing time. It can be seen from Fig. 8(a). that the terahertz wave will attenuate after passing through the sample, and the attenuation degree of terahertz wave of LSR samples are almost the same. After passing through LSR sample, the time position of the main wave of terahertz wave was delayed as can be seen in Fig. 8(a). From the sample thickness in Table 2 and the local peak diagram of the main wave in Fig. 8(a), it can be inferred that the main wave delay is mainly determined by the thickness of the sample. The thicker the sample is, the later the main wave delay is. Table 2. Sample thickness for THz-TDS. Ageing time
Thickness (mm)
0 days
1.010
20 days
1.006
40 days
1.006
60 days
1.001
Figure 9 below shows the dielectric constant and dielectric loss of LSR with different ageing degrees in the frequency range of 0.5–2.0 THz. It can be seen from Fig. 9(a) that the dielectric constants of all LSR samples decreased with the increase of frequency. After 1.8 THz, the dielectric constants tended to be stable, stable in the range of 2.5–2.55. The dielectric constant of LSR was in the range of 2.5–2.7 in the frequency domain of 0.5–2.0 THz. In this frequency domain, the dielectric constant increased with ageing time. It can be seen from Fig. 9(b) that the dielectric loss of all samples first increases and then decreases with the increasing frequency, showing a Debye like relaxation process with peak frequency of 0.9 THz. In the frequency range of 0.5–0.9 THz, the dielectric loss increases with the increase of ageing time. But after 0.9 THz, the dielectric loss gradually decreases and the gap between different samples gradually narrows.
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Fig. 8. (a) THz time domain spectrum and (b) THz frequency domain spectrum
Fig. 9. (a) Dielectric constant and (b) Dielectric loss of LSR with different ageing time in the range of 0.5–2.0 THz
5 Discussion With the increase of thermo-oxidative ageing time, although the further crosslinking might enhance the mechanical strength, the side chain breaking and the precipitation of nanoparticles reduced the adhesion of polysiloxane matrix. In addition, during the aging process, some pores and cracks appeared in LSR, which also reduced the mechanical strength of LSR. Finally the mechanical strength of silicone rubber declined as a whole. With the increase of ageing time, the effects of re-crosslinking and chain structure fracture occurred in LSR polymer. According to the results of FTIR in Fig. 2, after thermo-oxidative ageing, the Si-O absorbance first increased and then decreased, which meant the increase of crosslinking degree. Then ageing occurred, resulting in the breaking of Si-O bond. The absorbance of Si-CH3 and Si-(CH3 )2 decreased, indicating that the branching fracture occurred and the internal order decreased. In the early stage, the side chains such as methyl group are broken, which makes the overall convergence increase [7]. The main chain of polysiloxane was broken and part of the cross-linking structure was destroyed. Therefore, the internal diffusion coefficient and ion mobility of the polymer decrease, which makes the conductivity of LSR decrease with the increase
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of ageing time [7]. In the later stage, with the main chain breaking, more SiO2 were generated, which maked the interface between nanoparticles more and more. Due to the low convergence of polymer at the interface, the ion mobility rose and the conductivity increased. The above reasons make the conductivity first decrease and then increase. Due to the separation of the side chain and the breaking of the main chain, the overall order decreased, so the DC breakdown field strength gradually increased in the early period. However, with further ageing, pores or cracks appeared in the LSR due to the precipitation of SiO2 and the convergence of polymer at the nano particles interface was lower. As a result, the breakdown channel was easier to form and led to the decrease of breakdown field strength. Therefore, the breakdown field strength first increases and then decreases. Figure 9 showed that the ε and ε of LSR increased with the increase of ageing time in terahertz frequency domain. The first reason was that some C-H and Si-CH3 broke after thermal aging, resulting in the increase of internal small molecular groups and ions. Then the ion displacement polarization and partial dipole scheme increased. The second reason was that the ε was positively related to the refractive index, and the refractive index was often related to the density of the material [16]. In the process of thermal aging, more oxygen was involved to increase the density, and the invading oxygen atoms caused electronic displacement polarization [16]. Lastly, nano-SiO2 particles precipitated in LSR during ageing. With the increase of Nano-SiO2 content and the shortening of molecular chain, more polar groups appeared at the end of polysiloxane chain, and the dielectric constant increased. Therefore, based on these three reasons, the ε and ε increased with thermo-oxidative ageing.
6 Conclusion The main conclusions are as follows: (1) With the increase of thermo-oxidative ageing time, the mechanical strength of LSR decreased because the breaking of side and main chain as well as the precipitation of nano-SiO2 particles. (2) The breakdown field strength first increased and then decreased with ageing and the high field conductivity had the opposite trend due to the destruction of molecular structure and the decrease of connectivity because of precipitation of nano-SiO2 particles during ageing. (3) In terahertz frequency domain, the dielectric constant and dielectric loss gradually increased with ageing time. The main reasons were side chain breaking, oxygen invasion, the increase of density and the precipitation of nano-SiO2 particles.
Acknowledgment. This work was supported by foundation project of Shaanxi Electric Power Corporation (Breakdown characteristics of insulating materials and its influence on operation characteristics of power equipment) under contact 5226SX1800FB.
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References 1. Zhu, M., Li, C., Liu, Y., et al.: Micro structural characterization of liquid silicone rubber’s aging phenomenon used for composite bushing. Power Energy 36(01), 68–73 (2015) 2. Seifert, J.M., Winter, H. -J., Barsch, R., et al.: Tracking and Erosion Performance Of Liquid Rubber HV composite in housings. In: 2007 Annual Report - Conference on Electrical Insulation and Dielectric Phenomena, pp. 329–337. IEEE, Vancouver (2007) 3. Zhang, F., Wu, X., Guo, Q., et al.: Research progress on functional LSR. Silicone Mater. 24(6), 380–384 (2010) 4. Yan, N., Jia, Z., Ye, W., et al.: Study on thermal aging characteristics and mechanism of LSR used for outside insulation. High Volt. Apparatus 55(06), 145–150 (2019) 5. Yan, Z., Hao, J., Tian, L., et al.: Micro characterization of aged liquid silicone rubber used for 220 kV current transformer jacket. In: 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials (ICPADM), pp. 716–719. IEEE, Sydney (2015) 6. Fei, Y., Zhang, Y., Zhou, Y.: Thermo characteristics of Silicone rubber and its effects on operational reliability of extra-high voltage cable accessories. Adv. Technol. Electr. Eng. Energy 33(12), 30–34 (2014) 7. Chen, Q., Shang, N., Wei, X.: Influence of thermal oxygen aging on dielectric and mechanical properties of liquid silicone rubber. Electr. Mach. Control 24(04), 141–148 (2020) 8. Lin, Y., Liu, Y., Wu, K., et al.: Influence of UV-A radiation on liquid silicone rubber and high temperature vulcanized silicone rubber for outdoor insulation and its mechanism. Proc. CSEE 41(05), 1–13 (2021) 9. Smith, R.M., Arnold, M.A.: Terahertz time-domain spectroscopy of solid samples: principles, applications, and challenges. Appl. Spectrosc. Rev. 46(8), 636–679 (2011) 10. Hangyo, M., Tani, M., Nagashima, T.: Terahertz time-domain spectroscopy of solid samples: a review. Int. J. Infrared Millimeter Waves 26(12), 1661–1690 (2005) 11. Zhang, C., Mu, K.: Terahertz spectroscopy and imaging. Laser Optoelectron. Prog. 47(02), 1–14 (2010) 12. Kaneko, T., Ito, S., Minakawa, T., et al.: Degradation mechanisms of silicone rubber under different ageing conditions. Poly. Degrad. Stab. 168, 108936 (2019) 13. Qin, Y., Yu, L., Fu, J., et al.: Research on microscopic properties and hydrophobicity of high temperature vulcanization silicone rubber under long-wave ultraviolet radiation. Trans. China Electrotech. Soc. 29(12), 242–250 (2014) 14. Xie, D., Min, D., Li, S., et al.: Nano-doping effects on dielectric breakdown and coronaresistance properties of polymeric nanocomposites. Proc. CSEE 38(19), 5909–5918+5949 (2018) 15. Cheng, L., Xia, Y., Gao, S., et al.: Application of terahertz time domain spectroscopy in moisture content detection of insulating pressboard. Smart Power 48(08), 104–109 (2020) 16. Basharat, M., Mohammad, A., Rahmat, U.: Accelerated aging affect on temperature vulcanized silicone rubber composites under DC voltage with controlled environmental conditions. Eng. Fail. Anal. 118, 104870 (2020)
Design of Control Loop of Three-Phase Z-source Inverter Sisi Bai(B) , Yingna Guo, Zhao Ma, and Huan Cheng School of Electrical Engineering, Xi’an Shiyou University, No. 18 Electronic Second Road, Yanta District, Xi’an, Shaanxi, China [email protected]
Abstract. Z-source inverter(ZSI) is a new type of inverter. Its main difference from ordinary inverter is that ZSI can increase or reduce the output voltage of inverter according to the actual situation. Firstly, this article analyzes the working principle of the ZSI, Secondly, it establishes mathematical models of Z-source network and three-phase inverter, the transfer function is derived, and then it designs the double closed-loop control ZSI. Finally, saber is used to simulate the circuit to verify the accuracy of the whole system design. Keywords: Z-source inverter · Mathematical model · Closed-loop control · Saber simulation
1 Introduction Over the years, people demand a growing number kinds of electric energy, people have been paying attention to how to convert the direct current in the battery into a stable alternating current (AC), in which inverters play an important role. The traditional inverter needs DC-DC converter in series during the use, which not only increases the workload of designers, but also reduces the working efficiency of inverted systems. On this basis, researchers continue to propose new inverter topologies, among which ZSI [1] is currently the most widely studied inverter topology. This type of inverter is different from traditional inverters, not only can directly achieve the goal of step-up or step-down and reduce the cost of the circuit, but also broaden the application field of the inverter in the industry. Therefore technical research on ZSI is also of great practical significance. For the sake of studying the dynamic performance of the inverter system, the ZSI must be mathematically modeled to achieve closed-loop control. Literature [2] obtains the transient model of the Z-source system through the state-space method and the high-frequency small-signal disturbance method; Literature [3] gives a complete signal flow graph modeling procedure for the Z-source system, and uses the state averaging method to establish an average and small signal model on the inverter bridge side; Literature [4] uses a simple boost control method for the ZSI, establishes a closed-loop control system, and conducts simulation modeling; Literature [5] models the ZSI, and proposes a more reliable voltage and current double closed-loop control method for © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 764–771, 2022. https://doi.org/10.1007/978-981-19-1528-4_78
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the circuit. In summary, this paper set up the system’s mathematical model of the Zsource that uses the state-space averaging method, and use coordinate transformation to build mathematical model of three-phase inverter bridge, thereby determining the transfer function, and establishing the ZSI double closed loop control system. At last, it is verified by simulation.
2 Working Principle of ZSI The main research object of this article is three-phase voltage type ZSI, the topology of which is shown in Fig. 1. Since the Z-source system that composed of inductors and capacitors is used as the input of the three-phase bridge, two MOSFETs on the same bridge arm can be turned on at the same time, so ZSI has special buck-boost characteristics. idc D
L1 Q1
udc
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a
uin
ud L2
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Fig. 1. Topology of three-phase Z-source inverter
The ZSI has two states of direct and non-direct in a period T, and the equivalent circuit diagram is shown in Fig. 2.
(a) through state
(b) non-through state
Fig. 2. System equivalent circuit diagram
In the Z-source network, L1 = L2 and C1 = C2 . It can be seen from the symmetry and equivalent circuit uC1 = uC2 = uC , uL1 = uL2 = uL
(1)
When the system is in the through state, the inverter bridge side is short-circuited and the diode D is reversely blocked. If the operating time of the non-through state in a period T is T1 , it can be obtained from Fig. 2(a) uC = uL , ud = 2uC , uin = 0
(2)
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When the system is in the non-through state, the inverter bridge side can be seen as a current source and the diode D forward conduction. If the running time of the through state in a period T is T0 , and T0 = T − T1 , it can be obtained from Fig. 2(b) udc = uL + uC , ud = uC − uL , uin = 2uC − udc
(3)
Derived according to the formula, the relationship between the output voltage u0m of the ZSI and the input voltage udc is u0m = mB
udc 2
(4)
T Where B is the boost factor, and B = T1 −T ≥ 1; m is the modulation ratio of the inverter. 0 It can be seen from Eq. (4) that for ZSI, as long as the appropriate boost factor and modulation degree are selected, the output voltage can be control that greater or less than the input voltage [6].
3 Mathematical Model Establishment The topology of the ZSI can be regarded as a combination of the two parts of the Z-source system and the inverter bridge, so the two parts can be modeled separately [7]. 3.1 Mathematical Model of Z-source System The equivalent circuit diagrams of the Z-source system are shown in Fig. 2. The modeling is completed in an ideal state, so the parasitic resistance of the inductor and the capacitor are both 0. The through duty cycle d0 = T0 /T , and the non-through duty cycle is d1 =
T − T0 = 1 − d0 T
(5)
Since in a period T, the time occupied by the through state and the non-through state can be determined by the through duty cycle. In this calculation select the appropriate state variable, and write the state equation according to Kirchhoff’s law as ⎡
⎤ ⎡ 0 dL0 0 − dL1 iL1 d1 ⎥ ⎢ d0 d⎢ 0 C ⎢ uC1 ⎥ = ⎢ − C 0 d dt ⎣ iL2 ⎦ ⎣ 0 − L1 0 dL0 d1 uC2 0 − dC0 0 C
⎤ ⎡ d1 0 iL1 L ⎥⎢ uC1 ⎥ ⎢ 0 − d1 C ⎥ ⎢ ⎥⎢ ⎦⎣ iL2 ⎦ + ⎣ d1 0 L uC2 0 − dC1 ⎤⎡
⎤
⎥ udc ⎥ ⎦ iin
(6)
Due to the symmetry of the Z-source system, by introducing small signal disturbances, it be obtained uˆ C =
[(1−2D0 )(2UC −Udc ) + sL(Iin1 −2IL )] s2 LC + (1−2D0 )2
+
(1−D0 )(1−2D0 )ˆudc + sL(D0 −1) ˆiin s2 LC + (1−2D0 )2
(7)
According to the formula uˆ in = 2ˆuC − uˆ dc , uˆ in can be obtained, and then the transfer function can be obtained [8].
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iin Q1
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ia ib ic
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uoa uob uoc Ca Cb Cc
Fig. 3. Mathematical model of three-phase inverter circuit
3.2 Mathematical Model of Three-Phase Inverter Circuit When establishing a mathematical model for a three-phase inverter circuit, since the three-phase output of the circuit is symmetrical and equal, so La = Lb = Lc = L, and Ra = Rb = Rc = R. The mathematical model is shown in Fig. 3. In the three-phase coordinate system, choose the current that the filter inductor as the state variable, and write the equation: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ dia
⎢ dt ⎢ ⎢ L · ⎢ dib ⎢ dt ⎣ dic dt
⎢ ia ⎥ ⎥ ⎢ ua ⎥ ⎢ uoa ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥ = ⎢ ub ⎥ − ⎢ uob ⎥ − R · ⎢ ib ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎦ ⎣ ⎦ ⎣ ⎦ uc uoc ic
(8)
We can use the matrix equation of the Park transformation that to convert the system from the three-phase stationary coordinate system to the dq rotating coordinate system, so as to obtain the mathematical model of the three-phase inverter in the dq rotating coordinate system as follows ⎧ ⎪ ⎨ L did = ud − u0d + ωLiq − Rid dt (9) ⎪ di ⎩ L q = u − u − ωLi − Ri q 0q q d dt Where ω is the rotational angular velocity of the dq coordinate system. Carrying out Laplace transform on formula (9), the transfer function of the threephase inverter can be obtained as (sL + R) Id (s) = Ud − U0d + ωLIq (s) (10) (sL + R) Iq (s) = Uq − U0q − ωLId (s)
4 Design of Control System The control system adopts the design of double closed loop. The outer ring is voltage, which controls the capacitance voltage of Z-source network, and the current inner ring controls the current of output inductance. Figure 4 shows the control diagram of ZSI.
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id
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Fig. 4. Z-source inverter system control diagram
4.1 Inductor Current Inner Loop Control In the dq coordinate system, considering that the two axes are completely symmetric, and the control is exactly the same, the analysis is made from the d-axis control as an example. It can be seen from formula (9) that there is coupling between the two axes, so it is necessary to add decoupling control in the control. It can be deduced from this ⎧ ⎪ ⎨ L did = ud − Rid dt (11) ⎪ ⎩ L diq = u − Ri q q dt Where ud and uq are the outputs of the two axes current inner loop, respectively [9]. It can be seen from Eq. (11) that, due to the introduction of current feedback in the control vector, the coupling between the two axes is cancelled, and the two-axis currents are controlled independently. At the same time, the feedforward quantities u0d and u0q of the output voltage are introduced in the control that in order to obtain a stable and fast response output voltage. In this design, the first-order inertia link kSPWM /(sT + 1) is used as the equivalent bridge gain of the ZSI, kSPWM is the ZSI gain and kSPWM = uin = uC . The control block diagram of the current inner loop is simplified as shown in Fig. 5. i*d
+
-
Gi s
sT+1
1 sL
id
Fig. 5. Current inner loop control block diagram
We can get the open-loop transfer function of the current loop from Fig. 5 as Gio (s) = Gi (s) ·
kSPWM sL(sT + 1)
(12)
For purpose of enable the current loop to respond quickly, only when the current loop transfer function is proportional control, can the current inner loop control be corrected to a typical I-type system. So let Gi (s) = Kp , where Kp is the gain that the proportional regulator.
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Correspondingly, the transfer function of the current inner loop is deduced as follows Go (s) = Where K =
Kp kSPWM L
s2 +
K T K T
+
(13)
s T
.
4.2 Capacitor Voltage Outer Loop Control The voltage outer loop control of the whole system is realized by controlling the capacitance voltage of the Z-source system. According to the circuit equivalent diagram of the inverter bridge in different working states, the s-domain formula of the Z source capacitor voltage uC can be deduced as uC =
(1 − 2d0 ) iL − (1 − d0 )iin iL − iin iC = = sC sC sC
(14)
is the current of DC side when the ZSI is in the non-through state. Where iin According to formula (14), the control block diagram of the voltage outer loop can be drawn, as shown in Fig. 6. iL
1-2d0 u*c
+ -
Guc s
Gi s
uo 1-d0 2uin
+ -
uc
Fig. 6. Voltage outer loop control block diagram
So as to track uC∗ in the steady state of the system, a PI controller can be used: Ki s Where Kp is the proportional coefficient, Ki is the integral coefficient. Then the voltage outer loop open loop transfer function is Guc (s) = Kp +
Guo (s) = Kp ∗
K u0 τs + 1 ∗ 2 ∗ (1 − d0 ) τs Ts + K + s 2sCuin
(15)
(16)
Where τ = Kp /Ki . It can be seen from formula (16) that the highest order of the open-loop transfer function is 4th order, so the order of the formula (16) can be reduced and simplified to τs + 1 u0 Guo (s) = Kp ∗ 1 (1 − d0 ) ∗ 2 2τ Cuin Ks+1 s
(17)
According to the numerator and denominator form of the above formula, it can be determined that the voltage outer loop is a type II system, and then the appropriate proportional coefficient and integral coefficient can be determined.
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5 Simulation Circuit Design and Result Analysis In this design, to prove that the above conclusions is correct, the simulation circuit of ZSI is built in Saber software and the simulation analysis is carried out, as shown in Fig. 7 and Fig. 8. In the circuit, the parameters are set as follows: the input voltage is 30V, the switching frequency is 10 kHz, and the through duty cycle is 0.2.
Fig. 7. Open loop simulation circuit diagram
Fig. 8. Closed loop control circuit
The circuit output waveform is given in Fig. 9, which includes current and voltage. From this waveform, we can be seen that the ZSI can not only stably output three-phase AC, but also the output voltage is measured 49.5V, which is higher than the input voltage. Therefore the ZSI can complete the boost when there is a through duty cycle.
Fig. 9. Simulation output waveform
Figure 10 shows the waveform of the circuit input, the open-loop and closed-loop output voltage. As you can see from the picture that when the input voltage of the circuit is disturbed and changed, the output waveform of the closed-loop simulation can respond faster and the signal is more stable, than the open-loop.
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Fig. 10. Disturbed open-loop and closed-loop output waveforms
6 Conclusion This paper mainly introduces the working principle of the ZSI, and establishes a mathematical model for closed-loop control. Through the open loop simulation analysis, it can be seen the ZSI has the function of boosting voltage. And it can be from the results that the dynamic response after adding the closed-loop is faster and the stability is better. Acknowledgment. This work is supported by the natural science foundation research program of Shaanxi Province (Program No. 2020JM-542) and the postgraduate innovation and practice ability training program of Xi’an Shiyou University (Program No. YCS20213167).
References 1. Peng, F.: Z-source inverter. IEEE Trans. Ind. Appl. 39(2), 504–510 (2003) 2. Liu, J., Hu, J., Xu, L.: A modified space vector PWM for Z-source inverter-modeling and design. In: 2005 International Conference on Electrical Machines and Systems, pp. 1242–1247 (2005) 3. Qu, K., Zhang, Q., Zhou, H.: A closed-loop controlled Z-source inverter. J. Shanghai Univ. Electr. Power 25(06), 530–533 + 542 (2009). (in Chinese) 4. Chen, W.: Signal Flow Graph Modeling and Control Analysis of Z-source Inverter. Hefei University of Technology, 2007. (in Chinese) 5. Cui, B., Qian, Z., Ding, X., Peng, F.: Voltage and current double closed loop control of Z-source inverter. Power Electron. 09, 1–3 (2007). (in Chinese) 6. Li, Y., Zhou, Z., Ma, S.: Research on Z-source inverter. J. Shijiazhuang Railway Vocat. Tech. Coll. 19(01), 79–84 (2020). (in Chinese) 7. Rajakaruma, S.: Steady-state analysis and designing impedance network of Z-source inverters. IEEE Trans. Industr. Electron. 57(7), 2483–2491 (2010) 8. Xu, D.: Power Electronic System Modeling and Control. Machinery Industry Press, 2006. (in Chinese) 9. Li, J., Wang, D., Chen, G., Song, D., Ma, Y.: Modeling and control of three-phase Z-source grid-connected inverters for direct-drive wind power generation systems. Trans. Chin. Soc. Electr. Eng. 24(02), 114–120 (2009). (in Chinese)
Effect of Surface Treatment on Surface Flashover Performance and Multi-factor Aging Characteristics of Epoxy Resin Bingnan Li1 , Huan Niu1 , Mingru Li1 , Zhen Li1 , Yafang Gao1 , Shengtao Li1,2(B) , and Hangyin Mao1,2 1 Xi’an Jiaotong University, No. 28 West Xianning Road, 710049 Xi’an, China
[email protected] 2 State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, China
Abstract. In order to study the performance of the modified epoxy resin in the operating environment, the epoxy resin samples after surface modification treatment, including ozone oxidation treatment and electron beam irradiation treatment, were subjected to electric-heat-gas combined aging experiments. The surface flashover voltage was measured regularly, and the infrared spectrum and surface trap characteristics of the aged samples were compared. The surface flashover voltages of three kinds of samples show a downward trend with aging time. The samples which have been surface modified still have better performance in surface flashover voltage than that have not been modified. The sample oxidized by ozone has the highest surface flashover voltage both aged and unaged. Keywords: Surface flashover · Electron beam irradiation surface modification · Ozone oxidation surface modification · Aging
1 Introduction As a hot issue in the field of high voltage and insulation technology, flashover on solid insulation surfaces is also a key technical issue that hinders the development of electrical industry, like pulse power drive sources, and spacecraft power systems, especially for HVDC equipment [1–4]. In response to this problem, many experts have done a plenty of research to modify insulation materials, which are mainly divided into two categories, bulk modification and surface modification. Bulk modification including heat treatment [5], nanoparticle doping [6–9], etc. Surface modification includes polishing to change the surface roughness [10, 11], fluorination treatment [12, 13], ozone treatment [14], electron beam irradiation [15–17], etc. These treatment methods can improve the surface flashover characteristics for basin insulators to a certain extent. Among them, the ozone oxidation can greatly improve the surface flashover voltage of solid insulation, and the environmentally friendly treatment method of electron beam irradiation has great engineering application prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 772–782, 2022. https://doi.org/10.1007/978-981-19-1528-4_79
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However, the surface flashover performance of insulation materials will seriously decline in the operating environment on account of electrical, heat and other reasons, which will change its physical structure or chemical properties. At present, the research on the aging performance of insulation material is mostly the research on the original material [18–20], and there is a lack of research on insulation materials after surface modification in aged environment. Therefore, this paper points at the two kinds of surface treated samples (ozone surface treatment and electron beam irradiation) and untreated epoxy resin samples in electricheat-gas combined aging experiments. Fourier infrared spectroscopy experiments and surface potential attenuation experiments were performed on the three samples, and the changes of surface flashover voltage, as well as surface trap characteristics of epoxy resin with different treatment methods during the aging process were analyzed.
2 Experimental Preparation 2.1 Sample Preparation and Surface Treatment 2.1.1 Sample Preparation To make the micro-Al2 O3 /epoxy resin sample, we put 298g micro-Al2 O3 particles into 100 g epoxy resin and mixed them well. Add 39 g curing agent and cast the mixture onto the heated mold, and the curing temperature should be set at 120 °C for 3 h and 140 °C for 13 h. Finally, we can get micro-Al2 O3 /epoxy resin sample when it cooled to room temperature. 2.1.2 Ozone Treatment The ozone surface modification system is composed of fume hood, oxygen source, ozone generator, ozone concentration meter, reaction kettle, vacuum pump, pressure gauge, valve and other equipment. The reaction system is shown in the Fig. 1. The ozone concentration used in the experiment is 140 mg/L and the treatment time is 4 h.
Fig. 1. Ozone treatment system
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2.1.3 Electron Beam Irradiation The electron beam irradiation system used in this article consists of a vacuum experiment chamber, an electron gun, a high-voltage electrode, a molecular pump, and a loading platform. The diagram of the electron beam irradiation is shown in Fig. 2. The electron beam radiation energy used is 15 kV, the beam current is 5 µA, and the irradiation time is 2 min.
Fig. 2. Electron beam irradiation system
2.2 Electric-Heat-Gas Combined Aging Experiment System Figure 3 shows the specific structure of this aging experiment system. It mainly consist of an experiment cavity, electrodes, cylinder, and heating device. The three modules can be adjusted separately at the same time to simulate the complex environment in service. In this experiment, the epoxy composite is placed under the finger electrode, the electrode spacing is set to 5 mm, and then the ends of the finger electrode and the high voltage electrode inside the experimental cavity are connected with the high voltage wire. After checking that the wiring is correct, the experimental chamber is evacuated, and then filled with 0.1 MPa SF6 , then the entire chamber is heated to 50 °C. Finally, connect the aging voltage source and set the specified voltage to 10 kV. Every 24 h, remove the aging voltage source, connect the special voltage source for flashover, conduct the surface flashover performance test of the epoxy composite insulation material after electricheat-gas multi-condition combined aging, and record the surface flashover voltage value at time. 2.3 Properties Test 2.3.1 Fourier Transform Infrared Spectrometer This experiment uses Nicolet 6700 advanced Fourier transform infrared spectrometer which can detect the molecular structure characteristics of the sample, and can also perform quantitative analysis of each component in the mixture. We choose Attenuated Total Reflection (ATR) mode for this experiment, which can effectively reflect the composition of the test sample’s surface from a chemical point.
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Fig. 3. Electric-heat-gas combined aging experiment platform (1 Electrode, 2 Sealing cap, 3 Insulation board, 4 Heating ring, 5 Temperature sensor probe, 6 Temperature controller, 7 Observation window, 8 Experimental cavity, 9 Power source).
2.3.2 Surface Potential Decay Test The surface trap density and energy level are important parameters that influence the charge transformation characteristics of solid surface. The expression of trap energy level on solid surface is ET = kB T ln(vATE t)
(1)
E T is the trap energy level on the solid surface, in eV, k B is Boltzmann’s constant, with a value of 1.38 × 10−23 J·K–1 , t is the time for the surface potential to decay, in s. ν ATE is the electron’s attempted escape frequency, in s−1 . The expression of trap density on a solid surface is NS T (ES T ) = (ε0 εr )/elLt(∂φS (t))/∂t
(2)
N S T (E S T ) is the density of solid surface traps, in eV−1 ·m–3 , ε0 is the vacuum permittivity, εr is the relative permittivity of solid, l is the distribution interval of surface traps, in m, φ S (t) is the surface potential, in V, L is the thickness of the solid sample, in m.
3 Results and Discussion 3.1 Experimental Results The changes on flashover voltage of three kinds of samples are shown in Fig. 4. From the Weibull distribution curve, the scale parameter of the three samples can be obtained. For the two different surface treatment methods, the effect of ozone treatment on the flashover voltage is slightly higher than that of electron beam irradiation.
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Fig. 4. Weibull distribution of surface flashover voltage along three kinds of samples
Fig. 5. Changes in surface flashover voltage with aging time
As shown in Fig. 5, an electric-heat-gas combined aging experiment was performed on all kinds of samples. Among them, the flashover voltage of the samples treated with ozone is always maintained at a relatively high level, and it decreases with the increase of aging time as well, but it is always significantly higher than another two kinds of samples. The flashover voltage of the sample treated by electron beam radiation decreased but still higher than the sample with no surface modified.
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Fig. 6. Infrared spectrum comparison before and after aging
Figure 6 shows the results of Fourier infrared spectroscopy experiments performed on three samples before and after aging. Different absorption peaks correspond to different
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chemical groups. Figure 6 (a), Fig. 6 (b), Fig. 6 (c) represent untreated, ozone treated and electron beam irradiated samples respectively. The infrared spectrum of the untreated sample before and after aging changed significantly, indicating that the chemical bond changed during the aging process. It can be preliminarily judged that the benzene ring structure in the epoxy resin is relatively stable without much change. The absorption peak of the carbon-oxygen bond has increased, indicating new synthesis has occurred during the aging process. A small and broad absorption peak appears near 3300 cm−1 , showing the generation of free hydroxyl groups and free hydrogen. The generation of these polar groups is one of the main reasons for the decrease of flashover voltage [22]. The peak of the carbon-oxygen double bond in the fingerprint area increased significantly because of a large amount of oxidation during the process. Since the ozone treatment itself has already oxidized most of the chemical groups on the surface, the changes in the absorption peaks of the carbon-oxygen double bonds in the fingerprint region before and after aging are not obvious. The carbon-oxygen double bond at 1728 cm−1 , carbon-carbon double bond at 1449 cm−1 , and the carbon-oxygen single bond peak of absorption at 1242 cm−1 all increase, and there is a slightly chemical bond varies during aging experiment. The infrared spectra of the sample irradiated by the electron beam before and after aging basically did not change, only the peak value of the carbon-oxygen double bond in the fingerprint area increased significantly.
Fig. 7. Traps distribution characteristics of surface modified samples before and after aging
Figure 7(a) is the trap distribution curve of the sample treated by ozone before and after the aging experiment. Ozone treatment increased the shallow trap density of the epoxy micron composite insulating material [14], but after the aging test, the shallow trap density on the sample’s surface decreased, while the trap level increased evidently. Figure 7(b) is the trap distribution curve of the sample irradiated by electron before and after the aging experiment. Electron beam treatment introduces deep traps to the surface of micro-composite epoxy insulating materials [19], but the density and level of deep traps on the surface of the sample decreases significantly after aging.
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Fig. 8. Relationship between surface flashover voltage and surface trap level
Figure 8 shows the relationship between flashover voltage and surface trap level of surface modified epoxy resin samples. According to the results of Fig. 7, we extract trap energy levels of ozone oxidized and electron beam irradiated samples before and after aging, correlate these parameters with their flashover voltage and get an “U-shaped” curve. 3.2 Discussion (1) After the strong oxidizing ozone molecules react with the material surface, the chemical structure and physical morphology of the material surface are changed, resulting in physical surface morphology great changes have taken place. The change of surface physical morphology and the change of surface chemical group together lead to an increase in the density of shallow traps on the surface, which result of the surface roughness increasing, the surface charge dissipation rate will increase in case of the surface carrier mobility increasing, which caused by the changes on shallow surface trap distribution [14]. Electron beam irradiation causes high-energy electrons to hit the surface of epoxy micro-composite materials and act on the epoxy matrix on the surface, resulting in a substantial increase in the content of free radicals on the surface [16], free radicals have the ability to capture charged particles. The content of free radicals on the surface has increased significantly, which is equivalent to introducing deep chemical traps on the surface. On the one hand, resulting in a decrease in surface conductivity, which hinders surface charge transport. (2) The surface flashover property of ozone-treated sample is the best both before and after aging, ozone-treated sample is always maintained at a relatively high level on surfaceiflashoverivoltage during the entire aging process. The reason is that the ozone treatment has a chemical reaction on the external of micro-composite material based on epoxy [14], however, in the aging process, caused by the harsh
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environment, the molecular chains on the epoxy surface are degraded and cracked, a plenty of chemical defects can be generated, leading to an addition in the density of traps. A certain concentration of trapped meridian can inhibit the emission of secondary electrons, thereby enhancing the surface flashover property, the trap energy level introduced by ozone treatment is on the left side of the “U-shaped” curve [22], so as the trap energy density and level increase, the surface flashover voltage shows a downward trend. Because the free radicals introduced by the electron beam irradiation process are not stable and exist for not a very long time, although the flashover property will be much more outstanding than that of untreated one under the same aging period after a long time of electric-heat combined action. However, as time goes by, the surface free radicals introduced by electron beam irradiation treatment are likely to re-bond with the molecules on the epoxy surface under aging conditions, which leads to a large reduction in free radicals and a decrease in the density of deep traps on the surface. The trap energy level introduced by the electronic beam irradiation is coincides with the right part of the “U-shaped” curve [22]. As the trap density and energy level decrease, the surface flashover voltage gradually decreases.
4 Conclusion (1) After surface modified by ozone oxidation, the surface shallow trap density of the material continues to rise, the deep one continues to decrease. The increase in the density of shallow traps on the surface promotes the dissipation of charges [23]. While the influence of beam irradiated by electron on the micro-composite material is that it hits the epoxy matrix on the surface, resulting in a substantial increase in the deep traps’ density, hinders the emission of secondary electrons by adjusting the density of deep traps on the surface of the material, thereby increasing the surface flashover voltage. (2) On the external of ozone oxidized micro-composite sample, chemical defects will be generated in a large number in the aging experiment. The trap energy level increase so the flashover voltage along the surface shows a downward trend. The deep traps introduced by the electron beam irradiation on the micro-composite are easily to decrease in trap level and density, so the flashover voltage of this kind of sample reduces during aging experiment.
Acknowledgment. Supported by the National Grid Science and Technology Project Funding (Contract No.:52110418000R).
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References 1. Gurevich, E.L., Liehr, A.W., Amiranashvili, S., et al.: Role of surface charges in DC gasdischarge systems with high-ohmic electrodes. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(3), 036211 (2004) 2. Nitta, T., Nakanishi, K.: Charge accumulation on insulating spacers for HVDC GIS. IEEE Trans. Electr. Insul. 26(3), 418–427 (1991) 3. Koch, H., Schuette, A.: Gas insulated transmission lines for high power transmission over long distances. Electr. Power Syst. Res. 44(1), 69–74 (1998) 4. Goll, F., Witzmann, R.: Lightning protection of 500-kV DC gas-insulated lines (GIL) with integrated surge arresters. IEEE Trans. Power Delivery 30(3), 1602–1610 (2015) 5. Li, S.T., Huang, Q.F., Sun, J., et al.: Effect of traps on surface flashover of XLPE in vacuum. IEEE Trans. Dielectr. Electr. Insul. 17(3), 964–970 (2010) 6. Lewis, T.J.: Nanometric dielectrics. IEEE Trans. Dielectr. Electr. Insul. 1(5), 812–825 (1994) 7. Nelson, J.K., Fothergill, J.C., Dissado, L.A., et al.: Towards an understanding of nanometric dielectrics. In: IEEE Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico, pp. 295–298 (2002) 8. Cheng, Y.H., Wang, Z.B., Wu, K.: Pulsed vacuum surface flashover characteristics of TiO2 /epoxy nano-micro composites. IEEE Trans. Plasma Sci. 40(1), 68–77 (2011) 9. Li, Z., Huang, Y., Min, D., et al.: DC vacuum flashover characteristics of epoxy-based carbon nanotube nanodielectrics. High Volt. Technol. 43(9), 95–103 (2017). (in Chinese) 10. Shioiri, T., Shindo, T., Kamikawaji, T., Kaneko, E., Ohshima, I.: Effect of annealing and polishing on flashover characteristics of ceramic in vacuum. Dielectr. Electr. Insul. 9(3), 416–420 (2002) 11. Kirkici, H., Coker, J.K., Walker, J.: Surface flashover characteristics of polishedpolycrystalline diamond and diamond-like carbon (DLC) thin films in vacuum. Electr. Insul. Dielectr. Phenom. 191–194 (1995) 12. Mohamad, A., Chen, G., Zhang, Y., et al.: Surface fluorinated epoxy resin for high voltage DC application. IEEE Trans. Dielectr. Electr. Insul. 22(1), 101–108 (2015) 13. Xinbo, H., Wenbo, Y., Yi, T., Zhengkang, Y.: The influence of DC corona aging on the properties of cycloaliphatic epoxy resin and HTV silicone rubber. Power Syst. Technol. 44(11), 4454–4463 (2020). (in Chinese) 14. Huang, Y., Min, D., Xie, D., Li, S., Wang, X., Lin, S.: Surface flashover performance of epoxy resin micro-composites influenced by ozone treatment. In: Electrical Insulating Materials, pp. 235–238 (2017) 15. Xing, Y., Shen, Y., Song, X., Yu, Y., Xiao, M.: Effects of electron beam irradiation on insulation characteristics of epoxy/AlN nanocomposites. Appl. Supercond. 29(2), 1–4 (2019) 16. Li, M., et al.: Effect of electron beam irradiation on epoxy and its stability. In: Electrical Insulation and Dielectric Phenomena, pp. 495–498 (2019) 17. Zhang, Z., Zheng, X., Wu, W., Yang, P., Peng, P.: DC polyimide surface flashover in vacuum under electron beam irradiation. In: 2013 IEEE International Conference on Solid Dielectrics (ICSD), pp. 322–324 (2013) 18. Youyuan, W., Liu, Y., Wang Shiyou, X., Haiying.: The influence of electrothermal aging on the properties of epoxy resin in dry-type transformers. Trans. Chin. Soc. Electr. Eng. 33(16), 3906–3916 (2018). (in Chinese) 19. Youyuan, W., Shiyou, W., Yanguang, H., Yi, L., Yanan, C.: Research on the thermal aging characteristics of dry-type transformer epoxy resin. High Volt. Technol. 44(01), 187–194 (2018). (in Chinese) 20. Liu, W.: Epoxy resin insulation aging characteristics and life evaluation under high frequency electrothermal stress. North China Electric Power University (2015). (in Chinese)
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21. Zhang, S., Zhao, Y., Yang, K., Wang, X.: Study on aging mechanism of bisphenol a epoxy resin based on molecular simulation. In: International Electrical and Energy Conference, pp. 523–526 (2018) 22. Li, S., et al.: Unraveling the U-Shaped dependence of surface flashover performance on the surface trap level. IEEE Access 7, 180923–18093 (2019) 23. Cai, Z., et al.: Influence of surface trap parameters on AC/ DC flashover of epoxy resin. In: 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), pp. 1–4 (2020)
Analysis of the Influence of Active Filter on the Unstable Resonance for Traction Power Supply System Chen Niu1(B) and Guo Wang1,2,3 1 School of Automation and Electrical Engineering,
Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China [email protected] 2 Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China 3 Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou, China
Abstract. This paper analyzes the influence law of resonance of traction power supply system. First, establish a unified impedance model of the traction power supply system with a source filter in the dq coordinate system; secondly, use the Nyquist criterion to analyze the influence of the parameters of the active filter on the resonance stability of the traction power supply system; Finally, a time-domain simulation model is constructed for verification. Keywords: Traction power supply system · Resonance · Active filter · Impedance model
1 Introduction The resonant instability of the electrified railway traction power supply system is mainly caused by the resonant matching of the impedance of the load and the system. ACDC-AC high-speed trains, as moving time-varying nonlinear loads, have a nonlinear strong coupling relationship with the traction power supply network. When they are parametrically coupled with the distributed inductance and capacitance of the traction network transmission line, it will cause the traction power supply system to produce Resonance instability. [1–5]. There are mainly the following methods for resonance analysis: (1) Time domain simulation method [7, 8]: The real-time changes of the system can be observed, but there is a certain gap between this method and the actual control strategy; (2) Modal analysis method [1, 9]: Establish a multi-conductor transmission line model of the traction network to analyze the resonance phenomenon, but did not consider the dynamic characteristics of the locomotive; (3) Impedance analysis method [10–14]: Use impedance-based system stability analysis method, Analyzed the stability of the system under different © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 783–790, 2022. https://doi.org/10.1007/978-981-19-1528-4_80
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impedance conditions, but did not consider the influence of the compensation device. In this paper, a unified impedance model of the traction power supply system with active compensation filter is established under dq coordinates. The Nyquist criterion is used to analyze the stability of the entire system, and the influence of active filter parameters on resonance is explored.
2 Unified Mathematical Model of Traction Power Supply System 2.1 Mathematical Model of Active Filter As shown in Fig. 1, the circuit structure of the active filter is mainly composed of a DC side capacitor inverter and an output filter inductor. Among them, udc is the voltage across the DC side capacitor, is the system current, is the load current, is the output current of the inverter, L is the filter inductance, and C is the DC side capacitor. es is
iL
Non-linear load
ic
L
C udc
Current tracking control
ic* Drive circuit
Harmonic detection
Fig. 1. Working principle of active filter.
In the process of modeling, the harmonic current detection link can be regarded as the first-order inertia link shown in Eq. (1), where k id is the compensation coefficient (0 < k id ≤ 1). i∗ kid = iL Taf · s + 1 id∗ iLd = G CD iq∗ iLq
GCD =
(1)
(2)
The current tracking control of the APF is shown in Fig. 2, the output voltage of APF is:
uad uaq
=
KPi + 0
KIi s
0 KPi +
KIi s
id∗ KPi + KsIi −ω0 L icd Ed − + iq∗ ω0 L KPi + KsIi icq Eq
(3)
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Fig. 2. Block diagram of current loop decoupling control.
For the input and output of the main circuit, as shown in Fig. 1, there are the following relationships: Ed sL ω0 L icd uad = + (4) uaq Eq icq −ω0 L sL Combining formulas (1)–(4), the relationship between load current and compensation current is: 0 0 sL + KPi + KsIi icd KPi + KsIi iLd = G (5) CD icq iLq 0 sL + KPi + KsIi 0 KPi + KsIi Therefore, the transfer function of the active filter is: APF (s) =
ic = A1−1 A2 GCD iL
(6)
among them,
sL + KPi + 0 KPi + A2 = 0
A1 =
KIi s KIi s
0 sL + KPi + 0 KPi + KsIi
KIi s
2.2 Traction Net-Locomotive Mathematical Model The car network system can be expressed as a small signal multivariable closed-loop feedback system, as shown in Fig. 3. The input of the feedback system is the output voltage of the substation, and the output is the load current [13].
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Fig. 3. Traction network-locomotive closed loop feedback system
According to Fig. 3, the open-loop gain of the traction network-locomotive closedloop feedback system can be obtained, as shown in Eq. (7): T1 (s) = Zs∗ (s)Ytrains (s)
(7)
Among them, the locomotive model Ytrain is equivalent to a 2 × 2 input admittance matrix under dq coordinates [14], as shown in Eq. (8): IL Ydd Ydq (8) = Ytrain = Yqd Yqq U In this paper, the AT traction network is equivalent to an RLC circuit. The equivalent impedance model on the grid side after reduction is shown in Eq. (9): 1 R + sLs ω0 Ls ZS∗ (s) = 2 s (9) ω0 Ls Rs + sLs K In the formula, K represents the transformation ratio of the traction transformer; Rs and Ls are the equivalent resistance and inductance on the grid side, respectively. 2.3 Unified Mathematical Model After the active filter is added to the traction power supply system, its small signal closed-loop system is shown in Fig. 4:
Fig. 4. Closed-loop feedback system of traction power supply system with active filter
From Fig. 4, the open-loop gain of the traction power supply system with active filter can be obtained, as shown in Eq. (10): T1 (s) = Ytrains (s)Zs∗ (s)(APF (s) + 1)
(10)
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3 Analysis of the Resonance Stability of the Active Filter to the Traction Power Supply System 3.1 The Impact of Active Filter Access on the Resonance Stability of the Traction Power Supply System Set the length of the traction power supply network to 15 km, and some system parameters are shown in Table 1.
Symbol
Description
Parameter
L/mH
Filter inductance
2
Kpi
Filter current loop proportional gain
5
KIi
Filter current loop integral gain
1
Tαf
Detection link calculation delay
0.1
Kid
Compensation coefficient of detection link
0.8
1.5
1.5
1
1
I m a gin ary Axis
ImaginaryAxis
Table 1. Partial parameter table of traction power supply system.
0.5
0 -0.5 -1 -1.5 -1.5
-1
-0.5
0
Real Axis
(a) Without active filter
0.5
1
0.5
0 -0.5 -1 -1.5 -1.5
-1
-0.5
0
0.5
1
1.5
Real Axis
(b) Contains source filter
Fig. 5. Nyquist curve of traction power supply system with and without active filter
In Fig. 5(a), λ1 bypasses the point (–1, j0) in the clockwise direction, indicating that the system cannot operate stably in the current state. In Fig. 5(b), the trajectories of λ1 and λ2 do not bypass the point (–1, j0), and there is a certain distance from the point (–1, j0). 3.2 The Influence of the Parameters of the Active Filter on the Resonance Stability of the Traction Power Supply System It can be seen from Fig. 6 that when the proportional gain of the current loop Kpi = 10, the Nyquist curve of the characteristic value λ1 will bypass the (–1, j0) point clockwise, indicating that the system cannot operate stably when Kpi = 10, and Kpi = 4 At this
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0.5
0 -0.5
0.5 0 -0.5
-1 -1.5 -1.5
-1
-1
-0.5
0
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1
-1.5 -1.5
1.5
-1
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-0.5
0
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Real Axis (b) Kpi=10
(a) Kpi=4
1.5
1.5
1
1
I m a gin ary Axis
I m a gin ary Axis
Fig. 6. Nyquist curve for changing the proportional gain of the current loop
0.5 0
-0.5 -1
-1.5 -1.5
0.5 0 -0.5
-1 -1
-0.5
0
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(a) L=10mH
1
1.5
-1.5 -1.5
-1
-0.5
0
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1
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Real Axis
(b) L=5mH
Fig. 7. Nyquist curve for changing the proportional gain of the current loop
time, the system can run stably, indicating that the system will lose stability when Kpi = 10. In Fig. 7(a), the filter inductance of the active filter is L = 10 mH, and the Nyquist curve of the characteristic value λ1 will bypass the point (–1, j0), the system cannot operate stably, and resonance instability will occur; and In Fig. 7(b), when L = 5.5 mH, neither of the two eigenvalues bypasses the (–1, j0) point, and the system is in a stable operating state.
4 Simulation This paper builds a simulation model based on the simulation parameters in Table 1 to verify the law of the active filter’s influence on resonance. Figure 8 shows the voltage waveform of the catenary at the grid connection point. Figure 8(a) shows that Kpi increases from 4 to 10 at 2.2s, and Fig. 8(b) shows that L increases from 5 mH to 10 mH at 2s. It can be clearly seen that after the parameter changes, the harmonic content of the grid voltage has increased significantly, the voltage amplitude has also begun to rise, and the
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Fig. 8. Contact line voltage waveform when changing control parameters and filter inductance
system stability has decreased. The simulation results are consistent with the theoretical results.
5 Conclusion This paper adopts the impedance-based system stability analysis method to establish a unified impedance model of the traction power supply system, analyzes the law of the influence of active filter parameters on resonance, and uses simulation to verify and obtain the following conclusions: (1) Establish an impedance-based traction power supply system stability analysis model, and use the Nyquist stability criterion to effectively judge the stability of the traction power supply system. (2) When the active filter is connected to the traction power supply system, its stability is significantly improved. When the other parameters of the system remain unchanged, increasing the proportional gain of the active compensation current loop will increase the stability of the system. The filter inductance is the opposite, and other parameters are also kept unchanged. If the filter inductance parameter is increased, the stability of the system will decrease. (3) Since the traction power supply system is dynamic, and it is difficult to change the parameters of the traction network or electric locomotive, and the feasibility is poor,
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the use of active filters can effectively change the system parameters and suppress the resonance instability of the traction power supply system.
References 1. Wang, S., Cai, Q., Xu, J., Chang, Y., Xue, Q.: Research on harmonic resonance overvoltage of traction power supply system based on modal analysis. J. China Railway Soc. 35(07), 32–41 (2013) 2. Wang, J., Liu, M.: Research on harmonic resonance of traction network based on impedance partial voltage principle. Power Syst. Prot. Control 45(16), 79–84 (2017) 3. Wang, B., Qiu, Z., Han, X., Jiang, X., Gao, S.: High-speed railway traction network series resonance analysis based on improved virtual branch method. Electr. Power Autom. Equip. 35(11), 90–95 (2015) 4. Wenren, Z.: Research on suppression scheme of harmonic resonance in traction power supply system of high-speed railway. J. Beijing Jiaotong Univ. 41(05), 106–113 (2017) 5. Yanling, Q.: AT Traction Network Harmonic Resonance Analysis and Suppression Technology Application Research. Southwest Jiaotong University, pp. 74–75 (2019) 6. Qiao, G., Ding, N., Yu, K.: Active and passive hybrid compensation method for high-speed railway power quality. Power Grid Clean Energy 28(7), 19–24, 30 (2012). https://doi.org/10. 3969/j.issn.1674-3814.2012.07.004 7. Cui, H., Feng, X., Lin, X., Qingyuan, W.: Simulation study on harmonic resonance characteristics of traction network and AC-DC-AC train coupling system. Proc. Chin. Soc. Electr. Eng. 34(16), 2736–2745 (2014) 8. Wei, F., Li, X., Xu, L.: Research on the high-order harmonic coupling of traction power supply system vehicle network. Power Supply Technol. 37(07), 1228–1232 (2013) 9. Wei, F., Xin, L., Xu, L.: Research on high-order harmonic coupling of traction power supply system vehicle network. Power Supply Technol. 37(07), 1228–1232 (2013) 10. Harnefors, L., Bongiorno, M., Lundberg, S.: Input-admittance calculation and shaping for controlled voltage-source converters. IEEE Trans. Industr. Electron. 54(6), 3323–3334 (2007) 11. Cespedes, M., Sun, J.: Impedance modeling and analysis of grid-connected voltage-source converters. IEEE Trans. Power Electron. 29(3), 1254–1261 (2014) 12. Sun, J., Bing, Z., Karimi, K.J.: Input impedance modeling of multipulse rectifiers by harmonic linearization. IEEE Trans. Power Electron. 24(12), 2812–2820 (2009) 13. Tao, H., Hu, H., Zhu, X., Yang, X., He, Z.: Analysis of resonance instability mechanism of traction power supply system coupled with vehicle network. Proc. Chin. Soc. Electr. Eng. 39(08), 2315–2324+14 (2019) 14. Zhou, Y., Hu, H., Yang, X., He, Z.: Analysis of Low frequency oscillation of electrified railway car-network coupling system. Proc. Chin. Soc. Electr. Eng. 37(S1), 72–80 (2017) 15. Mu, X., Wang, Y., Chen, S., Wang, Y., He, Z.: Research on the stability of the high-speed railway train-network electrical coupling system based on the improved sum-norm criterion. J. Electrotech. Eng. 34(15), 3253–3264 (2019)
Research on Digital Vehicle Inverter Power Supply Zhaohua Yu(B) , Gaili Yue, and Luyao Li Xi’an University of Science and Technology, Xi’an, China [email protected]
Abstract. With the development of economy and the improvement of the level of science and technology, the car has become a set of office, entertainment as one of the vehicles, the car electronic products are more and more rich, in order to ensure the normal use of the car electronic equipment, must be configured to convert the 12 V direct current of the car into the appropriate alternating current. This paper designs a TMS320F28335 as the main control chip of the vehicle inverter power supply, the car DC voltage into 220 V/50 Hz AC voltage. The system adopts a two-stage conversion circuit structure. SG3525 chip is used as the main control chip of the push-pull circuit at the front stage, and voltage closed-loop control is adopted. The post-stage DC-AC inverter adopts a double closed-loop PI control strategy of the outer loop of output voltage and the inner loop of current. The system control strategy is simulated by MATLAB simulation software, and the correctness of the system design scheme and control strategy is verified. Keywords: Vehicle inverter power supply · DSP · SPWM modulation · Double closed loop control · The MATLAB simulation
1 Introduction Inverter technology is developing towards the direction of high power density, large capacity, high reliability and intelligence. High-speed DSP is used to control, detect and protect the parallel inverter, realize load current sharing and output synchronization, realize automatic removal of fault modules, and realize high reliability and maintainability [1–3]. This paper designed a TMS320F28335 as the main control chip of the vehicle two-stage inverter power supply, in order to ensure the normal work of the premise with high efficiency, high reliability, small size, easy to control and other advantages. The technical indicators of inverter power design in this paper are as follows: • • • • •
DC input voltage: The rated voltage: 12 ± 3 V; AC output voltage: 220 V ± 3%; Output voltage frequency: 50 ± 0.2 Hz; The output power: 500 W; Conversion efficiency: η ≥ 90%;
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 791–798, 2022. https://doi.org/10.1007/978-981-19-1528-4_81
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• Waveform distortion rate: THD ≤ 3%; The overall system design block diagram is shown in Fig. 1.
Fig. 1. Block diagram of the overall system design
2 The Main Circuit 2.1 DC-DC Boost Circuit In this paper, the push-pull circuit is selected as the main circuit topology of the frontstage DC-DC booster part. This circuit has the characteristics of simple structure, less switching devices, higher power utilization rate, etc [4, 5] and meets the application characteristics of low voltage input and medium and high power output of vehicle inverter power in this paper. As shown in Fig. 2.
Fig. 2. The circuit diagram of push-pull conventer
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2.2 Push-Pull Circuit Schematic Diagram The schematic diagram of the push-pull circuit is designed and drawn in Altium Designer software. First of all, the input DC voltage is fed into the two switches through two filter and voltage regulator capacitors of the same type 1000 μF/50 V, and then the two complementary PWM drive signals provided by the control chip SG3525 enable the two switches to operate alternately in an ordered manner. When the system inputs 12 V DC voltage, the voltage is increased to 320V ripple voltage through the transformer, and then converted to 320 V DC voltage through the back-stage bridge rectifier. Finally, two filter capacitors of the same model are used to filter the DC voltage. At the same time, the output DC bus voltage will be fed back to the chip SG3525 to realize the voltage closed-loop control. 2.3 Design of DC/AC Inverter Circuit Full-bridge inverter circuit is selected in this paper, which has flexible control and smaller volume under the same output power, and is more suitable for inverter power with medium and high power characteristics [6]. The circuit topology is shown in Fig. 3. The output filtering circuit of the inverter adopts LC filtering, that is, low-pass filtering.
Fig. 3. Topological structure of full bridge conversion circuit
After the voltage boost from the front stage push-pull circuit, the system will transfer the increased DC voltage to the rear stage full-bridge inverter circuit.DSP provides SPWM wave with frequency of 20 kHz to control the orderly on-off of two pairs of switch tubes, and converts the transmitted DC voltage to 220 V/50 Hz AC voltage. At the same time, a voltage and current acquisition device is connected to the output end, and the collected output signal is fed back to DSP, which then adjusts the output according to the load condition, so as to maintain a real-time optimal output state.
3 System Control Strategy and Slmulation 3.1 Push-Pull Circuit Control In this design, the DC bus voltage is set to 320 V, in order to make the output voltage of the push-pull circuit stable at 320 V, the need for a closed-loop design of the push
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pull circuit. The error comparator inside the control chip SG3525 can be conveniently configured as a PI regulator [7, 8], and the output voltage can be closed loop controlled by the configuration of the peripheral circuit of the chip. The voltage closed-loop control principle block diagram of the push-pull part is shown in Fig. 4.
Fig. 4. Push-pull circuit control principle block diagram
As shown in Fig. 4, the output voltage is sampled and the closed-loop control is carried out. Theoretically, the output can be stabilized. The collected voltage is sent to the voltage regulator of SG3525 chip through feedback coefficient k1, and the voltage regulator is PI regulator. According to the output conditions, the duty cycle of PWM is adjusted by SG3525 chip to realize stable output of 320 V DC voltage. The bus voltage in the front-stage push-pull booster circuit has closed loop. The output voltage is controlled in closed loop to ensure that the output voltage is not affected by the output load changes when the inverter is open loop. In order to improve the stability of the system, the proportional integral control algorithm must be introduced. This design will adopt the incremental proportional integral control algorithm [9]. 3.2 SPWM Inverter Circuit Control Strategy According to the above analysis, the voltage and current double closed-loop control principle of the back-stage inverter is shown in Fig. 5.
Fig. 5. The voltage and current double closed loop control principle of the inverter part
As shown in Fig. 5, the output voltage is sampled and controlled in a closed loop, which theoretically stabilizes the output. After voltage collection, the voltage regulator GV (S) in DSP is fed through the feedback coefficient K3 . According to the output, the duty cycle of SPWM is adjusted by DSP to achieve a stable 220 V AC output voltage. In practical application, with the increase of load, the output voltage will drop to a
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certain extent, In order to solve the problem of output voltage reduction under heavy load, the output current can be used as a closed-loop feedback quantity, and the feedback coefficient is k 2 , the current regulator is PI regulator to improve the precision adjustment error of the output voltage and improve the robustness of the system [10].
4 Analysis of Simulation Results 4.1 Push-Pull Circuit Simulation Analysis According to the calculated parameters, a push-pull circuit simulation model was established in Simulink, and the control strategy of the circuit was simulated and verified. The duty cycle of the trigger pulse of the switch tube was set as 48%, the input voltage was 12 V, the switching frequency of the switch tube was 30 kHz, and the total simulation time was 0.5s. The waveform of the trigger pulse of the switch tube and the voltage waveform of the DC bus after rectification and filtering were tested and analyzed respectively. The push-pull circuit simulation model is shown in Fig. 6.
Fig. 6. Push-pull circuit simulation model
Fig. 7. PWM Module Internal simulation model
PWM signal generation module in push-pull circuit simulation model can generate PWM pulse signal and control voltage in closed loop. The output voltage is collected as the feedback quantity, and its internal model is shown in Fig. 7. The switch trigger pulse waveform and DC bus voltage waveform output by the push-pull circuit can be obtained, by running the push-pull circuit simulation model. as shown in Fig. 8 and Fig. 9 below.
Fig. 8. Trigger pulse waveform of switch Q1
Fig. 9. DC bus voltage waveform and Q2
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As shown in Fig. 9, the output voltage waveform of the push-pull circuit can be seen from the simulation figure. The output DC voltage of the push-pull circuit rises from 0 to 317.5 V in only 0.02 s, which basically stays at 317 V, meeting the design requirements of the system. 4.2 Inverter Circuit Simulation Analysis SPWM simulation model and full bridge inverter simulation model are established in Simulink simulation environment, and the double closed-loop control strategy of the system is simulated and verified. SPWM drive signal generation simulation model, as shown in Fig. 10 (Fig. 11).
Fig. 10. SPWM drive signal generation simulation
Fig. 11. SPWM trigger pulse waveforms of Q3 and Q4 model
The simulation model of the full bridge inverter circuit 5 consists of four switches and LC low-pass filter at the rear stage. The simulation model is shown in Fig. 12.
Fig. 12. Simulation model of full-bridge
Fig. 13. Output voltage waveform and inverter circuit current waveform at 0% load
As can be seen from the simulation results in the figure above, the output current waveform after filtering is relatively smooth, and the current amplitude remains unchanged, which fully verifies the rationality of the design of the low-pass filter. The output voltage waveform is also highly sinusoidal without significant distortion. The voltage amplitude is stable at 320 V and the cycle is 0.02 s. In other words, the effective value of the AC output is stable at 220 V/50 Hz, which meets the preset requirements.
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5 Experimental Results Analysis 5.1 Analysis of Drive Waveform of Switching Tube In the full-bridge inverter circuit, the driving waveform of switch Q3 is shown in Fig. 14. It can be seen that the driving waveform of the switching tube and the drain source voltage waveform of the switching tube have better quality and less burr, which can verify the correctness of the driving circuit designed in this paper.
Fig. 14. The driving waveform of switch Q3 in full-bridge circuit
Fig. 15. The driving waveform of switch Q3 and Q4
The driving waveform of the upper and lower two switch tubes Q3 and Q4 of the same bridge arm in the full bridge circuit is shown in Fig. 15. In this design, under the output load of 25 W and 70 W respectively, the output voltage waveform of the system is tested to determine the output voltage waveform characteristics under different output loads. Output voltage waveforms under different loads are shown in Fig. 16.
(a) Output voltage waveform at 25W load
(b) Output voltage waveform at 70W load
Fig. 16. Output voltage waveforms corresponding to different output loads
As can be seen from the output voltage waveform in Fig. 13, the output voltage waveform of the inverter power supply is of good quality and has no obvious distortion. At the same time, it can be seen that the system output voltage waveform is basically the same under different input voltages, the frequency is 50 Hz, the effective value is 220 V, the amplitude is about 315 V. The results show that the input voltage of the system has no effect on the output voltage of the inverter.
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6 Conclusion This design takes DSP as the core of the overall structure of the controller, using a two-stage converter topology, namely the front-stage push-pull booster module and the back-stage full-bridge inverter module. The main inverter chip uses TMS320F28335 to convert 12V DC voltage to 220 V/50 Hz AC voltage. The control strategy of the system is simulated by using MATLAB simulation software, and the simulation results verify the correctness of the system design scheme and control strategy. Then based on the simulation and circuit design of the on-board inverter power supply, a 500-W experimental prototype was built, and various performance indicators were tested. The test results show that the indicators of the vehicle inverter designed in this paper meet the design requirements.
References 1. Smith, T.F., Waterman, M.S., Zhang, X., Lian, X.: Identification of common molecular. Full electronic vehicle system. Autom. Eng. 534–539 (2012) 2. Zhang, Z., Sitong, Z.: Design of a low-power vehicle-mounted inverter power supply. Electromech. Technol. (04), 46–47 (2017). (in Chinese) 3. Xia, X., Ning, P., Gang, M., Yuan, C.: Control and research of digital vehicle inverter power supply. J. Hebei North Acad. Sci. (Natl. Sci. Ed.) 35(11), 26–31 (2019). (in Chinese) 4. Reddy, N.R.S., Reddy, T.B., Amarnath, J., Rayudu, D.S.: Performance improvement in vector controlled induction motor drive by combining the principle of hybrid PWM and ANFIS. Acta Electrotechnicaet Inform. 12(4), 6168 (2012) 5. Li, J., Qu, Y., Zeng, S., Cheng, R., Zhang, R., Zhao, Y.: Ge complementary tunneling fieldeffect transistors featuring dopant source/drain. Chin. Phys. Lett. 35(11), 80–83 (2018). (in Chinese) 6. Wang, M., Wei, L., Guo, W., Ding, X.: Design and implementation of adaptive wide range DC input inverter. Chin. J. Power Sour. 44(11), 1675–1678 (2020). (in Chinese) 7. Kaisha, K., Jidoshokki, T.: Patent issued for vehicle inverter device and motor-driven compressor (USPTO 10003241). J. Eng. 8–9 (2018) 8. Liu, Y., Wu, W.: Research on SPWM inverter power supply using compound control. Electr. Drive 48(03), 33–40 (2018). (in Chinese) 9. Yi, Y., Wang, X., Tang, Z., et al.: Research on the control circuit of vehicle inverter power supply. Power Technol. 36(7), 1019–1021 (2012). (in Chinese)
Effects of Rail Shape on Recoil of Electromagnetic Energy Equipment Guochao Li1,2 , Weiqun Yuan2,3(B) , Wen Tian1,2 , Chen Miao1,2 Weikang Zhao2,3 , Ying Zhao2,3 , and Ping Yan2,3
,
1 University of Chinese Academy of Science, Beijing 100049, China 2 Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
[email protected] 3 Key Laboratory of Power Electronics and Electric Drive, Chinese Academy of Science,
Beijing 100190, China
Abstract. In the launching process of electromagnetic energy equipment, the armature will be accelerated by an electromagnetic force in the bore. At the same time, the launcher will also receive a reverse thrust to enable the launcher recoil. The force acting on the gun frame will not only deform the gun frame, but also affect the launch accuracy of the system. Therefore, it is very helpful to study the recoil motion and reduce the recoil force for prolonging the life of the system and improving the launch accuracy. This paper analyzes the force of the launcher (including the rail and breech busbar) in the recoil process of the electromagnetic energy equipment, and obtains the source, distribution and value of the force. The recoil force consists of two parts, one in breech busbars and the other in rails. At the same time, the influence of rail shape on recoil force was analyzed. The influence of rectangular, convex and concave rail on the recoil force is studied. It is found that the convex rail has the most obvious effect on reducing the recoil force, which provides some supports for the design of electromagnetic launcher. Keywords: Electromagnetic energy equipment · Free recoil · Rail shape · Convex rail
1 Introduction As a new type of equipment, electromagnetic energy equipment has become research focus. As an equipment that relies on huge kinetic energy to cause damage, it has advantages of fast projectile export velocity, high precision and high safety performance [1]. In military, high firing speed is a very effective means of anti-armor. Theoretically, the initial velocity of projectile will not be limited [2]. Therefore, many countries have invested a lot of human capital in the research of electromagnetic energy equipment, and the technology has become a hot topic in the world. In the firing process of conventional gun, because the breech is tightly closed, in the process of gunpowder combustion, it will not only produce a forward thrust on © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 799–811, 2022. https://doi.org/10.1007/978-981-19-1528-4_82
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the projectile, but also produce a backward force on the closed breech, which is the main reason for the recoil movement of gun launcher [3]. Similarly, the electromagnetic energy equipment will produce recoil motion in the launching process, the recoil force of electromagnetic energy equipment mainly comes from electromagnetic force [4]. In artillery, the power of recoil motion on launcher is the resultant force of gun bore, and the resistance is recoil resistance [5]. In the electromagnetic energy equipment, we define the motive force of the backward movement of the launcher as the recoil force, which is in the opposite direction of the armature movement, and the resistance is the recoil resistance, which is in the direction of the armature movement. The recoil force produced in the recoil movement will cause an irreversible deformation of the gun frame and affect the shooting accuracy. It is necessary to install anti recoil device to reduce the force acting on the gun frame. The design of anti-recoil device needs to be based on the variation of recoil force. Therefore, it is very important to study the recoil process of electromagnetic energy equipment in the launching process. The research on recoil movement has done a lot at home and abroad. In the early stage, it mainly focused on the source of recoil force. By analyzing the action characteristics of Ampere force, A. K. T. Assis [6] concluded that recoil force does not exist in rails, but should exist in the joint part of rails and breech busbars. Sadedin [7] regards the behavior of “electromagnetic matter” as a kind of gas from the perspective of momentum conservation by establishing a theoretical model, and the formation and distribution of recoil force are explained through theoretical calculation. By establishing lumped parameter model of electromagnetic energy equipment, Qiang J [8] found that the recoil force is equal to the thrust of armature, and the direction is opposite. An ideal force model is established, and it is found that the recoil force mainly exists in the tail feeding structure. Yong L [9] uses the finite element software to build the launch system model. Through simulation analysis, it is found that the breech busbar is the main source of recoil force, and the influence of different breech busbar structures on recoil force is analyzed and compared. The simulation results show that the dovetail breech busbar structure has the smallest recoil force, which provides a certain idea for the design of breech busbar. But the recoil force is determined by the force of the busbar and the rail, they rarely talk about the impact of rail on recoil force. In this paper, based on the finite element software ANSYS, the model of electromagnetic energy equipment was established, the value, distribution and source of recoil force during the movement of armature in bore were discussed. The force on launcher is divided into two parts, that is, breech busbars and rails. The reason and locations of recoil force on rails were analyzed. The models of rectangular, concave and concave rail were established. The influence of different rail shapes on recoil force was studied. The calculation results have certain guiding significance for the design of the rail.
2 Rail Force Analysis of Electromagnetic Energy Equipment The electromagnetic energy equipment is regarded as magnetoquasistatic in launching process, ignoring the influence of displacement current and electric field changes on the
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magnetic field. Combined with Maxwell equations, the following analytical formula of electromagnetic field distribution is obtained: →
→
∇ × H= J
(1)
→
∇·B =0
(2) →
∂B ∇× E= − ∂t →
→
∇·J =− →
(3)
∂ρ ∂t
(4)
→
J =γ E
→
(5)
→
→
H is the magnetic field intensity, J is the current density, B is the magnetic induction, → E is the electric intensity, ρ is volume current density, γ is the conductivity. For the convenience of the analysis, the launcher and armature are simplified as a 2-D model for force analysis, as shown in Fig. 1. We divide the launcher into two parts: the breech busbar and the rail. Breech busbar
Jy2
Rail Jy1
y
O
Jx2 Jx1 Armature
Electromagnetic force
x
Fig. 1. 2-D schematic diagram of electromagnetic energy equipment
In the vicinity of the rail-armature interface, the current will flow through the rail in the way shown in the figure. A current along the X direction will be generated near the contact surface. The electromagnetic force here can be obtained as follows [10]: → → × Bz1 fy1 = Jx1
(6)
→ is the X-direction current density of the rail-armature interface and → is the where Jx1 Bz1 magnetic induction intensity perpendicular to the paper. fy1 is the electromagnetic force of rails in rail-armature surface. Similarly, the electromagnetic force between breech busbars and rails can be obtained as follows: →
fy2 = Jx2 × Bz2
(7)
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In other areas of the rail, most of the current is symmetrically distributed along the Y direction. Therefore, according to the Fleming’s rule, the electromagnetic forces of this part of the rail are symmetrically distributed along the X direction, and the total resultant force is 0.
3 Simulation Analysis 3.1 3-D Finite Element Model The launching process of electromagnetic energy equipment is a coupling with electromagnetic, thermal and structure. This paper establishes a 3-D model of the launching device based on the finite element software ANSYS, and studies the recoil phenomenon in the dynamic launching process by combining the finite element method with the boundary element method. The caliber of the launcher was 15.6 mm × 12 mm, the rail size was 600 mm × 16 mm × 8 mm. C-type solid armature was used to move along Y direction, and the model was established as shown in Fig. 2. The specific material parameters are shown in Table 1. Table 1. Material parameters Armature
Rail
Density (kg/m3 )
2700
8260
Young’s modulus (Pa)
6.89 × 1010
1.25 × 1011
Poisson’s ratio
0.33
0.3
Specific Heat (J/(kg/K))
896
420
Thermal conductivity (W/(m/K))
180
118
Conductivity (S/m)
3.7 × 107
5.998 × 107
Fig. 2. 3-D finite element model
Based on the above model, the driving current curve as shown in Fig. 3 is loaded. The total loading time is 2.5 ms, the current rising time is 0.5 ms, the current peak is 120 kA, the duration is 1ms, and the current dropping time is 1 ms.
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Fig. 3. Driving current curve
Because the friction between the armature and the rail is very small compared with the recoil force [11], in the simulation, the influence of friction was ignored. The breech busbar and the rail were divided into one grid to ensure that there was no relative movement between them. The rail and breech busbar were free in Y direction. So that the armature moved along the Y positive direction and the rail moved along the Y negative direction. 3.2 Analysis of Simulation Results Under the above driving current, the armature velocity curve and launcher velocity curve were obtained as shown in Fig. 4. The armature exited the muzzle at 1.83 ms, and the muzzle velocity was 595.85 m/s. The influence of aftereffect period on recoil process was not considered in this paper. Therefore, the launcher and armature accelerated during the movement of the armature in bore, after the armature exits the muzzle, launcher and armature maintained a uniform velocity. The velocity of the launcher is 0.78 m/s at the time of ejection.
Fig. 4. Velocity curve of armature and launcher
According to the acceleration curve, the force of armature and launcher in Y direction can be calculated. As shown in Fig. 5, the thrust of armature is not the same as the recoil
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force of launcher. During the current flat, the maximum thrust of armature can reach 2.9 kN, and the force of armature has a decline process, because the value of magnetic induction intensity of armature decreases due to the magnetic diffusion [12]. The force of the launcher is similar to that of the current curve, but the electromagnetic force of the launcher is smaller than that of the armature. The calculated results are similar to those of Liu [9] et al.
Fig. 5. Force curve of armature and launcher in Y-direction
In the force analysis of the launcher, through the summation and integration of the model, the force on breech busbars and the rails are obtained, as shown in Fig. 6. It can be seen that the direction of the electromagnetic force on breech busbars and rails are opposite. The electromagnetic force on breech busbars which is the driving force that can reach 3.1 kN is along the Y negative direction to push the launcher move [13], and it is mainly concentrated in the contact areas between breech busbars and rails. The closer to the contact surface, the greater the electromagnetic force, as shown in Fig. 7. The electromagnetic force on rails moves along the Y positive direction, the maximum is 1.4 kN, which hinders the recoil movement of the launcher. However, the electromagnetic force on breech busbars is more than twice of rails, which is the main power source of the recoil movement.
Fig. 6. Y-direction force of the rail and breech busbar
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Fig. 7. Electromagnetic force distribution vector diagram of the breech busbar
In the analysis of electromagnetic force on the rail, it is found that the electromagnetic force on different parts of the rail is different. Figures 8 show the electromagnetic force vector distribution of rail on the 0.4 ms, 1 ms and 1.7 ms. It can be seen from the figure that the electromagnetic force distribution on the rail is basically the same at each time, which is divided into three parts. The first part is the rail-armature interface and its nearby area. In this part, the current can be divided into X and Y direction, and only the current in X direction will generate electromagnetic force in Y direction on the rail, which provides resistance for recoil motion. The second part is the rail in contact with the busbar. When the current in the breech busbar flows to the rail, a X-direction current will be generated, which will lead to the formation of Y-forward electromagnetic force on the rail and provide resistance for recoil movement. The third part is the other parts of the rail besides the above. The electromagnetic force is symmetrically distributed along the X direction. Theoretically, the resultant force in the X direction is 0. This is consistent with the above theoretical analysis.
(a) Electromagnetic force vector distribution of the rail in 0.4 ms, (b) Electromagnetic force vector distribution of the rail in 1 ms, (c) Electromagnetic force vector distribution of the rail in 1.7 ms
Fig. 8. Distribution of electromagnetic force vector in rail at different times
In summary, how to reduce the damage of the recoil movement to the gun frame mainly depends on the reasonable design of the breech busbar, but the influence of electromagnetic force on the rail in the recoil movement cannot be ignored.
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4 Influence of Rail Shape on Recoil The above research shows that there is also a part of electromagnetic force along the Y direction on the rail as the resistance of recoil motion, so this section mainly studies the influence of different rail shapes on the recoil process. The rectangular, convex and concave rails are designed respectively to study the value and distribution of electromagnetic force in Y direction during recoil. 4.1 Rail Shape Design Rectangular, convex and concave rails were designed respectively. Ensure that the length, width, cross-sectional area of the three rails were the same [14], as shown in (a), (b) and (c) of Fig. 9. The convex part in figure (b) was an arc, the center of the arc was O, the radius was R, and the radian was 2θ. Similarly, graph (c) was the same arc as graph (b). The volume, mass and starting position of armature were the same. There was no preloading between the armature and the rail. y
O
y
y
R
r
2ϴ r
h2
h1
h
R
b
O
x
(a) Section of rectangular rail
2ϴ
b
(b) Convex rail section
x
b
x
(c) Concave rail section
Fig. 9. Rail section
4.2 Analysis of Simulation Results Ensure that all simulation parameters are the same. The force curves of launcher, breech busbar and rail in three cases are obtained respectively. Figure 10 shows the electromagnetic force curve in the Y direction of the launcher. It can be seen that the recoil force is similar in the rising stage of the current. In the flat top stage and falling stage, the recoil force of launcher on convex rail is the smallest, and that on rectangular rail is the largest.
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Fig. 10. Electromagnetic force in Y direction of launcher
Figure 11 shows the electromagnetic force curve in the Y direction of the breech busbar. It can be seen that the electromagnetic force of breech busbar along the negative direction of Y-axis provides power for recoil movement, and the peak value can reach 3.2 kN, which is greater than the force of launcher. Under the convex rail shape, the electromagnetic force in Y direction of breech busbar is the smallest. The results show that the electromagnetic force in Y direction is basically the same for concave and rectangular rail. Generally, the rail shape has little influence on the force of breech busbar.
Fig. 11. Electromagnetic force in Y direction of the breech busbar
Figure 12 shows the electromagnetic force curve in the Y direction of the rail. It can be seen that the electromagnetic force on the rail is along the positive direction of the Y-axis, which hinders the recoil movement. This part of the force is used to offset the recoil force on the breech busbar. For the convex rail, the electromagnetic force in Y direction is the largest and that in rectangle is the smallest.
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Fig. 12. Electromagnetic force in Y direction of the rail
Table 2 shows the electromagnetic force in the Y direction of the launcher and the rail under three kinds of rail shapes when the current load is 1ms. In order to compare the influence of rail shape on the force of launcher and rails, the following formula is defined: Table 2. Electromagnetic force at 1 ms Rail type
Convex rail Rectangular rail Concave rail
Y direction electromagnetic forces Launcher 1602.25 (N) Rail 1401.00
1782.44
1742.09
1256.31
1352.77
β1 =
F LR − FL × 100% FL
(8)
β2 =
F LC − FL × 100% FL
(9)
α1 =
F RR − FR × 100% FR
(10)
α2 =
F RC − FR × 100% FR
(11)
where FL is the electromagnetic force of the launcher for convex rail, F LR is the electromagnetic force of the launcher for rectangular rail, F LC is the electromagnetic force of the launcher for concave rail, FR is the electromagnetic force of the rail for convex rail, F RR is the electromagnetic force of the rail for rectangular rail, and F RC is the electromagnetic force of the rail for concave rail. β1 and β2 is the percentage of recoil force increase of rectangular and concave rail relative to convex rail, α1 and α2 is the percentage of the rail force increase of
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rectangular and concave rail relative to convex rail. It can be calculated from the table, β1 = 11.24% , β2 = 8 .72% , α1 = − 10.32% , α2 = −3 .44% . It shows that the recoil force of rectangular rail is increased by 11.24% relative to convex rail, and that of concave rail increases by 8.72% relative to convex rail. The force of the rectangular rail is 10.32% less than that of the convex rail, and that of concave rail is 3.44% less than that of convex rail. Therefore, under the same conditions, the convex rail can reduce the recoil force of the system. According to the calculation results, the influence of the rail shape on the electromagnetic force of the launcher and the rail itself cannot be ignored. A reasonable rail shape can reduce the recoil force properly and improve the accuracy of the launch system. In the results of the force on the launcher, we find that the recoil force is different from the armature thrust, but the curve of recoil force is similar to the current curve. Therefore, we consider the formula of armature thrust is: Fa =
1 2 LI 2
(12)
The armature thrust is proportional to the square of the current. Based on this formula, we suppose that the recoil force is also proportional to the square of the current. Therefore, we get the formula of recoil force as follows: FL = kI 2
(13)
where k is constant and I is current. Through the analysis of the current curve, we can get that when k is equal to 1.1e−7 , the recoil force of the convex rail is the same as that calculated by formula 13, as shown in Fig. 13. When k is equal to 1.24e−7 and 1.2e–7 , the recoil forces of rectangular rail and concave rail are also equal to the calculated forces respectively. The above analysis is based on the simulation results, and further experiments are needed to verify it.
Fig. 13. Electromagnetic force of convex rail
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5 Conclusion This paper studies the distribution of electromagnetic force on rails in the process of recoil motion, and obtains the recoil force of different shapes of rail. The main conclusions are as follows: Firstly, on the distribution of electromagnetic force in recoil motion, the electromagnetic force on breech busbars is the main driving force of recoil motion, and the electromagnetic force on rails are the resistance of recoil motion. The force on rails is mainly divided into two parts, the first part is rail-armature interface, the electromagnetic force along the direction of the armature movement, hinders the recoil movement, the second part is the contact areas between breech busbars and rails, the electromagnetic force along the direction of the armature movement. Therefore, in the process of recoil, the electromagnetic force on rails cannot be ignored. Secondly, convex, rectangular and concave rail are designed to study the influence of rail shape on recoil force. The recoil force of convex rail is the smallest, and that of rectangular rail is the largest. When the rail is convex, the electromagnetic force on the rail is the largest, which plays an important role in reducing the recoil force. The shape of the rail has little influence on the electromagnetic force on the busbar. Therefore, it is suggested that the convex rail is the most suitable one for rail design. Starting from the rail shape, this paper explores the recoil force of the launcher, which supplements the previous research, and also provides a reference for reducing the recoil force of the launcher. Acknowledgment. This work was supported in part by the National Natural Science Foundation of China 51875546.
References 1. Jun, L., Ping, Y., Weiqun, Y.: Development and status of electromagnetic railgun launching technology. High Volt. Technol. 40(4), 1052–1064 (2014). (in Chinese) 2. Longxia, Z., Biqing, L., Min, H.: Development of foreign electromagnetic gun. Aerodyn. Missile J. 10, 23–27 (2011). (in Chinese) 3. Deng W.: Research on dynamic simulation test technology of gun recoil. North University of China (2020). (in Chinese) 4. Sadedin, D.R.: Conservation of momentum and recoil in the railgun. IEEE Trans. Plasma Sci. 33(1), 599–603(1997) 5. Yuefei, G.: Design of gun recoil device. National Defense Industry Press, Beijing (2010).(in Chinese) 6. Assis, A.K.T., Bueno, M.A.: Equivalence between ampere and Grassmann’s forces. IEEE Trans. Magn. 32(1), 431–436 (1996) 7. Sadedin, D.R.: Conservation of momentum and recoil in the railgun. IEEE Trans. Magn. 33(1), 599–603 (1997) 8. Qiang, J.: Research on technology and application of electromagnetic railgun. North Central University (2012). (in Chinese) 9. Yong, L., Tao, Z., Pinhua, H., Yan, Z.: Influence of breech busbar on the recoil force of Railgun launcher. IEEE Trans. Plasma Sci. 48(6), 2301–2307 (2020)
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10. Zengji, W., Lixue, C., Shengguo, X., Chengxian, L.: Experiments and analysis of downslope low-voltage transition in C-type solid armature rail gun. IEEE Trans. Plasma Sci. 48(7), 2601–2607 (2020) 11. Jiangbo, S., Baoming, L.: Research on recoil process of electromagnetic railgun. Acta Armamentar II 36(2), 227–233 (2015). (in Chinese) 12. Mengxian, T., Zhaoxing, R., Qingao, L.: Study on electromagnetic field diffusion effect of solid armature. J. Ballistics 10(2), 29–32 (1998). (in Chinese) 13. Zizhou, S., Wei, G., Bin, C., Yanhui, C., Kai, H., Xia, G.: The study of the simple breech-fed Railgun recoil force. In: 2012 16th International Symposium on Electromagnetic Launch Technology, pp. 1–4 (2012) 14. Chaoyong, Z., Feng, L., Liyang, H., Huihui, Z.: Influence of section shape of electromagnetic rail launcher on launching performance. J. Chin. Acad. Electr. Sci. 12(5), 213–522 (2017). (in Chinese)
Low-Intensity Pulsed Ultrasound for Killing Tumor Cells: The Physical and Biological Mechanism Jianhao Lin, Shoulong Dong(B) , Wencheng Peng, Hongmmei Liu, Penghao Zhang, Haoxiang Lv, Liang Yu, and Chenguo Yao State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China {linjianhao,dsl,pengwencheng,liuhongmei,ph,lvhaoxiang,yu_liang, yaochenguo}@cqu.edu.cn
Abstract. Low-intensity pulsed ultrasound (LIPUS) can kill tumor cells noninvasively by acting on human tissues with millimeter-level precision. However, the physical and biological killing mechanisms of low-intensity pulsed ultrasound on tumor cells are still unclear. In this study, we rationally set up the electric pulse to stimulate the ultrasonic transducer, and obtained the relationship between the LIPUS sound field and the distribution of tumor cells, as well as the results of the mechanism of tumor cell apoptosis. Results found that the distribution of the acoustic field on LIPUS had an influence on the distribution of tumor cells on the Petri dish. It also induced tumor cell apoptosis by disrupting the nucleus and cytoskeleton in the next few dozen hours after treatment. In addition, we compared the killing effect of LIPUS on different tumor cells varies and found that LIPUS showed a higher killing effect of tumor cells while no significant killing effect presented on normal cells. It indicated a potential selectivity of LIPUS on tumor cells. This study provides an experimental and theoretical basis for the research on the mechanism of killing tumor cells via LIPUS and is beneficial to advance its non-invasive and targeted killing of tumor cells. Keywords: Low-intensity pulsed ultrasound · Tumor cells · Acoustic pressure distribution · Killing mechanism
1 Introduction Clinically, ultrasound with low energy density (ISPTA < 3 W/cm2 ) and pulse propagation is defined as low-intensity pulsed ultrasound (LIPUS) [1]. It was approved by the FDA for the promotion of fresh fracture healing and bone discontinuity as early as the end of the last century [2]. Recent research findings have shown that LIPUS can also kill tumor cells [3], which opens up new possibilities for noninvasive tumor treatment. So far, conventional cancer treatments such as surgery, radiotherapy, and chemotherapy always tend to cause physical and psychological trauma to patients or have toxic side effects © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 812–820, 2022. https://doi.org/10.1007/978-981-19-1528-4_83
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[4–6]. Novel cancer therapies [7], including radiofrequency therapy [8], microwave therapy [9], and cryogenic freezing [9], are more difficult to approach heat-sensitive organs. Unlike focused ultrasound that damages healthy tissues and requires expensive MRI positioning to avoid ablation of normal tissues [10], LIPUS could be applied to tumor treatment of special organs with its non-invasive and non-thermal advantages and would greatly enhance patient comfort. However, the mechanism of tumor cell killing by LIPUS is not fully understood, and there are still different views on its mechanism of tumor cell killing [11–14]. Therefore, in this paper, we built a self-built experimental platform and observed the association between the acoustic field distribution of LIPUS and the distribution of tumor cells to explore the killing effect of LIPUS on tumor cells and the potential killing mechanism. Then we investigate the effectiveness of LIPUS on tumor cells at different varies and proposed a “cell resonance” explanation for its selective killing effect, which was verified by cellular experiments.
2 Materials and Method 2.1 LIPUS Experimental Platform The electrical pulses with a frequency of 670 kHz is generated by a Tektronix AFG3102C signal generator. Then, the signal was amplified by a Falco systems WMA 300 power amplifier and excited the ultrasonic transducer to generate LIPUS. The voltage and current of ultrasonic transducer were collected by HDO6034A 350 MHz High Definition Oscilloscope during the pulse period (as shown in Fig. 1). The sink is used to hold ultrapure water sterilized by LDZX-50KBS vertical autoclave. The carrier table is used to place cell culture well plates, and the transducer is fixed by the white emission tower. The schematic and physical diagram of the experimental platform are shown in Fig. 1(a) and (b), respectively. The whole study was performed in an ultra-clean table in a bio-sterile laboratory.
Fig. 1. LIPUS experimental setup. (a) Schematic diagram of LIPUS experimental platform. (b) Digital picture of LIPUS experimental platform.
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2.2 Parameters Setting During the action of LIPUS, the ultrasonic transducer and the cell culture plate were coupled with ultra-pure water to reduce the loss of ultrasonic energy. The signal generator output sinusoidal electric pulses with an in-string frequency of 670 kHz, which was compatible with the central frequency of the transducer, and the pulse duration was set to 30 ms. The appropriate number of electric pulse cycles was selected to control the duty cycle 10%, and the treatment of LIPUS was 2 min, as shown in Fig. 2(a). The peak value of each parameter during the experiment was collected. As shown in Fig. 2(b), the voltage was taken as ±125 V; the current was taken as ±125 mA; the duty cycle is 10% and ultrasonic transducer diameter is 14 mm; thus, the maximum electrical energy density was calculated as less than 0.508 W/cm2 with the phase effect was ignored during the calculation. The electroacoustic conversion efficiency was generally taken as 50%, so the acoustic energy density was less than 0.254 W/cm2 , which is still in the clinically defined low-intensity ultrasound range (ISPTA < 3 W/cm2 ).
Fig. 2. Parameters setting. (a) The pulse duration was set to 30 ms, and the duty cycle was 10%. (b) The voltage was ±125 V and the current was ±125 mA.
2.3 Cells Lines and Media Experiments were performed using the HaCaT (Cell Bank of the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences), A375, A549, and HeLa cell lines (Institute of Human Respiratory Diseases, Xinqiao Hospital, Chongqing Third Military Medical University). All cell lines were cultured in DMEM medium (Dulbecco’s modified eagle medium, GIBCO, USA) with 10% Fetal bovine serum (GIBCO, USA) and 1% double antibodies (100 µg/mL penicillin and 100 µg/mL streptomycin), GIBCO, USA). The cell incubator (Thermo Fisher Scientific, USA) culture environment is set to 37 °C with 5% CO2 in air.
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3 Results 3.1 Tumor Cell Distribution After 2 min of LIPUS and 2 days of incubation, the tumor cells are stained with CalceinAM fluorescent dye. Compared with the control group (Fig. 3(a)), the tumor cells showed a distribution of “Poisson’s bright spots” (Fig. 3(b)), or the distribution of antique copper coins (Fig. 3(c)). Figure 3(d) is the distribution of cells after the addition of soundabsorbing material.
Fig. 3. Tumor cell distribution. (a) No LIPUS applied. (b) The tumor cells are distributed in a “Poisson bright spot” pattern. (c) The tumor cells are distributed in an antique bronze pattern. (d) Distribution of tumor cells after adding sound-absorbing materials.
3.2 Physical Mechanism of the Different Distribution of Tumor Cells It is speculated that the different distribution of cells may be caused due to the distribution of the sound field. Because of the significant difference in the acoustic impedance between cell culture solution (acoustic properties are similar to water) and the air, when the LIPUS enters the air from the cell culture medium, part of the pulsed ultrasound will be reflected at the “water-air interface.” Based on the properties of piezoelectric materials, the LIPUS waves excited by sinusoidal electrical pulses are also sinusoidal waves. Ignoring the energy loss of the acoustic wave propagation in the solution, it can be assumed that the incident sinusoidal pulsed ultrasonic waves and the reflected waves interfere with each other in the cell culture solution, thus forming the LIPUS standing wave field. Through the simulation, we can see that in the acoustic standing wave field, the area with the smallest pressure change is the pressure node area, such as the light blue area, and the area with the largest pressure change is the pressure station area, such as the dark red and dark blue areas in Fig. 4(a). When the tumor cells are located at the pressure node in the vertical Z-axis, the tumor cells will show a “Poisson’s bright spot” distribution, which corresponds to the sound pressure level distribution in Fig. 4.(b). When the cells are located at the pressure node in the vertical Z-axis, the tumor cells will show a “bronze coins” distribution, which corresponds to the sound pressure level distribution in Fig. 4(c). When the absorbing material is installed at the “water-air interface”, the reflected waves of low-intensity pulsed ultrasound waves are weakened, resulting in
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almost no standing wave field in the solution, and therefore, the effect on tumor cells was also weakened (Fig. 3(d)), which reminds us that the existence of a standing wave field may also be one of the mechanisms that LIPUS kills tumor cells.
Fig. 4. Simulation of LIPUS sound field. (a) Simulation of LIPUS standing wave sound field. (b) Simulation of LIPUS sound field sound pressure level. (c) Top view of the sound pressure level of the simulated LIPUS sound field.
3.3 Biological Mechanism of Tumor Cell Killing by LIPUS The above experiments have shown that the distribution of cells is determined by the acoustic pressure distribution, but it is still not certain whether the black parts in Fig. 3(a), (b), and (c) were caused due to the cells being “pushed” by the acoustic waves or the cells being killed. Here, the biological mechanism of tumor cell killing by LIPUS needs to be investigated. The survival rate of A375 malignant melanoma cells is tested under the condition of standing wave field of LIPUS. The results show that the survival rate of tumor cells decreased gradually within 2 days after incubation, indicating that apoptosis may be caused by LIPUS (Fig. 5(a)). Using a fluorescence microscope to track the state of tumor cells in the same region in time, we found that after the treatment, the state of tumor cells gradually deteriorated and showed a trend of apoptosis. In order to explore the changes inside the cells, we stained the nucleus, cytoskeleton, and apoptotic proteins and compared them with the control group (see Fig. 5(b), no staining for apoptotic proteins). The results show that DAPI dye staining became lighter, indicating that the nucleus was damaged to a certain extent (Fig. 5(c)). And similarly, the cytoskeleton color changed to light green, and the cell morphology is observed to be deformed, which also indicated that the cytoskeleton was destroyed by LIPUS. In the Merged image, the apoptotic protein Bax dyed red can be seen entering the nucleus for apoptotic expression.
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Fig. 5. Changes in survival and cell structure of A375 after LIPUS. (a) Changes in A375 cell activity over time after LIPUS action. (b) Nucleus and cytoskeleton of tumor cells without LIPUS. (c) Nucleus and cytoskeleton of tumor cells after LIPUS.
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4 Discussion The nucleus is the most crucial regulatory center of the cell. When the nucleus is disrupted, the cell will not be able to perform normal metabolic and genetic behaviors. The cytoskeleton is a supporting system for cells to maintain normal morphology. When the cytoskeleton is destroyed, cells cannot maintain normal morphology and life activities [15]. We speculate that the cell death in this experiment may be caused due to the resonance of cells caused by LIPUS, which brings mechanical damage to the nucleus and cytoskeleton, then induces the incremental expression of the apoptotic protein Bax and ultimately lead to apoptosis of tumor cells. Suppose the damage of cells is indeed caused by resonance. In that case, since different cells have different structural and mechanical properties [16–18], we speculate that the same parameters of LIPUS acting on different cells might produce different effects. In a comparison experiment between one normal cell (human keratinocyte Hacat) and three tumor cells (malignant melanoma cell A375, lung cancer cell A549, and cervical cancer cell Hela), different cells did respond differently to the same LIPUS. As shown in Fig. 6, HaCaT was almost immune to LIPUS, A549 was the most sensitive, and LIPUS killed A375 and Hela at a slightly lower rate than A549. These results support the above “cellular resonance” hypothesis to a certain extent. The selective delivery of energy to the nucleus and cytoskeleton of tumor cells with little effect on normal cells may be attributed to the larger nucleus and more disordered and fragile cytoskeleton of tumor cells compared to normal cells [19]. Meanwhile, it is found that the presence of pressure standing points and pressure node regions in the standing wave acoustic field enhances the selective destruction of tumor cells by LIPUS.
Fig. 6. Survival rate of different cells under the same parameter LIPUS.
This study demonstrated that LIPUS had a killing effect on tumor cells, and the distribution of surviving cells is determined by the ultrasound field distribution. These
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results inspire us to control the sound field distribution to act on cells in specified areas in future studies for achieving precise treatment of tumor sites.
5 Conclusions The main results are as follows: (1) In this study, it is proved that low-intensity pulsed ultrasound has a killing effect on tumor cells, and the distribution of surviving cells is determined by the distribution of the LIPUS field. (2) LIPUS treatment of tumor cells will cause damage to the nucleus and cytoskeleton, destroying the normal morphology of cells and leading to apoptosis. (3) The same electrical pulse excitation of LIPUS had variable effects on different tumor cells (A549 > A375 > Hela) and almost no effect on healthy cells (epidermal cells HaCaT).
Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (51807016, 51877022), the Fundamental Research Funds for the Central Universities (2019CDCGDQ317, 2020CDJYGSX001), the Natural Science Foundation of Chongqing (cstc2020jcyj-msxmX0932) and the Postdoctoral Fund Project of Chongqing (XmT2018012). We thank the Institute of Human Respiratory Diseases of Third Military Medical University for providing the human melanoma cells.
References 1. Guo, Y., Xin, Z., Li, H., et al.: Embracing the third revolution in life sciences with emphasis on the development of microenergetic medicine. J. Peking Univ. Med. Ed. 47(004), 559–565 (2015). (in Chinese) 2. Ebrahim, S., Mollon, B., Bance, S., et al.: Low-intensity pulsed ultrasonography versus electrical stimulation for fracture healing: a systematic review and network meta-analysis. Can J. Surg. 57(3), E105–E118 (2014) 3. Lejbkowicz, F., Zwiran, M., Salzberg, S.: The response of normal and malignant cells to ultrasound in vitro. Ultrasound Med. Biol. 19(1), 75 (1993) 4. Wang, L., Bu, H., Yan, C., et al.: Experience of two pathways for laparoscopic adrenal tumor surgery (with 62 case reports). J. Mod. Genitourin. Oncol. 7(1), 52–53 (2015). (in Chinese) 5. Zhang, Y., Zhou, Y., Tian, L., et al.: A meta-analysis of the effect of health education pathways in oncology chemotherapy patients. China Pharm. Herald, 14(35), 139–143,167 (2017). (in Chinese) 6. Kang, J.: Technological advances and clinical applications of precision radiation therapy for tumors. J. Trans. Med. 5(2), 65–69 (2016). (in Chinese) 7. Li, C., Yao, C., Mi, Y., et al.: Recent advances on physical ablation for tumor. J. Biomed. Eng. (5), 1137–1140 (2009). (in Chinese) 8. Lencioni, R., Crocetti, L.: Radio frequency ablation of liver cancer. Tech. Vasc. Interv. Radiol. 10(1), 38–46 (2007)
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9. Yu, Z., Wu, J., Zhang, M., et al.: Mechanism of immunogenic cell death induced by microwave ablation treatment of osteosarcoma. Chin. J. Radiat. Oncol. 25(6), 602–608 (2016). (in Chinese) 10. Chenguo, Y., Yajun, Z., Chengxiang, L., et al.: Recent advances in tissue minimally invasive ablation with irreversible electroporation. High Volt. Eng. 40(12), 3725–3737 (2014). (in Chinese) 11. Tijore,A.,Margadant,F., Yao., M.,Hariharan, A., Sheetz, M., et al.: Ultrasound-mediated mechanical forces selectively kill tumor cells. bioRxiv https://doi.org/10.1101/2020.10.09. 332726 (2020) 12. Li, F., et al.: Mechanically Induced Integrin Ligation Mediates Intracellular Calcium Signaling with Single Pulsating Cavitation Bubbles. bioRxiv, https://doi.org/10.1101/2020.10.25. 353904 (2020) 13. Annette, G., Beate, R., Dennis, B., et al.: Characterization of dynamic behaviour of MCF7 and MCF10A cells in ultrasonic field using modal and harmonic analyses. Plos One 10(8), e0134999 (2015) 14. Heyden, S., Ortiz, M.: Oncotripsy: targeting cancer cells selectively via resonant harmonic excitation. J. Mech. Phys. Solids 92, 164–175 (2016) 15. Svitkina, T.: The actin cytoskeleton and actin-based motility. Cold Spring Harb. Perspect. Biol. 10(1), a018267 (2018) 16. Cross, S.E., Jin, Y.S., Rao, J., et al.: Nanomechanical analysis of cells from cancer patients. Nat. Nanotechnol. 2(12), 780–783 (2007) 17. Lekka, M., Gil., D., Pogoda, K., et al.: Cancer cell detection in tissue sections using AFM. Arch. Biochem. Biophys. 518(2), 151–156 (2012) 18. Ketene, A.N., Schmelz, E.M., Roberts, P.C., et al.: The effects of cancer progression on the viscoelasticity of ovarian cell cytoskeleton structures. Nanomed. Nanotechnol. Biol. Med. 8(1), 93–102 (2012) 19. Li, Q.S., Lee, G., Ong, C.N., et al.: AFM indentation study of breast cancer cells. Biochem. Biophys. Res. Commun. 374(4), 609–613 (2008)
Credible Capacity Evaluation Method of Distributed Generation in Distribution Network Based on Power Supply Reliability Bing Sun1(B) , Jiahao Chen1 , Xin Li2 , Zhicheng Liu2 , and Yunfei Li1 1 Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China
[email protected] 2 Central China Grid Company Limited, Wuhan, Hubei, China
Abstract. Distributed generation is widely integrated into the distribution network. The power supply capacity of the grid can be improved. However, the capacity value of distributed generation can’t be fully considered in power system planning due to the uncertainty of its output. A credible capacity evaluation method of distributed generation is proposed in this paper. The characteristic of which is that the credible capacity of distributed generation is denoted by load increment under the same power supply reliability. Firstly, the two states time series models of each element in distribution network are simulated by Monte Carlo sampling method. Secondly, the reliability index of the distribution network is calculated based on the two states time series models. In order to consider the time series characteristics of renewable energy output, annual time series simulation is carried out. Then, the credible capacity is searched through the dichotomy until the power supply reliability of system is the same as the original. Finally, the effectiveness of the proposed method is proved through the IEEE 33-bus system. Keywords: Credible capacity evaluation · Power supply reliability · Monte Carlo simulation · Dichotomy · Distributed generation
1 Introduction China is committed to adopting more effective policies and measures to reach the peak of CO2 emissions by 2030 and achieve carbon neutralization by 2060 [1]. The distributed generation (DG) is widely integrated into urban distribution network. In order to accurately evaluate the power supply capacity of wind turbine and photovoltaic (PV) equipment, credible capacity has been proposed as an evaluation index. From the perspective of power supply reliability, it can be defined as the capacity of conventional units that can be replaced by wind turbine and PV equipment under the premise of equal reliability [2]. The credible capacity evaluation of wind and PV power has become a research hotspot in the field of electric power. The core of credible capacity evaluation is the evaluation of power system reliability, which can be divided into three steps, set the model of wind turbine and PV equipment © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 821–829, 2022. https://doi.org/10.1007/978-981-19-1528-4_84
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output, calculate the system reliability, and search credible capacity. Different methods and models have been used in various steps in the research. In the first step, it can be divided into two models by considering time-series output of renewable energy or not. The former refers to multi-state unit models [3] and probability density models [4], et al. The latter includes historical output curves [5] and time-series output simulation [6], et al. In the second step, it can be divided into convolution method [7], Monte Carlo simulation method [8], universal generating functions method [9] and other new methods. In the third step, it can be divided into dichotomy, chord interception, simplified Newton’s method, intelligent algorithm, et al. [10, 11]. The number of iterations is not too much when these methods are used to calculate the credible capacity. There is little difference between the various methods. When carrying out the research on the credible capacity evaluation, more attention is paid to the centralized power plants, but the research on DG is insufficient. Aiming at the shortcomings of existing research, a method for evaluating credible capacity of distributed power generation is proposed in the paper. The characteristic of which is that the power supply support capability of DG during fault recovery is considered by time-series simulation considering the faults of distribution feeder. The credible capacity of DG is evaluated based on the index of expected energy not supplied (EENS). Firstly, the two states time series models of each element in distribution network are simulated by Monte Carlo sampling method. Secondly, the reliability index of the distribution network is calculated based on the two states time series models. In order to consider the time series characteristics of renewable energy output, annual time series simulation is carried out. Then, the credible capacity is searched through the dichotomy until the power supply reliability of system is the same as the original. Finally, the effectiveness of the proposed method is proved through the IEEE 33-bus system.
2 Introduction Credible Capacity Evaluation Method Based on the Same Power Supply Reliability 2.1 Output Model of Distributed Generation Setting the model of wind turbines and PV equipment output is the basis of calculating the credible capacity. Under the normal operation condition, the power generation state of the wind turbine and PV equipment and the output depend on the meteorological conditions. The output characteristics of wind turbines are as follows.
Pw
⎧ 0, ⎪ ⎪ 0 ≤ SWt < Vci ⎨ A + B ∗ SWt + C ∗ SWt2 Vci ≤ SWt < Vr = ⎪ Pr , Vr ≤ SWt < Vco ⎪ ⎩ 0, SWt > Vco
(1)
Among which, V ci denotes cut-in wind speed. V co denotes cut-out wind speed. Pr denotes rated power of wind turbine. V r denotes rated wind speed of wind turbine. The output characteristics of PV panels are mainly composed of the following three parts: nonlinear region, linear region and constant region, as shown in Fig. 1.
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Fig. 1. Schematic diagram of PV equipment output
2.2 Reliability Evaluation Based on Sequential Monte Carlo Simulation Reliability evaluation of the power system is the core work of calculating the DG credible capacity. The sequential Monte Carlo simulation method is adopted to carry out power supply reliability evaluation, the main steps of which can be as follows. Step 1: It is assumed that the initial state of all elements are in operation. Step 2: Sample the duration of the current working state for each element. The exponential distribution is taken as an example. Assuming that λi is the repair rate of the ith element, the repair duration of the element fault is denoted as follows. Di = −
1 ln gi λi
(2)
Among which, gi denotes a random number obeying a uniform distribution of [0,1]. Di denotes the repair time of the ith element. Similarly, λi can also be used as the faulty rate of the ith element to simulate the duration of the working state. Step 3: Repeat step 2 until the simulation under the research time span is completed. The time-series state transition process between the various elements in the time span can be obtained. Step 4: Construct the sequence state transition process of the system, The accident consequence analysis is carried out for each operating state. The outage conditions of distribution feeders and DG are taken into account when evaluating reliability. It is assumed that wind turbines and PV equipment will occur failure under certain probability conditions. The output of renewable energy is zero when the equipment is in repair state. The output of renewable energy is determined by wind speed and light intensity when the equipment is in operation state. The influence of the distribution feeder fault and repair time on EENS can refer to the distribution network reliability evaluation method [12]. It won’t be expanded here. Step 5: Repeat step 4 until the convergence condition of Monte Carlo simulation is met, and the reliability index of the system is calculated.
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2.3 Credible Capacity Evaluation Based on the Same Power Supply Reliability It is assumed that the capacity of the gth conventional generator unit is C g . d t denotes the load of the system at the t th time. Pre,t is the output of the renewable energy at the t th time. G denotes the sets of the DG respectively. R{a,b} denotes the reliability of the system when capacity of generator unit is denoted as a and load level is denoted as b. The credible capacity of the DG is assumed as C c . The time range of credible capacity evaluation is T. The ratio of the supplied load difference before and after the integration of wind turbine under the same reliability level to the installed capacity of wind turbine is the wind power capacity credibility. Therefore, the wind power credible capacity can be calculated as follows. ⎧ ⎧ ⎫ ⎫ ⎬ ⎬ ⎨ ⎨ R Pre,t + Cg , dt + Cc = R Cg , dt (3) ⎩ ⎩ ⎭ ⎭ t∈T
t∈T
g∈G
g∈G
The judgment criterion of the same reliability depends on the reasonable choice of reliability indicators. EENS is used to denote the system’s power supply reliability. The credible capacity of DG needs to be obtained through repeated search. It is a one-dimensional search process. The dichotomy is an effective method to realize the credible capacity search. The specific steps are as follows: Step1: Initialize the basic data of the original system, calculate and select the reference value of the system reliability index denoted as R0 . Step2: Increase the system load level to (1 + k) times more than the original load. Calculate the power supply reliability of the system denoted as R. The calculation method of C c is as follows:
∗ Pre,t (4) Cc = k max t∈T
Step3: The DG of which the capacity is Pre is integrated into the system. Update R and compare with the R0 . Increase or decrease the integrated capacity of the DG according to the comparison results of R and R0 and recalculate the R. Step4: Repeat step 3 until the following equation is satisfied. |R − R0 | < ε
(5)
Among which, ε is very small and constant, which denotes the convergence criterion of power supply reliability evaluation.
3 Case Study The IEEE 33-bus system is used to evaluate the credible capacity of DG. The detailed parameters of the system are shown in ref. [13]. The DGs are integrated into the 7th, 11th, 14th, 29th and 31st nodes of the system. The failure rate of distribution feeders is 0.2 times/year per km. The repair rate is 1000 times/year. The evaluation period is 10 years.
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Fig. 2. The topology of IEEE 33-bus system
3.1 Evaluation Result of Power Supply Reliability The partial faults of the distribution feeders by the sequential Monte Carlo sampling are showed in the Table 1. The initial time of the fault is the time when the fault occurs, and the duration of the fault is the difference between the end time and the initial time. Table 1. A feeder fault condition simulated by sequential Monte Carlo Fault line number
Initial time of failure
End time of failure
1
1760
1768
2
5155
2
6834
3
Fault line number
Initial time of failure
End time of failure
8
7375
7387
5161
8
2682
2682
6840
9
1760
1760
2312
2316
9
6753
6766
4
4242
4242
9
8154
8185
5
2328
2335
9
6055
6060
6
4228
4232
10
5654
5662
6
1073
1075
10
3735
3736
7
3463
3475
10
2965
2967
7
3490
3506
10
2403
2408
8
5758
5761
10
6557
6561
8
1544
1557
10
2137
2143
The failure of different lines will lead to power outages at different load nodes. The downstream load nodes can be affected by the upstream line, but the downstream lines faulty have little impact on the upstream nodes. Taking l5,6 broke down as an example, DG3 is firstly used to supply the load at nearby nodes in each hour during the failure period. Compare the output of DG3 with the lack of electricity of 14th node. If the output of DG3 is greater than the lack of electricity, the load of 14th node will be supplied. Otherwise, the load of the 14th node will not be supplied. The nearest principle
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is adopted for the power supply order of other node if the output of DG3 is greater than the load of the 14th node. In addition, if multiple DGs can be used to supply power when the fault occurs, the load of node under the DG downstream should be calculated firstly. The assessment results of the lack of power supply for 32 lines faults are shown in Fig. 3.
Fig. 3. The lack of power supply for each node of the distributed generation
3.2 Credible Capacity Evaluation Results The EENS of the IEEE 33-bus system is 284.2 MWh before the DG is integrated into the system. Then the maximum load of each node is increased to 1.1 times more than the original load. The sum of the maximum load is increased by 371.5 kWh. The index of EENS is increased to 324.2 MWh. Adjust the integrated capacity of the renewable energy until the EENS is equal to the 284.2 MWh. The evaluation results are shown in the Fig. 4, Fig. 5 and Fig. 6. • The EENS is the same as the original when the installed capacity of the PV equipment increases to 1.697 times more than the original level. The capacity of DG has been increased by 1254.6 kW correspondingly. So, the credible capacity of 1254.6 kW PV equipment is 371.5 kW. • The index of EENS is the same as the original when the installed capacity of wind turbine increases to 1.526 times the original level. The credible capacity of 946.8 kW wind turbine is 371.5 kW. • The wind turbine and PV equipment are integrated into distribution network at the same time. It is assumed that the ratio of the installed capacity to 1:1. The index of EENS is the same as the original when the installed capacity of wind turbine and PV equipment increases to 1.622 times the original level. The credible capacity of 1119.6 kW combined unit is 371.5 kW. It is found that the credible capacity of wind turbine is highest when used as the power supply, and the credible capacity of PV equipment is lowest. Because the utilization hours of wind turbines are much higher than that of PV turbines. Even if there is
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Fig. 4. Corresponding relationship between power supply reliability and PV equipment output
Fig. 5. Corresponding relationship between power supply reliability and wind turbine output
Fig. 6. Corresponding relationship between power supply reliability and wind turbine and PV equipment output
complementarity in the output of wind power and PV equipment, the power generation gap cannot be made up. Therefore, the credible capacity of wind turbines is highest.
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4 Conclusion An evaluation method of distributed generation capacity value is presented in the paper. The characteristic is to evaluate the credible capacity of the distributed generation based on the principle of the same power supply reliability. The core work is to carry out a sequential Monte Carlo simulation for a power distribution system with distributed generation and calculate the power supply reliability index denotes as R. In order to increase R to the original reliability denoted as R0 after the system load increases, the dichotomy is used to search the credible capacity of distributed generation. The load increase in system is used to denote the credible capacity of distributed generation. The credible capacity assessment of only PV equipment, only wind turbines, and wind turbines and PV equipment at the same time is carried out in case study. It is found that credible capacity of wind turbine is highest when wind resources is much richer than sunshine resources even if considering the complementarity in the output of wind power and PV equipment. Acknowledgment. This work was supported by the fund of the fund of Chinese National key R & D Plan (2018YFB0904502), Chinese Academy of Engineering Institute Local Cooperation Project (2020HENZDA02), and the 67th Postdoctoral Fund.
References 1. Ministry of Foreign Affairs of the People’s Republic of China. Statement by H.E. Xi Jinping President of the People’s Republic of China at the General Debate of the 75th Session of The United Nations General Assembly [OE/BL]. 09/22/2020. https://www.fmprc.gov.cn/mfa_ eng/zxxx_662805/t1817098.shtml 2. Zhang, N., Kang, C., Xiao, J., et al.: Review and prospect of wind power capacity credit. Proc. Chin. Soc. Electr. Eng. 35(1), 82–94 (2015) 3. Li, R., Li, Y., Guo, W., et al.: Simulation analysis of the influence of distributed generation access on the reliability of distribution network. Power Syst. Technol. 40(7), 2016–2021 (2016) 4. Han, S., Qiao, Y., Yan, P., et al.: Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles. Renew. Energy 157, 190–203 (2020) 5. Tapetado, P., Usaola, J.: Capacity credits of wind and solar generation: the Spanish case. Renew. Energy 143, 164–175 (2019) 6. Huang, W., Chen, B., Yi, Y., et al.: Reliability analysis of distribution network considering the sustainable load capacity of optical storage. Power Syst. Technol. 42(5), 1510–1517 (2018) 7. Guo, M.F., Yang, N.C., Chen, W.F.: Deep-learning-based fault classification using HilbertHuang transform and convolutional neural network in power distribution systems. IEEE Sens. J. 1–1 (2019) 8. Chen, F., Li, F., Feng, W., et al.: Reliability assessment method of composite power system with wind farms and its application in capacity credit evaluation of wind farms. Electr. Power Syst. Res. 166, 73–82 (2019) 9. Di Fazio, A.R., Russo, M.: Wind farm modelling for reliability assessment. IET Renew. Power Gener. 2(4), 239–248 (2008)
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10. Liu, Y., Wang, H., Han, S., et al.: Real-time complementarity evaluation method considering wind and solar output fluctuations. Power Syst. Technol. 44(9), 3211–3218 (2020) 11. Cai, J., Qingshan, X.U., Wang, X.: Evaluation of credit capacity for wind farms based on accelerated sequential Monte Carlo method. Autom. Electric Power Syst. 42(5), 86–93 (2018) 12. Li, R., Xu, H., Cai, J., et al.: Reliability Evaluation of Distribution System Considering Partitioning and Fusion of Hierarchical Structure[J]. Power System Technology 000(002), 494–499 (2015) 13. Wang, C., Cheng, H.Z.: Optimization of network configuration in large distribution systems using plant growth simulation algorithm. IEEE Trans. Power Syst. 23(1), 119–126 (2008)
Violation Detection of Transmission Line Construction Based on YOLO Network Lingjia Zhang1 , Lizhou Luo2 , Libin Chen1 , Jian Zeng1 , Xiaoyu Xin1 , Zhongshu Liu1 , and Nana Duan2(B) 1 State Grid Shaanxi Electric Power Company Construction Branch, Xi’an 710068, China 2 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
[email protected], [email protected]
Abstract. At present, the method of using drones for inspection has been widely used in the construction and maintenance of transmission lines, avoiding the inconvenience caused by manual tower climbing inspections. In this paper, the deep learning target detection algorithm is applied to the drone inspection work during the line construction to further improve the automation of the line construction inspection. The construction and maintenance site of the transmission line is fixed. Therefore, in the construction inspection, drones are used to take pictures of lines, towers and related places at fixed points, and then use the YOLOv3 network and YOLOv4 network to perform target detection on the pictures to identify violations of safety regulations. This paper selects three common violations for detection: workers are not wearing safety helmets, the steel wire ropes and metal components on the tower are not lined with soft objects, and the distribution box is not locked. After network training, violation detection can basically be achieved. This paper compares the mean average precision of using CIoU and GIoU intersection over union methods, and compares the mean average precision of whether to use K-means clustering to determine preselection boxes. The highest mean average precision can reach 96.82%, which can meet the needs of violation detection. Keywords: Violation detection of transmission line construction · YOLO · Intersection over union · K-means Clustering
1 Introduction In recent years, transmission line tower collapse accidents have occurred many times, mostly due to defects in key parts of the tower itself, and operators operating in violation of regulations [1, 2]. Traditional construction supervision requires a large number of steps such as manual tower climbing and total station measurement. The acceptance supervision process is very cumbersome, the sampling method is relatively extensive, and there are certain personal safety risks, which brings defects and hidden dangers to the later operation and maintenance work. In response to many problems in the construction and management of power transmission projects, the method of using drones instead of manual inspections has been widely used. In order to further improve the intelligence © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 830–837, 2022. https://doi.org/10.1007/978-981-19-1528-4_85
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of inspections, this paper uses the combination of drone aerial photography and deep learning target detection technology to supervise the construction process of transmission lines. Using drones to take fixed-point shooting of the construction site instead of manual inspections, and then using YOLO network deep learning to automatically interpret the photos taken and point out illegal operations. YOLO network [3] is currently the most widely used and most accurate network model in the field of target detection. YOLOv3 [4] was proposed for the first time in 2019, and YOLOv4 [5] was proposed for the first time in 2020. These two network models have once again pushed the mean average precision (mAP) and speed of target detection to a new level. There are many factors that affect the precision of target detection. This paper compares the YOLOv3 network and the YOLOv4 network. The intersection over union [6] uses two algorithms, CIOU and GIOU, with and without K-means clustering [7]. In the end, a high-precision result was obtained, which can be applied to the detection of violations in the construction of transmission lines.
2 The Application of Deep Learning YOLO Network in Violation Detection of Transmission Line Construction 2.1 YOLO Network Structure This paper mainly takes the YOLOv3 and YOLOv4 networks as examples to briefly introduce the structure of the YOLO network. The YOLO network is a convolutional neural network based on regression problems. Feature extraction, target location and classification are completed in one network, which is different from neural networks such as Fast-CNN [8] and Faster-RCNN [9] that have been proposed before. The main structure of YOLO is shown in Fig. 1, which can be divided into backbone network, neck network and head network.
Backbone network (CSPDarknet-53)
Neck network (SPP)
Head network
Fig. 1. YOLO network structure (YOLOv4)
The backbone network is a convolutional neural network for feature extraction, and it will output three feature maps of different sizes. The larger the feature map, the more comprehensive the location information, and the semantic information is not clear enough; the smaller the feature map, the more fuzzy the location information, and the clearer the semantic information. Therefore, the neck network merges feature maps of different sizes and outputs to the head network. The head network predicts the target category and position, and gives the position of the predicted frame, the confidence that the predicted frame contains the target, and the probability that the target belongs to various types of targets. In the training process of convolutional neural network, gradient descent method is used to optimize parameters, so a loss function must be set. The loss function of YOLO
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is shown in the following formula, including positioning loss, target confidence loss, and classification loss. Loss = λiou obj 1ij
B S2 i=0 j=0
=
obj
1ij Liou + Losscls + Lossclasses
1, have object 0, have on object
(1)
where, λiou is the coefficient of positioning loss, S is the length of the feature map, B is the number of anchor boxes on each element of the feature map, L iou is the positioning loss of each anchor box, Losscls is the target confidence loss, Lossclasses is the classification loss. In training, only using the gradient descent algorithm to optimize the weights and biases will lead to a local optimum. Therefore, in training, a vector can be added to the gradient direction, and the descending direction will slightly deviate from the gradient direction, so as to avoid falling into the local optimum. 2.2 Violation Detection of Transmission Line Construction The transmission line construction site is relatively fixed during the construction process, so the drone is used to take fixed-point shooting in the transmission line construction site at regular intervals to collect construction pictures. Then use the YOLO network for target detection and automatically identify violations of safety regulations. This paper selects three common violations for detection. They are workers not wearing safety helmets (No helmet), steel wire ropes and metal components on the tower are not lined without cushion (No cushion), and power distribution boxes are not locked(Unlocked). Some pictures of the target detection are shown in Fig. 2, Fig. 3 and Fig. 4.
Fig. 2. YOLO network recognizes that workers are not wearing safety helmets
After the YOLO network is trained, the variation curve of the precision of identifying workers without a helmet with the recall rate is shown in Fig. 5. It can be seen that the YOLO network can basically realize the function of violation detection with relatively high precision. Some violations in construction are shown in the pictures, and the design of the three-layer feature map output by the YOLO network can also better adapt to such changes.
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Fig. 3. YOLO network recognizes that there is not cushion at the wire rope and metal component binding on the tower
Precision
Fig. 4. YOLO network recognizes that the distribution box is not locked
Recall
Fig. 5. The variation curve of precision with recall rate
3 YOLO Network Uses Different Intersection Over Union Algorithms In the YOLO network, the intersection and union ratio is mainly used in the positioning loss in the loss function and the non-maximum value suppression algorithm, which has a certain impact on the precision of target detection. The intersection over union mainly describes the similarity of two frames, and is used to select a more accurate prediction frame in target detection. Assuming there are two boxes A and B, the most basic IoU definition is shown in the following formula. IoU =
|A ∩ B| |A ∪ B|
(2)
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The positioning loss in the loss function is expressed as Liou = 1 − IoU
(3)
When IoU is larger, A and B are considered to be similar. But if A is completely contained by B, then this means that the intersection over union can no longer accurately describe the degree of similarity between the boxes of A and B. So there are new intersection over union algorithms such as GIoU, DIoU, CIoU, etc. Here are three kinds of intersection over union algorithms in detail. 3.1 Intersection Over Union Algorithm GIoU
A
C
B
Fig. 6. Three boxes A, B, C
Suppose there are three boxes A, B, and C, as shown in Fig. 6, the C is the smallest box containing A and B. The expression of GIoU is GIoU = IoU −
|C\(A ∪ B)| |C|
(4)
Based on GIoU, the positioning loss in the loss function is Lgiou = 1 − GIoU
(5)
3.2 Intersection Over Union Algorithm DIoU As in Fig. 6, there are three boxes as shown in Fig. 7. The length of the line connecting the center points of the A and B is d, and the diagonal length of the C is c. The expression of DIoU is DIoU = IoU −
d2 c2
(6)
Based on DIoU, the positioning loss in the loss function is Ldiou = 1 − IoU +
d2 c2
(7)
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d
c B
Fig. 7. Three boxes A, B, C
3.3 Intersection Over Union Algorithm CIoU CIoU is proposed on the basis of DIoU, which is equivalent to an improvement of DIoU, and its expression is 2
GIoU = IoU − dc2 − αυ 2 gt υ = π42 arctan ωhgt − arctan ωh α=
(8)
υ (1−IoU )+υ
where, ωgt , hgt and ω, h represent the width and height of the label box and the prediction box, respectively. CIoU adds an additional item that contains box width and height information on the basis of DIoU, which can more fully describe the similarity of boxes. Based on CIoU, the positioning loss in the loss function is Lgiou = 1 − IoU +
d2 + αυ c2
(9)
3.4 The Impact of Using Different Intersection Over Union Algorithms on the Precision of YOLO Networks This paper uses the YOLOv3 network and the YOLOv4 network to train the network and make network predictions using the intersection over union algorithm of GIoU and CIoU, respectively. It is considered that the intersection over union of the prediction box and the label box is greater than 0.5, which means that the prediction is correct. Networks all use the default anchor box. The average precision(AP) and mean average precision(mAP) of each target are shown in Table 1. It can be seen from Table 1 that the YOLOv4 network is better than the YOLOv3 network in the detection of transmission line violations. The YOLOv4 network uses CIoU to obtain higher mAP, and mAP is about 2% higher than GIoU. The precision of the network for the second illegal operation is not high. This is because the relative position of the steel rope and the pole tower is relatively complicated, so it is easy to identify negative samples as positive samples.
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Table 1. The influence of GIoU and CIoU intersection over union algorithm on precision Algorithm
YOLOv3 with GIoU
YOLOv3 with CIoU
YOLOv4 with GIoU
YOLOv4 with CIoU
AP of no helmet
95.34%
96.56%
97.77%
98.25%
AP of no cushion
83.19%
86.23%
87.96%
91.26%
AP of unlocked
94.89%
96.45%
95.49%
96.78%
mAP
91.14%
93.08%
93.74%
95.43%
4 YOLO Network Uses K-means Clustering Algorithm 4.1 K-means Clustering Algorithm In the YOLO network, the size of some anchor boxes should be set in advance. The default number of anchor boxes in YOLOv3 and YOLOv4 is 9, and these anchor boxes are used to select the target in the picture. The K-means clustering algorithm is often used in deep learning. The dimensional clustering algorithm can give a suitable anchor box based on the training sample, which helps to improve the mAP. Taking the default value of 9 as an example, the K-means clustering algorithm divides the label boxes in all training samples into 9 categories through iteration, and the average value of all label boxes in each category is the selected anchor point box. The classification standard is that the Euclidean distance between the label boxes in each category and the anchor boxes of this category is the shortest, otherwise it will be classified into other categories. Until the category of all label boxes does not change, the iteration stops. 4.2 The Impact of K-means Clustering Algorithm on the Precision of YOLO Network Prediction The first paragraphs that follows a table, figure, equation etc. does not have an indent, either. The precision of the YOLOv3 network and the YOLOv4 network with and without the K-means clustering algorithm are shown in Table 2. The impact of K-means clustering algorithm on precision. If the intersection ratio of the prediction box and the label box Table 2. The impact of K-means clustering algorithm on precision Algorithm
YOLOv3 without K-means
YOLOv3 with K-means
YOLOv4 without K-means
YOLOv4 with K-means
AP of no helmet
96.56%
97.68%
98.25%
98.61%
AP of no cushion
86.23%
88.26%
91.26%
93.90%
AP of unlocked
94.89%
96.84%
96.78%
97.95%
mAP
93.08%
94.26%
95.43%
96.82%
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is greater than 0.5, the prediction is correct. The intersection over union uses the CIoU algorithm. It can be seen from the table that the use of K-means clustering can effectively improve the mAP of the network, and can increase the mAP of two networks by about 1.5%.
5 Conclusion The YOLO network was trained using illegal images of transmission line construction. After comparing different algorithms, it was found that the mAP reached the highest after using the CIoU cross-combination algorithm and the K-means clustering algorithm. The mAP of the detection of illegal construction of transmission lines can reach 96.82%, which can basically meet the requirements of practical applications.
References 1. Chen, H., Wang, X., Li, Z., Chen, W., Cai, Y.: Distributed sensing and cooperative estimation/detection of ubiquitous power internet of things. Protect. Control Mod. Power Syst. 4(1), 1–8 (2019). https://doi.org/10.1186/s41601-019-0128-2 2. Yu, Y.X.: Urgency and long-term nature of smart grid implementation. Power Syst. Prot. Control 47(17), 1–5 (2019). (in Chinese) 3. Zhao, X., Jia, H.H., Ni, Y.T.: A novel three-dimensional object detection with the modified you only look once method. Int. J. Adv. Robot. Syst. 15(2), 1729881418765507 (2018) 4. Wan, J., et al.: An efficient small traffic sign detection method based on YOLOv3. J. Sig. Process. Syst. 93(8), 899–911 (2020). https://doi.org/10.1007/s11265-020-01614-2 5. Xu, Z.R., Liu, M., Tan, Y.T.: Research on vehicle detection and traffic statistics based on YOLOv4. Mod. Inf. Technol. 4(15), 98–100+103 (2020). (in Chinese) 6. Chen, Z.F., Zhao, C.Y., Li, B.: A bounding regression loss function to improve IoU loss. Appl. Res. Comput. 37(S2), 293–296 (2020). (in Chinese) 7. Kazuo, A., Kazumi, S., Tetsuo, I.: CPI-model-based analysis of sparse k-means clustering algorithms. Int. J. Data Sci. Anal. 1–20 (2021) 8. Ji, C., Huang, X.B., Cao, W., et al.: Research on infrared insulator detection based on improved Fast-CNN mode. Comput. Modernization 284(4), 63–68 (2019). (in Chinese) 9. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Neural Inf. Process. Syst. 39(6), 1137–1149 (2017)
Optimization Design for 500 kV Four-Circuit Transmission Lines on Same Tower Gang Song1 , Haoyu Zhang2(B) , Chuang Liu2 , Zhiwei Ye2 , Yiyu Guo2 , Peng Liu2 , and Zongren Peng2 1 China Energy Construction Group Zhejiang Electric Power Design Institute, Hangzhou, China
[email protected]
2 State Key Laboratory of Insulation and Key Equipment, Xi’an Jiaotong University, Xi’an,
China [email protected]
Abstract. With the development of society and economic growth, the research on the multi-circuit transmission lines on same tower has been put on the agenda. Multi-circuit lines on the same tower can greatly save power transmission costs, but due to the small distance between phases, the mutual influence of the phases causes the electric field to change. This paper calculated the electric field in the design drawings of the 500 kV four-circuit linear tower on the same tower provided by North China Design Institute, and found that the electric field in some areas did not meet the design requirements. The parts that do not meet the requirements are optimized, and the final maximum surface electric field is 2100 kV/mm, which is lower than the control value of 2300 kV/mm, and the maximum surface electric field at the connection is lower than 800 kV/mm, which meets the design requirements. Keywords: Multi-circuit transmission line on same tower · Finite element method · Optimization design
1 Introduction With the rapid development of society and economy, the planning scope of cities and towns is constantly expanding, and the electricity load in cities and towns is also increasing day by day. There are more and more high-voltage lines erected around cities and towns, and the line corridors are becoming scarcer. Opening up new line corridors will inevitably involve large-scale demolition, and various facilities have been newly built on the outer edge of the original line corridors. It is very difficult to expand outward on the basis of the original line corridors [1, 2]. The existing power corridors have become indispensable. Renewable resources. Land resources around cities and towns are extremely scarce, and new power grid construction must try to increase the transmission capacity of unit corridors. The distance between the straight towers of the multi-circuit transmission line on the same tower is small, and the shielding effect of the tower cross arm on the phase © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 838–846, 2022. https://doi.org/10.1007/978-981-19-1528-4_86
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and the phase is weak, resulting in a greater influence between the three phases, so that the electric field distribution has different characteristics from the single-circuit line [3–5], higher requirements are put forward for phase-to-phase insulation [6]. Literature [7–10] studied and optimized the electric field distribution and the characteristics of the equalizing ring of the composite crossarm, but these studies did not consider the influence of the multi-circuit line on the same tower. Literature [11–14] studied the mechanical properties of the composite crossarm, and designed the connection structure of the composite crossarm. Based on the previous research, this paper uses the finite element analysis method to construct the finite element calculation model of the linear tower. The electric field distribution of the preliminary design is calculated, and some of the parameters are optimized, and finally the maximum electric field intensity is lower than the control value 2.3 kV/mm. The first part of this article briefly describes the background of the entire project, narrates the research results of predecessors, and briefly explains the research methods of this article. The second part shows the specific calculation model and results. The third part introduces the specific optimization process and the final result. The fourth part summarizes the conclusions of this article.
2 Calculation Model and Initial Results According to the preliminary design plan provided by the North China Design Institute on the composite cross arm of the linear tower for 500 kV power transmission projects, the main part of the pole and tower is a steel structure, and the cross arm part is made of composite materials. Establish a simulation model as shown in Fig. 1.
(a) tower model
(b)ring position Fig. 1. The calculation model and ring position
The parameters of the equalizing ring are listed in Table 1.
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Pipe diameter (mm)
Ring diameter (mm)
Under insulator
50
340
Upper insulator
50
200
V-type insulator
50
400
The distance between the shielding ring and the upper arm insulator is L = 300 mm, the relative position H = 404 mm to the end fittings, the pipe diameter D = 50 mm, the straight part length C = 600 mm, and the circular part diameter d = 900 mm (Fig. 2).
(a) Top view
(b) Front view Fig. 2. Position of shielding ring
Due to the shielding effect of the tower and the symmetry of the model, in the actual calculation, a quarter of the model is selected for calculation, reducing the amount of calculation. Among them, the pole tower and the ground are given the ground potential, and the ABC three-phase wire and the end connection fittings are given the highest √ 2 potential 525 kV × √ = 428661 V in turn. When the single phase reaches its peak 3 value, the other two potentials are −1/2 of the peak value −214330 V. The remaining air boundaries are electrically insulating boundaries. The calculation result is shown in the Fig. 3.
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(a) A phase
(b) a phase
(c) B phase
(d) b phase
(e) C phase
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(f) c phase
Fig. 3. Electric field distribution of initial design
Through the three-dimensional finite element electric field calculation and analysis of the initial straight tower design scheme, the maximum value of the overall field strength on the surface of the fittings is 2770 V/mm, which does not meet the control value of 2300 V/mm. The field strength at the connection between the V-type insulator string and the lower clamp is too high, reaching above 800 V/mm, which will accelerate the thermal aging of the insulator (Fig. 4).
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(a) E-field of shielding ring
(b) E-field of connection
Fig. 4. Unqualified E-field
3 Structure Adjustment and E-field Optimization Since the surface field strength of the fittings is the highest under the voltage peak conditions of phase A and phase a, the structure of the composite cross arm shielding ring and the V-type insulator equalizing ring is optimized when the phase Aa peak is selected. 3.1 Optimization of Shielding Ring The pipe diameter of the shielding ring is optimized, and the electric field intensity changes are shown in Fig. 5. It can be seen that the larger the pipe diameter, the lower the surface field strength. Choose a pipe diameter of 70 mm, and the highest field strength on the surface meets the requirements of the control value. Taking the economy of materials into account, no longer continue to increase the pipe diameter.
Fig. 5. Maximum surface E-field strength of shielding ring with different pipe diameter
Moving the shielding ring away from the tower can further reduce the field strength. Each time displacement value is reduced by 50 mm the ring spacing, and the relative position H is reduced by 50 mm. The electric field intensity change is shown in Fig. 6. It can be seen that after optimization, the highest field strength on the surface of the shielding ring is reduced to 2.1 kV/mm, meet the control value 2.3 kV/mm.
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Fig. 6. Maximum surface E-field strength of shielding ring with different displacement times
3.2 Optimization of Connection Considering that the field strength at the junction of the fitting and the insulator is too high, the large and small ring structure is adopted for improvement. Optimize the three parameters of pipe diameter, shielding depth, and ring diameter separately (Fig. 7).
Fig. 7. Optimized structure
The Fig. 8, 9, 10 shows the variation of maximum surface E-field strength with pipe diameter, shielding depth and ring diameter. After optimization, the pipe diameter is 55 mm, the ring diameter is 400 mm, and the shielding depth is 90 mm. E-field distribution after optimization is shown as Fig. 11, 12. Its electric field distribution and maximum field strength meet the requirements.
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Fig. 8. Maximum surface E-field strength of equalizing ring with different pipe diameter
Fig. 9. Maximum surface E-field strength of equalizing ring with different shielding depth
Fig. 10. Maximum surface E-field strength of equalizing ring with different ring diameter
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Fig. 11. E-field distribution of the shielding ring
(a) E-field distribution at the left joint
(b) E-field distribution at the right joint
Fig. 12. E-field distribution at the joint
4 Conclusion The finite element electric field calculation is carried out on the preliminary design scheme of 500 kV four-circuit composite crossarm on the same tower, and the results are analyzed. After optimizing each parameter, there are the following conclusions: 1. Considering the maximum surface field strength of the equalizing ring at the outer end fittings and the V-shaped insulator, in the case of the Aa phase peak, affected by the negative potential of the upper B and b phases and the tower ground potential, the maximum surface field strength of the a-phase shielding ring is the highest; Affected by the negative potential of phase B and phase b and the ground potential of the tower, the maximum field strength of the A phase V-shaped insulator string equalizing ring is the highest. 2. In the case of peak values of each phase, the maximum value of the overall field strength on the surface of the initial design fittings is 2770 V/mm, which does not meet the requirements. After optimization, the maximum value of the overall field
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strength on the surface of the fitting is 2100 V/mm, which meets the control value of 2300 V/mm. 3. The initial design field at the connection between each V-shaped insulator string and the lower clamp is stronger than 800 kV/mm. After optimization, the field strength is lower, which meets the design requirements.
References 1. Li, J., Wang, Z., Xia, Z., et al.: Research for the maintenance way of partial outage on 500 kV/220 kV four-circuit mixed-voltage transmission lines on the same tower. In: Proceedings of the 2016 IEEE International Conference on High Voltage Engineering and Application (ICHVE), 19–22 September 2016 (2012) 2. Peng, Y., Lei, X., Liu, T., et al.: Analysis of safe maintenance methods for 750 kV four-circuit transmission lines on the same tower. In: Proceedings of the The 16th IET International Conference on AC and DC Power Transmission (ACDC 2020), 2–3 July 2020 (2020) 3. Liu, Y., Chen, S., Huang, S.: Evaluation of corona loss in 750 kV four-circuit transmission lines on the same tower considering complex meteorological conditions. IEEE Access 6, 67427–67433 (2018) 4. Shen, W., Li, W., Wang, S., et al.: Study on electromagnetic environment and conductor selection for 750 kv same-tower four-circuit lines. In: Proceedings of the The 16th IET International Conference on AC and DC Power Transmission (ACDC 2020), 2–3 July 2020 (2020) 5. Xie, T., Liu, P., Li, J., Cheng, J., Peng, Z.: Electric field distribution of composite insulators for AC 1000 kV double-circuit transmission lines on the same tower. High-Voltage Technol. 36, 224–229 (2010). (in Chinese) 6. Bouhaouche, M., Mekhaldi, A., Teguar, M.: Improvement of electric field distribution by integrating composite insulators in a 400 kV AC double circuit line in Algeria. IEEE Trans. Dielectr. Electr. Insul. 24(6), 3549–3558 (2017) 7. Xi, Y., Qingyu, W., Peng, L., Zongren, P.: Structure optimization of 750 kV composite crossarm voltage equalization shielding device using multi-objective optimization method and artificial fish school algorithm. High Voltage Technol. 42, 3666–3675 (2016). (in Chinese) 8. Yang, X., Wang, Q., Liao, J., Wei, X., Peng, Z.: Peng Z Calculation of transient electric field of 750 kV composite crossarm under standard lightning voltage. High Voltage Technol. 43, 645–653 (2017). (in Chinese) 9. Wang, L., Zhu, Y., Yu, J.: Electric field analysis of the voltage equalizing ring of the new 220 kV composite crossarm tower. Smart Grid 4, 66–74 (2016). (in Chinese) 10. Zhu, K., Qiu, X., Ning, X., Zhou, Z., Li, Y.: 220 kV composite crossarm transmission tower electric field distribution and voltage equalization optimization analysis. J. Electr. Power Sci. Technol. 34, 217–222 (2019). (in Chinese) 11. Zhang, F.: Structural characteristics and operational stability of 35 kV composite cross arm insulators. Electr. Porcelain Arrester, 14–17 (1997). (in Chinese) 12. Sun, Q., et al.: Force calculation of 750 kV composite crossarm connection node. Power Grid Clean Energy 30, 38–42 (2014). (in Chinese) 13. Sun, P., Bai, Y., Cheng, K.: Design analysis of composite cross arm tower. Hebei Electr. Power Technol. 36, 19–22 (2017). (in Chinese) 14. Zhao, H, Yu, J, Qiu, Y: The influence of the structural parameters of the composite cross arm on the bonding stress. FRP/Compos., 36–40 (2015). (in Chinese)
A New 3-D Multi-segment Modelling Method for Stator Transposition Bar and Its Application in Calculating the Circulating Current Loss of Large Hydro-generators Chenguang Wang, Yanping Liang(B) , Lei Ni, Xu Bian, and Dongmei Wang College of Electrical and Electronic Engineering, Harbin University of Science and Technology, No.52, Xuefu Road, Nangang District, Harbin, China [email protected]
Abstract. Transposition of stator bar is an important technique to reduce the circulating current loss in stator bar. The 3-D numerical calculation method is widely used in the generator design. However, the mesh of the stator transposition bar model is difficult to realize, especially in the case of short transposition pitch. The computable model of the actual transposition structure of stator bar for circulating current loss calculation is beneficial to the winding design. In this study, we present the 3-D multi-segment modelling method for stator transposition bar. In this method, the global model of slot model with stator bar is divided into multiple segment models, the segment strands are coupled by coupling circuit. The less than 360° stator transposition bar in large hydro-generator is taken as example, the circulating current loss is calculated. The calculation accuracy is verified by experiments. Finally, by comparing with the leakage magnetic flux distributions obtained by the proposed multi-segment method and the traditional 2.5-D multi-slice method, the results are discussed. Keywords: Circulating current loss · 3-D modeling method · Numerical calculation · Stator transposition bar · Large hydro-generators
1 Introduction The stator bars of large generators are made of parallel strands, which are embedded in the stator slot. Because the differences of each strand link leakage magnetic field, the useless circulating current flows between strands, and produces circulating current loss [1, 2]. The circulating current loss could result in local overheating and insulation failure of strands in large generator [2]. In engineering, the transposition of stator bar could suppress circulating current [3], such as 360° transposition with strands rotating 360° in the stator slot, less than 360° transposition with strands rotating less than 360°, etc. The accurately calculated circulating current loss of stator bars is one of the decisive factors of stator bar transposition design. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 847–861, 2022. https://doi.org/10.1007/978-981-19-1528-4_87
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In many references, the circulating current loss calculating methods are discussed, mainly including analytical algorithm and numerical method. The analytical algorithm is convenient for calculating and solving rapidly, especially in the global solving model. However, there are excessive assumptions of the magnetic field around stator bar in calculation, but the actual transposition structure of parallel strands is often ignored [4]. The numerical method is widely used in electromagnetic simulation of electric machines [5–7]. In [5], a multi-slice 2-D time stepping finite element (FE) model is used to analyse the loss distributions in the induction motors. In [6], the simulation of 3 kW squirrelcage induction motor with skewed slots by means of multi-slice FE model is studied. In [7], the 2.5-D multi-slice method is used to solve the skewed radial flux electrical machines. 2-D and 2.5-D Finite element method (FEM) is an effective method to analyse the electromagnetic characteristics of electric machines [8, 9]. Nevertheless, the special structure of stator transposition bar makes the 3-D FEM necessary. The 3-D FEM can completely simulate the arrangement and transposition of strands, especially in the end region of stator bar, also it can comprehensively and accurately consider the influence of various factors in the electromagnetic calculation [10–12]. However, owing to various sizes between strands and the disparate strands insulation, the strands must be twisted especially in the case of short transposition pitch, which makes the mesh difficult to realize.Therefore, the solvable modelling method of stator transposition bar is one of the key problems of the circulating current loss calculation. In this paper, we proposed a new 3-D multi-segment modelling method for stator transposition bar and calculated the circulating current loss of stator bar in large hydrogenerator. The stator bar end segment models and the incline angle of strand in the slot segment models are completely modelled to ensure the calculation accuracy of proposed method. The outline of this paper is: In Sect. 2, the establishment process of proposed 3-D multi-segment modelling method is discussed in details. In Sect. 3, the proposed method is verified by comparison with the experimental data. In Sect. 4, based on the proposed method, we studied the electromagnetic properties of less than 360° stator transposition bars in 180 MW hydro-generator, and compared with the traditional 2.5-D multi-slice method. In Sect. 5, the conclusions of this study are summarized according to the contents of previous sections.
2 The Proposed Modelling Method The strands of stator bar are weaved according to transposition pitch. The strands in stator slot are bent at the transposition pitch and then transposed. The stator transposition bar and the transposition structure are shown in Fig. 1. The stator transposition bar includes end regions and slot region. The stator transposition bar end is mostly designed as space slant structure or space involute structure, and the strands in end region are nontransposed. This paper presents a new 3-D modelling method for stator transposition bar.
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Transposition Bend
Slot Region
End Region
Fig. 1. The stator transposition bar and local transposition structure
2.1 3-D Multi-segment Modelling Method The principle of this modelling method is to divide the stator transposition bar into segments and established physical model of each segment. In this article, the 180 MW hydro-generator is taken as an example. According to the design of it, the transposition type of stator bar is less than 360° transposition. The hydro-generator’s specifications are shown in Table 1. Table 1. Specifications of 180 MW hydro-generator Parameters
Value
Parameters
Value
Rated power/MW
180
Height of strand/mm
2
Rated current/A
7331.4
Width of strand/mm
8
Number of stator slots
594
Transposition pitch/mm
44.85
Stator bar layers
2
Number of strands column
2
Transposition type
Less than 360°
Number of strands
50
The strands of segment in slot region are modelled according to the actual position in the transposition bar. The length of segment model in slot region is one transposition pitch, and slot segment strands model is non-transposition. The strands of stator transposition bar in slot are inclined at a certain angle in the axial direction. The incline angle is: θ = arctan(
H ) L
(1)
where H is the height of strand, L is the transposition pitch. The strands in same column are paralleled and the strands in each segment are independent conductors. The model of less than 360° stator transposition bars is divided into N segments, and the slot segment models are numbered from M2 to MN−1 . For better illustration, the segment strands in slot segment model are numbered according to the transposition scheme.
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The segment models of less than 360° transposition bar in slot, the segment strand number and the strands in slot segment model are shown in Fig. 2. According to the transposition regularity of stator bar in slot region, the segment models in different axial position established by this method are all the same. Hence, the segment models of slot region can be fast established.
Fig. 2. Segment models of the less than 360° stator transposition bar in slot, the segment strand number and the strands in slot segment model
The magnetic field at the stator transposition bar end is complex, and will affect the circulating current distribution. In order to guarantee the calculation precision, the stator transposition bar end is completely modelled in this modelling method basing on the design parameters of stator bar. The stator transposition bar end and part of strands without transposition in slot region constitute one segment model. The segment models of less than 360° stator transposition bar in end region and the segment strand number are shown in Fig. 3. For the stator bar with transposition end, the modelling method of segment model in slot region can be applied to establish the transposition end segment model.
Fig. 3. Segment models of the less than 360° stator transposition bar in end region
2.2 Boundary Condition and Coupling Circuit The 3-D multi-segment models include upper stator bar and lower stator bar, and the surrounding air domain of the stator bar. S1 , S2 , S3 , S4 , S5 and S6 are external surfaces of the solving regions. The solving model and boundary conditions are shown in Fig. 4. The solved region includes non-eddy current region V1 and eddy current region V2 . The non-eddy current region V1 includes the air, stator core and insulation of stator bars. The
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S4 S6
S6
S2
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S3
Fig. 4. Solving model and boundary conditions
eddy current region V2 includes upper and lower stator bar. The electromagnetic field is calculate d by T, − method. In this method, the vector field in eddy current region represented by the vector-edge element, and the non-eddy current region is solved by scalar potential. Based on electric ˙ the 3-D mathematical model is given vector potential T˙ and magnetic scalar potential , as: ˙ s) ˙ = div(μ H V1 divμgrad (2) ˙s ˙ = −jωμ H rotρrot T˙ +jωμ(T˙ −grad ) V2 ˙ s) ˙ = −div(μ H divμ(T˙ −grad ) With ˙s= 1 H 4π
V
J˙ s ×r dv r3
(3)
where ρ is the resistivity, μ is the permeability, ω = 2π f , f is the current frequency, J˙ s is source current density. It is well known that the tangential component of the slot leakage field is much greater than that of the other components. The radical and axial components are too small and could be ignored. So, the boundary conditions of each independent solving region are given as: T˙ S1 ,S2 ,S3 ,S4 = 0 ˙ ⎧ S1 ,S2,S3 ,S4 = 0 (4) ⎨ n × T˙ S5 ,S6 = 0 ˙ ∂ S ,S = 0 ⎩ ∂n 5 6 where n is unit normal vector of region boundary. The magnetic field vector is:
˙ = μ T˙ −grad ˙s ˙ +H B˙ = μ H
(5)
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Under the influence of magnetic flux leakage, the current amplitude and current phase of strands are changed, then the circulating current is generated. The stator transposition bars of large generator are connected in the end by nose. Hence, the strands of stator transposition bar are connected in parallel at end region. In this proposed method, each strand in segment model is in series of resistance Rm,n and branch induced voltage Em,n , m is the segment strand number, n is the segment model number. Rm,n is DC resistance and same for all the strands. E˙ m,n is branch induced voltage of mth segment strand in Mn segment model, which induced by the other magnetic sources. The coupling circuit network of a single bar is shown in Fig. 5.
Fig. 5. The coupling circuit network
As is well known, the process of the strands transposition in the slot region is based on gradual change of the transposition pitch along the axial direction. The connection path of coupling circuit is substituted for the transposition process of physical model in this proposed method. The connection paths of the equivalent circuits of segment strands between the segment models are consistent with the transposition path, and the end strands are short connected in a parallel circuit network. As Fig. 5 shown, U˙ is voltage of stator bar, I˙ is source current of stator bar, the circuit network can be expressed as: E˙ m = U˙ m + I˙ m Rm
(6)
where E˙ m is the branch induced voltage of mth strand, U˙ m is the branch voltage of strand, I˙ m is the strand current, Rm is the mth strand resistance, m = 1, 2, · · · , Q(Q = 50), Q is the number of the strands. The voltage of stator bar is: U˙ = U˙ m
(7)
The strand resistance is: Rm =
N
Rm,n
(8)
n=1
where Rm,n is the resistance of mth segment strand in Mn segment model, N is the number of the segment models.
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The branch induced voltage is: E˙ m =
N
E˙ m,n
(9)
n=1
The branch induced voltage of mth segment strand in Mn segment model is: ˙ m,n d E˙ = − m,n dt
(10)
˙ m,n is magnetic flux link mth strand which induced by the other strand currents where in Mn segment model. ˙ m,n is: The magnetic flux ˙ m,n = (11) B˙ ·dSm,n Sm,n
where Sm,n is the cross-sectional area of mth segment strand in Mn segment model here the magnetic leakage density link. The unknown variable of electromagnetic field and the strand current can be approached by the iterative calculation of FEM governing Eqs. (2) and coupling circuit Eq. (6). The circulating current loss of mth strand is: N
2 (12) Pc,m = Rm,n · Im2 − Iav n=1
where Im is the mth strand current root mean square (RMS), I is the current RMS of single bar. Iav = I /Q. The circulating current loss of one single stator bar is: Pc =
Q
Pc,m
(13)
m=1
The vector field in the conductor region represented by the vector-edge element, and the mesh is realized by h-p hybrid adaption method of the software. To improve mesh quality, the size of tetrahedral in strands is 5 mm, the size of tetrahedral in stator core is 10 mm and the size of tetrahedral in air region is 12 mm.
3 Experimental Validation To verify the above electromagnetic calculation method, we performed experiments on the 308° stator transposition bars in a single slot. NF EC1000SA programmable single phase AC power source with step-down transformer is used to generate current. The experimental instruments are shown in Fig. 6. The single slot stator core is made of a number of silicon steel, and the 308° transposition bar is composed of 28 strands
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Power source
Stat
or c or e
Upper bar Lower bar
Step-down transformer Fig. 6. Stator transposition bars in a single slot and experimental instruments
connected together in the end. The total 50 Hz current RMS adopted in upper bar or lower bar are 100 A. The compared results of electromagnetic calculation method and measurement are shown in Fig. 7. The strand current obtained from the calculation is close to the measurement. The maximum related error of the upper and lower strand current RMS is 4.83% and 4.75%, and the maximum absolute error of the upper and lower strand current phase angle is 4.20°and 3.47°, which verify the modelling method and the accuracy of calculation.
Fig. 7. Comparison between the calculated and measured strand current
4 Calculation Results and Comparison with Traditional 2.5-D Multi-slice Method 4.1 Traditional 2.5-D Multi-slice Method The proposed traditional 2.5-D multi-slice model of less than 360° stator transposition bar in 180 MW hydro-generator has 21 slices. In slot region, 14 slices are selected
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at different positions along the axial direction, and the distance between the slices is equidistant. 3 slices are selected in the short end region and 4 slices are selected in the long end region, respectively. The slice position, the slice number and corresponding segment model number are shown in Fig. 8.
Fig. 8. The 2.5-D multi-slice method of less than 360° stator transposition bars. The number of slice in end region is: 1, 2, 3, 18, 19, 20, 21. The number of slice in slot region is: 4–17
To ensure the consistency of strand current in each slice, the strands in each slice belonging to a single strand are connected in series by the coupling circuit network. In order to facilitate the comparison, it is assumed in 2.5-D multi-slice method and the proposed 3-D multi-segment method that the current magnitude of the upper or lower stator bar is the rated current and the current phase is consistent. 4.2 Magnetic Field 4.2.1 Slot Leakage Magnetic Field The 31 sampling points are evenly selected at central line from slot rabbet to slot bottom in Fig. 9. The variation of the leakage magnetic flux density can be obtained by the traditional 2.5-D multi-slice method and the proposed 3-D numerical method. The tangential component and radial component of leakage magnetic flux density distribution in the slot are shown in Fig. 10. In Fig. 10, the tangential component of slot leakage flux gradually decreases from the slot rabbet to the slot bottom. The radial component of slot leakage magnetic flux along the slot rabbet to the slot bottom has a trend to increase first then decrease, and then increases slightly until it becomes stable. Moreover, the radial component of slot leakage magnetic flux is anti-symmetric about the middle of stator slot. We can see from the slot leakage magnetic flux distribution that the calculation results of the proposed two methods are consistent.
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Fig. 9. The sampling points in the stator slot
Fig. 10. The leakage magnetic flux density distribution in the slot
4.2.2 Magnetic Field Distribution in End Region The leakage magnetic field linked by stator bar to generate circulating current which includes the end leakage magnetic flux [13]. The sampling lines of segment model M 1 in end region are shown in Fig. 11. The sampling line A and sampling line C correspond to the lower bar in slice 1 and slice 3, respectively. The sampling line B and sampling line D correspond to the lower bar and upper bar in slice 2, respectively. The tangential and radial component of leakage magnetic flux density in sampling lines calculated by the proposed 3-D numerical calculation method and the traditional 2.5-D multi-slice method are shown in Fig. 12 and Fig. 13.
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Lower bar A B
C
Upper bar Y Z
D
X O
69.2
Sampling line A
51.9 34.6 17.3 0
-0.04
Proposed method Multi-slice method
-0.02
0
0.02
The bar height/mm
The bar height/mm
Fig. 11. Sampling lines in end region 69.2 51.9 34.6 17.3 0
0.04
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-0.04
-0.02
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0.02
0.04
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69.2
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Bx /T 69.2
Sampling line D
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17.3 0
-0.04
-0.02
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0.02
0.04
Bx /T
Fig. 12. Tangential component of leakage magnetic flux density in end region
In Fig. 12, the tangential component of leakage magnetic flux densities at A to D sampling lines in end region has similar distribution trends, and the calculation results of 2.5-D multi-slice method are greater than the proposed multi-segment method. In Fig. 13, the radial leakage magnetic flux densities at the 4 sampling lines are small, and the leakage flux distribution calculated by two methods is similar too. The tangential and radial component of leakage flux density at the sampling line D is greater than the other sampling lines, thus the upper bar will be greatly affected by the leakage magnetic field.
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The bar height/mm
The bar height/mm
The axial component of leakage magnetic flux density calculated by the proposed 3-D numerical method is shown in Fig. 14. We can see that the values of the axial component of leakage magnetic flux at some positions are greater than the tangential component of leakage magnetic flux, which cannot be ignored. Therefore, the calculation of the global magnetic field at the end of stator bar for circulating current loss calculation is necessary. The 3-D numerical calculation method proposed in this paper can simulate the spatial leakage magnetic field of stator bar end, which is suitable for accurately calculating the leakage flux distribution and circulating current loss of stator transposition bar.
-0.04
-0.02
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Sampling line B
51.9 34.6 17.3 Proposed method Multi-slice method
Multi-slice method
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69.2
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17.3 0
Multi-slice method
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69.2
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0
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-0.04
-0.02
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By /T
Fig. 13. Radial component of leakage magnetic flux density in end region
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B
C
D
0.02
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-0.04
-0.02
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Bz /T Fig. 14. Axial component of leakage magnetic flux density in end region
4.3 Circulating Current Loss
50
Upper bar
Proposed method Multi-slice method
40 30 20 10 2 -10
0
10
20 30 Strand number
40
50
Circulating current losses / W
Circulating current losses / W
The strand circulating current loss in upper and lower bar calculated by the traditional 2.5-D multi-slice method and the proposed 3-D multi-segment method are shown in Fig. 15. It can be seen that the multi-slice method calculation results are smaller than the proposed 3-D method in this paper, especially in the strands number 14 to 22. The calculation absolute error of the strand circulating current loss in upper and lower bar by two methods above is 8.796 W and 1.866 W, respectively. 10
Lower bar
6 2 -2 Proposed method
-6
Multi-slice method
0
10
20 30 Strand number
40
50
Fig. 15. Strand circulating current loss
5 Conclusion In this paper, we have proposed a new 3-D multi-segment modelling method for stator transposition bars in large hydro-generator. By comparing with experimental results and the 2.5-D multi-slice method respectively, the accuracy of the proposed method is verified and the differences between the results obtained by two methods are discussed. The following conclusions are obtained:
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(1) The 3-D numerical modelling for less than 360° stator transposition bars in large hydro-generator has carried out by using the modelling method in this paper. The slot segment models are the minimum simplification of the actual stator transposition bar, and there is no need to model the transposition bend of the transposition strand. The complete models of stator bar end guarantee the accuracy of the electromagnetic calculation. The proposed method is solvable and suitable for the stator transposition bar modelling. (2) By comparing with the experimental results, the maximum related error of strand current RMS is 4.83%, and maximum absolute error of strand current phase angle is 4.20°. Hence, the 3-D multi-segment modelling method provides satisfying accuracy for the electromagnetic calculation of stator transposition bar. (3) The leakage magnetic field distribution and the circulating current loss of less than 360° stator transposition bars in 180MW hydro-generator are calculated by both the proposed 3-D multi-segment method and traditional 2.5-D multi-slice method. Enough slices of stator transposition bar in end region are needed to ensure the calculation accuracy of circulating current loss, as the axial component of leakage magnetic flux density in end region is large enough to affect the stator bar. (4) The equivalent circuit connection of segment strands is substituted for the transposition path between segment strands. This method can be used to flexibly connect the segment strands of segment models according to actual strand transposition path. The proposed method is suitable for electromagnetic calculation of any transposition parallel conductors, especially the stator transposition bars in large generators.
Acknowledgment. This work was supported in part by the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant (UNPYSCT-2018210), the Natural Science Foundation of Heilongjiang Province under Grant (ZD2019E008), and in part by the National Natural Science Foundation of China under Grant (51807040) and (51977053).
References 1. Rehfeldt, A., Fricke, T., Schwarz, B., et al.: Measurement of sub-conductor circulating currents in roebel bars of a hydro generator. In: 2018 XIII International Conference on Electrical Machines (ICEM), pp. 1273–1277. IEEE, Greece (2018) 2. Yamazaki, K., Furuhashi, T., Yui, H., et al.: Analysis and reduction of circulating current loss of armature wires in permanent magnet synchronous machines. IEEE Trans. Ind. Appl. 55(6), 5888–5896 (2019) 3. Wang, D., Liang, Y., Gao, L., et al.: A New global transposition method of stator winding and its loss calculation in AC machines. IEEE Trans. Energy Convers. 35(1), 149–156 (2020) 4. Yanping Liang, X., Bian, L.Y., et al.: Numerical calculation of circulating current losses in stator transposition bar of large hydro-generator. IET Sci. Meas. Technol. 9(4), 485–491 (2015) 5. Ho, S.L., Fu, W.N., Wong, H.C.: Thermal study of induction motors by phantom loading using multi-slice time stepping finite element modeling. IEEE Trans. Magn. 35(3), 1606–1609 (1999)
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6. Gyselinck, J.J.C., Vandevelde, L., Melkebeek, J.A.A..: Multi-slice FE modeling of electrical machines with skewed slots-the skew discretization error. IEEE Trans. Magn. 37(5), 3233– 3237(2001) 7. Keränen, J., Ponomarev, P., Pippuri, J., et al.: Parallel performance of multi-Slice finiteelement modeling of skewed electrical machines. IEEE Trans. Magn. 53(6), 7201204 (2017) 8. Škofic, J., Boltežar, M.: Numerical modelling of the rotor movement in a permanent-magnet stepper motor. IET Electr. Power Appl. 8(4), 155–163(2014) 9. Wrobel, R., Mlot, A., Mellor, P.H.: Contribution of end-winding proximity losses to temperature variation in electromagnetic devices. IEEE Trans. Ind. Electron. 59(2), 848– 857(2012) 10. Khatab, M.F.H., Zhu, Z.Q., Li, H.Y., et al.: Comparative study of novel axial flux mag-netically geared and conventional axial flux permanent magnet machines. CES Trans. Electr. Mach. Syst. 2(4), 392–398(2018) 11. Yan, B., Wang, X., Yang, Y.: Parameters determination and dynamic modelling of line-start permanent-magnet synchronous motor with a composite solid rotor. IET Electr. Power Appl. 13(1), 17–23 (2019) 12. Cheng, M., Wang, J., Zhu, S., et al.: Loss calculation and thermal analysis for nine-phase flux switching permanent magnet machine. IEEE Trans. Energy Convers. 33(4), 2133–2142 (2018) 13. Bian, X., Liang, Y., Wang, C., et al.: Influence of stator end structures on the end leakage magnetic field for stator bars in large turbo-generators. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS). IEEE, China (2019)
Study of Decentalized Trade Based on Blockchain and Rolling Aggregation Mechanism in V2G Sixiang Zhao1 , Yachao Wang1 , Hanji Ju1 , Tianshu Hu2(B) , and Fengming Chu2(B) 1 State Grid Jibei Marketing Service Center (Fund Intensive Control Center and Metrology
Center), Beijing 102208, China 2 Beijing University of Chemical Technology, Beijing 100029, China
[email protected], [email protected]
Abstract. The number of electric vehicles involved in trading is larger and larger, as the electric vehicles are becoming more and more common. The electric vehicle trading rule based on the dynamic pricing strategy and rolling aggregation trading mechanism is established due to the randomness and uncertainty of electric vehicles charging. The smart contract of electric vehicle power trading is proposed based on the hyperchain. This smart contract is released to the Funchain platform to verify the feasibility of the proposed model. Keywords: V2G · Blockchain · Decentralization · Smart contracts · Energy trading
1 Introduction The electric vehicles are promoted by the development of the electric vehicles technologies and the energy crisis. The number of electric vehicles connected to the grid is becoming larger and larger [1–3]. The number of electric vehicles in China can be more than 60 million by 2030 according to “The Electric Vehicle Development Strategy Research Report”. V2G (Vehicles to Grid) is an important part of the grid. The electric vehicles play an important role in the Energy Internet owing to the large-scale energy storage capacity [4, 5]. The electric vehicles connected to the grid are the random loads and the mobile energy storage devices. When charging the grid provides energy for the electric vehicles. When the grid is needed more energy, the energy can be transferred from the electric vehicles to the grid [6]. Therefore, the electric vehicles are beneficial to the grid peak shaving, reduce the power loss of the grid, and smooth the fluctuation of power grid [7]. Blockchain technology [8] is regarded as the subversive innovation due to the distributed account and the smart contract, which can link the energy flow, information flow and capital flow effectively. The autonomous and dependable trade between the electric vehicles can be established automatically based on the blockchain technology and smart contracts. There will be amount of electric vehicles participating in the power market competition as the development of distributed power technology © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 862–871, 2022. https://doi.org/10.1007/978-981-19-1528-4_88
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and the increasing of the electric vehicle number. Therefore, the design of the trade mode methods is particularly important, which is safe, efficient, transparent and information symmetrical. The energy trade based on the blockchain technology were investigated by some scholars and some researchers focused on the impact of electric vehicles connected to the grid. Some scholars pointed out that the orderly charging of electric vehicles could be gained through the time-depending electricity prices [9–11]. However, the electricity trade involving electric vehicles in V2G were not studied. Some researchers only investigated the decentralized energy trade based on the blockchain technology by the theoretical method [12–14], which is independent of the real experiments. Some scholars studied the decentralized energy trade in the energy internet, but the power trade involving electric vehicles were not taken into consideration [15, 16]. Kang et al. [17] proposed a localized point-to-point (P2P) electricity trading model based on the iterative double auction mechanism, which can maximize social welfare in electricity trading. However, the new energy users were not taken into the power trade. A decentralized power trading model and rule is established based on the rolling aggregation mechanism. The smart contract of V2G power trade is proposed based on the hyperchain and the V2G power trade platform was built and operated. The distrust can be solved by the cryptography principles, which can ensure the fairness, transparency and non-discrimination of trades. Operator A
legend
Operator D
Operator B
Charging parking lot
Charging parking lot
Electric vehicle charging pile Electricityselling vehicle Electricitypurchase vehicle
Photovoltaic power users Operator C Wind power users
Fig. 1. Decentralized trading model diagram of V2G.
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2 Decentralized Trading Mechanisms and Models 2.1 Decentrized Trading Model in V2G The demand for electricity purchase and sale of the electric vehicles, the randomness and instability of the new energy generation are very large in the V2G network. It is of benefit to design a flexible trading model, which can satisfy the needs of users in V2G network. The V2G decentralized trade mode is presented as shown in Fig. 1, which contains electricity-selling electric vehicles, electricity-purchase electric vehicles, photovoltaic power users, wind power users and so on. All of the above users can declare trade requirements to the nearest operators. Operators conduct rolling matching of user’s quotation information every 30 min. After the successful matching, the trade is reached and the smart contract is generated. If not, the next round of trading goes on. The trading model of V2G contains the electric vehicles, new energy generator, operators, electric vehicle charging piles and the grid corporation, which are introduced in detail. Electric Vehicles: The electric vehicles can stay the electricity-selling state, electricity-purchase state and the off-line state, all of which play the different roles in the V2G trade. Electricity-selling electric vehicles can purchase electricity from the grid when the electricity price is low; sells the electricity to the grid when the electricity price is high. Therefore, some benefits can be gained by the electric vehicles. New Energy Generator: New energy generators including photovoltaic and wind power can transfer clean energy to the V2G network. The new energy generator can sell the electricity to the electric vehicle for storage when the new energy is in the high power state. And the electric vehicles can sell stored electric energy to grid during the peak power consumption of the grid, which can avoid the waste of the solar and wind. Operators: Operators act as energy agent, which is the link of power and wireless communications. Electricity-purchase electric vehicles send the requests of electricity to the nearest operator. The energy agent counts local electricity needs and publishes them to other local electric vehicle and new energy generators. The electricity sellers update the quotation information and the operators conduct rolling matching of user’s quotation information every 30 min. Electric Vehicle Charging Piles: Charging piles including the private charging piles and the charging piles of the grid corporation can record the charging amount by the smart ammeter. All of the trades reached according to the smart contract. Grid Corporation: The electricity purchase and sales services is delivered by the grid corporation. 2.2 Decentralized Trading Rules Based on Rolling Aggregation 2.2.1 Rolling Aggregation Model The rolling aggregation model based on the blockchain technology is shown in Fig. 2. All of the electricity-selling, electricity-purchase electric vehicles, photovoltaic and wind generators can send out the trade demand, which can be transferred to the blockchain network by the operators. Each quotation information is transformed into a triplet
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The Demand of electricity trading Electricityselling vehicle
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Transaction information Block height：172706 Header hash：00000000...ad Time summary： 1607746330000 Main content of the block： All transaction information in this block
Block height：172707 Header hash：00000000...5r Time summary： 1607746332000 Main content of the block： All transaction information in this block
Block height：172708 Header hash：00000000...gc Time summary： 1607746334000 Main content of the block： All transaction information in this block
Fig. 2. Rolling aggregated transaction model based on blockchain technology
component. Quotationi (tradeTimei , energyi , pricei )
(1)
where i ∈ (1, …,n), tradeTimei is the period for electricity trade; enengyi and pricei represent the quantity and price of the electricity, respectively. The users’ quotation information is rolling aggregated every 30 min. If the successful matching, the trade is reached and the smart contract is generated. The trade information can be storaged by the blockchain. If not, enter the next round of trading. 2.2.2 Clearing Rule of Rolling Aggregation It is very important of the clearance for the energy trade. The trade information is rolled every 30 min, and the clearing rule is presented in detail as follows: (1) The quotation of electricity purchaser is ranked descending and that of the seller is ranked ascending. (2) The declaration time preference principle is dominate when the quotation is equal. (3) The quotation of the electricity purchaser is not lower than the seller’s, and the trade is matched. The price is the arithmetic average of both quotations, and the quantity is the smaller one.
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(4) The quotation of electricity purchaser is low than the seller’s, and the matching is stop. The source is shown as follows: Algorithm 1 Smart Contract implements a quote matching algorithm Input: buyer quote queue buyerList, seller quote queue sellerList Output: Trading queue dealList Initialize: seller quote queue is sorted by price si from small to large sellerList = {(s1
supply1),…,(sn , supply)} buyer quote queue is sorted by price bi from large to small buyerList = {(b1 demand1),…,(bn,demandn)} 1 function ClearingAlgorithm (buyerList sellerList ) 2 3 4 5 6 7 8 9
10
while buyerList!={} & sellerList!={} exists do if supplyi ≥ demandi then dealPricei = (si + bi) / 2 dealEnergyi = min (supplyi
demandi)
dealList.addList (dealPricei dealEnergyi) end if end while return dealList end function
3 Smart Contract Based on Hyperchain 3.1 Smart Contract Based on Hyperchain and Blockchain Technology Blockchain is stored by the blocks, which are linked according to the generating time. The chain of the blocks constructed based on the hash value and the data of the blockchain can not be tampered [18]. The smart contract is more safety and can reduce the costs of the trade process [19]. The smart contract is similar to the if-then statement. Hyperchain blockchain platform is a basic technology platform developed by Hangzhou Hyperchain Technology Co., Ltd., which can be used by the enterprises and government agencies [20]. V2G power trading platform is established upon the hyperchain blockchain platform, by which the trading smart contract can be built based on the rolling aggregation model. 3.2 V2G Power Trading Smart Contract The users participating in V2G power trading should meet the following three conditions: 1) The trades is accomplished of all the users’ own accord; 2) The quotation information is confidential before the success of rolling matching contract; 3) The users failling perform the contract will be punished.
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Start Users release electricity demand information and pay margin Blockchain collation of demand information
Users are free to adjust quotation information
N
Is rolling matching started?
Y
Users provide real quotation and random string
Consistent with sealed quotation?
N
Y Close the transaction, generate a smart contract
Y
Is rolling matching successful?
Execution of transaction Settlement by contract return the remaining margin End
Fig. 3. The flow chart of V2G power trading based on fun chain
The trading process contains 4 steps: encrypting the quotation, quotation verification, rolling aggregation and trade settlement, which are accomplished by four main function functions. The diagram is shown in Fig. 3. (1) Encrypting the quotation function: electricity seller for electric vehicle, electricity purchasers electric vehicle, photovoltaic and wind power users can submit the quotation information on the trading platform. The security deposit should be paid before the trade. All of the quotation information is recorded by the smart contract and ranked according the time order. The quotation information is the hash encrypted, and shown as follows: E = SHA(q, x)
(2)
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where E is the encrypted quotation. SHA(,) is SHA-256 hash function. q is the real quotation information; x is the random string. Table 1. Information on power purchase customer offers Subscriber type
Trading period
Electric quantity /kWh
Electrovalence /yuan/kWh
Quotation time
Electric vehicle 1
09:00–10:00
80
0.6
08: 15
Electric vehicle 2
09:00–10:00
60
0.7
08: 05
Electric vehicle 3
09:00–10:00
70
0.65
08: 10
Electric vehicle 4
10:00–11:00
65
0.8
08: 10
Electric vehicle 5
09:00–10:00
50
0.7
08: 20
Electric vehicle 6
10:00–11:00
70
0.75
09: 10
Electric vehicle 7
10:00–11:00
68
0.65
09: 20
Electric vehicle 8
09:00–10:00
85
0.7
08: 25
2) Quotation verification function: In the quotation verification step, the quotation information q and the random string x are submitted. The encrypted quotation is checked by the smart contract. If mismatching, the quotation is invalid, and this user can not participate in rolling aggregation step. The smart contract are carried out after all the quotation information is eligible. Table 2. Information on electricity sales customer offers Subscriber type Trading period
Electric quantity/kWh
Electrovalence /yuan/kWh
Quotation time
Electric vehicle 09:00–10:00 1
60
0.5
08: 15
Electric vehicle 09:00–10:00 2
70
0.45
08: 05 (continued)
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Table 2. (continued) Subscriber type Trading period Electric vehicle 10:00–11:00 3
Electric quantity/kWh
Electrovalence /yuan/kWh
Quotation time
70
0.55
09: 10
Photovoltaic 4
10:00–11:00
120
0.4
08: 20
Photovoltaic 5
09:00–10:00
160
0.3
08: 25
Wind power generator 6
10:00–11:00
60
0.45
08: 10
Wind power generator 7
09:00–10:00
120
0.35
08: 15
3) Rolling aggregation function: The valid trade information is cleared by the rolling aggregation rule every 30 min. The user should confirm the trade in 15 min after the aggregation successfully. 4) Execution and settlement function: After the contract is generated, the trade is conducted. The smart ammeter send the trading message to the platform, and the trade the settled according to the smart contract.
4 Simulation In order to verify the effectiveness of the scheme, the V2G power trading smart contract is published on the Hyperchain platform to simulate the trade. It is assumed that there are eight power purchasers and seven power sellers in the V2G network, the information submitted by the power purchasers is shown in Table 1 and the information of the sellers is shown in Table 2. According to the above settings, the trading electricity price and quantity can be gained by the simulation, which is shown in Table 3. After two rolling aggregation trading, seven rounds of trades and 12 trades are reached. The order of completion is as follows: [(2,5,8),5], [(1,3,8),7], [1, 2], [4, 4], [6, (4,6)], [7, (3,6)]. In power trade, the clearance order of power purchasers is affected by the trading period, electricity price and trading time, while power sellers are affected by the trading period, electricity price and transaction time. From Table 3, it can be gained that the electric vehicle 2, electric vehicle 3, photovoltaic power generation 4, photovoltaic power generation 5, wind power generation 6 and wind power generation 7 of the electricity selling users can be taken into the trade. Because the price of photovoltaic power generation 5 is lower, this trade is completed first; while the price of electric vehicle 1 is higher, so the rolling aggregation fails. In this trade, the total amount of new energy is 480kwh, the average trading price is 0.508kwh/yuan, and the social green power benefit is 244.05 yuan. It is of benefit for reducing carbon emissions and increasing the new energy consumption through the V2G platform. This platform is based on the blockchain technology and independent of
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Power users
Electricity sales users
Trading period
Electric quantity /kWh
Electrovalence /yuan/kWh
Electric vehicle 2
Photovoltaic power generation 5
09:00– 10:00
60
0.5
Electric vehicle 5
Photovoltaic power generation 5
09:00– 10:00
50
0.5
Electric vehicle 8
Photovoltaic power generation 5
09:00– 10:00
50
0.5
Electric vehicle 8
Wind power generation 7
09:00– 10:00
35
0.525
Electric vehicle 3
Wind power generation 7
09:00– 10:00
70
0.5
Electric vehicle 1
Wind power generation 7
09:00– 10:00
15
0.45
Electric vehicle 1
Electric vehicle 2
09:00– 10:00
65
0.525
Electric vehicle 4
Photovoltaic power generation 4
10:00– 11:00
65
0.6
Electric vehicle 6
Photovoltaic power generation 4
10:00– 11:00
55
0.575
Electric vehicle 6
Wind power generation 6
10:00– 11:00
15
0.6
Electric vehicle 7
Wind power generation 6
10:00– 11:00
45
0.55
Electric vehicle 7
Electric vehicle 3
10:00– 11:00
23
0.6
the third party, which has the advantages of decentralization, non-tampering, traceability and multi-party mutual trust [21, 22].
5 Conclusion The rapid development of blockchain is beneficial to the V2G power trade. A decentralized trade model is built based on the blockchain in this paper. The decentralized trade process of electricity is established based the rolling aggregation rule. And the V2G electric vehicle power trading smart contract is designed based on the Hyperchain technology, and a practical trading platform is built. The effectiveness of this platform is proofed by the comparison between simulation and traditional power trading schemes. Acknowledgments. The financial supports from the Science and Technology Project of State Grid Jibei Electric Power Company Limited. "Key Technology Research on Reliability Improvement of Electric Vehicle Energy Service Based on Blockchain" (52018520002T), is gratefully acknowledged.
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References 1. An, Q.: Research and application of key technologies for decentralized transactions based on blockchain. Donghua University (2017) 2. Chu, F., Su, M., Xiao, G., et al.: Analysis of electrode configuration effects on mass transfer and organic redox flow battery performance. Ind. Eng. Chem. Res. 61(7), 2915–2925 (2022) 3. Merz, M.: Potential of the blockchain technology in energy trading. Blockchain Technology (2016) 4. Peters, G.W., Vishnia, G.R.: Overview of emerging blockchain architectures and platforms for electronic trading exchanges. Soc. Sci. Electron. Pub. (2016) 5. Aune, R.T., Krellenstein, A., O’Hara, M., et al.: Footprints on a blockchain: trading and information leakage in distributed ledgers. J. Trading 12(3), 5–13 (2017) 6. V Viana, D.: Two technical images: blockchain and high-frequency trading. Philosophy Technol. 1–26 (2016) 7. D’Antona, J., Snags, R.: Three more banks for blockchain trading system. Tradersmagazine Com (2015) 8. Molinari, V., Latona, J., Pallotta, C.J., et al.: Systems and methods for trading, clearing and settling securities transactions usin 9. Wei, D., Zhang, C., Sun, B., et al.: Multi-objective optimal scheduling of electric vehicle charging and discharging based on time-sharing tariff. Power Grid Technol. 38(11), 2972– 2977 (2014) 10. Zhang, H., Wang, S., Zhao, X., et al.: Electric vehicle charging and discharging scheduling control strategy based on load interval tariff incentive. Guangdong Electric Power, 30(3), 42–46 (2017) 11. Sun, X., Wang, W., Su, S., et al.: Design of an orderly charging control strategy for electric vehicles based on time-sharing tariff. Power Syst. Autom. 37(1), 191–195 (2013) 12. Zhang, N., Wang, Y., Kang, C.Q., et al.: Blockchain technology in energy internet: a preliminary investigation on research framework and typical applications. Chin. J. Electr. Eng. 36(15), 4011–4022 (2016) 13. Sun, H.: Demand-side response analysis of energy internet integrating blockchain technology and smart contracts. Modern Manuf. (9), 64–65 (2017) 14. Zhou, G.-L., Lv, R.-J.: Application of blockchain technology in energy internet. In: 2016 Annual Conference on Information Technology in Electric Power Industry (2016) 15. Tai, X., Sun, H., Guo, Q.: Blockchain-based power transaction and blockage management method in energy internet. Power Grid Technol. 40(12), 3630–3638 (2016) 16. Ping, J., Sijie., C., Ning., Z., et al.: Smart contract-based decentralized trading mechanism for distribution networks. Chin. J. Electr. Eng. 37(13), 3682–3690 (2017) 17. Kang, J., Yu, R., Huang, X., et al.: Enabling localized peer-to-peer electricity trading among plug-in hybrid electric vehicles using consortium blockchains. IEEE Trans. Ind. Inform. 13(6), 3154–3164 (2017) 18. An, Q.: Research and application of key technologies for decentralized transactions based on blockchain. Donoghue University (2017) 19. Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media, Inc. 2015.G BLOCKCHAIN TECHNOLOGY: US20170011460[P] (2018) 20. Zheng, G., Lin, W.: Fun Chain technology: linking everything with the underlying core technology. Tsinghua Manage. Rev. 000(010), 104–108 (2018) 21. Baert, R.: FX trading not ready to benefit from safeguards of blockchain. Pensions & Investments (2015) 22. Ma, J., Deng, J., Song, L., et al.: Incentive mechanism for demand side management in smart grid using auction. IEEE Trans. Smart Grid 5(3), 1379–1388 (2014)
Hysteresis Loop Measurement for Steel Sheet Under PWM Excitation Condition Xinyang Gao, Nana Duan(B) , and Shuhong Wang School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China [email protected], [email protected]
Abstract. This article adopts a method of measuring the hysteresis loop of oriented silicon steel sheet under the excitation of pulse width modulation (PWM) signal. A measurement platform was built on the basis of the traditional Epstein frame method. According to international standards, the length of the equivalent magnetic circuit of the Epstein frame was calculated, and the harmonic control method was used to achieve the measurement of the hysteresis loop of the oriented silicon steel sheet under PWM excitation. Starting from the traditional sinusoidal excitation, this paper measures the variation of the hysteresis loop of the silicon steel sheet under different frequency excitation. On this basis, the waveform control and PWM signal output programming were carried out through the host computer, and the influence of high-order harmonics of different frequencies on the magnetic properties of the oriented silicon steel sheet under PWM excitation was compared. Provide theoretical basis for the design and research of transformer laminated core. Keywords: Pulse width modulation · High-order harmonics · Hysteresis loop
1 Introduction The laminated core has become a widely used magnetic circuit component in power transformers. The analysis of the magnetic field problem in the laminated core and the optimization of its structure are key issues in the design of power transformers. With the rapid development of power electronics technology, PWM technology widely used in modern electric drive systems. Compared with the traditional sinusoidal power supply, due to the rich high-order harmonics in voltage and current, the loss in the iron core of electrical equipment will increase significantly. According to the relevant standards of magnetic property measurement, the existing electrical steel sheet manufacturers only provide the magnetic property measurement data under the excitation of standard sinusoidal magnetic flux density. These data cannot accurately characterize the magnetic properties of electrical steel sheets under complex conditions [1]. In order to accurately evaluate and take effective measures to reduce core loss and improve the energy efficiency of electrical equipment in the design stage, it is necessary to accurately measure the hysteresis loop of electrical steel sheet under the excitation of Pulse Width Modulation (PWM) power supply [6]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 872–879, 2022. https://doi.org/10.1007/978-981-19-1528-4_89
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So far, domestic and international standards for measuring the magnetic properties of electrical steel sheets under the excitation of sinusoidal magnetic flux density have been published. Because the measurement of the magnetic properties of electrical steel sheets under PWM excitation is related to many factors, and each influencing factor is coupled with each other, there are few reports on related measurement standards. Literature [4] proposed the possibility of establishing a standard method for measuring the magnetic properties of soft magnetic materials under inverter power supply, discussed its influencing factors. Literature [5] uses Epstein frame to develop an automatic test system under PWM excitation mode, derives the analytical expression of the iron loss measurement error, and points out that the phase difference of the signal is a key factor affecting the measurement accuracy. After calibration, Repeatability is within 3%. Literature [2] uses a single-sheet tester method, considers the influence of harmonics on magnetic characteristics, determines the selection method of the reference waveform, controls the excitation waveform, and obtains the core data under PWM excitation. Literature [8] measured the magnetic characteristics of the toroidal core of a high-frequency transformer under PWM excitation, and constructed a core loss calculation model. In this paper, based on the original Epstein frame measurement standard, by improving the experimental platform and measurement system, a measurement system for the magnetic properties of electrical steel sheets under PWM excitation is built. The hysteresis loop of 30QG120 grain-oriented electrical steel sheet under PWM power supply was measured, and the influence of high-order harmonics on the hysteresis loop of grain-oriented silicon steel sheet was studied.
2 Experimental Device 2.1 Epstein Frame Parameters
Iron core 1.4m
Fig. 1. Epstein frame model
To use the Epstein frame method to measure the magnetic properties of silicon steel sheets, it is an essential and important link to determine the equivalent magnetic circuit length of the frame used. Figure 1 is a model diagram of the Epstein frame. For the
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sake of accuracy, the frame magnetic circuit length generally cannot be directly used for its geometric average length, but needs to be measured and determined separately. For example, for the more common standard 25 cm Epstein frame, the magnetic circuit length is not 1 m, but the equivalent magnetic circuit length (0.94 m) specified by the IEC (International Electrotechnical Commission) standard is generally used for measurement. With reference to the national standard, this article derives the calculation equation of the equivalent magnetic circuit length of its core model. According to the definition in the national standard, the calculation of the equivalent magnetic circuit is shown in Eq. (1): me (1) Le =Lm m In Eq. (1), me is the equivalent mass of the model, m is the total mass of the model; L e is the equivalent magnetic circuit length of the model. Then through the known data parameters in Table 1. Epstein frame parameters, the equivalent magnetic circuit length of the frame can be obtained. Calculated by the above equation, the equivalent mass of the square ring used is 23.8 kg, which is equivalent to 1.27 m in length. Table 1. Epstein frame parameters Technical Parameters
Silicon steel sheet sample
Model
30QG120
Density of iron core (kg/m3 )
7.65 × 103
Model core section area (mm2 )
2.87 × 103
Model core quality (kg)
26.3
Number of turns of excitation coil
312
Excitation coil wire density (kg/ m3 )
8.9 × 103
Induction coil turns
312
2.2 Experiment Platform In order to realize the measurement of the hysteresis loop of the grain-oriented silicon steel sheet under PWM excitation, this paper builds the experimental measurement platform shown in Fig. 2 based on the Epstein frame. The measurement system is mainly composed of the following two parts: (1) Signal generation, data acquisition and processing part. The signal generation and data acquisition and processing are completed by the PC and the data acquisition card (NI-USB-6363). The data acquisition card contains input channels and output channels. The PC can be programmed to output waveforms, and at the same time, use the channels of the data acquisition card to collect B and H signals. This article uses LabVIEW to realize the programming process, and writes the corresponding program in LabVIEW to collect and process the data collected by the data acquisition card;
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Fig. 2. Experiment platform
(2) Measuring part. The main part of the measurement consists of the Epstein frame, including the silicon steel sheet under test and the shunt resistor. There are two types of measured voltage signals: the voltage across the shunt resistor on the excitation winding side and the voltage on the induction winding side. The purpose of measuring the voltage across the shunt resistor is to obtain the current through the field winding side. The secondary coil and the primary coil are wound in parallel to enhance the coupling, and the number of turns of the two is equal. The two signals are synchronously collected by the analog input channel of the data acquisition card to strictly ensure the alignment of the signal sequence, and then the data is transmitted to the PC for post-processing.
3 Principle of the Experiment 3.1 PWM Excitation Generation Method The PWM excitation signal required for this measurement is realized by the analog output function of the data acquisition card. The output waveform is amplified by a high-precision power amplifier to the primary side of the Epstein frame as an excitation. In the traditional silicon steel sheet measurement method, the reference waveform frequency of the magnetic flux density is single, and it is only necessary to control the waveform of the magnetic flux density in the measured sample to be a sine wave according to the reference waveform of the magnetic flux density. Different from the traditional method of measuring the magnetic properties of silicon steel sheets, the PWM power supply contains high-order harmonics, and the order of harmonics can reach several hundred. Under such excitation conditions, to measure the magnetic properties of silicon steel sheets, it is necessary to consider non-sinusoidal magnetic flux density measurement. In order to determine the reference waveform of the magnetic flux density under non-sinusoidal conditions, it is necessary to determine the proportion of each harmonic in the reference waveform. The harmonics in the reference waveform should depend on the PWM power supply [2]. The reference waveform is determined according to Eq. (2): B(t) = B1 sin(2π ft) +
m n=2
kn B1 sin(2π nft + φn )
(2)
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In Eq. (2), f is the frequency of the fundamental wave, B1 is the amplitude of the fundamental wave, K n is the ratio of the amplitude of the n-th harmonic to the amplitude of the fundamental wave, and Fn is the phase of the n-th harmonic. 3.2 Experimental Measurement Principle In this paper, the Epstein frame method is used to measure the hysteresis loop of the silicon steel sheet. The number of turns of Epstein frame excitation coil (primary side) is N 1 , and the number of turns of induction coil (secondary side) is N 2 . Connect a shunt resistor in series, the size of the resistor is R. At the same time, according to the law of electromagnetic induction, an exciting magnetic field will be generated in the silicon steel sheet, and the magnetic field strength calculation equation H can be obtained by the law of Ampere’s ring: H=
N1 U1 N1 I = L RL
(3)
In Eq. (3), L is the equivalent magnetic circuit length of the Epstein frame, and U 1 is the voltage on both sides of the shunt resistor. According to Faraday’s law of electromagnetic induction and the definition of magnetic induction, the magnetic induction B is obtained according to Eq. (4): 1 φ (4) B= = U2 dt S N 2S In Eq. (4), S is the cross-sectional area of the coil, and U 2 is the voltage on both sides of the induction coil, which can be collected by the data acquisition card. U 1 and U 2 can be converted into H and B signals after being collected by the data acquisition card, thereby obtaining the hysteresis loop of the sample in the PC.
4 Result 4.1 Measurement of Hysteresis Loop at Different Frequencies In this section, we mainly study the influence of different excitation frequencies on the hysteresis loop of grain-oriented silicon steel sheet. Figure 3 shows the measurement results of hysteresis loops at 50 Hz, 100 Hz and 200 Hz excitation frequencies when the excitation source amplitudes are equal and the silicon steel sheet is not saturated. As the excitation frequency increases, the area of the hysteresis loop gradually increases, resulting in an increase in loss. 4.2 Influence of High Order Harmonics This section mainly studies the influence of high harmonics on the measurement results. Figure 4 is the comparison of the magnetic field intensity under the two excitations. It can be seen that under the PWM excitation, the internal magnetic field intensity of the silicon steel sheet contains a large number of high harmonics.
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1 50Hz 100Hz 200Hz
0.8 0.6 0.4
B(T)
0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -50
0
50
H(A/m)
Fig. 3. Comparison of calculated and measured hysteresis loops under different exciting frequencies 80
PWM SIN
60 40
H(A/m)
20 0 -20 -40 -60 -80
0
0.02
0.04
0.06
0.08
0.1
t(s)
Fig. 4. Comparison of magnetic field strength under different excitations
Figure 5 compares the effect of the controlled harmonic order on the hysteresis loop. (PWM excitation, 8 pulses, modulation index 1.2) When the controlled harmonic order is not more than 3, 5, 7, 9, and the hysteresis loop is as shown in the figure. It can be seen that compared with the sine excitation, the measurement result contains a local
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Fig. 5. Comparison of calculated and measured hysteresis loops under different exciting frequencies
hysteresis loop. In addition, the increase of high harmonics will affect the shape and size of the local hysteresis loop.
5 Conclusion In this paper, based on requirements under PWM excitation, a measurement method suitable for the hysteresis loop of the grain-oriented silicon steel sheet under this condition is adopted. The influence of the frequency characteristics of the hysteresis loop and the high-order harmonics on the magnetic characteristics of the silicon steel sheet is studied.
References 1. Yang, Q.X., Li, Y.J.: Characteristics and development of advanced magnetic materials in electrical engineering. Trans. China Electrotech. Soc. 31(10), 1–12 (2016) 2. Wang, X.Y., et al: Measurement and dynamic modeling method for grain-oriented silicon steel sheets under PWM power supply. Proc. CSEE 33(30), 153–158 (2016)
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3. Liu, Z., et al: Magnetic properties measurement and modeling for electrical steel sheet under PWM excitation condition. Adv. Technol. Electr. Eng. Energy 40(01), 34–42 (2021) 4. Zhang, D.H., et al.: Investigation on measurement of stress dependent vector magnetic properties of non-oriented electrical steel sheet. Trans. China Electrotech. Soc. 34(2), 449–456 (2019) 5. Zhang, C.G., et al.: An electromagnetism simulation methodology of laminated silicon steel by three axes orthogonal and wide frequency excitation. Proc. CSEE 37(7), 2167–2175 (2017) 6. Kong, Q.Y., et al: Research on magnetic characteristics of electrical steel sheet of large power transformer. Adv. Technol. Electr. Eng. Energy 37(9), 17–23(2018) 7. Chen, J.Q., Ma, W.M., Wang, D., et al.: Development of automatic iron loss measurement system with PWM excitation. Magnetic Mater. Dev. 4, 37–42 (2011) 8. He, R.Z.: Research on core loss performance of high frequency transformer under PWM excitation. Shenyang University of Technology (2019) 9. Zirka, S.E., Moroz, Y.I., Marketos, P., et al.: Generalization of the classical method for calculating dynamic hysteresis loops in grain-oriented electrical steels. IEEE Trans. Magn. 44(9), 2113–2126 (2006)
Influence of Trace SF6 on Surface Corona Characteristics in SF6 /N2 Mixtures Under DC Voltages Yanliang He(B) , Wei Ding, Anbang Sun, and Guanjun Zhang State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China [email protected], [email protected]
Abstract. Corona discharge in gas-insulated power equipment will cause potential harm to the inner insulation of the equipment. Insulation defects near insulator may induce surface corona. Because the insulation performance of insulator is generally lower than that of gas medium, it is easy to cause gap breakdown or surface flashover under relatively low applied voltage, making the equipment outage or damage. SF6 /N2 mixtures is a typical electronegative gas with synergistic effect, which is often used for inner insulation of power equipment. A small amount of SF6 can change the insulation performance of gas mixtures and make it retain the useful characteristics of electronegative gas. In this paper, the influence of gasmixture ratio on surface corona characteristics in SF6 /N2 mixtures was studied by setting up the discharge experimental setup of tip-surface compound defect. The variation of light emission patterns under different voltage polarities were analyzed, and the rules of discharge onset and breakdown voltages, current pulsetime parameters were compared. The results have shown that the surface effect mainly affects the streamer corona development. When the SF6 content reaches 1%, both the discharge light emission patterns and the parameters of pulse time under positive and negative polarity are virtually consistent with those in pure SF6 . With increasing SF6 content, the onset voltage and breakdown voltage of surface corona discharge increase, and the pulse-time parameters decrease and finally reach saturation. Keywords: Surface corona · SF6 /N2 mixtures · Surface effect · Discharge characteristics · DC voltages
1 Introduction With the application of gas-insulated switchgear (GIS), gas-insulated transmission lines (GILs) and other power equipment in DC systems, the inner insulation of power equipment under DC voltages has been widely concerned. As the weak part of inner insulation, the dielectric solid represented by epoxy resin spacers usually becomes the onset point of strong discharge defects [1, 2]. When the insulation defect is located near the dielectric © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 880–890, 2022. https://doi.org/10.1007/978-981-19-1528-4_90
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solid, the corona discharge generated under high electric field stress can be attracted towards the surface of dielectric. As the common filling gas medium of gas-insulated power equipment, SF6 /N2 mixtures is a typical synergistic effect gas, that is, a small amount of SF6 can change the insulation performance of gas mixtures [3]. Relevant researches show that when the SF6 content is 0%–5%, the change of insulation performance of SF6 /N2 mixtures is obvious [4, 5]. Therefore, to investigate the influence of trace SF6 on the characteristics of surface corona is of significance to understand the mechanism of surface corona discharge and to optimize the insulation design of power equipment. In view of the corona discharge characteristics in SF6 /N2 mixtures, scholars have done some research [6-8], and for the surface corona discharge with dielectric solid, there has been some studies in air environment [9-11]. For the surface discharge in SF6 /N2 mixtures, Xue et al. [12] investigated the accumulation property of surface charges, and obtained that the accumulation of surface charge decreased with increasing surface roughness. Chvyreva et al. [13] concluded that the surface discharge onset voltage increased as SF6 content increased. Hou et al. [14] researched the partial discharge characteristics and synergistic effect of gas mixtures by using the rod-plane electrode surface model, and thought that the synergistic effect under high pressure would be more obvious. Sadaoui et al. [15] investigated the DC creeping discharge on the dielectric solid surface, and found that the path length of creeping discharge decreased with the increase of SF6 content. At present, the researches on surface discharge in SF6 /N2 mixtures mainly focus on the surface charge characteristics and partial discharge onset characteristics, and rarely involves the SF6 /N2 mixtures of which SF6 content is not more than 5%. The strong electronegativity of SF6 will obviously affect the discharge characteristics of gas mixtures, especially in the environment of trace SF6 . However, the research on the influence of trace SF6 on surface corona characteristics in mixtures is less and needs to be improved. In this paper, the influence of trace SF6 on surface corona characteristics in SF6 /N2 mixtures is studied by establishing the discharge experimental setup of tip-surface compound defect. In the case of a certain defect location, the researched surface corona characteristics mainly include the light emission patterns, discharge onset and breakdown voltages, and current pulse-time parameters.
2 Experimental Setup and Method 2.1 Experimental Setup Figure 1 shows the discharge experimental setup of tip-surface compound defect. The tip-surface compound defect is composed of the needle electrode, plane electrode and Al2 O3 -filled epoxy resin spacer. The radius of the plane electrode is 50 mm, the radius of the needle electrode tip is 100 µm, and the needle-plane spacing is 20 mm. The epoxy resin spacer is a cylinder, the diameter of the bottom surface is 40 mm, and the column height is 40 mm. The high-voltage DC power supply is used to supply voltage; the current-limiting resistance R1 = 2 M, is used to limit the breakdown current; the sampling resistance R2 = 75 , is used to measure the discharge current from the plane
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electrode to the ground. The needle-electrode voltage, U 1 , and the sampling resistance voltage, U 2 , are both measured and saved by the oscilloscope. The discharge current, I 2 = U 2 /R2 . The discharge in the chamber is observed through the quartz windows, and the light emission patterns are taken by the intensified charged coupled device.
Fig. 1. Schematic diagram of the discharge experimental setup.
2.2 Experimental Method (1) The tip-surface compound defect placed in the discharge chamber is shown in Fig. 2. (2) First pump the chamber to vacuum and fill in pure N2 to a pressure of 0.2 MPa for 12 h. Then vacuum again, and fill in SF6 /N2 mixtures to 0.1 MPa through a trace-gas regulating device. (3) Increase the applied voltage at a uniform speed. When a light spot produced by the corona was taken by ICCD, the U 1 was defined as the corona onset voltage. When the current had a sudden increase, and there was a visible spark discharge channel in the discharge chamber accompanied by a clear discharge sound, the U 1 was defined as the breakdown voltage. After each voltage measurement 6 times, take the mean value as the result. (4) The gate width of ICCD was set to 50 µs during the positive corona, 0.5 µs during the negative corona, and 1 ns during the gap breakdown.
Fig. 2. Photo of tip-surface compound defect.
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3 Experimental Results and Discussion 3.1 Light Emission Patterns Under Different SF6 Contents As shown in Fig. 3, the light emission patterns of the positive corona-streamer in pure N2 is given. Since the needle electrode tip is made into a cone, there will be the surface mapping of the dielectric. It can be found that the initial stage of corona formation is mainly concentrated near the needle tip, and the light emission is weak, as shown in Fig. 3(a). With the increase of voltage, the streamer corona gradually forms, the length of main streamer channel increases, and the number of branched streamer channels increases, as shown in Figs. 3(b)–3(d). With the development of streamer, part of the branched streamer is attracted by the dielectric solid surface, thus forming two kinds of streamers, including the gas streamer and surface streamer. The corona discharge intensity increases evidently. As the applied voltage increases, the current had a sudden increase, and the gap breaks down accompanied by a clear discharge sound. As shown in Fig. 3(e), the discharge breakdown channel is the surface flashover channel.
Fig. 3. The positive corona-streamer development process in N2 under tip-surface compound defect. (a) V = 5.5 kV (b) V = 7.7 kV (c) V = 9.7 kV (d) V = 13.5 kV (e) V = 14.5 kV.
Figure 4 shows the light emission patterns of the positive corona-streamer in SF6 /N2 mixtures with 0.01% SF6 . After mixing a minute amount of SF6 with pure N2 , the development of streamer corona is obviously suppressed. The length of main streamer channel is shortened, and the discharge dispersion is weakened. However, the branched
Fig. 4. The positive corona-streamer development process in SF6 /N2 mixtures with 0.01% SF6 . (a) V = 5.8 kV (b) V = 9.6 kV (c) V = 12 kV (d) V = 15.5 kV (e) V = 18 kV.
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streamer channels still maintains the gas streamer and the surface streamer, as shown in Figs. 4(d)–4(e). The discharge breakdown channel presents a random situation of gas breakdown or surface flashover. Figure 5 shows the light emission patterns of the positive corona-streamer in SF6 /N2 mixtures with 0.1% SF6 . As the SF6 content increases, the diffuse corona discharge disappears and the discharge area becomes more concentrated. Due to the strong electronegativity of SF6 , the branched streamer channels gradually disappear, and only the development of the main streamer channel is retained.
Fig. 5. The positive corona-streamer development process in SF6 /N2 mixtures with 0.1% SF6 . (a) V = 6.2 kV (b) V = 10 kV (c) V = 15 kV (d) V = 20 kV (e) V = 23 kV.
Figures 6, 7 and 8 show the light emission patterns of the positive corona-streamer in SF6 /N2 mixtures with 1% SF6 , 10% SF6 , and pure SF6 , respectively. When the SF6 content reaches 1%, the light emission patterns are virtually stable, which are consistent with those in pure SF6 . When the applied voltage is low, the discharge area is mainly concentrated near the needle tip, showing a circular light emission region. When the applied voltage is high, due to the long distance of needle-plane spacing and the strong electronegativity of SF6 , the main streamer channel can always be generated and maintained before the gap breaks down completely.
Fig. 6. The positive corona-streamer development process in SF6 /N2 mixtures with 1% SF6 . (a) V = 7 kV (b) V = 15 kV (c) V = 20 kV (d) V = 30 kV (e) V = 40 kV.
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Fig. 7. The positive corona-streamer development process in SF6 /N2 mixtures with 10% SF6 . (a) V = 7.5 kV (b) V = 20 kV (c) V = 30 kV (d) V = 35 kV (e) V = 48 kV.
Fig. 8. The positive corona-streamer development process in SF6 under tip-surface compound defect. (a) V = 12 kV (b) V = 20 kV (c) V = 40 kV (d) V = 60 kV (e) V = 80 kV.
Figure 9 shows the light emission patterns of the negative corona-streamer in pure N2 . Compared with the positive polarity, the discharge dispersion in the negative polarity is weaker, the luminous intensity of the main streamer channel is weaker, and there is no obvious branched streamer channel. With increasing the applied voltage, the streamer corona discharge in pure N2 is converted into normal glow discharge, and the current increases rapidly. However, there is no obvious breakdown channel.
Fig. 9. The negative corona-streamer development process in N2 under tip-surface compound defect. (a) V = 3.7 kV (b) V = 5 kV (c) V = 8 kV (d) V = 9.5 kV (e) V = 10 kV.
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Figures 10 and 11 show the light emission patterns of the negative corona-streamer in SF6 /N2 mixtures with 0.01% SF6 , and 0.1% SF6 , respectively. With the introduction of SF6 gas in pure N2 , the discharge area is suppressed near the needle tip, showing a circular corona region. The main streamer channel and the normal glow discharge gradually disappear. As the electronegativity of the gas mixtures is not strong, once the streamer channel is generated, the breakdown channel will be formed.
Fig. 10. The negative corona-streamer development process in SF6 /N2 mixtures with 0.01% SF6 . (a) V = 4.4 kV (b) V = 10 kV (c) V = 20 kV (d) V = 28 kV (e) V = 30 kV.
Fig. 11. The negative corona-streamer development process in SF6 /N2 mixtures with 0.1% SF6 . (a) V = 4.5 kV (b) V = 10 kV (c) V = 20 kV (d) V = 30 kV (e) V = 35 kV.
Figures 12, 13 and 14 show the light emission patterns of the negative coronastreamer in SF6 /N2 mixtures with 1% SF6 , 10% SF6 , and pure SF6 , respectively. When the SF6 content reaches 1%, the main streamer channel with strong luminescence can be formed before gap breakdown. The discharge light emission patterns in this situation are virtually consistent with those in pure SF6 , and the result is the same as in positive polarity. In pure N2 environment and the gas mixtures with low SF6 content, the light emission patterns of corona discharge under different polarities are quite different. Under positive polarity, the streamer corona near the needle tip will dissipate when it develops to a certain extent, and the secondary electron avalanches generated by photoionization will form the new secondary streamers. Due to the surface effect of dielectric solid, some secondary streamers will be attracted by the dielectric surface to form surface streamers, while the secondary streamers that continue to develop in the gas will form gas streamers.
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With the introduction of SF6 , the secondary streamers formation are inhibited, thus the branch streamer channels gradually decrease. Under negative polarity, the secondary streamers will not be generated during the discharge. The streamer corona will not dissipate, and will continue to develop along the main streamer channel. In pure N2 , due to the lack of negative ion inhibition, the radial distance of the main streamer channel is long. The discharge is easy to form the pattern similar to normal glow discharge before reaching the plane electrode. With the introduction of SF6 , the suppression of negative ions makes the main streamer channel narrow, thus it is easy to form a breakdown channel.
Fig. 12. The negative corona-streamer development process in SF6 /N2 mixtures with 1% SF6 . (a) V = 5 kV (b) V = 10 kV (c) V = 20 kV (d) V = 30 kV (e) V = 35 kV.
Fig. 13. The negative corona-streamer development process in SF6 /N2 mixtures with 10% SF6 . (a) V = 5.5 kV (b) V = 10 kV (c) V = 20 kV (d) V = 30 kV (e) V = 42 kV.
Fig. 14. The negative corona-streamer development process in SF6 under tip-surface compound defect. (a) V = 9 kV (b) V = 20 kV (c) V = 40 kV (d) V = 60 kV (e) V = 80 kV.
3.2 Discharge Characteristics Under Different SF6 Contents As shown in Fig. 15, the threshold voltages variation under different SF6 contents is given. Both the corona onset voltage and breakdown voltage increase with the increase
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of SF6 content under different polarities, and the breakdown voltage is more sensitive to the addition of trace SF6 . Figure 16 shows the current pulse-time parameters variation under different SF6 contents, including the rise time, fall time and pulse duration. It can be found that with the increase of SF6 content, the pulse-time parameters under different polarities tend to decrease first and then saturate. The introduction of trace SF6 will have a significant impact on the pulse-time parameters of the gas mixtures. When the gas mixtures contains 1% SF6 , the pulse-time parameters are virtually the same as those in pure SF6 .
Fig. 15. The variation of corona onset voltage and breakdown voltage under different SF6 contents.
Fig. 16. The variation of pulse-time parameters under different SF6 contents. (a) Positive polarity (b) Negative polarity.
The pulse-time parameters depend on the formation and diffusion of positive ions during the ionization process. Taking the rise time of current pulse as an example, the positive-ion cloud formation time approximately equals to the electron migration time. When the SF6 content is low, the electron avalanches will occur far away from the
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needle tip, so the electron migration time is longer. With the increase of SF6 content, the inhibition effect of negative ions on ionization development increases rapidly and tends to be saturated, and the ionization region decreases and tends to be stable. Thus, the pulse-time parameters decrease first and then saturate. Due to the different polarities of the needle electrode, under the positive corona discharge, during the process from the initial streamer to the ions cloud formation, the electrons will first develop toward the ionization boundary, and then migrate to the needle electrode. Therefore, the time required for the electrons to complete the entire migration under the positive polarity is longer than that under the negative polarity, and the current pulse-time parameters are also longer.
4 Conclusion In this paper, the discharge experimental setup of tip-surface compound defect was established to investigate the influence of trace SF6 on surface corona characteristics in SF6 /N2 mixtures. The light emission patterns under different voltage polarities were studied, and the variation of discharge characteristics was compared. The conclusions can be drawn as follows: (1) Surface effect mainly affects the streamer corona development. Under the positive polarity, with increasing SF6 content, the streamer corona development will be significantly inhibited. The length of main streamer channel, the number of branched streamer channels, and the surface effect decrease. Compared with the positive polarity, the discharge dispersion under the negative polarity is weakened, and there is no obvious surface effect. (2) When the SF6 content reaches 1%, the discharge light emission patterns in SF6 /N2 mixtures under positive and negative polarity are virtually consistent with those in pure SF6 . (3) With increasing SF6 content, the onset voltage and breakdown voltage of surface corona discharge increase under different polarities, and the breakdown voltage is more sensitive to the addition of trace SF6 . The pulse-time parameters decrease and finally reach saturation under different polarities, and when the SF6 content is 1%, the pulse-time parameters in SF6 /N2 mixtures are virtually the same as those in pure SF6 .
Acknowledgement. The authors appreciate the supported of the National Natural Science Foundation of China (51777164), and the Fundamental Research Funds for the Central Universities, China (PY3A083 and xtr042019009).
References 1. Xue, J., Chen, J., Dong, J., Deng, J., Zhang, G.: Enhancing flashover performance of alumina/epoxy spacers by adaptive surface charge regulation using graded conductivity coating. Nanotechnology 31(36), 364002 (2020)
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2. Sobota, A., Veldhuizen, E.M., Stoffels, W.W.: Discharge ignition near a dielectric. IEEE Trans. Plasma Sci. 36(4), 912–913 (2008) 3. Guo, C., Zhang, Q., Wen, T.: A method for synergistic effect evaluation of SF6 /N2 gas mixtures. IEEE Trans. Dielectr. Electr. Insul. 23(1), 211–215 (2016) 4. He, Y., Sun, A., Zhang, X., Xue, J., Zhang, G.: Effect of trace SF6 on negative corona characteristics in SF6 /N2 gas mixtures under DC voltages. AIP Adv. 10(8), 085303 (2020) 5. Okubo, H., Yamada, T., Takahashi, T., Toda, T.: Partial discharge inception and breakdown characteristics in gas mixtures with SF6 . In: Christophorou, L.G., Olthoff, J.K., Gaseous Dielectrics VIII, p. 289, Plenum Press, NY (1998) 6. Van Brunt, R.J., Leep, D.: Characterization of pointplane corona pulses in SF6 . J. Appl. Phys. 52(11), 6588–6600 (1981) 7. Morrow, R.: Theory of positive onset corona pulses in SF6 . IEEE Trans. Electr. Insul. 26(3), 398–404 (1991) 8. He, Y., Ding, W., Sun, A., Zhang, G.: Effect of gas-mixture ratio on the characteristics of positive DC corona discharge in SF6 /N2 gas mixtures. IEEE Trans. Dielectr. Electr. Insul. 28(3), 829–837 (2021) 9. Allen, N.L., Mikropoulos, P.N.: Streamer propagation along insulating surfaces. IEEE Trans. Dielectr. Electr. Insul. 6(3), 357–362 (1999) 10. Meng, X., Mei, H., Chen, C., Wang, L., Guan, Z., Zhou, J.: Characteristics of streamer propagation along the insulation surface: influence of dielectric material. IEEE Trans. Dielectr. Electr. Insul. 22(2), 1193–1203 (2015) 11. Li, X., Sun, A., Teunissen, J.: A computational study of negative surface discharges: characteristics of surface streamers and surface charges. IEEE Trans. Dielectr. Electr. Insul. 27(4), 1178–1186 (2020) 12. Xue, J., Wang, H., Chen, J., Li, K., Liu, Y., Song, B., Deng, J., Zhang, G.: Effects of surface roughness on surface charge accumulation characteristics and surface flashover performance of alumina-filled epoxy resin spacers. J. Appl. Phys. 124(8), 083302 (2018) 13. Chvyreva, A., Pemen, A.J.M., Christen, T.: Influence of SF6 admixtures to nitrogen on electrodeless streamer inception at an epoxy resin surface. IEEE Trans. Dielectr. Electr. Insul. 23(3), 1683–1689 (2016) 14. Hou, Z., Guo, R., He, C., Li, J., Yao, X.: Synergistic effect of SF6/N2 gas mixtures on surface partial discharge under DC voltage. IEEE Trans. Dielectr. Electr. Insul. 27(2), 692–699 (2020) 15. Sadaoui, F., Beroual, A.: DC creeping discharges over insulating surfaces in different gases and mixtures. IEEE Trans. Dielectr. Electr. Insul. 21(5), 2088–2094 (2014)
The Influence of Different Fillers on the Properties of Carbon-Matrix Composites Qichen Chen, Guangning Wu, Zefeng Yang(B) , Jiahui Lin, Wenfu Wei, Guoqiang Gao, Hao Li, Guofeng Yin, and Chunmao Li School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610000, China [email protected]
Abstract. The inherent cracks of carbon-matrix composites are important factors that affect the properties. Adding fillers is an effective way to reduce cracks in order to enhance the properties of composites. Herein, reinforced carbon-matrix composites were prepared by introducing nanodiamonds (NDs) and strong alkalimodified carbon fibers (CFs) into the powders by blending, respectively. Compared with CFs, NDs could effectively fill cracks with the volume expansion in composites, which promoted the deflection of the crack propagation path. It also had excellent thermal conductivity, and the electrical conductivity was improved after high-temperature sintering. Compared with the properties of original sample (OS), the compressive strength (97.833 MPa), flexural strength (21.342 MPa), thermal conductivity (2.809 W/(m·K)), and electrical conductivity (141.2 S/cm) of the composite sample with 0.05 wt% NDs (SNDs) increased by 78.2%, 67.1%, 16.03%, and 7.72%, respectively. NDs is a kind of filler to efficiently enhance the performance of carbon-matrix composites, which can achieve higher performance at low addition. Keywords: Carbon-matrix composite · Crack · Filler · Nanodiamond · Carbon fiber
1 Introduction Carbon-matrix composites have excellent mechanical properties, thermal conductivity, electrical conductivity, etc., and are widely used in aerospace, transportation, civil engineering, and so on [1, 2]. However, because of the difference in thermal expansion coefficients of different materials, the phases are separated in composites, resulting in defects such as cracks [3, 4]. These cracks have created a serious impression on the properties. Therefore, reducing the internal cracks has become the key to improving the performance of carbon-matrix composites. Fillers are considered to be an effective method to reduce the internal cracks of carbon-matrix composites. In recent years, people have used CFs as an external fillerreinforced composites and achieved excellent performance [5, 6]. However, there is little research on CFs reinforced composites after surface modification, which may substantially improve the properties. In addition, nanomaterials are also used as fillers to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 891–899, 2022. https://doi.org/10.1007/978-981-19-1528-4_91
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reinforce carbon-matrix composites, such as carbon nanotubes (CNTs) and graphene [7, 8], but they are not easy to produce on a large scale due to hard-disperse and high price. With the improvement of NDs preparation level and mass production, some people directly use NDs as a matrix to prepare high-strength graphite materials, but it has not been studied as a filler to reinforce composites [9]. Here, we compared the methods of adding NDs and alkali-modified CFs to enhance carbon-matrix composites. Specifically, 0.05 wt% NDs and 1 wt% CFs were added to the powders to prepare carbon-matrix composites. Compared with CFs, the addition of NDs reduces cracks in the composites, and the composites with NDs has excellent mechanical strength, electrical and thermal conductivity.
2 Experimental 2.1 Materials Pitch-coke (~50 µm in diameter) were purchased from Quzhou Zhongchi New Material Co., Ltd (China). Binder pitch (the properties were listed in Table 1) and flake graphite (6.5 µm in diameter) were purchased from Shanghai Mopeng Electric Carbon Technology Co., Ltd (China). CFs (7 µm in diameter and 0.01–0.2 mm in length) were obtained from Shanghai Lishuo Composite Technology Co., Ltd (China). NDs (5–15 nm in diameter) and sodium hydroxide (NaOH) were provided by Aladdin Reagent (Shanghai) Co., Ltd (China). Table 1. Properties of the binder pitch. Softening Point (°C)
Ash (%)
Toluene insoluble matter (%)
Quinoline insoluble matter (%)
Volatile matte (%)
Sulfur (%)
Moisture (%)
~109
0.25
29.5
8.2
51
0.25
2.2
2.2 Preparation of Composite Sample The preparation process is shown in Fig. 1. First, kneaded the pitch-cokes (95 wt%), flake graphite (5 wt%) and molten binder pitch (additive 35 wt%) at 180°C for 1 h. Then, the cool powder was tableted three times and passed through a screen to get the partials about 75 µm in diameter after being crushed. Next, form the bulk with a pressure of 130 MPa for 5 min [10]. Finally, embed the bulk and use a temperature gradient (the temperature rise curve mainly refers to Fig. 2) up to 1050°C to get the final carbon-matrix composites [11]. The original sample was recorded as OS. The composite sample added 1 wt% CFs and 0.05 wt% NDs at the first step after ultrasonic stirring for 0.5 h and mechanically stirring for 0.5 h in alcohol solution were recorded as SCFs and SNDs, respectively.
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Fig. 1. The preparation process of composite sample.
Fig. 2. The TG curve of mixture.
2.3 Characterization Scanning electron microscopy (SEM) were measured on a Nova NanoSEM 450 (FEI, USA). The transmission electron microscope (TEM) images was obtained by a TALOS F200X (FEI, USA). The thermal decomposition temperature of mixture was obtained by a TG209 F3 (Netzsch, Germany) at a heating rate of 10 °C·min−1 in the range of 50– 800 °C under N2 atmosphere. Fourier transform infrared (FT-IR) spectra was measured by a Spectrum 100 (PerkinElmer, USA). The density of composites was measured using Archimedes’ principle. Boiling method was used to measure the open porosity. The mechanical properties of compressive strength (10 × 10 × 10 mm, 2 mm/min) and flexural strength (4 × 8 × 32 mm, 3-point bending fixture method, 1 mm/min) are tested by a universal testing machine TY-9000D1 (Yangzhou Tianyou Instrument Equipment Co., Ltd., China). Electrical conductivity of composites ( 12.7 × 10 mm) was measured with an RTS-9 four-point probe system (Guangzhou FourProbes Tech. Co. Ltd., China). Thermal conductivity was measured by LFA467 HyperFlash (Netzsch, Germany) at room temperature. The thermal conductivity (λ, W/(m·K)) was calculated by Eq. (1) [12]: λ=ρ × CP × D
(1)
where ρ is the density of composites (g/cm3 ), C P is the specific heat (J/(g·K)) and D is the thermal diffusivity (mm2 /s), respectively.
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3 Results and Discussion 3.1 Characterization of the Raw Materials Figure 3(a) shows the SEM image of the pitch-cokes. One can see that the structure of pitch coke is irregular, and the diameter of pitch-coke is 20–50 µm. It can be seen from the EDS energy spectrum that the main components of pitch-coke are C and O in Fig. 3(b-c). The surface of purchased CFs often has a layer of polymer or impurities as shown in Fig. 3(d). As shown in Fig. 3(e), through NaOH modification treatment, the polymer and impurities on the surface of the CFs were removed, which provided conditions for improving the interface combination of CFs and composites. In addition, Fig. 3(f) is TEM of the NDs, showing that the diameter of NDs is 5–15 nm. Nano-scale of NDs provides the possibility to form interface layer for the coated pitch-coke. But the NDs were prone to agglomeration, ultrasonic and mechanical stirring were used to disperse them during the experiment.
Fig. 3. (a) SEM of the pitch-coke. (b-c) EDS of the pitch-coke with C K and O K. (d-e) SEM of the CFs before and after treatment. (f) TEM of the NDs.
Fig. 4. FT-IR spectra of the pitch-coke, CFs and NDs.
Moreover, as shown in Fig. 4, FT-IR spectra characterized the surface coronal energy clusters of pitch-coke, CFs and NDs. The peak at 3436 cm−1 and 1629 cm−1 are
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attributed to the stretching vibrations of O-H and C = C, respectively [3]. These are reflected on the surface of the three materials. In addition, C-O stretching vibrations is reflected on peaks at 1383 cm−1 , which can only be found on the surface of CFs and pitch-coke. This shows that modification by NaOH had a positive effect on the surface activation of CFs, and the presence of surface functional groups was conducive to the formation of hydrogen bonds. It can effectively improve the interface between materials and also have a positive effect on the uniform dispersion of nanomaterials. 3.2 Microstructure and Mechanical Properties of Composites The photograph of three composite samples of OS, SCFs, and SNDs after sintering are shown in Fig. 5. The appearance of composite samples is basically the same and the shape is well maintained without obvious cracks, which indicates that the external filler will not interfere with the molding and sintering of the composites. Moreover, the shape of the material is consistent with that of the mold, which shows that the carbon-matrix composites have workability. This makes it possible to process composites into devices suitable for various working conditions.
Fig. 5. The bulks of composites.
Density and open porosity of the composites are shown in Table 2. The density of SCFs and SNDs are higher than that of OS, which shows that the fillers can effectively increase the density of the composites. Among them, NDs (1.474 g/cm3 , the highest density of three composite samples) has a greater contribution to the increase in density. In addition, the open porosity of SNDs (14.12%) is the lowest, which shows that NDs play a positive role in filling cracks in composites. And it is beneficial to the improvement of material mechanical strength [1]. Table 2. Density and open porosity of the composites. OS Density (g/cm3 ) Open porosity (%)
1.440 19.21
SCFs 1.453 19.01
SNDs 1.474 14.12
The compressive strength-strain and flexural strength-strain curves of the composite samples at room temperature are shown in Fig. 6 (a) and (b). One can see that the
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compressive strength (97.833 MPa) and flexural strength (21.342 MPa) of SNDs is the highest and increase by 78.2% and 67.1% to that of OS, which is a breakthrough when the content of NDs is only 0.05 wt%. Certainly, the compressive strength (75.1 MPa) and flexural strength (16.266 MPa) of SNDs increase by 36.8% and 27.4% to that of OS. Compared with that of OS, the compressive strength and flexural strength of SCFs and SNDs increase, which proves that fillers can promote the improvement of the mechanical properties. However, the different reinforcement mechanisms of CFs and NDs on the composites lead to the different properties.
Fig. 6. (a) Compressive strength-strain and (b) flexural strength-strain curves of samples.
Fig. 7. SEM of the fractured surface of (a) OS, (b) SCFs, and (c) SNDs.
In order to study the mechanism of the filler in the composites, the fractured surface of three bulks was characterized, as shown in Fig. 7. Carbon-matrix composites have obvious internal cracks, with an average width of ~0.5 µm and a length of >20 µm, which is an important factor affecting the performance from Fig. 7(a). Figure 7(b) shows the SEM of the composite of SNDs. One can see that CFs plays a role in connecting the matrix and filling the cracks [13]. Moreover, the existence of CFs changes the form of material damage, from the original damage mainly caused by crack propagation to that caused by the interface between the matrix and CFs. However, the internal cracks are still obvious, and the crack size changed a little. Surprisingly, in Fig. 7(c), significant changes have taken place in the internal structure of SNDs, and composites tend to be more integrated. At the same time, the number and size of cracks are reduced, which is mainly due to the expansion of NDs during the sintering process to fill the cracks. According to the Griffith micro-crack equation, the relationship between strength (σ c)
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and crack size (c) was studied, which is given by Eq. (2) [9]: 2Gr σc = (2) πc where G is the elastic modulus, r is the surface energy per unit area and c is the length of the crack, which means that can improve the strength by shortening the crack length. The presence of NDs made the matrix form a better whole, and the crack decreased, which significantly hindered cracks growth and promoted the deflection of the crack propagation path. 3.3 Thermal and Electrical Conductivity As shown in Fig. 8(a), the thermal conductivity of SNDs (2.809 W/(m·K)) is the highest, which increases by 16.03% to that of OS. This is mainly due to the excellent thermal conductivity of NDs itself. Although graphitization of NDs occurs during high-temperature sintering, the thermal conductivity of composites is maintained. Moreover, NDs fills the internal cracks, which effectively reduces the phonon scattering caused by the cracks [14, 15]. However, it is possibly difficult to connect a thermal conductivity network for SCFs with the CFs content of 1 wt%, which results in the thermal conductivity of SCFs being only slightly higher than that of OS [16].
Fig. 8. (a) Thermal conductivity and (b) electrical conductivity of composite samples.
Figure 8(b) shows the electrical conductivity of the composite samples. One can see that it has not changed significantly among three composite samples. The electrical conductivity of carbon-matrix composites mainly comes from flake graphite, but internal cracks restrict the electrical conductivity. The effect of CFs as a filler on electrical conductivity is similar to its effect on thermal conductivity. Interestingly, NDs itself is insulating materials, but the conductivity of SNDs has improved a little. This is mainly due to a certain degree of graphitization phase transition in NDs, and graphite have excellent electrical conductivity [17]. Moreover, NDs reduce material cracks, which makes the electrical conductive network more perfect.
4 Conclusion In summary, NDs and surface alkali-modified CFs as fillers successfully prepared carbonmatrix composites with enhanced performance. NDs improves the internal cracks of
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the material better than CFs due to the different reinforcement mechanisms of them. The compressive strength (97.833 MPa) and flexural strength (21.342 MPa) of SNDs increased by 78.2%, and 67.1% to that of OS, respectively. The expansion of NDs during the sintering process can effectively fill the pores, increase the density and reduce the open porosity, which significantly hinders cracks growth and changes the crack propagation path. Moreover, the integrated structure of SNDs is more conducive to the formation of a connected thermal network and reduces phonon scattering to improve the thermal conductivity (2.809 W/(m·K)). At the same time, a certain degree of graphitization is also conducive to maintaining the electrical conductivity of composites (141.2 S/cm). This is the main reason why the performance of SNDs is better than that of SCFs. The low-cost and efficient method with low content NDs for preparing composites can effectively enhance the properties of the carbon-matrix composites, which provides a chance for further wide application in aerospace, transportation, and so on. Acknowledgment. This project was supported by the National Natural Science Foundation of China (No. 51837009, 51807167, and U19A20105).
References 1. Gao, V., Song, H., Chen, X.: Self-sinterability of mesocarbon microbeads (MCMB) for preparation of high-density isotropic carbon. J. Mater. Sci. 38, 2209–2213 (2003). https://doi.org/ 10.1023/A:1023740517269 2. Liu, X., Yang, Z., Xiao, S., et al.: Multi-physics analysis and optimisation of high-speed train pantograph-catenary systems allowing for velocity skin effect. High Volt. 5, 654–661 (2020). https://doi.org/10.1049/hve.2019.0388 3. Tu, C., Hong, L., Song, T., et al.: Superior mechanical properties of sulfonated graphene reinforced carbon-graphite composites. Carbon N Y 148, 378–386 (2019). https://doi.org/10. 1016/j.carbon.2019.04.001 4. Delport, M.R., Badenhorst, H.: Production of a self-adhering mesophase powder from anthracene oil for low pressure forming of graphite artefacts. J. Mater. Sci. 51(13), 6309–6318 (2016). https://doi.org/10.1007/s10853-016-9927-2 5. Gao, Y., Song, H., Chen, X.: Preparation of C/C composite using mesocarbon microbeads as matrix. J. Mater. Sci. Lett. 21, 1043–1045 (2002). https://doi.org/10.1023/A:1016025127367 6. Zambrzycki, M., Tomala, J., Szczypta, A.F.: Electrical and mechanical properties of granularfibrous carbon-carbon composites with recycled carbon fibres. Ceram. Int. 44, 19282–19289 (2018). https://doi.org/10.1016/j.ceramint.2018.07.154 7. Song, Y., Zhai, G., Shi, J., Guo, Q., Liu, L.: Carbon nanotube: carbon composites with matrix derived from oxidized mesophase pitch. J. Mater. Sci. 42(22), 9498–9500 (2007). https://doi. org/10.1007/s10853-007-2049-0 8. Zhang, F., Fan, K., Saba, F., Yu, J.: Graphene reinforced-graphitized nanodiamonds matrix composites: fabrication, microstructure, mechanical properties, thermal and electrical conductivity. Carbon N Y 169, 416–428 (2020). https://doi.org/10.1016/j.carbon.2020.08.011 9. Ran, J., Lin, K., Yang, H., Li, J., Wang, L., Jiang, W.: A new family of carbon materials with exceptional mechanical properties. Appl. Phys. A 124(3), 1–7 (2018). https://doi.org/ 10.1007/s00339-018-1691-5 10. Méndez, A., Santamaría, R., Granda, M., Menéndez, R.: Structural changes during pitchbased carbon granular composites carbonisation. J. Mater. Sci. 43, 906–921 (2008). https:// doi.org/10.1007/s10853-007-2214-5
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11. Fanjul, F., Granda, M., Santamaría, R., Menéndez, R.: The influence of processing temperature on the structure and properties of mesophase-based polygranular graphites. J. Mater. Sci. 39, 1213–1220 (2004). https://doi.org/10.1023/B:JMSC.0000013877.95022.2d 12. Lin, Y., Chen, J., Jiang, P., Huang, X.: Wood annual ring structured elastomer composites with high thermal conduction enhancement efficiency. Chem. Eng. J. 389, 123467(2020). https:// doi.org/10.1016/j.cej.2019.123467 13. Feng, W., Qin, M., Lv, P., et al.: A three-dimensional nanostructure of graphite intercalated by carbon nanotubes with high cross-plane thermal conductivity and bending strength. Carbon N Y 77, 1054–1064 (2014). https://doi.org/10.1016/j.carbon.2014.06.021 14. Feng, W., Qin, M., Feng, Y.: Toward highly thermally conductive all-carbon composites: structure control. Carbon N Y 109, 575–597 (2016). https://doi.org/10.1016/j.carbon.2016. 08.059 15. Liu, Z., Guo, Q., Shi, J., et al.: Graphite blocks with high thermal conductivity derived from natural graphite flake. Carbon N Y 46, 414–421 (2008). https://doi.org/10.1016/j.carbon. 2007.11.050 16. Hu, X.B., Zhao, B.Y., Hu, K.A., et al.: Thermal behaviors of mesocarbon microbeads and physical properties of carbon plates. J. Mater. Sci. 9, 1735–1741 (2004) 17. Oluwalowo, A., Nguyen, N., Zhang, S., et al.: Electrical and thermal conductivity improvement of carbon nanotube and silver composites. Carbon N Y 146, 224–231 (2019). https:// doi.org/10.1016/j.carbon.2019.01.073
Study on the Mechanism of Electrodynamic Behavior of Metal Particles in DC GIL Han Cheng1 , Wei Wei2(B) , Li Zhang1 , Tong Zhao1 , and Liang Zou1 1 Shandong University, Jinan 250061, China 2 Jiaxing Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd, Jiaxing
314000, China [email protected]
Abstract. Forced motion of linear metal particles in the DC GIL can reduce the insulation performance of the GIL and affect the reliable and safe operation of the DC transmission system because of air-gap breakdown or insulator flashover along the surface, which. To study the electrodynamic behavior mechanism of linear metal conductive particles in DC GIL, a free particle experimental platform were built, and a particle electrodynamic model under DC was established. Through a combination of experiment and simulation, the charging characteristics, lift and motion characteristics of linear metal particles, and the gas-gap breakdown characteristics caused by particle movement are obtained, and the reasons for the phenomenon of lift and movement of particles are explained from a microscopic point of view. Keywords: DC GIL · Linear metal particles · Motion · Electrodynamic behavior
1 Introduction Gas insulated transmission line (GIL) is has broad development prospects due to its high voltage level and transmission capacity [1, 2]. The metal particles will be producedin the actual production, assembly, transportation and operation of the GIL, which can cause air-gas breakdown or insulator flashover along the surface [3, 4]. The selection of coating materials and coating methods is useful for revealing the failure of gas insulation caused by moving metal particles. The research on the movement characteristics of metal particles mostly adopts experimental methods, and there is a lack of microscopic analysis of the typical movement phenomenon of particles and the mechanism of gas insulation breakdown caused by the movement of particles. The current model calculations are mostly spherical particles [5, 6], and there are few studies on the relationship between particle charge and force on the particle and the surface charge. In fact, the linear conductive particles have the strongest electric field distortion ability and the greatest threat to the equipment insulation performance [7, 8], the existing research is lack of research on the mechanism of space-time interaction between metal particles and space charge under multi-field coupling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 900–906, 2022. https://doi.org/10.1007/978-981-19-1528-4_92
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2 Electrodynamic Behavior of Linear Particles Since the internal electric field of the coaxial cylindrical GIL is mostly a slightly uneven electric field, it is only uneven electric field in the vicinity of the insulator support, the end of the high-voltage conductor, the metal particle driving device and the joints, etc., so flat electrodes that are convenient for analysis and research can be used. Simulate GIL [9]. Therefore, this article uses a flat-plate electrode in the air for experiments. The experiment uses linear particles with a radius of 0.25mm with different lengths. The linear particle motion and the resulting air gap discharge are related to the length and voltage polarity: firstly, one end of the particle will be lifted (Fig. 1a). As length increases, under positive polarity, jump up and down movement, multiple collision breakdown (Fig. 1b, Fig. 2a) → lower plate rotation, increased voltage breakdown (Fig. 1c, Fig. 2b and Fig. 2c) → down The pole plate is upright and penetrates directly (Fig. 1d, Fig. 2d); under negative polarity, jump and go back and forth (Fig. 3a) → “Flying” (Fig. 3b) → Upper pole plate rotation (Fig. 3c) → Upper pole plate upright, There is a high probability of breakdown as soon as it moves (Fig. 3d, Fig. 4).
Fig. 1. Movement of linear particles of different lengths under positive polarity.
Fig. 2. Air-gap discharge caused by linear particles of different lengths under positive polarity.
As voltage increases, if the voltage reaches the lifting voltage of the particle, the particle moves vertically with one end as the fulcrum. In this process, the particle ends are accompanied by violent partial discharge. When the particle approaches the vertical state, serious electric field distortion is caused, and at this time, the air gap discharge breaks down, and the particle acts as a part of the air gap conductive channel. In the process of linear metal particles moving vertically, the electric field distortion is weakened due to the decrease of their length, and the air gap will not break down immediately. At this point, partial discharge will occur at the end of the particle due to electric field
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distortion, and partial discharge will ionize the gas molecules in the air into positive ions, electrons and negative ions and other space charges and form ion wind. Under the action of gravity, electric field force and ionic wind, the linear metal particles rotate near the lower electrode.
Fig. 3. Movement of linear particles of different lengths under negative polarity.
Fig. 4. Air-gap discharge caused by linear particles of different lengths under negative polarity.
Under negative polarity, the particle is lifted and moves vertically with one end as the fulcrum. When the particle is close to the vertical state, it will directly lead to air gap breakdown discharge Electrodynamic simulation of linear metal particles. Particles have a stronger distortion effect on the electric field, which will lead to air gap breakdown in the process of vertical movement. At that time, the particle leaves the lower electrode and moves up to the electrode, collides with the upper electrode, and then changes the direction of motion to move down. After several collisions with the electrode through the air gap, the accumulation of space charge leads to the breakdown of the air gap. The particle is lifted from the right side of the lower electrode and breaks down when it moves to the left side close to the lower electrode. At that time, the motion of particles has two forms, one is through the air gap motion; The other is the phenomenon of flying fireflies, that is, after the particles collide with the upper electrode, they move downward and rotate. When moving in mid-air, they will change direction and continue to move upward, and there is a process of repeated collision between particles and the upper electrode.
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3 Electrodynamic Simulation of Linear Particles 3.1 Initiation Analysis Experiments have observed that regardless of the positive or negative polarity, the linear particles tend to lift up first at one end, as shown in Fig. 1(a). The simulation settings are the same as the 2.1 experiment. After one end of the linear particle is lifted, the polarity of the applied voltage has a greater influence on the movement of the particle. In the initial stage of movement, particles with positive polarity have greater “vibration” compared to negative polarity: particles with positive polarity jump or rotate and tilt around the direction of the electric force, as shown in Fig. 2(b) -(d); Under negative polarity, all jumps directly, as shown in Fig. 3(a). When the lifting voltage is reached, one end of the linear particle is lifted, and there is a small gap between the other end and the bottom plate. The small gap produces serious electric field distortion, causing the bottom end of the particle and the bottom plate to produce micro-discharge and equipotential [10]. Therefore, the particles can be regarded as protrusions on the pole plate, which is equivalent to “standing” particles. The applied voltage is as follows. With voltage increases, the initial stage, the electric field distribution at the electrode axis under the positive and negative polarities is shown in the figure below:
(a) positive polarity
(b) negative polarity
Fig. 5. Electric field intensity along the electrode axis at the initial stage.
In the positive polarity, the particles are negatively charged, and electrons formed on the surface immediately enter the strong electric field, causing electrons to collapse, and the slower positive ions move toward the particles. Therefore, the positive space charge near the particles is more concentrated, causing the electric field to be distorted, which is easy to satisfy self-sustainability. The discharge conditions are converted to streamers to form corona discharge. From the perspective of field emission, the Schottky effect makes some of the electrons in the conduction band in the metal obtain energy from the external electric field, and the metal surface barrier is distorted and tunneling occurs.
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The barrier penetrates and the electrons are separated from the surface, which forms field emission [10]. Under the positive and negative polarity, the electric field intensity distribution is shown in the leftmost figure in Fig. 5. The electric field distortion at the top of the particles is the most serious. Therefore, under the positive polarity, the particles are negatively charged, and the barrier at the end with the most severe field intensity distortion is very thin., Electrons easily penetrate the barrier and leave the surface to form field emission. Under the action of a strong electric field, electrons collapse and corona discharge occurs. According to Townsend discharge theory [15], α ionization and β ionization are basically the same. The corona discharge makes the particle charge and charge redistribute on the particles, and the rotating torque forces the particles to move. The stronger the corona effect, the more uneven the charge distribution and the more obvious the movement of the particles. 3.2 Analysis of Air Gap Breakdown Caused by Motion Corona discharge causes a large number of ion jets to move near the electrode with relatively large curvature. The ion jets strongly disturb the flow of the surrounding fluid, forming a fluid movement from the electrode with greater curvature to the electrode with less curvature, which is called ion wind. “Electrically induced secondary flow” [13].
(a) positive polarity
(b) negative polarity Fig. 6. Electron density.
As shown in the figure above, during the discharge process, there is a relatively large electric field intensity near the corona linear particles. The electric field force repels the electrons generated by the electron avalanche, which quickly moves them away from the corona pole, so that the particle near the particle always maintains a The large potential gradient ensures that the corona discharge can continue. The vertical particles and the flat electrode form a needle plate electrode structure with an extremely uneven electric field. There is a difference in the electric field strength between the corona zone and the non-corona zone, and the electron density is relatively high. With a large gradient difference, the electrons entering the non-corona zone with weak field strength will
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weaken the electric field force and reduce the acceleration. Under the action of collision resistance and viscous resistance, their speed will quickly tend to a constant value, and the charged ions will be The movement under the action of the electric field force will drive the air flow to form an ion wind flow in the discharge space, and the space electric field and the space ion density together affect the size of the ion wind speed [14]. The area with high corona electric field intensity is mainly concentrated in the conical area with the linear particles as the apex and the plate electrode as the bottom surface. Under the effect of ion collision and extraction, the electrode air gap forms an emissive shape that approximates a cone with the axis as the centerline. In the high-speed particle jet space, the speed of the ion wind decreases from the axis outwards, with a straight flow in the middle and a vortex flow on both sides. Due to the particle collision theory and the point-to-point theory, the area with higher electric field intensity in the corona-producing needle plate electrode is mainly concentrated in the conical area with the needle electrode as the apex and the plate electrode as the bottom surface, such as As shown in the figure, under the effect of ion collision and extraction, the electrode air gap forms an emission-like high-speed particle jet space with the axis as the centerline, which is similar to a cone. The ion wind speed decreases outward from the axis and flows in a straight line in the middle. It flows in a vortex on both sides. Under the action of corona, the high-speed ion wind in the cone area causes the particles to rotate and tilt with the direction of the power line as the axis when they “stand”. The movement of the particles reduces the distance between the needle plate electrodes, increases the unevenness of the electric field, and causes the difference in wind speed. When the particles move upward, they make irregular rotations.
4 Conclusion Linear lifting voltage is dependent on material and radius, independent of length and voltage polarity. With the change of length and voltage polarity, the movement of linear particles and the resulting gas-gap breakdown show obvious laws: one end of the particle is lifted first, and as the length increases, under the positive polarity, it jumps back and forth and repeatedly collides. Breakdown → bottom plate rotation, increased voltage breakdown → bottom plate upright, direct breakdown; under negative polarity, jump up and down movement → “flying firefly” → upper plate rotation → upper plate upright, a high probability movement will break down. The phenomenon of lift and movement of linear particles, such as lifting one end and “standing", and the phenomenon of air gap breakdown caused by movement such as collision breakdown and direct breakdown are related to the polarity effect of the corona. The linear metal particles have a small radius and increase in length, which will easily lead to air gap breakdown; the irregular shape of the linear metal particles makes the electric field distortion stronger and the polarity effect is more obvious. The corona polarity effect causes the lifting and movement of linear particles under positive and negative polarities and the movement-induced air gap breakdown characteristics to show obvious laws.
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References 1. Cookson, A.H., Farish, O., Sommerman, G.M.L.: Effect of conducting particles on AC corona and breakdown in compressed SF6. IEEE Trans. Power Appar. Syst. 91(4), 1329–1338 (1972) 2. Wang, J., et al.: Research progress on metal particle contamination in GIS/GIL. J. Power Energy Syst. 10(3), 418–424 (2019) 3. Li, Q.M., et al.: Characteristics of partial discharges caused by free metal particle and influencing factors in DC gas insulated line. High Voltage Eng. 43(2), 367–374 (2017). (in Chinese) 4. Li, Q.M., et al.: Review on metal particle contamination in GIS/GIL. High Voltage Eng. 42(3), 849–860 (2016) 5. Zhang, Y., et al.: Molecular dynamics modeling of sintering phenomena and mechanical strength of metal particles. Multiscale Model. Additively Manuf. Metals 15(1), 39–53 (2020) 6. Lu, F.C., et al.: Influence mechanism of dielectric coated electrodes on metallic particle lift-off in SF6 gas under DC voltage. Trans. Chin. Electr. Soc. 32(13), 239–247 (2017) 7. Ni, X.R., et al.: Polarity effect of DC GIL electrode coating on particle suppression. High Voltage Eng. 44(8), 2695–2703 (2018) 8. Li, X., Li, J.: Acoustic method for multiple free metallic particle recognition in GIS/GIL. IEEE Trans. Power Delivery 6(99), 1 (2021) 9. Gao, Y., et al.: Surface charge accumulation on a real size epoxy insulator with bouncing metal particle under DC voltage. IEEE Trans. Plasma Sci. 99, 1–10 (2021) 10. You, Z., Liu, J.: Review of motion and discharge characteristics of wire metallic particle in dc gil. Gaoya Dianqi/High Voltage Apparatus 53(10), 36–43 (2017) 11. Wang, J., et al.: Experimental studies on the motion and discharge behavior of free conducting wire particle in DC GIL. J. Electr. Eng. Technol. 12(2), 858–864 (2017) 12. Zhan, Z., et al.: Motion characteristics of metal powder particles in AC GIL and Its trap design. IEEE Access 9, 68619–68628 (2021) 13. Li, Q.M., Jian, W., Botao, L.: Review on metal particle contamination in GIS/GIL. High Voltage Eng. 42, 849–861 (2016) 14. Vaulina, O.S., Sametov, E.A., Lisin, E.A.: Spectral characteristics of charged particles in limited chain structures. J. Exp. Theor. Phys. 131(2), 361–373 (2020). https://doi.org/10. 1134/S1063776120060072
Analysis of the Surrounding Magnetic Field Distribution Under the Parallel Condition of AC and DC Cable Lines Jianjun Yang1,2 , Zhijie Zhu3 , Jingyi Li1,2 , Nana Duan3(B) , Ke Wang1,2 , Shuhong Wang3 , and Xuehuan Wang3 1 Key Laboratory for Far-Shore Wind Power Technology of Zhejiang Province,
Hangzhou 311122, China 2 Powerchina Huadong Engineering Corporation Limited, Hangzhou 311112, China
{yang_jj,li_jy3,wang_k2}@hdec.com
3 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
{zzj1264,w1406668657}@stu.xjtu.edu.cn, [email protected], [email protected]
Abstract. In recent years, offshore wind power has entered the stage of largescale development, and the submarine cables are laid in parallel. In order to study the distribution of magnetic field around AC and DC cable laying together, a complete two-dimensional model of submarine cable tunnel is established, and the magnetic field simulation analysis is carried out by using finite element software in this paper. The results show that when AC cable and DC cable are laid together, the magnetic field generated by AC cable will affect the magnetic field around DC cable. In order to reduce the influence of electromagnetic induction, the distance should be increased as far as possible, and the AC cables should be arranged in triangle. Keywords: Cable · Electromagnetic induction · Finite element method
1 Introduction With the increasing of electricity load and the development of urbanization in China, more and more electricity loads need to be transmitted by power cables, the transmission space of underground cables is becoming more and more tight [1–3] In order to save land resources, AC and DC cables are laid in a common trench [4, 5]. Although the cable structure has better shielding performance than bare conductors, due to the limited space of underground cable tunnels, the distance between the cable lines is shorter, and the mutual coupling between the lines is also more obvious, so the problems caused cannot be ignored, which needs further research. Some colleage team studied the electromagnetic coupling between AC and DC in AC and DC lines erected on the same tunnel, and analyzed the capacitive and inductive coupling effects between AC and DC transmission lines [6, 7]. The United States E. V. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 907–914, 2022. https://doi.org/10.1007/978-981-19-1528-4_93
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Larsen team analyzed the impact of the power frequency induced current generated by the AC transmission line on the parallel DC transmission line on the DC transmission control system and protection system, as well as the magnetic bias current in the converter transformer for the parallel erection of AC and DC transmission lines phenomenon [8]. Randy Horton and others in the United States established a simulation model for the mutual induction between AC parallel erected transmission lines, evaluated the impact of electromagnetic coupling on the working environment of maintenance personnel, and put forward relevant safety recommendations and measures. Zeng Rong of Tsinghua University and others analyzed the AC power frequency current generated by the AC transmission line through electromagnetic induction on the DC transmission line, and the non-power frequency component formed by the power frequency current after passing through the converter equipment. The influence of frequency component on converter transformer. In this paper, a two-dimensional overall model of the submarine tunnel is established and simulated by the finite element method to obtain specific electromagnetic effects and improvement measures. which have guiding significance for the actual laying of cables.
2 Tunnel Model and Calculation of Cable Magnetic Field Figure 1a shows the overall layout of the submarine cable tunnel. The left side of the tunnel is a ± 320 kV DC cable, and the right side are two 220 kV three-phase AC cables A1 , B1 , C1 and A2 , B2 , C2 . The outside is the size which unit is cm. Figure 1b shows the internal structure of the cable.
B2
A2
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C2 C1 B1
A1
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a) The layout of cable tunnel
b) Cable structure
Fig. 1. Cable tunnel layout
The formulas of magnetic induction intensity given in B = μ0 I /2π r
(1)
where μ0 is vacuum permeability, I is the current in the cable, r is the distance from the line core to the measured position. Assuming that the current reference direction is the z direction, the magnetic induction intensity generated by a single core wire with a current of I k are [9] Bk =
μ0 Ik μ0 Ik = 2π r 2π (x − xi )2 + (y − yi )2
(2)
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where (x, y) are the coordinates of the measured point, (x i , yi ) are the coordinates of the cable center. In the same way, the magnetic field of the three-core cable can be calculated, the three-phase current is expressed as a transient form, and the phase angle of phase A is ϕ, the three-phase current can be expressed as ⎤ ⎡ cos ϕ ⎡ ⎤ iA ⎢ 2 ⎥ ⎢ ⎥ √ ⎢ cos(ϕ − π )⎥ ⎥ ⎢ (3) ⎣ iB ⎦ = 2I ⎢ 3 ⎥ ⎦ ⎣ 4 iC cos(ϕ − π ) 3 where iA , iB , iC is three-phase alternating current. It can be deduced from the superposition principle that the horizontal component of the composite magnetic induction intensity of the three-phase core wire at the measuring point is Bx =
3
Bix = B1x + B2x + B3x
i=1
μ0 I y1 − y0 y2 − y0 2 4 y3 − y0 = cos ϕ + cos(ϕ − π ) + cos(ϕ − π )] [ 2 2 2 2π 3 3 r1 r2 r3
(4)
Introducing a variable yi − y yi − y = 2 2 + (x − x)2 (y − y) ri i i
Si =
(5)
by introducing the variable S i , the horizontal component of the magnetic induction intensity can be expressed as Bx =
3 i=1
√ μ0 2I 2 4 [S1 cos ϕ + S2 cos(ϕ − π ) + S3 cos(ϕ − π )] Bix = 2π 3 3
2 4 2 4 = K[(S1 + S2 cos π + S3 cos π ) cos ϕ + (S2 sin π + S3 sin π ) sin ϕ] 3 3 3 3 = K[M cos ϕ + N sin ϕ]
(6)
in order to obtain the expression of the vertical component of the magnetic induction intensity, a variable is introduced in the same way Wi = By =
3 i=1
Biy =
xi − x xi − x = 2 (yi − y)2 + (xi − x)2 ri
(7)
√ μ0 2I 2 4 [W1 cos ϕ + W2 cos(ϕ − π ) + W3 cos(ϕ − π )] 2π 3 3
= K[(W1 + W2 cos
2 4 2 4 π + W3 cos π ) cos ϕ + (W2 sin π + W3 sin π ) sin ϕ] 3 3 3 3
= K[M1 cos ϕ + N1 sin ϕ]
(8)
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The resultant magnetic induction intensity of the measuring point is
B = Bx2 + By2 = K[(M cos ϕ + N sin ϕ)2 + (M1 cos ϕ + N1 sin ϕ)2 ]1/2
(9)
√
where K = μ02π2I . In the actual situation, due to the different materials around the cable, there will be more factors to consider, and the expression may be more complicated, but the basic method is the same.
3 Simulation Analysis The finite element method is used to analyze the magnetic field distribution generated by the cable. The vector magnetic potential A satisfies the following formulas. ⎧ ∇ ×H =J ⎪ ⎪ ⎨ B=∇ ×A ⎪ E = −∂A/∂t ⎪ ⎩ J = ∂E
(10)
Y-axis position
Y-axis position
where A is the magnetic vector potential, H is the magnetic field intensity, J is the current density. Figure 2a shows the overall magnetic density distribution without AC cable excitation. The current of the two DC cables is ±1617 A, and the current of the AC cable is zero. At this time, the magnetic density is concentrated near the DC cable, and the magnetic density is very small in other parts of the tunnel and the AC cable. Figure 2b shows the overall magnetic density distribution with AC cable excitation. The effective value of AC cables current is 800 A. Due to the influence of the AC cable, the magnetic density distribution of the DC cable has changed, and the magnetic density of the AC cable has changed greatly compared with Fig. 2a.
X-axis position
X-axis position
a) Without excitation of AC cable
b) With excitation of AC cable
Fig. 2. The overall magnetic density distribution of the tunnel
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B (mT)
B (mT)
Figure 3a and b respectively show the DC cable magnetic density distribution along the diameter when the AC cable is excited or not. The magnetic density is almost nonexistent at the center of the cable, while the magnetic density is the largest at the junction of the conductor shielding layer and XLPE. The maximum magnetic density in Fig. 3a is 11.56 mT, and the maximum magnetic density in Fig. 3b is 13.25 mT. Due to the existence of the AC cable, the maximum magnetic density of the DC cable has increased by 14.6%. And the AC cable will have a great impact on the magnetic density distribution around the DC cable.
X-axis Position (mm)
X-axis Position (mm)
a) Without excitation of AC cable
b) With excitation of AC cable
Fig. 3. Magnetic density distribution of DC cable
B (mT)
In order to compare the influence of different arrangement of AC cables on the magnetic density distribution of DC cables, the AC cables are changed from triangular arrangement to horizontal arrangement, and the simulation results are shown in Fig. 4 when the AC and DC cables are acting at the same time.
X-axis Position (mm)
Fig. 4. DC cable magnetic density distribution after changing the arrangement of AC cables
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Current magnitude (A)
It can be seen from Fig. 4 that when the arrangement of AC cables becomes horizontal, the magnetic density distribution trend along the diameter of the cable remains unchanged, but the maximum magnetic density becomes 13.77mT, which increased by 3.9% comparing to Fig. 3b. From the above comparison, it can be seen that the magnetic field generated by the different arrangement of AC cables has different effects on the DC cable. In addition, we also studied in the case of triangular arrangement, The magnitude of the induced current generated by the AC cable excitation at the metal sheath of the DC cable. Since the DC cable flows through DC, the metal sheath of the DC cable will not induce current, the changing magnetic field generated by the AC cable will generate a certain induced current at the position of the metal sheath of the DC cable. The presence of this current will increase the temperature of the DC cable. And it will reduce the current carrying capacity of the DC cable and reduce the life of the DC cable. Therefore, it is necessary to study the magnitude of the induced current generated by the AC cable at the DC cable. We changed the distance between AC and DC cables and studied the magnitude of the induced current at different distances.
Time(s)
Fig. 5. The magnitude of the induced current when the AC and DC cables are 0.2 m apart
Figure 5 shows the magnitude of the induced current at the metal sheath of the DC cable when the AC and DC cables are 0.2 m apart. It can be seen from the figure that the peak value of the induced current at this time is 12 A, and this current will produce a large resistance heat loss in the metal sheath. Next, increase the distance of the AC and DC cables and observe the magnitude of the induced current again.
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Analysis of the Surrounding Magnetic Field Distribution
Time(s)
Fig. 6. The magnitude of the induced current when the AC and DC cables are 0.8m apart
Current magnitude (A)
Figure 6 shows the magnitude of the induced current when the distance between the two increases to 0.8 m. It can be seen from the figure that the peak value of the induced current has been reduced to 4.5 A. Continue to increase the distance between AC and DC cables.
Time(s)
Fig. 7. The magnitude of the induced current when the AC and DC cables are 1.5 m apart
Figure 7 shows the magnitude of the induced current when the distance between the two is 1.5 m. At this time, the current amplitude has been reduced to 2.4 A.
4 Conclusion In this paper, a two-dimensional model of the submarine tunnel is established, the magnetic density distribution in the tunnel is calculated using FEM, and the magnetic density distribution generated by the AC cables on the DC cables is obtained. By changing the arrangement of the AC cables, it is found that when the AC cables are arranged in a
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triangle the effect is reduced. By changing the distance between AC and DC cables, it is found that as the distance between AC and DC cables increases, the induced current at the DC cable decreases significantly.
References 1. Yan, Y.., Ting, Z., Zhaohui, C.: The electromagnetic field environment in the tunnel for the common laying of AC and DC cables. GuangDong Electric Power 31(12), 20–26 (2019) 2. Yan, Y., Ting, Z., Weiwei, Z.: Study on the influence of common trench for AC and DC cables. FuJian Soc. Electr. Eng. 21(14), 294–299 (2018) 3. Hu, M., Xie, S., Zhang, J., Ma, Z.: Desing selection of DC & AC submarine power cable for offshore wind mill. In: 2014 China International Conference on Electricity Distribution, pp. 1675–1679 (2014) 4. Niu, H., Zeming, C., Tianyou, Y., Xingjia, T., Cheng, N.: Tunnel multi-loop cable optimization based on cultural gene algorithm. J. South China Univ. Technol. 49(10), 141–150 (2021) 5. Li, Q., Chengke, Z., Hang, W., Zhi, T., Zhanran, X., Meng, C.: The influence of the shunting of underground multi-cable lines on the operating life. High Voltage Technol. 45(05), 1576–1583 (2019) 6. Zhu, T., et al.: Study on the electromagnetic field in HVDC/AC hybrid submarine cable tunnel. In: 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), pp. 1–4 (2018) 7. Herre, L., Wouters, P., Steennis, F., de Graaff, R.: On the electromagnetic coupling of AC and DC circuits on hybrid transmission towers. In: 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6 (2016) 8. Maneepeth, P., Thararak, P., Jirapong, P., Karaaom, C.: Mitigation of induced currents and overvoltages in metallic sheath of 115 kV underground cable using sheath voltage limiter and parallel ground continuity conductor. In: 2021 9th International Electrical Engineering Congress (iEECON), pp. 61–64 (2021) 9. Xu, Y.: Research on Electromagnetic Environment of 35kV Submarine Photoelectric Composite Cable. Dalian University of Technology (2018)
Parameter Design and Simulation of Unsymmetrical 18-Pulse Phase-Shifting Autotransformer Jiahui Zhang1 , Dongsheng Yuan2 , and Shuhong Wang1(B) 1 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China [email protected], [email protected] 2 Xi’an University of Technology, Xi’an, China [email protected]
Abstract. Aiming at the requirements of small size, high power density and low harmonic content of rectifier transformers for aircraft, unsymmetric 18-pulse phase-shifting autotransformer is selected as the research object. Its working principle and parameter design method are introduced. Based on C# language, the user interface parameter design and finite element model generation software of this type of transformer is developed, which realizes one-click parameter calculation and finite element modeling analysis and improves the efficiency of preliminary design analysis. The design and analysis of an 18-pulse phase-shifting autotransformer with a rated power of 20 kW, a rated voltage of 100 V and frequency of 400 Hz is completed based on the software. The results show that the software’s parameter calculation results are reliable and the finite element model generation is accurate and efficient. The transformer is more effective to suppress harmonic wave. Keywords: Phase-shifting autotransformer · Parameter design · Finite element modeling analysis
1 Introduction More-electric aircraft (MEA) refers to the fact that most of the secondary power requirements of the aircraft are provided in the form of electricity, and it has become a major technical solution in the modern aviation industry [1]. Many functions traditionally operated by hydraulic and pneumatic have been updated to electrification, which reduces the quality and overall size of the system and improves the overall reliability and maintainability. MEA usually uses a high-power rectifier system to convert AC power to DC power. The nonlinearity and time-varying nature of power electronic converters will bring a large number of harmonics to the entire power supply system, which gives the stability and quality of the MEA power system new challenges [2]. Two methods are usually used to solve the harmonic problem of high-power rectification systems: one is to install a harmonic compensation device. The other is to use PWM © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 915–922, 2022. https://doi.org/10.1007/978-981-19-1528-4_94
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rectification or multi-pulse rectification [3]. The PWM rectifier works at high frequency so that the switching loss is large when the power is high making the power conversion efficiency is not high. Multi-pulse rectification scheme has lower cost and higher efficiency and reliability. The ability of this scheme to suppress harmonics depends on the number of rectified pulses. The relationship between number of rectified pulses and Theoretical value of total harmonic distortion is shown in Table 1. Table 1. Theoretical value of total harmonic distortion Number of rectified pulses
Harmonic component
Theoretical value of total harmonic distortion
6 pulses
(6K ± 1)
31.08%
12 pulses
(12K ± 1)
15.2%
18 pulses
(18K ± 1)
10.1%
24 pulses
(24K ± 1)
6.8%
The phase-shifting transformer is the key equipment to realize the multi-pulse rectification scheme. Through the proper connection of the primary winding and the secondary phase-shifting winding, multiple sets of three-phase voltages with a certain phase difference are generated, and then multiple sets of three-phase rectifier bridges are connected to achieve multi-pulse rectified voltage output. The common phase shifting methods are Extension Triangle Method and Zigzag Method [4]. When using the above two methods to shift the phase, the output voltage of multiple rectifier bridges will have a voltage difference, and a balancing reactor needs to be added to the rectifier output end, which increases the volume and complexity of the system [5]. Aircraft rectifier transformers not only require low total harmonic content (THD) of the input current, but also have high requirements for volume, weight, equipment stability and reliability. Generally, the phase-shifting transformer of aircraft adopts autotransformer to reduce the required transformer capacity, thereby reducing the volume and weight. Literature [6] analyzed the working principle of the asymmetric 18-pulse rectifier transformer system and designed a prototype, the efficiency of which can reach 98.2%, and the THD is only 8.49%. Literature [7] developed a DP 18-pulse phase-shifting autotransformer based on Labview. The design software reduces the design complexity. Literature [8] proposed a step-down 18-pulse autotransformer, which realized a wide-range step-down adjustment of the output voltage through the connection of the primary side extension winding of each phase and the secondary side phase-shifting winding. Literature [9] used the finite element method to analyze the transient characteristics of the AC side three-phase current of the symmetrical 18-pulse rectifier transformer and calculated the loss of the amorphous alloy iron core. At present, researches on phase-shifting transformers mainly focus on the new topological structure, the parameter design of phase-shifting transformers with different capacities and voltage levels, etc. The verification method of the final design parameters mainly adopts Simulink for circuit time-domain simulation or manufacturing prototype
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for verification. Circuit simulation is not accurate enough for the analysis of phaseshifting transformers with complex structures, and it takes a long time and a lot of money to make prototypes for testing. Performing finite element simulation analysis on phase-shifting transformers with known design parameters can save time and ensure analysis accuracy. At the same time, design parameters can be modified according to the simulation results to improve equipment performance. For the unsymmetric 18-pulse phase-shifting autotransformer, there are prototype experiments with different capacities and voltage levels. However, there is no special parameter design software and finite element modeling analysis for this topology. Based on the theory of Literature [6], we develop a C# based transformer parameter design and finite element model generation software for this type of transformer, as well as obtaining a set of phase-shifting transformer body design parameters with a rated power of 20 kW, a rated voltage of 100 V and frequency of 400 Hz. The one-click finite element analysis modeling analysis function of the software is used to establish a three-dimensional model and an external circuit model. Its primary current and load voltage are analyzed. The final analysis results show that the software’s parameter calculation results are reliable, and the finite element model generation is accurate and efficient. Multi-pulse technology can effectively reduce the harmonics of grid-side current.
2 Research Methods We first introduce the working principle of an asymmetric 18 pulse phase-shifting autotransformer. Then, the basic method of transformer design is introduced. 2.1 Principle of Rectifier The circuit topology of the 18-pulse asymmetric phase-shifting autotransformer and its rectifier system is shown in Fig. 1. The power input is a set of sinusoidal voltages U A (t),U B (t),U C (t) with a phase difference of 120°, which directly supplies power to the main three-phase rectifier bridge. Autotransformer has three iron cores, each of which has five windings. The delta type primary windings are connected to the threephase voltage input. The two taps are used to connect the secondary phase-shifting windings on the other two iron cores, respectively. Thus, the other two sets of phase voltages (U a ’,U b ’,U c ’) and (U a ”,U b ”,U c ”) can be constructed by means of voltage vector superposition, which supply power to the auxiliary bridges, respectively. The three rectifier bridges are connected in parallel to supply power to the load [6]. Among the three sets of voltage vectors, the phase voltage vector input by the power supply is the longest, which is called the main vector. The phase shifting voltage is called the auxiliary vector. The nine voltage vectors are output to the load through three parallel rectifier bridges. The voltage output from the rectifier to the load is the maximum value of the line voltage at any time so that there is no need to connect a balanced reactor at the output of each rectifier bridge.
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Fig. 1. Circuit topology of pulse phase-shifting autotransformer for rectification system
2.2 Transformer Design Iron Core Design. Compared with the cylindrical iron core, the cylindrical iron core has a larger magnetic permeability area. Therefore, the cylindrical core is a better choice for phase-shifting transformers. Figure 2 shows the geometric structure of the three-stage stepped cylindrical iron core [10].
Fig. 2. Diagram of three-step cylindrical core structure
For the three-stage stepped cylindrical iron core shown in Fig. 2, the parameter design formula is: p (1) Dc = KD 4 3 where Dc is the diameter of cylindrical iron core. KD is the transfer coefficient between the power and the iron core and P is the rated power of the transformer. According to the calculation result of the diameter of the circumscribed circle of the iron core, the dimensional parameters of each level of iron core can be calculated according to the following formula and the parameters of windings can be designed after the iron core
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design. ⎧ a1 = 0.905Dc ⎪ ⎪ ⎪ ⎪ a = 0.707Dc ⎪ ⎪ ⎪ 2 ⎨ a3 = 0.424Dc ⎪ bi = Dc2 − ai2 i = 1, 2, 3 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ h = z · Dc c = x · Dc
(2)
Winding Design. When designing the number of winding turns, the number of primary winding turns can be determined according to Eq. 3 and then the number of winding turns of each section can be determined according to Eq. 4. V = 4.44fNp Bm Ae
(3)
V is the effective value of the power input line voltage. f is the frequency of the power supply. Bm is the magnetic density. Ae is the effective area of the iron core and N p is the number of turns of the primary winding. Assuming that the number of turns of the primary winding is N p , the number of turns of the long winding is N p2 , the number of turns of the short winding is N p1 , and the number of turns of the phase-shifting winding is N s . According to the electrical angle between the phase voltage vectors and the principle of vector superposition given in the literature [6], it can be found that the transformer turns ratio satisfies: ⎧ Ns ⎪ ⎪ = 0.137 ⎪ ⎪ Np ⎪ ⎪ ⎪ ⎨ Np1 = 0.258 (4) Np ⎪ ⎪ ⎪ ⎪ ⎪ Np2 ⎪ ⎪ = 0.484 ⎩ Np
3 Results This section introduces the parameter design software of the 18-pulse phase-shifting transformer and the final design results. Automated modeling and field-circuit coupling simulation based on script files are carried out in the finite element analysis software. Figure 3 shows the basic progress of the parameter design, which is realized on C#. According to the requirements of the transformer, the design parameters of iron core and windings are obtained by the software, which are shown in Table 2 and Table 3. The field-path coupling analysis method can accurately and effectively analyze the electromagnetic characteristics like voltage and current waveforms the transformer. Firstly, a 3D finite element model is established. Then, circuit modules are used to build the peripheral circuit of the model and measurement components such as voltmeter and ammeter are added. The modeling steps mentioned above are both automated by using the script files generated by the software.
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Start
Enter the Required Parameters of the Phaseshifting Transformer
Software Initialization
Yes
Automatic Modeling and Simulation
End
Initial Calculation
Parameter Adjustment
Reasonable Design
Adjusted Calculation
No
Fig. 3. Flow chart of software design
Table 2. Design parameters of the iron core Parameter/cm
Data
Diameter of the core Dc
5.22
First stage width a1
4.72
Second stage width a2
3.69
Third stage width a3
2.21
First stage thickness b1
2.22
Second stage thickness b2
3.69
Third stage thickness b3
4.72
Height of window h
20.87
Width of window c
6.68
Height of yoke hz
4.72
Table 3. Design parameters of the windings Winding
Np2
Np1
Ns
Number of turns
22
12
6
Width of lines (mm)
1.76
3.5
4.3
Length of lines (mm)
10
10
10
Figure 4 shows the DC load voltage waveform with a load resistance of 10 and the filtering inductance of 0.01H and Fig. 5 shows the current waveform of AC side. The average load voltage is 239.8 V when the simulation reaches stability, which is somewhat different from the theoretical average of 243.7 V. The reason should be that there is a certain impedance in both primary winding and phase-shifting winding, which leads to a certain voltage drop in the output line voltage. At the same time, there is a certain voltage drop in the conduction of the diodes. However, these two voltage drops are ignored in theoretical derivation.
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Fig. 4. Load Voltage Waveform of DC Side
Fig. 5. Current Waveform of AC Side
The current harmonic content can be calculated by Fig. 5. According to the formula, the total distortion rate THD is 12.39% when the current of AC side reaches stability.
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4 Conclusion This article research object for the isolation and pulse wave phase-shifting autotransformer and rectifying system, compared with traditional rectifier transformer, more effective to suppress harmonic wave pulse rectifier transformer, power grid current harmonic distortion rate is much smaller than the traditional six-pulse. The topological structure, working principle, output voltage characteristics, harmonic content and other system indexes of an asymmetrical phase shift autotransformer are analyzed. Combined with the general transformer design formula, the design software of phase-shift autotransformer is written in C# language under the window application framework of Visual Studio. The design and simulation modeling of the transformer are integrated, which greatly facilitates the follow-up research of the phase-shift autotransformer.
References 1. Weimer, J.: Electrical power technology for the more electric aircraft. In: Conference Proceedings of IEEE DASC 1993, vol. 3, pp. 445–450 (1993) 2. Wu, T., Bozhko, S.V., Asher, G.M., Thomas, D.W.P.: A fast dynamic phasor model of autotransformer rectifier unit for more electric aircraft. In: 2009 35th Annual Conference of IEEE Industrial Electronics, pp. 2531–2536 (2009) 3. Singh, B., Singh, B.N., Chandra, A., AI-Haddad, K., Pandey, A., Kothari, D.P.: A review of three-phase improved power quality AC-DC converters. IEEE Trans. Ind. Electron. 51(3), 641–660 (2004) 4. Ma, X., Bai, L.: Analysis and matlab simulation of 18 pulse rectifier system based on autotransformer. Comput. Aided Eng. 2008(03), 27–31 (2008) 5. Karnath, G.R., Benson, D., Wood R.: A novel autotransformer based 18-pulse rectifier circuit. APEC. In: Seventeenth Annual IEEE Applied Power Electronics Conference and Exposition (Cat. No.02CH37335), vol. 2, pp. 795–801 (2002) 6. Li, S., Cheng, G., Zhang, W., Cheng, X.: Analysis and realization of 21kW18 Pulse autocoupled variable voltage rectifier. Radar Sci. Technol. 8(04), 376–381 (2010) 7. Zhu, D., Yang, C., Wang, H.: Parameter design of 18 pulse ATRU transformer based on LabVIEW. J. Power Electron. Technol. 47(01), 24–25+69 (2013) 8. Niu, L., Ge, H., Yang, G., Jiang, F.: Optimization design of step-down 18-pulse autotransformer. Chin. J. Eng. Sci. 39(03), 456–461 (2017) 9. Roginskaya, L., Gusakov, D., Masalimov, D.: Multi-phase auto- and transformer rectifier system for aircraft. In: 2019 International Conference on Electrotechnical Complexes and Systems (ICOECS), pp. 1–4 (2019) 10. Tao, X., Wang, S.H., Huang, F.Y.P., Yuan, D.S., Wang, S.: Optimal design of rectifier transformer. In: 2015 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD), pp. 376–377 (2015)
Research on the Influence of AC Cable Lines on the Electric Field Intensity of Parallel DC Cable Lines Zhijie Zhu1 , Jianjun Yang2,3 , Nana Duan1(B) , Jingyi Li2,3 , Shuhong Wang1 , Hongke Li2,3 , and Xuehuan Wang1 1 School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China {zzj1264,w1406668657}@stu.xjtu.edu.cn, [email protected], [email protected] 2 Key Laboratory for Far-Shore Wind Power Technology of Zhejiang Province, Hangzhou 311122, China 3 Powerchina Huadong Engineering Corporation Limited, Hangzhou 311112, China {yang_jj,li_jy3,li_hk}@hdec.com
Abstract. In recent years, the transmission voltage level of the power grid has been continuously improved, and the layout of a large AC-DC interconnected power grid has formed with the original AC power grid. Due to the limitation of urban space and beauty, more and more overhead lines are transformed into cable lines. As a result, the underground cable transmission space is more and more tense, and even the phenomenon of parallel laying of AC and DC lines appears. In this paper, the actual cable tunnel model is established, and the electric field distribution of AC and DC cables parallel laying in the tunnel is simulated and analyzed. The results show that the alternating magnetic field of the AC cable has a certain influence on the electric field of the DC cable. In order to reduce the impact, the arrangement of the AC cables and the location of the AC and DC cables in the tunnel should be changed. Keywords: DC cable · AC cable · Electromagnetic induction
1 Introduction With the acceleration of urban development, urban land resources are becoming more and more precious. In order to alleviate the contradiction between power construction needs and land resources, urban areas have reduced the use of overhead lines and increasingly used power cables for power transmission. At the same time, the voltage grade of transmission cables continues to improve, and the number of large-capacity, long-distance, and large-section cable projects is increasing [1]. Therefore, the electromagnetic coupling of power lines to adjacent metal pipelines has attracted the attention of various countries [2, 3]. P. Wong, et al. in consideration of the personal safety of the maintenance personnel, the N. V. Buyakova team simulated and calculated the induced voltage generated by the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 923–930, 2022. https://doi.org/10.1007/978-981-19-1528-4_95
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power failure maintenance line under the coupling of parallel operation lines, and studied the different factors affecting the induced voltage [4, 5]. Reasearchers established the electromagnetic coupling simulation of AC and DC transmission which model calculates the different layouts of the pole conductors of the DC transmission line, the power frequency induction component under different operating modes, and the maximum bias current allowed by the converter transformer [6, 7]. Regarding the installation of AC transmission lines and DC transmission lines on the same tower, domestic and foreign experts also calculated the induced voltage coupled by the AC line on the DC line, analyzed the factors affecting the induced voltage, and evaluated the maintenance work of the DC line during power outages [8]. However, the research objects of the mutual coupling of these transmission lines are all overhead lines, and there is no research on the related problems of cable lines. In this paper, a complete two-dimensional model of the submarine tunnel cable is established, and the influence of the AC cable on the electric field of the co-layed DC cable was gotten by the finite element method.
2 Submarine Tunnel Model and Research Method Figure 1 shows the overall layout of the tunnel, which includes two rows of three-phase AC cables A1, B1, C1 and A2, B2, C2, DC cables, metal return lines, communication brackets. And established the cable tunnel model according to the modeling method of [9].
59.32
B2
C2 A2
218
69.18
77
260
A1 C1
B1
Fig. 1. Submarine tunnel model
In the process of modeling, in order to observe the electric field distribution of the DC cable in detail, a more accurate model was established for the DC cable. It has a total of 9 layers of structure. From the inside to the outside, they are copper, conductor shielding layer, XLPE, insulating shielding layer, and water blocking layer, aluminum,
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Fig. 2. Cable structure
PE sheath. The AC cable structure is simplified, with only two layers of copper and XLPE materials. The cable can be regarded as infinite, so the field studied is a two-dimensional parallel plane field. The solution field is a transient magnetic field, ignoring the displacement current and only the conduction current is left. And the divergence of B is 0, the solution equation is shown in the following equation ⎧ ∇ ×H =J ⎪ ⎪ ⎪ ⎨B = ∇ × A (1) ⎪ E = −∂A/∂t ⎪ ⎪ ⎩ J = σE where A is the magnetic vector potential, H is the magnetic field intensity, J is the current density.
3 Simulation Analysis The excitation of the AC cable is three-phase power frequency alternating current, the effective value of the phase voltage is 220 kV, and the excitation of the DC cable is the rated voltage, its value is ±320 kV. The following formula is the expression of phase A voltage √ (2) UA = 220000 × 2cos(100π t) Figure 3 shows the distribution of the overall electric field intensity in the tunnel, with the situation of DC cable excitating lonely. When there is no excitation of the AC cable, the electric field intensity is large near the DC cable.
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Y-axis Position
Electric field intensity(V/m)
X-axis Position
Fig. 3. The electric field intensity distribution of the tunnel when the DC cable is excited alone
The electric field intensity near the AC cable increases obviously after the excitation of the AC cable is added, which also changes the electric field distribution around the DC cable. The overall electric field intensity in tunnel is shown in Fig. 4
Y-axis Position
Electric field intensity(V/m)
X-axis Position
Fig. 4. The distribution of the overall electric field intensity in the tunnel when AC and DC cables are excited at the same time
The schematic diagram of the distribution of the electric field intensity of the DC cable along the diameter in the two cases is shown in Fig. 5 and Fig. 6. Since the solver is a transient solver in the simulation, we compare the situation with or without AC excitation at the same time in the steady state. The presence or absence of AC cable excitation does not change the distribution of electric field intensity, when there is no AC cable excitation, the electric field intensity at the DC cable at 0.4 s is 27900 V/m.
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X-axis Position
Fig. 5. Electric field intensity distribution of DC cable without excitation of AC cable
Electric field intensity(V/m)
Figure 6 shows the electric field intensity distribution of the DC cable at 0.4 s when the AC and DC cables are excited at the same time, the electric field strength increased to 28030.5 V/m. The difference between the results in the two cases is not very obvious.
X-axis Position
Fig. 6. Electric field intensity distribution of DC cable with excitation of AC cable
In order to observe the influence of DC cables on AC cables. We have obtained the electric field intensity distribution of the AC cable only under the action of the DC cable. Figure 7 shows the electric field strength of the AC cable when the DC cable is excitation alone (at 0.4 s time). At this time, the maximum electric field strength is 4576 V/m. This electric field intensity also has a certain influence on the AC cable.
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X-axis Position
Fig. 7. The electric field strength of the AC cable when the DC cable excitation alone
Electric field intensity (V/m)
Subsequently, the arrangement of AC cables was changed to analyze the influence of different arrangements. Figure 8 shows the electric field intensity generated on the DC cable when only the AC cable is excited and the AC cables are arranged horizontally.
X-axis Position (mm)
Fig. 8. DC cable field strength distribution when AC cables are arranged horizontally
Figure 9 shows the result when the AC cables are arranged in a triangle. It can be seen that when the AC cable is in a triangular arrangement, the overall electric field intensity decreases significantly.
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X-axis Position (mm)
Fig. 9. DC cable field strength distribution when AC cables are arranged in a triangle
4 Conclusion In this paper, a two-dimensional model of the submarine tunnel is established. The finite element method is used to calculate the distribution of electric field intensity in the tunnel when the AC and DC cables are laid together. Then the arrangement of the AC cables is changed. The results show that when the AC cables are arranged in a triangle the impact on DC cable is greatly reduced. Which has certain guiding significance for the actual layout of the cable.
References 1. Yan, Y., Ting, Z., Weiwei, Z.: Study on the influence of common trench for AC and DC cables. FuJian Soc. Electr. Eng. 21(14), 294–299 (2018) 2. Maneepeth, P., Thararak, P., Jirapong, P., Karaaom, C.: Mitigation of induced currents and overvoltages in metallic sheath of 115 kV underground cable using sheath voltage limiter and parallel ground continuity conductor. In: 2021 9th International Electrical Engineering Congress (iEECON), pp. 61–64 (2021) 3. Herre, L., Wouters, P., Steennis, F., de Graaff, R.: On the electromagnetic coupling of AC and DC circuits on hybrid transmission towers. In: 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6 (2016) 4. Lo, W.K., Wong, P., Leung, S.W., Siu, Y.M., Sun, W.N.: Analysis of induced voltage at close proximity with high voltage cables of electrified railways. In: 2018 IEEE International Symposium on Electromagnetic Compatibility and 2018 IEEE Asia-Pacific Symposium on Electromagnetic Compatibility (EMC/APEMC), pp. 944–947 (2018) 5. Buyakova, N.V., Kryukov, A.V., Nguyen, T.: Simulation of induced voltages created by hightension cable with cross-linked polyethylene insulation. In: 2020 International Russian Automation Conference (RusAutoCon), pp. 488–492 (2020)
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6. Yan, Y., Ting, Z., Zhaohui, C.: The electromagnetic field environment in the tunnel for the common laying of AC and DC Cables. GuangDong Electric Power 31(12), 20–26 (2019) 7. Hu, M., Xie, S., Zhang, J., Ma, Z.: Desing selection of DC & AC submarine power cable for offshore wind mill. In: 2014 China International Conference on Electricity Distribution, pp. 1675–1679 (2014) 8. Li, Q., Chengke, Z., Hang, W., Zhi, T., Zhanran, X., Meng, C.: The influence of the shunting of underground multi-cable lines on the operating life. High Voltage Technol. 45(05), 1576–1583 (2019) 9. Zhu, T., et al.: Study on the electromagnetic field in HVDC/AC hybrid submarine cable tunnel. In: 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), pp. 1–4 (2018)
Cause Analysis and Preventive Measures for Breaking of Strain Clamp in 500 kV Transmission Line Jiankun Zhao1(B) , Jianli Zhao1 , Liang Xu2 , Jialin Qin2 , and Kaiyue An3 1 Inner Mongolia Power Research Institute, Hohhot, China
[email protected]
2 China Electricity Council Power Construction Technology and Economic Consulting Center,
Beijing, China 3 Inner Mongolia Power Information and Communication Center, Hohhot, China
Abstract. The strain clamp is a fitting used to fix the wire or lightning cable to the strain insulator string. Corrosion, heat or breakage during operation directly threatens the safe operation of high-voltage transmission lines.The root of a 500 kV line wire strain clamp drainage plate was broken. Material inspections such as morphology analysis, X-ray digital imaging inspection, and metallographic structure analysis were carried out. The material inspection results, connection structure and operating environment were analyzed to analyze the cause of the breakage of the clamp According to the cause of the fracture, treatment methods such as improving the fatigue resistance of the strain clamp and changing the rigid connection structure are proposed, and preventive measures are given in the operation and maintenance and design stages. Keywords: Transmission line · Strain clamp · Breakage of drainage plate · TJ spacer · Breeze vibration
1 Introduction The strain clamp is a fitting that fixes the conductor or ground wire on the strain insulator string and bears the tension of the wire. According to the different connection types, there are three types: bolt type, wedge type and compression type. In order to meet the requirements of the holding force of the clamp, the compression type strain clamp is usually used for 500 kV lines. In recent years, due to the development of hydraulic technology, the hydraulic type strain clamp has basically replaced the explosion type and has been widely used in the power grid [1–3]. The hydraulic type strain clamp is composed of aluminum tube and steel anchor. The steel anchor is used to connect and anchor the steel core of the steel core aluminum stranded wire. The aluminum wire is connected to the aluminum tube, and the aluminum tube is connected to the root of the steel anchor. Defects in the strain clamp design, crimping, installation, and harsh operating environment may cause problems such as © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 931–940, 2022. https://doi.org/10.1007/978-981-19-1528-4_96
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corrosion, heat generation or breakage of the clamp [4–11], which directly threaten the safe operation of high-voltage transmission lines. This article takes a strain clamp of 500 kV transmission line drainage plate root fracture accident as the research object, carries out morphology analysis, X-ray digital imaging detection, metallographic analysis and other material characterization methods, comprehensively considering the material inspection results, connection structure and the environment, analyzed the reasons for the break of the clamp, and proposed treatment methods to improve the fatigue resistance of the strain clamp, change the rigid connection structure, etc., and put forward preventive measures during the operation and maintenance and design stages, which are important for preventing similar accidents.
2 Failure Overview In July 2020, the operation and maintenance personnel found that the strain clamp of the lower right sub-conductor of the large side of the middle phase of the tower 201 of a 500 kV line was broken, causing the drainage line to disconnect. At the same time, they found the tensile clamp of the upper right sub-conducting conductor of the large side of the phase was cracked, as shown in Fig. 1. The tower type of No. 201 is JGC2, erected in a single loop, and the large side span is 417 m. The line was put into operation in October 2011, the conductor design ice thickness is 10 mm, and the design basic wind speed is 27 m/s. The wire model is
(a) Failure site situation
(b) Fracture strain clamp Fig. 1. Failure situation of strain clamp
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JL/G1A-400/35 steel core aluminum stranded wire, the failure strain clamp model is NY-400/35, the pistol-shaped appearance, the aluminum tube (drain plate) is made of 1050A aluminum, and the material is hot extruded.
3 Material Inspection 3.1 Macroscopic Morphology Analysis The macroscopic morphology of the fracture strain clamp is analyzed. The strain clamp breaks at the bending part of the aluminum tube drainage plate, and the fracture originates from the sharp groove formed by the extrusion of the inner arc side of the bending part, and expands toward the inner wall in the “beach-like” shining pattern along the wall thickness direction. The fracture surface is flush with no obvious plastic deformation, and has a typical fatigue fracture macroscopic morphology, as shown in Fig. 2.
(a) Fracture zone
(b) Sharp groove Fig. 2. Macro morphology of fracture strain clamp
3.2 Microscopic Morphology Analysis A scanning electron microscope was used to analyze the microscopic fracture morphology of the fracture of the fracture strain clamp, as shown in Fig. 3. It can be seen that the
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fracture started on the surface of the sharp groove formed by bending the inner arc of the aluminum tube, and most of the area on the fracture showed a “beach-like” fatigue band.
(a) Initiation zone
(b) Extension area Fig. 3. SEM morphology of fracture of strain clamp
3.3 X-ray Digital Imaging Inspection Perform overall X-ray digital imaging inspection (DR perspective inspection) on the fracture strain clamp, as shown in Fig. 4. It can be seen that the crimp quality of each part of each strain clamp basically meets the requirements of GB/T 5285-2018 “Hydraulic crimping process specification for overhead conductor (below 800 mm2 ) and ground wire of transmission and transformation project construction”. No defects such as cracks other than the fracture were found in the site.
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Fig. 4. DR morphology of strain clamp
3.4 Chemical Composition Detection The aluminum pipe part of the fracture strain clamp was sampled for chemical composition test. The test results are shown in Table 1. The results show that the content of aluminum in the chemical composition of the tensile clamp aluminum tube is 99.60%, which meets the chemical composition requirement of 1050 A aluminum. Table 1. Test results of chemical composition of aluminum tubes. Detection element Si
Fe
Cu
Ti
GB/T 3190
≤0.25 ≤0.40 ≤0.05 ≤0.05
Measured value
0.15
Detection element Mg
0.18
0.02
Zn
Mn
0.001 Al
GB/T 3190
≤0.05 ≤0.07 ≤0.05 ≥99.50
Measured value
0.02
0.02
0.009 99.60
4 Failure Cause Analysis 4.1 Material Inspection Result Analysis During the bending process of the aluminum tube drainage plate at the wire clip production stage, a sharp groove with edges and corners is formed on the inner arc side, and a large stress concentration is formed in the groove part. Under the action of the alternating cyclic load during operation, fatigue cracks are formed along the sharp grooves where the stress is concentrated and gradually expand until the overall fracture of the aluminum pipe of the wire clamp is caused. 4.2 Connection Structure Analysis The fault phase is the middle phase of the tension tower, which is different from the direct jump connection of the side phase conductors. The middle phase is connected in
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a jump-around way, as shown in Fig. 5. The direction of the jumper is changed due to the jump, which makes the stress of the strain clamp drainage plate more complicated, and it is more prone to fatigue and damage under vibration.
Fig. 5. Intermediate jump mode of tension tower
When the drainage wire of the two sub-conductors on one side of the tensile string passes through the extension rod of the other side, use a TJ-type small spacer to fix it on the extension rod. The TJ-type spacer is used to support and isolate the drainage line to avoid extending the rod and the drainage line. Collision and friction. The distance between the TJ spacer and the crimping tube port of the drainage line clamp is very close, within 0.1 m. Thus, a rigid connection body is formed, which acts as a whole “link”, which connects the tension line on one side. The clip is connected to the extension rod on the other side as a whole, as shown in Fig. 6. When the two sub-conductors vibrate, through the mutual influence of this “link”, the up and down can be pulled together to move together, and on this rigid “link”, the vibration force of the left and right sub-conductors can be absorbed and delivered at the same time.
Fig. 6. TJ spacer and strain clamp form a rigid connection
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4.3 Operating Environment Analysis The route runs from east to west, and the terrain of the passing area is flat. The average annual wind speed in this area is 5 m/s, and the northwest and north winds dominate throughout the year. The wind in this area lasts for a long time and the wind speed is uniform and stable. The wind speed range is basically the same as the wind speed range corresponding to the breeze vibration of the ground wire of the overhead transmission line. The wind speed is also relatively stable, which easily leads to long-term stable breeze vibration of the ground wire. 4.4 Comprehensive Analysis In summary, the terrain where the tower is located is flat, and the wind is stable and continuous throughout the year. When the wind is steadily and uniformly blown towards the split conductors, due to the “Karman Vortex Street” effect, the leeward side subwire is inevitably in the wake, causing the two sub-conductors to vibrate up and down at different periods, which is the limiting effect of the rigid connecting rod. Bottom, a bending stress is formed directly at the connection point of the strain clamp and the drainage clamp. After repeated cycles and long-term action, the weakest link of the structure (the root of the drainage plate) will be fatigued and damaged.
5 Preventive Measures 5.1 Governance Method According to the above research and analysis, there are two key points that need to be improved: 1) The strength of the drain wiring board of the strain clamp needs to be improved; 2) The TJ spacer is originally used for isolation, but actually produces a rigid connection. Therefore, it is necessary to re-optimize and change the connection structure of the fittings. Hoe Type Strain Clamp. The failure strain clamp is a pistol type, and the drainage plate is made of aluminum tube. Large stress concentration is easily formed on the inner arc side, and the strength is low and the vibration fatigue resistance is weak. The strain clamp drainage plate is improved to “hoe type” to improve the performance of anti-vibration and fatigue resistance, as shown in Fig. 7. Flexible TJ Spacer. In order to improve the rigidity between the TJ spacer and the drainage plate through the equipment clamp, and reduce the influence of breeze vibration and dancing on the strain clamp drainage plate, the existing TJ-type small spacer is improved, and a special design with a damping spring The flexible TJ spacer rod, as shown in Fig. 8.
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(a) Design diagram
(b) In kind Fig. 7. Hoe type strain clamp
The flexible TJ spacer has the following functions: 1) It has the basic functions of the TJ spacer; 2) It can release the alternating vibration tensile stress transmitted to the strain clamp and prevent the force from acting on the drain plate of the clamp.
6 Preventive Measures Hidden danger investigation. Focus on investigating micro-weather areas such as large windy passes or flat and open areas, large spans, mid-phase (jumping) of tensile towers, and locations where TJ spacers are installed to form rigid connections. Focus on checking the damage of the TJ spacer and the cracks of the strain clamp. X-ray flaw detection was carried out in important tower sections in conjunction with power outages. Remove the TJ spacers installed with a large span (greater than 500 m), replace and install the flexible TJ spacers, and eliminate the rigid connecting rod effect formed after the installation of the TJ spacers. For newly-built lines, jumper wire jumps are used and when TJ spacers are installed, the line path should avoid micro-weather areas such as tuyere, and preferentially use “hoe-type” strain clamps with thicker shear forces at bends.
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(a) Design diagram
(b) In kind Fig. 8. Flexible TJ spacer
7 Summary In a flat and open environment with stable and continuous wind, the wire breeze vibrates severely. Under the influence of factors such as the rigid connection between the TJ spacer and the strain clamp, and the formation of grooves during the bending of the aluminum tube drainage plate of the pistol-type strain clamp, the strain clamp drainage plate is broken. During the operation and maintenance phase, focus on investigating micro-weather areas such as large windy passes or flat and open areas, large spans, middle phases of tensile towers, and locations where TJ spacers are installed to form rigid connections. Remove the TJ spacer rods installed in the large span, replace and install the flexible TJ spacer rods to eliminate the rigid connecting rod effect formed after the TJ spacer rods are installed. In the design stage, the jump-around method avoids microclimate areas such as tuyere, and preferentially selects the “hoe type” strain clamp. Acknowledgments. Inner Mongolia Electric Power (Group) Co., Ltd. 2020 Science and Technology Project (2020-01-53).
References 1. Ji’e, D.:. Handbook of Electric Power Fittings. Beijing: China Electric Power Press, December 2009. (in Chinese)
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2. Cefeng, Z., Ruomin, W., Guohong, C., et al.: Evaluation test of crimping quality of tensile clamp based on digital rays. Zhejiang Electr. Power 37(11), 41–45 (2018). (in Chinese) 3. Jianfeng, Y.: Analysis and prevention of a drainage line break failure caused by breeze vibration. Ningxia Electr. Power 01, 8–12 (2020). (in Chinese) 4. Shao, W., Zhen, Y.D.: Analysis of the causes of breaking of the tension clip of 220 kV transmission line. Shandong Electr. Power Technol. 44(6), 77–80 (2017). (in Chinese) 5. Dong, X., Qu, F., Li, Y., Fang, B., Wang, Y., Liu, G.: Experimental analysis of temperature distribution in high-voltage strain clamp. In: 2018 12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM) 92018) 6. Wang, W., Gang, Y.B., et al.: Failure analysis of strain clamps for high-voltage power transmission. Heat Treat. 33(4), 41–47 (2018). (in Chinese) 7. Fan, S., Chen, Y., Zhang, X., Wang, X.: Experimental Investigation on Breeze Vibration Fatigue of ACCC. In: 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE) (2020) 8. Shibin, T., Xiao, Z., Xiaolong, Y.: Fracture analysis of the connecting plate of the strain clamp of a 500 kV compact tower. Zhejiang Elect. Power 39(10), 69–73 (2020). (in Chinese) 9. Ji, K., Rui, X., Li, L., Yang, F., McClure, G.: Dynamic response of iced overhead electric transmission lines following cable rupture shock and induced ice shedding. IEEE Trans. Power Deliv. 31(5), 2215–2222 (2016) 10. Wei, K., Yinjian, C.: Measures of introduction and look forward to prevent breeze vibration of overhead power transmission lines. In: 2011 Second International Conference on Mechanic Automation and Control Engineering (2011) 11. Kun, W., Shuai, Z., Zhanjun, Y., et al.: Cause analysis of disconnection of a 220 kV transmission line. Phys. Test. Chem. Anal. (Phys. Sect.) 54(9), 692–697 (2018). (in Chinese)
Comprehensive Evaluation of Diversified and High Elastic Power Grid Based on Entropy-AHP-TOPSIS Method Hanyun Wang1(B) , Tao Wang1 , Xinyi Wang2 , Bing Li2 , and Congmin Ye2 1 Huzhou Power Supply Company of SGCC, Huzhou, China
[email protected] 2 North China Electric Power University, Beijing, China
Abstract. In order to adapt to the current trend of distribution network system with large access to renewable energy in China, the diversified and high elastic power evaluation index system has been constructed, and a resilience evaluation index system, reliability evaluation index system, economic evaluation index system and environmental protection evaluation index system have been established. Using the evaluation method of the combination evaluation with Entropy -AHP-TOPSIS, the paper makes a comprehensive evaluation of the diversified and high elastic power grid, compares the investment and construction effect of diversified and high elastic power grid in different regions through the analysis of the study. Keywords: Renewable energy, · Diversified and high elastic power grid · Evaluation index system · Comprehensive assessment
1 Introduction Elasticity refers to the ability of the system to combat external environmental interference and maintain its original state. Elastic power grid requires power systems to be flexible in dealing with risks, adapting to changing environments, and recovering system performance in a timely and rapid manner when exposed to risks [1]. Multi-fusion refers to distributed energy, including distributed power generation, distributed energy storage, demand response, etc. [2]. Among them, distributed power generation mainly includes distributed photovoltaic power generation, distributed wind power generation, etc. Distributed energy is a potential resource to enhance the flexibility of power grid, and the fusion and complementarity of multi-distributed energy is an important feature of the future power grid. Based on the above analysis, diversified and high elastic power grid can be attributed to vertical fusion source network lotus storage elements, horizontal integration of energy systems, physical information, socioeconomic, natural environment in various fields, with high carrying, high interaction, high self-healing, high efficiency four capacity of the power grid system. Diversified and high elastic power grid can realize various energy interconnections through operations such as “source-source complementary”, “source-network © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 941–953, 2022. https://doi.org/10.1007/978-981-19-1528-4_97
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coordination”, “network-load-storage interaction”, “source-network-load-storage cooptimization”, and greatly improve the wide area optimization and allocation capacity of energy resources and the level of comprehensive social energy efficiency. (1) Source-source complementary: “Source-source complementarity” emphasizes effective coordination and complementarity between different power sources, and overcomes the randomness and volatility of clean energy generation due to environmental and meteorological factors through the coordination and complementarity between flexible power generation resources and clean energy, and forms a multi-energy aggregation energy supply system [3]. (2) Source-network coordination: “Source-network coordination” requires improving the power grid’s ability to accept diversified power supplies, using advanced regulatory technology to optimize the combination of decentralized and centralized energy supply, highlighting the complementary coordination between different combinations, playing the buffer role of micro-network and intelligent distribution network technology, and reducing the adverse effects of the acceptance of new energy power on the safe and stable operation of the power grid [4]. (3) Network-load-storage Interaction: “Network--load-storage interaction” requires the further expansion of the definition of demand-side resources, energy storage, distributed energy as a broad term demand-side resources, so that demand-side resources as a relative resources relative to the supply side to participate in the system regulation and operation, guide the demand side to actively pursue renewable energy power fluctuations, with the orderly charging and discharge of energy storage resources, thereby enhancing the system’s ability to accept new energy sources, to achieve deprecation efficiency [5]. (4) Source-network-load-storage co-optimization: “Source-network-loadstorage” collaborative optimization refers to the power supply, power grid, load and energy storage four parts through a variety of interactive means, more economical, efficient and safe to improve the power dynamic balance of the power system, in order to maximize the use of energy resources operating mode and technology, the model is including “power grid, power grid, load, energy storage” as a whole of diversified and high elastic grid operation model [6].
2 Research Strategy and Methods 2.1 Diversified and High Elastic Grid Evaluation Index System 2.1.1 Evaluation Index System of Resilience The resilient of the distribution network frame structure is an extension of the security and stability of the whole distribution network, which refers to the ability of the system to withstand changes. The system can maintain the ability of all node voltages to be within the permissible range without crashing in the event of possible interference or failure, and can still operate safely and steadily (Table 1).
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Table 1. Diversified and high elastic power grid resilience indicator system One-level indicators
Two-level indicators
Three-level indicators
Resilience indicators
The structure of the rack
Total installed capacity Average single-back line length High-quality engineering rates N-1 pass rate Gap power supply adds peak capacity rate Flexible ERV transmission capacity Load ratio Load rate
Grid transmission capacity
Smart substation ratio
System resilience
Proportion of disaster prevention equipment Disaster monitoring capacity
2.1.2 Evaluation Index System of Reliability Diversified and high elastic power grid reliability evaluation includes distribution power supply safety, power outage time and power failure loss, etc., In order to explain the security of power supply, avoid or reduce the loss of power failure, avoid or reduce the time of power failure and so on, the indicator system is shown in the table below (Table 2). Table 2. Diversified and high elastic power grid reliability indicator system One-level indicators
Two-level indicators
Three-level indicators
Reliability indicators
The power supply is safe
Voltage pass rate Average life of decommissioned equipment Switch no oiling rate Maximum load utilization hours Power supply reliability System frequency pass rate Substation automation rate Distributed power/storage/microgrid capacity Maximum load (continued)
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One-level indicators
Two-level indicators
Three-level indicators Electricity consumption is increasing through the whole society
Time of power outage
The average power outage time for the user’s failure
Power outage loss
Loss of direct power outage on average per user unit time Loss of indirect power outages per user unit of time on average The average direct power outage loss per unit of power grid time The average indirect power outage loss per unit of power grid time
2.1.3 Evaluation Index System of Economic Diversified and high elastic power grid economic evaluation includes power grid construction input, power grid operation and maintenance benefits, social benefits and other three aspects, and respectively with three levels of indicators to fully interpret, the indicator system as shown in the table below (Table 3). Table 3. Diversified and high elastic power grid economic indicator system One-level indicators
Two-level indicators
Three-level indicators
Economic indicators
Grid operating costs
Total investment in the grid Power supply load per unit of grid assets The unit grid assets sell electricity Investment in user information collection and construction Smart investment Increase the supply load per unit of power grid investment Discount on grid losses
The cost of electricity purchase
Revenue from the sale of electricity per unit of power grid assets (continued)
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Table 3. (continued) One-level indicators
Two-level indicators
Three-level indicators The rate of change in the average purchase price of electricity Transmission and distribution prices Increased electricity sales per unit of power grid investment Average electricity price change rate for sale
2.1.4 Evaluation Index System of Environmental From the perspective of clean energy power generation development rate, clean energy power generation utilization rate, average coal consumption rate, unit power supply pollutant emission rate, etc., the index system is constructed, and the environmental friendliness is evaluated (Table 4). Table 4. Diversified and high elastic power grid environmental protection index system One-level indicators
Two-level indicators
Three-level indicators
Firmness indicators
Clean energy generation
The proportion of new energy power generation installed Clean energy generation emission reduction Reduce sulfur dioxide emissions Save standard coal Renewable energy generation
Clean energy efficiency
EIA adoption rate Water and soil conservation pass rate
Investment in environmentally friendly equipment
Proportion of use of environmentally friendly materials Proportion of energy-efficient equipment used Percentage of environmental protection facilities for power generation in conventional power plants
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2.2 Theoretical Model In choosing the diversified and high elastic power grid multi-attribute evaluation method and model, according to the characteristics of each attribute and index, combined with the scope of application of the method and the advantages and disadvantages of the characteristics, the use of combination evaluation theory ideas, the selection of appropriate evaluation methods organic combination, integration of different methods and model advantages, to avoid short, so as to obtain more accurate and reasonable evaluation results. In this paper, the diversified and high elastic power grid is evaluated by the Entropy-TOPSIS-AHP combination evaluation method [7]. 2.2.1 Weight Determination Facing the shortage of a single comprehensive evaluation method, in order to improve the level and precision of evaluation, the combination and application of evaluation method has become an important research direction in the field of comprehensive evaluation [8]. “Combination evaluation method” is through the combination of various methods, can achieve the effect of making up for each other. The combination of a single evaluation method can be combined for the weights of a single evaluation method, or the evaluation sequencing results of a single evaluation method can be combined. In other words, the combination evaluation method can be divided into “combination of weight coefficients” and “combination of evaluation results” combination of the method of empowerment is generally used linear weighted combination method, the calculation formula is (1) θi = αωi + (1 − α)μi ωi means subjective weight vector, ωi = 1; μi means objective weight vector, μi = θi = 1; α represents the importance of 1; θi represents combined weight vector, subjective empowerment methods, 0 ≤ α ≤ 1. 2.2.2 Entropy-TOPSIS-AHP Combination Evaluation Model The ideal point approximation evaluation method is often used for multi-objective decision-making on limited scenarios. The TOPSIS method first normalizes the original data matrix [9]. On this basis, the optimal and worst of the finite schemes are selected and recorded as the optimal and worst vectors. The distance between the subject being evaluated and the optimal or worst scheme is indicated as the approximation of the optimal or worst scheme, and this is the main basis for evaluation [10]. There are m evaluation scenarios, n evaluation indicators, assigning values to each indicator to form the initial matrix M and Mij representing the j indicator value of the i evaluation object. The steps for a comprehensive evaluation using Entropy-TOSIS -AHP are as follows: (1) According to the corresponding data information of the evaluation index, give each evaluation index a specific indicator value, and then list the initial matrix, that is,
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the original data matrix M: ⎡
(Mij )m×n
M11 ⎢ M21 =⎢ ⎣ ... Mm1
M12 M22 ... Mm2
⎤ ... M1m ... M2m ⎥ ⎥ ... ... ⎦ ... Mmm
(2)
(2) According to the above method, the indicator data is standardized, and the standardized decision matrix is obtained (Pij )m×n . ⎤ ⎡ P11 P12 ... P1m ⎢ P21 P22 ... P2m ⎥ ⎥ (3) (Pij )m×n = ⎢ ⎣ ... ... ... ... ⎦ Pm1 Pm2 ... Pmm (3) The weight is determined by the Entropy-TOSIS -AHP combination method. (4) Determine the indicator weighted evaluation value matrix. Because of the different importance of each evaluation indicator, all should consider the weight of each indicator, the normalization data weighted into a weighted normalization matrix.
V = ωi Pij mn (4) ⎤ ... ω1 P1m ... ω2 P2m ⎥ ⎥ ... ... ⎦ ... ωn Pmm
⎡
V = (vij )m×n
ω1 P11 ω1 P12 ⎢ ω2 P21 ω2 P22 =⎢ ⎣ ... ... ωn Pm1 ωn Pm2
(5)
(5) Define positive ideal scenario V + and negative ideal scenario V −
+ + + + max vij |j ∈ J1 , min vij |j ∈ J2 |i = 1, 2.K.m V = v1 , v2 , K, vn = i
−
V − = v1− , v2− , K, vn
i
(6)
= min vij |j ∈ J1 , max vij |j ∈ J2 |i = 1, 2.K.m i
i
(7) J1 represents a collection of benefit metrics, and J2 represents a collection of cost metrics. (6) Calculate the European distance The distance from scenario i (i = 1,2, …, n) to the positive ideal scenario is Si+ , and the distance from the negative ideal scenario is Si− . 2 n + vij − vj+ (8) Si = j=1
2 n − vij − vj− Si = j=1
(9)
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(7) Calculate relative proximity The proximity of scenario i (i = 1,2, …, n) to the ideal scenario is: ei =
Si−
(10)
Si+ + Si−
The TOPSIS evaluation value of each scheme is calculated by the above formula, and the evaluation object is sorted according to the evaluation value.
3 Example Analysis Collect three representative regional power grid data for study analysis, according to the Entropy-AHP-TOPSIS combination evaluation model. First of all, the weight is calculated, including subjective weight and objective weight, calculated weight distribution of each indicator as shown in Table 5. Table 5. Metric weight distribution Three-level indicators
Objective weight
Subjective weight
Comprehensive weights
Total installed capacity
0.084405
0.186782
0.135593
Average single-back line length
0.095743
0.036741
0.066242
High-quality engineering rates
0.086177
0.059037
0.072607
N-1 pass rate
0.112825
0.17761
0.145218
Gap Power adds a peak capacity rate
0.098924
0.055339
0.077132
Flexible ERV transmission 0.094148 capacity
0.049894
0.072021
Load ratio
0.058724
0.13351
0.096117
Load rate
0.075568
0.166064
0.120816
Smart substation ratio
0.133635
0.050079
0.091857
Proportion of disaster prevention equipment
0.087429
0.041898
0.064664
Disaster monitoring capacity
0.072423
0.043045
0.057734
Voltage pass rate
0.012511
0.152494
0.082502
The average life of the decommissioned equipment
0.088647
0.023381
0.056014
(continued)
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Table 5. (continued) Three-level indicators
Objective weight
Subjective weight
Comprehensive weights
Switch has no oiling rate
0.042441
0.018569
0.030505
Maximum load take advantage of hours
0.03796
0.030164
0.034062
Power supply reliability
0.000878
0.13497
0.067924
System frequency pass rate 0.032844
0.112975
0.07291
Substation automation rate 0.045889
0.058029
0.051959
Distributed power/storage/microgrid capacity
0.113739
0.036884
0.075311
Maximum load
0.075466
0.052154
0.06381
The electricity consumption of the whole society has increased
0.075694
0.044512
0.060103
The average power outage time for the user’s failure
0.089954
0.062217
0.076086
Operating and maintenance 0.065784 fees per million yuan of grid assets
0.05007
0.057927
Loss of direct power outage on average per user unit time
0.082075
0.057873
0.069974
Loss of indirect power outages per user unit of time on average
0.082075
0.055975
0.069025
The average direct power outage loss per unit of power grid time
0.077021
0.057912
0.067467
The average indirect power 0.077021 outage loss per unit of power grid time
0.051821
0.064421
Total investment in the grid 0.071699
0.044258
0.057979
Power supply load per unit 0.078888 of grid assets
0.113087
0.095988
The unit grid assets sell electricity
0.111441
0.092667
0.073893
(continued)
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Three-level indicators
Objective weight
Subjective weight
Comprehensive weights
User information acquisition and construction investment
0.073213
0.057884
0.065549
Highly flexible investments 0.077887
0.064391
0.071139
Unit grid investment increases supply load
0.0879
0.07711
0.082505
Grid loss discount
0.084575
0.096668
0.090622
Revenue from the sale of electricity to power grid assets per unit
0.087458
0.071134
0.079296
The rate of change in the average purchase price of electricity
0.082045
0.08078
0.081412
Transmission and distribution prices
0.067078
0.092524
0.079801
Increased electricity sales per unit of power grid investment
0.075677
0.058439
0.067058
The rate of change in the average electricity price sold
0.06863
0.071902
0.070266
Rate of change in sales power
0.071057
0.06038
0.065718
The proportion of new energy installations
0.093704
0.091578
0.092641
Clean energy generation emission reduction
0.106174
0.165984
0.136079
Reduce sulfur dioxide emissions
0.093997
0.156893
0.125445
Save standard coal
0.09922
0.156893
0.128057
Renewable energy generation
0.116555
0.074406
0.095481
EIA adoption rate
0.098034
0.057981
0.078008
Water and soil conservation pass rate
0.098034
0.058951
0.078493
Proportion of use of environmentally friendly materials
0.103929
0.067537
0.085733
(continued)
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Table 5. (continued) Three-level indicators
Objective weight
Subjective weight
Comprehensive weights
Proportion of 0.103454 energy-efficient equipment used
0.079962
0.091708
Percentage of environmental protection facilities for power generation in conventional power plants
0.086898
0.089814
0.088356
AMI scale
0.205821
0.174904
0.190363
Based on the resulting comprehensive weights, the index standardized values under each attribute are calculated based on the TOPSIS evaluation method, as well as the comprehensive evaluation values, as shown in Table 6. Table 6. Attributes score and comprehensive evaluation values Attribute score
Firmness
Reliability
Economy
Environmentally friendly
Evaluation value
Region 1
0.722154
0.81457
0.358897
0.967279
0.715725
Region 2
0.597562
0.779904
0.596864
0.920753
0.723771
Region 3
0.603618
0.839898
0.347981
0.969283
0.690195
Based on the regional comprehensive evaluation values, a comparison of the regional evaluation values is drawn, as shown in Fig. 1. As can be seen from Fig. 1. The comprehensive evaluation values of the three areas are in the range of 0.69–0.73, of which the comprehensive evaluation value of region 2 is the highest, indicating that the construction investment of region 2 in the diversified and high elastic power grid management is better. Combined with Table 6, Region 2 has a higher overall evaluation value, which is due to its leading economy in the diversified and high elastic power grid. Of the three regions, the 2nd highest overall rating is Region 1, which has the best attributes in firmness, while Region 3 has the best reliability and environmental protection. It can be seen that in the investment and construction of diversified and high elastic power grid in the three regions, the focus of investment construction is focused on each other, and the comprehensive evaluation value can better reflect the investment and construction effect of diversified and high elastic power grid.
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Region 1
Region 2
Region 3
Fig. 1. Comparing the comprehensive evaluation values of each region
4 Conclusion The subjective empowerment method in the combination evaluation method mainly refers to AHP, and the objective empowerment method refers to the entropy right method. Combined with the connotation of the attributes of diversified and high elastic power grid and the characteristics of various indicators, a variety of evaluation methods are combined to effectively carry out a scientific, rational, comprehensive and effective comprehensive evaluation of diversified and high elastic power grid. Theoretically, the combination evaluation method is more reasonable and applicable than the single evaluation method, it can be said that the combination evaluation method plays a role in promoting the long and short to a certain extent, improving the accuracy and applicability of the method, and helping to make a more reasonable evaluation of the evaluation object. Acknowledgment. The study is funded by Huzhou Power Supply Company of SGCC, project title “Re-search on theoretical framework of diversified and high elastic power grid” under Grant no. SGZJHU00FZJS2000611.
References 1. Zhou, B., Yang, X., Li, J., Nong, R., Chen, W.: Summary of Key Technologies for Multi-Fusion High Elastic Grids 2. Yu, J., Xia, Q.: The concept design and exploration of multi-fusion high elastic power grid under the form of energy internet China. J. Electr. Eng. 41(02), 486–497 (2021) 3. Xia, Y., Wu, H., Xin, J.: Micronet economic operation evaluation method that takes into account the complementary characteristics of wind/light/water/storage multi-source. Power Autom. Equip. 37(07), 63–69 (2017) 4. Zeng, X., Liu, T., Wang, S., Li, B., Li, X., Zeng, W.: The flexible DC grid of wind farms coordinates control strategies with the source network of traditional DC delivery. Grid Technol. 41(05), 1390–1398 (2017)
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5. He, D.: Source network lotus storage interaction to drive energy change. Business Administration (01), 73–74 (2019) 6. Peng, C., Yu, H., Sun, H.: Joint system planning for distribution network optical storage based on the collaborative optimization of the source network load. Grid Technol. 43(11), 3944–3951 (2019) 7. Hua, S.I.: Study on the Theory and Methodology of Multi-indicator Comprehensive Evaluation. Xiamen University (2000) 8. Mei, Q., Lu, Y., Ji, M.: Analysis and research on the AHP-fuzzy comprehensive evaluation method. J. Chin. Saf. Sci. (07), 89–92 (2004) 9. Laxminarayan, S.: Some score functions on Fermatean fuzzy sets and its application to bride selection based on TOPSIS method. Int. J. Fuzzy Syst. Appl. (IJFSA) 10(3) (2021) 10. Wang, Z., Qian, Z.: Indicator empowerment based on the worst-case scenario and its application. Syst. Eng. Electron. (11), 5–7–93 (2002)
A Dual-Loop Control Strategy for Interlinking Converters in Hybrid AC/DC Microgrids Yuwei Zhang1 , Qian Xiao1(B) , Zhipeng Jiao2 , Wenbiao Lu1 , Jin Xu1 , Yunfei Mu1 , and Hongjie Jia1 1 Key Laboratory of Smart Grid of Ministry of Education,
Tianjin University, Tianjin 300072, China {yuweizhang,xiaoqian,wenbiaolu,xujin,yunfeimu,hjjia}@tju.edu.cn 2 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Abstract. A dual-loop control strategy for interlinking converters (ICs) has been proposed in this paper. Firstly, in the microgrids (MGs), droop control provides frequency and voltage reference. Then, for IC, a dual-loop control structure is adopted. The outer loop is based on the droop characteristic to achieve the control objective; the inner loop is based on the idea of constant power control to achieve the tracking without static error of the power reference value. This strategy reduces the communications effectively, and the IC only operates in two modes that operation mode and power distribution mode are optimized. The control structure is simple and extensible. Finally, the hybrid AC/DC MG system has been built in MATLAB/Simulink, and the simulation results verify the effectiveness of the control strategy. Keywords: Microgrid · Hybrid AC/DC microgrid · Interlinking converter · Dual-loop control
1 Introduction In recent years, the massive exploitation of traditional fossil energy has led to many environmental pollution problems. At the same time, in order to actively respond to the “carbon peak” and “carbon neutrality” policy, and to build a new power system with new energy as the main part, MG is an effective way to connect a large amount of new energy to the power grid, which can effectively improve the efficiency of renewable energy utilization, reliability of power supply on the customer side, and promote the optimization of distribution grid system structure and enhance the operation quality. The MGs can be classified into AC MGs, DC MGs and hybrid AC/DC MGs according to the form of power supply. At present, AC MGs have been studied in terms of control strategies and operational stability [1–4], and DC MGs will become the most important form of MG energy supply in the future because of their high energy utilization and low control complexity, which have been studied by many scholars [5–8]. Hybrid AC/DC © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 954–964, 2022. https://doi.org/10.1007/978-981-19-1528-4_98
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MGs can adapt to different types of power sources and loads, effectively reduce the input rate of customer-side converters, and reduce unnecessary harmonic waves. In addition, it saves a lot of cost in the construction process, and the AC part can directly realize grid-connected operation, which can comfortably cope with the diverse demands of the generation side and load side with more flexible control forms. To name a few, Mi., Yang. et al. focus on the control strategies, energy storage devices and new topologies in hybrid AC/DC MGs in the direction of in-depth research [9–11]. Although the hybrid AC/DC MGs have the above advantages, the operational control of ICs between MGs has been a research challenge. The primary purpose of IC control is to transfer power to achieve reasonable cooperation among controllable units in the MGs, effectively cooperate with two MGs to deal with external disturbances, and improve the stability of hybrid AC/DC MG system [12, 13]. Most of the previous studies need large of communications or divide the working process of IC into multiple modes [14], which is not only complex in control, but also relies heavily on communications and has poor operational reliability. In order to remedy the above defects, this paper proposes a dualloop control strategy for ICs, where the outer loop is based on the droop characteristic to achieve the control objective that distributes the output power of the leading unit according to its capacity ratio; the inner loop is based on the idea of constant power control to achieve static-free tracking of the outer loop power reference value. Mi, Y. et al. have proposed a power coordinated control strategy for hybrid AC/DC MG [9], which realizes error free operation of frequency and voltage. However, the droop control of IC is divided into several sections; in the simulation, the output power of the controllable units of each subnet is greatly different. Although the frequency and voltage are stable, the purpose of optimizing power allocation is not achieved. Compared with the traditional strategies, this strategy has the following characteristics: (1) The communication of each unit in the system is reduced. This strategy only collects frequency and voltage, and does not need each controllable unit to transmit its own power, load and output power status information, which greatly reduces the communication traffic. (2) The operation modes of the IC are simple. The IC of this strategy only contains two modes: positive power transmission and negative power transmission which does not require a large number of event determination links. (3) The adjustment speed is fast. The proposed control strategy uses the droop formula as a linear equation to calculate and control the power directly, with less error and faster regulation speed. (4) The strategy is extensible. Different compensation terms can be added to the droop control of the leading unit to achieve various control objectives of the system. (5) The energy efficiency of DG is improved and the energy distribution of the system is optimized.
2 Research on Microgrid Control Strategy The hybrid AC/DC MG system studied in this paper is shown in Fig. 1, which includes an AC MG and a DC MG, and the IC to achieve reasonable power mutual aid. The
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MGs are equipped with a leading unit and an auxiliary unit. The leading unit (such as the energy storage system (ESS)) provides voltage and frequency reference for the system through droop control. The auxiliary unit (such as PV, constant power load) adopts constant power control to simulate the source side output fluctuation and load side disturbance. AC system and IC both use 2-level voltage source inverter topology, DC system in order to achieve bidirectional power transmission, using bi-directional DC-DC converter (BDC) [15]. Since this paper focuses on the power interaction between the AC MG and the DC MG, both distributed generations (DGs) and ESSs are equivalent to a DC voltage source. The following content will classify the two control strategies according to the power supply mode and describe them.
DC MG
AC MG PV
PL,ac
DC
PL,dc
AC
DC DC
PIC
PV
AC ESS
DC
PA,ac
DC
PA,dc
IC
AC
Local load
DC DC
ESS
Local load
Fig. 1. The hybrid AC/DC MG topology.
2.1 Introduction of ACMG Control Strategy In the AC MG system, droop control connects frequency with active power and voltage amplitude with reactive power, so as to realize the load allocation of each DG in a certain proportion. A common droop control process is shown in Fig. 2. The structure of “Droop control + PR control” is adopted and the PR controller is used to realize the droop characteristic by adjusting the voltage of the inverter port. ⎧ PL,ac ∗ ⎪ ⎪ ⎨ ωac = ωac − k P,ac Q ⎪ L,ac ∗ ⎪ ⎩ Eac = Eac − kQ,ac
(1)
where PL,ac and QL,ac are the actual output power of the leading unit in the ACMG ∗ are respectively the angular respectively; kP,ac and kQ,ac are the droop gains; ωac and ωac ∗ are respectively frequency reference value and its rating value of the AC MG; Eac and Eac the reference value and rating value of the output voltage amplitude. It is worth noting that: the above parameters are the per-unit values, the corresponding reference value is the rating value. The essence of constant power control in AC MG is to realize the constant output of power by decoupling active power and reactive power, independently controlling the inductor current id with reference active power, and independently controlling the
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AC Bus Inverter iLabc Udc
iabc
uabc
Cdc L
C
Sampling
Droop control *
ω ωac+ -
SPWM vabc + PR
θ ω /s 0 AC Voltage Eac,ref Eac Loop E0
-
ac
PL,ac
1/kP,ac
-
1/kQ,ac + * QL,ac E ac
dq Power calculation
dq
iabc abc uabc abc
Fig. 2. Droop control block diagram of ACMG’s leading unit.
inductor current iq with reference reactive power. According to the above analysis, the structure of “Outer power loop + Inner current loop” can be obtained. However, the structure of double loop control is not only complex, but also difficult to adjust the parameters. In order to reduce the complexity, it can be assumed that the voltage vector coincidences with the D-axis in the Park transformation, and the capacitor current of the inverter is considered to be small enough. Then, it can be simplified according to the structure of “Inductor current single loop” shown in Fig. 3, and the reference power can be tracked without static error by controlling the inverter port voltage.
AC Bus
Inverter iLabc Cdc
iabc
L
C
Constant power control
SPWM ud +
+
uPd abc
PI -
ωL
dq
ωL +
uPq +
+
+ iref,Ld 2/(3u ) Pref d
iLacd iLabc dq abc iLacq Sampling uabc ud dq abc uq
iLd iLq -
PI
uabc
+ iref,Lq
-2/(3uq)
Qref
uq
Fig. 3. Constant power control block diagram of ACMG’s auxiliary unit.
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2.2 Introduction of DCMG Control Strategy The droop control of DC MG is similar to that of AC MG. When the system enters a steady state, the bus voltage and output active power of the leading unit satisfy the following droop characteristic: ∗ − uref,dc = udc
PL,dc kdc
(2)
∗ are respectively the reference voltage and rating value of the DC Where uref,dc and udc MG bus; PL,dc is output power; kdc is the droop gain. Then, based on Eq. 2 and taking the duty ratio of BDC as the controlled object, the structure of “Outer drooping loop + Voltage current double closed-loop”, as shown in Fig. 4, can be obtained. By controlling the duty ratio [16], this structure realizes the tracking of bus voltage to reference value without static error by using PI controller.
iL,dc Droop control PL,dc
1/kdc
-
+ u
* dc uref,dc
VT1 iLL L VT2
Inner loop control +
- udc PI
iref,LL +
- iLL PI
dL
uL,dc
VD1 C
udc
VD2
Fig. 4. Droop control block diagram of DCMG’s leading unit.
Traditional DC MG constant power control using “Outer power loop + Inner inductor current loop” structure. Assuming that there is no loss in the process of power transmission in BDC, that is, the input power is equal to the output power, then the reference value of inductor current can be directly calculated by using the power reference value, which saves the outer loop PI controller and reduces the control complexity. Finally, the structure of “Inductor current single loop” is shown in Fig. 5. iA,dc
Pref,i
Constant power control + i dA PI 1/uA,dc ref,AL iAL
uA,dc
VT1 iAL L VT2
VD1 C
udc
VD2
Fig. 5. Constant power control block diagram of DCMG’s auxiliary unit.
3 Research on Control Strategy for the IC At present, most of the research targets of droop control are to distribute the load reasonably according to the rated capacity of each unit. In the hybrid AC/DC MG system
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studied in this paper, the control objective can be understood as making the ratio of the actual output power of the leading unit in each MG equal to the ratio of their rated capacities. It can be further explained as follows: First, assume that the ratio of the rated capacities of the two leading units is as follows: ∗ ∗ : PL,dc =m : 1 PL,ac
(3)
∗ ∗ where PL,ac and PL,dc are the rated capacities. According to Eq. 3, it is expected that the IC can make the output power of each leading unit operate according to the capacity ratio by transferring power between the two MGs, and finally achieve reasonable load distribution. In order to achieve the above objective, this paper proposes a dual-loop control strategy for ICs in hybrid AC/DC MG systems. The following will be described in the order of outer loop and inner loop control.
3.1 Realization of Outer Loop Control Strategy for the IC Design outer loop control structure of the primary purpose is to achieve the control objective, the IC control strategy is based on the characteristic that the droop control establishes the relationship between power and frequency/voltage, by collecting the AC system frequency and DC bus voltage, the actual output power of each MG’s leading unit is obtained by using the droop formula. Finally, the power uncoordinated quantity of the leading unit required by the outer loop is obtained by using Eq. 4: ∗ ∗ − udc ) − kac (ωac − ωac ) (4) PL = m kdc (udc where PL is the power uncoordinated quantity of leading units. After the power uncoordinated quantity is obtained by Eq. 4, it is introduced into the PI controller to form an outer loop structure. When the two MGs run smoothly in the ideal state, PL should be zero. Thus, the control objective is realized. This strategy only collects frequency and voltage, and does not need each controllable unit to transmit its own power, load and output power status information, which greatly reduces the communication traffic. What’s more, the method is extensible. Different compensation terms can be added to the droop control of the leading unit to achieve various control objectives of the system. For example, the adjustment items of cost can be introduced in the droop control, the economic power distribution is achieved by the control strategy. 3.2 Realization of Inner Loop Control Strategy for the IC After the power reference value is obtained by PI controller in the outer loop, the main objective of the inner loop control is to make the IC output power track the reference value accurately. After the actual transmission power of IC controlled by the inner loop structure meets the reference value, the control objective of the interconnected system can be achieved together with the outer loop control. The outer loop power reference value is introduced into the “Inductor current single loop” structure mentioned above to obtain PWM voltage. Finally, the power reference value is tracked without static error by controlling the output voltage. To sum up, the outer loop and inner loop of IC are carried
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out, and the following control block diagram is obtained in Fig. 6. The expressions of the outer loop and inner loop can be obtained as follows: Pref = (kop +
koi )PL s
(5)
⎧ 2 Pref ⎪ ⎪ ⎨ iref,Lacd = 3 ud ⎪ ⎪ u = (k + kii )(i ⎩ ip Pd ref,Lacd − iLacd ) − ωLiLacq + ud s
(6)
where Pref is the reference value of inner power loop; iLac and iref,Lac are inductor current and its reference value respectively; kop , koi , kip , kii are PI controller coefficients respectively. DC Bus AC Bus
IC iLabc Cdc
L
+
-
ωL
dq
ωL +
iLacd
Outer loop control
+
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2/(3ud)
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+
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Qref
iLacq ud uq
ωac udc
Eq.4 u
iLacd -
PI +
+
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uq
C
ω*ac +
uPd
uPq
uabc
Inner loop control
SPWM ud
abc
iabc
dq dq
*
dc
iLabc abc abc
uabc Sampling
Fig. 6. The proposed control strategy block diagram for IC.
Through the above control strategy, the multi-aspect control objectives of the hybrid AC/DC MG system can be achieved. Firstly, from the perspective of MGs, the output power of the leading unit on both sides is distributed according to its capacity ratio, which improves the energy utilization efficiency of the leading unit and reduces the reserve capacity in the MGs. From the perspective of hybrid AC/DC MG system, the operation mode and power distribution mode of the system are optimized, so that it can effectively deal with various disturbances and achieve reasonable energy mutual assistance between the two MGs.
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4 Simulation Analysis 4.1 Simulation Parameters In order to verify and test the effectiveness and anti-disturbance ability of the AC/DC hybrid MG control strategy, the system structure as shown in Fig. 1 is built in MATLAB/Simulink. The basic parameters of the simulation model are as follows: (1) in order to facilitate observation, the ratio of the rated capacity of the leading unit of the two subnets is set as 1:1 in this paper, and the rated power is selected to be 100 kW; (2) in the AC system, the rated frequency and voltage are selected to be 50 Hz and 380 V; (3) the bus voltage of the DC system is selected to be 800V. Other detailed parameters in the model are shown in Table 1, 2 and 3. Table 1. Hardware and control parameters of the AC MG. Unit
Subsystem
Parameter
Value
Leading unit
Hardware parameters
Voltage source
900 V
LC filter
0.25 mH; 150 µF
Switching frequency
5000 Hz
P/ω droop control
Rated frequency; Droop gain
1; 40
PR controller
kP ; kR ; ωc
8; 10; 0.5 rad/s
Hardware parameters
Voltage source
900 V
LCL filter
0.4 mH; 100 µF; 0.1 mH
Switching frequency
5000 Hz
kP ; ki
0.2; 50
Auxiliary unit
PI controller
Table 2. Hardware and control parameters of the DC MG. Unit
Subsystem
Parameter
Value
Leading unit
Hardware parameters
Voltage source
600 V
LC filter
0.5 mH; 10000 µF
Auxiliary unit
Switching frequency
5000 Hz
P/U droop control
Rated voltage; Droop gain
1; 10
Voltage PI controller
kwp ; kwi
5; 100
Current PI controller
knp ; kni
0.01; 1
Hardware parameters
Voltage source
600 V
LC filter
0.5 mH; 10000 µF
Switching frequency
5000 Hz
kp ; ki
0.01; 1
PI controller
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Unit
Subsystem
Parameter
Value
IC
Hardware parameters
LC filter
0.6 mH; 150 µF
Switching frequency
10000 Hz
Outer loop PI controller
kop ; koi
1; 50
Inner loop PI controller
kip ; kii
0.1; 50
4.2 Simulation Results In the simulation process, the power reference values of the two MGs’ auxiliary units step repeatedly to simulate the continuous fluctuation of the source side output and the load side demand in the system. The AC side is connected with a load which rated power is 30 kW. Four disturbances are set in the simulation to test the response ability of the system under source side fluctuation and load side disturbance. The output power of the two leading units, the frequency of the AC system and the DC bus voltage, the IC transmitted power and the power reference instruction of the two auxiliary units are shown in Fig. 7. All the simulation results are expressed in per-unit values. When the model reached the first steady state, the reference power of the MGs’ auxiliary units were −1 and 0.5, respectively. According to simple calculation, the total load demand of the current system was 0.8. At this time, the theoretical output power of the leading unit on both sides was 0.4. As shown in Fig. 7(a) and (b), the actual output power was about 0.4 on both sides, which proved the effectiveness of the control strategy in steady state. t = 0.5 s: The load disturbance occurred in the system, and the reference power of the ACMG auxiliary unit stepped from −1 to −0.5 in Fig. 7(f). The simulation results showed that the system can respond to the disturbance in time. The IC reduced its positive transmission power to make the system transition to a new steady-state operation point, and the leading unit on both sides of the two MGs re-equalized the load. t = 1.5 s: The reference power of ACMG’s auxiliary unit was −0.8. At this time, AC side load increased, IC increased its positive transmission power to share the load. t = 2.5 s: The reference power of the DCMG’s auxiliary unit stepped from 0.5 to 0.2, the output of DG at DC side decreased. At this time, IC reduced its positive transmission power to cope with the output fluctuation and achieved equal power distribution. t = 3.5 s: The reference power of the ACMG’s auxiliary unit stepped from −0.8 to −0.4, and the reference power of the DCMG’s auxiliary unit stepped from 0.2 to −1.3. A large load disturbance occurred in the DCMG. As can be seen from Fig. 7(e): at this point, IC quickly responded by switching from the positive power transmission mode to the negative power transmission mode to re-equalize the load. From the observation of Fig. 7(a)–(d), it can be seen that the output power of the leading units followed the drooping characteristic under four disturbances, and the frequency and voltage support are quickly established after the disturbances. As can be seen from Fig. 7(e), IC can respond quickly to system disturbance in the simulation process
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to maintain the realization of the control objective. The simulation results showed that the system had strong anti-disturbance ability. 1.15 1 0.8 0.6 0.4 0.2 0 -0.2 -0.3 0.5 1 1.5 2.0 2.5 3.0 3.5 4.0 4.5
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Fig. 7. Simulation results: (a) output active power of AC MG’s leading unit, (b) output power of DC MG’s leading unit, (c) frequency of AC system, (d) voltage of DC bus, (e) output active power of IC, (f) reference power of auxiliary units.
5 Conclusion This paper proposes a dual-loop control strategy for ICs in hybrid AC/DC MGs. The simulation results show that the IC can achieve reasonable power distribution according to the established control strategy even after the system is continuously disturbed. In addition, the IC only operates in two modes, and only needs to collect frequency and voltage to achieve the control objective. This strategy effectively reduces the control complexity and communications, achieves the control effect of simple and easy to implement. In the future, the corresponding compensation items can be added to the MG droop control, and the diversified control objectives can be achieved through the dual-loop control strategy.
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Acknowledgment. This work was funded in part by China Postdoctoral Science Foundation (Grant No. 2020M680880), and partly supported by the joint project of NSFC of China and EPSRC of UK (No. 52061635103 and EP/T021969/1).
References 1. Lu, R., Wang, J., Wang, Z.: Distributed observer-based finite-time control of AC microgrid under attack. IEEE Trans. Smart Grid 12(1), 157–168 (2021) 2. Tu, C., Huang, H., Lan, Z., et al.: Coordinated control strategy of power electronic transformer and energy storage in microgrid. Trans. China Electrotech. Soc. 34(12), 2627–2636 (2019). (in Chinese) 3. Hong, H., Gu, W., Qiang, H., et al.: Power oscillation damping control for microgrid with multiple VSG units. Proc. CSEE 39(21), 6247–6255 (2019). (in Chinese) 4. Saim, A., Houari, A., Guerrero, J.M., et al.: Stability analysis and robust damping of multiresonances in distributed-generation-based islanded microgrids. IEEE Trans. Ind. Electron. 66(11), 8958–8970 (2019) 5. Chaturvedi, S., Fulwani, D.: Adaptive voltage tuning based load sharing in DC microgrid. IEEE Trans. Ind. Appl. 57(1), 977–986 (2021) 6. Wang, K., Zhang, X., Chung, H.S.: Solid-state single-port series damping device for power converters in DC microgrid systems. IEEE Trans. Power Electron. 34(1), 192–203 (2019) 7. Sahoo, S., Peng, J.C., Mishra, S., et al.: Distributed screening of hijacking attacks in DC microgrids. IEEE Trans. Power Electron. 35(7), 7574–7582 (2020) 8. Xiao, Q., Chen, L., Jia, H., et al.: Model predictive control for dual-active-bridge in naval DC micro-grids supplying pulsed power loads featuring fast transition and online transformer current minimization. IEEE Trans. Industr. Electron. 67(6), 5197–5203 (2020) 9. Mi, Y., Song, G., Cai, H.: Autonomous coordinated control of hybrid AC/DC microgrids based on segmented droop. Power Syst. Technol. 42(12), 3941–3950 (2018). (in Chinese) 10. Jia, H., Xiao, Q., He, J.: An improved grid current and DC capacitor voltage balancing method for three-terminal hybrid AC/DC microgrid. IEEE Trans. Smart Grid 10(6), 5876–5888 (2019) 11. Xiao, Q., Mu, Y., Jia, J., et al.: Modular multilevel converter based multi-terminal hybrid AC/DC microgrid with improved energy control method. Appl. Energy 282, 116–154 (2021) 12. Li, X., Guo, L., Li, Y., et al.: A unified control for the DC-AC interlinking converters in hybrid AC/DC microgrids. IEEE Trans. Smart Grid 9(6), 6540–6553 (2018) 13. Eghtedarpour, N., Farjah, E.: Power control and management in a hybrid AC/DC microgrid. IEEE Trans. Smart Grid 5(3), 1494–1505 (2014) 14. Xu, Q., Xiao, J., Wang, P., et al.: A decentralized control strategy for economic operation of autonomous AC, DC, and hybrid AC/DC microgrids. IEEE Trans. Energy Convers. 32(4), 1345–1355 (2017) 15. Zhu, S., Wang, F., Guo, H., et al: Overview of droop control in DC microgrid. Proc. CSEE 38(1), 72–84+344 (2018). (in Chinese) 16. Li, X., Guo, L., Li, Y., et al.: Flexible interlinking and coordinated power control of multiple DC microgrids clusters. IEEE Trans. Sustain. Energy 9(2), 904–915 (2018)
Force Simulation of Four Bundle Spacer Under Short Circuit Condition Wang Lei1 , Zhao Jianli1 , Wang Liang2(B) , and Zhao Zijian1 1 Inner Mongolia Power (Group) Co., Ltd., Inner Mongolia Power Research Institute Branch,
Hohhot, China 2 Huazhong University of Science and Technology, Wuhan, China
[email protected]
Abstract. The application of spacer is more and more widely, which will greatly affect the safe operation of transmission line when it is damaged. In this paper, ANSYS finite element simulation software is used to simulate the centripetal force of FJZ-445-27-80 spacer in case of short circuit. The stress status and weak points of each component of the spacer in the case of short circuit fault are defined. The simulation results show that: under the condition of short circuit, the displacement of the end of spacer clamp and the middle of frame changes the most; The maximum stress appears in the root and middle part of the connection between the spacer clamp body and the frame. The simulation results can provide a reference for the optimal design of the spacer, and provide a theoretical basis for the popularization and application of the spacer in EHV transmission lines. Keywords: Transmission line · Spacer · Clamp · Framework · Centripetal force
1 Introduction Most 500 kV transmission lines in China adopt four bundle conductor structure, and they all adopt frame type integral spacer. In order to ensure that the bundle spacing of bundled conductors remains unchanged to meet the electrical performance, reduce the surface potential gradient, and in the case of short circuit, there will be no electromagnetic force between the bundle of conductors, causing mutual attraction and collision, or even causing instantaneous attraction and collision, but the accident can be restored to the normal state after it is eliminated, so the spacer is installed at a certain distance in the span. As a kind of power fittings, the role of spacer in the transmission line is to keep a certain relative spacing position of multiple sub conductors in a phase conductor. Spacer plays a role in fixing conductor, preventing whipping between sub conductors, restraining breeze vibration and sub span oscillation. When the short-circuit fault occurs in the transmission line, the spacer will bear the centripetal force due to the shortcircuit current, which requires the material and structure of the spacer to meet certain requirements. The spacer itself can be regarded as a rigid body, but after connecting with © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 965–973, 2022. https://doi.org/10.1007/978-981-19-1528-4_99
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the splitting line, it can adjust to the uneven deformation between the splitting lines, so the interaction between the force system is complex. When the spacer is installed, it needs high-altitude operation, and when it is replaced, it needs live line operation. The operation effect is affected by the level difference of field workers, and it is easy to cause the failure of clamp loosening, falling off, rubber pad separation and so on. Once the spacer fails, the conductor is easy to be worn, which will affect the reliability of the transmission line [1, 2]. Because the spacer plays an important role in bundled conductors, many scholars at home and abroad have studied the spacer. In reference, the calculation results of ordinary bundle conductor spacer and rotary spacer are compared and analyzed [3–7]. In reference, the anti galloping of phase to phase spacer is studied. The calculation results of iced three-phase four bundle conductor after installing all kinds of phase to phase spacer are analyzed [4–11]. The change of line tension and galloping response before and after using spacer are compared. References analyzed the faults of the spacer such as short circuit and uneven icing. The stress of the spacer was analyzed by using the finite element analysis theory [12–17]. The stress of the spacer under the condition of short circuit and uneven icing was compared with that under normal operation. Literature studies the anti galloping effect of the rotating spacer under different conditions [18– 21]. The installation scheme of clamp rotary spacer is optimized, and the maximum sub span and the length of End Sub span are calculated. The effect of spacer on galloping suppression of double bundle conductors is analyzed in reference [22]. In this paper, based on ANSYS finite element simulation software, the simulation model of four bundle FJZ-445-27-80 spacer is established. By applying centripetal force to the spacer, the working condition of transmission short circuit fault is simulated. The simulation results can provide reference for the optimization design of spacer, and provide theoretical basis for the popularization and Application of spacer in UHV transmission line.
2 Modeling of Spacer 2.1 Physical Model The simulation model of four split FJZ-445-27-80 spacer is established, as shown in Fig. 1. The model size is the same as the real object, and the model includes four cantilevers, spacer middle frame, bolts, pins, etc. Due to the complex structure of spacer, in order to simplify the mesh generation process and shorten the solution time, the model is simplified appropriately. In the static analysis of the spacer, the damping effect of the rubber pad of the clamp and the damper at the joint of the clamp is not considered. Considering that the damper is in the initial state of high-pressure stress, it is difficult to impose prestress on the damper, and the damper is not the focus of our research, so the damper here uses the same material as the middle frame body. The model material is ZL102 cast aluminium alloy with tensile strength of 145 MPa. According to the working characteristics, the spacer is divided into damping spacer and non damping spacer. Its main function is to ensure that the spacing of split wire harness remains unchanged, so as to meet the electrical performance, reduce the surface potential gradient, prevent short circuit, and suppress aeolian vibration and sub span
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oscillation. Among them, FJZ-445-27-80 damping spacer is between the body and four holding claws with support, which uses the rubber pad embedded in the holding claw to damp, so as to eliminate the conductor vibration. At the same time, the movable elastic joint between the support and the body can be used to resist the conductor oscillation and torsion, so as to achieve the function of damping and vibration elimination of spacer. The splitting times of FDZ-445-27-80 damping spacer is 4, the splitting spacing is 0.45 m, the self weight is 86.8 N, and the splitting radius is 0.318 m. According to the material performance test results, the elastic modulus is 6.9 × 1010 N/m2 , Poisson’s ratio 0.33 and material density 2.7 × 103 kg/m3 , yield strength 2.76 × 107 N/m2 . The spacer damper is simulated by solid element, which is used to simulate three-dimensional solid structure. It has the characteristics of plasticity, creep, expansion, strong stress, large deformation and large strain capacity. Pressure can be applied as a surface load on each surface of the unit (positive pressure points to the inside of the unit). In the solid modeling of the spacer damper, the bottom-up method is adopted. Firstly, the key points of the spacer body are defined in the overall coordinate system, and then the lines, surfaces and bodies are defined in turn. The geometric model of FDZ-445-27-80 spacer damper can be obtained through the subtraction, pasting and other operations in Boolean operation.
Cantilever Bolt Middle frame
Wireway
Fig. 1. Three dimensional model of FJZ-445-27-80 spacer
2.2 Parameters and Loads The wire parameters are shown in Table 1. The failure points of the spacer are mostly concentrated around the clamp. This phenomenon is partly caused by the structure of the spacer itself, and the other part is related to the load on the spacer during operation. There are two kinds of loads that may exist in the operation of the spacer: during normal operation, the load of the spacer of the bundled conductor includes the load of the conductor transmitted through the clamp and the dead weight of the spacer, as shown in Formula 1. Where n is the number of splitters, F T is the tension of splitters, and L is the distance between two spacers, γ Is the radius of spacer split circle, β is the maximum
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torsion angle of conductor, and G is the dead weight of spacer. Under the condition of short circuit of transmission line, the current of split line increases. Due to the strong electromagnetic force between each other, the pressure pointing to the center of spacer is generated. This part of force acts on the spacer through the clamp, as shown in Formula 2. Where, I m is the maximum short circuit current, S is the diameter of split circle, and D is the diameter of sub conductor. When the line is short circuited, the spacer mainly bears the load, as shown in Formula 3. 1 FT γ sinβ + G L 4 √ n−1 S F2 = 3.132 Im FT lg n D F1 = −3n
(1) (2)
F = F1 +F2
(3)
Table 1. Parameters of fjz-445-27-80 spacer Elastic modulus
Conductor diameter
Conductor tension
Maximum short circuit current
Maximum torsion angle
69000 MPa
26.82 mm
25.975 kN
75 kA
50°
3 Results and Analysis 3.1 Boundary Condition Setting Under short-circuit condition, the bundle conductor generates pressure to the center of the spacer. The boundary conditions imposed on the model in the simulation are shown in Fig. 2. Meanwhile, considering that gravity and other factors are very small relative to the centripetal force load, its influence is not considered here. According to the requirements of centripetal force test for sub spacer in technical conditions and test methods for spacer (DL/T 1098-2009), this test is to test the ability of spacer to bear compressive force during conductor short circuit. If the tension is applied in the horizontal direction, and then the tension is gradually increased, so that the centripetal force acting on the sub spacer reaches the required value, and the sub spacer does not appear deformation and damage, the test passes. In this paper, according to the above experimental content, the finite element simulation analysis of the quad splitter spacer is carried out. 3.2 Force Analysis After the stress is applied, the stress and deformation of the spacer are shown in Fig. 3. According to the stress nephogram 3a of the spacer, under the condition of centripetal
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Fig. 2. Boundary condition setting of spacer
force, there is stress concentration at the cantilever clamp of the spacer, and the maximum stress exceeds the yield strength of the material, and the material has yielded at the cantilever corner. The strain nephogram 3b of the spacer also shows that the deformation at the corner of the cantilever clamp is large. It can be seen that the clamp is the most vulnerable component in the whole structure. Under the condition of short-circuit centripetal force, the stress nephogram and strain nephogram at the cantilever clamp are shown in Fig. 4, and the stress nephogram and strain nephogram at the middle frame are shown in Fig. 5. The maximum stress at the cantilever clamp is 143 MPa, the maximum stress at the middle frame is 75 MPa, and the maximum stress at the cantilever is about twice that at the middle frame. The maximum displacement in X direction of the middle frame is 0.06 mm, which appears in the middle
(a)Stress nephogram Fig. 3. Simulation results of spacer.
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of the frame; The maximum displacement in Y direction of cantilever clamp is 0.08 mm, which appears at the end of spacer clamp.
(a)
Fig. 4. Simulation results of cantilever clamp
(a)
Strain nephogram
Fig. 5. Simulation results at middle frame.
3.3 Comparison of Results As shown in Fig. 6, through the physical test of power characteristics, the parameters such as energy dissipation, resonant frequency and impedance of the damper can be obtained. The power characteristics of the damper are tested on an electromagnetic shaking table. The damper clamp is fixed on the shaking table, a sinusoidal exciting force is applied to it, the vibration speed, exciting force, amplitude and other signals of the damper are measured, and the power consumption characteristics and resonance
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frequency characteristics of the damper are obtained through data processing. The test frequency range is 5–100 Hz, and the linear scanning speed is 0.2 Hz/s. The test data are collected, processed and analyzed by the signal.
Fig. 6. Schematic diagram of damper power characteristic test platform
The frequency power curve of FDZ damper obtained through theoretical calculation and measured test is shown in Fig. 7. As can be seen from Fig. 7, there are three peak frequencies in the theoretical value power curve and the measured test power curve. Moreover, the peak power and peak frequency of the theoretical power curve are close to the measured test data, and the curve trend is basically consistent. At the same time, the positions of the peaks and troughs of the curve are roughly the same, and the overall trend is the same. In addition, there are differences in some minor parts in Fig. 7. The main reasons are as follows: ➀ during the test, the frequency increment of the pen involved is too large,
Theoretical Test
Fig. 7. Comparison test results of power characteristic curve of FDZ shock hammer
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and some special frequencies may be omitted; ➁ The external environment of the test will interfere with the data acquisition, which will cause that the data collected by the force sensor is not standard sine wave or cosine wave, and there is a certain error when reading the corresponding amplitude and phase difference; ➂ The theoretical model is not perfect, and the nonlinear influence of steel strand is not considered.
4 Conclusion In this paper, through the finite element modeling of four split FJZ-445-27-80 spacer, the centripetal load is applied to simulate the short circuit condition of transmission line. The conclusions are as follows: a) Under short-circuit condition, the displacement of the end of spacer clamp and the middle of frame changes the most. b) Under short-circuit condition, the maximum stress appears at the root and the middle of the frame. c) The simulation results indicate the direction for the future structural improvement, and the calculation model lays the foundation for the analysis of the short-circuit fault condition of the spacer in the EHV transmission line system.
References 1. Rathore, B., Mahela, O.P., Khan, B., et al.: Protection scheme using wavelet-alienation- neural technique based for UPFC-compensated transmission line. IEEE Access 9, 13737–13753 (2021) 2. Fan, W., Zhang, S.H., Zhu, W.D., et al.: Vibration analysis and band-gap characteristics of periodic multi-span power transmission line systems. Eng. Struct. 238, 111669 (2021) 3. Gonalves, V.M., Bolonhez, E., Campos, G., et al.: Transmission line routing optimization using rapid random trees. Electr. Power Syst. Res. 194(4), 107096(2021) 4. Hou, H., He, J., Pan, J., et al.: A K-band high-gain power amplifier with slow-wave transmission-line transformer in 130-nm RF CMOS. Int. J. Circuit Theory Appl. 49, 1347–1357 (2021) 5. Shang, X., Yang, J., Zhu, B., et al.: Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents (2021) 6. Chen, H.: The research on six breaks damping conductor spacer for 750 KV transmission line. Northwest China Electr. Power (2004) 7. Han, K., Lee, et al.: Fault type classification in transmission line using STFT. In: International Conference on Developments in Power Systems Protection. IET (2012) 8. Page. Describe road motorcycle components, systems, dynamics, and handling characteristics (2015) 9. Mukherjee, A., Kundu, P.K., Das, A.: Transmission line fault classification under high noise in signal: a direct PCA-threshold-based approach. J. Inst. Eng. (India) Ser. B 1–15 10. Khazaei, J.: Cyberattacks with limited network information leading to transmission line overflow in cyber-physical power systems. Sustain. Energy Grids Netw. 27, 100505 (2021) 11. Shin, H., Song, Y., Kong, P.Y.: Robust online overhead transmission line monitoring with cost efficiency in smart power grid. IEEE Access (2021)
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12. Xiang, L.X., Chen, B.F., Huo, G.Q., et al.: How organizational slack effect SMEs’ product innovation? Mediating role of centrifugal and centripetal forces. R&D Manage. 58(24), 33–38 (2013) 13. Huang, F.Y., Zhou, T., Wang, C.: Discussion on transmission line lightning fault detection. Hunan Electr. Power (2009) 14. Omar, A., Osman, M.K., Ibrahim, M.N., et al.: Fault classification on transmission line using LSTM network. Indonesian J. Electr. Eng. Comput. Sci. 20(1), 231 (2020) 15. Wei, L.I., Zhao, H., Jin, Y.Y.: Simulation of fault transmission line with PSCAD/EMTDC. Northeast Electr. Power Technol. (2015) 16. Varan, M., Ergüzel, A.T., Genç, H.H., et al.: Design and implementation of an open source transmission line impedance matching educational framework. Comput. Appl. Eng. Educ. (1) (2020) 17. Ghaedi, A., Golshan, M., Sanaye-Pasand, M.: Transmission line fault location based on threephase state estimation framework considering measurement chain error model. Electr. Power Syst. Res. 178, 106048.1-106048.11 (2020) 18. Davis, R.J., Bisharat, D.J., Sievenpiper, D.F.: Classical-to-topological transmission line couplers. Appl. Phys. Lett. 118, 131102 (2021) 19. Wang, D., Wu, K.: Mode-selective transmission line Part I: theoretical foundation and physical mechanism. IEEE Trans. Compon. Packag. Manuf. Technol. 10, 2072–2086 (2020) 20. Wang, Z., Dong, Y., Itoh, T.: Transmission line metamaterial inspired circularly polarized RFID antenna. IEEE Antennas Wirel. Propag. Lett. (2020) 21. Mukherjee, A., Kundu, P.K., Das, A.: Classification and fast detection of transmission line faults using signal entropy. J. Inst. Eng. (India) Ser. B (1) (2021) 22. Li, W., Qiao, L., Feng, C., et al.: Fault analysis of cement pole and wire in 110 kV transmission line. IOP Conf. Ser. Earth Environ. Sci. 634(1), 012081 (5pp)
Numerical Simulation of Diffusion Characteristics of SF6 Decomposition Products in Gas-Insulated Switchgear Yifan He1,2 , Chen Li1 , Yanliang He2(B) , Xiaoxin Chen1 , Wei Ding2 , Anbang Sun2 , and Guanjun Zhang2 1 Research Institute of State Grid Zhejiang Electric Power Limited Company, Hangzhou,
Zhejiang, China 2 State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical
Engineering, Xi’an Jiaotong University, Xi’an, China [email protected]
Abstract. As a mature insulation diagnosis method, chemical detection method based on SF6 decomposition products has been widely used in the operation and maintenance of gas-insulated switchgear (GIS). Due to the large volume of GIS, the axial diffusion process of decomposition products in the gas chamber has obvious influence on its concentration distribution, thus affecting the accuracy of insulation diagnosis. In this paper, a three-dimensional mass transfer model was established based on the Fick’s law and Fuller-Schettler-Giddings (FSG) equation. By setting several sampling ports, the diffusion characteristics of SF6 decomposition products, including SOF2 , SO2 F2 , S2 OF10 , CO2 , CF4 and C2 F6 , are investigated under the influence of gas generation rates, defect locations and partial discharge (PD) types. The corresponding time-domain characteristic curves are also obtained. The results show that the concentrations of decomposition products are in direct proportion to the gas generation rate. Locations of defects in radial direction have little influence on the concentration distribution. And the axial concentration has an exponential decrease with distance. The time-domain characteristic curves can be well applied to predict the defect positions and PD types. Keywords: Decomposition products · SF6 · Diffusion characteristics · Gas-insulated switchgear · Insulation diagnosis
1 Introduction Gas-insulated switchgear (GIS) is widely used because of its small footprint, long maintenance cycle and high reliability. However, as the number of GIS increases year by year, the faults caused by insulation defects such as partial discharge (PD) and partial overthermal are increasing [1, 2]. As the main insulating gas in GIS, SF6 gas will produce a series of decomposition products under the effect of insulation defects, which will affect the inner insulation performance of equipment [3]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 974–983, 2022. https://doi.org/10.1007/978-981-19-1528-4_100
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Compared with the traditional electrical detection method, SF6 decomposition products detection method is a kind of chemical detection method with strong antiinterference ability. It is conducive to fault type identification and has a good application prospect. In recent years, it has been widely concerned [4, 5]. At present, the decomposition characteristics of SF6 obtained by most research are mainly based on the discharge chamber in the laboratory [6, 7]. Different from the actual GIS in operation, the discharge chamber is small in volume and the discharge scale is relatively large. The products generated by discharge can quickly and evenly fill the whole space. In the actual GIS, the distance between insulation defects location and gas sampling port is far, and the discharge scale is smaller than the volume of gas chamber. During the diffusion process of decomposition products to the sampling port, the diffusion speed of different products is significantly different, and the product concentration will change with the diffusion distance and diffusion time, which will affect the accuracy of insulation diagnosis. Therefore, it is necessary to study the diffusion process and concentration variation of SF6 characteristic decomposition products in GIS. For the gas diffusion in SF6 , Taylor et al. [8] studied the thermal diffusion coefficient and intermolecular interaction potential of noble gas in SF6 . Zhong et al. [9] investigated the influence of Cu content and gas pressure on the four combined diffusion coefficients in SF6 /Cu gas mixtures, and the research is suitable for the decomposition and diffusion process of arc simulation in high voltage circuit breaker. Liu et al. [10] used one-dimensional model to simulate the time-domain characteristics of the main characteristic products in SF6 and SF6 /N2 gas mixtures, and analyzed the effects of temperature, pressure and gas-mixture ratio on the gas diffusion process. To sum up, there lacks a universal method of defect diagnosis and the significant role of gas diffusion characteristics has been neglected. The coverage of SF6 characteristic decomposition products is not comprehensive as well. In this paper, a three-dimensional simulation model is established in the actual chamber size of GIS, and the diffusion characteristics of decomposition products generated by partial discharge in SF6 gas are studied. Based on the diffusion laws of decomposition products, corresponding timedomain characteristic curves are obtained to predict defect positions and PD types.
2 Simulation Model 2.1 The 3D Model Geometry To simulate the actual diffusion process of the decomposition products in GIS, this paper constructed a 3D model according to the 252 kV GIS in [11], as shown in Fig. 1. The outer layer of the concentric cylinder is grounding shell. The inner layer is the surface of conductor. The side walls are the insulators. Sampling ports are situated on the outer layer, and the insulation defect is situated on the inner layer. The length of the model, the outer radius and the inner radius are set to 1 m, 0.2 m and 0.05 m, respectively.
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Fig. 1. 3D simulation model geometry.
2.2 The Governing Equations and Transfer Parameters According to Fick’s second law, the diffusion process of SF6 decomposition gas products in GIS can be described by solving the Eq. (1): ∂nj + ∇ · −Dj ∇nj = 0 ∂t
(1)
Where nj , Dj are the concentration (mol/m3 ) and the diffusion coefficient (m2 /s) which correspond to different decomposition products (SOF2 , SO2 F2 , S2 OF10 , CO2 , CF4 and C2 F6 ). t is the time (h). The boundary conditions and initial values are set as Eq. (2). ⎧ ⎪ ⎨ nSDefect = k · t ∇nSNDefct = 0 (2) ⎪ ⎩ n(x, y, z, t = 0) = 0 Where nSdefect is the concentration (mol/m3 ) on the surface of defect, nSNDefect is the concentration on the rest surface. k is the gas generation rate (mol/(m3 ·s)). The diffusion coefficient is the main factor determining the diffusion velocity and the flux of components. Its value is corelated with the gas pressure p, the temperature T, the relative molecular mass M and the molecular diffusion volume V d . It can be calculated through the Fuller-Schettler-Giddings (FSG) Eq. (3): 1 0.0101 × T 1.75 × M1G + MSF 6 DG/SF6 = (3)
1/3 1/3 2 p × VdG + VdSF6 Where T and p are the temperature (K) and gas pressure (Pa) of the system and in this model, they are set to 300 K and 0.45 MPa, M G and M SF6 is the relative molecular
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mass (g/mol) of the deposition gas product and SF6 , V dG and V dSF6 are the molecular diffusion volume (cm3 /mol) of the deposition product and SF6 . The diffusion coefficient of SOF2 , SO2 F2 and S2 OF10 under the gas pressure of 0.45 MPa have been calculated in the previous study [10]. We use the Tyn-Calus Eq. (3) to estimate the molecular diffusion volume of CO2 , CF4 and C2 F6 , and then calculate the diffusion coefficients using Eq. (4). V = 0.285VC1.048
(4)
Where V is the diffusion volume (cm3 /mol), V C is the critical volume (cm3 /mol). The relative parameters of decomposition products are listed in Table 1. Table 1. The critical volume and diffusion coefficient. Species
Critical volume (cm3 /mol)
Diffusion coefficient (m2 /s)
CO2
93.9
1.64 × 10–6
CF4
141
1.06 × 10–6
C 2 F6
221
8.2 × 10–7
SOF2
–
1.1 × 10–6
SO2 F2
–
9.9 × 10–7
S2 OF10
–
5.86 × 10–7
3 Simulation Results and Discussion The diffusion process of the decomposition products of SF6 in GIS is simulated in this section. The gas pressure is set to 0.45 MPa and the temperature 300 K, which is the working condition in GIS. The gas generation rate is set as 0.001 mol/(m3 ·s). 3.1 The Concentration Distribution The concentration distribution rules of different components are similar, thus here we take SO2 F2 at t = 500 h as an example, the spatial concentration contribution, the concentration curves at axial direction and their fitting curves are shown in Fig. 2.
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Fig. 2. Concentration distribution of SO2 F2 at t = 500 h. (a) When defect occurs at the end of conductor, the spatial distribution, the concentration curve along x-axis and its exponential fitting. (b) When defect occurs in the middle of conductor, the spatial distribution, the concentration curve along x-axis and its gauss fitting.
The concentration reaches highest point at the defect and it decreases with the distance increases. When the defect occurs at the end of the conductor, the distribution curve can be approximated to an exponential decay curve, defined by Eq. (5). When the defect occurs in the middle of conductor, the distribution curve can be approximated to a gauss curve, defined by Eq. (6).
y = 250 + 216 × e
−(x−2.85) 41
2597 ×e y = 452 + 30 × π 2
−2×
(5) x−50 30
2
(6)
3.2 The Effects of Defect Location on Diffusion Concentration To investigate the effect of axial location of defect on diffusion characteristics of decomposition products, we set the defect at the end of conductor (x = 0 cm), the middle of the conductor (x = 50 cm) and amid the two point at (x = 30 cm), as shown in Fig. 3. And for
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each axial location, we set three positions on the circle, signified as Position 1, Position 2 and Position 3. Eleven Sampling ports are situated on the top line of grounding shell, and interval is 10 cm.
Fig. 3. Defect and sampling point locations.
The Effect of Defect Location on the Circle of Conductor: We select Position a as the defect axial location and Position 1, 2, 3 as the location on the circle. Figure 4 shows the concentration variation of SO2 F2 and CO2 with time at sampling port 1, 6. The defect location on the circle of conductor at the same axial location has little effect on the concentration curves. And with the increasing axial distance between defect and sampling point, the effect weakens and the concentration curves are virtually the same. Moreover, the increase of diffusion coefficient will also abate the effect. Therefore, the position of the defect on the circle of conductor can be neglected and it renders insulation diagnosis convenient, which can be focused on the axial location of defects.
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Fig. 4. Concentration variation curves of SO2 F2 and CO2 at sampling port 1, 6 when defect occurs at Position 1, 2, 3 at x = 0 cm. (a) SO2 F2 at sampling port 1. (b) SO2 F2 at sampling port 6. (c) CO2 at sampling port 1. (d) CO2 at sampling port 6.
The Effect of Defect Location in Axial Direction: We select Position a, b, c as defect axial location, and for each axial location, position 1 is selected. Figure 5 shows the concentration variation of SO2 F2 at the sampling port 50 cm away from the defect in the axial direction. When the defect is close to the side walls (below 10 cm in axial direction), the concentration has a decrease even if in the same axial distance. Therefore, we can divide the GIS into two parts for the insulation diagnosis. When the distance away from insulator was above 10 cm, it can be classified as tip discharge or suspended discharge. And if the distance was below 10 cm, it can be classified as surface discharge.
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Fig. 5. Concentration variation curves of SO2 F2 at the sampling port 50 cm away from the defect in the axial direction. The defect occurs at Position a, b, and c.
3.3 The Time-Domain Characteristic Curves The defect situated at Position c can represent the tip discharge and the suspended discharge, the defect situated at Position a can represent the surface discharge, and the defect at position b can be treated as the critical point of these two states. Figure 6 is the time-domain characteristic curves of different decomposition products when the defect occurs at position b. From the Eq. (1), we can deduce that the diffusion concentration is in the direct proportion to the gas generation rate. Therefore, the obtained time-domain characteristic curves can be zoomed to match the time-domain characteristic curves obtained through experiment or during the overhaul of GIS. The type of PD can be inferred based on the calculated gas generation rate and the location of defects will also be possible to be determined.
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Fig. 6. Time-domain characteristic curves of different decomposition products when the defect occurs at position b.
4 Conclusion In this paper, a three-dimensional mass transfer model was established based on the Fick’s law and Fuller-Schettler-Giddings (FSG) equation. The diffusion characteristics of SF6 characteristic decomposition products is investigated, including SOF2 , SO2 F2 , S2 OF10 , CO2 , CF4 and C2 F6 . The results calculated by the 3D mass transfer model show that when the defect occurs at the end of the conductor, the distribution curve can be approximated to an exponential decay curve, and when the defect occurs in the middle of conductor, the
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distribution curve can be approximated to a gauss curve. Different positions of defect on conductor have little influence on the concentration distribution if the axial location is the same. The effect of axial position on the diffusion characteristics can be applied to insulation diagnosis through time-domain characteristic curves. Acknowledgements. This work was supported by State Grid Zhejiang Electric Power Limited Company Technology Project (5211DS190030).
References 1. Tang, J., Zeng, F., Pan, J., Zhang, X., Yao, Q., He, J., Hou, X.: Correlation analysis between formation process of SF6 decomposed components and partial discharge qualities. IEEE Trans. Dielectr. Electr. Insul. 20(3), 864–875 (2013) 2. Zhu, M., Wang, Y., Liu, Q., Zhang, J., Deng, J., Zhang, G.: Localization of multiple partial discharge sources in air-insulated substation using probability-based algorithm. IEEE Trans. Dielectr. Electr. Insul. 24(1), 157–166 (2017) 3. Zeng, F., Tang, J., Fan, Q., Pan, J., Zhang, X., Yao, Q., He, J.: Decomposition characteristics of SF6 under thermal fault for temperatures below 400°C. IEEE Trans. Dielectr. Electr. Insul. 21(3), 995–1004 (2014) 4. Li, J., Han, X., Liu, Z., Li, Y.: Review on partial discharge measurement technology of electrical equipment. High Voltage Eng. 41(8), 2583–2601 (2015). (in Chinese) 5. Fu, Y., et al.: Theoretical study of the neutral decomposition of SF6 in the presence of H2 O and O2 in discharges in power equipment. J. Phys. D: Appl. Phys. 49(38), 385203 (2016) 6. Ji, S., Zhong, L., Wang, Y., Li, J., Cui, Y., Wang, W.: SF6 decomposition of typical CT defect models. IEEE Trans. Dielectr. Electr. Insul. 22(5), 2864–2870 (2015) 7. Wang, Y., Ji, S., Zhang, Q., Ren, J., Li, J., Wang, W.: Experimental investigations on lowenergy discharge in SF6 under low-moisture conditions. IEEE Trans. Plasma Sci. 42(2), 307–314 (2014) 8. Taylor, W.L., Hurly, J.J.: Thermal diffusion factors and intermolecular potentials for noble gas-SF6 systems. J. Chem. Phys. 98(3), 2291–2297 (1993) 9. Zhong, L., Wang, X., Rong, M., Wu, Y., Murphy, A.B.: Calculation of combined diffusion coefficients in SF6 -Cu mixtures. Phys Plasmas 21(10), 103506 (2014) 10. Liu, M., Zou, J., Qiu, R., Li, Z., Zhou, W.: The computation of diffusion characteristics of decomposition gases in SF6 and SF6 /N2 within gas insulated transmission lines. Trans. China Electrotech. Soc. 35(11), 2478–2490 (2020). (in Chinese) 11. Wang, Y., Chang, D., Qin, S., Fan, Y., Mu, H., Zhang, G.: Separating multi-source partial discharge signals using linear prediction analysis and isolation forest algorithm. IEEE Trans. Instrum. Meas. 69(6), 2734–2742 (2020)
Electromagnetic Transient Calculation and Experiment of Intelligent Transformer Under DC Bias Magnetic Field Dongliang Lan1 , Minghua Zhu2 , Tengteng Hou2 , Zhiwei Chen3(B) , Fenglinzi Dan4 , and Beibei Liang3 1 East China Electric Power Test and Research Institute of China Datang Group Science and
Technology Research Institute Co., Ltd., Hefei, Anhui, China 2 Linggan Energy Science and Technology (Suzhou) Co., Ltd., Suzhou, China 3 School of Electric Engineering and Automation, Hefei University of Technology, Hefei,
Anhui, China [email protected] 4 Dangtu Power Supply Company of State Grid Anhui Electric Power Co., Ltd., Maanshan, Anhui, China
Abstract. DC magnetic bias is a phenomenon that occurs when the transformer is in a abnormal working state. It is of great significance to study the formation and influence of DC magnetic bias in power transformers. In this paper, based on the single-phase transformer model which is lightly to be affected by DC bias, the saturated electromagnetic transient of transformer core under the DC magnetic bias is calculated by using the finite element model. The effect of the DC bias on the transformer is studied. The excitation current, magnetic density distribution, core loss and current harmonic changes before and after the DC bias are given. The reasons for the increase of the dual harmonic are analyzed in detail. An analog circuit of DC magnetic bias is designed to simulate the factual working state of the DC bias condition. The DC magnetic bias simulation test circuit is built, the electromagnetic transient changes caused by DC magnetic bias are studied which verify the correctness of the proposed analog circuit scheme. At the same time, the electromagnetic transient calculation in this paper provides guidance for DC magnetic bias protection of DC transmission project. Keywords: Electromagnetic transient · DC bias · Transformer · Excitation current
1 Introduction DC magnetic bias is a phenomenon that occurs when the transformer is in abnormal working state. Generally, due to some reasons, the DC component existing in the transformer winding leads to the DC flux, finally making the core quickly enter the half-cycle saturation state. At this time, the shape of excitation current is positive and negative half-cycle asymmetry [1]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 984–993, 2022. https://doi.org/10.1007/978-981-19-1528-4_101
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The research and measurement of the DC bias at home and abroad shows that the geomagnetic storm and the large current flowing through the grounding pole of the DC system are the main reasons for the DC current flowing through the neutral grounding transformer [2–5]. There have been many cases of geomagnetic storms affecting the safe operation of power grids around the world, with many reports in Canada, the United States, Japan, Finland and other countries. Two powerful geomagnetic storms in 1989 and 2003 even caused blackouts in Quebec, Canada and Malmo, Sweden. From 2005 to 2007, China Electric Power Research Institute, North China Electric Power University and State Grid Electric Power Research Institute conducted in-depth research on the geodetic electric field distribution of the ground pole body of UHVDC transmission projects. The prediction calculation method of geodetic DC distribution was established. From 2006 to 2008, North China Electric Power University cooperated with Baoding Tianwei Group, China Southern Power Grid Technology Research Center and other units to conduct research on the magnetic field calculation model and calculation method of large power transformers in the case of DC bias. Preliminary research results have been obtained in the aspects of calculation methods and models of power transformers DC bias [2]. At present, a variety of measures to restrain DC magnetic bias have been formed. The following methods are commonly used, such as the reverse injection current method, the neutral point series resistance method and the neutral point series capacitance method. Now scholars have also put forward the neutral series resistance-capacitance method, line series capacitance method, potential compensation method and other methods. Generally, the magnitude of DC current can be limited by changing the impedance or the purpose of DC isolation is achieved by connecting capacitors in series. In addition, researches on DC magnetic bias mainly focus on the DC magnetic bias caused by the geomagnetic storms and DC grounding poles, while researches on the DC magnetic bias caused by the stray currents are few. Researches on the treatment of the DC magnetic bias caused by the stray currents are still in the theoretical research stage, with insufficient data support. Several obvious transformer DC bias magnetic phenomenon has occurred in China, which has serious impact on the security and stability of the power system, and even causes accidents. Because the transformer core saturation in a short time belongs to the electromagnetic transient process, will inevitably bring complex electromagnetic parameter changes [3]. Therefore, it is significant to carry out the distribution of transformer core magnetic density, excitation current, current harmonic and the change of core loss distribution under DC bias.
2 Generating Mechanism of DC Magnetic Bias In Fig. 1, the OA section of the core magnetization curve represents the normal operation of the transformer. In the design of the transformer, the rated operating main magnetic flux is usually set at A point. When the DC current enters into the transformer winding, the superposition of the DC magnetic flux and AC magnetic flux would form the total magnetic flux. When the AC current and the DC bias are in the same direction, the half cycle of the magnetic flux density increases greatly. And the other half cycle of the
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Fig. 1. Mechanism of transformer DC bias
Fig. 2. Distortion process of excitation current
magnetic flux density decreases. The excitation current waveform forms a sharp wave with positive and negative half-wave asymmetry [1, 5]. For the sake of highlighting the excitation current distortion process, the nonlinear saturation characteristic of the core is represent as a broken line of two slopes in Fig. 2. The slope of line segment O-A is the normal slope of the linear section of the magnetization curve, and the slope of line segment A-B is the slope of the saturated section of the magnetization curve. Excitation current waveform can be obtained from the broken-line magnetization characteristics. So as to ensure the high effectiveness of the principle simulation, the current waveform preserves the major characteristics of the initial excitation current waveform [3]. Among them, i is the excitation current.ib is the DC bias current. im is the fundamental frequency excitation current. ih is the magnetic saturation additional excitation current. I is the effective value. I m is the peak value. ϕ is the magnetic flux. ω is the angular frequency. t is the time [4]. The coordinate of the intersection of the two broken lines in the broken-line magnetization characteristic model is (im , ϕ m ), the saturation depth of core material and the initial angle of excitation current distortion are indicated by h (magnetic saturation) and α (folded corner), respectively [6–8]. The sum of the DC bias current ib , the normal fundamental frequency excitation current im and the magnetic saturation additional excitation current ib is equal to the total excitation current if . Among them, the fundamental frequency excitation current im = I m sin(ωt). The magnetic saturation additional excitation current ih in the interval α ≤ ωt ≤ π -α can be expressed as [9]: ih = (h − 1)(Im sin(ωt) − Im + Ib )
(1)
In other intervals from 0 –2π, ih = 0. The DC component I contained in the additional excitation current ih of magnetic saturation is I=
(h − 1) 2 Ib (2Im − Ib ) − (π − 2α)(Im − Ib ) 2π
(2)
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The total DC current in the excitation current is I = I + Ib
(3)
It is equivalent to the DC component forced to flow into the transformer winding through the neutral wire. The expression of the peak value of the excitation current, the effective value of the excitation current, the fundamental frequency component and the high-order harmonic component can also be derived.
3 Harmonic Analysis As shown in Fig. 2 above, it is the no-load current waveform of the transformer under the DC bias. When there is no DC bias current, the excitation reactance is only related to the induced potential and the no-load current. When DC bias occurs, the excitation reactance is related to the induced potential and the excitation current is generated under the combined action of the AC and DC component [10]. Analyzing Fig. 2, it can be seen that when the core is in the unsaturated zone. A Le = L0 = 0.4π μ0 Ne2 l Considering the unsaturated zone, there is:
(4)
die = Rie (5) dt When the core enters the saturation zone, the inductance of the excitation winding is approximately L e = 0. So there is: ⎧ ⎨ U = L die (Unsaturated zone) 0 0 (6) dt ⎩ U0 = ie R(Saturation zone) L0
Assuming that the even harmonic components in ie are iek (k = 2,4, · · · ), then U0k = Uek + iek R(k = 2, 4, · · · )
(7)
Since the superposition of even harmonics of sinusoidal voltage is equal to 0, there is U0k = −iek R(k = 2, 4, · · · )
(8)
Therefore, an even harmonic voltage appears in the excitation winding and an even harmonic magnetic flux is generated through the feedback of the loop resistance.
4 Finite Element Modeling of DC Bias The DC bias simulation model of the single-phase transformer was carried out. Due to the symmetry of the transformer, its physical model is reasonably simplified. A twodimensional symmetrical single-phase transformer model is established in the finite element simulation software comsol as indicated in Fig. 3.
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Fig. 3. Finite element model of single-phase transformer
4.1 Excitation Current and Harmonic Analysis When the transformer is under the no-load condition and the DC bias current I dc = 0 A, the current waveform and harmonic distribution on the primary side are shown in Fig. 4. Obviously, the waveform of the figure is not a sine wave in the strict sense, which shows that the transformer core has already saturated at this time. Although the transformer works in the saturation zone, the waveform is still symmetrical because there is no bias field. The harmonic distribution shows that the current waveform is mainly composed of the fundamental component. The odd harmonics also have a certain proportion, mainly the third and fifth harmonics. And there are almost no even harmonics.
0.4
0.2
FFT/%
I/A
0.2 0
0.1
-0.2 -0.4 0.14
0.0
0.16
0.18
T/S
(a) Excitation current
0.2
100
200 300 400 Frequency/Hz
500
600
(b) Excitation current spectrogram
Fig. 4. Excitation current waveform and harmonic distribution at I dc = 0 A
When the transformer is under the no-load condition, the excitation current waveform and harmonic distribution on the primary side of the winding are shown in Fig. 5. Obviously, with the increase of the DC current, the transformer’s operating current shifts to the saturation and the flat area of the iron core magnetization curve. Therefore, the excitation current has a serious positive deviation. The current waveform in the positive semi-axis shows an obvious peak wave and the current amplitude increases, while the value of the current waveform in the negative semi-axis gradually shows a flat top wave. From the spectrum diagram, it can be seen that the harmonic components and the specific gravity of each harmonic increase when the DC current enlarges.And the current contains a large number of even harmonics.
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6 1.0
3
FFT/%
i/A
4.5
1.5
0.5
0 0.14
0.16
0.18
t/s
0.2
0.22
0.0
0
(a) Excitation current
100
200 300 400 500 600 Frequency/Hz
(b) Excitation current spectrogram
Fig. 5. Excitation current waveform and its harmonic distribution under DC bias
4.2 Magnetic Density Distribution Under DC Bias Figure 6 shows the direction of the magnetic field lines and the magnetic flux density distribution of the transformer core under the normal operation when the DC bias current I dc = 0 A. As shown in the figure, the direction of the magnetic field lines of the transformer core is opposite, and the value of the magnetic flux density is the same in the positive and negative half cycles. The magnetic density at the corners of the core is relatively large. Owing to the influence of the distribution of the magnetic field lines, the magnetic field lines at the corners are relatively dense.
Fig. 6. Distribution of magnetic flux density of transformer core without DC bias
Figure 7 shows the magnetic flux density of the transformer core and the direction of magnetic field lines under the DC bias. It is easy to see from the figure that when the DC bias current increase, the magnetic flux density of the transformer core will show an increasing trend, but the growth rate of the magnetic flux density of the transformer core will become slower and slower. This is mainly because the transformer core is already at the saturation stage of the transformer core magnetization curve.
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Fig. 7. Distribution of magnetic flux density of transformer core under DC bias
4.3 Transformer Core Loss Distribution The core loss density cloud diagram under the different DC bias is calculated by Comsol shown in Fig. 8. When the transformer operates normally without DC bias, the maximum loss is 1.2 × e4 W/m3 , and the maximum loss is located at the corner of the transformer core. When the DC current enlarges, the core loss distribution law is the same as the loss distribution without DC bias. When the transformer is connected to the DC current, the maximum loss density of the core is about 3.12 × e4 W/m3 , from which we can conclude that the loss of the transformer core is positively correlated with the DC current content, and the maximum loss part is at the corner of the transformer core place.
Fig. 8. Distribution cloud diagram of transformer core loss
5 DC Magnetic Bias Simulation Experiment An experiment of transformer DC bias phenomenon was implemented out to prove the effectiveness of the finite element calculation results [11]. Figure 9(a) and (b) is the experimental circuit and the experimental site under the DC bias. The transformers are composed of two single-phase transformers with exactly the same size and parameters. The primary side is connected in shunt connection and connected to AC excitation, and
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the secondary side is connected in backward chaining to offset the counter electromotive force caused by the introduction of DC. Each winding current obtained in the experiment is shown in Fig. 10. Based on the design experimental circuit, two 1 kV single-phase transformers are made (Table 1).
A
Parallel transformers
K1
T1
T1
DC
Data collection
T2
DC bias AC
T2
A
Load resistance
(a) Experimental circuit diagram
DC power supply
(b) Experimental site
Fig. 9. DC bias simulation experiment
Fig. 10. Excitation current waveform before and after DC bias
Table 1. Transformer prototype parameters Rated capacity/VA
1000
Rated short circuit impedance %
2.16
No-load loss/W
24.13
Load loss/W
36.45
Rated voltage of high voltage winding/V
220
Rated current of high voltage winding/A
4.55
Low voltage winding rated voltage/V
110
Low voltage winding rated current/A
9.09
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Fig. 11. Harmonic distribution of excitation current before and after DC bias
Figure 10(a) is the transformer excitation current waveform when the voltage transformer is under the no-load condition, and the excitation current waveform has no offset and distortion. Figure 10(b) is the excitation current waveform when DC bias is added. It can be seen that the excitation current is distorted and the amplitude increases at this time. In order to better observe transformer excitation current, FFT (fast Fourier analysis) is performed on the above-mentioned current results obtained in the experiment. As shown in Fig. 11 when the bias current increases, the even harmonics appear on the primary side current, and the odd harmonics also increase to a certain extent.
6 Conclusion Based on the single-phase transformer model, this paper gives a detailed introduction to the process of the transformer DC bias. The excitation current and harmonic changes of the transformer under the DC bias was given. The reason for the increase of the odd harmonics and the even harmonics was analyzed. In the process of the DC bias experiment, DC current and AC current are introduced on both sides of the transformer to simulate the actual DC bias engineering problem. The results in this paper could provide some effective methods, reference basis and technical means for the research of power transformers in the power system and the resistance of the DC bias intrusion.
References 1. Xu, W., Wu, Y., Wu, F., Shen, X., Ruan, J.: Study on the influence factors of power transformer DC magnetic bias. In: 12th IET International Conference on AC and DC Power Transmission (ACDC 2016), pp. 1–6 (2016) 2. Wang, S., Dehghanian, P., Li, L., Wang, B.: A machine learning approach to detection of geomagnetically induced currents in power grids. IEEE Trans. Ind. Appl. 56(2), 1098–1106 (2020) 3. Zhao, Z., et al.: Measurements and calculation of core-based B-H curve and magnetizing current in DC-biased transformers. IEEE Trans. Appl. Supercond. 20(3), 1131–1134 (2010)
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4. Li, X., Wen, X., Markham, P.N., Liu, Y.: Analysis of nonlinear characteristics for a threephase, five-limb transformer under DC bias. IEEE Trans. Power Delivery 25(4), 2504–2510 (2010) 5. Biro, O., Buchgraber, G., Leber, G., Preis, K.: Prediction of Magnetizing current wave-forms in a three-phase power transformer under DC bias. IEEE Trans. Magn. 44(6), 1554–1557 (2008) 6. Fuchs, E.F., You, Y., Roesler, D.J.: Modeling and simulation, and their validation of threephase transformers with three legs under DC bias. IEEE Trans. Power Delivery 14(2), 443–449 (1999) 7. Bíró, O., Koczka, G., Leber, G., et al.: finite element analysis of three-phase three-limb power transformers under DC bias. IEEE Trans. Magn. 50(2), 565–568 (2014) 8. Zhang, X., Liu, X., Guo, F., Xiao, G., Wang, P.: Calculation of DC bias reactive power loss of converter transformer via finite element analysis. IEEE Trans. Power Delivery 36(2), 751–759 (2021) 9. Wang, Y., Liu, Z.: Estimation model of core loss under DC bias. IEEE Trans. Appl. Superconduct. 26(7), 1–5 (2016) 10. Lu, S., Liu, Y., De La Ree, J.: Harmonics generated from a DC biased transformer. IEEE Trans. Power Delivery 8(2), 725–731 (1993) 11. Wang, Fh., et al. Simulation and experiment research on the effects of DC-Bias current on the 500kV power transformer. In: Electronics and Signal Processing (EEIC 2011 LNEE V4). Ed., pp. 245–252. Springer (2011)
Torque Density Optimization of an Axial Flux Permanent Magnet Synchronous Machine Using Genetic Algorithm Combined with Simplex Operator Jiayue Zhou , Jianyun Chai, Xi Xiao, Haifeng Lu(B) , and Chaosheng Huang Tsinghua University, Beijing 100084, CN, China [email protected]
Abstract. Axial flux permanent magnet synchronous machine (AFPMSM) has the characteristics of high torque density and power density for its unique structure compared to traditional radial flux permanent magnet synchronous machine (RFPMSM), which makes it preferable for some applications such as in-wheel electric machine of electric vehicles (EV). In this paper, a novel double-statorsingle-rotor AFPMSM for EV is designed. To accomplish the optimization with the goal of maximum output torque density with constraints, genetic algorithm (GA) combined with simplex operator is applied in this paper. The objective function is derived according to equivalent magnetic circuit of the machine on the basis of seven variable geometry parameters. Early-maturing which often occurs and GA optimization process is easily trapped into local optima, missing the real global optimal results. To achieve better solution, simplex operator is applied and an advanced GA optimization algorithm for AFPMSM is proposed. The precision of the objective function and the optimal solutions are testified using finite element analysis (FEA). Keywords: Axial flux permanent magnet synchronous machine · Genetic algorithm · Finite element analysis
1 Introduction Axial flux permanent magnet synchronous machine (AFPMSM) is of high interest in EV in-wheel applications for its unique structure, high torque density and high efficiency [1, 2]. More and more effort has been devoted to further improving the torque density of double-stator-single-rotor AFPMSM, which is a fatal index for in-wheel electric machines [2]. However, the correlation between the input excitation and resulting torque are nonlinear, which are too complicated to be directly solved by linear optimization methods. Therefore, designers have to attempt many combinations of parameters to acquire the best solution of a certain motor design under certain circumstances, which is time-wasting and hard for new designers. GA, an effective method in optimization of nonlinear problems, can be applied to motor design and optimization problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 994–1005, 2022. https://doi.org/10.1007/978-981-19-1528-4_102
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Some researches targeted on AFPM optimization based on GA have been proposed. A coreless single-stator-double-rotor AFPM is designed and optimized in [3] with the analytical method via magnetic vector potential based on the solution of the system of Maxwell’s equations. In [4], analytical model based on equivalent magnetic circuit of an embedded single-rotor-double-stator AFPM synchronous machine is derived and the optimization of the machine is accomplished by multi-objective genetic algorithm. [5] provides a design method consisting of analytical method based on equivalent magnetic circuit and GA-based design optimization method for an axial flux induction machine. The characteristics of AFPM can be calculated by numerical method or analytical method. Numerical method refers to numerical solutions of partial differential equations, namely FEA. However, FEA calculation is time-consuming to be the objective function of GA. In this paper, an analytical model based on magnetic circuit is derived and acts as the objective function of GA optimization. Many locally optimal solutions exist near globally optimal solutions of the objective function of motor design. Therefore, early-maturing happens frequently in motor optimization based on GA, lowering the precision of the solutions as well as rate of convergence. Downhill simplex is an effective way to increase the ability of local search and the quality of solutions of GA [6]. In contrast to previous research on GA-based motor optimization, this paper first proposes combining simplex method with GA to prevent early-maturing of GA. The analytical model is based on magnetic circuit, which is fast for designing. In this article, an AFPMSM for EV in-wheel applications is designed and optimized by advanced GA algorithm. Section 2 derives the analytical model for AFPMSM based on equivalent circuit method. It shows the equation describing the output torque density of AFPMSM. Section 3 proposes the optimization model of AFPMSM and introduces the GA algorithm combined with complex operator. Section 4 demonstrates GA process and the optimal design parameters for AFPMSM. It also presents the FEA results of the optimal design parameters including output torque, which is compared to analytical results calculated in Sect. 2.
2 Analytical Model The overall structure of AFPMSM for EV in-wheel applications is designed as sandwiched stator-rotor-stator geometry, with double stators and single rotor. This topology, depicted in . Figure 1, has great advantages in heat dissipation, which is a key problem for inwheel electric machines. The Analytical model of AFPMSM is derived using equivalent magnetic circuit method [7].
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Fig. 1. AFPMSM structure.
2.1 Magnetic Field The magnetic field inside permanent magnet can be calculated as. Bm = Br + μ0 μr Hm
(1)
Ignore the leakage flux of permanent magnet, the continuity of magnetic flux is expressed as βBg = Bm
(2)
The air gap field generated by permanent magnet is calculated as Bgav = βBr
1 2 hPM 1 2 K F hPM
+ μr g
(3)
where KF is set as 1 and μr is considered as 1. The magnetic field of stator core teeth is calculated as Bt =
αt + α s Bg αt
(4)
where αt is the minimum width of stator core tooth and satisfies αt Ri = hyoke
(5)
2.2 Motor Weight The weight of different parts of motor is calculated as π MPM = β Do2 − Di2 hPM ρPM 4 = 2* π4 Do2 − Di2 hyoke ρsteel Msteel = Myoke + Mtooth t Do2 − Di2 hslot ρsteel +2* π4 αt α+α s 1 Mcopper = 2 ∗ βαs Ri lwav hslot ρcopper 2
(6) (7) (8)
Average length of winding is calculated as lwmin = αt Ri + Do − Di + αt Ro
(9)
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lwmax = αt Ri + αs Ri + Do − Di + 2αs Ri + αt Ro + αs Ri lwav =
3 (lwmin + lwmax ) = αt Ri + Do − Di + 2αs Ri + αt Ro 2 2
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(10) (11)
2.3 Output Torque Density The sizing equation of AFPMSM is written as Tem = kw ABg
Do + Di 2
2 (Do − Di )
(12)
The winding factor of 40-pole-48-slot machine equals to 0.966. A is linear current load, which is calculated by A=
π
NI1 Z Do +Di 2
n
(13)
The sizing equation can be derived as Tem
NI1 Z = kw Bg π
2
Do2 − Di 2
(14)
Output torque density refers to output torque per unit weight and can be calculated as ρT =
Tem MPM + Msteel + Mcopper
(15)
3 Optimization Model and GA Algorithm Design 3.1 Optimization Model The AFPM design is formulated to following optimization model.
Tem OB : max Msteel + Mcopper + MPM
(16)
subject to C1 : Do ≤ 335
(17)
C2 : 200 ≤ Di ≤ 250
(18)
π Di 1 ∗ ≥ 2hair 40 2
(19)
C3 :
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Variable name
Maximum value
Minimum value
1
Do
330
310
2
Di
200
250
3
hpm
20
12
4
NI
800
1300
5
αt
1 72 π
1 45 π
6
β
0.78
0.92
7
hslot
15
8
C4 : Bt ≤ 1.6
(20)
C5 : 0.6 ≤ Bg ≤ 0.9
(21)
C6 : A =
(22)
Do +Di 2 1 2 NI1
≤ 17
(23)
1 hpm + hair + hslot + hyoke ≤ 30 2
(24)
C7 : ρA = C8 :
π
NI1 Z ≤ 60
1 2 αs Ri hslot fill_factor
The optimization goal is maximum torque density, as shown in (16). As in (17), the maximum outer diameter is restricted by the size application circumstances. The tangential gap between two adjacent permanent magnets is required to be longer than double of air gap in order to avoid severe leakage flux of permanent magnet. (20) (21) requires that to avoid saturation of stator iron core, the maximum magnetic flux density in the stator iron tooth and iron yoke are restricted within 1.6T and the maximum air gap magnetic flux density are within 0.9T. (22) and (23) put restrictions on linear current load and current density respectively which determine the heat dispersion ability of motor. The axial size is limited in (24). 3.2 Chromosome Design According to analytical model of AFPM, the output torque density can be determined by seven variables, and their range are shown in Table 1. The physical meaning of variables in Table 1 are depicted in Fig. 2 and Fig. 3, and the chromosome of GA is designed accordingly.
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Fig. 2. Intersection surface of AFPMSM
Fig. 3. Side view of AFPMSM structure
3.3 GA with Constraints The optimization of motor is a restriction problem. Restrictions include linear ones and nonlinear ones. In GA, there are different methods to deal with constraints. For linear constraints c1 , c2 and c8 , simply applying special operator can keep the solutions always within feasible region. Special operator includes initialization operator, crossover operator and mutation operator. Initialization operator is written as initial value = rand ∗ (upper limit − lower limit) + lower limit
(25)
offspring = α ∗ parent1 + (1 − α) ∗ parent2
(26)
Crossover operator is a convex combination of parent chromosome. It is proven that the offspring will maintain in feasible region if both parent1 and parent2 are feasible, so the individual will always remain feasible with initialization and crossover as (25) (26). Mutation operator is offspring = parent ∗ α + up(/lo)(1 − α)
(27)
c3 , c4 , c5 , c6 and c7 are nonlinear, so punishment function is applied to adjust the fitness of individuals. punishment = p3 + p4 + p5 + p6 + p7 where pi = max{0, Ci }, i = 3, 4, 5, 6, 7
(28)
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The probability of individuals for selection are adjusted as (29). probability = fitness − punishment ∗ 10
gen 1+ maxgen
(29)
Obviously individuals will not always stay in feasible area of c3 , c4 , c5 , c6 and c7 . They will, however, gradually tend to be within the feasible area due to the increasing punishment force as (29). 3.4 Combining Simplex Operator GA-based motor optimization always suffers from early-maturing due to the complication of the output equations [8]. In this design, simplex operator is adopted and combined in GA to replace traditional GA to prevent early-maturing. There are two methods to determine early-maturing. One is that the best fitness value among the population remains unchanged for several generations, and the other is that the difference between the best and the worst fitness value among the population is less than a certain value. The former one is adopted in this paper because it prevents the algorithm from running into a dead loop.
Fig. 4. Flowchart of GA combined with simplex operator
Figure 4 depicts the flowchart of GA. In contract to traditional GA, after fitness calculation, if there exists early-maturing phenomenon in accordance with the criterion stated above, the algorithm goes into simplex calculation. When conducting simplex operator, several individuals are randomly picked as the original simplex, vertex of the polygon formed by selected individuals. The worst individual among the simplex has to move, to form a new simplex and replace the original one.
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Figure 5 shows the difference of average torque density between applying simplex operator and not applying simplex operator. It is clear that simplex operator can effectively prevent early-maturing. With no simplex operator, the objective function matures early at about 15 generation. It is shown that applying simplex operator can save objective function from early-maturing.
Fig. 5. Average torque density (a) with simple operator (b) without simplex operator
4 Optimization Result and 3-D FEA Result The optimal topology variables of AFPMSM acquired by GA are much different from the original ones, which are shown in Table 2. It would take much effort to artificially searching for the best solutions, but the proposed GA optimal methods merely spend 10 s in getting them, which free designers from wasting time and energy. Compared to the original design, the optimal topology can produce higher torque density within dimensional limits, significantly improve the output characteristics of AFPMSM. The output performance is compared through 3D FEA analysis using ANSYS MAXWELL. The output torque from analytical calculation and 3D FEA is depicted in Fig. 6, and the analytical calculation results are in good agreement with FEA, which verified the precision of the analytical methods. The output torque of the optimal AFPMSM and the original one is depicted in Fig. 7. The optimal design produces higher torque, which is originated from higher fundamental air gap axial flux density as well as higher inductances. The air gap axial flux density and the FFT analysis are demonstrated in Fig. 8 and Fig. 9 respectively. Air gap flux density of AFPMSM has lower fundamental value and higher harmonics compared to RFPMSM. The fundamental air gap axial flux density of optimal design is +8% with relation to the original one, leading to higher magnetic loading. To prove the magnetic linearity and avoid saturation, magnetic flux density under rated load in stator core is depicted in Fig. 10. In-wheel electric machines will suffer deteriorated performance from magnetic saturation due to the reduction on efficiency. Therefore, the optimal AFPMSM can operate well in EV applications.
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Variable name
Original value
Optimal value
1
Do
310
326
2
Di
220
227
3
hpm
12
14
4
NI
30 ∗ 35.35 = 1060.5
1078 = 30 ∗ 36
5
αt
3 200 π
7 450 π
6
β
0.8
0.83
7
hslot
9
10
Fig. 6. Output torque of the final selected AFPM
5 Conclusion This paper first proposes combining simplex method with GA for AFPM optimization to prevent early-maturing. The algorithm can affectively and quickly increase the output torque density of AFPM, saving the time of designers and achieve better results. The objective function of the GA algorithm is derived based on equivalent magnetic circuit. The performances of optimal AFPMSM are testified by 3-D FEA analysis.
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Fig. 7. Output torque of (a) original design and (b) optimal design
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Fig. 8. Air gap axial flux density of (a) original design and (b) optimal design
Fig. 9. FFT analysis of air gap axial flux density
Fig. 10. Magnetic flux density of stator core
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Acknowledgment. Thanks to the State Key Laboratory of automobile safety and energy conservation for its independent research project “high power density electric vehicle direct drive hub motor” (ZZ2021–041) for funding this paper.
References 1. Lim, D.K., Cho, Y.S., Ro, J.S., Jung, S.Y., Jung, H.K.: Optimal design of an axial flux permanent magnet synchronous motor for the electric bicycle. IEEE Trans. Magn. 52(3), 1–4 (2016). https://doi.org/10.1109/TMAG.2015.2497374 2. Pei, Y., Wang, Q., Bi, Y., Chai, F.: A novel structure of axial flux permanent magnet synchronous machine with high torque density for electrical vehicle applications. In: Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, vol. 2017-Janua, pp. 1717–1722 (2017). https://doi.org/10.1109/IECON.2017.8216291 3. Virtiˇc, P., Vraži´c, M., Papa, G.: Design of an axial flux permanent magnet synchronous machine using analytical method and evolutionary optimization. IEEE Trans. Energy Convers. 31(1), 150–158 (2016). https://doi.org/10.1109/TEC.2015.2477319 4. Benlamine, R., Dubas, F., Randi, S.A., Lhotellier, D., Espanet, C.: Design by optimization of an axial-flux permanent-magnet synchronous motor using genetic algorithms. In: 2013 International Conference on Electrical Machines and Systems, ICEMS 2013, pp. 13–17 (2013). https://doi.org/10.1109/icems.2013.6754546 5. Mei, J., Zuo, Y., Lee, C.H.T., Kirtley, J.L.: Modeling and optimizing method for axial flux induction motor of electric vehicles. IEEE Trans. Veh. Technol. 69(11), 12822–12831 (2020). https://doi.org/10.1109/TVT.2020.3030280 6. Ren, Z., San, Y.: A hybrid optimized algorithm based on simplex method and genetic algorithm. In: 2006 6th World Congress on Intelligent Control and Automation, pp. 3547–3551 (2006). https://doi.org/10.1109/WCICA.2006.1713029 7. Geng, W., Zhang, Z., Li, Q.: Concept and electromagnetic design of a new axial flux hybrid excitation motor for in-wheel motor driven electric vehicle. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), pp. 1–6 (2019). https://doi.org/10.1109/ ICEMS.2019.8922430 8. Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010)
Application of Knowledge Mapping and Fault Diagnosis in Power Communication Network Yi Zhang, Can Qi, Ye Zhao(B) , Kunrui Tong, Zilan Zhao, and Lin Zhang State Grid JiBei Information and Telecommunication Company, Beijing 100053, China [email protected]
Abstract. After years of construction and application, the power communication management system has accumulated massive amounts of real-time data and operating data. However, the core operation and maintenance work such as defect diagnosis analysis and business routing planning still mainly relies on manual experience, which is not only inefficient and difficult to meet the increasingly large and complex communication network security production requirements, but also cannot quickly locate when complex faults occur in the communication network Fault points and formulate emergency alternative routes. It is necessary to use artificial intelligence technology research to explore the influencing factors that affect the reliability of channel routing, establish a business channel reliability model, and assist mode personnel to find the best routing channel. Improve the speed and quality of repairs. The thesis first introduces the main goals of this research, and makes a preliminary analysis of the ideas for the realization of the research goals. Secondly, it introduces the knowledge map technology and the actual application status of the knowledge map technology at home and abroad. Then confirm the specific content and specific implementation plan of the project research. Finally, by comparing the failure work order data before and after the implementation of the plan, the implementation effect of the plan is analyzed. Keywords: Knowledge graph · Network monitoring · Fault diagnosis · Work order dispatch
1 Introduction 1.1 Background Power communication network is an indispensable part of power system, it is the basis of power grid dispatching automation and production management modernization, and an important technical means to ensure the safe, economic and stable operation of power grid. As the basic support of building a unified and strong smart grid and enterprise informatization, power communication network provides a safe information transmission channel for power grid dispatching, automation, relay protection, safety automatic control, power market transaction and enterprise informatization in the whole power system. Its high-quality management is the key to keep the whole power grid smooth. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1006–1019, 2022. https://doi.org/10.1007/978-981-19-1528-4_103
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1.2 The Purpose and Significance of This Paper Using the advantages of artificial intelligence and knowledge mapping technology in experience summary, knowledge mining, and continuous evolution, we can improve the cognitive ability of large-scale complex communication network through “diagnosis disposal feedback learning”. In order to improve the level of intelligent operation and maintenance of communication network, experience accumulation and continuous evolution of defect diagnosis and analysis are realized, and intelligent diagnosis support knowledge base is continuously expanded and improved. The significance of the study is as follows. (1) Maintenance impact business analysis based on knowledge map can improve the accuracy and integrity of the analysis results, so as to avoid potential safety hazards caused by communication maintenance. The objectives of the study are as follows: (1) Form the research results of maintenance impact business analysis based on knowledge map. The main ways of application and promotion of related achievements are: Based on the association relationship between communication channels and the carried communication services, through rule definition, knowledge mapping and intelligent association methods to realize the analysis of communication maintenance impact business, and realize the intelligent decision-making of communication maintenance risk. Guide the business impact analysis of TMS and improve its timeliness. (2) Form the research results of automatic order dispatch based on knowledge map and knowledge base. The way of application and promotion of related achievements is: the automatic order distribution based on knowledge map and knowledge base is different from the traditional manual rule order distribution, improves its accuracy and timeliness, and guides the construction of automatic order distribution function. 1.3 Research Status at Home and Abroad Knowledge mapping technology is also one of the hot issues in the field of intelligent research. It is similar to the well-known social network, but it contains more categories and more complex. Build knowledge map, make diverse, heterogeneous, complex, multidimensional data linked together, through reasoning to obtain other knowledge. Knowledge map is essentially an arbitrary subset of all the facts known by human beings, which is usually represented as a huge complex semantic network. Each node of the network represents an entity, and each edge represents a binary relationship between nodes. The basic unit of the network is a triple shaped like (head entity, relationship, tail entity), such as (Beijing, located in, China), (Huang Xiaoping, wife, Yang Ying), etc. In the field of electric power, with the continuous emergence of innovative and cross research results, the technical system in the field of electric power has been expanded. Therefore, in order to effectively describe the technical system and track the changes of some of the technical systems in the process of research and development, we must work
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out a perfect knowledge map, so as to extract the technical terms from various angles, and then analyze the relationship between the data sources. Therefore, in the process of building this atlas, technicians can extract some terms from the relevant database and analyze the terms related to the power field, and analyze the relationship between the top and bottom. 1.4 Chapter Arrangement of the Thesis This paper mainly consists of five chapters. The first chapter is “Introduction”, which describes the background of this research, studies the current situation of knowledge mapping technology, introduces the knowledge mapping technology, and the practical application of knowledge mapping technology at home and abroad. It also introduces the main objectives of this study and makes a preliminary analysis on the realization of the research objectives. The second chapter is “knowledge mapping theory research”, which studies knowledge mapping algorithm and technology. The third chapter is “research and implementation of knowledge mapping and fault diagnosis”, which defines the research content and technical implementation of knowledge mapping and fault diagnosis. The fourth chapter is “research results and effect inspection”, through the comparison of the fault work order data before and after the implementation of the scheme, the implementation effect of the scheme is analyzed. The fifth chapter is “summary and prospect”, which summarizes the effect and existing problems of this research, and analyzes and prospects the work direction that can be improved and cooperated in the future.
2 The Theoretical Research of Knowledge Mapping Algorithm 2.1 Theoretical Research on Knowledge Mapping Algorithm Knowledge graph is a concept formally proposed by Google in 2012. Its main purpose is to improve the intelligence and efficiency of search engine. Knowledge map is a kind of semantic network in essence. Nodes represent entities or attributes, and edges represent various semantic relationships between entities and between entities and attributes. Among them, entity refers to the object [1] or thing that objectively exists in the real world and has distinction, such as China, peach, etc. Attributes are information that describes the characteristics of entities, such as area, maturity, etc. Relationship is the most important feature of knowledge map, which can realize the interconnection of everything, and support semantic understanding, information retrieval and other applications. Knowledge mapping construction technology mainly includes: knowledge extraction, knowledge fusion, knowledge representation, knowledge verification and knowledge reasoning. The construction process framework is shown in Fig. 1.
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Fig. 1. Knowledge map construction process
The main framework of knowledge mapping can be divided into the following modules: knowledge source, knowledge acquisition, knowledge fusion [2], knowledge storage, distributed computing framework and knowledge service (Fig. 2).
Fig. 2. Knowledge map architecture
2.1.1 Sources of Knowledge In reality, there are many kinds of data sources, good and bad. There are a lot of structured information and unstructured documents on the Internet. Unstructured documents
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include unstructured documents [3] headed by natural language documents and semistructured documents headed by log configuration. The knowledge in these documents needs to be preprocessed. With the help of text analysis method, the entity, relationship and attribute triples are extracted to construct the knowledge map. A large number of unstructured documents need to be manually annotated in order to achieve better processing results, but the manually annotated knowledge or open knowledge base can be used as the basic corpus, also known as prior knowledge. The existing knowledge base can help to build a new knowledge map. 2.1.2 Knowledge Acquisition The emergence of a large number of innovative and cross research results in the field of electric power makes the technical system in the field of electric power expand rapidly. How to accurately describe and quickly track the technical system and its changes in this field has become an urgent problem [4]. It is very important to analyze and extract technical terms from various data sources and analyze the relationship between them. Mapping this problem to the domain of knowledge mapping, we can find that this is a typical process of building domain knowledge mapping. One solution is to analyze and extract the professional terms and the relationship between the upper and lower levels from the paper data, encyclopedia website and other data sources. Among them, the term extraction method is consistent with the term extraction method of building Chinese professional dictionary in power field. Aiming at the problem of hierarchical relationship learning between terms, this paper adopts a threestage solution, including semantic modeling of label document, semantic relationship measurement of label and hierarchical structure modeling based on active learning, as shown in Fig. 3.
Fig. 3. Power domain knowledge acquisition
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2.1.3 Knowledge Fusion In order to improve the scale and quality of knowledge map by fusing data from different sources, knowledge fusion technologies such as entity disambiguation and anaphora resolution emerge as the times require [5]. At the same time, knowledge fusion also aims to further integrate the complex knowledge acquired from multi-source data and expand the original knowledge base. Knowledge fusion is an important method for knowledge sharing, which is based on the multi-source heterogeneous, semantic diversity and dynamic evolution knowledge obtained from the open fragmented data of network big data. Through conflict detection and consistency check, it can judge the correctness of the knowledge, eliminate the rough and extract the fine, and organize the verified correct knowledge into a knowledge base through alignment, association and merge calculation. According to the above definition, knowledge fusion mainly involves entity alignment, knowledge processing and knowledge updating. 2.1.4 Knowledge Storage Knowledge storage is mainly responsible for storing knowledge in NoSQL and DB. Combined with the characteristics of power data, the adaptive power knowledge storage scheme is constructed [6]. 2.1.5 Knowledge Service Combined with practical application scenarios, the Intelligent Question Answering System Based on knowledge map is completed. The overall framework of the intelligent question answering system is shown in Fig. 4. Aiming at the power communication system, combined with the knowledge map, the knowledge service section is applied to intelligent alarm, anomaly detection and defect location. 2.2 Key Points and Difficulties of Research Power communication operation and maintenance knowledge map is the carrier of defect diagnosis knowledge base operation. Whether the entities in the communication domain and the relationship between entities are comprehensive and accurate plays a key role in the smooth implementation of the project. Therefore, the research on power communication operation and maintenance knowledge map is particularly important in this project. Among them, the modeling of knowledge map in the vertical domain [7] of power communication is one of the difficulties in this research, which is embodied in the entity extraction of knowledge map, the construction of relationship between entities, domain text corpus and so on.
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Fig. 4. Knowledge service
3 Construction and Implementation of Knowledge Map and Fault Diagnosis Model 3.1 Main Research Contents Based on the power communication operation and maintenance domain knowledge map model, this paper studies the entity abstraction technology of power communication channel, topology, resources and business; based on the power communication knowledge map, this paper studies the relationship between power communication operation and maintenance entities [8]; studies the calculation technology of power communication knowledge map, so as to realize the real-time query and feedback calculation of relevant entities and contacts when defects or maintenance. The knowledge map model of power communication operation and maintenance domain is proposed. 3.1.1 Research on Iterative Updating Learning Model of Defect Diagnosis Knowledge Base Based on Artificial Intelligence This paper studies the iterative updating learning technology of defect diagnosis knowledge base based on artificial intelligence, mainly uses neural network and unsupervised learning model for training, and achieves the goal of correcting wrong labels and updating knowledge base regularly [9].
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3.1.2 Research and Development of Defect List Automatic Distribution Prototype Based on Knowledge Map and Knowledge Base Develop the prototype of automatic work order distribution based on knowledge map and knowledge base, improve the intelligent application level of TMS, improve the work efficiency of communication dispatchers and establish typical application demonstration through the improvement of existing functions and the development of automatic distribution function. 3.2 Construction and Implementation of Knowledge Map and Fault Diagnosis Model 3.2.1 Construction Technology of Power Communication Operation and Maintenance Knowledge Map Realization As the carrier of knowledge base operation, knowledge mapping of power communication operation and maintenance is mainly divided into knowledge source and acquisition, knowledge fusion, knowledge storage, Distributed Knowledge Computing Framework and knowledge service. This paper studies the knowledge acquisition technology based on natural language processing, taking the historical work ticket, defect list, maintenance information [10], operation guidance database, manufacturer network management platform and TMS structured database as the source, using semantic analysis, keyword extraction, synonym construction and entity relationship analysis and other
Fig. 5. Technical route of power communication operation and maintenance knowledge map construction
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technologies to realize the basic entity construction of knowledge map; through the graph database and structured database storage, the basic entity is constructed In the distributed computing framework such as spark and Flink, the relationship between entities is constructed to realize the real-time query and feedback calculation of relevant entities and contacts during defects or maintenance; finally, the knowledge services such as Atlas visualization analysis, entity search, defect diagnosis and mode optimization are realized to improve the efficiency of operation and maintenance. The overall technical roadmap is shown in Fig. 5. 3.2.2 Implementation of Iterative Updating Learning Model for Defect Diagnosis Knowledge Base Based on Artificial Intelligence
Fig. 6. Technical route of knowledge base iterative learning update
The research of knowledge base iterative updating learning technology based on artificial intelligence is mainly divided into two parts, namely knowledge error correction model and knowledge discovery model. The knowledge error correction model, based on the previous related research [11], uses the feedback information of defect list and error list information to train through neural network model, and realizes the knowledge correction including error tag correction, error list correction and tag indentation Knowledge discovery model is based on real-time alarm information and topology channel update information, and realizes regular learning of knowledge base through incremental unsupervised learning model, which belongs to iterative update (Fig. 6);
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3.2.3 Automatic Distribution Tool of Defect List Based on Knowledge Map and Knowledge Base This paper studies the automatic work order distribution technology based on knowledge map and knowledge base, develops the automatic work order distribution system tool with practical function, solves the problems of low efficiency of original work order distribution and inaccurate defect diagnosis[12], improves the intelligent application level of TMS, improves the work efficiency of communication dispatcher’s defect disposal, and establishes a typical application demonstration. The technical route is shown in the figure below (Fig. 7).
Fig. 7. Technical route of defect list automatic distribution system
4 Application and Effect of the Model The statistical results show that the alarm compression rate reaches 99.927%, and the proportion of automatic defect distribution in all defect orders increases steadily. See the figure below for details: (1) Improve alarm compression ratio By applying the power communication knowledge map to fault diagnosis, the effect of alarm compression, alarm filtering and alarm merging is achieved. Further reduce the number of network alarm monitoring, liberate the dispatcher from the network alarm monitoring work, and effectively improve the monitoring efficiency and operation and maintenance efficiency. Detailed statistics are as follows (Table 1):
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Original alarm Root alarm
Data volume
Compression ratio
5168646
–
803755
Defect auto dispatch
588
84.45% 99.927%
(2) Improve the rate of defect automatic dispatch Through the establishment of the closed-loop process of communication automatic order dispatching, the transformation from monitoring alarm to monitoring defect, from manual one-way automatic order dispatching, and from offline to mobile disposal is realized, which promotes the homogeneity improvement of communication operation and maintenance management [13], reduces the influence of the difference of operation and maintenance personnel's skill level, and makes the defect disposal more standard, avoids the risk of undetected alarm and missing handling, and makes the operation and maintenance management [14] more efficient Standardize and strongly support the safe production of large power grid. Detailed statistics are as follows (Table 2):
Table 2. Improve the rate of defect automatic dispatch Month
Auto dispatch quantity
Total number of defects
Proportion of automatic dispatch
202006
3
44
6.82%
202007
28
69
40.58%
202008
84
125
67.20%
202009
118
143
82.52%
202010
103
127
81.10%
202011
86
109
78.90%
202012
113
152
74.34%
202101
53
59
89.83%
The implementation and application of the algorithm promote the rational allocation and orderly use of massive data resources in various units, and open up the interactive channel between information and communication technology and traditional power grid business management practice. The merging rate of equipment alarm is more than 90%, the average time of equipment fault handling is reduced by 70%, and the average labor cost is reduced by 67%. It leads the innovation and reform of information and communication dispatching management development, improves the ability of electric power to
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serve economic and social development, and realizes the win-win situation of enterprises and society. In addition, it promotes the transformation of power communication operation and maintenance from traditional passive operation and maintenance to data intelligent operation and maintenance. By establishing a closed-loop process of communication dispatch monitoring, wrong dispatch analysis and mobile order receiving, the transformation from monitoring alarm to monitoring defect, from manual dispatch to one-way automatic dispatch, and from offline flow to mobile disposal is realized, which promotes the homogeneity improvement of communication operation and maintenance management and strongly supports the safety production of large power grid.
5 Summarizes and Prospects 5.1 Summary Through the research of communication knowledge map and the application of related research results in fault diagnosis, the following results are achieved. (1) Complete the construction of power communication operation and maintenance knowledge map model, realize the visual analysis of the map, and improve the efficiency of operation and maintenance. (2) The analysis model of maintenance impact business is constructed to realize the analysis of communication maintenance impact business and realize the intelligent decision of communication maintenance risk. (3) Complete the construction of iterative updating learning model of defect detection knowledge base based on artificial intelligence, and realize the regular learning of knowledge base by using the feedback information of defect list and error list. (4) Complete the construction of automatic work order distribution technology model based on knowledge map and knowledge base, develop practical automatic work order distribution system tools, improve the intelligent application level of TMS, and improve the work efficiency of communication dispatcher. The stable operation of the transmission network is the basis for the normal operation of various business networks carried by it. With the development trend of social informatization and the rapid development of Internet applications, the demand for network bandwidth is also increasing rapidly, which brings the demand for large-scale construction of transmission network. Therefore, the development direction of intensive network maintenance is put forward. Through the construction of automation system, the maintenance efficiency is improved, the work pressure of grass-roots maintenance personnel and the technical level requirements of grass-roots maintenance personnel are reduced, and finally the standardization and integration of on-site maintenance are realized. The research on the application of knowledge mapping and fault diagnosis in power communication network is a part of the process of network maintenance intensive transformation. Through this research, the automation and standardization of transmission
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network alarm monitoring are preliminarily realized, the efficiency of network monitoring is improved, the manpower required for network monitoring is saved, and the number of alarm work orders received by front-line maintenance personnel is reduced. 5.2 Expectation This research has completed the preliminary construction of power communication knowledge map. In the next stage, we will carry out further research on the construction of power communication knowledge map, and apply the popular big data analysis, neural network, AI artificial intelligence technology to the construction of knowledge map. To achieve more automatic and intelligent aspects of knowledge mapping, such as entity extraction, relationship construction between entities, domain text corpus and so on.
References 1. Zhou, J., Sun, X., Zhang, B.: Research on intelligent brain technology of power communication network based on knowledge map. In: 2019 Annual Meeting of Power Industry Informatization (2019). (in Chinese) 2. Wang, Q.: Research on entity relationship extraction and knowledge mapping in power field (2020). (in Chinese) 3. Hu, H., Zhai, X., Hu, G.: Analysis of flow connection behavior in communication network based on flow knowledge map. Comput. Eng. (2019). (in Chinese) 4. Li, X., Xu, J., Guo, Z., et al.: Construction and application of knowledge map of dispatching automation system. China Electric Power (2019). (in Chinese) 5. Zhang, X., Li, J., Meng, W., et al.: Research and application of power knowledge management system based on modern service system. Guizhou Electric Power Technol. 021(010), 82–86 (2018). (in Chinese) 6. Fang, L.: Research on fault diagnosis algorithm of power communication based on case base. Hebei Electric Power Technol. 038(004), 38–41 (2019). (in Chinese) 7. Mo, S., Gao, G., Li, R., et al.: A fault diagnosis method for power communication network intelligent operation and maintenance system (2020). (in Chinese) 8. Wang Teng. Research on SDH alarm association rules and fault diagnosis in power system communication network [D].(in Chinese) 9. Wang, C.Y., Jiang, Q.Y., Tang, Y.J., et al.: Power dispatching fault diagnosis based on text mining of alarm signal. Power Autom. Equip. 039(004), 126–132 (2019). (in Chinese) 10. Drakaki, M., Karnavas, Y.L., Karlis, A.D., Chasiotis, I.D., Tzionas, P.: Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi-agent system approach using intelligent classifiers. IET Electric Power Appl. 14, 245–255 (2020) 11. Kulkarni, S., Gu, Q., Myers, E., Polepeddi, L., Lipták, S., Beyah, R., Divan, D.: Enabling a decentralized smart grid using autonomous edge control devices. IEEE Internet Things J. 6(5), 7406–7419 (2019) 12. Zhou, M., Liu, X.B., Yang, J.B., Chen, Y.W., Wu, J.: Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment. Knowl.-Based Syst. 163, 358–375 (2019)
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13. Durresi, M., Subashi, A., Durresi, A., Barolli, L., Uchida, K.: Secure communication architecture for internet of things using smartphones and multi-access edge computing in environment monitoring. J. Ambient Intell. Hum. Comput. 10(4), 1631–1640 (2019) 14. Rivera, J., Nasirifard, P., Leimhofer, J., Jacobsen, H.A.: Automatic generation of real power transmission grid models from crowdsourced data. IEEE Trans. Smart Grid 10(5), 5436–5448 (2018)
A Mix Peer-To-Peer Energy Trading Hierarchy Modal for Prosumers Songnong Li1,2 , Lefeng Shi3 , and Song Wang3(B) 1 State Grid Chongqing Electric Power Research Institute, No. 80, Middle
Section of Huangshan Avenue, Yubei District, Chongqing, China 2 Chongqing Key Laboratory of Energy Internet Advanced Measurement and Energy Big Data,
No. 80, Middle Section of Huangshan Avenue, Yubei District, Chongqing, China 3 National Center for Applied Mathematics in Chongqing, Chongqing Normal University,
Chongqing 401331, China [email protected]
Abstract. The microgrids operating in peer-to-peer (P2P) energy trading model are widely viewed as the backbone of the future energy system, supporting the penetrations of distributed energy resources (DERs). How to combine the physical and virtual structures (microgrid and P2P) in order to reach the most system efficiency has become the hot topic in current academic community. Around this point, a body of related methods were proposed, whereas majority of them lack necessary theoretical argument, making them short of adequate theoretical ground. To fill this gap, in this paper, a mix P2P energy trading hierarchy in a microgrid environment is discussed. Firstly a theoretical models are built and derived to identify the optimal trading hierarchy considering the electricity supplying uncertainties from prosumers; accordingly, secondly, a pertain layered trading structure and its internal trading matching methods are introduced. Simulation results demonstrate superiority of the proposed hierarchy, compared with current model. Keywords: Peer-to-peer trade · Microgrid · Prosumer · Local electricity market
1 Introduction Confronting increasingly aggravated energy crisis and environmental problems, advancing the rapid development of distribution energy resources (DERs) representing as wind energy and solar energy, in order to displace traditional fossil-based energy, has become the common consensus in the world. Based on this, the International Energy Agency (IEA) projected that till 2040, the two-thirds of the world’s electricity would come from DERs which would occupy at least the 40% of the total installed capacity of the entire power system [1]. Nevertheless, the IEA meanwhile caveated that the judgment is based on a idealistic vision in which lots of aspects of current power system should adjust concurrently with the penetration of DERs in both technological terms and relevant business model. For example, [2] and [3] argued that the massive penetration of renewable generation would bring new challenges and opportunities to the power system, leading to new technological scheme and business models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1020–1028, 2022. https://doi.org/10.1007/978-981-19-1528-4_104
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However, the concomitant impact with DERs development is far from being so. Basically, this transform will lead to far-reaching changes in the existing power supply and trading mode, bringing about a set of latent uncertainties. The primary uncertainty comes from their technical limitation. Serious dependency on the real-time sate of natural renewable resources, e.g., the situation of wind strength and solar shining, makes their generated power usually unstable and even low-quality [4, 5]; let alone, whether or not the participation of irrational prosumers could cause the operational messy in the level of local distribution power trading, still is an open question [6–8]. As for the aspect of management for DERs, a set of studies suggested that the technical and business modes ought to be considered jointly. Based on this thought, recently two innovative local power trading models were proposed and became buzzwords, i.e., the aggregator trading model and peer-to-peer (P2P) model. Some fundamental settings are accepted generally that in a smart grid background, there needs a micro-level P2P matching platform responsible to balance local power supply of the local prosumers with DERs or aggregators and the power consumption of local consumers, under the assistance with the support of complementary facilities like natural gas and storage infrastructure. However, numerous devices addition needs a big body of investment; who should be responsible for investing the ancillary equipment, or how to migrate the cost pressure of some main stakeholders such as grid operators, still is scarcely addressed and given an appropriate scheme. To this end, this paper firstly in Sect. 2, examines the effectiveness of the wildly accepted trading models: pure P2P trading structure and mix trading structure, from the starting points of motivating more prosumers’ participation in the consciously self-enhancing their generation power quality so as to reduce the cost burden of other relevant agents; based on relevant results, in Sect. 3, a multi-stage matching method with relevant algorithms, considering the P2P platform trading criteria; next, a case study is conducted to validate the proposed measure in Sect. 4; Sect. 5, at last, presents the conclusion of this article.
2 Structured Peer-To-Peer Energy Trading Hierarchy Analysis 2.1 Pure P2P Trading Model As illustrated in Fig. 1, N prosumers owning DERs and M consumers constitute a local power trading together, which is mediated and matched via P2P platform to balance their power supply and demand; additionally, when there exists a power shortage due to the uncertainty of power generation of DERs, the P2P platform also is in charge of filling it through purchasing power from the upper-level grid. Considering the inaccuracy of forecasting the power generation of DERs, assume that the trading model of P2P platform executes dispatching-ahead trading ( e.g., day-ahead trading), where the platform matches the power supply and demand according to the supply-demand information. In the trading process, in order to eschew the risk caused by the power supply uncertainty of DERs, a two-part tariff is employed, wherein the first-part price is pija ($/kWh) which is the transaction price between the prosumer i (i ∈ N = {1, 2, ..., N }) and consumer j (j ∈ M = {1, 2, ..., M }); their transnational power amount is Qija (kwh); so do the second part price which is denoted as pijr ($/kWh), while the real trading power
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is Qijr (kwh). Take into account the power generation uncertainty of DERs, hence set the power-supply quality of DER prosumers (i.e., power supply stability) follow a probability θi with θi ∈ [0, 1], which is related to the types, and other intrinsic features of DERs. However, the power supply quality could be enhanced, if the prosumer add ancillary equipment such as storage system; set the enhanced supply quality as: θie = θi + b · ei , θie ∈ [0, 1]
(1)
where θie the enhanced power supply quality after the presume i invests the ancillary equipment; the increasement of the power supply quality is b · ei , where ei is the investment degree of the prosumer i for ancillary equipment; b is the conversion coefficient from investment to power supply quality. The investment degree, ei can be measured by cost, following the function: kci 2 · ei (2) 2 For corresponding to practices, assume that the function (2) obeys the discipline of incremental marginal cost, where Ci is the investment cost of prosumer i; kci is the cost-conversation coefficient. Based on the above setting, the profit function of prosumer i is: Cie =
p
πi = pija Qija + pijr (Qija θie ) − ηij Qija (1 − θie ) − Cie
(3)
where πi is the profit of prosumer i in the power trading; pija Qija is the prepaid revenue at the first part price; pijr (Qija θie ) is the real-trading revenue at the second part price, where p Qija θie is the amount of realistic trading power, i.e., Qijr ; ηij Qija (1 − θie ) is the penalty cost p when the power of prosumer i can not reach the promised power trading amount Qija ; ηij ($/kWh) is the penalty coefficient. Correspondingly, as the consumer of prosumer j’s power, the consumer j’s cost function could be set to: p
Cj = pija Qija + pijr (Qija θie ) + (pg − ηij )Qija (1 − θie )
(4)
where Cj is the expenditure of consumer j in the trading with prosumer j; pg is the grid power price when consumer j resorts to the high-level grid to fill the power gap Qija (1 − θie ), caused by the uncertainty of prosumer i. In general trading context wherein both prosumers and consumers do not own market power, a rational consumer j could choose trading with prosumer i if the aggregated expenditure is not more than that of purchasing power from the upper-level grid. Accordingly, the proposition 1 can be obtained: Proposition 1: In the pure P2P trading model, to ordinary consumer j, the optimal p p choice in terms of power quality is θie ≥ (pg − ηij + pija )/(2pg − ηij + pijr ). The feedback effects to prosumer would be obtained as shown in Proposition 2, while consumers follow proposition 1. Proposition 2: In the pure P2P trading model, the optimal investment degree of the p prosumer i for enhancing its power supply quality, is ei = b(pijr + ηij )Qija /kd .
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Fig. 1. P2P trading structure
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Fig. 2. Mix P2P trading structure
2.2 Mix P2P Trading Model The distinction of mix P2P trading model with pure the P2P model rests on the adding of an aggregator h which plays as mediators between the P2P platform and some prosumers. As illustrated in Fig. 2, the aggregator is in charge of aggregating the distributed power from prosumer g (g ∈ N ) which is not meet the power quality criteria of direct P2P trading, transforming the aggregated power to qualified power by virtue of scale effects of synthesized DERs and in assistance of relevant ancillary facilities, so as to trade the power in P2P platform; otherwise, for the prosumer k (k ∈ N ) capable of satisfying the power quality criteria, they still could sell their power via P2P platform, skipping over aggregators. Note that N ∪ N = N. Assume aggregator h following the price tactics of a two-part tariff, thus the profit function of the prosumer g is: a a r a h a Qgh + pgh (Qgh θge ) − ηgh Qgh (1 − θge ) − Cge πg = pgh
(5)
where the profit function of the prosumer g, πg , is consisted of the prepaid revenue a Q a at the first part price (pa ), the real-trading revenue pr (Q a θ ) at the second pgh gh gh gh gh ge r ), and penalty cost ηh Q a (1 − θ ), as well as the investment cost C part price (pgh ge ge gh gh a (kWh) is the transnational of prosumer g for enhancing its power quality. Besides, Qgh power amount reported by the prosumer g to aggregator h. θge the power supply quality of prosumer g’ DERs, considering that the presume g invests the ancillary equipment for k promoting the power quality; the corresponding cost is Cge = 2cg · eg2 , wherein eg is the investment degree of the prosumer g for ancillary equipment; kcg is the cost-conversation coefficient. Following the same thought of settings, the profit function of the prosumer k is: p
a a r a a Qgj + pkj (Qkj θke ) − ηkj Qkj (1 − θke ) − Cke πk = pkj
(6)
Different from function (5), the function (6) represents the obtained profit of the qualified prosumer k trading with consumer j via P2P platform. The settings of other a , ηp , Q a , θ , C , are similar to the function (3). counterparts, for example pkj ke kj kj ke During the context of mixed P2P trading, the profit function of aggregator h is: p
a Q a + pr (Q a θ ) − η Q a (1 − θ ) − pa Q a − pr (Q a θ ) πh = phj he hj hj hj hj hj he gh gh gh gh ge h a +ηgh Qgh (1 − θge ) − Che
(7)
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The function (7) consists of three parts: namely, the revenue from power trading with a Q a + pr (Q a θ ) − ηp Q a (1 − θ ), the cost from consumer j via the P2P platform phj he hj hj hj hj hj he a Q a − pr (Q a θ ) + ηh Q a (1 − θ ), and aggregating lower qualified prosumers −pgh ge gh gh gh ge gh gh the investment cost Che for enhancing its overall trading power quality. The settings of a , ηp , Q a , θ , C , are similar to the function (3) too. Notably, for expressing and later phj he hj hj he derive convenience, we set the prosumers aggregated by aggregator are represented by the prosumer g uniformly, without influencing the ultimate results. In addition, though k Che = k2ch · eh2 similar with Cge = 2cg · eg2 in terms of form, regarding to the facility usage efficiency, we set the kch < kcg , because the ancillary usage efficiency of the aggregator is higher than the prosumers normally, leading its unit investment cost lower. Based on the function (5)–(6), accordingly, the corresponding function of consumer j is: p
a a a r a − ηxj )Qxj (1 − θxe ) + prxj Qxj + pxj Qxj θxe , x ∈ {h, k} Cj = (pxj
(8)
Analyzing the interactive relationships among function (5)–(8), Proposition 3 can be got: Proposition 3: In the mix trading model, the optimal investment degree of the prosumer g and aggregator h for enhancing its power supply quality, respectively, is: eg =
r +ηh )Q a b(pgh gh gh kcg
p
and eh =
r +η )Q a b(phj hj hj . kch
According to Proposition 1–3, and analyzing them comprehensively, corollary 1 can be obtained: p
Corollary 1: The mix trading model can embrace B − A = (pg − ηij + pija )/(2pg − p p a )/(2p − ηp + pr ) − be ] more prosumers to participate ηij + pijr ) − [(pg − ηhj + phj g h hj hj their DERs power supply than the pure P2P model, in the precondition of B ≥ A. According to the obtained propositions and corollary, some enlightenments can be got; in addition, some intuitively sensible governing approaches for DERs proposed in some studies have been justified here in theory. Proposition 1 indicates that health P2P trading needs an admittance criterion, so as to guarantee the overall trading effectiveness and aggregated cost minimization. This result corroborates the validity of the notion of the active network. Contrasting Proposition 2 and Proposition 3, related results can be obtained: a good tariff strategy is effective to incentivize prosumers for active enhancing their power quality [9], in terms of which the mixed trading model is more resultful. Through Corollary 1, it can be validated that the mixed P2P trading model appears more inclusivity for accommodating more prosumers participating power trading, compared with the pure P2P trading model.
3 Structured Peer-To-Peer Energy Trading Hierarchy Construction 3.1 Mix P2P Trading Hierarchy Referring to Fig. 2, the trading model purposed in this paper contains multiple layers. The fundamental layer lies at the bottom layer of the whole DERs trade where the consumers
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having electricity demand and the prosumers having extra electricity are marched through market mechanism. Yet, accounting into potential supply-demand imbalance in this layer, there need more trading layers which resident in higher voltage level so as to absorb more prosumers, aggregators to join into the trade. In addition, the participants in the different trading level should obey the trading permission indicators suggested in the prior propositions and corollary. The trading structure and the corresponding trading criteria constitute the whole trading hierarchy of the mix P2P trading. The behind logic of applying this layered trading model sources from the diversity of prosumers and technology-economic consideration of trading plat-form. The miscellaneous prosumers who provide different quality of electricity should be categorized depending on this layered trade structure. Moreover, due to this categorization, the computation efficiency of the layered trade platform would be higher than building one large platform. Correspondingly the investment cost would be lower. 3.2 Mix P2P Trading Algorithms Matching Algorithm Between Aggregators and Consumers. Set the market mode in the first-layer trade constituted by n percent prosumers and 1−n percent aggregators. Because the trading of the first-layer is consumer-orientation, the aiming function is set as a function of consumption cost minimization, in which trading price pf and penalty fee ηM if not meeting trading deal, are the main independent variables. M min C M = nCjM + (1 − n)Caj
(9)
s.t.πjM ≥ 0
(10)
πajMM ≥ 0
(11)
pg Qij θi − CjM ≥ 0
(12)
M pg Qaj θaj − Caj ≥0
(13)
Based on function (9)–(13) and using Kuhn-Tucker conditions, build a Lagrange M ); function: L1 = −C M + λ1 πjM + λ2 πajMM + λ3 (pg Qij θi − CjM ) + λ4 (pg Qaj θaj − Caj solving the function, the optimal solution of the independent variables can be obtained: pf ∗ = [kp (1 + 2pg ) + Y (1 − 2pg ) − 2Qij Z 2 ] · [2Y 2 (1 − 2pg ) + 2Qij2 Z 4 + (2Y + kp )bQij Z − (A + 2pg Y )X + (2kp − 2Y + 4pg Y + 2X )Qij Z 2 ]/[(4kp Qij Z 2 + 4bkp Qij Z + b2 kp Qij ) · W ] − pg (kp + 2Y )/W ηM ∗ = [2Y 2 (1 − 2pg ) + 2Qij2 Z 4 + (2Y + kp )bQij Z −(A + 2pg Y )X + (2kp − 2Y + 4pg Y + 2X )Qij Z 2 ]/ (4kp Qij Z 2 + 4bkp Qij Z + b2 kp Qij )
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Y = θˆi kp , Z = bpg , W = 2Qij Z 2 + 2pg Y − Y . where X = 2(2Qij2 Z 4 + 2kp Qij Z 2 + 2bQij YZ + bkp Qij Z + 2Y 2 ) Matching Algorithm Between Aggregators and Prosumers. Following the same logic with the pure market trading mode of the first-layer trade, set in the secondlayer trade, for participating prosumers and VPPs, the percent of choosing MM mode is u; choosing CM mode is 1-u. In this situation, the trading objective function can be constructed: max πajM = uπajMM + (1 − u)πajCM
(14)
s.t.πiaMM ≥ 0
(15)
πiaCM ≥ 0
(16)
πajMM ≥ 0
(17)
πajCM ≥ 0
(18)
Solving the functions (14)-function (18), the results of related variables can be got:
wM ∗ where U =
ps∗ = (b2 Qia pr∗ − kp + Y )· [YU + b2 Qia pr∗ (U − bQia pr∗ ) − 2bpr∗ Y ]/ [bkp (b2 Qia pr∗ + Y )] − pr∗ Y /(b2 Qia pr∗ + Y ) = [−YU − b2 Qia pr∗ (U + bQia pr∗ ) + 2bpr∗ Y ]/bkp
pr∗ (b2 Qia pr∗ + 2Y )/Qia .
3.3 Two-Stage Marching The electricity supply and demand between prosumers and consumers in a proposed trading model of this paper could be matched by a trade platform in a two-stage process. At the first stage, the platform attempts to match a sole prosumers/ or aggregators with one consumers, based on the comprehensive assessment of price and electricity quality. If the first match can not be met, the platform would turn to match the consumers with aggregators, and match prosumers and aggregators. Notably, the prosumers matched with aggregators usually are the ones whose services can not satisfy the requirement of consumers in the first stage. Hence, for them, it is a rational choice to be absorbed into the pool of aggregators to trade as a whole. The electricity demand of other left consumers who unmatch in the above stages, could be met by the distribution grid. The specific principles of the two-stage matching algorithm obey the formula (9)– (18).
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4 Case Study To verify the feasibility of the trading suggestion set forth by this paper, a realistic electricity system scenario is employed. The scenario bases on the ‘LSOA W01000897’ area of Neath Port Talbot, Wales, UK [10]. Notably, in this background, all the electricity consumers get electricity from the distribution network of 0.4 kV; the electricity from prosumers also upload from this voltage level. The trading hierarchy complies the setting of Fig. 2. That is, there exists a microgrid market below the voltage level of 0.4 kV. In the level of 11 kV, a distribution market exists, servicing for the electricity balance in this jurisdiction. The installed DERs all are PV. The ratio of prosumers account for 10% in all consumers. In this trading structure, a electricity trade of an 1-h interval (i.e., 10 a.m.–11a.m.) was discussed. For simplicity, posit in this time interval, the power generation of various prosumers is constant, though their feasible generation capacities are different in the prior setting. The generation capacities follow a standard normal distribution. Referring to [10], the demand of consumers were sampled evenly from [VARdom Pdom , PARdom Pdom ] and [VARnondom Pnondom , PARnondom Pnondom ], where Pdom was sampled from the normal distribution N (P dom , 0.2P dom ) and Pnondom ∼ N (P nondom , 0.2P nondom ). From the perspective of electricity users, the suggested business model of this article also is effective. As shown in Fig. 3, because of DREs trading business models enhance the participation ratio of the prosumers owning DREs in the precondition of securing the electricity-providing quality, thus, the overall cost of electricity users in terms of electricity consumption would be reduced. For the users, compared with both their cost of buying electricity from the distribution grid as well as the one from the other DREs trading model which don’t have detailed trading structures and corresponding trading participation criteria, gaining electricity from our proposed model is more economic.
Fig. 3. Cost saving of electricity users
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5 Conclusion In this paper, a P2P energy trading hierarchy and related trading model are proposed in a distribution grid environment with a high penetration of DERs. Focusing on the model feasibility, the trading hierarchy is discussed firstly from the lens of trading theory; accordingly a mix P2P trading model is suggested. Next, a corresponding trading algorithm is proposed. And a case study is given to validate the model and the algorithm at last. Compared with other existing studies, the theoretical argument of P2P trading hierarchy has some certain enlightening meaning. Acknowledgements. This project was fully supported by the National Social Science Fund of China (19BJY077); the Humanities and Social Sciences Projects of the Ministry of Education of China (18YJC790137), the Humanities and Social Sciences Key Projects of Chongqing Municipal Education Commission of China (grant number: 20SKGH036), and the Natural Science Foundation of Chongqing (grant number: cstc2020jcyj-msxmX0808).
References: 1. Agency, I.E.: World Energy Outlook 2018 examines future patterns of global energy system at a time of increasing uncertainties. https://www.iea.org/newsroom/news/2018/november/ world-energy-outlook-2018-examines-future-patterns-of-global-energy-system-at-a-t.html, Accessed 5 May 2021 2. Parag, Y., Sovacool, B.K.: Electricity market design for the prosumer era. Nat. Energy 1(4), 16032 (2016) 3. Morstyn, T., Hredzak, B., Agelidis, V.G.: Control strategies for microgrids with distributed energy storage systems: an overview. IEEE T. Smart Grid 9(4), 3652–3666 (2018) 4. Khodaei, A., Bahramirad, S., Shahidehpour, M.: Microgrid planning under uncertainty. IEEE T. Power Syst. 30(5), 2417–2425 (2015) 5. Zou, K., Agalgaonkar, A.P., Muttaqi, K.M., Perera, S.: Distribution system planning with incorporating DG reactive capability and system uncertainties. IEEE T. Sustain. Energ. 3(1), 112–123 (2012) 6. Hansen, P., Liu, X., Morrison, G.M.: Agent-based modelling and socio-technical energy transitions: a systematic literature review. Energy Res. Soc. Sci. 49, 41–52 (2019) 7. Mazzucato, M., Semieniuk, G.: Financing renewable energy: who is financing what and why it matters. Technol. Forecast. Soc. 127, 8–22 (2018) 8. Huang, Z., Yu, H., Peng, Z., Zhao, M.: Methods and tools for community energy planning: a review. Renew. Sust. Energ. Rev. 42, 1335–1348 (2015) 9. McKenna, E., Thomson, M.: Photovoltaic metering configurations, feed-in tariffs and the variable effective electricity prices that result. IET Renew. Power Gen. 7(3), 235–245 (2013) 10. Shi, L., Zhou, Y., Long, C., Abeysinghe, S., Cipcigan, L., Wu, J.: A peer-to-peer energy trading hierarchy for microgrids in distribution networks. In: International Conference on Applied Energy (2019)
Research and Application System of Power Communication Network Data Mining and Intelligence Based on Regulatory Cloud Zhao Zilan(B) , Yu Ran, Yu Meng, Zhang Jiaojiao, Zhang Yi, Wan Ying, and Xu Hongfei State Grid JiBei Information and Telecommunication Company, Beijing 100053, China [email protected]
Abstract. Through data mining, cloud computing, knowledge atlas and other technical means, the data value of power communication network is studied deeply. The functions of power communication network one chart state monitoring based on Regulatory cloud, automatic business analysis and intelligent fault disposal, communication hidden trouble inspection and intelligent auxiliary disposal of artificial intelligence, communication operation simulation and event disposal drill platform, communication support intelligent dispatching command system are realized. Improve the communication guarantee ability of electric network and improve the intelligence level of communication operation management. Based on the analysis existing communication dispatching work, combined with the Regulatory cloud platform, the paper puts forward the research of power communication data value mining and intelligent decision making technology based on Regulatory cloud, which can improve communication data quality and assist in intelligent decision making. Keywords: Power communication network · Intelligent scheduling · Data mining · Regulatory cloud
1 Introduction At present, the communication system is in the state of “information island” in both data and application. There are some problems in the communication business data, such as insufficient value mining, poor professional integration and collaboration, weak professional management support, weak system application performance and so on. Under the demand of “large secondary” dispatching operation, it is urgent to enhance the ability of communication and power grid integration management and collaborative dispatching management. At present, we mainly rely on professional network management and communication management system to carry out communication dispatching operation, but we haven’t built communication dispatching auxiliary support platform. The lack of technical support means has caused the following problems: the graphic visualization degree of communication network is not high, the communication network architecture © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1029–1037, 2022. https://doi.org/10.1007/978-981-19-1528-4_105
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is mainly displayed by topology diagram, which can’t reflect the complex geographical environment, It is difficult to provide visual guidance close to the reality for the support work; For the communication failure events, it lacks intelligent and automatic impact assessment, disposal scheme analysis function, and online work platform of unified dispatching command; Lack of intelligent means to actively analyze communication risks, it is difficult to achieve accurate and efficient prevention in major activities[1–3]. Therefore, this paper deeply carries out the value mining of communication data based on regulation cloud, carries out the research and application of professional data sharing and sharing, management aided decision-making, performance improvement and flexible application, studies the research and application system development of communication scheduling aided decision support platform, and makes full use of the increasingly mature cloud computing, artificial intelligence knowledge mapping, deep learning and other technical means, Improve the level of communication support ability quickly.
2 Characteristics and Development Requirements of Power Grid Communication At present, the United States, Europe, Japan and other countries and regions have carried out a lot of research work on data mining and intelligent decision-making. The use of artificial intelligence, big data analysis and other technologies has been widely studied and applied in intelligent logistics, intelligent city, intelligent medical and other aspects. At present, the application of inspection robot and dynamic risk assessment in domestic power grid is also developing rapidly. The assistant decision-making based on artificial intelligence has achieved initial success in the aspect of dispatching operation information. However, in the field of electric power communication, due to the too scattered distribution of communication resource data information and the insufficient depth of data analysis and mining, it is difficult to adapt to the current development of intelligent assistant analysis, In the field of electric power communication, the research on intelligent decision-making of communication operation based on data mining of communication resources is still lacking [4–7]. At the first mock exam, the first mock exam is not based on the unified data specification, nor is the unified model management of the regulation cloud. The data structure, key fields, public basic models and public dictionaries are different from the unified scheduling model, which results in the barrier [8] of data communication and sharing between the power grid and communication network. Therefore, the power communication network needs to carry out the following functional research: it is necessary to design a communication regulation cloud data model that meets the requirements of communication scheduling and grid architecture. Design the unified public model of the whole network communication for the common basic objects of communication, establish the unified model in the national sub cloud, and distribute the provincial and local cloud (public domain) [9, 10];The customized demand of each province can be modeled on demand in the provincial cloud (private domain). Need to complete the communication and power grid data through sharing. At present, the communication management data is independent of the regulation cloud
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storage, and does not synchronize the data to the regulation cloud. In the power regulation cloud database, there are only various kinds of data of the power grid, but lack of the resources, monitoring and operation data of the communication network. Compared with the overall business scheduling, the communication data has become a data island to a certain extent. Therefore, there is an urgent need to use TMS as the data source to realize three kinds of data such as communication resources, monitoring and operation, and to complete the matching, docking and sharing with the grid data in the control cloud [11]. It is necessary to design and develop micro applications integrating communication and power grid. At present, as an information business system of the company, TMS has provided application functions such as communication real-time monitoring, resource management, operation management and professional management. On the one hand, the coupling between functional modules is close, the development and deployment cycle is long, and the application effect is slow; On the other hand, there is a lack of rich data analysis and display applications, especially the association analysis and business collaboration applications of communication network and power grid integration [12]. Therefore, it is urgent to realize the core basic micro application of communication resource management, dispatching monitoring and operation control based on the regulation cloud, and deeply integrate with the power grid dispatching. With the data association mining analysis as the main means, it is necessary to realize the intelligent micro application of deep integration of power grid communication support guarantee, power grid communication fault joint positioning, power grid communication demand trend, etc.[13, 14].
3 Construction of Communication Intelligent Scheduling Based on Regulation Cloud In order to meet the above requirements of the development of power grid communication, a solution is urgently needed to realize the deep integration of intelligent micro applications, such as power grid communication support guarantee, power grid communication fault joint location, power grid communication demand trend and so on. Relying on it technologies such as cloud computing, big data and mobile Internet, the dispatching control cloud (hereinafter referred to as the dispatching cloud) has inherent advantages [15, 16]. It uses data association mining analysis as the main means to realize the core basic micro applications of communication resource management, dispatching monitoring and operation control, and can be deeply integrated with power grid dispatching. Through the construction of regulatory cloud, we can gradually form a regulatory technical support system of “resource virtualization, data standardization, application service”. The construction of regulation cloud can provide the underlying technology foundation for power automation business, so as to meet the needs of model data services, infrastructure services and other basic services in the process of power production control, and then provide power dispatching automation decision analysis report for power regulation center and other departments.
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This paper studies from the aspects of communication data visualization, fault alarm automatic disposal, hidden danger intelligent patrol, event disposal drill, etc., and finally forms the value mining and intelligent decision-making technology of power communication data based on regulation cloud. 3.1 State Monitoring Technology of Communication Map Based on Regulation Cloud With the new HTML5 vector graphics engine, lightweight map engine and data statistics bi component of what you see is what you get, all kinds of communication thematic display views that can be independently deployed, upgraded and run can be rapidly expanded according to different needs. Typical examples are “communication status monitoring” and “communication Overview”. The first is the ‘communication Overview” map, which is used to show the geographical distribution and statistical overview of communication resource information, and supports the drilling down according to the data and graphics of State Grid, branch, province and city. It is the gateway and entrance to control the cloud overview and access the data of communication network resources at all levels (Fig. 1).
Fig. 1. Electric power communication network resource
The second is "Communication Map status monitoring", which is used to realize the comprehensive display of all kinds of information such as communication site, equipment, power supply, optical cable and business on the same geographic map. It not only reflects the scale and usage information of communication resources, but also reflects the operation status of the network and the progress of operation management in real time. It is a centralized display of the operation status of the whole communication network in the cloud (Fig. 2). 3.2 Automatic Business Analysis and Intelligent Fault Disposal Technology Combined with the current situation of power grid communication and pain points, the characteristics of alarm data are studied. At the same time, the feature extraction is
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Fig. 2. Electric power communication network monitor
carried out. At the same time, the common fault knowledge base is constructed. By matching with the features of new alarms, the risk is associated, the early warning is issued, and the intelligent fault elimination scheme is recommended. The details are as follows: using the method of feature extraction and feature matching (vector distance calculation) of alarm data, combined with the topology analysis of alarm nodes, fault diagnosis and location are realized; According to the data of equipment, business and mode safety rules, combined with the line topology, the equipment fault knowledge base is constructed, and the intelligent repair scheme is recommended; At the same time, according to the existing knowledge base, infer the associated risk and send out early warning. Through the application of the above technology, the communication and real-time alarm of power grid can be pushed each other, the fault occurrence link can be diagnosed, and the end-to-end fault diagnosis and location of power grid professional integration can be realized; At the same time, for communication failure, according to the business level, resource margin, equipment operation history, mode safety rules, intelligent planning of fault repair scheme is proposed; When a fault occurs, the fault influence range is diagnosed and the associated risk is given early warning (Fig. 3).
Fig. 3. Automatic business analysis and intelligent fault handing
3.3 Inspection and Intelligent Assistant Disposal Technology of Communication Hidden Danger Based on Artificial Intelligence By extracting all kinds of hidden danger knowledge from unstructured document data, relational database and other hidden danger data sources, a rich hidden danger knowledge
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base is established. Through knowledge reasoning, it provides auxiliary decision-making for daily hidden danger investigation and governance. It is mainly reflected in the following two aspects: the establishment of communication hidden danger knowledge base, including the historical operation records of communication resources within the scope of guarantee, historical performance records, alarm records, communication hidden danger identification and risk point assessment documents. Combined with knowledge reasoning technology, it provides daily automatic patrol, manual fixed-point patrol, trend routing analysis and other functions to realize the active detection of communication hidden danger (Fig. 4).
Fig. 4. Communication hidden trouble inspection and intelligent auxiliary technology
Through in-depth mining of communication hidden danger database, combined with daily automatic inspection, manual fixed-point inspection, trend routing analysis and other functions, the active detection of communication hidden danger is realized. 3.4 Build Communication Operation Simulation and Event Handling Drilling Platform Combined with power communication network topology information and business system data, the communication knowledge map is constructed based on knowledge map technology, and the power communication operation simulation platform is established. Through artificial intelligence technology (data analysis, graph analysis, path planning, feature matching clustering), the power communication operation simulation, risk assessment, state prediction, training and teaching ability are realized. It can realize: in case of abnormal communication events, it can quickly give an alarm and early warning, and automatically complete the evaluation of the scope and degree of influence on the grid business; Based on deep learning technology and emergency plan knowledge base, the auxiliary analysis and decision-making of communication emergency disposal scheme are realized according to the influence of different grading and different business; The main scenarios of communication support are online drilling, and heuristic interaction technology is used to provide drilling and training tools for support personnel (Fig. 5). Through the operation simulation and event handling drilling platform, the operation situation of the whole power communication network can be visually displayed. After the fault occurs, the involved scope, influence business and correction method can be
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Fig. 5. Communication operation simulation and event handing drill platform
pointed out through simulation; Carry out system verification and risk assessment in case of major operation mode change and maintenance arrangement; At the same time, it supports the accident prediction of the whole network operation, and puts forward pre control measures and optimization suggestions; Support to carry out simulation training and anti accident drill, improve the professional ability of dispatching operation and maintenance personnel, and test the level of communication emergency management. 3.5 The Intelligent Dispatching Command System of Communication Support Based on Regulation Cloud Based on the primary and secondary basic data of communication and power grid, carries out the technical research of artificial intelligence knowledge mapping and deep learning, and realizes the intelligent auxiliary decision-making of communication and power grid operation, which is the advanced goal of regulation Cloud Application construction and the main direction of communication application customization and expansion. Driven by business requirements and technology development, the intelligent level of communication management and operation is continuously improved (Fig. 6).
Fig. 6. Intelligent dispatching and command system for communication assurance based on regulatory cloud
Through the intelligent dispatching command system, the communication and power grid data are integrated, and the research on data fragment fusion, feature fusion and
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decision-making level fusion is carried out, so as to realize the joint analysis ability of communication and power grid data and improve the business decision-making level. At the same time, the application of big data platform, combined with machine learning algorithm for intelligent data extraction, cleaning and calculation, optimize data quality, mining data value. Furthermore, the application scenarios of collaborative scheduling and association analysis of power grid and communication are expanded, and the related algorithm engine is studied to support the realization of cloud micro application function.
4 Conclusion Through the research and application of communication data value mining and intelligent assistant decision-making technology based on regulation cloud, it can provide information, intelligent, reliable management and control and intelligent assistant decisionmaking means for power communication management decision-makers, which is in line with the actual needs of power grid enterprises, and can effectively enhance the value of communication data and improve the work efficiency of communication operators, It is applicable to the promotion and reference of intelligent assistant decision-making in communication system by power companies of all provinces (autonomous regions, municipalities directly under the central government) and units outside the State Grid system.
References 1. Kai, X., Meng, H., Ning, D., et al.: Research on cloud control construction under the new situation of power Internet of things. Power Internet things 26(10), 167–173 (2020). (in Chinese) 2. Chen, Z., Liu, D., Xing, X.Y., et al.: An efficient operation and maintenance method for power regulation cloud. Power Syst. Prot. Control 48(14), 175–181 (2020). (in Chinese) 3. Wang, W., Huang, D., Wang, S., et al.: Design and implementation of grid regulation cloud platform. Electric. Technol. 21(12), 92–96 (2020). (in Chinese) 4. Zhu, W., Guo, Q.: Research on association rules mining algorithm for microgrid fault detection. Electric. Technol. 16(8), 7–10 (2015). (in Chinese) 5. Wei, J., Ying, Z., Shuqi, C., et al.: Research on data mining and knowledge map construction technology of power grid infrastructure engineering. Res. Power Inf. Commun. Technol. 19(2), 15–22 (2021). (in Chinese) 6. Xu, H.: Structural design and application of general data object for power dispatching oriented to regulation cloud. Power Grid Technol. 43(12), 109–114 (2018). (in Chinese) 7. Zhang, L., Que, L., Han, X., Ji, X.: Application of grid graphics integrated maintenance technology based on control cloud. Power Syst. Autom. 43(22), 151–155 (2020). (in Chinese) 8. Shen, G., Sun, L., You, D., et al.: Integrated visualization method of intelligent dispatching system information. Prot. Control Power Syst. 42(13), 129–134 (2014) 9. Zhang, L., Que, L., Han, X., Ji, X.: Application of grid graphics integrated maintenance technology based on regulation cloud. Autom. Power Syst. 21(7), 56–61 (2019). (in Chinese) 10. Xu, K., Han, M., Dong, N., et al.: Research on cloud control construction under the new situation of power Internet of things. Power Supply Consum. (9) (2020) 11. Juan, F., Hongjie, Z., Jiafeng, S., et al.: Deep level detection technology for massive operation data of smart grid based on regulation cloud. Autom. Instrument. 230(12), 162–165 (2018)
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12. Zhang, Q., Liu, H., Li, N., et al.: Research on architecture and key technologies of “regulation cloud” in Shandong Province. Shandong Electric Power Technol. (5) (2018). (in Chinese) 13. Huang, Y.C., Huang, C.M., Liao, C.C., et al.: A new intelligent fast Petri-net model for fault section estimation of distribution systems. In: International Conference on Power System Technology, 2000, Proceedings, Power, vol. 1, pp. 217–222. IEEE (2000) 14. Rather, Z.H., Chen, Z.: Wide area measurement based security assessment & monitoring of modern power system: a danish power system case study. In: Horvat, G., Vinko, D., Zagar, D. (eds.) 2013 2nd Mediterranean Conference on Innovative Smart Grid Technologies - Asia (ISGT Asia), Household Power Outlet Overload Protection And Monitoring Using Cost Effective Embedded Solution, Embedded Computing (MECO). IEEE (2013) 15. Qiu, M., Gao, W., Chen, M., Niu, J.W., Zhang, L.: Energy efficient security algorithm for power grid wide area monitoring system. IEEE Trans. Smart Grid 2(4), 715–723 (2011) 16. Salehi, V., Mazloomzadeh, A.: Real-time power system analysis and security monitoring by WAMPAC systems. In: Innovative Smart Grid Technologies (ISGT) (2012)
Research on Simulation Training System of Substation Based on HTC Vive Yuhan Wang(B) and Xiaohui Liao School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China [email protected]
Abstract. The purpose of this paper is to improve the operation level of substation staff and make up for the immersion of the existing simulation training system. Accordingly, we design a 500 kV substation virtual reality simulation train system, which based on the prototype of a 500 kV substation in Henan. The substation scene is restored 1:1 though the 3Ds Max modeling software. In the Unity software, the substation scene is optimized, and the built-in UGUI system is used to realize the function of users registration and login, and the four simulation functions such as the site inspection, cognitive learning, operating practice, and fault handing are developed. Finally, we can realize the synchronous operation of desktop virtual reality and immersive virtual reality, combined with the HTC Vive virtual reality device. The simulation training system designed in this paper greatly improves the learning efficiency and the sense of presence of operators. Keywords: HTC Vive · Virtual reality · 3Ds Max modeling · Substation · Simulation training system
1 Introduction The stable operation of substations is crucial to people’s daily life and industrial production [1, 2]. And with the progress of society, the structure of the power grid is becoming more and more complex, which puts forward stricter requirements on the operation level of substation staff [3]. At present, most of the existing training methods cannot combine theory with practice. Usually the theoretical learning part is relatively boring, and the practical part cannot predict the occurrence of faults in advance or set faults artificially due to the particularity of the working environment of the substation. It is difficult to carry out on-site learning and operation in the substation. Therefore, this paper aims to establish a virtual reality simulation training system for substations based on HTC Vive, which can highly restore the actual operating conditions of substations. At the same time, the students’ sense of immersion is increased by interacting with the VR device. The paper provides a safe and economical training method to improve the training effect. With the rapid development of virtual reality technology, i.e., from ‘3I’(Interactivity, Immersion, Imagination) to ‘4I’(Artificial Intelligence, AI), this technology has been widely used in the field of substation simulation training [4]. In 2012, Wang Yanfang [5] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1038–1044, 2022. https://doi.org/10.1007/978-981-19-1528-4_106
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adopted the development tool-Open GL (open graphics library) and OSG (open scene graph) has built a substation accident simulation platform and assessment system. The system is still in desktop virtual reality and lacks immersion. In 2015, Chen Yongbo [6] an immersive substation simulation training system. Through some interactive equipment, stereo projection, helmet-mounted display, etc., users’ sense of presence can be increased and students can be placed in a more realistic virtual environment, but the system only Stay in the link of roaming inspection and equipment learning, there is no fault simulation function. In 2019, Wu Bing [7] developed a virtual simulation system for the main system of an immersive substation based on a 110 kV step-down substation in a certain city. The voltage level of the system still has limitations. In response to the above problems, the paper develops a 500 kV substation virtual reality simulation training system based on HTC Vive. The system uses 3Ds Max (3D Studio Max) software to build models of all substation equipment and civil structures, and then completes the development of virtual scenes and hardware HTC Vive system in Unity. The system puts students in a more realistic virtual environment through head-mounted displays(HMD), operating handles and other device, which can not only stimulate students’ interest in learning, but also increase students’ sense of presence and immersion.
2 Overall Design 2.1 Selection of Modeling Software The paper chooses 2016 version of 3Ds Max for building the model of the substation. Almost all equipment models of substation are irregular, such as transformers, isolation switches, etc. At this point, polygons equipment are executed the transformation operations on point-level or surface-level by Polygon Modeling [8–12] in the 3Ds Max modifier. After completing the substation modeling, making it effect optimization and memory optimization in 3Ds Max material editor. In addition, the animation development process of the system adopts the interpolation between key frames to process the object image. The three-dimensional model effect diagram of substation is shown in the Fig. 1.
Fig. 1. Virtual substation rendering.
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2.2 Virtual Reality Engine Unity The system chooses Unity 5.6.1 Person Edition to develop the virtual scene of the substation. Unity engine that a multi-platform comprehensive game development tool is developed by Unity3D Technologies, which is suitable for a wide range of applications, for example, Web design, simulation training [13]. If used properly, Unity ‘interface has many view windows that promotes the development of virtual substation scenes and the implementation of function. Some optimization methods, such as light rendering, adding collision bodies, importing resource packages and applying the technology of LOD(level of detail) [14], makes the visual effect of system model more realistic and makes the virtual scenes of the substation more abundant in Unity. Then the system uses C# language to editing Unity scripts in Visual Studio, including login script, menu script, animation script, HTC Vive handle interactive script, equipment learning script, particle system script and so on. The login interface and menu of the 500 kV virtual reality simulation system are design though the UGUI system in Unity. 2.3 Virtual Reality Development Tool HTC Vive HTC Vive, an external device that realizes the interaction between virtual and real world, was released in 2015 [15–17]. It mainly consists of a head-mounted display, two positioning based stations and two operating handles [18]. The configuration of the virtual reality development environment is divided into two parts: hardware installation and software installation. Firstly, we should set up the positioning based station, HMD and handles according to the instructions of the HTC Vive. Then though the data transmission line and the connector, the motion data of the HMD and screen space are transmitted, and the HMD is connected with the PC. Secondly, downloading and installing the Steam VR plugin from the Unity 3D resource store. And the HMD and the handles can be monitored in real time when it is imported into the Unity 3D virtual reality platform. After all devices are connected, setting the game area by the handles, and selecting the appropriate operation mode: sitting mode and standing mode. Finally, the user’s immersive experience in the virtual reality substation training system is increased by the Steam VR Plugin and VRTK interactive plug-ins. The function of the Steam VR Plugin is to realize the virtual imaging of the HMD, which is to render the picture to human eyes by reading the motion data of the HMD, and transmit the picture split-screen display to the HMD. The function of VRTK interactive plug-ins is to realize the displacement and interaction of objects in the virtual scene through the left and right handles respectively. The specific methods for realizing the displacement of objects are generally divided into normal roaming and instantaneous movement. And common interactive operations with objects including touching, grasping, using, and so on.
3 Function Introduction 3.1 The Site Inspection In order to ensure the stability of the power system in daily life, the staff need to patrol the substation to ensure the normal operation of the power equipment. At the same time,
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during the inspection process, the staff can learn about the equipment of the substation. Therefore, this system has developed two patrol modes: automatic roaming and autonomous roaming. In automatic roaming, the camera is installed on the automatic cruising robot car to make it patrol according to a fixed route. During the movement, the equipment model of the substation can be observed by adjusting the direction of the lens. In autonomous roaming, the user, as the first point of view, can move freely in the virtual substation to complete the inspection work of the substation. 3.2 Cognitive Learning The substation staff need to be proficient in the basic information of each equipment, but the traditional way of learning substation equipment is relatively boring, and students lack a sense of immersion. Therefore, the system has developed a equipment learning module to enter the name, model, function and other information of the main equipment of the substation into the system, and the important information of the equipment can be displayed through basic human-computer interaction (HCI). 3.3 Operating Practice When the grid load is adjusted or the equipment is scheduled to be overhauled, it is necessary for the substation staff to be proficient in the operation steps and processes of the substation, so as to ensure the switching operation successfully and steadily. However, in real life, due to the high risk of substations and expensive equipment, it is difficult for staff to practice on-site switching operations. Therefore, this paper develops a switching simulation operation based on line switching. 3.4 Fault Handing It is inevitable that the substations equipment may break down during the normal operation of substation. When it happens, the staff should solve the problem in a timely and correct manner for troubleshooting. At present, it is impossible for the equipment to malfunction deliberately during learning, and there are few practical examples of fault handling. So it is difficult for students to master the methods of handling faults proficiently. Consequently, this paper designed a fault simulation system. Through the VR technology, to simulate the common faults types of substation and to learn the operational approach of dealing the power equipment faults.
4 Function Implementation 4.1 The Site Inspection Automatic Roaming. First, we build a model of robot car in 3Ds Max, which is stored as an obj file. We transform it to a fbx file and import it into the virtual scene of the substation in Unity. And it was renamed to Robot. Then, we import the First Person Controller from the assert packages in the Project view, which is named Robot-Camera
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and is attached to the Robot. After that, we adjust the size of the Robot and the height and angle of the Robot-Camera. Finally, we can determine the path by adding key points to the DOPath function in the DOTween component. Automatic roaming is shown in the Fig. 2. Antomatic Roaming. First, we select the First Person Controller as the control object. Then, we set the coordinates and parameters about it in the Scene view and the Inspector view. In particular, it is impossible to observe it on the ground for the higher substation equipment. Therefore, the first role controller can be raised or lowered by clicking the keyboard H or L. The aerial view of the system is shown in the Fig. 3.
Fig. 2. Automatic roaming.
Fig. 3. Aerial view of the system.
4.2 Cognitive Learning First, we create a UI interface in the system, and input the parameter information of the substation equipment into the system in txt file. Then, we create a file named Empty, and add the recorded audio to the Audio component to realize synchronous learning of voice and text. 4.3 Operating Practice Switching operation is a dynamic transition process, which is a link that needs to be mastered by substation staff. The system uses VR technology to develop a switching simulation operation process based on line switching. In 3Ds Max, we use the Keyframe Interpolation method to build the animation model, and convert its storage format to import it into Unity. Then we decompose and control the animation model in Unity. The former, animation segmentation, requires the Mecanim animation system in the Unity engine. The latter, that is, animation control, requires the Animator Controller component in Animator. And at the same time, we should set the State Machines to control the progress and termination of the animation. Finally, we need to write a control script to control the normal operation of the animation.
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4.4 Fault Handing Generally, substation fault simulation is divided into static fault simulation and dynamic fault simulation. Static faults that do not need to be simulated by animation, such as equipment identification plates falling off, mechanism box closure faults, etc., can be realized by calling the RotateAround function in 3Ds Max through editing a script program or by rendering alternatives. The Mechanism closure fault is shown in the Fig. 4. Dynamic faults that need to be simulated by animation, such as transformer explosion and fire, shunt reactor smoke, etc., can be be set to the corresponding parameters to achieve fire, smoke and other phenomena through the particle system in Unity. The transformer fire fault is shown in the Fig. 5.
Fig. 4. Mechanism closure fault.
Fig. 5. Transformer explosion effect.
5 Conclusion The paper uses 3Ds Max software and Unity engine to develop a 500kV virtual reality substation simulation training system, Based on virtual reality technology. And combined with the HTC Vive hardware development platform, the system realizes humancomputer interaction, enhances immersion, changes the traditional learning mode, and improves learning Interesting. At the same time, the system has the advantages of low development cost, high safety and easy maintenance.
References 1. Wang, Y.: Research on Application of Power Supply Reliability Evaluation Technology in Substation. Shandong University, Jinan (2009). (in Chinese) 2. Wu, Y., Fang, X., Men, F., Qin, X., Zhou, L.: Research on simulation training system for substation equipment maintenance. J. Phys. Conf. Ser. 1550(5), 052006 (2020) 3. Liu, Y., Zhao, J., Lin, Y., Zhang, R., Xue, J.: 110 kV skills training system for substation operation and maintenance based on VR. Electron. Meas. Technol. 42(21), 131–136 (2019). (in Chinese) 4. Wang, H.: Research on Virtual Reality Simulation System of 500 kV Substation. Zhengzhou University, Zhengzhou (2019). (in Chinese)
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5. Wang, Y.: Research and Implementation of Three-Dimensional Simulation System for Substation Accidents. North China Electric Power University, Beijing (2012). (in Chinese) 6. Chen, Y., Lin, C., Li, J., et al.: Design and realization of immersive substation simulation training system. Power Syst. Technol. 39(7), 2034–2038 (2015). (in Chinese) 7. Wu, B., Huang, C., Zhu, X.: Development of fault simulation system for immersed substation. Power Syst. Prot. Control 45(21), 102–108 (2017). (in Chinese) 8. Xu, Y.: Research on Immersive Virtual Simulation System of Substation Electrical Main System. Henan Polytechnic University, Henan (2019). (in Chinese) 9. Zai, X.F., Zhu, J.J., Pan, Z.G.: 3ds MAX modeling and its application in virtual reality. Comput. Simul. 21(4), 94–97 (2004) 10. Cai, W., Cheng, G., Zhu, Z.M.: Design of mine virtual simulation system based on 3D max and virtools. Coal Eng. 1, 111–113 (2011) 11. Wainer, G.A.: Visualization in 3ds Max for Cell-DEVS Models Based on Building Information Modeling. Cellular Model. Tech. (2013) 12. Wang, W.L., Wang, Y.J., Li, R.M.: Design of drilling simulation training system based on virtual reality. Comput. Technol. Dev. (2011) 13. Li, H., Lu, P.P.: Design of virtual animation system based on unity 3D]. Modern Electron. Tech. 44(08), 164–168 (2021). (in Chinese) 14. Qi, Y., Tian, M., Chen, X., Zhang, H.: Construction of virtual simulation training system for substation based on unity 3D. J. Lanzhou Jiaotong Univ. 40(01), 53–59 (2021). (in Chinese) 15. Hou, G.: Development of Immersive Tower Crane Operation Simulation Training System. Shandong Jianzhu University, Jinan (2019). (in Chinese) 16. Nikitin, A., Reshetnikova, N., Sitnikov, I., Karelova, O.: VR training for railway wagons maintenance: architecture and implementation. Procedia Comput. Sci. 176, 622–631 (2020) 17. Casey, P., Lindsay-Decusati, R., Baggili, I., Breitinger, F.: Inception: virtual space in memory space in real space – memory forensics of immersive virtual reality with the HTC vive. Digital Investigation 29, S13–S21 (2019). https://doi.org/10.1016/j.diin.2019.04.007 18. Li, H., Si, Z., Zhou, Z.: Research on virtual laboratory system of printing color science based on HTC vive. Digital Print. 01, 22–28 (2020). (in Chinese)
Development of Vehicle Charger with High Power Factor Operation Fucun Li1(B) , Zhou Wang2 , Yan Zhang1 , Danwen Yu1 , Lijun Liu3 , and Guanglei Li1 1 State Grid Shandong Electric Power Research Institute, Jinan, China
[email protected]
2 State Grid Jinan Power Supply Company, Jinan, China 3 State Grid Shandong Electric Power Company Marketing Service Center, Jinan, China
Abstract. In order to alleviate the pressure of energy shortage and environmental pollution, Accelerating the wide access of distributed energy, electric vehicles and other interactive energy is one of the important ways to realize low-carbon operation of power grid and help achieve “carbon peak, carbon neutral. At present, more and more electric vehicles come into the life of family residents, and the charging infrastructure is one of the important factors that affect its large-scale promotion, Portable vehicle charger is generally small in size, light in weight and low in cost, which has the characteristics of flexible charging at anytime and anywhere. In order to reduce the impact on the power grid, especially on harmonic and reactive power, and to reduce the impact on battery life, it is necessary to control the output voltage and current of the charger, therefore, it is necessary to put forward relevant index requirements for chargers, In this paper, how to improve the output power factor, output voltage and current range controllable, etc. developed a car charger. Keywords: Electric vehicle · Car charger · High power factor · Voltage and current
1 Introduction With the increasingly serious problems of energy shortage and environmental pollution, electric vehicles have become the focus of research and development in various countries. There will be more and more family electric vehicles. A large number of electric vehicles will be connected to the power grid, and the impact on the power quality of the power grid cannot be ignored, In order to reduce the impact on the power quality of power supply, it is necessary to put forward relevant requirements for the electric vehicle charger connected to the power grid, especially the operation power factor. If the power factor is too low, it will bring harmonic pollution to the power grid. In the future, in terms of the number of electric vehicles, home electric vehicles will become the mainstream of electric vehicles. In order to meet the convenient charging of owners, on-board charger will become the standard configuration of electric vehicles. The power grid provides single-phase or three-phase power for vehicle charger. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1045–1053, 2022. https://doi.org/10.1007/978-981-19-1528-4_107
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2 Main Index and Topological Structure In order to meet the convenient access of vehicle charger and meet the requirements of grid access, it is necessary to put forward requirements for relevant indicators of charger. The details are shown in Table 1: Table 1. Main index requirements of on board charger Index name
Index requirements
Input voltage
380 V ± 10%
Output voltage
600 V ± 5%
Output power
3.3 kW
Power factor
0.98
Cascade function
Cascadable
Temperature range
−20–60 °C
To meet the above requirements, the main circuit structure is APFC and DC/DC cascade and the specific circuit structure is shown in Fig. 1: DC/DC Converter
PWM Rectifier
E
VT1
C1
VT2
C2
Battery
L2
L1
VT3
Fig. 1. Single module main circuit topology
Fig. 2. Charging unit module cascade structure
The main structure topology of single module main circuit is uncontrollable rectification and boosts active power factor correction, and then connected to DC/DC. The output side is filtered by LC, and the controllable devices used in boost power factor correction and DC/DC link can reduce the device pressure through parallel connection of multiple devices. In order to improve the output power, multiple modules can be used in parallel, as shown in Fig. 2. In this paper, the theoretical analysis and prototype verification of the single module structure are carried out.
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3 Control Theory Analysis 3.1 Power Factor and Harmonics Power factor is one of the main indicators of the influence of power equipment on power grid. PF =
P U1 I1 cos ϕ1 I1 cos ϕ1 = = = γ cos ϕ1 S U1 Ir Ir
(1)
In Eq. (1), P is the input active power, S is the apparent power, and Ir is the effective
value of the input current, Ir can be expressed as:Ir = I12 + I22 + · · · In2 , I12 , I22 , In2 are the fundamental and harmonic components of Ir respectively, U1 is the effective value of the grid side voltage, γ is the distortion coefficient of the current waveform, and ϕ1 is the phase shift angle between the fundamental current and the input sinusoidal voltage. The relationship between THD and input current, fundamental component and harmonic component is as follows: ∞ Ir2 − I12 In2 × 100% = × 100% (2) THD = I1 I2 2 1 From formula (1), the relationship between γ , PF and THD is as follows: 1 γ=√ 1+THD2 1 PF = √ cos ϕ1 1+THD2
(3) (4)
It can be concluded that whether PF is close to 1 depends not only on the phase angle of voltage and current, but also on the harmonic distortion rate. 3.2 Theoretical Analysis of Boost APFC Control Boost APFC usually has peak current control, average current control and hysteresis current control, the peak current control is simple and the frequency is fixed, but the switching current is large, the loss is large and the noise is large; The average current control switch frequency is fixed, the noise is small, but it needs to increase the current detection, and the control is complex. The control accuracy of hysteresis current control is related to the hysteresis width. The larger the hysteresis width, the lower the switching frequency and loss, but the poor current control effect. On the contrary, the higher the switching frequency and loss, the higher the working frequency of the device. To sum up, the average current control is adopted in this paper. According to Fig. 1, based on Kirchhoff’s voltage law and current law, boost current is modelled and analysed, when the inductance current is continuous, the relationship between inductance voltage and inductance current is as follows: diL L dt = vin , 0 < t < dT (5) L didtL = vin − vo , dT < t < T
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Here, iL is the inductance current, vin is the input voltage, vo is the output voltage, D is the duty cycle, T is the switching cycle. In a switching cycle, the average current on the load is: iD = (1 − d )iL
(6)
According to Eq. (6), Eq. (5) can be transformed into: L
diL = dvin + (1 − d )(vi − vo ) = vi − (1 − d )vo dt
(7)
By Laplace transform, the Eq. (7) can be obtained as follows: sLiL = vi − (1 − d )
(8)
vo = (R/sC) (1 sC + R) ∗ iD = RiD (RCs + 1)
(9)
The output voltage is:
When s = 0, the formula (7) - formula (9) can be transformed into: ⎧ ⎨ vo /vi = 1/(1 − D) I = vo /R ⎩ D IL = ID /(1 − D) ∧ ∧
(10)
∧ ∧ ∧
In Eq. (10), small signal disturbance vi , iL , vo , d , iD is added ⎧ ∧ ∧ ∧ ∧ ⎪ ⎪ ⎨ sL(IL + iL ) = Vo + vi −[1 − (D + d )](Vo + vo ) ∧
∧
∧
(ID + iD ) = (IL + ii )[1 − (D + d )] ⎪ ⎪ ∧ ⎩ ∧ Vo + vo = R(ID + iD )/(RCs + 1)
(11)
After adding the disturbance, assuming that the small signal is much smaller than the corresponding large signal and ignoring the second-order product term, the small signal model can be obtained ⎧ ∧ ∧ ∧ ∧ ⎪ ⎪ ⎪ sL iL (s) = vi (s) − (1 − D) vo (s) + Vo d (s) ⎨ ∧ ∧ ∧ RID (12) vo (s) = R(1−D) RCs+1 iL (s) − (RCs+1)+(1−D) d (s) ⎪ ⎪ ∧ ∧ ⎪ ⎩ ii (s) = iL (s) The average current control is a double loop control strategy, that is, current loop control and voltage loop control. The output sampling voltage is compared with the reference voltage to produce error, and the voltage loop output is generated through the voltage compensation link. The voltage loop output and a certain proportion of the rectified voltage pass through a multiplier. The generated signal is used as the positive
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input of the current loop, and the negative end of the current loop inputs the current sampling signal, After passing through the current loop, the PWM signal is generated by the PWM comparator, which controls the switch S to turn on and off to control the inductance current to follow the rectified voltage signal, so as to achieve the purpose of power factor correction. The current inner loop control includes current error amplifier, The block diagram of the transfer function s of PWM comparator and inductance current to duty cycle is shown in Fig. 3. + -
C2
R2 GCA(s)
GPWM (s)
GId(s)
GRs(s)
Fig. 3. Current loop control block diagram
R1
C1
R3
CA
Fig. 4. Structure of current error amplifier
The current reference signal is the voltage signal output by the multiplier. The reference current signal generated by the multiplier and the inductance current in the main circuit pass through the current sampling resistance sampling signal. After passing through the current error amplifier, the driving signal is generated by the PWM comparator to control the action of the switch. The structure of the current error amplifier is shown in Fig. 4. The output voltage is compared with the output voltage reference signal through the sampling network and enters the voltage error amplifier. The output voltage of the voltage error amplifier is multiplied by a certain proportion of the input voltage signal to get the given current loop. The current loop gives the current signal of the output inductor after passing through the current loop. The output voltage Vo is obtained through the transfer function Gvi (s) of the output voltage to the inductor current, the structure block diagram of voltage control loop is shown in Fig. 5. R3
-
GVA (s)
K1 Vin
Gib(s)
Gvi (s)
Vo
C1
R1
+
R2
Vref
Vref
CA
GRs(s)
Fig. 5. Structure block diagram of voltage control loop
Fig. 6. Voltage error amplifier
The voltage error amplifier Gvi (s) adopts PI controller, and its structure is shown in Fig. 6.
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3.3 Main Circuit Parameter Designs In Sect. 3.1 and 3.2, we have completed the analysis of the circuit model and average current control of boost APFC. The parameter design of the main circuit is also a very important link. Whether in the simulation or prototype production, the parameter indexes of the main circuit are shown in Table 1. The boost inductor plays the role of energy transfer, storage and filtering in the main circuit, especially has a great impact on the ripple current of the input side. When the output power is large and the input voltage is small, the input side current is large and the ripple current is large. If the output power is too small, the grid side current will fluctuate up and down along the command value too much, and if the input voltage is too large, it will cause the current distortion. Here we choose 3 mH. If the output capacitance is too small, the output DC voltage ripple will be too large, and if the output capacitance is too large, the start-up charging time will be long, here we determine it as 3000 uF. Fast recovery diode: the maximum voltage on both ends is 400 V. Considering the circuit overvoltage, parasitic parameters, voltage oscillation and other reasons, four MUR20100 with voltage withstand capacity of 1000 V are selected here. The controllable device involved in the main circuit is MOSFET. The voltage and current bearing capacity of the device are mainly considered here, and the price factor is also considered. Six types of IRFP460 MOSFETs are selected here. Switching frequency of the switch tube: when the frequency increases, the size of the magnetic element can be reduced and the power density can be improved. However, too high switching frequency will increase the loss of the switch device and the fast recovery diode, resulting in the decrease of the overall circuit efficiency and the difficulty of heat dissipation; However, if the switching frequency is too small, the volume of the magnetic component will be too large, which will make the overall volume larger. Therefore, the volume and efficiency of the circuit should be comprehensively considered, and 140 kHz is selected here.
4 Prototype and Experimental Analysis 4.1 Prototype Production a) Main circuit part According to the design and selection of main circuit parameters in Sect. 3.3, first, AC voltage is filtered by LCL and electromagnetic interference, and a better sine wave voltage waveform is obtained. Then, through bridge rectifier and boost boost chopper circuit, voltage control and power factor correction on the network side can be realized. The prototype of main circuit is shown in Fig. 7.
b) Control circuit part The control part is mainly composed of current feedback circuit, voltage feedback circuit and driving pulse generation circuit, and the protection circuit is mainly composed of under voltage protection circuit and over voltage protection circuit. The drive and protection circuit board is shown in Fig. 8.
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Fig. 7. Main circuit prototype
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Fig. 8. Control circuit prototype
4.2 Analyses of Experimental Results a) Driving pulse waveform: The output driving waveform is shown in Fig. 9.
Fig. 9. Control output pulse waveform
Fig. 10. Voltage closed loop experimental waveform
From the driving waveform shown in Fig. 9, it can be seen that the peak value of driving voltage is 16 V, and the MOSFET is a voltage type driver, which can be driven when the driving voltage is greater than 12 V. b) Results and analysis of voltage closed loop The results of voltage closed-loop debugging are shown in Fig. 10. It can be seen from Fig. 10 that when the RMS value of AC voltage on the input side changes, the output DC voltage does not change; At the same time, by changing the output voltage command, the output voltage can be changed without changing the input voltage, that is, the voltage closed-loop is realized. c) Current closed loop results and analysis When the control signal is not enabled, that is, when the network side current is not closed-loop, the voltage and current waveforms at the input side of the system are shown in Fig. 11. From the figure, it can be seen that the system is connected to a
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standard sinusoidal voltage, but the network side current has large distortion at zero crossing. Through FFT analysis, it can be concluded that the power factor is 0.8, the current distortion rate is 64.5%, and the network side distortion is serious.
Fig. 11. Voltage and current waveform of network side when current control is not closed loop
Fig. 12. Voltage and current waveform of network side when current control is not closed loop
When the control signal is enabled, the waveform diagram of the voltage current and output side voltage of the system is shown in Fig. 12. It can be seen from Fig. 12 that when the control signal is enabled, that is, after the current closed loop is adopted, the grid side current is improved significantly. Through FFT analysis, the power factor is 0.99 and the harmonic distortion rate is 5.7%. That is to say, the output voltage of the system can be controlled and a unit power factor operation can be realized on the grid side.
5 Conclusions An on-board vehicle charger doesn’t need too large volume and high power, which is often used in residential home charging at night. The main performance index is to achieve high power factor when the cost is not high. The vehicle charger designed in this paper can realize the controllable output voltage and current, the grid side voltage and current are in phase and less harmonic, that is, high power factor operation, high efficiency of effective power transfer, and has very high practical value. Acknowledgements. This work was supported by the State Grid Shandong electric power company’s science and technology project: Research and application of grid connected state evaluation technology for large-scale distributed generation (520626200014).
References 1. Shi, C., Tang, Y., Khaligh, A.: A single-phase integrated onboard battery charger using propulsion system for plug-in electric vehicles. IEEE Trans. Veh. Technol. 66(12), 10899–10910 (2017)
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2. Shi, C., Tang, Y., Khaligh, A.: A three-phase integrated onboard charger for plug-in electric vehicles. IEEE Trans. Power Electron. 33(6), 4716–4725 (2018) 3. Saber, C., Labrousse, D., Revol, B., Gascher, A.: Challenges facing PFC of a single-phase on-board charger for electric vehicles based on a current source active rectifier input stage. IEEE Trans. Power Electron. 31(9), 6192–6202 (2016) 4. Kim, S., Kang, F.S.: Multifunctional onboard battery charger for plug-in electric vehicles. IEEE Trans. Ind. Electron. 62(6), 3460–3471 (2015) 5. Kesler, M., Kisacikoglu, M., Tolbert, L.: Vehicle-to-grid reactive power operation using plugin electric vehicle bidirectional offboard charger. IEEE Trans. Ind. Electron. 61(12), 6778– 6784 (2014). https://doi.org/10.1109/TIE.2014.2314065 6. Praneeth, A.V.J.S., Williamson, S.S.: A wide input and output voltage range battery charger using buck-boost power factor correction converter. In: 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2974–2979 (2019) 7. Shi, C., Tang, Y., Khaligh, A.: A single-phase integrated onboard battery charger using propulsion system for plug-in electric vehicles. IEEE Trans. Veh. Technol. 66(12), 10899–10910 (2017). https://doi.org/10.1109/TVT.2017.2729345 8. Ye, J., Shi, C., Khaligh, A.: Single-phase charging operation of a three-phase integrated onboard charger for electric vehicles. In: 2018 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 681–686 (2018) 9. Singh, U., Pal, Y., Nagpal, S., et al.: Single-phase on-board integrated Bi-Directional charger with power factor correction for an EV. In: 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 716–721 (2019) 10. Gachovska, T., Scarlatescu, G., Radimov, N., et al.: Bi-directional 3.3 kW on-board battery charger. In: 2020 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 1884–1890 (2020)
A Novel Adaptive Low Voltage Locking Protection Strategy for Distributed Generation Access Zhiren Liu, Weilin Tong(B) , Jinghua Xie, Cheng Li, Yajuan Lv, and Tianyu Fang State Grid Wuxi Power Supply Company, Wuxi, Jiangsu, China [email protected]
Abstract. Overcurrent protection for distribution network(DN) is locked by low voltage, which can effectively prevent the misoperation of protection caused by the large-capacity load starting. With the access of distributed generation (DG), boost fault current will increase the bus voltage value where relay protection installed. The conventional low voltage locking strategy uses fixed setting value, causing the problem of low voltage mis-locking and refusal-operation of protection. To solve the problem, this paper proposes an adaptive low voltage locking protection strategy based on time-fixed value for overcurrent protection. The novel strategy adopts exponential dynamic setting value, which realizes the setting value changes dynamically with the fault duration time. By applying the strategy, the problem of low voltage mis-locking caused by the boost fault current is effectively prevented, and reliable protection operation is realized. The effectiveness of the proposed strategy is verified by MATLAB/Simulink simulation and case analysis. Keywords: Distributed generation (DG) · Low voltage locking · Adaptive protection strategy
1 Introduction DN is an important part of power system. In recent years, photovoltaic, wind power and other DGs are developing rapidly. DN is rapidly changing into active distribution network. Distributed new energy has the advantages of cleanness and non-pollution [1], but the negative impacts on the power system are also gradually highlighted, including effects on relay protection [2–4]. After a large number of DGs are connected to the DN, the simple network of traditional single-source radial power supply becomes a complex network of multi-source and multi-terminal power supply. The network structure, operation mode and short-circuit current characteristics have changed, and the conventional current protection will no longer apply. In order to reduce or even eliminate the impact of DG access on the relay protection of DN, many domestic and international scholars began to study the adaptive relay protection scheme of DN. In [6], authors proposed the concept of branch contribution factor to eliminate the influence of DG. According to the calculation of different fault © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1054–1062, 2022. https://doi.org/10.1007/978-981-19-1528-4_108
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characteristics, the adaptive adjustment of the setting value of the protection scheme is realized in [7]. The authors of [8] proposed to realize the adaptive voltage protection according to the measured real-time phase current and corresponding fault types. In [9], authors improved the traditional calculation method of protection setting value, reduced the operation time of protection device in specific scenarios, and improved the protection performance. The traditional DN adopts overcurrent protection. When the fault occurs, the current protection is affected by the boost current from the DG access, which is easy to cause protection mis-operation [10]. In this regard, the authors of [11] has proposed the correction scheme using voltage factor, and established the simulation model based on PSCAD/EMTDC to verify the selectivity and rapidity of the scheme. By low voltage locking, overcurrent protection can effectively prevent protection mis-operation caused by large capacity load startup. With the access of DG, the boost fault current will increase the bus voltage, resulting in the problem of low voltage mislocking. To solve the problem, this paper proposes a novel adaptive low voltage locking strategy based on time-fixed value, which changes the conventional low voltage locking mode using fixed value. The strategy adopts exponential dynamic setting value to realize the dynamic change of setting value with fault duration time. The strategy can effectively prevent the problem of low voltage mis-locking caused by the output fluctuation of DG, and realize reliable protection operation. This paper verifies the effectiveness of the proposed strategy through MATLAB/Simulink simulation and case analysis.
2 Background Overcurrent protection is usually used as the main protection of DN with DG. In order to prevent protection malfunction caused by inrush current and back electromotive force when large capacity load starts, low-voltage operation locking will be put into use when needed for overcurrent protection. 2.1 Basic Principle of Low Voltage Locking Overcurrent protection with low voltage locking is essentially the protection requiring low voltage component to start. Low voltage locking component is a voltage measuring element which reflects transformer fault by low voltage and prevents overcurrent protection mis-operation. When symmetrical fault occurs, the overcurrent protection can be started only when the three-phase voltage of the protection installed is lower than a certain value. Using this method, the current setting value of overcurrent protection is greatly reduced, and the sensitivity is improved. The conventional overcurrent protection logic is shown in Fig. 1. The criterion of low voltage locking component is: min(Uab , Ubc , Uca ) < Uzd
(1)
where U zd is the low line voltage value of local bus. The setting value of low voltage component is calculated according to the following conditions, and the minimum value of them is selected as the setting value.
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Meet criterion of low voltage This side exit The other side operate
Correct direction t1
I>I1zd Correct direction
t2
I>I2zd Correct direction
t3
I>I3zd
trip
trip
trip
Fig. 1. Conventional overcurrent protection logic block diagram.
(1) Set voltage according to the minimum operating voltage that may occur during normal operation. Uset = UL min /K rel K re
(2)
where U Lmin is the minimum operating voltage of the transformer during operating normally, K rel is the reliability coefficient, and K re is the return coefficient of low voltage relay. (2) Set voltage by avoiding the self-starting of the load. When the low voltage value comes from potential transformer (PT) at low voltage side of the main transformer, the setting operating voltage is: Uset = (0.5 ∼ 0.6)UN
(3)
Where U N is the line voltage at the installed relay protection. When the low voltage value comes from PT at high voltage side of the main transformer, the setting operating voltage is: Uset = 0.7UN
(4)
2.2 Influence of DG Access on Low Voltage Locking The overcurrent protection with low voltage locking component can effectively prevent the protection mis-operation caused by the startup of large capacity load. However, when the DN undergoes a high-resistance three-phase grounding symmetrical fault, the bus voltage where relay protection installed is high, which does not meet the operating conditions of low voltage component. In particular, after the grid-connected photovoltaic,
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wind turbine and other DGs, the boost fault current will increase the bus voltage, which further aggravates the problem of low voltage mis-locking. As a result, the fault cannot be removed in time, and eventually the fault scope expanded. As shown in Fig. 2, there are DGs connected on Bus P and Bus M. When a symmetrical fault occurs on Bus N through high resistance three-phase grounding, both Bus P and Bus M provide short-circuit current to the fault point. The booster current passes through the short-circuit point impedance and leads to the corresponding voltage drop. While the fault occurs, the voltage of relay protection installed on Bus M is higher than that without DG. Therefore, the low voltage component does not meet operating condition to start, prone to mis-locking, and overcurrent protection will not operate. K1
DG Bus P
DG
Bus M
Bus N
Fig. 2. Diagram of DN fault with DG access.
3 Adaptive Low Voltage Locking Strategy In order to solve the problem above, the function logic of overcurrent protection with low voltage locking component need to be optimized to prevent the protection refusaloperation with the access of DG. In this paper, on the basis of retaining original locking logic function of overcurrent protection, a novel adaptive locking strategy is proposed. The low voltage setting value varying with time is added to the locking logic. The basic principle is to dynamically adjust the low voltage setting value according to the fault duration time, which is positively correlated with time. Firstly, the strategy avoids the voltage sag caused by various inrush currents, prevent the failure of low voltage locking function, and ensure that the protection does not cause mis-operation due to excessive inrush currents. Secondly, it improves the operating voltage value of low voltage component and prevent low voltage mis-locking caused by the boost fault current from DG access. 3.1 Operating Characteristic Curve of Strategy For the time-based low voltage component, the operating equation should meet the following requirements: 1. Set low voltage value U FX1 and time value t FX1 . t FX1 represents the starting operation time of time-based overcurrent protection, and its setting value should avoid the voltage drop caused by various inrush currents, which can be consistent with the operation time of overcurrent protection at the end of backup protection. U FX1 is the maximum operating voltage at t FX1 . When the fault voltage is lower than the voltage, the low voltage operation locking function is opened.
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2. Set the low voltage setting U FX2 and time value t FX2 . t FX2 represents the longest operating time of time-based overcurrent protection, and its setting value is based on the tolerance fault time of primary equipment when the most serious fault occurs. U FX2 is the maximum operating voltage at t FX2 , UFX 2 = KFX Umin
(5)
where K FX represents the reliability coefficient, which can be taken from 0.9 to 0.95 according to the actual application, and U min represents the minimum operating voltage of the power system. 3. The maximum operating voltage of time-based low voltage relay increases with time, presenting the characteristics of time-based change. The natural exponential form equation is selected as the operating equation of the low voltage component, and good operating characteristics are obtained. The operating voltage at t FX1 is U FX1 , and the operating voltage at t FX2 is U FX2 . The operating equation can be derived as follows. Uϕϕ =
UFX 1 − UFX 2 t UFX 1 − UFX 2 tFX 1 e + UFX 1 − t e t t FX 1 FX 2 e −e e FX 1 − etFX 2
(6)
The meaning of most variable s in (8) has been described. In addition, Uϕϕ is the operating line voltage of relay protection. The operating characteristics of time-based low voltage relay are shown in Fig. 3. Uφφ /V Umin UFX2 UFX1 Novel operating zone
Conventional operating zone
tFX1
tFX2
t/s
Fig. 3. Characteristic curve of the novel low voltage locking strategy.
3.2 Protection Logic Design The time-based low voltage component is added and the logic block diagram is shown in Fig. 4.
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Meet criterion of low voltage based on time This side exit The other side operate
Correct direction
trip
I>IFXzd Fig. 4. Block diagram of novel adaptive low voltage locking strategy.
It can be seen from the diagram that a time-based low voltage operation locking function is added on the basis of original protection. The correct direction means that the protection device judges the fault direction as the setting positive direction, and the low voltage criterion based on time means that the operating equation of the low voltage component based on time is satisfied. For the time-based low voltage component, the operating voltage is taken as the voltage between the phases of the bus, and the time-based characteristics change with the fault duration t, and the operating characteristics need to be consistent with the diagram.
4 Simulation and Analysis In this paper, the simulation model is built in MATLAB/Simulink to analyze the proposed strategy. The simulation topology is shown in Fig. 5. Relay protection I is installed at the outlet of Bus A. A
B
C
I
DG
Load
Fig. 5. Simulation topology diagram.
Figure 6 and Fig. 7 show the voltage and current waveforms at Protection I before and after DG access. The three-phase grounding short circuit fault occurs at Bus C. Before the access of DG, the voltage of Protection I drops to about 9000 V when fault occurs, and the fault current is about 400 A, higher than the normal current. After the access of DG, the DG provides boost current to the fault point, which reduces the fault current flowing through Protection I, only 200 A. It reduces the sensitivity of the Protection I, leading to the smaller protection range, and even refusal-operation of Protection I. At
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the same time, the access of DG increases the fault voltage at Protection I, about 12 000 V, which makes the protection mis-locking and affects the reliability of protection. According to the analysis, Protection I is prone to the refusal-operation.
Fig. 6. Voltage and current waveform of Protection I before DG access.
Fig. 7. Voltage and current waveform of Protection I after DG access.
To solve the problem, this paper proposes the novel adaptive strategy. According to (3) proposed in Sect. 3: U FX1 = 6000 V, U min = 9000 V, K FX = 0.9, t FX1 = 1s, t FX2 = 1.8s, and the protection startup time is calculated as follows. In Table 1 and Table 2, S1 means the conventional strategy,and S2 means the novel strategy.
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Table 1. Protection I operation statistic before DG access. Grounding resistance/
5
10
15
20
25
30
Protection 1 voltage /V
4152.47
5684.77
5995.89
7035.34
7162.62
7226.26
Protection 1 current /A
775.74
558.15
400.34
358.08
295.47
251.25
Locked or not for S1
N
N
N
Y
Y
Y
Locked or not for S2
N
N
N
N
N
N
Protection startup time /s
0
0
0
1.47
1.52
1.54
For S1, when the voltage value of Protection I is higher than 6000 V, the low voltage component locks protection. As shown in Table 1 before DG access, when the grounding resistance is greater than 20, low voltage component mis-locking occurs. For S2, the mis-locking does not occur. The overcurrent protection operates within 1.47 ~ 1.54s, and the fault is removed in time. Table 2. Protection I operation statistic after DG access. Grounding resistance/
5
10
15
20
25
30
Protection 1 voltage /V
4352.12
6473.44
7293.69
7689.67
7901.8
8014.94
Protection 1 current /A
594.85
392.72
252.46
178.14
127.33
124.22
Locked or not for S1
N
Y
Y
Y
Y
Y
Locked or not for S2
N
N
N
N
N
N
Protection startup time /s
0
1.24
1.56
1.69
1.75
1.78
Table 2 shows the operation of Protection I after DG access. For S1, when the grounding resistance is greater than 10 , low voltage mis-locking occurs. For S2, the mis-locking does not occur, and overcurrent protection operates within 1.24–1.78 s. By comparing strategies, it can be found that the conventional strategy is prone to mis-locking and refusal-operation of protection. The novel strategy avoids mis-locking and removes the fault by delay operation.
5 Conclusion Overcurrent protection with low voltage locking effectively prevents protection misoperation caused by large capacity load startup. With the DG access, the boost fault current will increase the bus voltage where relay protection installed, resulting in the problem of low voltage mis-locking and refusal-operation of protection. To solve the problem, this paper proposes a novel adaptive low voltage locking strategy based on time-fixed value, which changes the conventional low voltage locking mode using fixed value.
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The strategy adopts exponential dynamic setting value to realize the dynamic change of setting value with fault duration time, effectively prevents the problem of low voltage mis-locking, and realizes reliable protection operation. This paper verifies the effectiveness of the proposed strategy through MATLAB/Simulink simulation and case analysis. Moreover, the device using the strategy has been applied in practical application.
References 1. Xiao, Y., Ouyang, J., Xiong, X., Wang, Y., Luo, Y.: Fault protection method of single-phase break for distribution network considering the influence of neutral grounding modes. Prot. Control Modern Power Syst. 5(1), 1–13 (2020) 2. Suliman, M.Y., Ghazal, M.: Design and implementation of overcurrent protection relay. J. Electric. Eng. Technol. 15(4), 1595–1605 (2020) 3. He, J., Liu, L., Ding, F., Li, C., Zhang, D.: A new coordinated backup protection scheme for distribution network containing distributed generation. Prot. Control Modern Power Syst. 2(1), 1–9 (2017). https://doi.org/10.1186/s41601-017-0043-3 4. Zhan, H., Wang, C., Wang, Y., et al.: Relay protection coordination integrated optimal placement and sizing of distributed generation sources in distribution networks. In: Power and Energy Society General Meeting, pp. 1. IEEE (2016) 5. Zhou, C., Zou, G., Du, X., et al.: A pilot protection method based on positive sequence fault component current for active distribution networks. Proc. CSEE 40(07), 59–69 (2020) 6. Jing, M., Xi, W., Chao, M., et al.: A new adaptive protection approach for distribution network containing distributed generation. Power Syst Technol. 35(10), 204–208 (2011). (in Chinese) 7. Ma, J., Ma, W., Qiu, Y., et al.: An adaptive distance protection scheme based on the voltage drop equation. IEEE Trans. Power Delivery 30(4), 1931–1940 (2015) 8. Ma, J., Ma, W., Wang, X., et al.: A new adaptive voltage protection scheme for distribution network with distributed generations. Can. J. Electr. Comput. Eng. 36(4), 142–151 (2013) 9. Coffele, F., Booth, C., Dusko, A.: An adaptive overcurrent protection scheme for distribution networks. IEEE Trans. Power Delivery 30(2), 561–568 (2015) 10. Nguyen, T.T., Truong, A.V., Phung, T.A.: A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. Int. J. Electric. Power Energy Syst. 78, 801–815 (2016) 11. Xie, M., Wang, T., Xu, J., et al.: Effect of distributed generation on relay protection of distribution network and comprehensive improvement of protection scheme. Power Syst. Protect. Control 47(19), 78–84 (2019)
The Influence of Load Model on the Accuracy of Power Grid Simulation Xiang-yu Liu1(B) , Hui-bin Li2 , Xiao-ming Li1 , Shuai Li3 , Ning Gong4 , and Shi-bo Yang5 1 State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
[email protected]
2 School of Electrical and Electronic Engineering, North China Electric Power University,
Baoding 071003, China 3 Institute of Equipment Fault Diagnosis and Testing Technology, North China Electric Power
University, Baoding 071003, China 4 State Grid Hebei Electric Power Co. Ltd. Cangzhou Power Supply Branch, Cangzhou 061000,
China 5 State Grid Hebei Electric Power Co. Ltd., Maintenance Branch, Shijiazhuang 050011, China
Abstract. The influence of load model on simulation accuracy is studied, and the engineering analysis method considering the accuracy of load model is put forward. Firstly, the common load model and engineering application model for power network analysis are introduced, and the influence of static load model is analyzed qualitatively by theoretical analysis. Secondly, the actual power grid is introduced to study the effect of electric motor proportion on the simulation results of short circuit current calculation, static stability calculation, transient stability calculation, dynamic stability calculation, frequency stability calculation and voltage stability calculation. It is concluded that there is no consistent safe load model for different simulation problems. Then, the error interval caused by different problems is analyzed when typical load model is adopted to simulate the actual power grid. Based on the results of calculation and analysis, a safety analysis method considering the accuracy of load model in engineering application is proposed. The results of this paper have reference value for developing grid control strategies. Keywords: Load model · Simulation err · Model accuracy · Engineering calculation
1 Introduction Simulation calculation is a significant method to recognize and discover the characteristics of power system, and it is also an important tool to guide the planning and operation of power grid [1]. The accuracy of simulation directly affects the operation safety of power grid. Model is the basis of simulation calculation, and the accuracy of model is the key to ensure the accuracy of simulation. At present, the generator control system in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1063–1075, 2022. https://doi.org/10.1007/978-981-19-1528-4_109
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China has realized the measurement modeling [2], and the accuracy of load model is the key factor affecting the simulation results [3]. The analysis results of several blackouts in the United States show that the inappropriate load model will lead to the simulation results inconsistent with the actual fault curve [4], and the excessively optimistic load model will cause potential threat to the electric power system security [5]. Time-varying and nonlinear are the characteristics of load, but the parameters of the model are required to be accurate and robust. Accurate modeling for multiple load nodes in large power grid is not only a huge workload, but also difficult to achieve, which makes load modeling a recognized problem in power system [5]. In recent years, scholars mainly carry out theoretical research on load modeling [6–9] and parameter identification [10–12]. A real-time estimation model for dynamic load model parameters is presented [13]. A load model containing voltage information for all boundary is presented in the literature [14]. In engineering application, the practical technology of load modeling [15] has been paid more attention, and some studies have found that the load model has a powerful effect for simulation results through calculation and experiment [5]. In [16], it is found that the transmission limit of transmission section is improved after Hunan power grid adopts a more realistic load model. The influence of motor proportion in load on transient stability of Henan power network has been studied in [17]. It is concluded that the influence of some dynamic parameters of load on system damping is not obvious in [18]. These studies take the actual power grid as an example to verify the important influence of load model on the simulation conclusion, but the research conclusion is lack of systematicness, which is difficult to fully guide the calculation and analysis of the actual power network. In this paper, on the basis of existing research, on the one hand, qualitative analysis of the effect of load model, on the other hand, quantitative simulation analysis of the typical problems of the actual power grid, study the impact of load model changes on the simulation results, and summarize the influence tendency of load model changes on different problems. Furthermore, it puts forward how to analyze the simulation error caused by the inaccuracy of the load model and how to carry out the engineering calculation and analysis of the safety, so as to provide experience and basis for the security assessment of the power network.
2 Research Strategy and Methods 2.1 Load Model Common Load Models. In the calculation and analysis of power grid, the commonly used load models mainly include model of static load, model of induction motor and comprehensive load model. The model of static load is shown in formula (1) and formula (2), where P0 and Q0 represents the active power and reactive power of the load at rated voltage and rated frequency, and U 0 represents rated voltage. P1 and Q1 are the proportion of active load and reactive load with constant impedance, P2 and Q2 are the proportion of active load and reactive load with constant current, P3 and Q3 are the proportion of active and reactive load with constant power, P4 and Q4 are the proportion of active and reactive
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load related to frequency, and L DP and L DQ are the coefficients of active and reactive load change caused by frequency change. U 2 U + P3 + P4 (1 + f · LDP ) PD = P0 P1 + P2 (1) U0 U0 U 2 U + Q3 + Q4 (1 + f · LDQ ) + Q2 (2) QD = Q0 Q1 U0 U0 The model of induction motor is displayed in Fig. 1. in which Rs is stator resistance, X s is stator reactance, X m is excitation reactance, X r is rotor reactance, Rr is rotor resistance, and s is initial slip.
Fig. 1. Induction motor load model
The comprehensive load model considering branches of distribution network is shown in Fig. 1. Its main feature is to consider branches of distribution network below 220 kV, reactive power compensation devices, and the combination of different load types. The model is most close to the actual situation, but it is not widely used because of the complexity of modeling (Fig. 2).
Fig. 2. The comprehensive load model considering distribution network branch
Engineering Application Model. Considering the difficulty of accurate modeling of large power network, the typical load model of constant impedance plus induction motor
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is generally used in practical engineering applications [5]. The typical model of power grid in various regions of China can be referred to [19]. In practice, the types of motor load are various, and typical models are usually used in engineering simulation. Some parameters of typical motor model are displayed in Table 1, in which T j is the motor inertia time constant and K l is the motor load rate. Table 1. Partial parameters of motor load T j /s
Kl
Rs
Xs
Xm
Xr
Rr
2
46.9%
0
0.18
3.50
0.12
0.02
2.2 Theoretical Analysis of Static Load Effect The power output by the ideal non-salient pole generator are shown in Eqs. (3) and (4): Eq U sin δ xd
(3)
Eq U U2 cos δ − xd xd
(4)
PG = QG =
Considering the correlation between E q and U, E q = kU can be made at a certain operation point, where k is the correlation coefficient, then Eq. (3) and Eq. (4) can be written as: kU 2 sin δ xd
(5)
kU 2 U2 cos δ − xd xd
(6)
PG ≈ QG ≈
Calculate the variation of generator active power and reactive power with voltage, as shown in Eq. (7) and Eq. (8): 2k sin δ dPG ≈ ·U dU xd
(7)
2k sin δ dPG ≈ ·U dU xd
(8)
According to the static load model shown in Eq. (1) and Eq. (2), the voltage differential calculation is carried out, and the variation of active and reactive power of static load with voltage is as follows: 2P1 P2 dPD = P0 ( 2 U + ) dU U U0 0
(9)
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dQD 2Q1 U Q2 = Q0 ( + ) 2 dU U0 U0
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(10)
Comparing with Eq. (7) and Eq. (9), it can be seen that with the occurrence of voltage fluctuation, constant impedance load and generator power have the same trend change characteristics when the order is equal, constant current load and generator power change have the same trend change characteristics when the order is reduced, and there is no correlation between constant impedance load and generator power change. There is a similar relationship between the load and the reactive power characteristics of the generator as shown in Eq. (8) and Eq. (10). The above load characteristics show that the constant impedance load can better follow the change of generator output, which is conducive to the balance between generator and load and the stability of the system. There is no correlation between constant power load and generator output. When the system fluctuates, it damages the stability of power network. The effect of constant current load is between the two loads. In other words, among the static loads that contribute to the stability of the power network, the first is the constant impedance load, the second is the constant current load, and the last is the constant power load.
3 Results 3.1 Influence of Engineering Load Model on Simulation Results The engineering load model with motor load is difficult to carry out theoretical analysis. In order to research the impact of engineering load model on the calculation and analysis of actual power network, taking the typical load model of 40% constant impedance and 60% motor as an example, the simulation analysis of various typical problems is carried out, and the quantitative difference of results is obtained when the load model changes. Short-Circuit Current. When the bus is short circuited, only the static load in the model is changed, and the PSD-SCCP calculation program can be used to calculate the short-circuit current. The average value of bus short circuit level of different voltage levels is shown in Table 2.
Table 2. Short circuit current under different static load models Load model
Constant impedance
Constant power
Constant current
500 kV/kA
45.22
45.22
45.22
220 kV/kA
31.13
31.13
31.13
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According to the national standard GB/T15544.1–2013, the motor load is considered. In the static load model, which is considered as constant impedance load, when motor ratio changes from low to high, calculate the three-phase short-circuit current of buses in the power network. The average values of bus short-circuit current of different voltage levels are shown in Table 3 and Fig. 3. Table 3. Short circuit current at different motor load ratios The proportion of motor
0
0.2
0.4
0.6
0.8
1
500 kV/kA
39.24
42.25
44.02
45.22
46.09
46.76
220 kV/kA
25.18
27.82
29.70
31.13
32.28
33.24
Fig. 3. Effect of motor ratio on short circuit current
It can be displayed from Table 2 that the static load model is equivalent to the constant impedance model in PSD short circuit calculation, and changing the static load model does not affect the calculation results. It can be displayed from Fig. 3 that when the bus with different voltage levels is short circuited, the proportion of lifting motor type load will raise the short-circuit current. Compared with the full constant impedance model, the average short-circuit current of buses with different voltage levels differs by 19.2% and 32.0% respectively. Static Stability. Set the load model of power grid as constant impedance and motor, select a typical sending and receiving system in regional power grid, slowly increase the transmission power of section, and calculate the static angle stability limit of sending and receiving system under different motor ratios, as shown in Fig. 4.
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Fig. 4. Effect of motor ratio on static stability limit
From Fig. 4, we can find that the static stability limit of the sending and receiving end system gradually decreases when motor ratio is raised. The minimum value occurs when the motor load ratio is about 0.8. When all the load types are motors, the static stability limit begins to increase again. The maximum value of static stability limit is about 2.8% higher than the minimum value. Transient Stability. Set the load model of the power grid as constant impedance and motor, select the typical sending and receiving system in the large regional power grid, set the transient N–1 fault in the transmission section, and calculate the transient angle stability limit of the sending and receiving system under different motor ratios, as shown in Fig. 5.
Fig. 5. Influence of motor ratio on transient stability limit
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From Fig. 5, we can find that the transient stability limit of the sending and receiving system under the same fault condition gradually decreases with the increase of motor proportion, and reaches the lowest value when the load is all motors. The maximum value of transient stability limit is about 11.3% higher than the minimum value. Dynamic Stability. Set the load model of the power grid as constant impedance and motor, select a certain inherent oscillation mode existing in the regional power grid as the research object, and use PSD-SSAP to calculate the oscillation mode of the power grid. The changes of the typical oscillation mode under different motor ratios are displayed in Fig. 6 and Fig. 7.
Fig. 6. Effect of motor ratio on damping ratio
Fig. 7. Influence of motor ratio on oscillation frequency
It can be displayed from Fig. 6 that the oscillation damping ratio of the inherent oscillation mode increases with the increase of motor load ratio. When the ratio continues to increase, the oscillation damping ratio begins to decrease, and the maximum value appears when the motor ratio is 0.4. The difference between the maximum value and the minimum value is about 16.1%. It can be displayed from Fig. 7 that the oscillation frequency of the natural oscillation mode in the power grid gradually decreases with the increase of the motor load proportion, and reaches the minimum value when all the load models are motors.
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Frequency Stability. Set the load model of power grid as constant impedance and motor. When 3000 MW power failure occurs in the regional power grid, calculate the maximum frequency deviation of the system under different motor ratios, as shown in Fig. 8.
Fig. 8. Effect of motor ratio on frequency
From Fig. 8, we can find that under the same grid fault, when the motor proportion increases, the maximum frequency difference increases gradually, and reaches the maximum value when the load is all motors. The maximum value of frequency difference is about 13.6% higher than the minimum value. Voltage Stability. Set the load model of power grid as constant impedance and motor, select a city level power grid in the regional power grid as the research object, when a fault occurs in the power grid, calculate the critical load of voltage instability in the regional power grid. Calculate the critical load of regional power grid under different motor ratios, as shown in Fig. 9.
Fig. 9. Effect of motor ratio on critical load
From Fig. 9, we can find that the critical load of voltage instability in regional power grid decreases with the increase of motor load proportion, and reaches the minimum value when all loads are motors. The maximum value of load is about 92.8% higher than the minimum value.
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3.2 Engineering Calculation Considering Load Model Error Simulation Error Analysis. Taking the regional power grid mentioned in Sect. 3.1 as a case. When the proportion of motor increases from 0 to 100%, the maximum and minimum values of each calculation content are shown in Table 4. From this, the maximum error ratio of the load model used in the engineering analysis can be calculated due to the inaccurate motor proportion. Table 4. Simulation error analysis of different calculation contents Calculation Content
60% motor
Minimum
Maximum
500 kV short circuit/kA
45.22
39.24
46.76
Error ratio 3.4%
220 kV short circuit/kA
31.13
25.18
33.24
6.8%
Static limit/MW
6363
6354
6531
0.14%
Transient Limit/MW
1226
1163
1294
5.1%
Dynamic Stable Damping Ratio
0.064
0.056
0.065
12.5%
Frequency deviation/Hz
0.1799
0.1693
0.1923
6.9%
Voltage Stability Critical Load/MW
2729.6
2025.2
3903.6
25.8%
From Table 4, we can find that the error ratio of simulation results of each calculation and analysis content is quite different. The maximum error ratio occurs in the study of voltage stability, and the maximum error can reach 25.8%. The minimum error ratio occurs in the study of static stability limit, and the minimum error ratio is only 0.14%. In fact, the error ratio delimits the error range of simulation results to a certain extent, which can provide reference for the determination of safety margin and the formulation of control strategy. Engineering Calculation Considering Accuracy of Load Model. According to the theoretical analysis in Sect. 2 and the simulation conclusion in Sect. 4.1, the load model has a powerful impact on the analysis results of power grid, and it is difficult to find a unified conservative model for the study of different problems. The conservative load model in the analysis of one problem may have risks in the analysis of another problem, so the accurate load modeling is the key to ensure the accuracy of calculation and analysis. When the load is not accurately modeled, this paper proposes to use the process shown in Fig. 10 to analyze the security problems of power grid. As shown in Fig. 10, the process shown by the dotted line can refer to the analysis contents in Sect. 3. Through the calculation and analysis of the power grid to be studied, the conservative load model of a problem to be analyzed can be determined, and the conservative load model can be directly used for calculation and analysis to determine the operation limit and control strategy of the power grid. The solid line process in Fig. 10 refers to the analysis content in Sect. 4.1. Through the analysis of known power grid security and stability characteristics, the error range of load model on different calculation conclusions is determined. By modifying the safety margin of the calculation results of typical load model, it can guide the operation and control strategy.
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Fig. 10. Engineering analysis methods that considers the accuracy of load models
4 Conclusion and Suggestion 4.1 Conclusion In this paper, the effect of static load model on security and stability is theoretically analyzed. Taking a large regional power grid as an example, the influence of load model on power grid simulation results is quantitatively analyzed. The maximum error range of typical load model in simulation analysis is evaluated. Through theoretical analysis and quantitative calculation of different power grid security problems, the conclusions are as follows: 1. In the static load, the load types which are beneficial to the stability of power system are constant impedance, constant current and constant power load in turn. 2. In the research of PSD short circuit, the static load is treated according to the constant impedance model. In the load model, the higher motor ratio, the greater the shortcircuit current of bus. The short-circuit current gap between 500 kV and 220 kV bus can reach 20%–30%. 3. The motor proportion in the load has little effect on the research results of the static stability limit. When the motor load proportion is about 80%, the static stability limit reaches the minimum. 4. When the proportion of motor load increases, the transient stability limit of power grid decreases, and the maximum gap is about 11%. 5. The dynamic stability characteristics of the power grid increase and then de crease with the increase of the motor proportion, reaching the maximum value when the motor proportion reaches about 40%, and the range of change is about 16%. 6. The frequency stability of power network decreases with the increase of motor load ratio, and the maximum difference is about 14%. 7. The voltage stability of power grid decreases with the increase of motor load ra tio, and the maximum difference is about 93%. 8. For different types of calculation and analysis, it is difficult to establish a unified conservative load model. The calculation and analysis based on the fixed typical load model will cause some safety risks. 9. The effect of load model on simulation results is voltage stability, short-circuit current, dynamic stability, frequency stability, transient stability and static stability from large to small.
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4.2 Suggestion 1. In order to ensure the safety and reliability of the calculation results, different load models should be used to analyze different problems in engineering application, so as to ensure the conservatism of the calculation results. 2. When the typical load model is used for calculation and analysis, the calculation conclusion should be appropriately corrected within the possible error range according to the characteristics of the actual power grid. 3. For the calculation problems which are greatly affected by the load model (such as voltage stability problems), the accurate load modeling should be carried out as far as possible.
Acknowledgment. This work is supported by National Natural Science Foundation of China (No. 5170000), the Chinese Fundamental Research Funds for the Central Universities (201888888).
References 1. Lin, J., Liu, Y., Xu, Z., et al.: A new time-domain simulation method of power systems based on improved sparse probabilistic collocation method. Proc. CSEE 39(08), 2297–2306 (2019). (in Chinese) 2. Lu, Y., Liu, Y., Sun, P., et al.: A study on the modeling of governor and prime mover of power unit for power system analysis. Power Syst. Technol. S2, 123–126 (2007). (in Chinese) 3. Qu, X., Li, X., Song, J., et al.: Composite load model considering voltage regulation of distribution network. Trans. China Electrotech. Soc. 33(04), 759–770 (2018). (in Chinese) 4. Arif, A., Wang, Z., Wang, J., et al.: Load modeling: a review. IEEE Trans. Smart Grid 9(6), 5986–5999 (2018) 5. Zhao, J., Ju, P., Shi, J., et al.: Review and prospects of power system load modeling. J. Hohai Univ. (Nat. Sci.), 1–8 (2020). Accessed 05 Jan 2020, http://kns.cnki.net/kcms/detail/32.1117. TV.20191107.1510.002.html. (in Chinese) 6. Zhang, K., Feng, X., Tian, X., Hu, Z., Guo, N.: Partial Least Squares regression load forecasting model based on the combination of grey Verhulst and equal-dimension and new-information model. In: 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), pp. 915–919 (2020). https://doi.org/10.1109/IFEEA51475.2020.00192 7. Ji, Y., Buechler, E., Rajagopal, R.: Data-driven load modeling and forecasting of residential appliances. IEEE Trans. Smart Grid 11(3), 2652–2661 (2020). https://doi.org/10.1109/TSG. 2019.2959770 8. Marma, H.U.M., Liang, X.: Composite load model and transfer function based load model for high motor composition load. In: 2019 IEEE Electrical Power and Energy Conference (EPEC), pp. 1–5 (2019). https://doi.org/10.1109/EPEC47565.2019.9074786 9. Rodríguez-García, L., Pérez-Londoño, S., Mora-Florez, J.J.: Methodology for measurementbased load modeling considering integration of dynamic load models. In: 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), pp. 1–6 (2020). https://doi.org/10.1109/ROPEC50909.2020.9258723 10. Meng, D., Zhang, X., Zeng, Y., Feng, C., Hu, X., Wang, C.: Two-level correction method of load parameters based on an aggregation-identification structure. In: 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), pp. 3733–3738 (2020). https:// doi.org/10.1109/EI250167.2020.9347278
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11. Khodayar, M., Wang, J.: Probabilistic Time-varying parameter identification for load modeling: a deep generative approach. IEEE Trans. Ind. Inf. 17(3), 1625–1636 (2021). https://doi. org/10.1109/TII.2020.2971014 12. Bu, F., Ma, Z., Yuan, Y., Wang, Z.: WECC composite load model parameter identification using evolutionary deep reinforcement learning. IEEE Trans. Smart Grid 11(6), 5407–5417 (2020). https://doi.org/10.1109/TSG.2020.3008730 13. Paidi, E.S.N.R., Nechifor, A., Albu, M.M., Yu, J., Terzija, V.: Development and validation of a new oscillatory component load model for real-time estimation of dynamic load model parameters. IEEE Trans. Power Delivery 35(2), 618–629 (2020). https://doi.org/10.1109/ TPWRD.2019.2918059 14. Ji, Y., et al.: An equivalent modeling method for multi-port area load based on the extended generalized ZIP load model. In: 2018 International Conference on Power System Technology (POWERCON), pp. 553–558 (2018). https://doi.org/10.1109/POWERCON.2018.8601588 15. Ju, P., Wang, Y., Xiang, L., et al.: Load modeling for ningxia grid with special loads. Electric Power Autom. Equip. 32(8), 1–4, 21 (2012).(in Chinese) 16. Song, J., Chen, H., Tang, W.: Influence of different load models on transient stability level of hunan power grid. Power Syst. Technol. 31(5), 29–33 (2007). (in Chinese) 17. Lu, C., Gang, L., Han, W.: Impact of induction motor model on transient stability of East China Power Grid. Power Syst. Technol. 31(5), 6–10 (2007). (in Chinese) 18. Ke, E.W., Haque, M.E.: Dynamic load modelling of a paper mill for small signal stability studies. IET Gener. Transm. Distrib. 8(1), 131–141 (2014) 19. Feng Xuefeng,Lv Kunlu. Research status of comprehensive load modeling[J]. Electric Age, 2017(10): 94–95, 98. (in Chinese)
Research on Recognition Strategy of Oil-paper Insulation Aging State Based on Deep Residual Network Tao Li1 , Zihao Wang1 , Yongdao Wang1 , Linfeng Mao1 , Zhensheng Wu2(B) , and Deling Fan2 1 Yuxi Power Supply Bureau of Yunnan Power Grid Co., Ltd.,
42 Hongta Avenue, Yuxi, Yunnan, China 2 School of Electrical Engineering, Beijing Jiaotong University,
No. 3 Shang Yuan Cun, Hai Dian District, Beijing, China [email protected]
Abstract. In order to solve the existing problems of oil paper insulation aging identification technology, this paper proposes an oil paper insulation aging state identification method based on deep residual network. Firstly, the partial discharge experiment was carried out based on the column plate electrode model to obtain the partial discharge pulse phase distribution maps of oil paper insulation at different thermal aging stages. Then, the test data were preprocessed by sample expansion and image graying. Finally, the deep residual network was used for pattern recognition of samples to determine the aging stage. The experimental results show that the recognition rate of oil paper insulation aging can reach 97%, which is 24% and 17% higher than traditional algorithms such as SVM and BPNN, in the aging stage with no obvious features, the recognition effect is more significant. Keywords: Deep residual network · Partial discharge · Pattern recognition · Oil paper insulation · Thermal aging
1 Introduction Oil paper insulation is the main form of internal insulation in oil immersed power transformer. Thermal aging is an important reason for the decline of mechanical properties of insulating paper. It is of great significance to deeply study the thermal aging insulation mechanism and establish a reliable identification and evaluation for the aging state of oil paper insulation to ensure the safe and stable operation of transformer. Literature [1, 2] proposed the air gap defect model of oil paper, and used genetic algorithm back propagation neural network and Levenberg Marquardt optimization neural network to identify its aging respectively. Factor analysis was used to select 10 principal component factors of 29 statistical characteristic parameters as the input of neural network, and the radius of unit circle as the output, The back propagation neural network is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1076–1087, 2022. https://doi.org/10.1007/978-981-19-1528-4_110
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improved, and its fuzzy clustering is studied [3]. In reference [4], an air gap defect discharge model was proposed and the aging experiment was carried out. After denoising the partial discharge signal, the empirical mode decomposition (EMD) - singular value decomposition (SVD) feature was combined with the random forest classifier to identify the aging stage. In reference [5], dissolved gas analysis in oil and partial discharge waveform are used for feature fusion, and particle swarm optimization algorithm is used to optimize support vector machine to improve the recognition effect of traditional support vector machine.
2 Theory The test results of initial voltage at different aging stages are shown in Fig. 1. The average values of three groups of experiments at this aging stage are used for each initial voltage. In general, with the aging time prolonging, the starting voltage presents a downward trend, which indicates that the oil paper insulation performance declines, and the partial discharge is easier to start and develop. During the aging process, the degree of polymerization of the paper will decrease, while the dielectric constant will increase [6]. The increase of electric field inhomogeneity caused by this is a possible explanation for this phenomenon. After aging for 20 days, the change range of the initial voltage decreases, and the effect of aging on the initial voltage is not significant.
Fig. 1. The initial voltage changes with aging stage
According to the test results of initial voltage in different aging stages, the overall idea of this paper is as follows: firstly, the partial discharge test is carried out based on the column plate electrode model to obtain the partial discharge pulse phase distribution map of oil paper insulation in different thermal aging stages, then the test data is preprocessed by sample expansion and image graying, and finally the pattern recognition of samples is carried out by using the deep residual network, determine the aging stage.
3 Algorithm and Strategy 3.1 Data Expansion The tests under different aging stages and different voltages are shown in Table 1.
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Test voltage (Test Times)
A(0 days)
20(6), 21(2), 22(2)
B(10 days)
20(4), 21(5), 22(2)
C(20 days)
18(2), 19(2), 20(2), 21(3), 22(3), 23(2)
D(30 days)
20(2), 21(6), 22(3), 23(3), 24(2)
E(40 days)
20(3), 19(3), 18(3), 17(2)
To ensure the comparability of each data, 60000 discharge points above 10pC are selected as a group of data for each long-time recorded data stream. Among the data obtained, the test data of group B in the 10 days aging stage was less, and the sliding data clipping [7] was used to expand the test data of group B, and the data segmentation was performed by equal discharge times (60000 times) in the other stages. The sliding data is clipped as one data for every 60000 discharge points from the first discharge point, and the starting point of the next data is the previous data starting point plus 15000 discharge points. The diagram is shown in Fig. 2. Except for group B, the experimental data were directly segmented with 60000 discharge points at equal intervals, and the schematic diagram is shown in Fig. 3.
Fig. 2. Schematic diagram of sliding step size cutting
Fig. 3. Schematic diagram of data segmentation
3.2 Image Preprocessing The q − ϕ image of the original data is drawn every 60000 times of discharge above 10 pc. For each image of the original data, 0.7 times of the maximum discharge value of the current group of data is used as the maximum value of the spectrum; The original image is divided into 100 parts × 100 image panes, count the number of discharge times
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in each pane, and become the q−ϕ density matrix; The obtained image is logarithmically calculated as shown in Eq. (1) to avoid a large number of small discharges and mask a small number of large discharges. xij = log(zij )
(1)
zij is the number of discharge points in the pane, and xij is the logarithm result of the corresponding pane. Normalize the processing results as shown in Eq. (2), The original numerical matrix after logarithmic operation is transformed into the range of 0~1, then multiply by 255 to form a standard 8-bit gray image matrix, which is saved as gray image. A total of 13817 image data sets are obtained, 90% of which are used for network training and 10% for testing. yij =
xij × 255 max(x)
(2)
yij is the gray value of the pixel corresponding to the pane in the gray image, and x is the matrix after the whole logarithm operation obtained in the previous step. The effect before and after graying is shown in Fig. 4. In the gray-scale image of Fig. 4(b), the black area below the image represents a large number of small discharges, and the dark point above represents a discharge with larger amplitude and less discharge times.
(a)Original data of 20kV after thermal aging at 130 °C for 40 days
(b)The result of gray processing
Fig. 4. Image comparison before and after graying
3.3 Deep Residual Network The deep residual network is a kind of convolutional neural network, which has stable training and excellent performance. In each layer of convolution network operation, the dimension of feature subgraph is increasing, and automatic feature extraction is realized through training iteration [8].
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In this paper, the simplified ResNet-18, ResNet-18 and ResNet-34 are constructed to find the network with better performance and compare the influence of different network depth. The structural parameters of each network are shown in Table 2. Table 2. Structure parameters of deep residual network Net
Modules number
The number of basic modules of residual module No. 1
No. 2
No. 3
No. 4
Simplify ResNet-18
4
1
1
1
1
ResNet-18
4
2
2
2
2
ResNet-34
4
3
4
6
3
4 Experimental Simulation 4.1 Construction of Test Platform The test platform follows IEC60270 partial discharge test standard [9], and the test circuit diagram is constructed by pulse current method, as shown in Fig. 5.
Fig. 5. Partial discharge test circuit diagram
The power supply part of the test circuit uses a programmable signal generator to generate the signal waveform. The sampling rate of the signal output is 6.5 ms/s, which is converted into a 50 Hz power frequency cycle. There are 1.3 × 105 signal
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sampling points in a cycle. After being amplified into power signal by high-voltage power amplifier, the input signal can be converted into test high-voltage power signal with fixed gain amplification of 1:5000. According to IEC60270, the center frequency of charge frequency domain integration is 250 kHz and the bandwidth is 300 kHz. The high voltage probe was used to measure the voltage of the test object by 1000:1 partial voltage. The 5000:1 divider output port is monitored by the voltage of the power amplifier, and the outlet voltage of the amplifier is measured. There is no partial discharge when the partial discharge circuit is raised to the RMS voltage of 30 KV, and the noise of the test system is less than 10 Pc. Karamay No. 25 transformer oil without any other impurities is used. It is placed in a vacuum drying oven and dried for 48 h under 80 °C and pressure less than 10 Pa. The 1 mm thick insulating paperboard for converter was cut and dried in vacuum for 48 h at 105 °C and pressure less than 10 PA. Then the oil paper sample was placed in the environment of 80 °C and pressure less than 10 Pa for 48 h. After that, it was cooled at room temperature and stored at 20 °C. According to the MUNCHINGER thermal aging rule [10] shown in equation (3), the aging time of the experimental sample at high temperature is estimated as the equivalent time of normal operation. T = T0 e−a(θ−θ0 )
(3)
θ0 is the reference working temperature of insulating materials in normal operation; T0 is the equivalent working time of insulating material under the reference working temperature; θ0 is the thermal aging temperature of the sample; T is the experimental time of the sample under the condition of thermal aging; a is the thermal aging coefficient, generally 0.1155. In this paper, the reference working temperature of transformer is 80 °C, the actual working temperature is 130 °C, which is divided into five stages, each stage interval is 10 days. The results are shown in Table 3. Table 3. Equivalent working time of each sample Aging stage
A
B
C
D
E
Sample aging time (hours)
0
240
480
720
960
Sample aging time (days)
0
10
20
30
40
Estimated equivalent time (years)
0
9
18
26
35
4.2 Network Evaluation Index In this paper, the network evaluation index includes accuracy rate, accuracy rate, recall rate, F1 value and algorithm confusion matrix. TP is the same number of samples in the current aging stage as the network identification aging stage, FP is the number of samples in other aging stages as the current aging stage, FN is the number of samples in
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other aging stages as the current aging stage, and TN is the number of samples in other aging stages as the other aging stages. The definition of accuracy is as follows (4), the number of correct samples, which accounts for the proportion of all sample. accuracy =
TP + FN TP + TN + FP + FN
(4)
The definition of precision is shown in Eq. (5), the proportion of all samples in the current aging stage that are the same as those in the network identification aging stage in all samples identified as the current aging stage. precision =
TP TP + FP
(5)
Recall is defined as Eq. (6), that is, the proportion of all samples in the current aging stage that are the same as those in the network judgment aging stage to all samples in the current aging stage. recall =
TP TP + FN
(6)
The definition of F1 value is shown in Eq. (7), which takes into account the accuracy and recall rate, and is a high optimal parameter. 2 1 1 = + F1 precision recall
(7)
Confusion matrix can clearly give the classification results, its number of columns is the real aging stage, and the number of rows is the aging stage it judges. The elements on the diagonal of confusion matrix are the number of samples with correct classification, while the rest are the number of samples with wrong components. The larger the value on the main diagonal is, the better the network performance is. 4.3 Identification Results The training process of deep residual network is shown in Fig. 6(a). With the increase of iterations, the recognition accuracy of each network increases gradually. The accuracy of the simplified ResNet-18 decreases many times in the iterative process due to the small number of parameters and poor stability; ResNet-34 has a deep network structure, which has been stable at a high accuracy rate after about 20 iterations; The stability of ResNet-18 is between the two. According to Fig. 6(b), their accuracy can reach more than 95%. The deepest ResNet-34 has the best stability, and the iterative accuracy has been stable at 96.2%; The highest accuracy of ResNet-18 is 97%; The simplified ResNet-18 showed significantly greater volatility, with accuracy between 95.6% and 96.6%.
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(a)Change of accuracy in iterative process of deep residual network
(b)The stable value of the final accuracy of deep residual network training Fig. 6. Recognition results of deep residual network
Taking the highest accuracy parameter of ResNet-18 which has better effect, its classification report by stages is shown in Table 4. It can be seen that for each stage of aging, the deep residual network has achieved good classification effect. For the 40 days aging samples with obvious characteristics, 100% recognition can be achieved, and the network evaluation indexes of other stages are all above 90%. Table 4. Deep residual network classification result report Aging stage (days)
Accuracy
Recall
F1
Number of test data
0
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From the confusion matrix shown in Fig. 7, it can be seen that only a small number of samples are distributed on the non-diagonal line, the misjudgment is rare, and most of the aging stages are correctly matched. The results of confusion matrix show that the
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method in this paper can be used as an effective method for oil paper insulation aging diagnosis.
Fig. 7. Confusion matrix of deep residual network
Using the same algorithm to calculate the two traditional algorithms of support vector machine and back propagation neural network, the classification report and confusion matrix of these two algorithms can be obtained, as shown in Table 5, Table 6 and Fig. 8, Fig. 9. Table 5. Classification result report of support vector machine. Aging stage (days)
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The average accuracy of deep residual network in different aging stages can reach 97%, which is 17% higher than that of back propagation neural network and 24% higher than that of support vector machine. The main reason for this phenomenon is that the automatic feature extraction of the depth residual network has advantages over the traditional artificial feature extraction. It can find the deep features that are not easy to detect and describe the image better, so that the depth of the network can be greatly increased.
Research on Recognition Strategy of Oil-paper Insulation Aging State Table 6. Classification results report of BP neural network Aging stage (days)
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F1
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Fig. 8. Confusion matrix of support vector machine
Fig. 9. Confusion matrix of BP neural network
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In terms of accuracy, in the first few aging stages where the traditional algorithm features less obvious, the accuracy rate is insufficient. SVM is particularly poor for 30 days sample recognition, with an accuracy rate of only 55%, which is 34% different from the deep residual network in the same stage; The accuracy of BPNN is 70%, which is 29% lower than that of deep residual network. The deep residual network can not only achieve high accuracy in 40 days samples with obvious characteristics, but also can effectively identify the aging stage. In terms of F1 value, the higher F1 value of the deep residual network represents the consideration of accuracy and recall. The accuracy rate of BPNN 30 days sample and SVM 30 days sample F1 value is lower, which means that the recall rate is insufficient, and more samples in this aging stage are missed. The F1 value of SVM and BPNN is low, which shows that the ability of both accuracy and recall is poor.
5 Conclusion In this paper, an aging stage identification method based on deep residual network is proposed. The oil paper samples aged at 130 °C for 40 days in five stages were prepared. The PRPD patterns of partial discharge in different aging stages were obtained by using the column plate electrode as a typical defect. After data expansion and image graying, the algorithm model based on the depth residual network was used to identify different aging stages, compared with support vector machine and back propagation neural network based on artificial feature extraction, the following conclusions are obtained: 1. The recognition rate of the deep residual network can reach 97%, which is 24% and 17% higher than the traditional support vector machine method and back propagation neural network method, respectively. The essential reason for the improvement of accuracy is that the deep residual network can analyze the PRPD gray image with more information, extract the deep features that are difficult to detect in the image through the automatic feature extraction ability, and describe the discharge features in different aging stages better. Therefore, compared with the traditional manual feature extraction algorithm, it has significant advantages. 2. The deep residual network can not only achieve high accuracy in the 40 days samples with obvious characteristics, but also can effectively identify in the lower aging stage. Compared with the traditional algorithm, each index has obvious advantages, which is conducive to identify the precursors of insulation deterioration, which is of great significance for the operation, repair and maintenance of equipment aging. 3. Under the conditions of different voltages, different development stages of partial discharge and different pressurization methods, the deep residual network still achieves good classification effect, showing its strong fitting ability and generalization ability. The complexity of the data leads to the low recognition rate of the traditional algorithm, which cannot achieve good performance.
Acknowledgment. This research was funded by the National Key Research and Development Program of China, grant number 2018YFB2100104.
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References 1. Liao, R.J., Wang, K., Zhou, T.C., et al.: A method of evaluating thermal aging state of oil paper insulation by partial discharge factor vector. J. Electrotech. 25(09), 28–34 (2010). (in Chinese) 2. Zhou, T.C., Yang, L.J., Liao, R.J., et al.: Oil paper insulation aging diagnosis based on partial discharge factor vector and BP neural network. J. Electrotech. 25(10), 18–23 (2010). (in Chinese) 3. Li, J., Liao, R., Grzybowski, S., et al.: Oil-paper aging evaluation by fuzzy clustering and factor analysis to statistical parameters of partial discharges. IEEE Trans. Dielectr. Electr. Insul. 17(3), 756–763 (2010) 4. Zhang, J.W., Wang, M., Xie, H., et al.: Aging stage evaluation of oil paper insulation based on random forest. Electr. Meas. Instrum. 55(09), 121–125 (2018). (in Chinese) 5. Wang, M.: Study on Evaluation Method of Oil Paper Insulation Aging State of Power Transformer. China University of Mining and Technology, Beijing (2018).(in Chinese) 6. Sun, P., Sima, W., Yang, M., et al.: Influence of thermal aging on the breakdown characteristics of transformer oil impregnated paper. IEEE Trans. Dielectr. Electr. Insul. 23(6), 3373–3381 (2016) 7. Fang, X.Q., Song, H., Luo, L.G., et al.: Application of convolution neural network in pattern recognition of partial discharge image. Power Grid Technol. 43(6), 2219–2226 (2019). (in Chinese) 8. Gao, X., Ji, J.W., Zhao, B., et al.: Multi classification method of smart meter fault based on CVAE-CNN model under unbalanced data sets. Power Grid Technol., 1–9 (2021). (in Chinese) 9. High-Voltage Test Techniques. Partial Discharge Measurements IEC 60270:2015[S] (2015) 10. Xie, J.: Partial Discharge Deterioration Law and Diagnosis Method of Transformer Oil Paper Insulation. North China Electric Power University, Beijing (2016).(in Chinese)
Leading Phase Operation Strategy of Multi-generators Based on Equivalent Impedance Method Xiang-yu Liu1(B) , Shuai Li2 , Xiao-ming Li1 , Hui-bin Li3 , Shi-bo Yang4 , and Ning Gong5 1 State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
[email protected]
2 Department of Mechanical Engineering, North China Electric Power University,
Baoding 071003, China 3 Department of Electrical Engineering, North China Electric Power University,
Baoding 071003, China 4 State Grid Hebei Electric Power Co. Ltd., Maintenance Branch, Shijiazhuang 050011, China 5 State Grid Hebei Electric Power Co. Ltd. Cangzhou Power Supply Branch, Cangzhou 061000,
China
Abstract. This paper investigates the leading phase operation strategy of multigenerators based on Equivalent Impedance Method. Firstly, the basic principle of generator leading phase operation and the problems of generator leading phase operation based on voltage control are described. Secondly, the technical idea is analyzed that using equivalent impedance to distribute reactive power among multi-units under the constant reactive power control mode. Then the method of calculating the equivalent impedance by using branch current and the implementation flow of multi- generators leading phase operation strategy are proposed. Finally, the effectiveness of the proposed method is verified by a real power grid example, and the leading phase operation strategy comparation between multigenerators and single- generator. The results show that the voltage regulation effect of the proposed method is better and the reactive power exchange between generators can be eliminated effectively. The achievements obtained in this paper have a positive significance to engineering application of multi-generators leading phase operation technology. Keywords: Equivalent impedance · Branch current · Leading phase operation of multi-generators · Reactive power control
1 Introduction The ground capacitance of cable lines and high voltage overhead lines provides a lot of charging reactive power. When the power grid is in the mode of small load operation, the voltage will rise [1, 2]. Moreover, due to the formation of UHV (Ultra High Voltage) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1088–1099, 2022. https://doi.org/10.1007/978-981-19-1528-4_111
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long distance transmission pattern [3] and low voltage grid access of distributed new energy [4, 5], the voltage control problem of power grid is increasingly prominent. It is an economic and mature technical way to regulate the grid voltage by generator leading phase operation [6]. Encouragingly, after long-term study [7, 8], the researchers have solved the technical problems in single generator leading phase operation, which lays the foundation for the research of multi-generator leading phase operation. At present, the simulation analysis [9, 10] and engineering experiment [11, 12] of unit leading phase capacity is relatively comprehensive. On this basis, the conclusion of single generator leading phase operation is consistent: There are six main limiting factors for the leading phase operation capacity of the unit [13]. Among them, the physical constraints of power plant and the security and stability constraints of power grid are the main aspects, in recent years, more and more attention has been paid to the latter [14, 15]. In the actual power grid, it is also found that the security and stability constraints may become the limiting factor of generator leading phase capacity [16]. In terms of multi-generators leading phase operation, Ref. [17–20] is representative. Ref. [17] discussed the superiority and stability of multi-generators leading phase operation and analyzed the voltage regulation capacity as well as the problem of multigenerator coordinated leading phase operation. Ref. [18] studied the voltage regulation characteristics of single machine leading phase operation and dual machine coordinated leading phase operation, and pointed out that multi machine leading phase needs to reasonably distribute reactive power. Ref.[19] emphasized the importance of multigenerator coordinated controlling. Thus, multi-generator coordinated leading phase operation and whole network voltage regulation have become a new research direction. As for the controlling strategy, in Ref. [20], scholars applied constant reactive power control strategy to study the coordination strategy of dual generator leading phase operation. However, the strategy was carried out for the given power grid structure, which is not suitable for multi-generator mode and complex operation mode. At the moment, a feasible strategy of multi-generator coordinated leading phase operation has still not been reported. Therefore, it is of great significance in engineering to conduct relevant study. On the basis of Ref. [17–20], in this paper, constant reactive power control mode is used and the strategy of multi-generator leading phase operation based on Equivalent Impedance Method is studied. Moreover, the correctness of using branch current to represent equivalent impedance and using equivalent impedance to represent correlation degree of each voltage regulating unit is analyzed. In order to verify the applicability of this method, the actual power grid is applied to compare the effects of single generator leading phase operation with multi-generators leading phase operation.
2 Generator Leading Phase Operation 2.1 Leading Phase Operation Mechanism Under Classical Model Leading phase operation is a kind of operation state in which the generator generates active power and absorbs reactive power. As is shown in Fig. 1, under normal conditions, most generators are in late phase operation, and the current I˙ lags behind the voltage
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U˙ . As can be known from Fig. 1, at constant voltage U˙ , inner potential E˙ q will reduce when the generator changes from late phase operation to leading phase operation.
δ ψ
δ
φ
Late phase operation
φ
Leading phase operation
Fig. 1. Vector diagram of generators’ operating state
The expression of generator active output without considering excitation regulation is: Pe =
Eq U sin δ Xd
(1)
where U and X d are regarded as constants. Leave the value of active power Pe unchanged, the reduction of E q , as a result of leading phase operation, will cause the raise of power angel δ, which will result in the reduction of the transient and static power angle stability limits of the system. 2.2 Leading Phase Operation Considering Excitation Control The conventional control mode of generator excitation system is constant voltageconstant power control mode, and different units are independent of each other. As Fig. 2 shows, the voltage control logic of generator set A is to keep U e1 (or U 1 ) a specified value. If the voltage control target exceeds the control capacity of unit A, then switch to power control mode. U1 S
U
a
Qa
Qs
XT1 XT2
Xs
Qb
Ue1
A
Eq1
c
Ue2 d
B
Eq2
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Fig. 2. A simplified model of a two-generator and infinite system
If leading phase operation is needed due to the voltage of system is too high, both single-generator leading phase operation and multi-generator exist some problems. For instant, if only the unit A joins to the leading phase operation, the excitation current of A need to be reduced. At the same time, reactive power Qa flows to bus
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c through bus b which will lead to the decreasing of U e1 and U 1 . As a result of the reduction of U 1 , unit B under constant voltage control mode will add reactive power Qb in order to increase the volage of bus b. On the one hand, unit B weakens the leading phase operation effect of unit A. On the other hand, it increases the reactive power flow between the two units, which has adverse effects on the safety and economy of system operation. If multi-generators share the leading phase operation task, it involves the reactive power coordination of multi units, which is also the difficulty of the problem.
3 Multi-generators Leading Phase Operation Coordination Under Constant Reactive Power Control 3.1 Equivalent Model of Reactive Power Distribution As shown in Fig. 3, it is assumed that the voltage of a hub node O in the power grid is too high, so it is necessary to adjust the voltage level by leading phase operation of nearby units. On this basis, the regional power grid can be equivalent to the model of Fig. 3-(a) where A, B and C represent the generator sets, S is the equivalent sets of system. There exists electrical connection between node O and generator A, B, C. Moreover, electrical connection also exists among A, B and C. Similar to the two-generator leading phase operation system, when the three generators operate leading phase at the same time, reactive power will flow from O to A, B and C separately. However, if the leading phase coordination between the generators is irrational, there will be reactive power flow among A, B and C. If the leading phase coordination is reasonable, the reactive power exchange among A, B and C due to leading phase operation shall be zero. The system can be equivalent to the model of Fig. 3-(b) because there is no incremental reactive flow between generators. S
S O
O
A
A
B
B
C (a)
C (b)
Fig. 3. Equivalent model of three-generator leading phase operation
Under the condition of the three generators A, B and C are all guaranteed to operate leading phase (or reduce reactive power output), there will not be reactive power exchange problem. At this time, if the electrical distance between the three generators and node O can be acquired, coordinated decision of leading phase depth of multi-generators can be conducted based on it.
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3.2 Reactive Power Control Coordination of Multi-generators Leading Phase As shown in Fig. 4, the voltage level of a node O is controlled by the added n generators. If the voltage of O is too high, generator sets A1 –An will reduce the reactive power output to lower the voltage. Constant-power control is applied to these n generator sets during the process of leading phase operation. S
zs
O
z1
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...
z2 A2
zn An
Fig. 4. Equivalent model of multi-generator leading phase operation
Assuming that the total reactive power from node O to the n generator sets is Qo, the needed voltage level can be reached. The reactive power flowed to An is set as: 1 zn Qn = n · Qo (2) 1 zn n=1
During the process of multi-generators leading phase operation, reactive power absorption ratio of each set is distributed based on formula (2) so as to adjust the voltage of node O to reach the target. 3.3 Equivalent Impedance Calculation Based on Branch Current As illustrated in Fig. 3-(a), calculate the three-phase short circuit current I O of node O based on normal operation mode. Under the normal mode, set the generator A to stop, and calculate the three-phase short-circuit current I O-A of node O again. According to the principle of linear circuit superposition, the branch current of generator A three-phase short circuit to node O can be known as I A = I O − I O-A . Therefore, in the equivalent mode as Fig. 3-(b) shown, the equivalent impedance from generator A1 to node O can be expressed as 1/I A . Thus formula (2) can be regarded as: Qn =
IAn · Qo n IAn n=1
where IAn represents the branch current of generator A1 .
(3)
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3.4 Steps of Multi-generators Leading Phase Operation Strategy It can be known from the analysis of Sect. 3.1, 3.2 and 3.3 that the value of leading phase reactive power can be distributed based on the branch current of three-phase short circuit of each generator to the node when the voltage of a node is controlled by method of multi-generator sets leading phase. The specific steps of proposed method are as follows (Fig. 5).
Acquire the operation data of power grid
Determine the relevant generators aiming at the node to be controlled
Calculate the three-phase shortcircuit branch current of relevant units Acquire the reactive power absorption ratio of the relevant units Relevant units absorb reactive power in fixed proportion according to constant reactive power mode
Do relevant generator sets reach the upper limit of leading phase operation capacity?
Y
Stop leading phase operation
N
Continue to increase the leading phase depth until the voltage of the target node reaches the requirement or all relevant sets reach the upper limit of leading phase capacity
Fig. 5. The implementation steps of multi-generator leading phase operation
Step1: Determine the node to be controlled with high voltage and the relevant generators based on the operation data and mode of power grid. Step2: Calculate the three-phase short-circuit current of the node to be controlled under the operation mode at present. Afterwards, shut down the relevant generators one by one, calculate the three-phase short-circuit current of the node to be controlled again, so as to obtain the branch current of the relevant sets. Step3: Acquire the reactive power absorption ratio, which is used to control the leading phase operation of relevant generator sets, according to the value of branch current.
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Step4: If the relevant generator sets reach the upper limit of operation capacity in the process of leading phase, the remaining sets will continue to increase the leading phase depth until the voltage level of the node to be controlled reaches the requirement (or all relevant sets reach the upper limit of leading phase capacity).
4 Case Application and Verification 4.1 Description of an Actual Case Taking the actual power grid shown in Fig. 6 as an example, it is a 220 kV power grid, which is connected to the 500 kV main system through substation I. There are three power plants and nine substations in the network. In a small operation mode, each power plant has only one generator and operates at full power. The rated power of generator in power plant 1–3 is 600 MW, 300 MW and 660 MW respectively. Under this mode, the operation voltage of power grid is on the high side, and the voltage of each node is shown in Table 1. B
A
C D
Power plant 1 G F The main system
E
Power plant 2
H
Power plant 3
Fig. 6. An example of actual power grid
Table 1. The voltage of power plants and substations under small operation mode Node name
Voltage/kV Node name Voltage/kV
Power plant 1 237.4
D
234.4
Power plant 2 234.1
E
236.4
Power plant 3 236.6
F
234.8
A
234.2
G
234.1
B
234.4
H
236.4
C
234.2
I
531.6
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The voltages of substation E and H are both 236.4 kV which is in a high level in this mode. Thus, generator leading phase operation is to be adopted to adjust the voltage level of this local grid. 4.2 Verification of the Multi-generators Leading Phase Strategy Herein, the multi-generators coordination operation strategy described in Sect. 3 is applied to the case in Sect. 4.1 in order to verify the method. In this case, substation E is set to be the controlled node. Leading phase operation mode will be used to lower the voltages of E and additional factory stations nearby. The three-phase short-circuit branch current of each generator to substation E, as shown in Table 2, is calculated by the method described in Sect. 3.3. Table 2. The branch current of three-phase short circuit Node name
Current/kA
Proportion/%
Power plant 1
4.303
20.54
Power plant 2
1.248
5.96
Power plant 3
0.848
4.05
14.552
69.45
System equivalent unit
The three power plants gradually reduce the excitation current and absorb reactive power in the proportion shown in Table 2. The voltage level of 220kV substation changes with the sum of reactive power absorption of the three power plants and the curve is shown in Fig. 7. 237 236 235
Voltage/kV
234 233
E
A
B
C
D
F
G
H
232 231 230 229 228 0
50 100 Reactive power/MVar
150
Fig. 7. The relationship between voltage and reactive power absorption
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It can be seen from Fig. 7 that in the process of reactive power absorption gradually increasing to 150 MVar, the voltage range of the substation gradually decreases from 234–237 kV to 228–231 kV, and the voltage of each substation is negatively correlated with the total reactive power absorption. When the sum of reactive power absorption of three power plants is 150 MVar, the voltage of each node is shown in Table 3. Table 3. The voltage of power plants and substations when 150 MVar reactive power is absorbed Node name
Voltage/kV Node name Voltage/kV
Power plant 1 230.0
D
230.4
Power plant 2 228.1
E
229.8
Power plant 3 231.9
F
229.9
A
228.4
G
230.7
B
228.9
H
229.8
C
229.6
I
528.0
It can be seen from Table 3 that the voltage of substation E decreases by 6.6 kV to 229.8 kV, which is similar to the average rated voltage. 4.3 Comparison of Multi-generators Leading Phase and Single Generator In order to realize compare the actual difference of multi-generators leading phase operation and single-generator, single generator leading phase operation is simulated with the target of controlling the bus voltage of substation E to 229.8 kV. Power plant 1 is selected as the leading phase operation unit, and other units adopt the default constant voltage control mode. When the voltage of substation E is reduced to 229.8 kV, the voltage of each station is shown in Table 4, and the reactive power output of each generator under different control modes is shown in Table 5. Table 4. The voltage of power plants and substations when one single power plant is in leading phase operation mode Node name
Voltage/kV Node name Voltage/kV
Power plant 1 229.4
D
231.7
Power plant 2 230.6
E
229.8
Power plant 3 233.9
F
231.1
A
230.5
G
231.6
B
230.4
H
229.9
C
230.9
I
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Table 5. Generator reactive powers under different operation modes Mode
Power plant 1 Power plant 2 Power plant 3
Reactive power under original mode/MVar
143.9
2.4
172.3
43.0
−17.5
143.0
Single generator leading phase reactive power −45.6 /MVar
30.4
238.9
Multi-generators leading phase reactive power/MVar
Comparing Table 3 with Table 4, it can be known that if the voltages of substation E are both 229.8 kV, the maximum voltage difference between 220 kV power stations under single generator leading phase operation mode is 3.8 kV shown in Table 3 while that under multi-generators shown in Table 4 is 4.5 kV. In addition, the difference quadratic sum between the voltage of each 220 kV station and the control target of 229.8 kV in Table 3 is 13.58 while that in Table 4 is 33.51. Therefore, the voltage level of the power grid is more balanced under the control of multi-generators leading phase. It is outstanding in driving regional grid voltage by controlling the voltage of hub nodes. It can be seen from Table 5 that compared with the original operation mode, under the multi-generators leading phase operation, the reactive power output of three generators is reduced, and there is no reactive power flow between units. On the contrary, under the single generator leading phase operation mode, when the reactive power output of power plant 1 is reduced and switched to leading phase operation mode, the reactive power of power plant 2 and power plant 3 is increased by 28 MVar and 62.6 MVar compared with the original mode, forming the reactive power exchange between power plants. Therefore, the multi generator coordinated leading phase operation mode has obvious advantages over the single generator.
5 Analysis of Multi-generators Leading Phase Coordination Strategy 5.1 Importance of Reactive Power Control Mode The conventional control strategy of generator is voltage control, that is, to keep the voltage of specified node as the target value within the reactive power control capacity of generator. When multiple generator sets control the voltage of a node at the same time, it involves the problem of division and coordination of the generator units. However, the voltage control method ignores these factors, it is difficult to achieve coordination. Voltage and reactive power are closely related to each other. The coordination task of multi-generators leading phase operation can be realized by reasonably distributing the reactive power consumption ratio of multiple units. Based on equivalent impedance, reactive power coordinated control can solve the problem that voltage control strategy cannot solve. Therefore, the reactive power control mode is superior to the voltage control mode in the aspect of multi-generators leading phase coordination.
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5.2 Adaptability of Equivalent Impedance Method Reasonable reactive power distribution needs to consider the operation mode of power grid, electrical distance, unit capacity and other factors. It is very complex to calculate the weight of reactive power allocation of each unit with analyzing the mentioned factors in detail. Considering the frequent changes of operation mode, the applicability of reactive power distribution by analytical method is not strong. Compared with the analytical method, the equivalent impedance method is simpler and more convenient to calculate the equivalent impedance by using the short-circuit current. When the leading phase unit changes and the operation mode is adjusted, the workload of re-leading phase calculation is less and the robustness is strong, which can be applied to online decision-making.
6 Conclusion In this paper, the coordinated control problem of multi-generators leading phase is studied. Firstly, the equivalent impedance of each voltage regulating unit to the node to be controlled is obtained by branch current calculation. Afterwards, the reactive power control proportion of the voltage regulating unit is distributed by the equivalent impedance. In addition, the voltage regulation effect of proposed method is verified by an actual power grid example. The main conclusions and suggestions are as follows: 1. Reactive power control method is more effective than voltage control method in dealing with multi-generators leading phase operation. 2. It is feasible to characterize the equivalent impedance between generators and controlled node based on branch current. 3. Under the control model of single generator leading phase operation, it is easy to cause reactive power exchange between units. At the same time, the voltage regulation effect is lower than that of multi-generators leading phase operation. 4. The control strategy based on equivalent impedance method proposed in this paper can effectively realize the coordination of multi-generators. Its voltage regulation effect is much better that that of single generator leading phase operation mode.
Acknowledgements. This work is supported by National Natural Science Foundation of China (5188884), Natural Science Foundation of Hebei Province, China (E2888883).
References 1. Ding, Y., Zhao, L., et al.: Strategies and measures for optimal balance of power flow in distribution network. Electron. Technol. Softw. Eng. 11, 229–230 (2021). (in Chinses) 2. Jia, T.R.: Research on the optimal configuration of reactive power compensation for urban 330kV cable network and unified power flow controller. Xi’an University of Technology (2021). (in Chinese)
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3. Ai, H., Huang, J., Wu, J., et al.: Reactive power control strategy for the Shanbei-Wuhan UHVDC transmission project. Power Syst. Protect. Control 49(14), 149–156 (2021). (in Chinese) 4. Le, J., Zhou, Q., Wang, C., et al.: Research on voltage and power optimal control strategy of distribution network based on distributed collaborative principle [J/OL]. In: Proceedings of the CSEE, pp. 1–10, 31 January 2020. (in Chinese). https://doi.org/10.13334/j.0258-8013. pcsee.182229 5. Chang, W., et al.: Simulation evaluation of fast frequency response capacity of new energy power stations. In: E3S Web of Conferences, p. 245 (2021) 6. Zhang, Y., et al.: Safety analysis of leading phase operation of a nuclear power plant #3 generator. Electr. Eng. 20, 112–114+117 (2021). (in Chinese) 7. Lv, Y., Du, Y., Liu, Q., et al.: Study and stability analysis of leading phase operation of a large synchronous generator. Energies 12(6), 1047 (2019) 8. Cheng-liang, W., Hong-hua, W., Gang, X.: System voltage regulation of power grid based on synchronous generator’s leading phase operation. In: CICED 2010 Proceedings, pp. 1–4. IEEE (2010) 9. Zhang, Y., Tong, X., Xu, S., et al.: Simulation of Influence of generator leading phase operation on voltage and loss of Gansu power grid. In: IOP Conference Series: Materials Science and Engineering, vol. 486, no. 1, p. 012155. IOP Publishing (2019) 10. Gu, F., Ge, B., Lin, P., et al.: The simulation analysis of leading phase operation of powerformer. In: 2009 Asia-Pacific Power and Energy Engineering Conference, pp. 1–4. IEEE (2009) 11. Li, K., Wang, Y., Gu, W., et al.: Modeling of generator leading phase ability based on ant colony optimization and support vector machine. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–5. IEEE (2017) 12. He, Z., Li, Y., He, S., Zhang, Q.: Analysis and test verification of turbine generator’s leading phase operation capability. Power Gener. Technol. 40(5), 488–492 (2015) 13. Xian, X., Xun, Z., Zhou, D.: Operation and reactive power control of large turbine-generators. Trans. China Electrotech. Soc. 30(05), 98–105 (2015). (in Chinese) 14. Cui, X., Bao, Y., Ma, M., et al.: On-line auxiliary decision-making calculation for united generators’ leading-phase operation in a large-scale power grid. Autom. Electr. Power Syst. 35(24), 79–83 (2011). (in Chinese) 15. Plotnikova, T.V., Sokur, P.V., Tuzov, P.Y., Shakaryan, Y.G.: Static stability of parallel operation of asynchronized generators in an electrical system. Therm. Eng. 61(13), 962–976 (2014). https://doi.org/10.1134/S0040601514130072 16. Xiangyu, L., Yuling, H., Wen, Z., et al.: Analysis on unit capability of leading-phase operation considering stability restriction of power grid. J. North China Electr. Power Univ. 44(01), 52–57 (2017). (in Chinese) 17. Wei, Y., Wei, Z., Sun, G., et al.: Discussion of several issues of leading phase operation of multi-generators. J. Hohai Univ. (Nat. Sci.) 40(05), 590–596 (2012). (in Chinese) 18. Lin, Y.D.: Research on multi units coordinated phase advance operation method used in typical daily voltage regulation of power grid. North China Electric Power University (2021). (in Chinese) 19. Wang, W., Liu, H., Wang, T., et al.: Coordinated leading phase based multi units reactive power distribution method. Sci. Technol. Eng. 21(20), 8465–8470 (2021). (in Chinese) 20. Hu, W., Wei, Z., Wang, C., et al.: Double-generator leading-phase operation based on constant reactive power control strategy. Electr. Power Autom. Equip. 32(10), 127–131 (2012). (in Chinese)
Improved Degaussing Power Supply Applied to Ship Degaussing System Chao Huang(B) , Shengdao Liu, Zhixin Li, and Ziwei Liu College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, Hubei, China [email protected]
Abstract. A full-bridge circuit is applied in the traditional degaussing power supply to achieve output polarity switching. The limited output minimum current and the complicated circuit configuration affect the equipment performance. Thus, a bipolar DC-DC converter is proposed in this paper to achieve the output polarity switching and adjust the current amplitude. Compared with traditional degaussing power supply, The improved degaussing power supply consists of fewer power semiconductor devices and the output voltage is smooth at zero crossings, especially suitable for low resistance coils. This paper analyzes the operation mode of the bipolar DC-DC converter, derives the voltage gain expression of the converter, and proposes an equivalent analysis method based on the Buck-Boost circuit to make the output characteristics of the converter and the features of the circuit more intuitive. Finally, the output capability of the improved degaussing power supply structure is verified by simulation. The result has verified the improved degaussing power supply structure meets the power requirements of the ship’s degaussing system. Keywords: Degaussing power supply · Bipolar DC-DC convert · Cuk converter
1 Introduction When a ship is sailing at sea, the ferromagnetic hull itself has an inductive magnetic field and a permanent magnetic field, which makes the ship exposed to the danger of being attacked by magnetic fuze mines. In order to compensate the induced magnetic field and the permanent magnetic field generated by the ship, winding coils are placed around the ship, and a certain current is passed through it so that the magnetic field generated by the coil can be able to compensate the magnetic field generated by the ship to almost zero, so as to avoid being attacked by magnetic fuze mines. At present, the direction and amplitude of the induced magnetic field of a ship are related to the direction of the ship’s navigation, longitude and latitude. Therefore, the shipboard degaussing control system will calculate the required current value according to the current state of the ship, and send it to the power supply of the coil to output the corresponding current. Affected by the ship’s attitude and heading, the current value © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1100–1110, 2022. https://doi.org/10.1007/978-981-19-1528-4_112
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may be positive or negative. In the extreme case, a sine wave with a current waveform of 1/3 Hz is required. This requires that the power supply for the coil not only has the capability of bipolar output but also the output voltage can smoothly transition at zero crossings. The degaussing coil is equivalent to a resistive load. During operation, the resistance of the coil is approximately constant, and the output voltage of the power supply will vary linearly with the output current. The use of a lower resistance coil can reduce its own loss and the output power of the power supply, which is very necessary for the ship’s power supply system. However, when a low-resistance coil is used and a large current is required, there are many problems, such as low input and output voltage gain, short conduction time of the internal power device of the power supply, and large peak current flowing. In power converters, a large peak current is likely to cause saturation of magnetic devices, increased power semiconductor device conduction losses, and obvious effects of circuit parasitic parameters, and a series of adverse effects. As a result, the reliability and life of the power supply are reduced. Even if frequency modulation methods such as LLC resonant converter topologies are used, when the output voltage changes in a large range, it’s difficult in designing the resonant gain curve, and the converter’s working frequency range is too large. Traditional degaussing power supplies mainly use phase-shifted full-bridge and symmetrical half-bridge topologies. For degaussing power supply such a wide range of output voltage applications, the literature [1] proposed A hybrid full-bridge converter that consists of a secondary-side phase-shift full-bridge converter (SPS-FBC) and a full-bridge LLC converter is Proposed, the literature [2] analyzed the design of a phase-shifted full bridge suitable for a wide output voltage range. Literature [3, 4] proposed a new LLC resonant converter for wide output voltage, which can greatly improve the efficiency of the converter while satisfying the wide voltage output. Literature [5] studied the current output characteristics and the realization of soft switching when the LCC converter works in constant current mode. Although the converter proposed in the above documents meets the requirements of working in a wide output voltage range, it can only output voltage and current of a single polarity, and still cannot meet the requirements of the degaussing system power supply. Literature [6] proposes a high-voltage bipolar square wave pulse source that uses a cascaded full-bridge inverter circuit on the output side to achieve output commutation. This working method is basically the same as the method widely used in the existing degaussing power supply system [7–9]. It’s working process is easy, when the given current value is positive, the main diagonal switch is turned on, connecting the load and the power supply output; when the given current value is negative, the auxiliary diagonal switch is turned on to make the power output polarity reverse to complete the current polarity switching operation. The magnitude of the current amplitude is still regulated by the front-end power converter. In this way, although bipolar output can be realized, the whole circuit is more complicated, and the added set of full bridge circuit only plays a basic polarity switching function. Based on this degaussing power system structure, literature [10] proposes another working mode: the back-end full-bridge circuit works in a unipolar PWM modulation mode, and its two bridge arms form two Buck circuits, whose output polarity are opposite to each other.
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The front-end power converter operates in voltage source mode and can supply to one or more back-end full-bridge inverter circuits. In this paper, we use a bipolar DC-DC converter based on a variant Cuk circuit as the back-end bipolar output circuit in the working mode proposed in the literature [10]. An equivalent analysis method based on Buck-Boost circuit is proposed for this bipolar DC-DC converter, which simplifies the circuit analysis and parameter design. Compared with the solution proposed in the literature [1], only two switching power semiconductor devices are needed to achieve bipolar output. By adjusting the on-duty ratio of the power MOSFET, output polarity switching and current amplitude adjustment are realized, which simplifies the hardware system. design.
2 Bipolar Degaussing Power Supply Structure The traditional degaussing power supply structure is shown in Fig. 1(a), and the improved degaussing power supply structure proposed in this paper is shown in Fig. 1(b). A complete degaussing power supply system incorporates multiple degaussing power supply devices with the structure shown in Fig. 1. Two power MOSFETs have been reduced caused by the improved degaussing power supply, which can simplify circuit design and reduce manufacturing costs. Moreover, the input and output of the subsequent converter circuit share the same ground. The design of the measurement circuit has been simplified, and the accuracy has been improved.
C in1
Q1
D1
Lr
S1
S2
Cr RL
Vg
Vo
D2
C in 2
S3
Q2
S4
(a) S2
C in 1
Q1
D1
Lr Cr
L1 S1
Vg
C in 2
Q2
C1
L2 C2
Vo
D2
(b) Fig. 1. (a) Traditional degaussing power supply structure. (b) Improved degaussing power supply structure
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2.1 Working Mode Analysis of Bipolar DC-DC Converter The difference between the bipolar DC-DC converter and the ordinary Cuk circuit is that the power diode originally used for freewheeling is replaced by a power MOSFET, and the drain terminal of MOSFET is changed from being connected to the ground to being connected to the input power source. This change makes the converter have a good bipolar output capability. The actual schematic is shown in Fig. 2. S2
L1 Vg
C1 S1
L2 C2
RL
Vo
Fig. 2. Improved Cuk circuit
The negative voltage output of the converter has been realized by the capacitor C1 and the power MOSFET S1 , the positive voltage output has been realized by the input power supply and the power MOSFET S2 . In a complete switching cycle, a bipolar square wave is generated at the source terminal of S1 , after passing through the LC filter circuit, the required DC voltage is obtained. Adjusting the respective conduction time of the S1 and S2 , the polarity and amplitude of the output voltage can be changed. Figure 3(a) and Fig. 3(b) show the working waveforms of the converter in positive and negative output respectively. 0 < t < DTs : the S2 is turned on, S1 is turned off. According to the principle of small disturbance approximation, the voltage across the inductor and the current flowing through the capacitor can be described as: ⎧ vL1 = −VC1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ vL2 = Vin − Vo (1) iC1 = IL1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ iC2 = iL2 − Vo R DTs < t < Ts : the S1 is turned on, S2 is turned off. Similarly, the voltage across the inductor and the current flowing through the capacitor can be obtained from: ⎧ vL1 = Vg ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ vL2 = −VC1 − Vo (2) iC1 = IL2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ iC2 = iL2 − Vo R
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S1
S1 t
S2
t
S2
t
t t
iL1 t iL1 VC1
VC1
t t iL 2
t
iL 2 t
VSW
VSW
t t
t
Vo
Vo
t
DTs (1-D)Ts Mode1 Mode 2 Mode1 Mode 2
DTs (1-D)Ts Mode1 Mode 2 Mode1 Mode 2
Fig. 3. Converter output waveform at (a) negative Vo. (b) positive Vo.
From Eqs. (1) and (2), according to the volt-second balance and ampere-second balance, the steady-state characteristics of the converter are deduced as: ⎧ ⎪ ⎪ VC1 = Vg ∗ D ⎪ ⎪ ⎪ D ⎪ ⎪ ⎪ 2D − 1 ⎪ ⎪ ∗ Vg ⎨ Vo = D (3) ⎪ −D ⎪ ⎪ ∗ IL2 IL1 = ⎪ ⎪ D ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ IL2 = Vo R The relationship between the converter voltage gain and the duty cycle is shown in Fig. 4. when D > 0.5, the converter input and output voltages are in phase; when D < 0.5, the input and output voltages are reversed. A good zero-crossing transition of the output voltage can be achieved by adjusting the duty cycle.
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Fig. 4. The relationship between converter voltage gain M(D) and duty cycle D
2.2 Equivalent Analysis Based on Buck-Boost Circuit The steady-state voltage gain relationship of the bipolar DC-DC converter is deduced above. The converter contains two inductors and two capacitors. Through the equivalent analysis method based on Buck-Boost circuit, the parameter design of the converter is simplified. Make the output characteristics and circuit characteristics of the converter more intuitive. The derivation process is shown below. (1) The Buck-Boost circuit is shown in Fig. 5(a). To prevent the converter from entering the DCM mode, the rectifier diode is replaced with a power MOSFET S2 . After the derivation of the volt-second balance rule, the input and output voltage gain expression of the converter is shown in Eq. (4), which shows that the converter can only achieve unipolar output. −D Vo = Vg (1 − D)
(4)
(2) Swap the positions of inductor L1 and switch S1 in the Buck-Boost circuit to obtain the circuit diagram shown in Fig. 5(b). (3) The drain terminal of switch S2 is directly connected to the input power, and the ground terminal of the filter capacitor C1 is connected to the drain terminal of switch S1 . The circuit diagram is shown in Fig. 5(c).
S1 Vg
L1
(a)
S2
L1
S2 C1
Vc1 RL
Vo Vg
S2 S1
L1
C1
(b)
Vc1
RL
Vo
Vg
Vc1 C1
S1
RL
Vo
(c)
Fig. 5. The derivation of the bipolar DC-DC converter. (a) step 1. (b) step 2. (c) step 3.
Perform switch mode analysis for the circuit shown in Fig. 5(c), and the equivalent circuit diagrams under different modes are shown in Fig. 6(a) and Fig. 6(b).
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Compared with Buck-Boost switching mode in a switching cycle, it can be seen that the switching modes are roughly the same. When S1 is off and S2 is on, the output capacitor C1 is not directly connected to the load, which is equivalent to a open-load state. The volt-second balance analysis is performed on the filter inductor L1 , and the relationship between the voltage across capacitor C1 and the input voltage gain is derived, which form is consistent with the Buck-Boost circuit. Vc1
iL1 L1
iS2 iL1
C1
Vg
RL
Vo
iRL
iRL
iL1
L1
Vc1 C1 RL
Vg
Vo
iRL
(a)
(b)
Fig. 6. (a) The MOSFET Q1 is turned on and Q2 is turned off. (b) The MOSFET Q1 is turned off, and Q2 is turned on.
(4) Finally, insert an LC filter between the load resistance and the converter to obtain the required DC voltage output, the structure is shown in Fig. 7. S2
L1 Vg
C1 S1
L2 C2
RL
Vo
Fig. 7. DC voltage output
3 Parameter Design According to the above-mentioned derivation process, the bipolar DC-DC converter can be regarded as an evolution from the Buck-Boost circuit. The filter inductance L1 and the filter capacitor C1 of the converter have the same functions as the inductance and capacitance in the Buck-Boost circuit. The inductor L2 and capacitor C2 play a role in filtering high-frequency switching ripple. 3.1 Parameter Design of Inductance L1 and Capacitance C 1 When the switch Q1 is turned on, the peak-to-peak value of the inductor current as: iL1 =
Vg DTs 2L1
(5)
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The average value of the inductor current can be derived as: IL1 =
−D ∗ IL2 D
(6)
Vg DTs 0.8IL1
(7)
Set the current ripple: iL = 0.4IL1 . L1 can be obtained as: L1 = −
Similarly, given the ripple voltage of the capacitor C1 , the value of the capacitor C1 can be described as: (1 − D)Vo Ts 2vc1 R
C1 =
(8)
3.2 Parameter Design of Inductance L2 and Capacitance C 2 When the switch Q2 is turned on, the peak-to-peak value of the inductor current can be described as: iL2 =
(Vg − Vo )DTs 2L2
(9)
The average value of the inductor current can be expressed as: Vo R
(10)
(Vg − Vo )DTs 0.8IL2
(11)
IL2 = Take the current ripple: iL2 = 0.4IL2 . L2 can be obtained as: L2 =
Under a given output voltage ripple condition, the capacitance charge relationship C2 can be described as: C2 =
iL Ts 8vo
(12)
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4 MATLAB Simulation The amplitude and polarity of the output voltage of the degaussing power supply change with the magnetic field, and the output voltage also changes within a certain range. The selected parameters of the circuit need to meet the worst working conditions. Combining Eqs. (5) to (12), the worst working conditions occur when the output voltage is maximum negative and light load. Therefore, the design converter parameters are shown in Table 1. Table 1. Converter parameters Parameters
Value
Input the voltage Vg /V
48
Output voltage Vo /V
−24~+24
Output current Io /A
−10~+10
Switch frequency Fs /kHz
200
Inductor L1 /uH
80
Capacitor C1 /uF
75
Inductor L2 /uH
180
Capacitor C2 /uH
100
The simulation results for −24 V, 24 V, 0 V are given respectively, and the waveform is shown in Fig. 8. When the output voltage is positive, there is a certain oscillation at the start phase, which will be stable after 0.02 s, and the controller parameters need to be optimized. The converter able to reach a stable state, when it output both positive and negative voltage.
Fig. 8. Voltage waveform when output –24 V, 24 V, 0 V
The comparison of Vo and VC1 are shown in Fig. 9, where Vo represents represents the output voltage waveform of the Buck-Boost circuit, and VC1 represents the voltage
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waveform across capacitor C1 in the bipolar DC-DC converter. The steady-state performance of the voltages is very similar in the time domain, so the Buck-Boost circuit can be used to analyze the characteristics of the inductance L1 and the capacitor C1 equivalently.
Fig. 9. Comparison of output characteristics
The converter outputs an AC voltage waveform with an amplitude of 24 V and a frequency of 1/3 Hz is shown in Fig. 10. It can be seen that the waveform is smooth at the zero-crossing point.
Fig. 10. V = 24 V f = 1/3 Hz AC voltage waveform
5 Conclusion The improved degaussing power supply structure proposed in this paper requires a small number of switching tubes and well done in terms of bipolar output capability, which can not only meet the positive and negative DC voltage output but also realize the AC voltage output. The smooth transition at the zero crossings can be achieved when outputting the AC voltage waveform. Also, the converter can output an approximately zero-volt voltage which is suitable for the use of extremely low resistance degaussing coils. In this way, it can greatly reduce the power rating of the equipment. Thus, the improved degaussing power supply structure is of great significance to the energy saving of the ship’s power system.
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References 1. Zhao, Q., Liu, W., Wang, D., Li, K., Wang, Y.: Hybrid full-bridge converter with wide output voltage for high-power applications. IET Power Electron. 13(3), 592–601 (2020) 2. Cetin, S.: High efficiency design procedure of a second stage phase shifted full bridge converter for battery charge applications based on wide output voltage and load ranges. J. Power Electron. 18(4), 975–984 (2018) 3. Choi, B.Y., Lee, S.R., Kang, J.W., Jeong, W.S., Won, C.Y.: A novel dual integrated LLC resonant converter using various switching patterns for a wide output voltage range battery charger. Electron. News weekly 8, 759 (2019) 4. Sun, W., Jin, X., Zhang, L., Hu, H., Xing, Y.: Analysis and design of a multi-resonant converter with a wide output voltage range for EV charger applications. J. Power Electron. 17(4), 849–859 (2017) 5. Wang, D., Duan, Y., Gao, H., Wang, Z., Zhao, Q.: Research on current output characteristics of LCC resonant converter and realization of soft switching. Trans. Chin. Soc. Electr. Eng. 33(12), 2788–2800 (2018). (in Chinese) 6. Xiong, L., Ma, L., Hu, G., Xie, Z., Zhang, D., Yang, Z., He, W.: Development of a high-voltage bipolar square wave pulse source with load universality. Trans. Chin. Soc. Electr. Eng. 30(12), 51–60 (2015). (in Chinese) 7. Wang, S., An, D.: Design and test method of degaussing system for degaussing outside ship. Ship build. Technol. (06), 59–64+98 (2016). (in Chinese) 8. Zhao, W., Yang, Z., Liu, S.: Summary of the application of high-temperature superconducting cables in the degaussing system of ships. Marine Electr. Technol. 36(09), 37–39 (2016). (in Chinese) 9. Teng, C.: Research on high-reliability energy storage and degaussing module combined power supply. Southeast University (2015). (in Chinese) 10. Jiang, C.: Research on high-precision current degaussing power supply. Nanjing University of Aeronautics and Astronautics (2019). (in Chinese)
Optimal Control Strategy of Fatigue-Oriented Wind Turbine Yaw System Zhiheng Liu , Qi Yao(B)
, and Bo Ma
Energy and Electricity Research Center, Jinan University, Zhuhai 519070, China [email protected]
Abstract. Long-term and frequent operation of the wind turbine yaw system may cause bearing damage due to the accumulation of fatigue. To explore the fatigue characteristics of the yaw system, two targeted fatigue indexes are designed and analyzed in this paper combined with the mechanical structure and operation principle. Then, dozens of yaw scenarios were simulated on the basis of FAST, and the coupling relationship between yaw fatigue and multiple factors was explored to describe the fatigue characteristics of the yaw system. Furthermore, according to the above fatigue characteristics, a fatigue-oriented yaw control strategy based on fuzzy control is proposed in this paper, which optimizes the yaw speed at different stages of the yaw process. The simulation results show that the proposed yaw control strategy can effectively suppress the fatigue load of the yaw bearing in multiple scenarios without affecting the accuracy of the yaw. Therefore, the research in this paper has obvious theoretical significance and engineering application value for the fatigue load suppression of the yaw system. Keywords: Wind turbines · Yaw system · Fatigue load · Fuzzy control
1 Introduction Yaw system is an important part of the wind turbine, which has an important impact on the power generation efficiency and safe operation of the wind turbine [1]. In recent years, scholars have done a lot of research on yaw system. Ref. [2] explores the influence of different air density, turbulence intensity, wind speed and other factors on the blade root, yaw bearing, hub and other parts of wind turbine. In Ref. [3], the effect of different yaw angles on the load of yaw bearing is considered. In Ref. [4], the influence of different yaw speed and yaw pressure on the yaw bearing was studied. Ref. [2–4] only considered the influence of different conditions on the load of wind turbine yaw bearing, and did not put forward the optimization scheme for different conditions. In Ref. [5], a yaw start-stop strategy based on decision tree is proposed, which can improve the stability of yaw system. In Ref. [6], a PID yaw strategy is proposed to make the nacelle face the wind in the shortest time and improve the utilization of wind energy. Although
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1111–1122, 2022. https://doi.org/10.1007/978-981-19-1528-4_113
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Ref. [5, 6] proposed the optimal yaw strategy, they only focused on the stability and wind accuracy, and did not consider the suppression of yaw bearing load. In Ref. [7], a yaw protection strategy is proposed to suppress the load of yaw bearing, which can effectively reduce the fatigue load of wind turbine. In Ref. [8], model predictive control based on multi-step predictive model is used to reduce the yaw fatigue load; In Ref. [9], a multi-objective optimization algorithm is proposed to minimize the yaw load. Ref. [7–9] have considered the optimal yaw strategy based on suppressing yaw load, but the selection of characterization parameters of yaw load is not comprehensive, so it is hard to comprehensively measure the effectiveness of the algorithm. Based on the research status, this paper makes a further analysis on the mechanical load of the yaw system, and two parameters are introduced to describe the fatigue of the yaw bearing. The experiments under multiple yaw working conditions through FAST [10]/Simulink are designed to summarize the laws affecting the yaw fatigue load. Based on the above laws, a fuzzy yaw control strategy is proposed, and the effectiveness of fatigue load suppression of the strategy is verified by some cases.
2 Structure and Load Analysis of Yaw System 2.1 Structure of Yaw System The yaw system of wind turbine is mainly composed of wind direction sensor, yaw controller, untwist program controller, yaw bearing, yaw hydraulic circuit and yaw brake [11]. The control block diagram of yaw system is shown in Fig. 1 [12].
Fig. 1. Control block diagram of yaw system
2.2 Fatigue Characterization Parameters of Yaw System The fatigue load will act on the yaw system of the wind turbine in the form of torque, which are: yaw bearing rolling torque (Mx), yaw bearing pitch torque (My), yaw torque (Mz), as shown in Fig. 2.
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Fig. 2. Torque diagram of yaw system
In the process of long-term operation, yaw system will suffer cumulative fatigue damage, which can be described by Damage Equivalent Load (DEL) [13]. DEL is based on rain flow counting method and Miner’s theorem, combined with load time series. The calculation formula is as follows: n Lm i i m i (1) DEL = fo T where m is fatigue index; L i is the load value of the level i; ni represents the number of rain flow cycles in level i; T is the total running time; f 0 represents the cycling rate of the equivalent cyclic load. Due to the uncertainty and randomness of wind direction, the wind turbine is in the state of frequent start-up and braking, which will produce a very large transient impact on the yaw bearing at the moment of start-up and braking. This transient impact can be expressed by the Maximum Transient Impact (MTI) of three yaw bearing torques, and the MTI calculation formula is shown in (2). ⎧ max (|Mxi − Mxi−1 |) ⎪ ⎪ MTIMx = t∈[t ⎪ 1 ,t2 ] ⎨ MTIMy = max (|Myi − Myi−1 |) (2) t∈[t1 ,t2 ] ⎪ ⎪ ⎪ ⎩ MTI = max (|Mz − Mz |) Mz
t∈[t1 ,t2 ]
i
i−1
where t represents the total operation time of yaw system; i represents the time point of yaw bearing torque time series; MTI Mx , MTI My and MTI Mz represent the maximum absolute value of the difference between adjacent sampling points of yaw bearing torque. 2.3 Load Experiments of Yaw System This section considers the influence of different working conditions on the MTI Mx , MTI My , MTI Mz and DEL Mx , DEL My , DEL Mz of the yaw system. The yaw system of wind turbine starts to yaw at a fixed speed until the wind wheel is facing the incoming wind direction. The experimental time is 200 s. Load of Yaw System Under Different Average Wind Speed. Ref. [14] points out that large-scale wind turbines will produce large inertia in the procedure of yaw. In
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order to avoid the inertia damage to the yaw system, the yaw speed of wind turbines is generally less than 1°/s. Therefore, based on the wind direction (30°) and safe yaw speed (0.4°/s), this section considers the influence of different steady-state wind speeds on load parameters, and the results are shown in Table 1. It can be seen from Table 1 that, MTI Mx and MTI Mz will decrease with the increase of wind speed, MTI My is a non-linear change. DEL Mx , DEL My , DEL Mz have increased in varying degrees. In conclusion, when the average wind speed increases gradually, the fatigue damage of yaw bearing will also increase. Table 1. Comparison table of load parameters of yaw bearing under different wind speeds Wind speed
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
6 m/s
161.4
183.8
3004.1
1122.9
2190.7
3438.0
8 m/s
160.1
164.5
2986.9
1595.1
2560.4
3393.5
10 m/s
158.3
151.8
2986.9
2092.1
2952.0
3390.4
12 m/s
157.6
162.8
2976.8
2844.2
3376.6
3379.4
14 m/s
158.1
174.5
2776.4
2921.4
4883.5
5290.9
16 m/s
156.5
191.4
2772.3
3064.2
7827.7
7575.5
18 m/s
155.6
236.5
2769.3
3227.7
10883.0
8952.0
Load of Yaw System at Different Yaw Speeds. Based on the same wind speed (12 m/s) and the same wind direction (30°), this section considers the influence of different yaw speeds on the load of yaw system. The simulation results are shown in Table 2.
Table 2. Comparison of load parameters of yaw bearing under different yaw speed Yaw speed
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
0.2°/s
76.0
158.8
1391.1
2801.8
3436.9
2546.2
0.3°/s
116.4
161.0
2076.2
2848.0
3361.1
2835.5
0.4°/s
157.6
162.8
2976.8
2844.2
3376.6
3379.4
0.5°/s
197.3
165.7
3456.5
2872.0
3333.7
3775.7
0.6°/s
239.2
167.5
4163.4
2901.1
3346.6
4136.2
0.7°/s
278.8
170.5
4836.6
2910.4
3349.9
5325.1
0.8°/s
320.8
172.7
5776.2
2950.0
3360.1
6448.0
In Table 2, MTI increase with the increase of yaw speed, especially MTI Mz . DEL Mx increases slightly, DEL My is insensitive to yaw speed, and DEL Mz increases greatly. It
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can be concluded that Mx are affected by the yaw speed, but the amplitude is small, My is not sensitive to yaw speed, Mz is greatly affected by the yaw speed. Therefore, with the increase of yaw speed, the damage to the yaw system will increase. Load of Yaw System Under Different Yaw Angles. Based on the steady wind speed (12 m/s) and safe yaw speed (0.4°/s), this section considers the influence of different yaw angles on the load of yaw system. The simulation results are shown in Table 3.
Table 3. Load comparison table of bearing under different yaw angles Angle
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
10°
170.1
164.2
2963.5
2959.8
2823.4
3173.3
15°
156.3
165.7
2775.1
3014.5
2960.6
3125.9
20°
156.8
164.4
2776.2
2896.0
3085.6
3034.7
25°
157.2
164.4
2776.8
2888.6
3211.1
3182.4
30°
157.6
162.8
2976.8
2844.2
3376.6
3379.4
35°
158.2
156.1
2779.1
2822.8
3405.5
3364.4
It can be seen from Table 3 that when the yaw angle increases gradually, except DEL My , the other indexes fluctuate irregularly, which indicates that the load of the wind turbine yaw system is not sensitive to the yaw angle. Yaw System Load Under Different Turbulence Intensity. Steady state wind and turbulent wind should be considered in turbines load condition. In this paper, Von Karman model in IEC standard [15] is adopted. The turbulence intensity increases from 0.12 to 0.16, the average wind speed is 12 m/s, the wind direction is 30° and the yaw system of the wind turbine yaws at 0.4°/s. The simulation results are shown in Table 4.
Table 4. Load comparison table of bearing under different turbulence intensity Intensity
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
0.12
156.7
224.7
3070.2
3351.1
4459.2
4747.8
0.13
156.7
241.2
3065.9
3439.3
4708.5
4912.1
0.14
156.6
261.8
3072.9
3511.6
5011.6
5103.4
0.15
156.6
277.7
3093.5
3601.3
5328.4
5351.3
0.16
162.1
292.4
3098.8
3722.4
5626.5
5626.4
It can be seen from Table 4 that when the turbulence intensity increases, each index of the yaw system increases, which proves that with the gradual increase of turbulence intensity, the fatigue load of the yaw system of the wind turbine will increase regularly.
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Summary of Load Characteristics of Yaw System. From the above experimental data, it can be seen that the yaw load is affected by the wind speed, yaw speed and turbulence intensity, but not sensitive to the yaw angle. Among them, with the increase of average wind speed, the DEL of yaw moment also increases, and the MTI of yaw torque is not sensitive to the increase of wind speed. When the yaw speed is gradually increased, the DEL My value is basically unchanged, and the other load parameters are increased in varying degrees. With the increase of turbulence intensity, each index of yaw bearing load increases, which proves that the influence of turbulence intensity is great.
3 Design of Fuzzy Controller for Yaw System The fuzzy controller is suitable for solving control problems with difficult modeling or imprecise system [16]. Therefore, this paper uses the designed fuzzy controller to achieve the purpose of suppressing the load of yaw bearing. 3.1 Overall Design of Fuzzy Controller The inputs of the fuzzy controller are the deviation between the actual yaw angle and the set yaw angle e and the wind speed w. The yaw speed v is obtained through fuzzy reasoning and defuzzification. The control block diagram is shown in Fig. 3.
Fig. 3. Fuzzy control block diagram
3.2 Input and Output Variables and Membership Function Inputs and Membership Functions. The inputs are the actual yaw angle and the deviation e of the set yaw angle and the wind speed w. The basic universe of deviation is (−30°, 30°), which is represented by radians (−0.5236, 0.5236), and the language variables are {NB, NMB, NM, NS, ZO, PS, PM, PMB, PB}, which respectively represent {negative big, negative medium big, negative middle, negative small, zero, positive small, positive middle, positive middle big, positive big}. The basic domain of wind speed is selected as (3, 25), and the language variables are selected as {L, M, B}, which represent {low, medium, high} respectively. The membership functions of the two input variables are triangular membership functions, as shown in Fig. 4(a) and (b).
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Fig. 4. Membership functions of inputs and output
Output and Membership Function. The output is selected as yaw speed v. The basic universe of argument is (−0.8°/s, 0.8°/s), transform to rad/s is (−0.014, 0.014), and the language variables are {NB, NMB, NM, NS, ZO, PS, PM, PMB, PB}, which respectively indicate {negative large, negative medium large, negative medium, negative small, zero, positive small, positive medium, positive medium large, positive large}. The membership function adopts triangular membership function, as shown in Fig. 4(c). 3.3 Fuzzy Rules According to the determined input and output variables and the rules summarized in the second section, the principles of designing the fuzzy control state table are as follows: when the wind turbine receives the yaw command, the yaw system starts to yaw slowly, that is, when the yaw error is the largest, the yaw rate is smaller, so as to reduce the transient impact of the wind turbine yaw system in the start-up stage. When the yaw error is moderate, the wind turbine begins to increase the yaw rate to eliminate the yaw error quickly. When the yaw error is small, different yaw rates are selected according to the wind speed to reduce the DEL and MTI at the end of yaw. These experiences are summarized as fuzzy control rules, as shown in Table 5. Table 5. Fuzzy control rule table W
E NB
NMB
NM
NS
ZO
PS
PM
PMB
PB
L
ZO
PB
PB
PB
ZO
NB
NB
NB
ZO
M
ZO
PMB
PMB
PMB
ZO
NMB
NMB
NMB
ZO
H
ZO
PS
PB
PMB
ZO
NMB
NB
NS
ZO
In Table 5, each fuzzy conditional statement determines a fuzzy rule, for example: R1 : If E = NB and V = L then Vy = ZO By analogy, 27 fuzzy rules can be obtained, and the total fuzzy relationship of fuzzy controller can be obtained by “union” operation: R : R = R1 ∨ R2 ∨ · · · ∨ R27
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3.4 Defuzzification In this paper, the area barycenter method [17] is selected as the defuzzification method, and the accurate value of yaw velocity v can be obtained by solving the ambiguity. So far, the design of fuzzy controller is completed.
4 Case Studies The case studies are based on FAST/Simulink, and the wind turbine is NREL 5 MW. In this section, the fatigue load of the yaw system under different working conditions will be obtained and analyzed based on the simulation results. Base on Ref. [18], this section takes the different yaw rates (0.8°/s and 0.4°/s) of traditional PID control strategy as the control group, combines the different wind speed, turbulence intensity and yaw angle, and verifies the feasibility of the designed fuzzy yaw controller under the working conditions shown in Table 6. Table 6. Working condition parameter table Case
Turbulence intensity
Average wind speed
Yaw angle
1
0
12 m/s
30°
2
0.16
12 m/s
30°
3
0
12 m/s
20°
4.1 Load Analysis Under Case 1 In Case 1, the load values of three different yaw strategies of wind turbine are shown in Table 7, and the time domain curve of yaw angle is shown in Fig. 5(a). The time sequence simulation diagram of yaw bearing torque is shown in Fig. 5(b)–(d). It can be seen from Fig. 5(b) that the fluctuation torque Mx with fuzzy control is obviously smaller than that of the other two control schemes in the period of 20–50 s. In Fig. 5(d), the fuzzy yaw control will not produce large transient impact at the end of yaw, while the 0.4°/s and 0.8°/s schemes will produce very large transient impact at 75 s and 37.5 s respectively, which will damage the yaw system. According to the results in Table 7, the fuzzy yaw strategy has an obvious effect on suppressing MTI. The MTI Mx of the fuzzy yaw strategy is 80.13% less than 0.4°/s and 90.24% less than 0.8°/s. MTI My decreased by 7% compared with 0.4°/s and 12.33% compared with 0.8°/s. MTI Mz is 96.62% less than 0.4°/s and 98.26% less than 0.8°/s. DEL Mx decreased by 3.57% and 7.03% compared with 0.4°/s and 0.8°/s, respectively. The DEL Mz decreased by 26.54% and 61.5% compared with 0.4°/s and 0.8°/s. However, the DEL My increases by 2.63% and 3.12% compared with 0.4°/s and 0.8°/s, respectively. Therefore, through the analysis, the fuzzy yaw strategy is effective on reducing the load of yaw system under the steady-state wind speed of 12 m/s.
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Fig. 5. Simulation curves of Case 1
Table 7. Load comparison table of yaw bearing in condition 1 Strategy
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
0.4°/s
157.6
162.8
2976.8
2844.2
3376.6
3379.4
0.8°/s
320.8
172.7
5776.2
2950.0
3360.1
6448.0
Fuzzy
31.3
151.4
100.6
2742.6
3465.5
2482.6
4.2 Load Analysis Under Case 2 In Case 2, the simulated scenario is turbulent wind, and the wind speed is shown in Fig. 6(a). The variation of yaw angle of three different yaw strategies is shown in Fig. 6(b), and the yaw bearing torque is shown in Fig. 6(c)–(e). It can be seen that the Mz curve with fuzzy yaw control has no large transient impact at the end of yaw, while the other two strategies will produce large transient impact at the end of yaw. The results Table 8 show that MTI Mx , MTI Mz and DEL Mx can be suppressed by fuzzy yaw control under the condition of turbulent wind. MTI Mx with fuzzy yaw strategy is 7.29% and 53% less than that of 0.4°/s and 0.8°/s, respectively. MTI Mz decreased by 84.89% and 91.58% compared with 0.4°/s and 0.8°/s, respectively. DEL Mx decreased by 5.14% and 9.89% compared with 0.4°/s and 0.8°/s, respectively. However, the fuzzy yaw control strategy is not as effective as the traditional strategy in suppressing MTI My , DEL My and DEL Mz . Therefore, the fuzzy yaw control can suppress MTI Mx , MTI Mz and DEL Mx under the turbulent wind condition, but the overall effect is not as good as the steady-state wind condition.
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Fig. 6. Simulation curves of Case 2
Table 8. Load comparison table of yaw bearing in condition 2 Strategy
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
0.4°/s
162.1
292.4
3089.8
3722.4
5626.5
5626.4
0.8°/s
319.7
278.5
5544.0
3918.7
5585.5
8355.7
Fuzzy
150.3
294.8
466.8
3531.1
5597.4
6298.1
4.3 Load Analysis Under Case 3 The yaw angle of Case 3 is 20° and the curve is shown in Fig. 7(a). The torque under different control strategies is shown in Fig. 7(b)–(d). It can be seen that the fuzzy yaw control strategy does not produce great transient impact at the beginning and end of yaw, while the other two strategies will produce great transient impact at the end. Table 9 shows that the fuzzy yaw control has a good effect on suppressing MTI in this case. Among them, MTI Mx with fuzzy yaw strategy is 76.37% less than 0.4°/s and 88.42% less than 0.8°/s. MTI My decreased by 5.78% compared with 0.4°/s and 12.78% compared with 0.8°/s, respectively. MTI Mz is 97.37% less than 0.4°/s and 98.69% less than 0.8°/s. DEL Mx decreased by 3.46% and 7.3% compared with 0.4°/s and 0.8°/s, respectively. The DEL Mz decreased by 9.27% and 51.79% compared with 0.4°/s and 0.8°/s. However, the DEL My increases by 3.91% and 4.13% compared with 0.4°/s and 0.8°/s, respectively. To sum up, the fuzzy yaw strategy still has a very good load suppression effect under the condition of 20° yaw angle.
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Fig. 7. Simulation curves of Case 3
Table 9. Load comparison table of yaw bearing in condition 3 Strategy
MTI Mx
MTI My
MTI Mz
DEL Mx
DEL My
DEL Mz
0.4°/s
156.8
164.4
2776.2
2896.0
3085.6
3034.7
0.8°/s
319.9
177.6
5548.5
3015.9
3078.9
5711.7
Fuzzy
37.0
154.9
72.9
2795.7
3206.1
2753.5
5 Conclusion In this paper, two indexes (MTI and DEL) are proposed to measure the yaw fatigue by analyzing the mechanical structure of the yaw system. Then, the laws of the yaw fatigue with the wind speed, yaw speed, turbulence intensity and yaw angle are summarized through a number of simulations. Based on the above laws, a fuzzy yaw control strategy is designed to achieve the purpose of suppressing the load of yaw bearing. The effectiveness of the proposed yaw control strategy is verified by the case studies. Under the steady wind, the fuzzy yaw control has a significant effect on suppressing the MTI and DEL, in which the MTI Mx and MTI Mz are reduced by more than 70%, and the DEL is reduced by more than 3%. In turbulent wind, the fuzzy yaw control also has a good suppression effect on the MTI and DEL Mx . Compared with the traditional control strategy, the MTI Mz and DEL Mx are reduced by 80% and 5% respectively. Acknowledgements. This work was supported by GuangDong Basic and Applied Basic Research Foundation (2020A1515110547).
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References 1. Piao, H.: Simulation of fuzzy-pid synthesis yawing control system of wind turbine. Trans. China Electrotech. Soc. 24(03), 183–188+202 (2009). (in Chinese) 2. Liu, W.: Effect of different wind conditions on equivalent fatigue load of wind turbine. Mech. Electr. Eng. Technol. 49(12), 114–117 (2020). (in Chinese) 3. Li, J.: Research on the influence of yaw angle on loads of wind turbine. Energy Conserv. 30(Z2), 49–52+4 (2011). (in Chinese) 4. Wang, H.: Study on the influence of yaw pressure and yaw rate on yaw load. Sci-tech Innov. Prod. (05), 55–57+60 (2016). (in Chinese) 5. Li, J.: Research on optimization of yaw start and stop for wind turbines based on decision tree. Renew. Energy Resour. 37(06), 921–926 (2019). (in Chinese) 6. Jiang, P.: Yaw strategy based on PID. Silicon Valley 7(14), 198–199 (2014). (in Chinese) 7. Yang, W.: Effect on yaw protection strategy on the loads of MW wind turbines. Mach. Design Manuf. Eng. 50(05), 116–121 (2021). (in Chinese) 8. Song, D.: Model predictive control using multi-step prediction model for electrical yaw system of horizontal-axis wind turbines. IEEE Trans. Sustain. Energy 10(4), 2084–2093 (2018) 9. Zhao, G.: Wind direction prediction and yaw optimization of wind farm based on deep learning. Northeast Electric Power University (2020). (in Chinese) 10. Jonkman, J.: FAST user’s guide. technical report NREL/EL-500-38230. Technical report, NREL (2005) 11. Wang, X.: Introduction and fault analysis of large wind turbine yaw system. Inner Mongolia Petrochem. Ind. 38(03), 75–76 (2012). (in Chinese) 12. Ye, H.: Control Technology of Wind Turbine. China Machine Press, Beijing (2006) 13. Buhl, M.: Mcrunch user’s guide for version 1.00. Technical report (2008) 14. Yao, X.: Design and Manufacture of Wind Turbine. China Machine Press, Beijing (2012) 15. IEC 61400-3:2005, Wind Turbines Part 3: Design Requirements for Offshore Wind Turbines 16. Liu, X.: Status and development of fuzzy control. Inf. Control 04, 283–292 (1999). (in Chinese) 17. Wang, H.: Study on fuzzy reasoning and unfuzzy based on the method of programming with Matlab. Mod. Electron. Tech. 23, 43–46 (2004). (in Chinese) 18. Zhang, J.: Research on simulation of sliding yaw system dynamic characteristic for wind turbine. North China Electric Power University (2016). (in Chinese)
The Fabrication Technology and Test Results of the NbTi Superconducting Racetrack Magnets Wanshuo Sun1,2(B) , Lei Wang1 , Jinshui Sun1,2 , Junsheng Cheng1 , Shunzhong Chen1,2 , and Qiuliang Wang1 1 Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
[email protected] 2 University of Chinese Academy of Sciences, Beijing 100049, China
Abstract. Superconducting racetrack-shaped magnet was fabricated successfully, and the goal is to apply it to a superconducting undulator. The coil was wound with NbTi superconducting wires by dry winding followed by vacuum pressure impregnation. First, the single coil was fabricated, and the critical current was tested in liquid helium. The critical currents of the six tested coils were in the range of 475 A to 483 A. The coils were all charged to the designated value of 400 A and did not quench. A single coil satisfied the design requirement. Keywords: Superconducting undulator · Racetrack-type magnet · Niobium titanium · Critical currents
1 Introduction Superconducting undulators and permanent magnet-based undulators have been studied for several years and have been built at several places around the world [1–3]. The superconducting undulator was fabricated using superconducting materials, as follows: low temperature superconducting materials NbTi and Nb3Sn, which work at a liquid helium temperature of approximately 4.2 K; and high temperature superconducting material, such as YBCO, that work at liquid nitrogen temperature [4–6, 7]. The superconducting undulator has the advantages of having small volume, high magnetic field, easy adjustment, and so on and has the potential to be applied to a synchrotron radiation facility [8]. Compared with a cryogenic permanent magnet undulator in the same magnetic gap and magnetic periods, a superconducting undulator can provide a higher magnetic field. The current direction can be changed in a superconducting undulator to switch the period length [9]. However, fabricating the superconducting undulator is difficult, because the superconducting materials need to work under extreme conditions, such as low temperature and strong stress, which pose a challenge to the superconducting undulator [10]. At same time, superconducting magnets in undulators wound by superconducting wires were sensitive to the operating temperature, magnetic field, and other external factors, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1123–1129, 2022. https://doi.org/10.1007/978-981-19-1528-4_114
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such as powder source, which possibly cause the quenching of coils. The superconducting materials used typically have high cost. Many problems still need to be addressed because of the complex processing technology and risk of magnet quench during the operation of superconducting undulator. The fabrication of coil is an important technology that has a significant influence on stability and safety and needs to become the focus of research. We reported the process of fabrication of superconducting magnets used in superconducting undulator. We conducted an electrical excitation test at low temperature by soaking in liquid helium. The superconducting magnets were wound by NbTi superconducting wires on racetrack-type magnetic poles made of iron to improve the magnetic field efficiency. We calculated the magnetic field distribution and heat stress of the superconducting coils at a low temperature of 4.2 K. The stress distribution of the superconducting coils was analyzed to examine the areas under the highest force and to promote the improvement of some mechanical fixtures.
2 Coil Design 2.1 Magnetic Field Analysis Table 1. The main design parameter of the magnet Item
Design parameters
Superconducting material
NbTi/Cu
Conductor diameter (mm)
0.6
Insulation thickness (um)
50
Thickness of magnetic pole (mm)
3.5
Width of magnetic pole (mm)
108.5
Fig. 1. The simulation model of superconducting coils and magnetic poles
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The design parameters for the magnets are provided in Table 1. We built the simulation model according to these design parameters. As shown in Fig. 1, the simulation model included two parts, i.e., upper and lower. Each part consisted of three superconducting coils, coil formers, and magnetic poles. The superconducting coils located at the upper and lower parts are distributed alternately.
Fig. 2. The magnetic field distribution of the superconducting coils
The magnetic field distribution of the superconducting coils was simulated, as shown in Fig. 2. The maximum magnetic field was near the inside of the circular sections and achieved approximately 3.8 T. 2.2 Force Analysis of Superconducting Coils
Fig. 3. Force analysis of superconducting coils
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According to the simulation model, when the exciting current reached 400 A, the force situation of superconducting coil was analyzed, as shown in Fig. 3. The force in the X direction was approximately zero because of the symmetry of the coil structure. The force in the Y direction was 95.38 N, which was small for the superconducting coils and the formers. The force in the Z direction reached 2961.6 N, which was a challenge, because in this direction, no mechanical support exists for the coils. The stiffness and strength in this direction can be due to the winding tension formed during coil winding and the mechanical strength of composite, which was made up of superconducting wires and the impregnated resin.
Fig. 4. The stress distribution of superconducting coils
The stress distribution of superconducting coils was analyzed based on the simulation model after energizing. As shown in Fig. 4, the maximum stress was 11.7 MPa, which was far below the yield strength of the superconducting material used. Thus, the superconducting magnets had adequate margin of safety. 2.3 The Wingding of Racetrack NbTi Superconducting Magnets Formvar-insulated NbTi superconducting wires (0.6 mm in diameter) were used for the 60-turn coils. These were wound very tightly around an iron core. According to the design parameters, the coils were assembled with magnetic poles. The NbTi superconducting wires and the magnet poles were insulated by insulating paint and polyimide membranes, respectively. To make the superconducting coils stronger and to minimize the influence of stomata on the performance of superconducting coils, the dry winding method was adopted. For the superconducting coils used in the undulator, the dry winding method can improve the consistency of superconducting wires and resin or other reinforced material and lower the number of magnet quenching and training, which would be detrimental to the whole system. The resin used to impregnate the coils needs to have a low difference with superconducting wires in terms of thermal expansion coefficient.
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For this particular racetrack-type coil with anisotropism, winding and controlling the shape of the magnet was difficult due to the discontinuous winding tension between the straight and circular sections. The process of fabrication is important for ensuring the final performance of superconducting magnets. The very high dimension accuracy of this racetrack-type coil was ensured by precision wire laying, i.e., the rows with an odd number of wires alternate with the rows with an even number of wires. 2.4 The Vacuum Pressure Impregnation of the Magnets The magnets prepared by wet winding had poor dimensional uniformity and precision and could not be inserted into the gaps of magnetic poles easily. In many cases, the magnets that were forced to assemble tightly caused shorts to the metallic former; hence, they had weak insulation performance. To meet the requirement of high dimensional precision of superconducting magnets, which affected the contact between the magnets and the magnetic poles, thereby cooling the magnets completely, we used the dry winding method followed by vacuum pressure impregnation to fabricate the magnets. CTD 101 K was chosen as the impregnating material to impregnate the superconducting magnets. It is an anhydride-cured epoxy system used for cryogenic applications at liquid helium temperatures; it is used for its resistance to high energy radiation. As a commonly used resin in a superconducting material, CTD 101 K has superior performance. For example, it has excellent mechanical properties under cryogenic temperature conditions. It had low viscosity (less than 100 cp at 65 °C), which ensured that the resin can fully fill the gap between the adjacent wires and improve the wetting of the wires. It had a long pot life (60 h at 40 °C), which was important for impregnating the magnets, and it indicated good handling characteristics. At same time, it had excellent adhesion to other materials, like fibers.
3 Results and Discussions 3.1 Excitation Tests A Keithley2000 multimeter was used to read the voltage signal output from the shunt meter and terminals of the magnets. A computer using Labview software was used to monitor the change of signal as a function of time. A Cryogenic power supply with 10 mA distinguishability was used to energize the superconducting magnets. As shown in Fig. 5, the superconducting coil was fabricated using the process described above. First, the size of coils prepared was accurately inspected. We ensured that the dimension accuracy was in line with the requirements and can be assembled with the magnetic poles tightly. Furthermore, the electrical insulation strength of the superconducting coils to the magnetic poles was tested after assembly. We excited the magnets after cooling them by liquid helium. The current flow was increased stepwise. At every current value, the current was held constant for some time to check the voltage fluctuation of superconducting magnets. Each single superconducting coil was excited separately. The single magnets were immerged in liquid helium and energized until the electrical field in the magnet exceeded 1 uV/cm.
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Fig. 5. A picture of a single superconducting coil after winding
Fig. 6. The energizing current I as a function of time for No.1 to No.6 coil
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The charging curves of superconducting magnets are shown in Fig. 6. All the energizing current values of superconducting magnets reached the designed parameter value of 400 A. The critical currents of superconducting magnets of No.1, No.2, No.3, No.4, No.5, and No.6 were tested at 483.53, 479, 475.14, 478, 475.61, and 476.41 A, respectively. All the current values were above 475 A and at the range of 475 A to 483 A. The prepared single superconducting magnets showed excellent repeatability and had enough margin of safety.
4 Conclusions A superconducting racetrack-shaped magnet used to fabricate the undulator was prepared successfully. The magnet was wound with NbTi superconducting wires by dry winding method followed by vacuum pressure impregnation. CTD 101 K resin was used to impregnate the superconducting magnets due to the low viscosity and long pot life, which made it suitable for the impregnation of compactly arranged superconducting wires. This resin had excellent mechanical properties under cryogenic temperature conditions, which helped control the shape of racetrack-type superconducting magnets. The critical currents of the six coils tested were in the range of 475 A to 483 A. The coil module was charged to 400 A and did not quench. The superconducting magnets had a large margin of safety.
References 1. Bahrdt, J., Gluskin, E.: Cryogenic permanent magnet and superconducting undulators. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 907, 149–168 (2018) 2. Tanaka, T., Hara, T., Bizen, T., et al.: Development of cryogenic permanent undulators operating around liquid nitrogen temperature. New J. Phys. 8(11), 287 (2006) 3. Ivanyushenkov, Y.: Magnetic simulation of a superconducting undulator for the advanced photon source. In: Proceedings of the Particle Accelerator Conference 2009, p. 310 (2009) 4. Gourlay, S.A.: Fabrication and test results of a prototype, Nb3Sn superconducting racetrack dipole magnet (1998) 5. Kim, S.H., et al.: R&D of short-period NbTi and Nb3Sn superconducting undulators for the APS. In: Proceedings of the 2005 Particle Accelerator Conference, pp. 2419–2421. IEEE (2005) 6. Hwang, C.S., Wang, B., Chen, J.Y., et al.: Design of a superconducting multipole wiggler for synchrotron radiation. IEEE Trans. Appl. Supercond. 13(2), 1209–1212 (2003) 7. Dietderich, D.R., Godeke, A., Prestemon, S.O., et al.: Fabrication of a short-period Nb3Sn superconducting undulator. IEEE Trans. Appl. Supercond. 17(2), 1243–1246 (2007) 8. Gehlot, M., Mishra, G., Trillaud, F., et al.: Magnetic design of a 14 mm period prototype superconducting undulator. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 846, 13–17 (2017) 9. Casalbuoni, S., et al.: Superconducting undulator coils with period length doubling. J. Phys. Conf. Ser. IOP Publ. 1350(1), 012024 (2019) 10. Ivanyushenkov, Y., et al.: Development and operating experience of a short-period superconducting undulator at the advanced photon source. Phys. Rev. Spec. Top. Accel. Beams 18(4), 040703 (2015)
Cluster Analysis Based Eigenvalue Extraction and Dynamic Time Regulation for Electricity Anomaly Detection Jingjing Jiang, Xinming Liu(B) , Wenzhuang Chen, and Aikun Mao Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, Liaoning, China [email protected]
Abstract. To solve the problem of anomaly detection, a cluster analysis based eigenvalue extraction and dynamic time scheduling algorithm is proposed for anomaly detection. The eigenvalues are extracted and the typical user load curves are summarized by applying the clustering algorithm to the similar clusters within clusters with “clusters” as the unit. The eigenvalue curve is compared with the typical user load eigenvalue curve by dynamic time sizing algorithm, and the similarity coefficient is obtained to determine the similarity between the two curves, and then to determine whether there is an abnormality in the user’s electricity consumption. In this paper, the method is simulated by PyCharm software, and the graph curves are drawn using Matplotlib plug-in. Keywords: Power anomaly detection · Cluster analysis · Eigenvalue extraction · Dynamic time sizing · Load eigenvalue curves
1 Introduction With the popularization of smart meters, smart grid terminals can collect and store massive amounts of user electricity consumption data, and some users’ abnormal electricity consumption data are also hidden among them. At this time, if the massive data can be analyzed quickly and the users with abnormal electricity consumption can be accurately detected, it will have important practical value. Traditional electricity anomaly detection has always been based on anti-electricity stealing technology, supplemented by on-site inspections. Methods such as regular inspections, regular calibration of electric meters, and electricity theft reports are commonly used to detect electricity theft or metering device failures [1, 2]. Although there are a variety of methods available for users to detect abnormal electricity consumption [3–6], these methods all have a strong dependence on the user’s parameter settings. The selection of data thresholds is subjective and the calculation accuracy is not high. Problems such as too large amount of calculation due to algorithm, difficulty of training samples, etc. To this end, this paper proposes a new method of power consumption anomaly detection based on the feature value extraction of cluster analysis © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1130–1138, 2022. https://doi.org/10.1007/978-981-19-1528-4_115
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and dynamic time warping algorithm. It can effectively solve the difficult problem of high-dimensional data processing. The extraction of feature values not only covers the characteristics of the data, but also effectively avoids them.
2 Data Processing This paper takes the user-side electricity consumption data of a certain area for one year as a research sample, including the electricity consumption information of 19,924 users for 365 days, and a total of 6314494 time series. Through on-site inspection, the user number of abnormal electricity consumption has been obtained. Use PyCharm software, program through Python language, and use Matplotlib plug-in to draw graphics. 2.1 Obtain User Electricity Consumption Data Please Note In order to illustrate the complexity of user electricity consumption data, and to show it more intuitively and clearly, this article only randomly selects 5 users from the sample, and draws a broken line statistics chart of their annual electricity consumption as shown in Fig. 2 (Fig. 1).
Fig. 1. Annual electricity consumption fold statistics for 5 customers
2.2 Delete and Complete Data For user data with missing data greater than 20% of the total data volume of the year, delete the user data, and use the interpolation mean method for user data with missing data less than 20% of the total data volume of the year [7] by formulas (1) and (2) For completion processing. AverageKWH =
KWHAfter − KWHBefore days
(1)
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in the formula, AverageKWH is the user’s average daily power consumption, KWHAfter is the meter reading one day after the default date, KWHBefore is the meter reading one day before the default date, and days is the default number of days. TodayKWH = KWHBefore + AverageKWH
(2)
in the formula, TodayKWH is the complement value of the default date meter reading. After deleting and completing the original data, 18176 valid user power consumption data were obtained, of which 15,134 were normal users and 3042 were abnormal users. The effective user electricity consumption data is divided into two parts: 70% (General Data, GD) is a randomly selected sample of 12723 normal electricity users, used to extract the characteristic value curve of the typical electricity consumption of users; 30% (The data to be tested, Test Data, TD) is a mixed sample of the remaining 5453 normal users and abnormal users, used to extract the characteristic value curve of the user’s electricity consumption and detect abnormal users.
3 Cluster Analysis Use GD for data dimensionality reduction and cluster analysis. Principal component analysis is used to reduce the dimensionality of the huge data, and the Canopy clustering algorithm is used to coarsely cluster the reduced data to determine the reference value of the number of clusters K in the K-Medoids clustering algorithm. 3.1 Data Dimensionality Reduction Each user’s electricity consumption data for each day is one-dimensional data, and the 365-day electricity consumption data of each user is 365-dimensional data. The 365-dimensional data of each user is reduced by PCA. In this paper, the decreasing dimension parameter is set to 1, and the dimension is reduced to 1 dimension, so that a large amount of high-dimensional data is reduced to low-dimensional data, so as to prepare for subsequent data clustering. The coordinate distribution of the scattered points after using PCA to reduce the dimensionality of GD is shown in Fig. 3.
Fig. 2. GD reduced dimensional scatterplot
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3.2 Canopy Clustering Algorithm and Implementation Canopy is generally used to perform coarse clustering before K-Medoids. K-Medoids must determine the size of the cluster number K before using it. If the value of K is unreasonable, it will cause a large error, so the Canopy algorithm is introduced here [8]. The Canopy clustering algorithm is used to coarsely cluster the electricity consumption data after PCA dimensionality reduction. As shown in Fig. 4, the algorithm divides the scattered point area formed by the PCA dimensionality reduction in Fig. 3 into 4 parts, and the center point of each part is represented by A, B, C, and D respectively, thereby roughly dividing GD There are four categories.
Fig. 3. Canopy clustering diagram
3.3 K-Medoids Clustering Algorithm and Its Implementation The K-Mediods clustering algorithm is an improved algorithm of the K-Means algorithm, which will not cause excessive deviation of the division results due to outliers [9]. 3.4 DBI Clustering Number Effect Analysis Use the K-Medoids clustering algorithm to cluster the data after dimensionality reduction, set the value of the cluster number K to 1, 2, 3, …, 11, 12, and use the DBI validity index to perform clustering analysis. The analysis results are shown in Fig. 5. The smaller the DB value, the lower the similarity between classes [10], which achieves the best effect of the above cluster analysis.
4 Extract User Power Consumption Characteristic Value Curve 4.1 Obtaining the Characteristic Value Curve of Electricity Consumption of the User to Be Tested 1. Using formula (3), the extreme difference in monthly electricity consumption of users can be calculated. MonthRange = xmax − xmin
(3)
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Fig. 4. Analysis of the effect of DBI clustering numbers
In the formula, x max represents the maximum power consumption in this month, and x min represents the minimum power consumption in this month. 2. Formula (4) can be used to calculate the variance of the user’s monthly electricity consumption. k MonthVariance =
i=1 (MonthAverage
− xi )2
k
(4)
In the formula, MonthAverage represents the average power consumption of the current month, x i represents the daily power consumption, and k represents the number of days in the current month. 3. Use formula (5) to calculate the standard deviation of the user’s monthly electricity consumption. k 2 i=1 (MonthAverage − xi ) (5) MonthStandard = k In the formula, x i represents the daily electricity consumption, and k represents the number of days in the month. According to formulas (3), (4) and (5), the TD is extracted from the eigenvalues of the three dimensions of range, variance, and standard deviation to obtain the characteristic value curve of the electricity consumption of the user under test. 4.2 Obtaining user’s Typical Electricity Consumption Characteristic Value Curve After clustering GD with K-Medoids algorithm, the classification number is 4. According to formulas (3), (4) and (5), the feature values are extracted from three dimensions, and the average of the sum of each feature value is taken to obtain four categories Characteristic value curve of user’s typical electricity consumption. As shown in Fig. 5, Fig. 6, Fig. 7 and Fig. 8 respectively.
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Fig. 5. Typical load eigenvalue curve for Category I users
Fig. 6. Typical load eigenvalue curve for Category II users
Fig. 7. Typical load eigenvalue curve for Category III users
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Fig. 8. Typical load eigenvalue curve for Category IV users
5 Electricity Abnormality Detection and Analysis The dynamic time warping algorithm (DTW) is a method to measure the similarity between two time series [11]. Use dynamic time warping algorithm to detect 5453 users in TD. The dynamic time warping algorithm is used to compare the similarity between the user’s electricity consumption characteristic value curve and the user’s typical electricity consumption characteristic value curve. When the similarity coefficient is lower than 0.869, it is determined that the user’s electricity consumption data is abnormal. The test results are shown in Table 2 (Table 1). Table 1. Abnormal power consumption test results
Normal user Abnormal user Total
Total number of users
GD
TD
Detect anomalies
15134
12712
2421
101
3042
0
3042
2998
18176
12712
5463
3099
A total of 5,453 users were detected this time, of which the number of abnormal users detected was 3099 users. After comparing the user numbers with the actual abnormal users checked on site and comparing the abnormal detection results, 3099 suspected abnormal users were found Among them, there are 2957 users appearing in the abnormal user list. Compared with the total data of 3042 abnormal users, the correct rate of abnormal user electricity detection in this article is 97.21%. Table 2 is the comparison result of the abnormal accuracy of electricity consumption with the literature [3–5] and [6]. The results prove the effectiveness of the method proposed in this paper.
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Table 2. Comparison of accuracy
Correct rate
Literature [3]
Literature [4]
Literature [5]
Literature [6]
This article
76.00%
95.90%
96.00%
19.60%
97.12%
6 Conclusion The cluster analysis-based feature value extraction and dynamic time regularization of electricity anomaly detection methods proposed in this paper give full play to the relatively large data value of the samples to be tested. Through cluster analysis and feature value extraction, the dynamic time warping algorithm is used to compare the characteristic value curve of the electricity consumption of the user under test with the characteristic value curve of the typical electricity consumption of the user to detect the users with abnormal electricity consumption, and the effectiveness of the method is proved through experiments. • To deal with the insufficient data accuracy caused by the excessively large test sample data, the use of feature value extraction can not only fully reflect all the data characteristics of high-dimensional data, but also greatly improve the accuracy of calculations without affecting the data accuracy. • Using Canopy algorithm and K-Medoids clustering algorithm not only effectively compensate for the accuracy of K value selection, but also use the advantages of low complexity and fast speed of the algorithm to effectively solve the problem of high-dimensional data processing accuracy. • The use of dynamic time warping algorithm overcomes the problems of strong dependence on users in the parameter settings of algorithms such as the local outlier factor and poor scalability of the algorithm. By effectively extending and shortening the time series, Calculate the similarity between the two-time series from different angles, thereby improving the accuracy of the calculation.
References 1. Xu, B.: Talking about the tactics and preventive measures of stealing electricity. Coal 67–68 (2003). (in Chinese) 2. Cao, W., Rongqi, A.: Practice and thinking on anti-stealing work power supply enterprise management, pp. 12–14 (2011). (in Chinese) 3. Hui, L., et al.: Intelligent electricity data screening method based on long and short-term memory network. Guangdong Electr. Power 32, 47–56 (2019). (in Chinese). https://doi.org/ 10.3969/j.issn.1007-290X.2019.002.007 4. Wenqing, Z., Zheji, S., Gang, L.: Detection of abnormal user power consumption patterns based on deep learning. Power Autom. Equip. 38, 34-38 (2018). (in Chinese). https://doi.org/ 10.16081/j.issn.1006-6047.2018.09.006 5. Ji, Y., Zeng, X., Long, Y., Wang, J., Li, J., Fei, X.: Research automation and instrumentation of abnormal power consumption monitoring based on big data mining, pp. 219–222 (2019). (in Chinese). https://doi.org/10.14016/j.cnki.1001-9227.2019.08.219
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6. Hao, W., et al.: Application of machine learning algorithms in anti-stealing. Anal. Hebei Electr. Power Technol. 39, 38–41 (2020). (in Chinese). https://doi.org/10.3969/j.issn.10019898.2020.01.010 7. Gang, C.: inference imputation for missing values in survey data-taking CGSS2013 as an example survey world, pp. 53–56 (2019). (in Chinese). https://doi.org/10.13778/j.cnki.113705/c.2019.05.009 8. Liao, D., Du, R., Wang, X.: The influence of linear correlation of variables on the contribution rate of principal component variance. J. Henan Norm. Univ. (Nat. Sci. Edit.) 38, 23–26 (2010). (in Chinese). https://doi.org/10.3969/j.issn.1000-2367.2010.06.007 9. Xu, C., Shan, S.P., Na, S.: Research on real-time network forensics based on improved data mining algorithm. In: Applied Mechanics and Materials, p. 2617 (2013) 10. Li, Y.: Medical material distribution model based on PCA and K-Means clustering. Technol. Innov. 16–17 (2020). (in Chinese). https://doi.org/10.15913/j.cnki.kjycx.2020.15.006 11. Li, H.: Time works well: dynamic time warping based on time weighting for time series data mining. Inf. Sci. 547 (2021)
Research on the Novel Nonlinear Robust Control Strategy of Power Grid Low Frequency Oscillation Suppression Based on CSMES Kaiji Li, Zhongjian Kang(B) , and Zheng Chang College of New Energy, China University of Petroleum (East China), Qingdao 266580, China [email protected]
Abstract. In order to solve the problem of low frequency oscillation of power grid, the cur-rent-superconducting magnetic energy storage (CSMES) is applied to suppress the problem in this paper. The charge-discharge process of CSMES is realized by space vector pulse width modulation (SVPWM), and a novel nonlinear robust additional controller based on extended state observer (ESO) and terminal sliding mode is put forward to control the exchange power be-tween the SMES and power grid in order to suppress the low frequency oscillation. The ESO can track the system states and the uncertainties and disturbances of the system in real time, and realize the fast suppression of low frequency oscillation. The simulation model of the single machine infinite-bus (SMIB) system with CSMES is built with MATLAB SIMULINK. The simulation results show that the CSMES can reduce the amplitude and shorten stability time of the frequency oscillation. This confirms that the controller has good control effect and robust-ness, and the CSMES has good effect on suppressing low frequency oscillation of power grid. Keywords: ESO · CSMES · Terminal sliding mode
1 Introduction With the development of new energy represented by wind power and photovoltaic, the use of modern fast, high top multiple excitation system, interconnected power grid makes the system more closely connected. The problem of low frequency oscillation in power grid is more frequent and more serious [1, 2]. At present, the most commonly used method to suppress low frequency oscillation is to install power system stabilizer (PSS) in the excitation system. Although PSS has a good effect on suppressing low frequency oscillation, there are some limitations. For example, the parameters of PSS need to coordinate with each other in order to suppress low frequency oscillation. If PSS parameters is not adjusted well, it can not only suppress the oscillation, but also cause the deterioration of the system operation environment. With the development of power electronics technology, a new technology has been developed to improve the dynamic stability of power system, which is the use of energy storage devices combined with power electronic controller [3, 4]. According to the types © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1139–1151, 2022. https://doi.org/10.1007/978-981-19-1528-4_116
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of energy conversion, energy storage technology can be divided into physical energy storage, chemical energy storage, electromagnetic energy storage and phase change energy storage [5]. Physical energy storage is represented by pumped energy storage, compressed air energy storage and flywheel energy storage. Chemical energy storage includes lead acid, lithium ion, nickel cadmium, liquid flow and sodium sulfur battery energy storage. Magnetic energy storage mainly includes supercapacitor energy storage [6] and supercon-ducting energy storage. Phase change energy storage includes phase change cold storage technology and phase change heat storage technology [7]. Compared with other energy storage devices, superconducting magnetic energy storage (SMES) have the advantages of low loss, high energy storage efficiency and long service life, especially the properties of superconducting coils fast charging and discharging. Thus, it is very suitable to solve the stability of the power grid. According to the difference of the converter, the SMES can be divided into voltage-source-converter SMES (VSMES) and current-source-converter SMES (CSMES). In comparison, the CSMES structure is simple because the energy of the super-conducting coil is stored in the form of current. Thus, the power exchange between the power grid and the power grid is more rapid. On the other hand, the VSMES control theory is more mature, and the conduction loss is less. At present, some research has applied the superconducting magnetic energy storage device to the system stability control. Reference [8] applies VSEMS technology, and introduces a new instantaneous d-q change detection method, which can quickly compensate instantaneous voltage waveform and reduce voltage disturbance. Reference [9] applies fuzzy logic control to the DC-DC chopper of VSMES to improve the voltage stability of the power systems with high wind power penetration. In this studies, the control of the converter is still applied to the PI control. However, because of the high nonlinearity and coupling of the energy storage converter, the PI controller do not achieve satisfactory robust-ness. Reference [10] proposes an energy shaping control based on interconnection and damping assignment to improve the robustness of SMES and battery energy storage sys-tem converters in microgrid. However, the research is still based on the topology of VSMES, and the speed of power exchange is slightly less than that of the CSMES topology. The extended state observer (ESO) is widely used in the active disturbance rejection control (ADRC). It is also the core of the auto disturbance rejection control technology. It can not only estimate the state variables in the system accurately, but also estimate the uncertainty of the system model and the unknown external interference in the system. Further, it does not need to give the definite mathematical model of system in advance [11, 12]. The essence of sliding mode variable structure control is a kind of special nonlinear control. The most outstanding advantage is that the design of sliding mode is independent of the object parameters and disturbance, and has the ability of complete self-adaptability to the disturbance and parameter perturbation of the system. On the basis of common sliding mode control, the terminal sliding mode controller (TSMC) realizes sliding mode control by constructing sliding mode with nonlinear term, which converges the system state to zero in the limited time. The TSMC solves the progressive convergence of the state of the ordinary sliding mode control under the condition of
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on-line sliding mode surface, and the chattering can be effectively eliminated because there is no switching term [13, 14]. This paper discusses the suppression effect of the SMES on low frequency oscillation. Current source converter (CSC) is used in SMES and SVPWM technology is applied to achieve power exchange with the system. Using the system frequency deviation or the generator speed deviation as the input signal, the ESO is applied to track the system parameters and the uncertainties of the system model in real time. The terminal sliding mode controller based on ESO is designed to compensate or absorb the power of the power grid after the low frequency oscillation.
2 Nonlinear Robust Control Based on ESO Subsequent paragraphs, however, are indented. The ESO is widely used in ADRC technology. It can not only estimate the state variables in the system accurately, but also estimate the uncertainty of the system model and the unknown external interference in the system, and transform the nonlinear system into a linear system. For a n order time-varying nonlinear system with external disturbances, its expression refer to (1): x(n) (t) = f (x(t), · · · , x(n−1) (t) + w(t) + bu(t))
(1)
where x(t), · · · xn−1 (t) are state variables, w(t) is external interference, u(t) is control variable and b is its gain factor. Set x1 = x(t),x2 = x (t), · · · ,xn = xn−1 (t), Eq. (1) can be rewritten as: ⎧ x˙ 1 = x2 ⎪ ⎪ ⎪ ⎨ x˙ 2 = x3 (2) .. ⎪ ⎪ . ⎪ ⎩ x˙ n = f (x1 , x2 , · · · , xn ) + w(t) + bu(t) Set a(t) = f (x1 , x2 , · · · , xn ) + w(t), where f (x(t), · · · , x(n−1) (t)) is uncertain factors of the system, w(t) is unknown system external interference. Equation (2) can be rewritten as: ⎧ x˙ 1 = x2 ⎪ ⎪ ⎪ ⎨ x˙ 2 = x3 (3) .. ⎪ ⎪ . ⎪ ⎩ x˙ n = a(t) + bu(t)
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For the system described in Eq. (3), a n + 1 order ESO with dynamic parameter compensation can be designed, which refer to: ⎧ k1 ⎪ ⎪ g(z1 − x(t)) ⎪ z˙1 = z2 − ⎪ g (z1 − x(t)) ⎪ ⎪ ⎪ ⎪ ⎪ k2 ⎪ ⎪ g(z1 − x(t)) z˙2 = z3 − ⎪ ⎪ g (z1 − x(t)) ⎪ ⎪ ⎨ .. (4) . ⎪ ⎪ ⎪ ⎪ kn ⎪ ⎪ ⎪ g(z1 − x(t)) + bu(t) z˙n = zn+1 − ⎪ ⎪ g (z − x(t)) 1 ⎪ ⎪ ⎪ ⎪ kn+1 ⎪ ⎪ ⎩ zn+1 = − g(z1 − x(t)) g (z1 − x(t)) where g(z) is a nonlinear function related to the state error of the system. When the appropriate k1 , k2 , · · · , kn+1 and g(z) are selected correctly, the state variables x1 , x2 , · · · , xn+1 and extended state a(t) in Eq. (3) can be accurately estimated by the state variables z1 , z2 , · · · , zn+1 in Eq. (4). For Eq. (3), set v = a(t) + bu(t), its spatial state expression refer to: ⎤⎡ ⎤ ⎡ ⎤ x1 010···0 0 ⎥ ⎢ 0 0 1 · · · 0 ⎥⎢ x2 ⎥ ⎢ 0 ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ .. .. .. . . .. ⎥⎢ .. ⎥ ⎢ .. ⎥ ⎢ ⎥ = ⎢ . . . . . ⎥⎢ . ⎥ + ⎢ . ⎥v(t) ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎣ x˙ n−1 ⎦ ⎣ 0 0 0 · · · 1 ⎦⎣ xn−1 ⎦ ⎣ 0 ⎦ 000···0 1 x˙ n xn ⎡
x˙ 1 x˙ 2 .. .
⎤
⎡
(5)
Thus, the nonlinear system becomes a pseudo-linear system, and the TSMC can be designed [15]. For a n order system, global fast sliding modes refer to (6): ⎧ q /p ⎪ s1 = s˙0 + α0 s0 + β0 s00 0 ⎪ ⎪ ⎪ q ⎨ s2 = s˙1 + α1 s1 + β1 s 1 /p1 1 (6) .. ⎪ ⎪ . ⎪ ⎪ ⎩ qn−2 /pn−2 sn−1 = s˙n−2 + αn−2 sn−2 + βn−2 sn−2 where αi , βi > 0 and qi , pi (qi < pi ) are positive odd integers. The global fast sliding mode control law refer to (7): n−2 n−2
1 d n−k−1 q/p (n−k−1) × f (x) + u(t) = − αk sk + βk n−k−1 + ϕsn−1 + rsn−1 (7) g(x) dt k=0
k=0
The state variables and uncertainties of the system can be obtained from the ESO, so the sliding surface refer to (8): s0 = c1 z1 + c2 z2 + · · · + zn
(8)
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Set fast sliding mode s˙1 = s˙0 + αs0 + βs0
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, control law refer to (9): q /p0
v = −c1 z2 − c2 z3 − · · · − cn−1 zn − αs0 − βs00
(9)
where c1 , c2 , · · · , cn−1 are positive constants. Original control variable refer to (10): u=
v − zn+1 b
(10)
3 System Principle and Control Graphics may be full colour but make sure that they are appropriate for print (black and white) and online (colour) publication. Coloured line graphs should use dotted or dashed lines, or shapes to distinguish them apart in print. Examples of graphs in acceptable formats are given in Fig. 1. Each figure should be explicitly referred to in numerical order and should be embedded within the text at the appropriate point. A maximum of four subfigures are allowed per figure. The power of the power grid is transmitted through the SMES converter to the superconducting inductor coil, which is stored in the superconducting coil in the form of electromagnetic energy. When the power grid needs energy compensation, the energy in the superconducting coil can be transmitted back to the power grid by the converter. If the coil is kept in a superconducting state, the energy stored in the coil is almost no loss until it is released. The superconducting coil is the core of SMES, and its storage energy refer to (11): W =
1 2 LI 2
(11)
where L is the inductance of the coil, I is the current in the coil. According to the different topology of the converter, SMES can be divided into VSMES and CSMES, as shown in Fig. 1 and Fig. 2, respectively. In contrast, the use of VSC is relatively common, the control theory is relatively mature and easy to operate in no load condition, and the conduction loss of power electronic devices is less. CSMES exchanges power directly with the system, so the dynamic response is faster. Because of no DC-DC chopper, the AC voltage component of the superconducting coil is less, and the AC loss is less. G1
G3
G5
G6
L1
AC
L2
C
L3 G4
G6
G2
C1 C2 C3
Fig. 1. Structure of VSMES
SMES Coil
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G3
G5
L1
AC
SMES Coil
L2 L3 G4
G6
G2
C1 C2 C3
Fig. 2. Structure of CSMES
The SMES applies the CSMES in this paper. Its structure includes superconducting coils, CSC and LC filters. The LC filter can filter the harmonic current of the net side and suppress the overvoltage when the switch is turned off. The control of CSMES control generally consists of outer loop control and inner loop control. Outer loop control can provide the reference value of active and reactive power required by the inner loop system according to the actual demand of the system. The inner loop control is to calculate the amplitude, phase and other control parameters by the reference value provided by the outer loop control, and then generate the trigger pulse of the power electronic device so as to realize the power exchange with the power grid. In this paper, in order to reduce the AC side harmonics and the DC side ripple, SVPWM technology is applied in inner loop control [16, 17]. The principle of SMES power control is shown in Fig. 3. The reference values of the active and reactive power are Pr and Qr respectively. Two PI regulators are used to make active and reactive feedback to reduce the power error caused by filtering and other factors. The output current amplitude Is and the phase difference of the voltage and current α can be calculated by Pr and Qr . The Is and α are used to produce trigger pulses P1 –P6 .
Fig. 3. Principle of CSMES power control
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The studied model adopts single machine infinite bus (SMIB) system. As shown in Fig. 4, CSMES is connected to the generator port to absorb or compensate active power. External grid
G
SMES Fig. 4. Studied model
The parameters of generator and CSMES system are presented in Table 1 and Table 2, respectively. Table 1. Generator parameters Component
Value
Rated power
35 MVA
Rated voltage
10.5 kV
Inertia coefficient
4s
Friction factor
0.5 p.u
Pole pairs
2
Table 2. CSMES system parameters Component
Value
SMES inductance
7.8 H
Stand-by SMES current
4800 A
In this paper, frequency control is applied to the terminal sliding mode additional controller based on ESO. The generator adopts the classical two order model. Assuming that the modulation ratio M and the conduction angle α are controlled by the first-order inertial link, SMES can be written as a second-order model [18]: P˙ s = − T1 Ps + T1 g1 (12) ˙ s = − 1 Qs + 1 g2 Q T T
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where, Ps is the active power to the CSMES, Qs is the reactive power to the CSMES, T is the response time, g1 g2 is the virtual control quantity, and its expression refer to (13): g1 = Qs α + (1.5Vmax Id cos α)u1 − Qs u2 (13) g2 = −Ps α + (1.5Vmax Id sin α)u1 + Ps u2 where, Vmax is the maximum system voltage of SMES access point,Id is the superconducting coil current,u1 is the modulation ratio control quantity, u2 is the conduction angle control quantity. Taking the reference active power P as the control variable and the reference reactive power Q given as 0. u1 and u2 refer to (14): √ 2 (P 2 +Q2 ) 2P u1 = 3Vmax Id = 3Vmax Id (14) u2 = arctan(Q P) = 0 Combined with the second-order equation of generator and the Eqs. (12) to (14), the system equation refer to (15): ⎧ δ˙ = ωω0 ⎪ ⎪ ⎨ D ω˙ = H1 (Pm − Pe ) − H ω 1 1 ˙ ⎪ = − P + (P + P) P s s ⎪ T T ⎩ Pe = Ps +Pl
(15)
where, Pl is the active power to the lines, and P is the additional controlled power. Set x = ω, Eq. (15) can be rewritten as: x¨ =
P 1 1 ˙ Ps (Pm − P˙ l + − − D˙x) − P H T T HT
(16)
For the system, the parameter design method of the extended state observer is listed in [19]. The poles of the compensation matrix are p1 = p2 = p3 = –50, so k1 = 150, k2 = 7500, k3 = 125000. The three-order extended state observer refers to (17). ⎧ 150 ⎪ ⎪ z˙1 = z2 − g(z1 − x(t)) ⎪ ⎪ ⎪ g (z1 − x(t)) ⎪ ⎪ ⎨ 7500 g(z1 − x(t))+bu(t) z˙2 = z3 − (17) ⎪ g (z1 − x(t)) ⎪ ⎪ ⎪ ⎪ 125000 ⎪ ⎪ g(z1 − x(t)) ⎩ z˙3 = − g (z1 − x(t)) 1 where b = − HT , u(t) = P, nonlinear function g(z) is listed in (18).
g(z) = fal(z, α, δ) = where α = 0.5, θ = 0.01.
|z|α sign(z) |z| ≥ δ z |z| < δ δ 1−α
(18)
Research on the Novel Nonlinear Robust Control Strategy
The principle of global fast sliding mode controller is listed in (19). ⎧ ⎪ ⎨ s 0 = c 1 z1 + z2 q/p s1 = s˙0 + αs0 + βs0 ⎪ ⎩ v = −c z − αs − βsq/ p 1 2 0 0
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(19)
where α, β > 0, c1 > 0, p, q are positive odd numbers and p > q. The ultimate active power control law refers to (20). v − z3 b
u(t) =
(20)
The integral control structure is shown in Fig. 5. The input signal is generator speed deviation ω. ESO + TSMC is the additional control of PI controller. The output of the two controllers is combined as the control signal of CSMES.
Fig. 5. Integral control structure
4 Simulation Results The simulation model has been shown in Fig. 6. A three phase short circuit fault occurs when the simulation is set at 10 s, and the fault is removed after 0.5 s, so as to simulate low frequency oscillation [20]. The contrast between the input and output power of the CSMES is shown in Fig. 6. 107
P/W
2 0 -2 -4
(a) 107
P/W
2 0 -2 -4 9
10
11
12
13
14
(b)
15
16
17
18
19
20
t/s
Fig. 6. (a) Input of CSMES, (b) Output of CSMES
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When the amplitude of the low frequency oscillation is the largest, the input and output errors are slightly larger. In the subsequent process, the output of CSMES can track the input of control very well. 5000
4900
Idc/A
4800
4700
4600
4500
4400 10
12
14
16
18
20
t/s
Fig. 7. Current of superconducting coil
Figure 7 shows the waveform of the superconducting coil current during the suppression process of low frequency oscillations. The energy of the superconducting coil is stored in the form of current, so the current of the coil fluctuates as CSMES absorbs and compensates the active power of the system. When the coil current is less than the initial current, a constant value will be added in the control input so that the coil current can be maintained at the initial current. In this paper, PI controller and the controller based on ESO and terminal sliding mode are applied in CSMES respectively. And the contrast of generator speed waveform of two controller is shown in Fig. 8. 1.015 Nonlinear robust control PI control
1.01
Without control
w/pu
1.005 1 0.995 0.99 0.985 0.98 9
10
11
12
13
14
15
16
17
t/s
Fig. 8. Contrast of generator speed
18
19
20
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In Fig. 8, the point line is the generator speed oscillation without CSMES control, the dotted line is the generator speed with simple proportional inertial control, and the solid line is the generator speed with nonlinear robust control. The detailed comparison parameters are presented in Table 3. Table 3. Parameters comparison of generator speed Control mode
Maximum amplitude
Stability time
No control
0.014 p.u
20 s
PI control
0.005 p.u
17.5 s
Nonlinear robust control
0.004 p.u
15 s
From the simulation results, it can be seen that the maximum amplitude of the rotational speed fluctuation can reach 0.014 p.u when the system does not connect with CSMES, and oscillation tends to be stable after 9.5 s. The amplitude of the oscillation is obviously reduced and the stable time becomes shorter when the CSMES is connected to the system and takes a certain control. Comparing the two control methods, in terms of the amplitude of the generator speed, the maximum amplitude of the fluctuation of the generator speed after the fault is up to 0.005 p.u with the traditional PI control method, and the value is 0.004 p.u with the additional nonlinear robust control. In terms of the oscillation stability time, the system with the traditional PI control tends to be stable in 7 s after the failure, and the system with the additional nonlinear robust control tends to be stable in 4.5 s after the failure. The results show that the nonlinear robust additional control has better control effect on suppressing oscillation.
5 Conclusions This paper briefly introduces the characteristics of SMES with different converters, and chooses CSMES which has faster response speed as the study object. A novel nonlinear robust controller based on ESO and terminal sliding mode which converts generator speed deviation signal into the power signal which CSMES exchanges with the system is put forward to suppress low frequency oscillation. The simulation results show that CSMES can effectively reduce the amplitude of the oscillation, shorten the stability time and improve the stability of the system. The effectiveness of CSMES in suppressing low frequency oscillation is proved. By comparing the effect of the nonlinear robust control and the traditional PI control, the nonlinear robust controller based on ESO and terminal sliding mode shows better control effect and robustness.
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References 1. Li, X., Li, D., Liu, Y., Li, H.: Study on power low frequency oscillation caused by primary frequency modulation. In: 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 2271–2276 (2020). https://doi.org/10.1109/ ITAIC49862.2020.9339160 2. Li, Y., Fan, L., Miao, Z.: Wind in weak grids: low-frequency oscillations, subsynchronous oscillations, and torsional interactions. IEEE Trans. Power Syst. 35(1), 109–118 (2020). https://doi.org/10.1109/TPWRS.2019.2924412 3. Topolyuk, Y.P.: Stability of the solutions to synthesis problems of radiating systems according to power radiation pattern. In: 2016 XXIst International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), pp. 166–168 (2016). https://doi.org/10.1109/DIPED.2016.7772246 4. Ortega, Á., Milano, F.: Generalized model of VSC-based energy storage systems for transient stability analysis. IEEE Trans. Power Syst. 31(5), 3369–3380 (2016) 5. Olabi, A.: Renewable energy and energy storage systems. Energy 136, 1–6 (2017) 6. Sun, D., Long, H., Zhou, K., Lv, Y., Zheng, J., Chen, Q.: Research on SCESS-DFIG DC bus voltage fluctuation suppression strategy for frequency inertia regulation of power grid. IEEE Access 8, 173933–173948 (2020). https://doi.org/10.1109/ACCESS.2020.3025292 7. Pielichowska, K., Szatkowski, P., Zambrzycki, M., Macherzy´nska, B.: Polyurethane/graphene nanocomposites as phase change materials for thermal energy storage. In: 2015 IEEE 15th International Conference on Nanotechnology (IEEE-NANO), pp. 105–108 (2015). https:// doi.org/10.1109/NANO.2015.7388808 8. Zheng, Z., Xiao, X., Chen, X.Y., Huang, C., Xu, J.: Performance evaluation of a MW-class SMES-based DVR system for enhancing transient voltage quality by using d–q transform control. IEEE Trans. Appl. Supercond. 28(4), 1–5 (2018). Art no. 5700805, https://doi.org/ 10.1109/TASC.2018.2812171 9. Said, S., Aly, M., Hartmann, B.: Application of SMES for voltage control of power systems with high wind power penetration. In: 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, pp. 461–466 (2018) 10. Li, T., Chen, Y., Gou, H.Y., Chen, X.Y., Tang, M.G., Lei, Y.: A DC voltage swell compensator based on SMES emulator and lead-acid battery. IEEE Trans. Appl. Supercond. 29(2), 1–4 (2019). Art no. 5700404, https://doi.org/10.1109/TASC.2019.2894017 11. Li, S., Gu, H.: Fuzzy adaptive internal model control schemes for PMSM speed-regulation system. IEEE Trans. Ind. Inf. 8(4), 767–779 (2012). https://doi.org/10.1109/TII.2012.220 5581 12. Changchao, L., Zhongjian, K.: Synchronization control of complex network based on extended observer and sliding mode control. IEEE Access 8, 77336–77343 (2020). https:// doi.org/10.1109/ACCESS.2020.2989895 13. Xu, S.S., Chen, C., Wu, Z.: Study of nonsingular fast terminal sliding-mode fault-tolerant control. IEEE Trans. Ind. Electron. 62(6), 3906–3913 (2015). https://doi.org/10.1109/TIE. 2015.2399397 14. Janardhanan, S., Bandyopadhyay, B.: On discretization of continuous-time terminal sliding mode. IEEE Trans. Autom. Control 51(9), 1532–1536 (2006). https://doi.org/10.1109/TAC. 2006.880805 15. Ouyang, Q., Chen, J., Zheng, J.: State-of-charge observer design for batteries with online model parameter identification: a robust approach. IEEE Trans. Power Electron. 35(6), 5820– 5831 (2020). https://doi.org/10.1109/TPEL.2019.2948253 16. Tao, H., Gao, Q., Li, B.: Research on dual-PWM variable-frequency converter based on current predictive control. In: 2010 Chinese Control and Decision Conference, pp. 1421–1424 (2010). https://doi.org/10.1109/CCDC.2010.5498206
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17. Berkovich, Y., Axelrod, B., Tapuchi, S., Ioinovici, A.: A family of four-quadrant, PWM DCDC converters. In: 2007 IEEE Power Electronics Specialists Conference, pp. 1878–1883 (2007). https://doi.org/10.1109/PESC.2007.4342288 18. Kaloust, J., Qu, Z.: Robust control design for nonlinear uncertain systems with an unknown time-varying control direction. IEEE Trans. Autom. Control 42(3), 393–399 (1997). https:// doi.org/10.1109/9.557583 19. Jain, R.K., Shetty, P.K., Shenoy, S.: Experimental evaluation of PID and ESO controller for instrument landing system. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 751–757 (2014). https:// doi.org/10.1109/ICCICCT.2014.6993059 20. Jing, L., Liqiu, W., Liang, H., Daren, Y.: Experimental study on low-frequency oscillation of the plume divergence angle of hall thrusters. In: 2015 IEEE International Conference on Plasma Sciences (ICOPS), 2015, p. 1 (2015). https://doi.org/10.1109/PLASMA.2015.717 9704
Research on the Data Security Enhancement Method Based on Encryption Paradigm Xiangsen Xu, Shuo Li, and Jing Zeng(B) State Grid Jibei Information & Telecommunication Company, Beijing 100053, China [email protected]
Abstract. As the network information developing, the network information security has become a focal issue in the field of information technology. Through the analysis of network security status, data encryption is used to guarantee the received information security. This paper introduces the knowledge of cryptography and proposes a ring signcryption scheme based on attributes. The signcryption method improves the efficiency compared with the traditional method of signature before encryption. A ring is established through the attribute set and relaxation attribute set in the system. The advantage of the proposed method is that the message receiver can determine the source of the message through the ring to confirm the reliability of the message while it also protects the identity of the signcrypter. Keywords: Cryptography · Network security · Encryption and decryption · Ring signature
1 Introduction As the computer network technology developing and improving continuously, accomplishments of prime importance have been made in the Internet application of various industries. Due to the huge effect, many people all around the world have conducted indepth study on the network technology, and the understanding of that has been strengthened due to the popularity of the network. The computer network is a vast open network that gives a good foundation for criminals in technology and platforms. Enterprise server in the network may be attacked at any time by the hackers, leading to the data leakage, damage and loss, furtherly causing huge economic losses. Therefore, network information security defense has become an important topic that needs continuous attention [1]. Network security involves protection of the computer network infrastructure, where network security issues are dealt with by network or system administrators who perform security policies and manage software and hardware to prevent unauthorized access to networks and resources. Cryptography can be used to protect data through wireless communication [2]. When we develop computer applications for transmitting confidential data, there is an urgent need for security. The intruder is endowed with a chance to capture information from the client computer to the server. Therefore, it is necessary to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1152–1158, 2022. https://doi.org/10.1007/978-981-19-1528-4_117
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use cryptography to provide data security by providing user ID and password to authenticate the user, while other methods are used to encode the information. The general asymmetric encryption and decryption process is shown in Fig. 1 [3]. This paper is mainly conducted according to the attribute-based encryption scheme to enhance the security of data. The attribute ring based signcryption scheme is proposed in this paper. This scheme is endowed with two advantages including that it encrypts and signs the message while ensuring the reliability of the message [4].
B
A Public key A
Exchange of Public keys
Public key A
Public key B
Private key A
Public key B
Verification
Encryption
Signature
Private key B
Decryption
Signature Message
Plaintext
Ciphertext
Ciphertext
Plaintext
Fig. 1. Asymmetric encryption and decryption process
2 Related Research 2.1 Cryptography Cryptography is a technique of converting plain text to ciphertext, and then the ciphertext is decrypted to plain text. Both sides can communicate securely by encrypting messages sent between them and using a key shared by the sender and receiver. 2.2 Industry Security Rules The industry provides measurement tools for security enforcement. These principles can be applied to the whole scope of the security analysis. Confidentiality prevents unauthorized access to sensitive data, which ensures that the necessary level of confidentiality is enforced. Cryptography based approaches can try to guarantee the data confidentiality during the information transmission from one node to another. Cryptography provides a secure mode of transmission that protects traversing sensitive data over shared media [5]. Integrity guarantees the information and systems accuracy through preventing unauthorized modification. Middleman is a common type of security attack. The intruder will intercept the data in transmission and modify it [6].
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Availability refers to prevention of losing access to resources and data to guarantee that information can be used as needed. Denial of service (DoS) is one of security attack types that typically attempt to deny access from appropriate users in order to interrupt service [7]. 2.3 Cyber Attacks The first common cybersecurity attack is eavesdropping, in which an opponent can easily get valuable information from packets that have been transmitted. The second common attack is message modification, where an attacker can intercept a packet for modification. A third common attack is message replay, in which an attacker can retransmit the contents of a packet at a later time. DoS attacks are another type of attack that is difficult to solve, which can be presented in several forms. The first form is a collaboration between nodes, where a group of nodes has malicious act and prevents broadcast messages from reaching certain network parts. The second form is a jamming attack, where the attackers block the channel of communication that prevents any network member in the affected area from sending or receiving any packets. The third form is the running out of power when the attackers repeatedly request packets from the sensors to exhaust the battery lives. Node hazard attack is another kind of attack, when the attacker gains the control or access to the sensor node itself after deployment, the sensor node is considered to be damaged. Because the compromised node is a formal member in the sensor network, it is easy to launch a variety of complex attacks from the infected node. Mock attack is a common type of attacks that could be launched by an infected node, where a malicious node pretends to be a legitimate node and adopts its identity to conduct active attack, such as the Sybil attack, in which a single node adopts different identities to trick other nodes. Node replication attacks, on the other hand, are the replication of sensor nodes. 2.4 Relevant Concepts of Network Security Definition of Network Security. During information transmission, the network security system should ensure that client applications and network services are managed when connected to the network without influencing information resources [8]. Definition of IDS for Network Security. To achieve the network security, the data mining and the security management of data mining information can be applied. Management means to effectively achieve organizational goals by comprehensive utilization of human resources and other related resources in accordance with the objective laws of the development of things. To fulfill a certain mission and realize a specific purpose, appropriate methods should be used in accordance with established principles and procedures [9]. IDS (Intrusion Detect System) for network security is an approach adopted to detect external attacks and privileges abuse by valid users. IDS will accomplish the intrusion detection and leaves the intrusion evidence on the basis of the users’ operation, which provides the foundation for data recovery and accident handling. IDS can detect the data generated during the user’s current and historical operation in accordance with a certain algorithm. Thus, whether the current operation is intrusion behavior can be judged, and
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the corresponding measures can be taken based on the detection results. The current operation in the IDS mainly takes form of data. It comes in two types, audit data from the operating system and packets flowing through the network.
3 Symmetric and Asymmetric Encryption 3.1 Asymmetric Encryption Public key cryptography, also called as asymmetric keys, is a cryptographic algorithm that needs two separate keys, one for encryption and the other for decryption [10]. Although there are certain differences, the two parts are mathematically relational. The public key is for encrypting the plaintext or verifying a digital signature, while the private key is for decrypting the ciphertext or creating a digital signature. Through asymmetric encryption certificate authority, network users can get public key and private key pairs. Any other user who wants to send an encrypted message can obtain the public key of the target recipient from the public directory. They can use this key to encrypt the message and send it to the recipient. When the recipient receives the message, their own private keys can be used to decrypt it, which is not accessible to anyone else. 3.2 Symmetric Key Encryption Symmetric encryption is one of the traditional cryptography algorithms, which uses the same key to encrypt and decrypt. These keys can be the same, or they can be simply converted between the two keys. The key is used in conjunction with plain text to create a ciphertext, and is decrypted with the ciphertext using the same key. Symmetric cryptography is divided into block cryptography and stream cryptography. Stream cipher is a symmetric key cryptography in which the plaintext is encrypted one byte at a time to give the number of the ciphertext stream. Plain text is combined with a pseudo-random cipher stream (key stream). Block cipher is a deterministic algorithm that operates on groups of fixed-length bits, called blocks. It divides the plaintext into groups with same size [11]. A block is just a set of characters, such as “cipherblock”. The commonly used block size is 8 characters or 64 bits. If the total characters number in the plain text is not completely divided by the block size, additional characters are usually added to the end of the plain text until a complete last block can be formed. The problem with block ciphers is that they duplicate the text, so the same cipher text is created. This could provide a clue to the intruder. To overcome this problem, a chaining pattern is used, in which the previous block of the ciphertext is combined with the current block for more security.
4 Attribute Based Ring Signature Scheme Attribute-based ring signcryption is suitable for conditions where both encryption and signature of the message are required. Based on the attribute ring signcryption scheme, the ring is constructed through the attribute set and the relaxed attribute set in the system [12], which has two major advantages and can effectively ensure the reliability of the message, at the same time, the user’s identity information can be protected.
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4.1 Algorithm Description Providing the set S is composed of d integers in Zp, then the Lagrange interpolation coefficient i,S (x) is: i,S (x) =
x−i i−j
j∈S,j=i
Setup(k): according to the security variable k, the trusted key generator selects two groups G1 and G2 whose order is prime p where p > 2k . Assuming that g is a generator of the group G1 . Then another bilinear mapping e : G1 × G1 → G2 can be selected. Supposing that the global attribute set in the system is u,and |u| = n. Suppose that a set of relaxation attributes = {1 , .., d −1 } is composed of d − 1 elements, t1 , . . . tn , tn+1 , .., tn+d −1 are random numbers in Zp and Ti = g ti (i = 1, . . . , n + d − 1). Then, one number α is randomly selected in Z∗p , and Y = e(g, g)α is assumed. Three hash functions H1 , H2 , H3 can be selected, which are defined as follows: H1 : G2 → {0, 1}|M | × Z∗p × G1 , H 2 : {0, 1}∗ → Z∗p , H3 : {0, 1}|M | × Z∗p → Z∗p Where, |M | represents the length of the ciphertext space. The public parameters PK of the system are: n+d −1 PK = {G1 , G2 , e, g, {Ti }i=1 , Y , H1 , H2 , H3 }
The master key MK of the system is: n+d −1 MK = {α, {ti }i=1 }
Key Exact(MK, w): w is the set of attributes of the user and MK is the master key of the system. The trusted key generation center runs the algorithm to generate the corresponding decryption key for the attribute set w. Firstly, a d − 1 degree polynomial q(x) is selected at random, in which q(x) satisfies q(0) = α. The set after the attribute
set w is relaxed can be set as w = w ∪ . For the attribute i in w, Di = g calculated, and output the decryption key Di .
q(i) ti
can be
4.2 Encryption Algorithm Signcryption(m, wS , wR ): After the sender (signer) S runs the algorithm and signcrypts the message m, the ciphertext is sent to the receiver R, where wS and wR are the set of attributes of the sender and the receiver respectively. Firstly, S selects a subset wS of d elements in wS , where f attributes can be selected f from the set of attributes wS , i.e. {i}i=1 ∈ wS . Based on the definition in Key Generation, d − f elements are selected from the set of relaxed attributes set . To randomly select r ∈ Z∗p in Z∗p with the assumption that s = H3 (m, r), u = g s and X = Y s = e(g, g)α·s . For attribute i in the attribute set wS , there is Ei = Tis . The attributes j in the attribute set wR can be calculated as Ej = Tjs . To construct a ring from the elements in the set wS wR . The specific process is as follows:
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a) Firstly, randomly select k ∈ wS from the attribute set wS to perform the following calculation: If l ∈ wS wR and l = k(l = 1, . . . , nR + d ), then ul is randomly selected ∗ in Zp , where nR represents the number of elements in the attribute set wR with the assumption that hl = H2 (m, ul , X , wS ∪ wR , l). Otherwise, if l = k, assume that rk is the random number in Z∗p , then uk and hk are calculated as follows: r
uk =
Ekk
l∈wS
wR ,l=k
=
ul · El
l∈wS
g tk ·rk ·s u · g tl ·hl ·s w ,l=k l R
r +hk
hk = H2 (m, uk , X , wS ∪ wR , k), ν = Ekk b) Calculation for y = (mrν) ⊕ H1 (x) The cipher text is as follows:
nR +d R CT = (y, wS , wR , u, {ul }l=1 , {Ei }di=1 , {Ej }nj=1 )
4.3 Decryption and Verification Algorithm Unsigncryption(CT): After receiving the ciphertext, the receiver R runs the algorithm to decrypt and verify it in the following steps. R +d R , {Ei }di=1 , {Ej }nj=1 ), a set wS with d elements is As CT = (y, wS , wR , u, {ul }nl=1 selected from the attributes wR and the calculation is as: X = e(Dj , Rj )t,S (0) j∈wR
=
e(g
q(j) tj
, g tj ·s )
t,S (0)
j∈wR
= e(g, g)α·s And then m , r and ν can be obtained through (m ||r ||ν ) ⊕ H1 (x). Providing that = H3 (m , r ), and u = g s can be verified. If u = g s , then calculate the equation hl = H2 (m , ul , X , wS wR , l), where l = nR +d 1, . . . , nR + d and then e(g, ul · g tl ·hl ·s ) = e(g, ν ) is further verified.
s
If e(g,
nR +d
l=1
ul · g
tl ·hl ·s
) = e(g, ν ), it indicates that CT is a valid ring signed cipher
l=1
text and its corresponding output plaintext information is m . nR +d ul · g tl ·hl ·s ) = e(g, ν ), it demonstrates that CT is Otherwise, if u = g s or e(g, l=1
not a valid signed cipher text.
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5 Conclusion Cyber security involves all kinds of business organizations, enterprises, and other types of institutions, and it has become a key function in building and maintaining modern high-speed networks. Cryptography is a technique used to provide information security by encoding data, providing highly secure data transmission by encrypting data that the third parties fails to access to. This paper proposes a new method for data verification, encryption and decryption. The main contribution of this paper is to adopt the attributebased encryption and signature to meet the characteristics of anonymity and effectively achieve privacy protection. Hence, the signature scheme also guarantees the reliability, authenticity and integrity of the source. Acknowledgments. This work is supported by the Science and Technology Project of State Grid Jibei Power Company Limited (No. 52018E20008H).
References 1. Ding, L., Lei, J.: Publication of network security monitoring data in July 2017. Netinfo Secur. 9, 160–165 (2019) 2. Ding, Z., Huang, R., Yang, H.: Development of cryptography technology and research on network security. Wirel. Internet Technol. 016(007), 38–39 (2019) 3. Ge, X.: Application and development of cryptography technology in network information security. Electron. Technol. Softw. Eng. 176(06), 242–243 (2020) 4. Liao, Y., Zhang, G., Chen, H.: Cost-efficient outsourced decryption of attribute-based encryption schemes for both users and cloud server in green cloud computing. IEEE Access 8, 1 (2020) 5. Liu, Y., Mu, D.: Research on network security evaluation method based on Cia attribute. Comput. Technol. Dev. 28(04):149–151+155 (2018) 6. Arnaldy, D., Perdana, A.R.: Implementation and analysis of penetration techniques using the man-in-the-middle attack. In: 2019 2nd International Conference of Computer and Informatics Engineering (IC2IE), pp. 188–192 (2019) 7. Anandaraj, I., Krishnamurthy, S.: Bayesian game approach to mitigate DoS attack in vehicular Ad-Hoc networks. Recent Patents Eng. 14(2), 150–160 (2021) 8. Gao, S.: Discussion on data communication network maintenance and network security. Telecom. Power Technol. 36(02), 187–188 (2019) 9. Wang, Y.: Application and research of intrusion detection technology based on data mining in network security. Electron. Manuf. (022), 77–78 (2019) 10. Wu, H.: Research and implementation of public key encryption. Sci. Technol. Vis. 10, 226–227 (2019) 11. Wei, Y., Shi, J., Li, L.: Impossible differential analysis of Lici block cipher algorithm. J. Electron. Inf. Technol. 041(007), 1610–1617 (2019) 12. Liu, N.: NDN Sensitive Information Protection Scheme Based on Attitude Ring Signcryption. Lanzhou University of Technology
Research on Early Warning and Disposal Technology of Intelligent IoT Terminal Security Threat Shijun Zhang, Shuo Li, and Jing Zeng(B) State Grid Jibei Information & Telecommunication Company, Beijing 100053, China [email protected]
Abstract. The intelligent IoT terminal has the characteristics of intelligence, networking and interaction, and are mostly deployed in uncontrolled environments, with problems such as weak self-protection and insufficient attack monitoring methods, which bring more risks to the security of the power grid. By carrying out the research and application of intelligent IoT terminal lightweight threat warning and linkage disposal technology, and building an intelligent IoT terminal security monitoring system, it can realize deep-level vulnerability mining of protocols, self-security reinforcement of terminal equipment, abnormal detection of self-security running status, and intelligent identification of terminal network attacks, and enhance the security risk perception and online management and control capabilities of intelligent IoT terminals. Keywords: Intelligent IoT terminal · Security risk · Lightweight threat early warning · Linked disposal
1 Introduction With the construction of energy Internet and new power system with new energy as the main body, various power grid smart terminals such as power distribution terminals and intelligent fusion terminals are widely used in all aspects of power system energy interconnection [1–3]. It strongly supports the comprehensive operation status perception, wide-area intelligent collaborative control and global natural human-computer interaction of the Energy Internet, and the terminal scale is over 100 million, which has become an important part of the new power system [4]. The safe operation of intelligent IoT terminals directly affects the development of the people and society. For example, the safety of payment terminals, production terminals, and smart meters may affect the reliability of electricity consumption, property and information security of residents [5, 6]. At present, the security protection of intelligent IoT terminals is mainly a security defense system based on encryption and isolation, with weak protection capabilities and insufficient monitoring methods. This paper study the security threat early warning and disposal method of intelligent IoT terminals, based on the threat perception and attack correlation technology © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1159–1167, 2022. https://doi.org/10.1007/978-981-19-1528-4_118
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of power grid smart terminal equipment. On the basis of the identification of system network and host application side network attacks covered by existing security monitoring and early warning, further improve the network attack monitoring and identification system covering intelligent terminal equipment, transmission network channels and platform applications covering the energy Internet site, and comprehensively improve the comprehensive defense capability of the energy Internet terminal layer.
2 Related Works In recent years, the network security situation has become increasingly severe. Network attacks initiated by intelligent terminals have occurred frequently, and the destructive power caused by the attacks has increased significantly [7, 8]. For example, in 2016, hackers launched a large-scale DDOS attack by illegally controlling a large number of IoT terminal devices, causing half of the US network to be paralyzed. In 2017, it was discovered abroad that malicious software targeting smart terminals in substations can maliciously control nearly 100 smart terminals in substations around the world. A large number of attack cases show that most of the attacks on critical infrastructure are initiated from terminals with weak on-site protection [9, 10]. The grid smart terminals have become the main target and springboard for attacking the grid, and the security problems caused by them also directly affect the safe and stable operation of the Energy Internet [11]. For various security issues caused by malicious attacks on grid smart terminals, there are no technical products that adapt to the characteristics of grid smart terminals and large-scale energy interconnection scenarios. There is an urgent need to study targeted technical means and security technical prevention measures, so as to achieve basic monitoring and protection against defects, abnormal behaviors, virus infections, and external threats of intelligent IoT terminals. In order to cope with attacks or avoid injuries, so as to ensure that various security elements of the IoT terminal are in a state of “no danger, no damage, and no accidents”.
3 Intelligent IoT Terminal Security Platform Construction 3.1 Overall Architecture Design The security protection of intelligent IoT terminals is carried out according to the idea of “grasping the scene, controlling the situation, strict traceability, strong early warning, and timely linkage”. Real-time perception of the security situation of the terminal site, terminal container and terminal communication network at all levels, record the attack process and trace the source of the attack through monitoring. Classified early warning and handling of security incidents of power intelligent IoT terminals, timely cutting off risk terminals, reducing risk threats in the communication network environment, and improving attack protection capabilities (Fig. 1).
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(1) Host System Security Monitoring For the self-perception of the power intelligent IoT terminal, the lightweight Agent technology is used to monitor the user login, running status, illegal process, illegal connection and illegal port of the integrated terminal host system, and to perceive its own status and external threats, which can improve the terminal’s own security and network security. (2) Network Traffic Monitoring Through the non-destructive bypass traffic collection technology, it can analyze the upstream network packets of intelligent terminals, and can identify abnormal terminal traffic and attack behaviors such as penetration scanning, denial of service, and brute force cracking in network traffic. (3) Container Security Monitoring Monitor the running status and security configuration of containers running in the fusion terminal to improve the security capabilities of terminal containers. (4) Linkage Control This part mainly includes two functional modules: linkage management and control disposal and linkage management and control strategy. Using the service agent, through the command transparent transmission technology, the intelligent terminal security monitoring module and the security access gateway are linked to block the abnormal power intelligent IoT terminal. By cutting off risk terminals in time, risk threats in the communication network environment are reduced, and attack protection capabilities are improved. (5) Configuration Management
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Remotely configure and manage the monitoring parameters of the security monitoring module, upgrade the security monitoring module, manage the license, restrict the installation and deployment of the security monitoring module through the license, and record the operation and operation of the security monitoring module behavior for auditing purposes. 3.2 Core Technology Research Through a number of core technology researches, the power grid smart terminal equipment firmware, protocol deep-level vulnerability mining, terminal equipment selfsecurity reinforcement, self-safety running status abnormal detection and terminal network attack identification capabilities have been enhanced. Realize “in-depth protocol analysis, scenario intelligent analysis, multi-level linkage blocking, terminal active early warning” to ensure the safe and stable operation of massive intelligent IoT terminals. The main technologies are as follows: (1) Business security traceability technology at the power grid smart terminal layer based on in-depth protocol analysis The intelligent terminal layer service security exception analysis technology based on protocol in-depth analysis judges whether service protocol packets are abnormal according to different protocol types. At the syntax level, this technology obtains the key fields of the message protocol and quickly locates them to obtain the field values. Through the parallel parsing technology, the key fields and their field values are matched with the whitelist. If they do not match the whitelist, it is regarded as a service security abnormality, so as to realize the rapid detection of network malformed packets. At the semantic level, the protocol behavior model is established through the frequency of occurrence of keywords, the frequency of combination and the time interval between keywords. Through the anomaly detection method based on the nearest neighbor node algorithm, discover service security anomalies, and combined with advanced event analysis and attack tracing and tracing, improve the ability to trace the source of terminal attack paths. (2) Attack correlation analysis method based on attack sequence and logical reasoning of power grid business scenarios Combined with various business application scenarios of intelligent terminals, attack correlation analysis is formed based on manual formulation and offline learning from the dimensions of different spatial levels of system objects, attack duration, and behavioral representations that disrupt normal business logic. Research the attack correlation analysis technology based on the attack sequence and logical reasoning of power grid business scenarios, and realize the accurate identification of multi-step coordinated attacks. At present, based on known attack cases and simulation verification results, 30 major categories and more than 130 sub-category attack correlation analysis models have been designed, which deeply integrates general security monitoring technology with business scenarios to greatly improve the accuracy of attack warnings.
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(3) Multi-level backup attack linkage blocking technology based on correlation analysis technology Aiming at the characteristics of the business logic structure of intelligent terminals, combined with the degree of abnormal attack threat, business impact, blocking impact range, blocking response time and other factors, a multi-level backup blocking method for network attacks is designed. A multi-level backup linkage blocking layer is formed, and then through strategic linkage control between devices at different levels, realize multi-level linkage blocking with fast response time, accurate blocking range, and reliable blocking action. (4) Terminal security threat perception technology of “terminal awareness and active early warning” Through technology, realize the transition from passive isolation-based security protection control to active monitoring and responsive security control. Focus on the security issues of massive heterogeneous power intelligent IoT terminals in an uncontrollable on-site environment, solve the problems of the generalization of the original network protection boundary and the difficulty of terminal equipment management and control brought about by the intelligent development of power grid terminals and the widespread access of IoT terminals. From the aspects of terminal hardware environment security, software computing environment security, business function security, communication security, etc., construct on-site terminal active immunity. 3.3 Technical Framework The intelligent IoT terminal security threat early warning and disposal platform mainly realizes the intelligent IoT terminal security monitoring, early warning and linkage disposal scheme. The technical architecture includes four aspects: the data source layer, the data processing layer, the data storage layer, and the data presentation layer, which can show the flow and logical relationship of the security threat information of the intelligent IoT terminal (Fig. 2). (1) Data source layer The data source layer supports various collection methods such as syslog, SNMP, and SNMP TRAP, and can obtain information such as switches, network security access gateways, IoT management platform master switch network traffic, and the security running status of edge IoT agents. Analyze this information and generate security events, which are sent to the specified data topic in the message bus. (2) Data processing layer The data processing layer includes four modules: message bus kafka, data processing service kafka Streams, data pipeline service kafka connector, and service proxy server. Message bus: It formulates corresponding source topics and result topics for sending and receiving different types of data, including uploading security events, alarm data, etc.
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Fig. 2. Platform technical architecture diagram
Data processing service kafka streams: Receive various security events uploaded by power-specific network security monitoring probe devices from the message bus kafka, and perform real-time analysis, statistics, auditing, etc. Send the corresponding result data to the message bus as a producer. Real-time processing is performed using the Kafka Streams streaming computing framework, and offline computing is implemented using database stored procedures. Data pipeline service kafka connector: Realizes receiving and uploading security events, alarm data, service agent processing data, etc. from the message bus, and stores them in the database. Service proxy server: Receive each command request from the data presentation layer, generate downlink messages for each command according to the command type, and send them to the power-specific network security monitoring probe device. The power-specific network security monitoring probe device uses the Netconf protocol to send commands to the edge IoT agent according to the corresponding device information. After the command is processed, the processing result of each command is returned to the service proxy service. The service agent performs statistical analysis on the received uplink packets, and stores the statistical analysis results in the database. (3) Data storage layer Data stores include real-time databases and relational databases. According to the business requirements of the panoramic security monitoring platform, real-time alarms, real-time monitoring and other real-time data are stored in the real-time
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database. Store historical data such as upload events, historical alarms, security audits, security monitoring, security analysis, security verification, asset management, platform management, device configuration, linkage management and control in a relational database for statistics, analysis, auditing and other functions. (4) Data presentation layer The data display layer includes the interactive background service spring boot and the foreground client electron for interaction. The interactive background service spring boot queries the mongodb real-time database by polling. When the data changes, it pushes the data to the front-end client electron in real time; at the same time, it queries, inserts and modifies the data in the database according to the request of the front-end client. For the service proxy, spring boot transparently transmits each command request of the foreground client to the service proxy server, and the specific processing is on the service proxy server. 3.4 Logic Deployment At the terminal layer, the edge IoT agent (integrated terminal) security monitoring and security access module is deployed in the edge IoT agent host system layer in a modular manner. Monitor the security of the host system, Docker system and container, generate monitoring data, and transparently forward it to the security access gateway (Type I) together with the business data of the business APP in each container. At the same time, it receives/responds to commands such as parameter configuration, license file, remote upgrade, and linkage management and control issued by the intelligent IoT terminal security threat warning and linkage disposal platform.
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At the platform layer, security monitoring probe devices are deployed to collect terminal monitoring data, and to collect and analyze core switch operation information and network traffic, generate monitoring data. Deploy an intelligent IoT terminal security threat warning and linkage disposal platform to collect, analyze and display all monitoring data/control data. It also manages the configuration, license and software upgrade of security monitoring probe equipment, edge IoT agent (integrated terminal) security monitoring and security access modules (Fig. 3).
4 Conclusion Based on the research and design ideas of the intelligent IoT terminal security threat warning and disposal platform, this paper establishes a simulation experiment platform, implants the security protection scenario model strategy, and verifies the security protection of a variety of typical intelligent IoT terminals. It focuses on testing the security threat monitoring from the intelligent IoT terminal ontology, the network analysis layer to the global monitoring layer, so as to achieve visual comprehensive analysis and early warning of the intelligent IoT terminal equipment. Through the simulation test, the effectiveness of the security threat early warning and disposal platform is verified. Acknowledgments. This work is supported by the Science and Technology Project of State Grid Jibei Power Company Limited (No. 52018E20008H).
References 1. Mukherjee, A.: Physical-layer security in the internet of things: sensing and communication confidentiality under resource constraints. Proc. IEEE 103(10), 1747–1761 (2015) 2. Tai, W.L., Chang Y.F., Lo, Y.L.: An anonymity, availability and security-ensured authentication model of the IoT control system for reliable and anonymous eHealth services. J. Med. Biol. Eng. (2018) 3. Peng, Y., et al.: Sensing network security prevention measures of BIM smart operation and maintenance system. Comput. Commun. 161 (2020) 4. Li, Q., Zhang, L., Zhou, R., et al.: Machine learning-based stealing attack of the temperature monitoring system for the energy internet of things. Secur. Commun. Netw. 2021(11), 1–8 (2021) 5. Blessy, R.: A survey on network security attacks and prevention mechanism. J. Curr. Comput. Sci. Technol. (2015) 6. Xi, R., Yun, X., Hao, Z.: Framework for risk assessment in cyber situational awareness. IET Inf. Secur. 13(2), 149–156 (2019) 7. Fei, J., Wang, X., Zhang, X., Yao, Q., Fan, J.: IoT terminal security monitoring and assessment model relying on grey relational cluster analysis. In: Liu, Q., Liu, X., Shen, T., Qiu, X. (eds.) The 10th International Conference on Computer Engineering and Networks. CENet 2020. AISC, vol. 1274, pp. 1507–1513. Springer, Singapore (2021). https://doi.org/10.1007/978981-15-8462-6_172 8. Vinayak, M., Jarin, T.: EAI endorsed transactions on energy web an overview of security issues in internet of things based smart environments. EAI Endorsed Trans. Energy Web 1–10 (2021)
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9. Kaˇnuch, P., et al.: E-HIP: An energy-efficient OpenHIP-based security in internet of things networks. Sensors (Basel Switz.) 19(22) (2019) 10. Dharfizi, A.: The energy sector and the internet of things–sustainable consumption and enhanced security through industrial revolution 4.0. (2020) 11. Krishnan, A.S.: Surplus and scarce energy: designing and optimizing security for energy harvested internet of things (2018)
Mechanism Analysis and Suppression Strategy of Ultra-low Frequency Oscillation in DC Asynchronous Networking Qingsong Liu1 , Lingfang Yang3 , Qingming Xin2 , Ziying Wang3 , Jun Deng1 , Shunliang Wang3(B) , and Chuang Fu2 1 Maintenance & Test Center, CSG EHV Power Transmission Company,
Guangzhou 510663, China 2 State Key Laboratory of HVDC, Electric Power Research Institute, CSG,
Guangzhou 51008, China 3 College of Electrical Engineering, Sichuan University, Chengdu 610065, China
[email protected]
Abstract. Recent years have witnessed emerging ultra-low frequency oscillation below 0.1 Hz issues in power systems with a high pro-portion of hydropower. This paper analyzes the mechanism of ultra-low frequency oscillation and principles of suppressing ultra-low frequency oscillation by frequency limit controller (FLC). First, a harmonic state-space model of a single-machine system under DC asynchronous networking is established. Second, the setting method of FLC parameters is proposed, and then, based on the harmonic state-space model mentioned before, this paper figured out the influence of FLC parameters on the ultra-low frequency oscillation. Finally, the PSCAD/EMTDC simulation verifications have been carried out. The simulation results are compared with the governor power system stabilizer (GPSS) suppression strategy, and the effectiveness of the proposed methods is proved. Keywords: Ultra-low frequency oscillation · Asynchronous operation · Linearized state space model · FLC · Root locus
1 Introduction HVDC transmission technology with long transmission distance, large capacity and high controllability has been widely applied in China’s large regional power network interconnection. However, in recent years, successive ultra-low frequency oscillation events with oscillation frequency lower than 0.1 Hz have been reported in the running test of DC asynchronous interconnection. In 2012, there was an abnormal fluctuation of 0.07 Hz oscillation frequency caused by the instability of governor in the island test of Jinsu DC transmission terminal [1]. In 2016, the Yunnan Power Grid experienced a longterm oscillation with a frequency of about 0.05 Hz during the asynchronous networking test between Yunnan Power Grid and the main network of China Southern Power Grid © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1168–1178, 2022. https://doi.org/10.1007/978-981-19-1528-4_119
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[2]. The characteristic of these oscillations mentioned before is that the rotational speed of the whole network shows the same variation while there is no relative oscillation among the units. Ultra-low frequency caused by negative damping and water hammer effect caused by speed regulation system are more conspicuous in the system with high proportion of hydropower units [3, 4]. In [5], a single machine and single load system is investigated in order to analyze the mechanism and characteristics of ultra-low frequency oscillation, such as oscillation frequency, damping, influencing factors, etc. However, multi-machine systems are much more common in engineering. A single machine equivalent (SIME) method is proposed to simplify multi-machine system into single machine and single load system [6], which makes it possible to analyze and mitigate ultra-low frequency oscillation in actual system. To date, a few alternative solutions have been proposed in the technical literature to reduce the risk of ultra-low frequency oscillation. The first solution is to optimize the parameters of regulator in hydro-turbine and the negative damping characteristic of the regulator is reduced at ultra-low frequency [7, 8]. However, this method is unrealistic in actual implementation process, for some hydraulic turbine parameters are fixed and some operating conditions of the system is not adjustable. Another method is based on the frequency limit controller (FLC) [9, 10]. When the frequency deviation exceeds the dead zone, the FLC automatically adjusts the DC transmission power, which can effectively suppress the ultra-low frequency oscillation. In [11], a linear state-space model considering hydro-turbine regulator is established to obtain system characteristic roots and a governor power system stabilizer (GPSS) is designed to phase compensation of the turbine speed regulator and the damping of control system, but this state-space model is not applied into ultra-low frequency oscillation of DC Asynchronous Networking. Based on further investigation of the ultra-low frequency oscillation phenomena in dc asynchronous networking with considerable hydropower, this paper analyzes the mechanism of ultra-low frequency oscillation and principles of suppressing ultra-low frequency oscillation by FLC. The remainder of this paper is as follows. In Sect. 2, a linear state-space model of the whole system is derived. In Sect. 3, the influence of FLC coefficients on ultra-low frequency oscillation is illustrated by root trajectory from the model proposed in this paper. Section 4 observes generator speed waveform on different FLC coefficients. The performance of generator coincides with what the theoretical root trajectory presents. Finally, Sect. 5 concludes this paper.
2 State-Space Model with Hydro-Turbine Regulator Effect 2.1 System Description Ultra-low frequency oscillation belongs to the category of frequency stability, which is the oscillation with a frequency lower than 0.1 Hz generated by frequency modulation. To focus on the analysis of ultra-low frequency oscillation, the sending system of dc asynchronous networking is equivalent to a single machine-infinity system illustrated in. As shown in Fig. 1, the sending-end is connected to the infinity system via a dc
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transmission line. Pdc represents the change of transmission power on the DC line. I 1 and I 2 are the current flowing to node 1 and 2, respectively. 2.2 Converter Model The linearized model of the converter can be expressed by (1). ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ VR IR Ka Kb Kc Kd ⎢ ⎥ ⎣ II ⎦ = ⎣ Ke Kf Kg Kh ⎦⎢ VI ⎥ ⎣ Idc ⎦ Vdc Ki Kj Kk Kl α
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where I R and I I represent the real and imaginary parts of the AC side current of the converter, V R and V I are the real and imaginary parts of the AC side voltage of the converter, V dc and I dc are the DC voltage and DC current and α is the trigger angle. The specific form of parameter K a -K l has been given in reference [12]. 2.3 Rectifier-Side Controller Model The rectifying side of the DC system adopts constant current control mode, and the structure is displayed in Fig. 2.
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The linearized state space equation of the rectifier-side controller can be represented as (2). X˙ αr = Idcr − Idc.order (2) αr = KIr Xαr + KPr Idcr − KPr Idc.order
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where X αr is a state variable in the process of calculation, I dcr is the DC current of the rectifier side, I dc.order is the reference value of the DC current on the rectifier side, α r is the rectifier trigger angle, K Ir and K Pr are the integral coefficient and proportional coefficient of the controller, respectively. 2.4 Inverter-Side Controller Model The inverter side of the DC system adopts constant arc extinguishing angle control, and the structure of it is depicted in Fig. 3.
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In the above equation, X αi is a state variable in the process of calculation, I dci is the inverter side DC current, V Ri and V Ii are the real and imaginary parts of the inverter AC side voltage, α i is the inverter trigger Angle, μi is the commutation Angle, γ order is the reference value of the arc extinguishing angle, K Ii and K Pi are the controller integral coefficient and proportional coefficient, respectively. 2.5 DC Transmission Line Model The topology of DC transmission line is shown in Fig. 4. In Fig. 4, Rdc is the total resistance of the DC line, V dcr and V dci represent the dc voltage on the rectifier side and the inverter, respectively. The voltage over the intermediate ground capacitor is V cap And the reactance L eff can be calculated by (4). Leff =
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2.6 AC Network Model After the DC asynchronous networking of hydropower units, the AC network at the turbine outlet is linearized as (5). ⎤ ⎡ ⎤⎡ ⎤ ⎡ G11 −B11 G12 −B12 V1R I1R ⎢ I1I ⎥ ⎢ B11 G11 B12 G12 ⎥⎢ V1l ⎥ ⎥ ⎢ ⎥⎢ ⎥ ⎢ (5) ⎣ I2R ⎦ = ⎣ G21 −B21 G22 −B22 ⎦⎣ V2R ⎦ I2I B21 G21 B22 G22 V2l Where I R , I I , V R and V I are the real and imaginary parts of the currents and voltages in Fig. 1, and subscripts 1 and 2 denote node 1 and 2, respectively. 2.7 State-Space Model of the Whole System Taking the dynamic characteristics of the excitation link into consideration, the generator adopts the third-order practical model, the excitation system employs the standard IEEE DC1A type excitation, and the turbine governor is expressed by the 8-type governor in PSASP [13]. Similarly, the state-space equations of generator, excitation system, governor and load can be derives. And combined with the above small signal models, the total state-space equation of the system after linearization can be described as (6). ˙ AX + BU X =
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In the Eq. (6), state variable X G = [X G X AVR X GOV X DC ]T , X G = [δ ω Eq ]T , X AVR = [E fd U F U R ]T , XGOV = [G1 G2 G3 G4 Pgv Pm ]T , X DC = [X αr Xαi I dcr I dci V cap ]T and input variable U = [I dc.order γorder ]T . A is a 17 × 17 square matrix, and B is a 17 × 2 matrix. Then the eigenvalues of the ultra-low frequency oscillation mode can be calculated.
3 The Influence of FLC on Ultra-low Frequency Oscillation Characteristics FLC is a significant for frequency stability control in power girds, which put frequency deviation into DC power modulation. The structure block diagram of DC FLC when its controller is PID is described in Fig. 5.
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sKd ωref
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According to the deduced small signal model and the control block diagram shown in Fig. 5, the state -space model with FLC can be obtained, so that the influence of each link in FLC on ultra-low frequency oscillation characteristics can be figured out by means of root locus. 3.1 Proportional Link
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In Fig. 6, the influence of the change of proportional coefficient K p on the trajectory of characteristic roots is expressed. As displayed in Fig. 6, the variation of K p has little effect on the oscillation frequency, but the damping ratio of the system increases when K p rises. Besides, the damping ratio is proportional to K p before K p reaches the upper limit. 3.2 Integral Link With integral coefficient T i descends, the root locus is shown in Fig. 7. It is found that T i has little influence on the characteristic roots. However, the damping ratio of the system will decrease slightly when T i falls. In other words, the integral part deteriorates the dynamic performance of the system slightly. But the damping ratio and oscillation frequency of the system are still mainly determined by the proportional coefficient.
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3.3 Differential Link In Fig. 8, how characteristic roots is influenced by K d , the coefficient of the differential link, is depicted.
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Fig. 8. Root locus with different K d
When K d climbs, the frequency hardly changes, and the damping of the system grows slightly. that is, an increase in K d can assuage the ultra-low frequency oscillation. However, the most significant suppression effect appears when the value of the proportional coefficient K p rises.
4 Coefficient Setting of FLC 4.1 Proportional Link Coefficient Simulation verification was carried out in PSCAD/EMTDC and the results are expressed in Fig. 9.
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Fig. 9. Influence of K P on the oscillation characteristics of ULFO
As shown in Fig. 9, the addition of proportional control can remarkably alleviate the ultra-low frequency oscillation phenomenon. The larger the proportional adjustment coefficient, the more obvious the suppression effect, which is consistent with the results of the characteristic root locus analysis. However, the actual adjustable capacity is limited in the operation of the DC system. Additionally, the inverter side can become the rectifier side while the rectifier side cannot be reversed. Nevertheless, the additional power injection cannot enable the power to transmit from the rectifier side to the inverter side lower than the minimum transmission power limit. Accordingly, the proportional coefficient K p has an upper limit. In addition, the limit of DC power transmission should be considered to keep the FLC in the linear adjustment range during the frequency oscillation process after disturbance. Here, the designed upper limit of K p is 15. 4.2 Integral Link Coefficient After proportional-integral FLC is introduced into the system, the waveform of turbine speed in simulation is described in Fig. 10. 1.01 1
ω/pu
0.99 0.98
KP=15 KP=15/Ti=2 KP=15/Ti=20
0.97 0.96 0
50
t/s
100
150
Fig. 10. Influence of T i on the oscillation characteristics of ULFO
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After adding the integral link, the smaller the integral link coefficient T i , the closer the system speed to 1 p.u. when it reaches the steady state, that is, the integral link can improve the static performance of the system and reduce the steady-state error while it will slightly weaken the damping ratio. 4.3 Differential Link Coefficient The generator speed waveform under the change of K d is shown in Fig. 11. 1.01 1
ω/pu
0.99 0.98
KP=15/Ti=2 KP=15/Ti=2/Kd=2 KP=15/Ti=2/Kd=10
0.97 0.96 0
50
t/s
100
150
Fig. 11. Influence of K d on the oscillation characteristics of ULFO
Comparing the proportional link, proportional-integral link and proportionalintegral-derivative link participating in the control of the DC FLC, it can be found that the proportional adjustment mainly affects the dynamic performance of the system. As the proportional coefficient increases, the system can quickly adjust the frequency to stability. And the integral link can reduce the steady-state error of the system, but it will slightly reduce the damping ratio of the system. Furthermore, the differential link can not only increase the damping ratio of the system, but also improve the dynamic performance of the system. Consequently, the FLC controller with proportional-integralderivative link involved in the adjustment is selected. The parameters are K p = 15, T i = 2, and K d = 10. Under this adjustment, the calculation results of the state-space model without FLC is displayed in Table 1. Table 1. Comparison of calculation results of state space model before and after FLC Category
Frequency/Hz
Damping ratio
No FLC
0.0419
0.00411
Adding FLC
0.0429
0.1221
After adding FLC, the system oscillation frequency is basically unchanged, but the damping ratio is greatly improved, indicating that the FLC under this adjustment can quickly adjust the frequency to stability and effectively suppress ultra-low frequency oscillations.
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5 Conclusion In this paper, the phenomenon of ultra-low frequency oscillation generated by the lower end system of DC asynchronous networking of hydropower units is analyzed and studied, and the conclusions are as follows: • A linearized state space equation is established for water turbine connected to the infinite system model through DC lines. • Based on ultra-low frequency oscillation in single-machine equivalent system, DC FLC is adopted to allay it. The eigenvalue analysis method was used to study the characteristic root curve under the change of each link coefficient of FLC. The root locus curve and simulation results illustrates that the ultra-low frequency oscillation can be rapidly attenuated by increasing the proportion parameter; the integral link can reduce the steady-state error; the differential link can improve the dynamic performance and damping characteristics of the system.
References 1. He, J., Zhang, J., Li, M., et al.: Frequency domain analysis and control for governor stability problem in islanded HVDC sending systems. Proc. CSEE 33(16), 137–143 (2013). (in Chinese) 2. Mo, W., et al.: Analysis and measures of ultralow-frequency oscillations in a large-scale hydropower transmission system. IEEE J. Emerg. Sel. Top. Power Electron. 6(3), 1077–1085 (2018) 3. Han, X., Jiang, Q., Liu, T., Li, B., Ding, L., Chen, G.: Research on ultra-low frequency oscillation caused by hydro power in hydro-dominant power system. In: 2018 International Conference on Power System Technology (POWERCON), pp. 1909–1914. Guangzhou, China (2018) 4. Chen, Y., et al.: Analysis of ultra-low frequency oscillation in Yunnan asynchronous sending system. In: 2017 IEEE Power & Energy Society General Meeting, pp. 1–5. Chicago, IL, USA (2017) 5. Lu, X., Chen, L., Chen, Y., Min, Y., Hou, J., Liu, Y.: Ultra-low-frequency oscillation of power system primary frequency regulation. Autom. Electr. Power Syst. 41(16), 64–70 (2017). (in Chinese) 6. Jiang, C., Zhou, J., Shi, P., Huang, W., Gan, D.: Ultra-low frequency oscillation analysis and robust fixed order control design. Int. J. Electr. Power Energy Syst. 104, 269–278 (2019) 7. Chen, G., et al.: Optimization strategy of hydrogovernors for eliminating ultralow-frequency oscillations in hydrodominant power systems. IEEE J. Emerg. Sel. Top. Power Electron. 6(3), 1086–1094 (2018) 8. Chen, L., et al.: Optimization of governor parameters to prevent frequency oscillations in power systems. IEEE Trans. Power Syst. 33(4), 4466–4474 (2018) 9. Li, J., Wang, B., Liu, C., et al.: Suppression scheme for ultra-low frequency oscillation based on frequency limit controller. High Volt. Eng. 45(7), 2126–2133 (2019). (in Chinese) 10. Li, W., Xiao, X., Guo, Q.: Ultra-low-frequency oscillation and countermeasures in YunnanGuangdong UHVDC sending-end system in islanded operating mode. Autom. Electr. Power Syst. 42(16), 161–166 (2018). (in Chinese)
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11. Liu, S., Wang, D., Ma, N., et al.: Study on characteristics and suppressing countermeasures of ultra-low frequency oscillation caused by hydropower units. Proc. CSEE 39(18), 5354–5363 (2019). (in Chinese) 12. Karawita, C., Annakkage, U.D.: Multi-infeed HVDC interaction studies using small-signal stability assessment. IEEE Trans. Power Deliv. 24(2), 910–918 (2009) 13. Jiang, Q., Li, B., Liu, T.: Investigation of hydro-governor parameters’ impact on ULFO. IET Renew. Power Gener. 13(16), 3133–3141 (2019)
Research on Transformer Fault Diagnosis Technology Based on Adaboost-Decision Tree and DGA Xiu Zhou1,2,3 , Tian Tian1,2,3(B) , Pengcheng Zhang1,2,3 , Yi Wang1,2,3 , Yan Luo1,2,3 , Yunlong Ma1,2,3 , Xiuguang Li1,2,3 , Ninghui He1,2,3 , and Jun Sun1,2,3 1 Power Science Research Institute of State Grid Ningxia Power Co., Ltd, Ningxia, China
[email protected]
2 Shizuishan Power Supply Company State Grid Ningxia Electric Power Co., Ltd,
Ningxia, China 3 PanDian SCI-Technology Company, Wuhan, China
Abstract. The fault of transformer is always manifest in the content of the gas dissolved in the oil. The combination of dissolved gas analysis (DGA) technology and machine learning method has been applied to the fault diagnosis of transformer. Aiming at the problem of insufficient accuracy when using a single learner for transformer fault diagnosis, the decision tree is used as a weak learner, and the weak learners with different weights are obtained through continuous iteration of AdaBoost ensemble learning algorithm, and four AdaBoost-decision tree are constructed by using the idea of binary tree. The transformer fault data set in the power field is used to verify the model. The results show that the diagnosis accuracy of the ensemble model is better than that of a single learner such as decision tree and support vector machine. It is a more suitable transformer fault diagnosis model. Keywords: Transformer · Adaboost · Decision tree · Fault diagnosis · Dissolved gas in oil
1 Introduction As the most expensive equipment in the power system, power transformer is irreplaceable. Its safety and stability will influence the security and constancy of the power system [1]. Transformer fault not only has irreversible harm to the power system, but also the economic loss caused by power failure is inestimable. Therefore, it is especially important to correctly diagnose the fault of transformer and prepare the maintenance scheduling reasonably [2]. When the transformer is faulty, transformer oil decomposes a large number of gases including hydrogen (H2 ), methane (CH4 ), ethane (C2 H6 ), ethylene (C2 H4 ), acetylene (C2 H2 ), carbon monoxide (CO), carbon dioxide (CO2 ) dissolved Project Supported by Science and Technology Project of SGGC(5229DK200004). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1179–1189, 2022. https://doi.org/10.1007/978-981-19-1528-4_120
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in the oil. The content of these gases is closely related to the internal transformer faults. According to the dissolved gas in the oil to analyze whether the transformer is malfunctioning, which is called dissolved gas analysis (DGA) [3]. The DGA is one of the most critical conventional detection methods for oil-immersed power transformers at present. It is widely used in transformer fault diagnoses due to its strong timeliness and the advantage of being almost immune to external electromagnetic fields [4]. At present, a large number of scholars are studying how to accurately diagnose whether potential faults have occurred in transformers according to the content of dissolved gas in the oil, such as IEC’s three-ratio method, David Triangle, etc. These ratio technique are easy to handle and are used by a large number of electric power workers in the on-site inspection of electric power [5, 6]. But in fact, the mapping relevance between the substance of gases dissolved in the oil and transformer fault types is complex. The ratio of traditional method has some shortcomings, such as missing codes and the accuracy being not high. Its hard to use these simple ratio technique to diagnose whether transformers has broken down. With the development of intelligent algorithm, the technology of transformer fault diagnoses has emerged as the times require, which combining machine learning algorithm and the technology of DGA. For example, the nonlinear mapping relevence between digging the substance of the dissolved gas in the oil and fault types by using artificial neural networks. By the popularity of machine learning, some researcher attempt to use machine belief networks for transformer fault diagnoses [7, 8]. Yet, deep neural network sometimes needs a mass practicing sample, and transformer fault is a small probability event, so it is difficult to construct a relatively complete sample set. It still has a very strong generalization ability to support vector machine (SVM) under the sample data set small in quantity, that means quite in line with the transformer fault feature of sample set. However, the transformer diagnostic model based on support vector machine is greatly affected by its parameters [9–16]. The research shows that the effect of single machine learning model is not good, and the entire learning method can effectively enhance the precision of transformer fault diagnosis [17–23]. In view of this, the Adaboost-decision tree ensemble algorithm diagnosis model is suggested in this paper. Firstly, the decision tree is used as the weak learner for fault diagnoses, and then the Adaboost ensemble framework is used to form a strong learning machine. Then, switch the multiclass problem into a binary classification problem which use the binary tree method and verify the results by the k-fold verification method. The results are compared with the support vector machine and decision tree method, and then the influence of different input features on the model performance is explored.
2 Adaboost-Decision Tree Adaboost (Adaptive Boosting) algorithm was suggested by Yoav Freund and Robert Shapire. Its adaptability is that the misclassified samples of the anterior weak classifier will be enhance, and all the weighted samples will be used to train the next weak classifier again. Meanwhile, a fresh weak classifier will be added in each round until the small enough error in advance or the maximum number in advance of iterations is attained [24].
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The detailed process of Adaboost algorithm is as follows: First, build a simple learner from the original data, then T training sets are generated by bootstrap method, use the T training set to form the T classifiers, these T weak learner are interrelated, finally assembled to form a strong learner. ➀ For a training data set is T = {(X1, Y1), (X2, Y2),···(XN, YN)}, each sample is given the same initial weight, which is: D1 = (w11 , w12 · · · w1N ), w1i =
1 , i = 1, 2, · · ·N N
(1)
➁ For m iterations, Gm(x) is used to represent the learner of the first m-wheel iteration, em is used to represent the error, and the weight formula is: αm =
1 − em 1 log 2 em
(2)
As can be known from the formula (3), the learner with small error will play a larger role in the final learner. ➂ The updated weight formula is as follows: wm+1,i =
wmi exp(−αm yi Gm (xi )) Zm
(3)
➃ Form a weak learner: f (x) =
M
αm Gm (x)
(4)
m=1
The ultimate learner is: G(x) = sign(f (x)) = sign(
M
αm Gm (x))
(5)
m=1
Decision tree (DT) is a very simple deep learning classification algorithm, that characterized by simple achievement, simple understanding and quick speed. Its regarded as a tree-like prediction model, and it includes three types of nodes: root node, internal node, and leaf node. The decision tree model is shown in Fig. 1.
Root Node
Internal Node
Internal Node
Leaf Node
Leaf Node
Leaf Node
Leaf Node
Fig. 1. Decision tree model.
Leaf Node
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Decision tree is a supervised learning. Supervised learning refers to a given set of samples, every sample has a set of quality and a type, which are decisioned in advance. Then a classifier is gained by learning, which can give the correct classification to the new targets. This machine learning is called supervised learning.In the formation process of decision tree, the basis which is divided into layers by the decision tree is called attribute value. Nowadays, the technique for dividing suitable qualities are: include information gain, information gain rate, Gini index, which corresponding algorithms of ID3 decision tree, C4.5 decision tree and CART. Based on the above principles, the Adaboost-DT diagnosis model is constructed, and it’s specific flow chart is as follows (Fig. 2):
T Sample training sets
Weight D(1)
Weight D(2)
ę Update weights
Weight D(n)
Training set with weight D(1)
Training set with weight D(2)
Training set with weight D(n)
DT error(1)e1
DT error(2)e2
DT error(n)en
Combination strategy
Adaboost-DT
Fig. 2. AdaBoost-DT model process.
3 Results and Analysis According to the relevant regulations of IEC, the internal faults of transformer are divided into normal (N), medium and low temperature overheating (T1), high temperature overheating (T2), low energy discharge (D1), high energy discharge (D2), transformer fault data collected from power field and various published papers, totaling 430 groups, contains 5 kinds of characteristic gas content, the five characteristic gases are hydrogen (H2 ), methane (CH4 ), ethane (C2 H6 ), ethylene (C2 H4 ), acetylene (C2 H2 ), and the sample data set is down is shown in Table 1. Because the Adaboost framework is a binary classification algorithm, in order to effectively identify the transformer states of all categories in the dataset, the binary tree technique is adopted, the multi classification matter is transformed into binary classification matter, since the transformer dataset used contains five transformer types, only four Adaboost-DT diagnosis models are needed, as shown in Fig. 3. Using the data set shown in Table 1 and the process adopted in Fig. 3, four different AdaBoost decision trees are constructed, in which the data of adaboost-DT1 includes 100 groups of fault free transformers and 330 groups of fault types; The data of adaboost-DT2
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Table 1. Sample data set. Transformer type
Sample data/Group
Coding rules
Normal Thermal malfunction
Electrical failure
Total
100
N
Medium-low temperature overheating
100
T1
High temperature overheating
100
T2
High energy discharge
70
D2
Low energy discharge
60
D1
430
Fig. 3. Binary tree AdaBoost-DT model.
includes 200 sets of thermal fault transformers and 130 sets of electrical fault transformers; The data of adaboost-DT3 includes 100 groups of medium and low temperature overheating and 100 groups of high temperature overheating; The data of adaboost-DT4 includes 60 groups of low energy discharge and 70 groups of high energy discharge. Randomly generated training set is used to train the samples, and the randomly generated test set is used to verify the accuracy of diagnosis. In order to reduce the impact of the random division of training set and test set on the accuracy of the model, the k-fold method is used to testify the accuracy of the model. The original samples are divided into k parts, each subset is made into a test set, and the other k-1 parts are treated as the training set, The average of the evaluation accuracy of the K models obtained in this way is used as the performance index of the final transformer state evaluation model (k is 10 in this paper).
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Fig. 4. Adaboost-DT 1 model simulation performance.
Fig. 5. Adaboost-DT 2 model simulation performance.
Fig. 6. Adaboost-DT 3 model simulation performance.
Fig. 7. Adaboost-DT 4 model simulation performance.
For AdaBoost algorithm, the number of weak learners generally obeys the law of large numbers. The more the number of weak learners T, the higher the accuracy of the final model. However, the too large number of weak learners in the model will lead to too slow training speed. This paper sets the number of weak learners to 100. The training errors of four different Adaboost-DT transformer diagnostic models are shown in Figs. 4, 5, 6, 7. From the model experiment results in Fig. 4, 5, 6, 7, it is shown that the error of training data decreases with the increase of the number of weak learners (decision trees) in the model. To explore whether the related methods have unique advantages, and to reduce the impact of the division of training set and test set on the model accuracy, the K-fold method is used to verify the model accuracy. The original samples are divided into K parts on average, each subset is tested once, and the other K-1 parts are used as training sets. The average diagnostic accuracy of the K models obtained in this way is used as the performance index of the final transformer fault diagnosis model. Also, since the performance of the model varies when different input characteristics are used, the common input characteristics are shown in Table 2.
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Table 2. Common model input characteristics. Input eigenvector
Input characteristics
Original features
H2 , CH4 , C2 H6 , C2 H4 , C2 H2 , total hydrocarbons
Three ratio
C2 H2 /C2 H4, CH4 /H2, C2 H4 /C2 H6
David triangle
CH4 /CH4 + C2 H2 + C2 H4 , C2 H2 /CH4 + C2 H2 + C2 H4 , C2 H4 /CH4 + C2 H2 + C2 H4
Original feature + Three ratio
H2 , CH4 , C2 H6 , C2 H4 , C2 H2 , total hydrocarbons, C2 H2 /C2 H4 CH4 /H2 C2 H4 /C2 H6
In order to explore the performance of different input features and different fault diagnosis models, different input feature quantities and different models are compared under the same training set and test set divided by K-fold. Among them, the number of iterations of AdaBoost is 100 times. DT does not do any branch reduction. SVM model uses grid search to optimize C and G parameters. The comparison results are shown in Table 3 at ➀ of the binary tree model nodes (that is identifying whether there is a fault type). The comparison results at node ➁ of the binary tree model (that is identifying the types of electrical and thermal faults) are shown in Table 4. The comparison results at node ➂ of the binary tree model (that is identifying the types of high and medium-low temperature overheating) are shown in Table 3. The comparison results at node ➃ of the binary tree model (that is identifying high and low energy discharge types) are shown in Table 5. Table 3. Performance impact of different algorithm models and different input characteristics (identification of fault types). Algorithm model
Input feature
Adaboost-DT
Original feature
0.934087
Three ratio
0.903319
David triangle
0.859974
Original feature + Three ratio
0.97183
DT
SVM
Average accuracy
Original feature
0.9172813
Three ratio
0.889517
David triangle
0.8441012
Original feature + Three ratio
0.95771
Original feature
0.9144506
Three ratio
0.8486033
David triangle
0.8386033
Original feature + Three ratio
0.957746
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Table 4. Performance impact of different algorithm models and different input characteristics (identification of electrical and thermal fault types). Algorithm model
Input feature
Average accuracy
Adaboost-DT
Original feature
0.780562
Three ratio
0.78046
David triangle
0.81493
Original feature + Three ratio
0.8682237
DT
SVM
Original feature
0.747521
Three ratio
0.739202
David triangle
0.7545456
Original feature + Three ratio
0.80155
Original feature
0.7633989
Three ratio
0.755173
David triangle
0.80155
Original feature + Three ratio
0.8256194
Table 5. Performance impact of different algorithm models and different input characteristics (identification of high and medium and low temperature overheating fault types). Algorithm model Adaboost-DT
DT
SVM
Input feature
Average accuracy
Original feature
0.759786
Three ratio
0.8057666
David triangle
0.72626
Original feature + Three ratio
0.796682
Original feature
0.664785
Three ratio
0.739892
David triangle
0.629464
Original feature + Three ratio
0.703062
Original feature
0.6625708
Three ratio
0.7114218
David triangle
0.6896464
Original feature + Three ratio
0.7168042
According to Table 3, 4, 5 and 6, Different transformer fault diagnosis models such as Adaboos-DT, DT and SVM are compared, and compared with the single algorithm
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Table 6. Performance impact of different algorithm models and different input characteristics (identification of high and low energy discharge fault types). Algorithm model
Input feature
Average accuracy
Adaboost-DT
Original feature
0.761805
Three ratio
0.775
David triangle
0.745
DT
SVM
Original feature + Three ratio
0.795825
Original feature
0.668055
Three ratio
0.6715275
David triangle
0.75
Original feature + Three ratio
0.609725
Original feature
0.706945
Three ratio
0.74514
David triangle
0.77523
Original feature + Three ratio
0.73125
model, the performance of AdaBoost integration method is improved, This is because AdaBoost connects weak learners through an ensemble method, and its accuracy is higher than that of single models; comparing the effects of different input features on the performance of each diagnostic model, there is no obvious law on the advantages and disadvantages of the three input features: original feature, three ratio and David triangle, but when almost all models adopt the input feature of original feature + three ratio, the diagnostic accuracy of the model is improved. Therefore, the input characteristic of original and three ratio can better characterize the fault type of transformer.
4 Conclusion According to the combination of DGA and AdaBoost-decision tree, this paper constructs a transformer fault diagnosis model, verifies the diagnosis accuracy of the model by using k-fold method, compares it with decision tree, SVM and other models, and compares the impact of different input characteristics on the performance of the model. The conclusions of this paper are as follows: (1) Compared with the single algorithm model, AdaBoost ensemble method has remarkable integration effect and improved the accuracy of diagnosis model. It is a more suitable transformer fault diagnosis model; (2) Comparing the effects of four different input features on the model performance, it is found that the model performance is the best when the input feature of original feature and three ratio is used.
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References 1. Gonzales Arispe, J., Mombello, E.: Power transformer condition assessment using DGA and FRA. IEEE Latin Am. Trans. 14(11), 4527–4533 (2016) 2. Faiz, J., Soleimani, M.: Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis. IEEE Trans. Dielectr. Electr. Insul. 25(5), 1798–1806 (2018) 3. Mansour, D.A.: Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases. IEEE Trans. Dielectr. Electr. Insul. 22(5), 2507–2512 (2015) 4. Moodley, N., Gaunt, C.T.: Low Energy Degradation Triangle for power transformer health assessment. IEEE Trans. Dielectr. Electr. Insul. 24(1), 639–646 (2017) 5. Gouda, O.E., El-Hoshy, S.H., E.L.-Tamaly, H.H.: Proposed three ratios technique for the interpretation of mineral oil transformers based dissolved gas analysis. IET Gener. Transm. Distrib. 12(11), 2650–2661 (2018) 6. Mendes Barbosa, T., Goncalves Ferreira, J., Antonio Ferreira Finocchio, M., Endo, W.: Development of an application based on the Duval triangle method. IEEE Latin Am. Trans. 15(8), 1439–1446 (2017) 7. Dai, Song, H., Sheng, G., Jiang, X.: Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network. IEEE Trans. Dielectr. Electr. Insul. 24(5), 2828–2835 (2017) 8. Dai, J., Song, H., Yang, Y., Chen, Y., Sheng, G., Zhang, X.: Relu DBN method for transformer fault diagnosis based on gas in oil analysis. Power Grid Technol. 42(2), 658–664 (2018). (in Chinese) 9. Zhang, Y., et al.: A fault diagnosis model of power transformers based on dissolved gas analysis features selection and improved krill herd algorithm optimized support vector machine. IEEE Access 7, 102803–102811 (2019) 10. Li, Zhang, Q., Wang, K., Wang, J., Zhou, T., Zhang, Y.: Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Trans. Dielectr. Electr. Insul. 23(2), 1198–1206 (2016) 11. Tian, F., et al.: Transformer fault diagnosis model based on feature selection and ICA-SVM. Power Syst Prot. Control 47(17), 163–170 (2019). (in Chinese) 12. Illias, A., Chan, K.C., Mokhlis, H.: Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis. IET Gener. Transm. Distrib. 14(8), 1575–1582 (2020) 13. Chen, H., Peng, H., Shu, N., Zhang, K., Wei, A.: Transformer fault diagnosis based on bat algorithm for least squares double support vector machine. High Volt. Technol. 44(11), 3664–3671 (2018). (in Chinese) 14. Min, Y., Ma, X., Qu, Z., Mo, J., Lv, X.: Research on improved PSO optimal SVM algorithm for transformer DGA fault diagnosis. Mod. Electron. Technol. 41(15), 124–128 (2018). (in Chinese) 15. Kari, T., Gao, W., Zhang, Z., Mo, W., Wang, H., Cui, Y.: Transformer fault diagnosis based on support vector machine and genetic algorithm. J. Tsinghua Univ. (Nat. Sci. Edn.) 58(7), 623–629 (2018). (in Chinese) 16. Kari, T., Gao, W., Zhao, D., Abiderexiti, K., Mo, W., Wang, Y., Luan, L.: Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm. IET Gener. Transm. Distrib. 12(21), 5672–5680 (2018) 17. Wang, X., Han, T.: Transformer fault diagnosis based on Bayesian optimized random forest. Electr. Meas. Instrum. 1–8, 4–336 (2020). (in Chinese)
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18. Liu, Y., Xu, Z., Li, G., Xia, Y., Gao, S.: Summary of application of artificial intelligence-driven data analysis technology in power transformer status maintenance. High Volt. Technol. 45(2), 337–348 (2019). (in Chinese) 19. Liu, Y., Fu, H., Xu, Z., Li, G., Gao, S., Dong, W.: Transformer fault diagnosis technology based on AdaBoost-RBF algorithm and DSmT. Electr. Autom. Equip. 39(6), 166–172 (2019). (in Chinese) 20. Huang, X., Wang, X., Tian, Y., Li, L., Cao, W.: Transformer fault diagnosis method based on PSO-ELM fusion dynamic weighted AdaBoost. High Volt. Electr. Appl. 56(5), 39–46 (2020). (in Chinese) 21. Zhou, Q., Wang, S., Liao, R., Sun, C., Xie, H., Rao, J.: Transformer fault diagnosis method based on AdaBoost optimized cloud theory. High Volt. Technol. 41(11), 3804–3811 (2015). (in Chinese) 22. Ghoneim, S.M., Taha, I.B.M., Elkalashy, N.I.: Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis. IEEE Trans. Dielectr. Electr. Insul. 23(3), 1838–1845 (2016) 23. Raichura, B., Chothani, N.G., Patel, D.D.: Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci. Meas. Technol. 14(1), 111–121 (2020) 24. Senoussaoui, M.E.A., Brahami, M., Fofana, I.: Combining and comparing various machinelearning algorithms to improve dissolved gas analysis interpretation. IET Gener. Transm. Distrib. 12(15), 3673–3679 (2018)
The Application of Pulsed Corona Discharge Plasma Technology in Air Pollution Control Lingang Weng, Xiaodong Shi, Qing Ye, Keji Qi, Shuai Zhang(B) , Licheng Zheng, and Yujie Liu Zhejiang Doway Advanced Technology Co., Ltd, Shang Cheng District, Hangzhou 310052, China [email protected]
Abstract. The application of pulsed corona discharge plasma technology in air pollution control is analyzed by experiment on double dielectric barrier discharge reactor (DDBD) and pulsed corona discharge reactor (PCD). The results show that the optimal operating frequency of PCD is 1600 Hz, operating power is 1–4.8 kW. PCD reactor is easier to control electric field intensity in appropriate value because of wider operating power and operating voltage. When it is applied in sewage pumping station with an activated carbon adsorption device at the tail, a better deodorization effect is obtained. In addition, different operating frequencies mainly affect the output power, but do not affect the development trend, and different application sites correspond to different output power, such as deodorization, dioxin purification, fresh air system. Pulsed corona discharge plasma deodorization technology has the advantages of good deodorization effect, controllable ozone escape and flexible operation. With the development of power supply technology, the process cost is gradually reduced, so it has good market application value. Keywords: Non-thermal plasma · Odor control · Dielectric barrier discharge · Pulsed corona discharge
1 Introduction As we all know, low-temperature plasma technology can be applied to environmental protection, energy, biomedicine, material modification and other fields [1, 2]. As a power source for plasma generation, high voltage pulse excitation power supply has incomparable advantages over general high frequency AC and DC. Under the action of the high-voltage pulse power source, a large number of low-temperature plasma, including high-energy electrons, ions, free radicals, excited molecules, etc., can produce strong physical and chemical effects on the material molecules entering the plasma reaction region, so as to achieve the purpose of dissociation, transformation, removal and modification [3, 4]. For the application of pulsed corona discharge plasma, the relevant research achievements have been accumulated. Ding Xinlong et al. [5] studied the removal of dust with © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1190–1198, 2022. https://doi.org/10.1007/978-981-19-1528-4_121
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high specific resistance by pulse discharge technology. Experiments by several scholars have proved that pulsed corona reactor has a good degradation effect on a variety of organic pollutants such as benzene, toluene, xylene, dichloromethane and trichloroethylene [6–8]. Ning Ping et al. [9] summarized the application of pulsed plasma in deodorization. Some scholars [10, 11] studied the effects of plasma application on novel Coronavirus inactivation. Yan Keping pointed out the key problems to be solved in the practical application of such technology and proposed the improvement direction [12]. Although some technical foundation has been accumulated, there is no systematic analysis of the trend of plasma action process and power supply parameter control. Based on this, this paper carries out experiments and analyzes the application trend and characteristics of pulsed discharge plasma combined with relevant theory and practice.
2 Experiment 2.1 Experimental Device The experimental device is shown in Fig. 1. During the experiment, the fan will introduce the gas into the reactor and discharge it after the plasma action. The outlet is set up with a detection hole to detect the gas composition. It is made of a stainless steel cylinder and an electrode line, and the discharge is of a toothed structure. Related structural dimensions are shown in Table 1. The discharge electrode wire is connected to the high voltage power supply. The power supply is high voltage pulsed plasma power supply (independently manufactured). The output voltage is 0–30 kV, the output frequency is 0–2000 Hz, and the pulse width is 1–5 μm. During the experiment, the output parameters of the power supply are controlled by changing the frequency and the conduction Angle. The discharge effect during the experiment is shown in Fig. 2. The discharge is relatively uniform and the device assembly size deviation is small.
Fig. 1. Schematic diagram of experimental equipment
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Wire diameter
Length
Gap
Number
ϕ140 mm
ϕ60 mm
1m
40 mm
64
Fig. 2. Plasma discharge effect
2.2 Experimental Method During the experiment, air was used as the working medium, and the fan air volume was controlled at 3000 m3 /h. Oscilloscope (OSC, DPO3034, Tektronix, USA) and mass analyzer (PQ3198, HIOKI, Japan) were used to detect the output signal of power supply, such as voltage, current, frequency, power, etc. Export ozone concentration by ozone analyzer (Shanghai Precision Instrument) and nitrogen oxide concentration by flue gas analyzer (F550CI, Wohler, Germany). The ozone yield is calculated by formula (1). Where: q Ozone power yield, g/kW; C is the export concentration of ozone, ppm; Q is the gas flow rate, m3 /h; P is the power supply, kW. q=
c ∗ Q ∗ 2.14 1000 ∗ P
(1)
In the process of reactor discharge, the corona field intensity was calculated by Peek empirical formula (2) [13]. Where, E0 represents the electric field strength, V/m; A is the discharge electrode radius, m; 1 indicates the roughness of the electrode surface (smooth surface is 1); Delta is the relative density of air. δ 6 E0 = 3.1 × 10 ε δ+0.03 (2) a Under the experimental conditions, it can be simplified as (3): 0.03 6 E0 = 3.1 × 10 1 + √ a
(3)
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The electronic characteristic energy is calculated according to Ma Tianpeng’s formula (4) [14] and Chen Junhong’s result [13]: e = EL
(4)
Where E is represented by reduced electric field intensity E/N (the ratio of electric field intensity and gas molecular density); (L = 7.9535 × 10−7 m).
3 Results 3.1 Electric Field Intensity According to the experimental conditions, the intensity of the corona onset electric field is calculated, which is about 36.4 kV/cm, and the corresponding reduced electric field is E/N = 146 Td, and the characteristic energy of the electron is about 3–4 eV. Under this energy condition, it is conducive to the separation of most organic molecules, but has no significant effect on oxygen molecules [15–17]. When the reduced electric field intensity is 100 Td, the electron characteristic energy distribution is in the range of 0–20 eV, and the distribution is mainly before 8.4 eV. At this time, ozone will not be generated basically [18, 19]. After the formation of stable plasma discharge area, discharge area is divided into corona discharge ionization and electron transfer, ionization zone internal electric field is stronger, the electronic characteristics of energy can be up to more than 50 eV, corona edge dizzy about the electric field intensity, electric field strength to stay away from discharge electrode electric field intensity decreases, the power supply output voltage is higher, the stronger the electric field intensity on average. Under the experimental conditions, MAXWELL is used to simulate the electric field distribution in the discharge area, and the results are shown in Fig. 3. When the peak pulse voltage is 17 kV, the electric field intensity in most areas reaches 3 kV/cm, and the maximum electric field intensity reaches 11.5 kV/cm. The obtained electric field intensity is far less than the theoretically calculated value, which is only for qualitative analysis, rather than quantitative investigation.
Fig. 3. Electric field distribution in the discharge area
In the application of pulse discharge device, the main principle of action is highenergy particle dissociation and chemical oxidation, according to the needs of different
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industries, can be targeted to enhance or weaken the high-energy particle dissociation or chemical oxidation. Such as the disinfection process mainly uses the electrostatic effect and high-energy particles, the electric field intensity does not have to be too strong, and denitrification application, the need for a sufficient amount of strong oxidizing substances, it is necessary to increase the voltage, raise the field strength, improve the average electron energy. According to this rule, power output parameters are controlled and process design parameters are regulated according to working conditions. In addition, the device adopts the superposition combination of multi-stage discharge plates to form the effect of step by step purification, which effectively reduces the required average electric field intensity and better realizes the control of ozone escape. 3.2 Secondary Voltage and Power Curve
25
peek voltage/kV
20 15 10
1200Hz(1) 1400Hz(2) 1600Hz(3) 1800Hz(4)
5 0 20
30
40
50
60
70
conduction angle/
80
90
100 110
o
Fig. 4. Relationship between conduction angle and secondary voltage
6 5
power/kW
4 3 2
1200Hz(1) 1400Hz(2) 1600Hz(3) 1800Hz(4)
1 0 20
30
40
50
60
70
conduction angle/
80 o
90
100 110
Fig. 5. Relationship between conduction angle and power
Figure 4 and 5 show the changes of secondary voltage and power in the process of adjusting the conduction Angle. As can be seen from the figure, in the process of voltage raising, the secondary voltage appears an inflection point between 15–16 kV, and the
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conduction Angle is between 48° and 53°. When the frequency is increased, the conduction Angle is larger under the same secondary voltage. Under the same conduction Angle, the power is larger. When the frequency is 1200 Hz, the maximum power reaches 3.1 kW, and when the frequency is 1800 Hz, the maximum power reaches 5.6 kW. 3.3 Ozone Yield and Power at Different Voltages The relationship between ozone yield and power at different voltages is shown in Fig. 6. Therefore, with the increase of voltage, power continues to increase. The ozone yield fluctuated from zero to high and from low to high, and experienced two turning points, corresponding to the voltage of 14–15 kV and 19–20 kV respectively. 30
1200Hz(1) 1400Hz(2) 1600Hz(3) 1800Hz(4)
yield of O3(g/kW)
25 20 15 10 5 0 5
10
15
20
25
peek voltage/kV
Fig. 6. Relationship between secondary voltage and ozone yield
With the increase of conduction Angle, the output voltage gradually increases, the electron energy near the electrode increases, the high-energy electron ionization region gradually expands outward, and the average field strength in the ionization region gradually increases. Before 14–15 kV, the average field strength in the region is low, oxygen molecular ionization is incomplete, and ozone is difficult to form. After 15 kV, an obvious corona phenomenon can be observed, forming a large and stable ionization region. The electric field intensity reaches above 8.4 eV, and the ozone concentration and ozone yield increase rapidly with the increase of the voltage. After 20 kV, the particle energy is too high, and the formation and decomposition of ozone tend to balance. 3.4 Power and Ozone Concentration The relationship between power and ozone concentration is shown in the Fig. 7. Below about 1 kW, ozone is barely produced, and then the concentration gradually increases. Parameters can be adjusted according to different process needs and different ozone requirements to control the amount of ozone within the design range. The parameters control of power supply and process in different places is shown in Table 2. (Note: The power frequency is 1800 Hz, and the air volume is about 3000 m3 /h).
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conce ntra tion of O 3
18 15 12 9
1200Hz(1) 1400Hz(2) 1600Hz(3) 1800Hz(4)
6 3 0 0
1
2
3
4
5
6
power/kW
Fig. 7. Ozone concentration under different power
Table 2. Parameters setting under different application conditions Application types
Fresh air system
Disinfection sterilization
Natural preservation
Odor purification
Remove NOx, dioxins
Power/kW
0.5–1
1–2
1–3
2–4
3–5
Ozone/ppm
0
0 indicates charging, and xi,t < 0 indicates discharging. T represents the total length of each predictive control time domain. N represents the total number of EVs in each predictive control time domain. The EVs’ charging and discharging should meet the following constraints: ⎧ EV ,i EV ,i ⎪ ⎨ Pdisc,max < xi,t < Pchar,max ∀i ∈ N (14) xi,t = 0 t > toff ,i ⎪ ⎩x = 0 t < tin,i i,t
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EV EV where Pchar,max and Pdisc,max indicate the maximum charging power and discharging power of the i-th vehicle. toff ,i indicates the off-grid time of the i-th vehicle. tin,i indicates the grid-connected time of the i-th vehicle. In addition, during charging and discharging, the vehicle is constrained by the target electric energy and state of charge, as shown in Eqs. (15)–(16): Eiin + xi,1 · t ≥ Eimin (15) off EV Eimin = Ei − (toff ,i − tcur − 1) · Pchar,max
where Eiin represents the initial electric energy of the i-th vehicle when connected to the grid. Eimin indicates the minimum electric energy allowed to meet the charging task. After the time charging and discharging command is issued until the vehicle is off the grid, when charging at the maximum power from Eimin , the charging target can be off guaranteed. Ei indicates the target electric energy when vehicle i is off the grid. tcur indicates the current time. 0 ≤ Eiin +
tT
cap
t · xi,1 ≤ Ei
(16)
t=tcur cap
where Ei
indicates the maximum capacity of the i-th vehicle.
4.3 Super Capacitor Assuming that, at time t, the voltage corresponding to the super capacitor (SC) access node i is U i (t), the susceptance of SC is Bsci(t), and the RP of SC Qsci(t) is as follows: QSCi (t) = Ui2 (t)BSCi (t) (17) BSCi (t) = BSCi,0 (20 δ0 (t) + 21 δ1 (t) + ... + 2λi δλi (t)) Where Bsci ,0 is the unit adjustment step of SC’s susceptance. 4.4 Wind Turbine The wind turbine is limited by the maximum apparent capacity of inverters during the operation: 2 2 2 PWT ,t + QWT ,t ≤ SWT ,max
(18)
where PWT ,t and QWT ,t respectively represent the AP and RP output of the wind turbine at time t. In addition, the following requirements for power factor should be met during the operation of the wind turbine: QWT ,t = PWT ,t · tan(arccos ϕ) (19) cos ϕmin ≤ cos ϕ ≤ cos ϕmax where cos ϕmax and cos ϕmin respectively indicate the maximum and minimum allowable power factor.
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4.5 Photovoltaic Cell The operation areas of photovoltaic cells (PV) adopting the OID strategy are as follows [12] (Fig. 3):
Fig. 3. Operation area of photovoltaic OID strategy
The operation of PV is limited by the maximum apparent power and power factor: 2 2 2 PPV ,t + QPV ,t ≤ SPV ,max (20) −PPV ,t tan θt ≤ QPV ,t ≤ PPV ,t tan θt where PPV ,t and QPV ,t respectively represent the AP and RP output of PV at time t. θt is the power factor angle. 4.6 SVC Model Using the simplified model, the RP output of SVC at time t QSVC (t) is continuously adjustable between the upper and lower limits: QSVC,min ≤ QSVC (t) ≤ QSVC,max
(21)
where QSVC,min and QSVC,max indicate the upper and lower limits of SVC.
5 Example Analysis 5.1 Basic Data Based on the IEEE-33 distribution network system (see Fig. 4), a simulation is conducted to verify the effectiveness of the important load access node voltage optimisation control strategy. Photovoltaic cells and wind turbines access nodes 9 and 28; nodes 12 and 29 are equipped with two SVCs, with adjustable ranges of [−100 to 100 kvar] and [−100 to 200 kvar]; SC access node 11, which unit adjustment step is 0.50 Mvar, and the maximum capacity is 5 Mvar. The important load is connected to node 26, and the local controllable equipment is battery energy storage and EVs. The MPC strategy starts every 15 min to optimise the voltage of the next three time periods (45 min) and execute the optimisation results of the first period. The total control time is 24 h, and the reference control voltage is the rated voltage.
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Fig. 4. IEEE-33 example system
Figure 5 shows the predicted and actual values of the wind power, photovoltaic power and load.
a) Daily load fluctuation
b) Daily fluctuation of wind power and photovoltaic
Fig. 5. Predicted and actual values of the wind power, photovoltaic and load
Tables 1 and 2 present the parameters of EV and energy storage battery, respectively. We assumed that the time and electric energy of EVs at the time of connection-grid and off-grid follow a normal distribution. Table 1. Electric vehicle parameters.
EV
Pchar,max /kW
Pdisc,max /kW
tin
toff
Ein /kW.h
Eoff /kW.h
4
−4
N(15.9, 4.0)
N(7.5, 4.0)
N(4.8,1.2)
N(9.6, 0.2)
Table 2. Parameters of battery energy storage
Battery
Pchar,max /kW
Pdisc,mx /kW
ηchar
ηdisc
Erate /kW.h
SSOC,ini
300
200
0.98
0.95
400
0.5
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5.2 Simulation Results and Analysis Taking the initial time as an example, Fig. 6 shows the voltage distribution of each node obtained by the state estimation and the actual voltage:
Fig. 6. Comparison between state-estimated voltage and the actual voltage
Compared with the actual voltage, the maximum deviation of the state estimation result after considering the measurement error is 0.2%, which has high accuracy and can effectively ensure the accuracy requirements of voltage control. Figure 7 depicts the control strategy of each controllable equipment and the voltage control effect. The MPC coordinates the local and wide-area controllable devices to control the voltage of the important load access nodes. According to the results, the proposed method can realise the node voltage tracking voltage reference value, effectively deal with the GV fluctuation caused by wind and solar uncertainty and achieve a good voltage control effect. In addition, the EVs can meet the charging demand whilst accepting the dispatching to ensure the travel of users. The SOC of the energy storage battery is maintained at approximately 0.5 during the dispatching period to provide the maximum adjustable range and avoid the life loss caused by deep charging and discharging. The operation times of the capacitor are limited by the cost constraint to ensure the service life.
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a) output of wind power, photovoltaic and SVC
c) Electric energy curve of electric vehicles
e) Capacitor switching plan
b) Charging and discharging strategy of electric vehicles
d) Charging and discharging strategy and SOC of the battery
f) Voltage control effect of node 26
Fig. 7. Control strategy of controllable equipment and voltage control effect
6 Conclusion In this research, a voltage optimisation control method based on MPC was proposed to suppress the voltage fluctuation of important load access nodes caused by the uncertainty of DG output, effectively coordinating the local and wide-area controllable equipment based on voltage sensitivity. Firstly, taking SCADA and local measurement information as the basis, the WLS method is used to estimate the state of the distribution network and extract the voltage control sensitivity online. Secondly, according to the basic principle of MPC, the linear voltage prediction model and rolling optimization model are established. By minimizing the planned control deviation of the important load voltage and regulation cost as the control objective, the mathematical model of local and wide-area controllable equipment is established. Finally, the method proposed by this research is verified by an IEEE 33 bus benchmark example. Acknowledgment. This paper is supported by the project: Key technology research and demonstration of green high reliable intelligent distribution network in Huangguoshu Scenic Area in 2020-Research on key technology of high reliable continuous power supply for important loads in core area (060400KK52190017).
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References 1. Yang, S., Wang, W., Liu, C., et al.: Coordinative strategy for reactive power and voltage control of wind farms cluster considering wind power fluctuation. Proc. CSEE 34(28), 4761–4769 (2014). (in Chinese) 2. Qiao, F., Ma, J., Li, Y.: Bi-level multi time-scale voltage/var optimization and control in a hybrid distribution network. In: 8th Renewable Power Generation Conference (RPG 2019), pp. 1–7 (2019) 3. Liu, W., Peng, G., Dan, Y., et al.: Double-time scale reactive power control with largescale wind power integrated into grid. Renew. Energy Resour. 34(12), 1811–1818 (2016). (in Chinese) 4. Gao, H., Zhang, Y., Ji, X., et al.: Scenario clustering based distributionally robust comprehensive optimization of active distribution network. Autom. Electric Power Syst. 44(21), 32–41 (2020). (in Chinese) 5. Liang, J., Lin, S., Liu, M., et al.: Distributed robust optimal dispatch in active distribution networks. Power Syst. Technol. 43(04), 1336–1344 (2019). (in Chinese) 6. Yu, T., Dong, L., Du, X., et al.: Distributionally robust optimization method of PV gridconnected capacity in a distribution network considering chance constraints. Power Syst. Prot. Control 49(10), 43–50 (2021). (in Chinese) 7. Li, P., Wu, Z., Zhang, C., Xu, Y., et al.: Multi-timescale affinely adjustable robust reactive power dispatch of distribution networks integrated with high penetration of PV. J. Mod. Power Syst. Clean Energy, 1–10 (2020) 8. Xia, P., Liu, W., Zhu, D., et al.: Multi-time scale optimal control method of reactive power and voltage based on model predictive control. Electr. Power Autom. Equip. 39(03), 64–70 (2019). (in Chinese) 9. Meriem, M., Bouchra, C., Abdelaziz, B., et al.: Study of state estimation using weighted-leastsquares method (WLS). In: International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), pp. 1–5 (2016) 10. Guo, Y., Wu, Q., Gao, H., et al.: Double-time-scale coordinated voltage control in active distribution networks based on MPC. IEEE Trans. Sustain. Energy 11(09), 294–303 (2020) 11. He, L., Yang, J., Yan, J., et al.: A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles. Appl. Energy 168(C), 179–192 (2016) 12. Zhen, N., Ding, X., Guan, Z., et al.: Coordinated optimization of active power and reactive power in distribution network based on scenario method. Power Syst. Technol. 43(05), 1640– 1649 (2019). (in Chinese)
Research on Evaluation Index System of Science and Technology Innovation Ability of Electric Power Information and Communication Enterprises Qionglan Na1(B)
, Dan Su1 , Yixi Yang2 and Jing Zeng1
, Huimin He1 , Jing Lou1
,
1 State Grid Jibei Information and Telecommunication Company, Beijing 100053, China
[email protected] 2 State Grid Information and Telecommunication Branch, Beijing 100761, China
Abstract. By exploring the evaluation methods of enterprise scientific and technological innovation capability, constructing the scientific and technical innovation evaluation index system of electric power information and communication enterprises, using the analytic hierarchy process to assign index weights, and striving to make a comprehensive evaluation of the scientific and technological innovation ability of electric information and communication enterprises more accurately and quantitatively. Furthermore, it proposes a path to enhance the technological innovation capability of electric power information and communication enterprises. The research results of this paper have positive practical significance for deepening the innovation-driven development strategy, advancing the demonstration construction of the new power system of the State Grid, and promoting innovative development and technological progress. Keywords: Analytic Hierarchy Process · Index system · Information and communication enterprise · Technological innovation
1 Introduction On March 15, 2021, General Secretary Xi Jinping proposed at the ninth meeting of the Central Finance and Economics Committee to build a new power system with new energy as the main body. Under this goal, as an essential link in the energy transition, the foundation for creating a new power system platform, the grid company will play a significant role in the energy system. Faced with the three critical challenges of “the extreme imbalance between clean energy supply and demand, the improvement of energy efficiency and the end-use electricity substitution entering the bottleneck stage, the high proportion of new energy brings uncertain systemic risks”, it is necessary to promote the traditional power system to use information New communication technologies are the driving force to accelerate the digital transformation of the power grid and promote the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1260–1273, 2022. https://doi.org/10.1007/978-981-19-1528-4_128
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upgrade of the conventional power network to the energy Internet. In this context, the information and communication industry of electric power companies has moved from the traditional basic communication network business scope to the integration and development of middle-level grid production business and information technology, and further extended to the upper level to become a solid foundation for supporting the demonstration and construction of new power systems. At present, the technological innovation capabilities of power information and communication companies are no longer limited to technical applications or competitive advantages. Still, they have become necessary capabilities for developing power grid companies, industrial integration, and technological innovation. In particular, the in-depth implementation of the “four revolutions, one cooperation” new energy security strategy and the “dual carbon” goal, as the most active field of scientific and technological innovation, the innovation capability of power grid information and communication enterprises is of great significance to innovation-driven development.
2 Literature Review The academic discussion on the technological innovation capability of the power information and communication industry has mainly experienced five stages: “Science and Technology Evolution-Technology Diffusion-Technology and Economic GrowthTechnological Innovation-Technology and Social Development”: First, early Shi Yellen [1], etc. Relevant research focuses on the technical field and analyzes the generation and evolution of information and communication technologies such as power wireless communication technology and power information security from a purely technical point of view. Second, the reform and dissemination of information and communication technology impact the traditional power industry, and related fields have become increasingly prominent. Tang Lei [2] and others proposed that the spillover effect of information and communication technology began to attract attention and found that information and communication technology has improved grid efficiency plays an essential role in the application of power system fault diagnosis and power system automation. Third, Su Bo [3] and others found that in advancing the development of the power system, information and communication technology plays an important role. The story of the country’s power information and communication industry promotes economic growth. This effect can be divided into substitution effect and penetration effect. Fourth, after 2015, guided by the innovation-driven development strategy, the relationship between information transmission capabilities and innovation has begun to become a new research topic. Wang Yi [4] and others have focused on technological innovation, regional innovation, corporate innovation, and green innovation. In the aspect of research into innovation. Fifth, the relationship between the innovation capability of electric power information and communication enterprises and the demonstration and construction of new power systems is a frontier topic in the current electric power information and communication field. In short, from the five stages of inspection, we can see that the innovation capabilities of electrical power information and communication companies have ranged from a single technical category to a more complex and diversified category. although electric power information and communication technology are still the core content, they have
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gradually been given innovation. with new connotations such as service, the evaluation of innovation ability also has a new category. Most of the research on evaluating the innovation ability of the power information and communication industry is still stuck in the research system at the information level. Mainly starting from the characteristics of electric power enterprises, analyzing the aspects of strategic positioning, infrastructure, business applications, human resources, information security, benefit index, etc., an evaluation index system for the informatization level of electric power enterprises was established. At the same time, the weight of each indicator system was determined according to the survey results and expert opinions, and a deeper exploration was made in the evaluation of the informatization level of power enterprises. Sun Qimeng [5] Starting from the current technological status and technological needs of electric power enterprises, constructing an evaluation index system from three levels of hard technological power, technological soft power, and technological innovation capability, applying a 10-level index system and 22 specific indicators, using robust Support vector machines optimize the index weights to reflect the scientific and technical innovation level of electric power enterprises more scientifically, and provide help for the quantitative calculation and empirical research of the scientific and technological innovation level of electric power enterprises. By the principles of continuous development, technical leadership, and critical focus, Zhang Lichen [6] and others have constructed a set of scientific and technological project acceptance evaluation index systems to meet the needs of modern power scientific and technical project management and provide evaluation methods and improvement suggestions for scientific and technological project management. Based on the literature review, the results show that the evaluation of the innovation capability of the country’s power information and communication enterprises is lagging behind the rapid development of information and communication technology. It is mainly manifested in three aspects: First, the innovation ability of electric power information and communication enterprises has changed from a purely technical category to general knowledge or basic service, which is closely related to scientific and technological innovation and enterprise development; but the academic world has a strong influence on the innovation ability of electric power information and communication enterprises. Evaluation is still at the technical and infrastructure levels, and attention to innovation and services, such as new trends and new features, is far from enough. Second, with the rapid development of power information and communication technology and industry, especially the rapid rise of 4G/5G in recent years, 6G research has quietly emerged. The technological potential of AI, the Internet of Things, and blockchain continue to appear. The innovation capability of information and communication technology has an increasing influence on the development of power grids. Third, after the innovationdriven development strategy was put forward, significant changes have occurred in my country’s electric power information and communication enterprises regarding development, technology application, and resource capabilities. The current research results and evaluation methods have lagged, and new explorations are urgently needed. In particular, the current power information and communication technology capabilities are of strategic significance to the demonstration and construction of new power systems. Therefore, it is necessary to study the evaluation index system of the innovation ability
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of electric power information and communication enterprises under the background of the demonstration and construction of new power systems [7].
3 Overview of Research Methods 3.1 Analytic Hierarchy Process Scholars at home and abroad have proposed a variety of methods to measure the innovation capabilities of enterprises, most of which use the Analytic Hierarchy Process (AHP method) to analyze the innovation capabilities of enterprises systematically. AHP is a systematic analysis method proposed by the American operations researcher Professor TLSaaty in the 1970s [8]. This method regards the problem as a system and breaks the problem into different components according to the nature of the problem and the overall goal to be achieved [9]. Based on the interrelationship and affiliation of the elements, the factors are summarized into different levels, and a multi-level analysis structure system is constructed [10]. The AHP method examines the primary status and development status of all aspects of the company’s technological innovation capabilities as a whole and can also use the correlation of indicators at various levels to compare relative levels, explore related reasons, and has been widely recognized and applied [11]. 3.2 Build a Hierarchical Structure The AHP structure model generally includes the following three levels: ➀ Target level (the highest level). There is only one element on the level that describes the goal of the problem; ➁ Guideliness (middle class). This layer contains the intermediate links involved in achieving the dream as a specific description and expansion goal; ➂ indicating layer (measure layer). This layer is also the bottom layer; it is the refinement of the guidance [12]. AHP adopts two comparison methods, which decision makers or experts should answer: Which of the two sub-indices under the same indicator is more important and how important? In addition, the 9-level system cited by AHP compares quantitative judgments of its importance. The value of the judgment matrix element is based on the subjective understanding and evaluation of the relative importance of each factor based on the actual objective situation. Generally, 1, 3,…, nine and its reciprocal should be used as the scale; 2, 4, 6, 8 are the adjacent mentioned above [13]. The median value of the judgment, its meaning are shown in Table 1. Table 1. Scale meaning. 1
This indicates that two elements are of equal importance
3
This indicates that compared to two elements, one element is slightly more important than the other
5
This indicates that compared to two elements, one element is more important than the other (continued)
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7
This indicates that compared to two elements, one element is more important than the other
9
This indicates that compared to two elements, one element is essential than the other
2 4 6 8 means that compared with two elements, the importance of a component than the other is between the above descriptions
3.3 Build a Hierarchical Structure According to the judgment matrix, one-level ranking refers to calculating the important weights of the elements of the previous layer and their related elements. Single decision sorting mainly calculates the maximum eigenvalue of the judgment matrix and the corresponding eigenvalue, and the judgment matrix C must have the top eigenvalue λmax ≥ n that |λI − C| = 0 (I am the identity matrix) and is a single root. If the judgment matrix is entirely consistent, λmax = n, other eigenvalues are all 0. Use the maximum eigenvalue of the discriminant matrix to calculate the corresponding eigenvalue, which should satisfy W of CW = λmax W, and the component of W is the weight of the corresponding element single sorting [14]. 3.4 Consistency Check The consistency index CI is used to test the consistency of the judgment matrix, and its −n value is calculated as CI = λmax n−1 by the following formula. If the judgment matrix is entirely consistent, then λmax = n, then CI = 0; if λmax it is slightly greater than n, the consistency test of the judgment matrix. Use CR = CIRI formula to calculate, CR = CIRI introduces the average random consistency index RI to calculate the consistency of the judgment matrix (see Table 2). For n = 1 or 2, the CR = 0 of the judgment matrix is defined as CR = 0; when n > 2, the CR value of the judgment matrix is 0, then the CR value of the judgment matrix is 0, which can be used as the consistency of one layer Analysis; if CR = 2 is obtained, the matrix needs to be adjusted and corrected. Table 2. RI value. Random consensus RI table N order
3
4
5
6
7
8
9
10
RI value
0.52
0.89
1.12
1.26
1.36
1.41
1.46
1.49
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4 Evaluation of Science and Technology Innovation Ability of Electric Power Information and Communication Enterprises 4.1 Construction of Evaluation Index System for Electric Power Information and Communication Ability Due to the characteristics of the power information and communication field, the evaluation criteria for the power information and communication system’s scientific and technological innovation capability lack a unified standard. Due to the different levels of development of the information and communication industries in various countries, the Information Society Development Index issued by the ITU is highly authoritative. As a result, some indicators cannot get valid data, which affects the applicability of research in various countries. Scholars at home and abroad are trying to construct an evaluation system from the aspects of public service and information communication degree of information and communication enterprises and study the information and communication industry and enterprise innovation from the perspective of R&D and innovation. In terms of service capability evaluation, the academic circle evaluated from the dimensions of service efficiency, service quality, public responsibility, public satisfaction, investment level, service process, service output, and social benefits. Based on the above-mentioned various index systems and dimensional composition, given the differences of different research objects, and the information exchangeability is a complex multi-factor system, the evaluation index system of the scientific and technological innovation ability of electric power information and communication enterprises is to select the critical technical innovation ability of each dimension. Indicators, including four first-level indicators and 12 s-level indicators for innovation funding, innovation workforce, innovation facilities, and innovation output, are synthesized and calculated using statistics and expert scoring methods. A scientific evaluation indicator system reflects power information and communication. The status quo of enterprises’ scientific and technological innovation capabilities truly measure, reflect, and dynamically evaluate the status quo of power information and communication enterprises’ scientific and technical innovation capabilities [15]. Based on the principles of science and completeness, level and unity, computable and operable, dynamic and universal, establish calculation rules, scoring methods, and weight settings for each sub-index, and build an evaluation index system for the fundamental technological innovation of electric power information and communication enterprises (see Table 3).
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Table 3. Evaluation index system of electric power information and communication capability. Evaluation index system of the scientific and technological innovation ability of electric power information and communication enterprises First level indicator
Secondary indicators
1. Innovation funding A1
(1) The proportion of innovation funding in the central business income B1 (2) R&D expenditure as a proportion of main business income B2 ……
2. Innovative human capital A2
(1) R&D personnel account for the proportion of all employees of the company B3 (2) Proportion of R&D personnel with a doctoral degree and above B4 ……
3. Innovation infrastructure A3
(1) Number of key technology laboratories B5 (2) Key technology equipment configuration rate B6 (3) Internet and other information facilities B7 ……
4. Innovation output A4
(1) Conversion rate of key technological achievements B8 (2) Number of patent applications B9 (3) Patent grant rate B10 (4) Number of participation in standard setting B11 (5) Scientific publications (papers) B12 ……
4.2 Use the AHP Method to Determine the Weight of the Evaluation Index Using the expert survey method, ten experts made independent judgments by understanding the relative importance of indicators at various levels and then scored the significance of each hand. This article introduces the weight calculation process by taking the score of the innovation achievement output index as an example. First, construct the judgment matrix (Table 4).
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Table 4. Judgment matrix. AHP data judgment matrix Key elements
The conversion Number of rate of critical patent technological applications achievements
Number of patents granted
Patent grant rate
Scientific publications (papers)
Conversion rate of key technological achievements
1.000
0.500
0.500
1.000
0.500
Number of patent applications
2.000
1.000
3.000
3.000
1.000
Number of 2.000 patents granted
0.330
1.000
2.000
0.330
Patent grant rate
1.000
0.330
0.500
1.000
1.000
Scientific publications (papers)
2.000
1.000
3.000
1.000
1.000
Then, by finding the maximum feature value and the feature amount of each judgment vector, and normalizing each judgment vector, finally, the row vector is normalized: ⎛ ⎞ 0.122 ⎜ 0.329 ⎟ ⎜ ⎟ ⎜ ⎟ Row vector normalization: ⎜ 0.157 ⎟. ⎜ ⎟ ⎝ 0.129 ⎠ 0.263 In this way, the weight set of the innovation output (A4) is calculated: W 4 = [0.122 0.329 0.157 0.129 0.263]. Calculate the largest characteristic root of the judgment matrix λmax : λmax = n 1 (AWi ) n Wi = 5.3267. i=1
Finally, the consistency check is performed to determine the vector weight. 1) Consistency index CI =
1 n
n i=1
λmax −n n−1
= (5.3267–5)/4 = 0.0817
2) Average random consistency index RI = 1.12 3) Random consensus ratio CR = CI/RI = 0.0817/1.12 = 0.0729 < 0.1 Therefore, it is considered that the judgment matrix has a good consistency (see Table 5).
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AHP level analysis results Item
Feature vector
Weights
Maximum eigenvalue
CI value
Conversion rate of key technological achievements
0.601
12.2%
5.3267
0.0817
Number of patent applications
1.578
32.9%
Number of patents granted
0.816
15.7%
Patent grant rate
0.678
12.9%
Scientific publications (papers)
1.328
26.3%
Similarly, through the expert questionnaire, the importance of innovation funds, innovation workforce, innovation facilities, and other indicators are ranked and consistently tested. The inspection results show that the overall ranking of the hands and the comprehensive indicators in the ten expert consultation forms is roughly the same. Finally, based on the ten expert opinion forms, the average value of each hand is calculated, and the weight of each indicator is calculated (see Table 6). Table 6. Evaluation index system for power information and communication capabilities (including weights). Evaluation index system for power information and communication capabilities (including weights) First level indicator
Secondary indicators
1. Innovation funding A1
(1) The proportion of innovation funding in the main business income B1
0.15
(2) R&D expenditure as a proportion of main business income B2
0.15
(1) R&D personnel account for the proportion of all employees of the company B3
0.14
2. Innovative manpower A2
Three-level indicators
Four-level indicators
Index Weight
(continued)
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Table 6. (continued) Evaluation index system for power information and communication capabilities (including weights) First level indicator
3. Innovation facility A3
4. Innovation outpour A4
Secondary indicators
Three-level indicators
Four-level indicators
Index Weight
(2) Proportion of R&D personnel with doctoral degree and above B4
0.06
(1) Number of key technology laboratories B5
0.06
(2) Key technology equipment configuration rate B6
0.08
(3) Internet and other information facilities B7
0.06
(1) Conversion rate of key technological achievements B8
0.06
(2) Number of 1) Innovation patent applications activity C1 B9
➀ Number of 0.00675 patent applications D1
(3) Number of patents granted B10
➁ Patent grant rate D2
0.00675
➂ Number of valid patents D3
0.009
2) Innovation concentration C2
➀ Patent share D4 0.00675 ➁ Inventor concentration D5
0.00675
➂ Proportion of invention patents D6
0.009
(continued)
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Evaluation index system for power information and communication capabilities (including weights) First level indicator
Secondary indicators
Three-level indicators
Four-level indicators
Index Weight
3) Innovation openness C3
➀ Proportion of Cooperative Patent Applications D7
0.00675
➁ Number of patent assignmentsD8
0.009
➂ Number of patent pledges D9
0.00675
➀ Average number of patent citation D10
0.00675
➁ Number of awarded patentsD11
0.009
4) Innovation value C4
➂ Average 0.00675 number of claims for granted patents D12 (4) standard B11
1) Number of participation in standard setting C5
0.03
2) Participation in standard setting contribution rate C6
0.03
5 Conclusion and Suggestions 5.1 Research Conclusions on the Evaluation Index System of Science and Technology Innovation Capability of Electric Power Information and Communication Enterprises The cultivation of technological innovation capabilities of electric power information and communication enterprises has become an important way to construct new power system model innovations. To this end, this paper uses the Analytic Hierarchy Process (AHP) to conduct a quantitative empirical study on the evaluation of the technological innovation capability of electric power information and communication enterprises. The results show that the evaluation index system of the technological innovation ability of
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electric power information and communication enterprises is a component of the weight of the criterion layer. The order of weight is in the order of innovation expenditure (A1) = innovation output (A4) > innovation manpower (A2) = innovation facility (A3). Among the weights of the indicator layer, “innovation expenditures accounted for the proportion of main business income” (η1 = 0.15) and “R&D expenditures accounted for the proportion of main business income” (η2 = 0.15) had enormous weights, indicating that the investment in technological innovation among all indicators Contains the most significant effect. The second-largest weight is the “key technology equipment allocation rate” (η6 = 0.08), which indicates that the research and development of scientific and technological innovation cannot be separated from advanced information and experimental communication equipment. The third weight ranking is “number of patent applications” (η9 = 0.0675), “patent grant rate” (η10 = 0.0675), “patent occupancy rate” (η12 = 0.0675), “inventor concentration” (η13 = 0.0675), cooperative application. The proportion of patients (η15 = 0.0675), the number of patent pledges (η17 = 0.0675), the average number of patent citations (η18 = 0.0675), the average number of claims of authorized patents (η20 = 0.0675), indicating that patent applications and authorizations are technological innovations of power information and communication enterprises Important indicators of ability evaluation. 5.2 Countermeasures and Suggestions for Cultivating Technological Innovation Ability of Electric Power Information and Communication Enterprises Affected by the innovation-driven development strategy, new-generation information and communication technology, artificial intelligence, digital economy, and other emerging business formats and many practical factors, improving the scientific and technological innovation capabilities of electric power information and communication enterprises in the future requires multiple channels to solve R&D funds and promote laboratory systems Measures to build and strengthen intellectual property management. Specifically, the following policy recommendations are put forward: First, strengthen the construction of the guarantee mechanism and solve the research and development funds through multiple channels. Carry out the reform of “delegation, management and service” in the field of scientific research, improve relevant systems, standards, and procedures, and strengthen business training and follow-up guidance. The “project leader system carries out the management,” and the power to manage people, money, projects and incentives are wholly given to the person in charge, which further enhances the innovation potential of the project leader. Focus on the allocation of power information and communication technology innovation resources, establish an effective organizational guarantee and resource input mechanism, rely on the State Grid Corporation, Jibei Company, and various unit cost funds to form an echelon continuous resource investment model, and concentrate limited resources on the core Technology and business orientation. Strive for the tilt of the company’s relevant functional departments in cutting-edge technology reserves and capital investment, lay a solid foundation for innovation and development, and continue to optimize investment in scientific and technological innovation. While increasing investment in scientific research, strengthen the guidance and supervision of the use of funds, ensure the closed-loop operation of the
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entire process of the capital chain, ensure reasonable use of funds, accurate investment, and meet the needs of innovation at all levels. Second, promote the construction of the laboratory system and enhance the overall innovation capability. To further consolidate the experimental research advantages of electric power information and communication major, expand basic supporting technology research capabilities, improve the company’s practical research system, strengthen the layout of core technology experimental research capabilities, and accelerate the development of helpful research capabilities in fields such as weak technologies and new business forms. Relying on the “Zhangjiakou (Winter Olympics) Energy Internet Comprehensive Demonstration Project,” focusing on the power of the Internet of Things, network security evaluation and offensive and defense, artificial intelligence, and other technical fields, two methods of independent declaration and joint declaration are adopted to integrate the company’s internal and external laboratory resources and prepare The construction of laboratories will cover the experimental research capabilities that the company plans to build in the next five years and above, promote the data integration of various sectors, realize the co-construction and sharing of master data, and promote the formation of research peaks in several fields. Third, improve intellectual property management and win the initiative in technological competition. In-depth implementation of the strategic deployment of intellectual property rights, guided by the development of power grids and the development of information and communications, strengthen patent layout, improve the patent creation system, continue to improve independent innovation and achievement transformation capabilities, and accelerate the construction of a world-class energy Internet enterprise with global competitiveness Strong technological support. With the purpose of “leading technology,” we will comprehensively sort out the company’s leading research and development results in information and communication, and focus on the patent layout of power Internet of Things, artificial intelligence, blockchain, and other leading technology research and development directions, seize the frontier of power technology and protect future competitive advantages. The patent layout is run through the entire scientific research process of project approval, R&D, and achievement output to enhance the value and efficiency of innovation achievements and improve the quality and depth of patent layout work. At the same time, the company actively carries out intellectual property-related training, deepens the knowledge and attention of scientific and technical personnel to intellectual property rights, and wins the initiative for its own scientific and technological achievements in market competition. Acknowledgments. This work is supported by the Science and Technology Project of State Grid Jibei Power Company Limited (No. 52018E20008H).
References 1. Yelan, S.: Suggestions on the site selection of the high mountain wireless communication relay station for electric power in Fuzhou. Fujian Electr. Power Electr. Eng. 03, 47–49 (1998). (in Chinese)
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2. Lei, T., Hongbin, S., Boming, Z., et al.: Power system online fault diagnosis based on information theory. In: Proceedings of the Chinese Society for Electrical Engineering, vol. 07, pp. 5–11 (2003). (in Chinese) 3. Bo, S., Hui, Y., Bicui, W.: Study on the development trend of power system communication science and technology information (Academic Research) 20, 175–176 (2008). (in Chinese) 4. Yi, W., Qixin, C., Ning, Z., et al.: Integration of 5G communication and ubiquitous power Internet of Things: application analysis and research prospects. Power Grid Technol. 43(05), 1575–1585 (2019). (in Chinese) 5. Qimeng, S.: Study on the construction of science and technology evaluation index system for my country’s electric power enterprises. China’s Circ. Econ. 23(11), 51–54 (2009). (in Chinese) 6. Lichen, Z., Guanghong, S., Ming, X., et al.: Construction of evaluation system for science and technology projects of Tianjin electric power company. Tianjin Sci. Technol. 06, 19–20 (2005). (in Chinese) 7. Qiuyan, T., Tengfei, G., Yan, H.: Research on my country’s information and communication capability evaluation under the background of innovation-driven technology and economy 38(11), 74–82 (2019). (in Chinese) 8. Xue, D., Jiaming, L., Haojian, Z., et al.: Analysis of analytic hierarchy process weight calculation method and its application research. Math. Pract. Understand. 42(07), 93–100 (2012). (in Chinese) 9. Ding, D., Chen, D., Zhai, X., et al.: Comprehensive evaluation of investment benefits of power grid enterprises based on AHP-TOPSIS method, pp. 535–539 (2018) 10. Li, L., Guan, C., Liu, X., et al.: Research on evaluation model of full voltage level operation efficiency of provincial power grid using analytic hierarchy process (AHP), pp. 60–63 (2019) 11. Mittal, V.K., Sindhwani, R., Shekhar, H., Singh, P.L.: Fuzzy AHP model for challenges to thermal power plant establishment in India. Int. J. Oper. Res. 34(4), 562 (2019). https://doi. org/10.1504/IJOR.2019.099109 12. Solangi, Y., Tan, Q., Khan, M., Mirjat, N., Ahmed, I.: The selection of wind power project location in the southeastern corridor of Pakistan: a factor analysis, AHP, and fuzzy-TOPSIS application. Energies 11(8), 1940 (2018). https://doi.org/10.3390/en11081940 13. Xiong, B., Fu, X.-N.: Research on evaluation of integrated competitiveness of power-selling company based on AHP-fuzzy evaluation method, pp. 355–362 (2018) 14. Jinyu, G., Zhongbin, Z., Qingyun, S.: Research and application of analytic hierarchy process. Chin. J. Saf. Sci. 05, 148–153 (2008). (in Chinese) 15. Sisi, Y., Henan, L., Wei, S., et al.: Research on the evaluation index system and analysis method of the comprehensive scientific and technological strength of Jilin Province. Value Eng. 35(36), 36–38 (2016). (in Chinese)
Design of High Torque Density Motor with Permanent Magnet/Reluctance Hybrid Double Rotor Xiang Li1(B) , Zhaoyu Zhang1,2 , Yujiang Sun3 , Siyang Yu1 , and Fengge Zhang1 1 School of Electrical Engineering, Shenyang University of Technology, Shenyang, China
[email protected]
2 Dalian SMART DRIVE Co., Ltd., Dalian, China 3 Fushun Coal Mine Motor Manufacturing Ltd., Fushun, China
Abstract. In order to improve the torque density to meet the requirements of the belt conveying field. A new structure with permanent magnet/reluctance hybrid double rotor motor (PMRHDRM) is proposed. Firstly, the structure and operating principle of the motor are introduced. Secondly, make a preliminary design of the electromagnetic scheme of the motor, and the design method based on the principle of increasing the torque density in the same volume with the traditional outer rotor PM machine. Thirdly, analyze how to get a large output torque of the motor and use finite element calculation to verify the rationality of the parameters of the motor. Finally, analyze the different load capacity of the motor to meet the requirement that the motor can select the appropriate working state according to different load conditions. The results show that the PMRHDRM has a large torque density, and at the same time the motor can meet both light-load and full-load operating conditions. Keywords: Permanent magnet/Reluctance hybrid double rotor motor · High torque density · Electromagnetic design
1 Introduction Traditional delivery system is applied to the field of belt transmission, which has a long structure and low efficiency [1]. Outer rotor permanent magnet synchronous motor (ORPMSM) is widely used in wind power generation, traction, electric vehicle (EV) and other fields, due to its special structure [2]. At the same time, scholars at home and abroad have carried out a lot of research on the electromagnetic design, structure optimization, temperature and stress of ORPMSM [3–6]. But the large inner space leads to a reduction in power density and torque density, which is the main problem for the ORPMSM. Subsequently, the motor with double stator or double rotor structure fully solves the problem that the inner cavity is too large. Literature [7] and [8] are double rotor permanent magnet synchronous motor (DRPMSM) structure, this structure provides a new option for the hybrid electric vehicle (HEV) field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1274–1282, 2022. https://doi.org/10.1007/978-981-19-1528-4_129
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Aiming at the operating environment where the application field is often in a lightload state, and the problem of too large internal cavity of the outer rotor direct drive motor, The new structure with permanent magnet /reluctance hybrid double rotor motor (PMRHDRM) is proposed.
2 Motor Structure and Principle of Operation The main structure of PMRHDRM is shown in Fig. 1. The outer unit motor is a ORPMSM and the inner unit motor is a synchronous reluctance motor (SRM). Both the two rotors are coaxially connected. The inner and outer stators are connected by a magnetic isolation ring and fixed inside the motor. The output torque of the whole motor is divided into two completely separated parts, one part is permanent magnet torque, and the other part is reluctance torque. The torque equation when two parts of the motor work at the same time is in (1), where the first half is permanent magnet torque, and the second half is reluctance torque.
Fig. 1. The schematic diagram of the permanent magnet/reluctance hybrid double rotor motor (PMRHDRM)
Tem = pψf iq,1 + p(Ld − Lq )id,2 iq,2
(1)
3 Motor Electromagnetic Design In order to improve the light-load operation ability of the motor, a 10 poles scheme is adopted for the inner and outer motors. Two sets of windings can be connected in parallel or in series. Through the phasor diagram analysis of the steady state operation of OPMSM and SRM, it is concluded that two sets of windings in series can ensure the continuous output characteristics, and at the same time, by adjusting the current angle, the output of the machine is maximized.
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3.1 Motor Power Calculation According to the initial parameters of the motor, the basic size of the outer unit motor is determined, and then the size parameters and power of the inner unit motor are deduced. The outer diameter of the outer unit motor stator is D1 and the rated power is P1N , the outer diameter of the inner unit motor stator is D2 and the rated power is P2N , the specific relationship can be calculated according to the following formulas. D12 Lef =
P1N 0.164A1 Bδ KW1 nηN cos ϕN
(2)
D22 Lef =
P2N 0.164A2 Bδ KW2 nηN cos ϕN
(3)
D2 = kj D1 − hi
(4)
In the formula, P1N and P2N are the rated powers of the outer and inner stator winding, D1 and D2 are the diameters of the outer and inner stator, K W1 and K W2 are the phase winding coefficients of the two unit motors, Br1 and Br2 are the air gap magnetic density effective value of the two unit motors, A1 is the line current density of the outer unit motor, A2 is the line current density of the inner unit motor, k j is the radio of outer diameter and inner diameter for the outer unit motor, k z is the radio of outer diameter and inner diameter for the inner unit motor, and hi is the thickness of the magnetic isolation ring. The parameters of the whole machine are shown in Table 1. Table 1. Parameters of PMRHDRM. Parameter
Numerical value
Rated power (kW)
47
Rated voltage (V)
1140
Outer diameter (mm)
740
Inner diameter (mm)
192
Rotating speed (r/min)
90
Core length (mm)
350
3.2 Design of Pole-Slot Combinations Outer Unit Motor This paper mainly takes the cogging torque and torque ripple as indexes to compare and analyze several pole-slot combinations with high harmonic frequency, as is shown in Fig. 2. And finally, a 10 poles 24 slots scheme is determined.
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Fig. 2. Diagram of pole-slot combinations scheme of outer unit motor.
Inner Unit Motor More slots will make the MMF fundamental wave generated by the stator winding closer to sinusoidal in the pole-slot combinations for the SRM, but it will also cause the problem of magnetic flux leakage. In Fig. 3, four schemes with higher harmonic frequency are selected for comparative analysis. By comparing the fundamental wave content of the air gap magnetic-density, the 10 poles 72 slots combination of the SRM is determined.
Fig. 3. Diagram of pole-slot combinations scheme of inner unit motor.
3.3 Rotor Structure Design Design of Permanent Magnet Due to the structural characteristics of PMRHDRM, the impact on the tensile stress of the permanent magnet is relatively small. The use of surface mount permanent magnet structure can save the space of the motor. And the parameters of the permanent magnet can be calculated with the following formulas. αp π D1 2p
(5)
λ0 μm Dδi 1 − λ0
(6)
bm = hm =
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In the formula, bm is the arc length of the permanent magnet, αp is the permanent magnet arc coefficient; hm is the thickness of the permanent magnet, λ0 is the noload magnetic leakage coefficient, μm is the relative permeability of the permanent magnet, and δi is the calculated air gap length of the motor. After formula calculation and simulation comparison analysis, αp and hm are respectively 0.79 mm and 4.7 mm. Design of Magnetic Barriers After calculation, the magnetic barriers are determined to be 4 layers. Equation (7) is the expression of the pole-arc coefficient α, where α1 is the arc length of d axis, and α2 is the arc length of q axis as is shown in Fig. 4(a). By comparative analysis, α is 0.7. Equation (8) represents the proportion of the n-th magnetic barrier layer, where t n represents the thickness of the n-th magnetic barrier layer, and hn represents the thickness of the n-th transverse magnetic conductive layer, as is shown in Fig. 4(b). After comparison, the final choices of β1 and β2 are 0.5 and 0.3. α=
α1 α1 + α2
(7)
βn =
tn hn + tn
(8)
Fig. 4. (a) Diagram of pole-slot coefficient of magnetic barrier rotor. (b) Diagram of the proportion of the magnetic barrier.
After design and analysis, the specific parameters of the two unit motors are determined in Table 2.
Table 2. Specific parameters of the PMRHDRM. Parameter
Outer unit motor
Inner unit motor
Rated output power (kW)
40
7
Rated voltage (V)
935
205
Rated current (A)
42
42 (continued)
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Table 2. (continued) Parameter
Outer unit motor Rated speed (r/min)
90
Inner unit motor 90
Rated output torque (kN·m)
4.2
0.7
Core length (mm)
350
350
Outer diameter of stator (mm)
670
440
Inner diameter of stator (mm)
455
350
Air gap length (mm)
2
0.6
Number of poles
10
10
Number of slots
24
72
Number of Conductors per Slot
220
28
Number of Parallel Branches
2
2
Coil pitch
2
7
Wire diameter (mm)
1.829
1.68
Limited slot fill factor (%)
74.6
77
3.4 Research on the Method of PMRHDRM to Obtain the Maximum Rated Output Torque In order to obtain the maximum rated output torque of PMRHDRM, ensure the output torque of the outer unit motor is maximized, so that its q axis coincides with the stator winding A phase axis, then adjust the rotor position angle of the inner unit motor so that its q axis is aligned with the outer unit motor. There is a deflection angle θ between the q-axis of the two motors, as is shown in Fig. 5(a). Figure 5(b) shows the influence of the rotor position deflection angle θ on the output torque of the inner unit motor and PMRHDRM. It can be seen from the figure that the output torque of the whole machine
Fig. 5. (a) Schematic diagram of the rotor position deflection angle of the inner and outer unit motors. (b) The influence of rotor position deflection angle on motor output torque.
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increases with the increase of the inner unit motor torque. In the subsequent research and analysis, θ is kept unchanged at 28 deg.
4 Simulation and Performance Analysis 4.1 Back Electromotive Force and Magnetic Field Analysis The rated voltages of outer and inner unit motors are 935 V and 205 V. In order to make the motor have a certain overload capacity, the no-load back EMFs should be 0.75–0.85 of the rated voltage. No-load back EMFs of outer and inner unit motors are shown in Fig. 6(a) and (b). With the effective values of 433.23 V and 90.51 V, which meet the requirements of the motor design.
Fig. 6. No-load back EMF waveforms of outer unit motor and induced voltage under rated load of inner unit motor. (a) outer unit motor no-load back EMF waveform. (b) inner unit motor induced voltage under rated load.
Figure 7 shows the magnetic density cloud diagram of the PMRHDRM. It can be seen from the figure that the whole motor magnetic density distribution is reasonable. Due to the limitation of the size of the motor, the magnetic density at the yoke of the rotor is about 1.58T. Local saturation phenomenon exists at the tooth edge, which has no significant effect on the overall performance of the motor.
Fig. 7. Magnetic density cloud map of the PMRHDRM.
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4.2 Analysis of Rated Output Torque Figure 8(a) is the output torque of the outer unit motor in rated load, the output torque in stable operation is about 4.25 kN·m, and the torque ripple is about 8.64%. Figure 8(b) is the output torque of the inner unit motor in rated load, the output torque in stable operation is about 734.25 N·m, and the torque ripple is about 3.29%. Figure 8(c) is the output torque of the PMRHDRM in rated load, the output torque in stable operation is about 4.98 kN·m, and the torque ripple is about 7.46%. Compared with the PMSM of the same volume and power, the output torque is increased by about 18%. But the ORPMSM with fewer poles used in the low-speed field may generates large torque, due to the long permanent magnet, the cogging torque is main reason that affects the large torque ripple of the motor. The next work is reducing the torque ripple by optimizing the structure of the motor.
Fig. 8. Rated output torque of each part. (a) Rated output torque of the outer unit motor. (b) Rated output torque of the inner unit motor. (c) Rated output torque of the PMRHDRM.
5 Conclusions The PMRHDRM is proposed in the paper, the results show that the output torque and the torque density of the motor is greatly improved then ORPMSM. The motor can meet the needs of different loads through different operation modes, as is shown in Table 3. After calculation and analysis, the motor performance is reasonable, which provides a new choice for the load-speed and high-torque transmission system. Table 3. The load capacity of PMRHDRM. Working condition
Output power (kW)
Output torque (kN·m)
Light-load
7
0.73
Heavy-load
40
4.25
47
4.98
52
5.5
Full-load
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Acknowledgments. We are very grateful to the other members of the research team for their help. This work is supported by the National Natural Science Foundation of China under Grant 51877139, and Department of Education of Liaoning Province (LQGD2020007).
References 1. Shi, Z., Sun, X., Cai, Y., Tian, X., Chen, L.: Design optimisation of an outer-rotor permanent magnet synchronous hub motor for a low-speed campus patrol EV. IET Electr. Power Appl. 14(11), 2111–2118 (2020). https://doi.org/10.1049/iet-epa.2020.0130 2. Fan, Y., Chen, S., Tan, C., Cheng, M.: Design and investigation of a new outer-rotor IPM motor for EV and HEV in-wheel propulsion. In: 2016 19th International Conference on Electrical Machines and Systems (ICEMS), pp. 1–4 (2016) 3. EL-Refaie, A.M., Jahns, T.M.: Optimal flux weakening in surface PM machines using fractional-slot concentrated windings. IEEE Trans. Ind. Appl. 41(3), 790–800 (2005) 4. Crider, J.M., Sudhoff, S.D.: An inner rotor flux-modulated permanent magnet synchronous machine for low-speed high-torque applications. IEEE Trans. Energy Convers. 30(3), 1247– 1254 (2015) 5. Reism, K., Binder, A.: Development of a permanent magnet outer rotor direct drive for use in wheel-hub drives. In: Proceedings 2014 International Conference Electrical Machines, Berlin, Germany, pp. 2424–2430 (2014) 6. Li, L., Zhang, J., Zhang, C., et al.: Research on electromagnetic and thermal issue of highefficiency and high-power-density outer-rotor motor. IEEE Trans 26(4), 52048805 (2016) 7. Pišek, P., Štumberger, B., Marˇciˇc, T., Virtiˇc, P.: Design analysis and experimental validation of a double rotor synchronous PM machine used for HEV. IEEE Trans. Mag. 49(1), 152–155 (2013). https://doi.org/10.1109/TMAG.2012.2220338 8. Li-Bing, J., Qi-Xing, G., Chong, W., Zheng-Hao, L., Li-Hui, X., Kang, H.: Optimization design and characteristic analysis of dual-rotor hybrid excitation motor. Electr. Mach. Control 9, 43–50 (2019)
Parametric Analysis and Performance Comparison of a Novel Brushless Double-Fed Generator with Series Cage Bar Assisted Magnetic Barrier Rotor Zhenyu Diao(B) , Siyang Yu, and Fengge Zhang School of Electrical Engineering, Shenyang University of Technology, Shenyang, China [email protected]
Abstract. Brushless doubly-fed generator (BDFG) shows great potential in the field of wind power due to its high reliability and cost advantages. The rotor is the basis of BDFG electromechanical energy conversion, and plays the role of “polenumber converter”. In order to improve the magnetic field modulation ability of the BDFG rotor, combining the advantages of different types of rotor structures of BDFG, this paper proposes a series cage bar assisted magnetic barrier rotor structure, the series cage bar and the radial magnetic barrier have an effect on the air gap magnetic field, which enhances the magnetic field modulation ability commonly. The structural characteristics of this kind of generator are introduced in detail, and the mapping law between the rotor structural parameters and the magnetic field modulation ability of the BDFG is studied. Finally, simulation research has verified the superiority of the above-mentioned generator structure and is suitable for application in the field of wind power generation. Keywords: Brushless doubly-fed generators (BDFG) · New hybrid rotor · Structural parameters · Magnetic field modulation ability
1 Introduction In order to meet the development direction of high-power, high-reliability, and low-cost wind power generation, with its brushless, sturdy structure, adjustable power factor, and small inverter capacity required, brushless double-fed generator BDFG will hopefully become the mainstream model of wind power generation in the future [1–4]. Unlike traditional generators, unlike traditional generators, there are two sets of three-phase windings with different poles on the stator of BDFG. The power winding is directly connected to the grid and the control winding is connected to the frequency converter and the magnetic coupling between the two sets of windings of the stator is realized by a rotor with a corresponding number of poles. Therefore, the rotor structure is very important to the operation of the BDFG. Commonly used BDFG rotor structures are cage type and reluctance type. The cage rotor has a simple structure, great starting ability and asynchronous operation ability, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1283–1291, 2022. https://doi.org/10.1007/978-981-19-1528-4_130
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but with low utilization rate and large harmonic content, cage rotors have been gradually being eliminated [5]. Sharif university of technology proposed a series cage rotor, this structure can effectively improve the sine of the cage bar modulation magnetomotive force [6]. The ordinary reluctance rotor has simple structure and no copper loss, but the magnetic field modulation ability is not strong and the eddy current loss is large, in order to reduce the eddy current effect of the rotor, a radial laminated magnetic barrier rotor appeared [7, 8]. For this reason, a new hybrid rotor structure which is composed by series cage bars and radial magnetic barrier laminations is proposed in this paper. The different structural parameters and magnetic field modulation ability of this kind of rotor are analyzed and introduced in detail. In addition, the influence of different rotor structures on the magnetic field of the generator is compared and analyzed.
2 Structure of the Proposed BDFG The rotor of the brushless doubly-fed generator plays a key role in the coupling capacity of the two sets of stators and the performance of the system. Therefore, the research on the rotor is very critical. The structure of the novel series cage bar assisted magnetic barrier rotor is shown in Fig. 1. The proposed hybrid rotor is based on the magnetic barrier rotor, in which series cage bars are added at the non-magnetic layer and the common cage bars connected to the ends are added at the center line of the salient pole. The advantage is that the auxiliary cage bar will strengthen the regulation of the magnetic flux path and the generated magnetomotive force and reluctance magnetomotive force will electromagnetically couple the two sets of stators. It can be seen that this kind of rotor has good magnetic field modulation ability and is easy to produce.
+ Radial magnetic barrier rotor
= Series short-circuit cage
New hybrid rotor
Fig. 1. Structure diagram of series cage bar auxiliary radial magnetic barrier rotor
The parameters of the generator are shown in Table 1. Stator power winding pitch y1 = 8; control winding pitch y2 = 15. Considering the generator manufacturing process, usually the coils with a large pitch are placed on the periphery of the slot, and the coils with a small pitch are placed inside the slot. Therefore, the 4-pole control winding is placed outside and the 8-pole power winding is placed inside.
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According to the generator parameters, the number of salient poles in the new hybrid rotor satisfies Zr = Pp + Pc , where Pp and Pc are the number of pole pairs of the power winding and the control winding. Table 1. Performance of the BDFG with new hybrid rotor Parameters
Value
Rated power (kW)
25
Stator outer diameter (mm)
400
Stator inner diameter (mm)
285
Number of stator slots
72
Core length (mm)
225
Air gap length (mm)
0.5
Rotor outer diameter (mm)
284
Rotor inner diameter (mm)
85
Rated speed (r/min)
1000
Number of poles of power winding
8
Number of poles of control winding
4
Rated voltage of power winding (V)
380
Rated voltage of control winding (V)
380
Rated frequency of power winding (Hz)
50
Rated frequency of control winding (Hz) 50 Frequency range (Hz)
[−20, 50]
3 Rotor Structure Paramter Analysis There is no direct electromagnetic coupling between the two sets of stator windings of 8 poles and 4 poles. The electromechanical energy conversion can only be realized by the magnetic field modulation of the generator. In order to increase the output power of the generator and improve the quality of the winding waveform, it is necessary to study the influence of the structural parameters of the rotor on the generator’s magnetic field modulation ability. 3.1 Magnetic Barrier Structure Aiming at the characteristics of the new hybrid rotor BDFG, this paper proposes two alternative radial magnetic barrier topologies, which are triangular magnetic barrier and tree magnetic barrier.
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Established a 25 kW BDFG model with magnetic barrier rotor to control the winding excitation, and analyzed the air gap flux density harmonic components of two different magnetic barrier rotor generators with the finite element method. The results are shown in Fig. 2 that through calculation and analysis of the ratio of each harmonic to the fundamental wave, it is concluded that the magnetic field modulation ability of the treeshaped magnetic barrier rotor is 7.42% higher than that of the triangular magnetic barrier rotor. Therefore, the new hybrid rotor uses the tree structure as the magnetic barrier part.
Fig. 2. Analysis of air gap magnetic density of BDFG with different magnetic barrier structures
3.2 Polar Arc Coefficient Table 2 shows corresponding magnetic layer width under different λ, and Fig. 4 shows variation curve of effective magnetic field component with different λ from 0.3 to 0.7, and as is shown in Fig. 3 that the fundamental wave of the air gap magnetic density increases with the increase of λ, the 8-pole magnetic field component which contribute to magnetic field modulation increases first and then decreases slowly, and reach the maximum value around λ = 0.5. Table 2. Polar arc coefficient structure parameter Magnetic layer width (mm)
Polar arc coefficient
5.58
0.30
6.51
0.35
7.44
0.40
8.36
0.45
9.29
0.50
10.22
0.55
11.15
0.60
12.08
0.65
13.01
0.70
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Fig. 3. Analysis of air gap magnetic density of BDFG with different polar arc coefficients
3.3 Number of Permeable Layers The reasonable selection of the number of magnetically permeable layers and the number of rotor slots is an important factor to improve the ability of magnetic field modulation. The BDFG generator models with magnetic layer number of 1~6 is established and analyzed. The results are shown in Table 3. It can be seen from Table 3 that with the increase in the number of magnetic layers, the magnetic field component that acts on magnetic field coupling first increases and then decreases. The reason for the decline is the saturation effect of the narrower width. Therefore, in order to avoid the saturation effect of the rotor and take into account the production cost of the generator, when the magnetic permeability layer of the rotor should be selected in 4 layers, the performance of the generator is the best. Table 3. The Influence of the Number of Permeable Layers on the Magnetic Field Modulation Ability Number of layers
Effective harmonic to fundamental wave ratio
Magnetic density amplitude (T) Bδc
Bδp
1
58.83%
0.643
0.3783
2
75.75%
0.6041
0.4576
3
83.41%
0.5746
0.4793
4
86.03%
0.5841
0.5025
5
92.62%
0.5
0.4631
6
94.54%
0.4543
0.4295
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3.4 Series Cage bar Parameters The number of cage bars corresponds to the number of slots in the rotor. The slots of the rotor are located in the non-magnetic layer. Therefore, the number of cage bars is related to the number of magnetic layers. Models with different numbers of cage bars are established, and the 2D model is shown in Fig. 4. Figure 5 shows the magnetic field modulation ability of the generator under no load under 5 rotor structures. With the increase of the number of cage bars, the magnetic field modulation ability shows an increasing trend. But the copper consumption of the generator will also increase, so it is necessary to comprehensively consider factors such as manufacturing cost and efficiency.
Fig. 4. The rotor with a common cage and four sets of cage bars in series
Fig. 5. Influence of the number of cage bars on the ability of magnetic field modulation
A series of cage bars composed of multiple turns of coils are embedded in the magnetic barrier under each pole of the hybrid rotor. Different positions of the cage bars under the salient poles will have different effects on the air gap magnetic field; at the same time, the span of the cage bars also affects the air gap. The key factor of magnetic field distribution, different short-distance coefficients will cause different harmonic content of induced electromotive force in the cage, which will ultimately affect the magnetic field modulation efficiency in the air gap. A rotor structure with 6 magnetic permeable layers and 1 cage bar group is selected for comparative analysis. The structure diagram is shown in Fig. 6. Taking the shortcircuit cage bar composed of R1 L 1 as an example, and the short-distance coefficient is y π · (1) k = sin τ 2
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A total of 25 groups of finite element models of the cage bar distribution at different positions were established for R1 L 1 , R1 L 2 , R1 L 3 · · · · · · R5 L 5 .
Fig. 6. Rotor structure diagram under different cage positions
The analysis process is shown in Fig. 7. According to figure (a), when the spans of the 5 groups of models are all the same, as the distance between the symmetry line and the center of the salient pole increases first and then decreases, the magnetic field modulation ability first increases and then decreases. When they are completely overlapped, the magnetic field coupling is strongest. According to figure (b), when the 5 groups of models are completely symmetrically distributed, the modulation effects of the cage bars under different spans are analyzed separately. With the increase of the cage span, the ratio of the effective harmonics of the magnetic field to the fundamental wave presents a normal distribution trend. When the span reaches 37, there are many effective harmonics and few high-order harmonics, and the modulation ability is the best. Therefore, when designing the position of the cage bars, the symmetry lines of the two cage bars under any salient pole should be as close as possible to the center line of the magnetic barrier salient pole. When the span angle of the cage bars is 30~37°, the magnetic field modulation ability of BDFG is the best.
Fig. 7. Magnetic field modulation ability of BDFG in different cage distribution
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4 Comparative Analysis of Different Rotors In order to analyze the auxiliary effect of the series bar on the magnetic field modulation ability, the influence of different rotor structures on the magnetic field of the generator is compared and analyzed. The stator remains unchanged, and the magnetic field modulation ability of the proposed rotor and the radial magnetic barrier rotor without auxiliary guide bars are compared and analyzed. In order to explain the modulation effect of different rotor structures more clearly, the air gap magnetic field distribution of the generator is analyzed by frequency spectrum, and its harmonic content is calculated. The magnetic density amplitude of the 1~30th harmonic is shown in Fig. 8. It can be seen from the figure that the 8-pole magnetic field component modulated by the proposed rotor structure BDFG is better than the ordinary radial magnetic barrier rotor BDFG through the auxiliary effect of the series bar and the modulation capacity increased by 15.4%. Therefore, the magnetic field modulation ability of the BDFG with a series cage bar assisted magnetic barrier rotor is better.
Fig. 8. Harmonic decomposition of BDFG with different rotors
5 Conclusion This paper proposes a new hybrid rotor structure for BDFG. The mapping law between rotor parameters and magnetic field modulation ability of BDFG is introduced and analyzed in detail. The characteristics of the proposed BDFG are simulated through finite element method. The results show that this type of generator has good performance and is suitable for wind power application. Acknowledgments. This work is supported by the Key International Cooperation of National Natural Science Foundation of China under Grant 52007124 and by Doctoral Start-up Foundation of Liaoning Province (2019-BS-180), and Department of Education of Liaoning Province (LQGD2020007).
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References 1. Jovanovic, M.G., Betz, R.E., Yu, J.: The use of doubly fed reluctance machines for large pumps and wind turbines. IEEE Trans. Ind. Appl. 38(6), 1508–1516 (2002)https://doi.org/10.1109/ TIA.2002.804749 2. Mathekga, M.E., Ademi, S., McMahon, R.A.: Brushless doubly fed machine magnetic field distribution characteristics and their impact on the analysis and design. IEEE Trans. Energy Convers. 34(4), 2180–2188 (2019). https://doi.org/10.1109/TEC.2019.2939183 3. Oraee, A., McMahon, R., Abdi, E., Abdi, S., Ademi, S.: Influence of pole-pair combinations on the characteristics of the brushless doubly fed induction generator. IEEE Trans. Energy Convers. 35(3), 1151–1159 (2020). https://doi.org/10.1109/TEC.2020.2982515 4. Olubamiwa, O.I., Gule, N.: Design and optimization of a Cage + Nested loops rotor BDFM. In: 2020 International Conference on Electrical Machines (ICEM), pp. 1868–1874 (2020). https://doi.org/10.1109/ICEM49940.2020.9270698 5. Zhang, F., Yu, S., Wang, Y., Jin, S., Jovanovic, M.G.: Design and performance comparisons of brushless doubly fed generators with different rotor structures. IEEE Trans. Industr. Electron. 66(1), 631–640 (2019). https://doi.org/10.1109/TIE.2018.2811379 6. Gorginpour, H., Jandaghi, B., Oraee, H.: A novel rotor configuration for brushless doubly-fed induction generators. Electr. Power Appl. IET 7(2), 106–115 (2013) 7. Yu, S., Zhang, Y., Chen, C., Zhang, F., Nian, H.: Loss estimation of brushless doubly-fed generator with hybrid rotor considering multiple influence factors. IEEE Access 8, 60043– 60051 (2020). https://doi.org/10.1109/ACCESS.2020.2983076 8. Abdi, S., Abdi,E., McMahon, R.: A new stator back iron design for brushless doubly fed machines. In: 2017 20th International Conference on Electrical Machines and Systems (ICEMS), pp. 1–6 (2017). https://doi.org/10.1109/ICEMS.2017.8056358
Analysis and Mitigation of Middle Frequency Resonance for Grid-Connected Inverter Under Weak Grid Gaoxiang Li(B) and Hongzhi Pan College of Electrical Engineering, Guangxi University, Nanning, China [email protected]
Abstract. Under weak grid, the grid-connected inverter can easily cause several hundred Hertz based middle-frequency resonance (MFR), which would seriously disturb the normal operation of system. To effectively address this issue, a parameter sensitivity analysis-based parameter optimization method is proposed to damp this MFR. Firstly, with considering the influences of inverter time delay and phaselocked loop (PLL), an accurate state-space model of inverter integrated in weak grid is built to reveal mechanism and characteristics of this MFR. With the increase of grid impedance, inverter is prone to undergo this MFR. By analyzing the control parameter sensitivity of the characteristic root for this MFR mode, the most sensitive control parameter can be scientifically identified, which should be optimized for suppressing this MFR. In addition, by theoretically analyzing the effect of control parameter change on system stability, a scientifically parameter optimization strategy is accurately obtained for optimally damping this MFR. At last the proposed mitigation strategy of this MFR are tested and verified by experimental researches. Keywords: Resonance · Grid-connected inverter · Weak grid · Eigenvalue analysis · Parameter optimization
1 Introduction With the increasing penetration of renewables, the stiffness of power grid declines gradually. Presently, power quality and stability issues induced by weak grid have been attracting more and more attention [1–3]. Inverter is the interface between grid and renewables, which plays a crucial role in the stability of renewable energy generation system [4]. The stability researches of inverter integrated in grid are significantly helpful for the analysis and mitigation of the oscillation in renewable energy generation system. Inverter can easily cause a series of stability issues under weak grid, for instance, LCL resonance [5] and subsynchronous resonance (SSR) [6]. Currently, LCL resonance of inverter has been intensively researched and many active-damping control strategies have been proposed to suppress this high-frequency oscillation [7]. For the SSR of grid-connected inverter, its oscillation frequency is very low, which is below than the fundamental frequency of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1292–1304, 2022. https://doi.org/10.1007/978-981-19-1528-4_131
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system. Due to the huge damage for power system, the SSR problem of grid-connected inverter also attracts widespread attention [8, 9]. Compared with LCL resonance or SSR, the several hundred Hertz based middle frequency resonance (MFR) are rarely studied [10]. Considering that MFR would seriously affect the power quality of system, the MFR of inverter integrated in weak grid has a great research value. For the stability of inverter integrated in grid, the analysis methods are generally divided into two large categories: they are frequency-domain and time-domain methods, respectively [11]. Currently, due to the simple and intuitive style, impedance method is the most widely used way to analyze the inverter’s stability [12, 13]. In addition, the widely used time-domain analysis methods consist of simulation analysis [14] and eigenvalue analysis [15]. Normally, simulation analysis method can hardly reveal the internal mechanism of system resonance, which is usually used as a verification method [16, 17]. The impedance method can reveal the mechanism of system resonance from the point of view of impedance, which has a certain degree of blindness in searching the dominant influence factor. Compared with the impedance method and simulation analysis method, the state space model-based eigenvalue analysis can scientifically and accurately identify the most critical control parameter which induces the system resonance, which can usually provide a clear direction for resonance suppression. On the issue of resonance mitigation, the widely used damping strategies of gridconnected inverter can be divided into two types: addition of the resonance damping device [8] and optimization of the original control algorithm [18]. Usually, optimization of the original control algorithm is more economical [19]. In addition, the control algorithm optimization methods can fall into two kinds according to whether the initial control structure is modified or not. Compared with the improvement of control structure, the optimization of control parameter is much simpler, which is usually the preferred method for damping system resonance [20]. However, due to the large number of control parameters, how to judge and choose the control parameter that should be optimized is usually a difficult and important point. Meanwhile, how to scientifically adjust the control parameter also requires further analysis and exploration. To address these issues, this paper proposes a parameter sensitivity analysis-based parameter optimization method, which can scientifically identify the control parameter that should be optimized for damping the MFR of inverter integrated in weak grid. Then, from systems stability viewpoint, a scientific parameter optimization way can be obtained by analyzing the characteristic roots of system under different control parameter values. The contributions of this researches are listed as below: 1) With considering inverter control delay and phase-locked loop (PLL), an accurate state-space model of inverter integrated in grid is built to thoroughly reveal mechanism and characteristics of this MFR. 2) By applying the parameter sensitivity analysis, the key control parameter which induces the system resonance can be theoretically identified, which is the basis for parameter optimization. 3) From the perspective of system stability, a concrete parameter optimization strategy is scientifically obtained by analyzing the influence of the control parameter change on the characteristic root. This paper is structured as below. A precise state-space model of inverter integrated in weak grid is built in Sect. 2. In addition, the underlying mechanism and characteristics of system resonance is studied according to eigenvalue analysis. To obtain the control
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parameter that should be optimized, the control parameter sensitivity of MFR mode is analyzed in Sect. 3. Then, a concrete parameter optimization method is obtained according to system stability analysis. Section 4 provides the results of experiments, and Sect. 5 summarizes the conclusions.
2 Middle-Frequency Resonance Analysis of Inverter Integrated in Weak Grid The circuit and control diagrams of inverter integrated in grid is depicted in Fig. 1, where Rg is grid resistance; L g is grid inductance; C f is filter capacitor; L f is filter inductor; V dc is dc-side voltage; va , vb and vc are the point of common coupling (PCC) voltages; ia , ib and ic are the currents of L f ; id c and iq c are the control signals of currents ia , ib and ic in dq domain; idref is reference value of id c and iqref is reference value of iq c ; Gi (s) = k p + k i s represents the effect of the PI controller for the current loop; K d is the decoupling coefficient and K f is feedforward coefficient. Q1
Vdc
Q3
Grid-Connected Inverter
Q5
Lf
ea
ib ic
eb
Q4
Q6
ec
PCC
iga
ia va vb vc
Q2
Lg , Rg
Grid
igb igc
vga vgb vgc
Cf Sampling
vabc,iabc vabc
vc
vb
va
Kf
Kf
Kf
mas ++ mbs mcs ++
++
PLL
θc iabc Kd
dq/abc
PWM
Control System
mdc mq ++ c
abc/dq
iqc idc
Kd
+
+ idref
Gi(s) Gi(s)
idc iqc
+
iqref
Fig. 1. Circuit and control diagrams of inverter integrated in grid
A. State-Space Model of Inverter Integrated in Weak Grid Figure 2 shows the diagram of synchronous reference frame phase-locked loop (SRFPLL), where vd c and vq c are the control signals of voltages va , vb and vc in dq domain; T pi (s) = T p + T i s is PI controller; ωc is the obtained angular frequency; θ c is the obtained phase. Due to the PLL, the control signals (id c and iq c ) used in controller are slightly different with the actual signals (id s and iq s ), as shown in Fig. 3. θ is the difference of the obtained phase, which is usually very small when PLL is stable.
Analysis and Mitigation of MFR for Grid-Connected Inverter
va vdc dq vb c vc abc vq
Tpi(s)
ωc
1/s
Fig. 2. Diagram of SRF-PLL.
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θc
Fig. 3. Actual signals and control signals.
From Fig. 2 and 3, there is θ c = θ + θ, where θ is the actual phase of grid. Generally, the transition matrix T dq/abc (θ c ) from abc domain to dq domain can be expressed as, ⎤ ⎡ cos θC cos(θC − 2π/3) cos(θC + 2π/3) 2⎣ (1) Tdq/abc (θC ) = − sin θC − sin(θC − 2π/3) − sin(θC + 2π/3) ⎦ 3 1/2 1/2 1/2 Considering that the value of θ is very small, there are cos(θ ) ≈ 1 and sin(θ ) ≈ θ. Therefore, by substituting θ c = θ + θ into (1), there is, Tdq/abc (θc ) = Tc (θ )Tdq/abc (θ ) where
⎤ ⎡ cos θ cos(θ − 2π/3) cos(θ + 2π/3) 2⎣ Tdq/abc (θ ) = − sin θ − sin(θ − 2π/3) − sin(θ + 2π/3) ⎦ 3 1/2 1/2 1/2 ⎡ ⎤ 1 θ 0 Tc (θ ) = ⎣ −θ 1 0 ⎦ 0 0 1
According to (2) and (4), there are, s mcd md − Kf vds 1 θ = mcq msq − Kf vqs −θ 1
(2)
(3)
(4)
(5)
where vd s and vq s are the transformed signals of voltages va , vb and vc in dq domain; md c and mq c are the intermediate variables of controllers, as shown in Fig. 1; and md s and mq s are the modulation signals in dq domain. From Fig. 1 and Eq. (5), the equivalent system diagram of inverter integrated in grid could be depicted in Fig. 4, where ed s and eq s are the transformed signals of voltages ea , eb and ec in dq domain; M d s and M q s are the rated values of md s and mq s , respectively; igd s and igq s are the transformed signals of currents iga , igb and igc in dq domain; and vgd s and vgq s are the transformed signals of voltages vga , and vgb vgc in dq domain. From Fig. 4, the control signals in dq domain are different with those of the actual signals in dq domain owing to the phase error caused by PLL. According to Eq. (2), the control signals in dq domain and the actual signals in dq domain exist the following relationships, expressed as follows, s vdc vd 1 θ = (6) vqc vqs −θ 1
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idc
Idref +
Gi(s)
iqc
+
mdc
++
+ Mqs
Kd θ
idc Iqref
+
Mds
Kd Gi(s)
+ mqc + +
mds
vds vqs
Gpwm(s)
eds
+ +
ids
1/(sLf )
+
+
ωLf
Kf
mqs
Gpwm(s)
iqc
eqs
+
1/(sCf )
vgds vds +
+
++
iqs igqs
1/(sCf )
1/(sLg+Rg)
igds
ωLg
ωLg
ωCf
1/(sLf )
vqs
Actual Circuit
ωCf
ωLf
Kf
+ +
igds
vds
++
vqs vgqs
1/(sLg+Rg)
igqs
Fig. 4. Equivalent system diagram of inverter integrated in grid.
idc iqc
1 θ = −θ 1
ids iqs
(7)
Substituting the state variables by their steady-state values and small perturbations (md c = M d c + md c ; mq c = M q c + mq c ; md s = M d s + md s ; mq s = M q s + mq s ; vd c = V d c + vd c ; vq c = V q c + vq c ; vd s = V d s + vd s ; vq s = I q s + vq s ; id c = I d c + id c ; iq c = I q c + iq c ; id s = I d s + id s ; iq s = I q s + iq s ) and neglecting the second-order perturbations, the following formulas can be obtained, ⎧
⎨ mc = ms − Kf vs + θ M s − Kf V s q q d d d (8) ⎩ mc = ms − K vs + θ M s − K V s f f d q q q d vdc = vds + θ Vqs (9) vqc = vqs + θ Vds idc = ids + θ Iqs (10) iqc = iqs + θ Ids According to Fig. 4, there are, ⎧ ic ⎪ ⎪ mc = −kp ic − ki d − Kd ic ⎨ q d d s c iq ⎪ ⎪ ⎩ + Kd idc mcq = −kp iqc − ki s Defining two new intermediate variables, expressed as follows, ⎧ ic ⎪ ⎪ x1 = d ⎨ s c i ⎪ q ⎪ ⎩ x2 = s Differentiating (12), there are, x˙ 1 = idc x˙ 2 = iqc
(11)
(12)
(13)
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Considering the controller time delay, the gain of inverter is usually expressed as Gpwm (s) = V dc /2/(1 + T s s). Therefore, the small signal gain of inverter can be obtained as, Vdc / 2 = eds msd 1+T ss (14) 2 V dc / msq 1+T = eqs ss where eq s and ed s are the small perturbations of voltages ed s and eq s , respectively. By equivalence transformation, (14) can be written as, d es msd Vdc / 2−eds d dt s = T (15) msq Vdc / 2−eqs d eq = dt T Assuming ω is the actual grid angular frequency, there is ω = ωc in steady state. According to the circuit in Fig. 4, there are, ⎧ s s s s s ⎨ d id = ed −R0 id +ωc Lf iq −vd dt Lf (16) s s s s s ⎩ d iq = eq −R0 iq −ωc Lf id −vq dt Lf ⎧ ⎪ ⎨
d vds dt
=
⎪ ⎩ d vqs = dt
⎧ d is gd ⎨ dt = s ⎩ d igq = dt
s ids −igd
Cf
s iqs −igq Cf
+ωc vqs
(17)
− ωc vds
s +ω L i s −v s vds −Rg igd c g gq gd
Lg s −ω L i s −v s vqs −Rg igq c g gq gd Lg
(18)
where igd s and igq s are the small perturbations of currents igd s and igq s , respectively; ed s and eq s are the small perturbations of voltages ed s and eq s , respectively; vgd s and vgq s are the small perturbations of voltages vgd s and vgq s , respectively. In addition, from Fig. 2, there are, 1 ω = Tp + Ti vqc (19) s θ =
ω s
(20)
where ω is the small perturbations of angular frequency ωc . Defining an intermediate variable, expressed as follow, x3 =
vqc s
(21)
Differentiating (21), there is, x˙ 3 = vqc
(22)
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Combing (13), (15)–(18), (20) and (22), the small-signal state-space model of inverter integrated in grid can be obtained, x˙ = Ax + Bu
(23)
where x = [id s , iq s , vd s , vq s , igd s , igq s , ed s , eq s , θ, x 1 , x 2 , x 3 ]T ; u = [vgd s , vgq s ]T . B. Middle-Frequency Resonance Analysis of Inverter Integrated in Weak Grid Based on the matrix B, the characteristic roots of the studied system with different grid stiffnesses is shown in Fig. 5. The parameters of the studied system are same as that of [4]. As shown in Fig. 5, when SCR decreases, the characteristic roots of the grid-connected inverter obviously shift. Generally, the system under study is stable only when the real part of all characteristic roots is negative. From Fig. 5, when the grid stiffness declines, the characteristic roots of system resonance mode gradually shift to the right-half plane, which means that the studied system will cause MFR under weak grid. When SCR = 3, the image part of the characteristic root for MFR mode is 211 Hz. Therefore, the resonance frequency of the studied system with SCR = 3 is about 211 Hz in dq domain.
Fig. 5. Root locus of the studied system when grid stiffness declines from SCR = 12 to SCR = 2.
To verify the analysis of Fig. 5, a grid-connected inverter is simulated in MATLAB/Simulink, as shown in Fig. 1. Figure 6 shows the simulation results. From Fig. 6(a), both voltages and current oscillate when SCR is abruptly dropped from 8 to 3. The frequency spectrum of the current shows that the oscillation frequency of current is about 261 Hz. Due to the middle-frequency oscillation of voltages and currents, the output power of inverter fluctuates too, as shown in Fig. 6(b). The oscillation frequency of active power is 211 Hz, which is consistent with its theoretical value because the MFR analysis is based on the dq domain. According to reference [24], a 211 Hz oscillation in dq domain can result in a 261 Hz oscillations in abc domain (50 + 211 = 261 Hz). Thus, the MFR analysis of current is also consistent with that of Fig. 5. Based on the analyses above, the simulation result of Fig. 6 is consistent with the MFR analysis.
Analysis and Mitigation of MFR for Grid-Connected Inverter 10
261Hz
iabc (A)
-500 40 0 -40 SCR=8 1
5
FFT analysis of ia
Current SCR=3 1.5 Time (s)
20
0 100 200 300 400 500 Frequency (Hz)
6
2 Pm (kW) / Qm (kVar)
vabc (V)
Voltage
1
Pm
4
211Hz
Qm 0 -1 SCR=8 1
2 SCR=3 1.5 Time (s)
20
(a)
Mag (% of Fundamental)
0
Mag (% of Fundamental)
500
1299
0 100 200 300 400 500 Frequency (Hz)
(b)
Fig. 6. Simulation results of system under different SCR. (a) Voltage and current. (b) Active and reactive powers.
3 Participation Sensitivity Analysis and Mitigation of Middle-Frequency Resonance
Table 1. Characteristic roots of system with SCR = 3. Characteristic root
Value
Oscillation frequency (Hz)
Damping ratio
λ1,2
−3048.4 ± j6237.6
992.8
0.4391
λ3,4
−2931.2 ± j5806.5
924.1
0.4507
λ5,6
−14.7 ± j1820.3
289.7
0.0081
λ7,8
43.3 ± j1326.0
211.0
−0.0326
λ9,10
−727.2 ± j22.1
3.5
0.9995
λ11,12
−11.9 ± j88.4
14.1
0.1334
The characteristic roots of inverter integrated in weak grid can be calculated and listed in Table 1. From Table 1, the studied system has five pairs stable modes and one pair resonance mode. The characteristic roots of the resonance mode are λ7,8 . The resonance frequency of MFR mode is 211 Hz, and the corresponding damping ratio is −0.0326. From Fig. 1, the number of the control parameters in the studied system are six and there are T p , T i , k p , k i , K d and K f . In order to suppress this MFR by optimizing the value of control parameter, it firstly needs to theoretically identify the most critical control parameter which induces the system resonance. Normally, the importance of parameter is proportional to its sensitivity of system mode. The key control parameter which induces this MFR mode can be theoretically obtained by applying the control parameter sensitively analysis. Figure 7 shows the control parameter sensitively of the characteristic roots λ7,8 .
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1 Kf
0
kp
Kd
Tp
Ti
1
2 3 4 5 Parameter number (a)
ki
6
Imaginary part sensitivity
Real part sensitivity
Seen from Fig. 7, the most sensitive control parameter for the characteristic roots λ7,8 is K f , which means that the value of parameter K f has the greatest influence on this MFR mode. Therefore, this MFR can normally be damped by optimizing the value of control parameter K f .
1
Kf 0
kp
Kd
Tp
Ti
1
2 3 4 5 Parameter number (b)
ki
6
Fig. 7. Sensitive degree of the control parameter for characteristic roots λ7,8 .
Fig. 8. Root locus of system with different control parameters. (a) K f decreases from 1 to 0. (b) K d decreases from ωn L f to 0.5*ωn L f . (a) k p decreases from 0.05 to 0.025. (b) k i decreases from 28 to 14. (a) T p decreases from 0.1 to 0.05. (b) T i decreases from 25 to 12.5.
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Figure 8 shows the root locus of system under different control parameters. From Fig. 8(a), when K f decreases from 1 to 0, the characteristic roots of MFR mode moves from the right half plane to the left half plane, which means that this MFR can be suppressed by properly decreasing the value of K f . When K f = 0.5, the distance between the characteristic roots of MFR mode and the right half plane is the largest. In addition, when other control parameters change by 50%, the characteristic roots of system are also depicted. As shown in Fig. 8(b), the characteristic roots of MFR mode are basically unchanged when K d decreases from ωn L f to 0.5 * ωn L f , which indicates that this MFR can hardly be damped by optimizing the value of K d . Similarly, the control parameters T p , T i , k p and k i , has a slight influence on the characteristic roots of MFR mode, as shown in Fig. 8(c)–(f). K f is the most sensitive control parameter for MFR mode, which means that it is most effective to mitigate this MFR by optimizing the value of K f . Based on the analyses above, the theoretical analysis of Fig. 8 and 9 are consistent.
4 Experimental Results To verify the above analyses, an inverter is built, where the inverter controller selects the DSP + FPGA scheme. The used control strategy of inverter is implemented in a DSP (TMS320F2812); voltage and current signals are collected by FPGA (EP2CQ208CN). In addition, the active and reactive powers are calculated and then output by DA module. The experimental parameters are identical to those of simulation, and the results are shown below. Experimental results of system under different SCR is shown in Fig. 9. From Fig. 9(a) and (b), both voltages and currents undergo an obvious middle-frequency oscillation when the SCR abruptly switches from 8 to 3. Due to the fluctuation of voltages and currents, the output power of inverter oscillates simultaneously, as shown in Fig. 9(c). Figure 9(d) shows the Discrete Fourier Transformation (DFT) of current and the oscillation frequency of current is about 261 Hz, which is completely consistent with the theoretical analyses. From Fig. 9(e), it can be seen that the active power oscillates at 211 Hz, which is completely consistent with the characteristic root analysis. Thus, the results of experiments are completely consistent with MFR analysis and simulation results, which verifies the correctness of the analysis in this paper. When K f = 0.5, the experimental results of system are presented in Fig. 10. The voltages, currents and output power fluctuate first and then run steadily when the SCR rapidly drop from 8 to 3. Therefore, this MFR issue can be effectively suppressed by properly adjusting of the value of K f , which tests the system stability analysis. The correctness of the proposed parameter sensitivity analysis based parameter optimization strategy is certificated by the experimental results of Fig. 10.
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Fig. 9. Experimental waveforms of system under different SCR. (a) Voltage waveform. (b) Current waveform. (c) Waveforms of active and reactive powers. (d) DFT results of the current. (e) DFT results of the active power.
[200V/div]
va vb vc
[20A/div]
ia ib ic
Pm [5kW/div] Qm [5kVar/div]
SCR =12
SCR=3
SCR =12
SCR=3
Time:[50ms/div]
Time:[50ms/div]
(a)
SCR =12
SCR=3
Time:[50ms/div]
(b)
(c)
Fig. 10. Experimental waveforms of system when K f = 0.5. (a) Voltage waveform. (b) Current waveform. (c) Waveforms of active and reactive powers.
5 Conclusion With considering the PLL and control delay, a several hundred Hertz based resonance issue of inverter integrated in weak grid is completely analyzed. To damp this MFR, a parameter sensitivity analysis-based parameter optimization method is proposed. The main conclusions are summarized as follows. 1) With the decline of grid stiffness, grid-connected inverter can easily cause several hundred Hertz based MFR, which would seriously affect the power quality of system. 2) By analyzing the sensitivity of the control parameter for system resonance, it finds that the voltage feedforward coefficient K f has a great influence on the MFR mode. It is most effective to mitigate this MFR by optimizing the value of K f . 3) A scientifically parameter adjustment strategy can be obtained by analyzing the influence of the control parameter change on the characteristic root of the studied system, which is designed from the perspective of system stability.
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4) Compared with the widely used impedance method, the eigenvalue analysis can scientifically and accurately identify the most critical control parameter which induces the system resonance, which can usually provide a clear direction for parameter optimization.
References 1. Song, Y., Blaabjery, F.: Overview of DFIG-based wind power system resonances under weak networks. IEEE Trans. Power Electron. 32(6), 3118–3128 (2017) 2. Li, G., Chen, Y., Luo, A., Wang, Y.: An inertia phase locked loop for suppressing subsynchronous resonance of renewable energy generation system under weak grid. IEEE Trans. Power Syst. 36(5), 4621–4631 (2021) 3. Li, G., Chen, Y., Luo, A., Liu, X.: Wideband harmonic voltage feedforward control strategy of STATCOM for mitigating subsynchronous resonance in wind farm connected to weak grid and LCC HVDC. IEEE J. Emerg. Sel. Top. Power Electron. 9(4), 4546–4557 (2021) 4. Zhu, D., Zhou, S., Zou, X., Kang, Y., Zou, K.: Small-signal disturbance compensation control for LCL-type grid-connected converter in weak grid. IEEE Trans. Ind. Appl. 56(3), 2852–2861 (2020) 5. Lin, Z., Ruan, X., Wu, L., Zhang, H., Li, W.: Multi resonant component-based grid-voltageweighted feedforward scheme for grid-connected inverter to suppress the injected grid current harmonics under weak grid. IEEE Trans. Power Electron. 35(9), 9784–9793 (2020) 6. Xie, X., Wang, S., Liu, H., Zhao, Q.: Hydrogen production equipment-based supplementary damping control to mitigate subsynchronous oscillation in wind power systems. IET Renew. Power Gener. 13(14), 2715–2722 (2019) 7. Li, G., et al.: Virtual impedance-based virtual synchronous generator control for gridconnected inverter under the weak grid situations. IET Power Electron. 11(13), 2125–2132 (2018) 8. Zhang, X., Xie, X., Shair, J., Liu, H., Li, Y., Li, Y.: A grid-side subsynchronous damping controller to mitigate unstable SSCI and its hardware-in-the-loop tests. IEEE Trans. Sustain. Energy 11(3), 1548–1558 (2020) 9. Li, G., et al.: Analysis and mitigation of subsynchronous resonance in series-compensated grid-connected system controlled by a virtual synchronous generator. IEEE Trans. Power Electron. 35(10), 11096–11107 (2020) 10. Song, Y., Blaabjerg, F.: Analysis of middle frequency resonance in DFIG system considering phase-locked loop. IEEE Trans. Power Electron. 33(1), 343–356 (2018) 11. Liu, H., Xie, X., Gao, X., Liu, H., Li, Y.: Stability analysis of SSR in multiple wind farms connected to series-compensated systems using impedance network model. IEEE Trans. Power Syst. 33(3), 3118–3128 (2018) 12. Cespedes, M., Sun, J.: Impedance modeling and analysis of grid-connected voltage-source converters. IEEE Trans. Power Electron. 29(2), 1254–1261 (2014) 13. Li, G., Chen, Y., Luo, A., Wang, H.: An enhancing grid stiffness control strategy of STATCOM/BESS for damping sub-synchronous resonance in wind farm connected to weak grid. IEEE Trans. Ind. Informat. 16(9), 5835–5845 (2020) 14. Ebrahimzadeh, E., Blaabjerg, F., Wang, X., Bak, C.L.: Reducing harmonic instability and resonance problems in PMSG-based wind farms. IEEE J. Emerg. Sel. Topics Power Electron. 6(1), 73–83 (2018) 15. Li, Y., Fan, L., Miao, Z.: Replicating real-world wind farm SSR events. IEEE Trans. Power Del. 35(1), 339–348 (2020)
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16. Salehi, F., Matsuo, I.B.M., Brahman, A., Tabrizi, M.A., Lee, W.: Sub-synchronous control interaction detection: a real-time application. IEEE Trans. Power Del. 35(1), 106–116 (2020) 17. Bi, T., Li, J., Zhang, P., Mitchell-Colgan, E., Xiao, S.: Study on response characteristics of grid-side converter controller of PMSG to sub-synchronous frequency component. IET Renew. Power Gener. 11(7), 966–972 (2017) 18. Zhou, S., et al.: An improved design of current controller for LCL-type grid-connected converter to reduce negative effect of PLL in weak grid. IEEE J. Emerg. Sel. Topics Power Electron. 6(2), 648–663 (2018) 19. Zhang, X., Xia, D., Fu, Z., Wang, G., Xu, D.: An improved feedforward control method considering PLL dynamics to improve weak grid stability of grid-connected inverters. IEEE Trans. Ind. Appl. 54(5), 5143–5151 (2018) 20. Zhu, D., Zhou, S., Zou, X., Kang, Y.: Improved design of PLL controller for LCL-type grid-connected converter in weak grid. IEEE Trans. Power Electron. 35(5), 4715–4727 (2020)
Conductor Selection of UHV Half-Wavelength AC Transmission Line Jialin Qin1(B) , Haiyan Mei2 , Jingwei Su1 , and Daping Liu3 1 CEC Technical and Economic Consulting Center of Electric Power Construction,
Beijing 100053, China [email protected] 2 China National Intellectual Property Administration, Haidian District, Beijing 100088, China 3 State Grid Anhui Electric Power Co., Ltd., Hefei 230022, Anhui, China
Abstract. UHV AC half-wavelength power transmission technology is a largecapacity, long-distance power transmission method, which has the characteristics of small loss, low annual cost, etc., and has significant social benefits. With the increasing attention to it word-wide, this technology has become an important direction for the development of power transmission. The selection of conductors for UHV AC half-wavelength transmission lines (HWTL) is an important subject, which has a greater impact on the transmission performance, transmission capacity, electromagnetic environment, and technical and economic indicators of power transmission line. This article mainly focuses on the selection of conductors for UHV AC half-wavelength transmission projects. Keywords: UHV · AC half-wavelength transmission · Conductor selection
1 Introduction AC half-wave transmission technology is mainly used to transmit power with an electrical distance close to one power frequency half-wave, that is, 3 000 km (50 Hz) or 2 500 km (60 Hz) ultra-long distance AC three-phase transmission technology [1, 2]. AC halfwavelength transmission does not require line reactive power compensation devices (such as high-voltage reactors) and intermediate switch stations. It has the characteristics of good economy and high reliability, and can realize the long-distance synchronous connection of the power grid. In recent years, scholars from various countries have begun to conduct practical research on related technologies of AC half-wavelength transmission [3]. In 2011, North China Electric Power University, China Electric Power Research Institute and State Grid Economic Research Institute jointly carried out research on the stability, transient analysis and economics of UHV AC half-wavelength transmission, and achieved a large number of research results [4–13]. Researchers from various countries have carried out in-depth research in various fields such as system analysis of UHV AC half-wavelength transmission and insulation coordination, but there is less research in the field of engineering construction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1305–1315, 2022. https://doi.org/10.1007/978-981-19-1528-4_132
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In this paper, researches are carried out on the selection of conductors for UHV AC HWTL projects. It mainly analyzes the electromagnetic environment characteristics, mechanical characteristics, load capacity and transmission loss, and annual cost of the conductor.
2 Parameter Model of HWTL According to circuit theory, when the length of the transmission line is much less than one electromagnetic wave wavelength, the voltage of the entire line is in the same change state, which can be described and analyzed by a centralized model; and when the length of the transmission line is close to the wavelength of the electromagnetic wave, the voltage along the line will be obvious fluctuations, and it is no longer appropriate to use centralized parameters for description and analysis, but to use distributed parameter models for analysis [8]. Assuming that the parameters along the HWTL are consistent, a uniform transmission line can be used to simulate it. Assuming that the voltage and current at the end of the line are known, the voltage and current equation at the distance l from the end is U˙ = U˙ 2 ch(γ l) + I˙2 Zc sh(γ l) (1) ˙ I˙ = I˙2 ch(γ l) + UZc2 sh(γ l) In the formula: U˙ 2 , I˙ 2 represents the voltage and current at the end of the line, and ˙I 2 is positive when the current flow out of the end of the line [13].
3 Voltage Characteristics of UHV HWTL The line parameters studied in this paper are shown in Table 1. Table 1. Unit parameters of UHV AC half-wavelength lines Phase R0 /(/km) jωL 0 /(/km) C 0 /(µF/km) sequence Positive 0.008 01 sequence
0.263 1
0.013 830
Zero 0.156 30 sequence
0.782 1
0.008 955
From Table 1, the corresponding wave impedance is ZC = 246.11 − j3.7455. A system with 21 nodes and 20 sections is adopted. The rated voltage of the system is 1000 kV and the line length is 3000 km. When the transmission power (TP) is 4500 MW, 5000 MW and 5500 MW, the voltage distribution along the line is calculated respectively, and the results are shown in Fig. 1.
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According to the results shown in Fig. 1, when the TP is 4500MW, the voltage value along the line does not change much. When the TP is increased to 5000 MW and 5500 MW, the voltage distribution along the line is high in the middle and low at both ends, and the highest voltage point appears in the middle of the line, the maximum voltage value can reach 1172 kV (1.12pu), 1289 kV (1.23pu). 14 00
55 00MW
Voltage (kV)
12 00 10 00 45 00MW
80 0
50 00MW
60 0 40 0 20 0
3000
2700
2400
2100
1800
1500
1200
900
600
300
0
0
Distan ce from startin g point (km )
Fig. 1. Voltage distribution along UHV AC HWTL
4 Research on Conductor Selection 4.1 Principles for Selection of Conductor Cross-Sections and System Conditions According to the current research of AC UHV projects, the electromagnetic environment is the most important limiting factor for the selection of AC UHV conductors. Therefore, this article comprehensively considers the following factors when carrying out the selection of UHV AC HWTL: 1) Economic current density; 2) Maximum current carrying capacity; 3) Electric field strength on the conductor surface; 4) Radio interference level; 5) Audible noise level; 6) Transmission loss [14]. In this paper, the line length is 3000 km, the rated voltage is 1000 kV, the power factor is 0.95, the maximum load utilization hours are 5000 h and 5500 h, and the TP is analyzed and compared according to 4500 MW, 5000 MW and 5500 MW respectively. 4.2 Economic Current Density and Total Conductor Cross-Section Regarding the economic current density, the valuestaken are also quite different because of the different situations in each country [14]. Combining with the past experience in conductor selection for AC UHV engineering, the reference value of the conductor economical current density selection is 0.9 A/mm2 .
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4.3 Conductor Splits and Split Spacing In UHV lines, the corona problem is generally solved by increasing the number and cross-section of the conductors. At present, the UHV lines that have been built use the regular octagonal splitting method. This paper recommends the conductor splitting distance is 400 mm. 4.4 Primary Selection of Conductor Combination According to the commonly used conductor types in domestic transmission line projects, four commonly used steel-cored aluminum stranded conductors with different crosssections are selected for comparative analysis. As shown in Table 2. Table 2. Conductor combination Conductor bundle
Aluminum section (mm2 )
Current density (A/mm2 ) 4500 MW
5000 MW
5500 MW
8 × JL/G1A-500/45
3976
0.69
0.76
0.84
8 × JL/G1A-630/45
4988
0.55
0.61
0.67
8 × JL/G1A-710/50
5802
0.47
0.52
0.58
8 × JL/G1A-800/55
6514
0.42
0.47
0.51
5 Electromagnetic Environment of Conductors The AC UHV half-wavelength transmission adopts a single-circuit transmission line. This paper selects the type II linear tower head for related calculations. 5.1 Calculation of Em /E0 According to the test results, the critical electric field strength and polarity of the conductor are relatively small, and the following formula is used in UHV AC transmission lines in China [14]: √ (2) Emo = 30.3mδ 2/ 3 1 + 0.3 r0 Where: m is the surface coefficient of the conductor, generally 0.82 for stranded conductor; δ is the relative air density; δ = 289 × 10–5 (p/(273 + t)); p is the air pressure, Pa; t is the air temperature, °C; r 0 is the radius of the conductor, cm. By formula (2), the critical electric field strength can be calculated, as shown in Table 3.
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Table 3. Critical electric field strength of conductor Conductor bundle
Diameter (mm)
Critical potential gradient peak (kV/cm) Altitude 500 m
Altitude 1000 m
Altitude 1500 m
Altitude 2000 m
8× JL/G1A-500/45
30
30.33
29.63
29.14
28.53
8× JL/G1A-630/45
33.8
29.98
29.29
28.79
28.19
8× JL/G1A-710/50
35.9
29.81
29.13
28.63
28.02
8× JL/G1A-800/55
38.4
29.63
28.95
28.44
27.84
Table 4. Maximum electric field strength of conductor Conductor
Maximum electric field strength of conductor (kV/cm) 4500 MW
5000 MW
5500 MW
Side phase
Middle phase
Side phase
Middle phase
Side phase
Middle phase
JL/G1A-500/45
14.89
15.10
17.45
17.69
18.34
19.34
JL/G1A-630/45
13.65
13.75
15.99
16.12
17.59
17.73
JL/G1A-710/50
13.07
13.13
15.32
15.39
16.85
16.92
JL/G1A-800/55
12.47
12.48
14.61
14.62
16.07
16.08
The maximum working electric field intensity E m on the surface of the conductor is related to the maximum operating voltage of the system, the diameter of the conductor, the split form and the distance between phases, etc. This paper uses the successive mirroring method to calculate, and the conclusions are shown in Table 4. For AC UHV HWTL, different TP lines have different maximum operating voltages. Now the E m and E m /E 0 values calculated from various conductor combinations under 4500 MW, 5000 MW, and 5500 MW conditions are listed in the Table. 5. The electric field intensity on the conductor surface should not be greater than 85% of the overall corona electric field strength to avoid the overall corona on the conductor. Therefore, JL/G1A-500/35 can only be used in areas with an altitude of 1000 m and below, at the same time the TP should less than 5000 MW; JL/G1A-630/45 can be used in areas with an altitude of less than 1500 m, or the TP is less than 5000 MW and the altitude is less than 2000 m; For areas with an altitude of less than 2000 m, JL/G1A-710/50, JL/G1A-800/55 can be used.
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Conductor
JL/G1A-500/45
JL/G1A-630/45
JL/G1A-710/50
JL/G1A-800/55
Altitude
4500 MW
5000 MW
5500 MW
Side phase
Middle phase
Side phase
Middle phase
Side phase
Middle phase
500 m
0.69
0.7
0.81
0.83
0.86
0.9
1000 m
0.71
0.72
0.83
0.84
0.88
0.92
1500 m
0.72
0.73
0.85
0.86
0.89
0.94
2000 m
0.74
0.75
0.87
0.88
0.91
0.96
500 m
0.64
0.65
0.75
0.76
0.83
0.84
1000 m
0.66
0.66
0.77
0.78
0.85
0.86
1500 m
0.67
0.68
0.79
0.79
0.86
0.87
2000 m
0.68
0.69
0.8
0.81
0.88
0.89
500 m
0.62
0.62
0.73
0.73
0.8
0.8
1000 m
0.63
0.64
0.74
0.75
0.82
0.82
1500 m
0.65
0.65
0.76
0.76
0.83
0.84
2000 m
0.66
0.66
0.77
0.78
0.85
0.85
500 m
0.6
0.6
0.7
0.7
0.77
0.77
1000 m
0.61
0.61
0.71
0.71
0.79
0.79
1500 m
0.62
0.62
0.73
0.73
0.8
0.8
2000 m
0.63
0.63
0.74
0.74
0.82
0.82
5.2 Radio Interference Calculation In this paper, the excitation function method is used to calculate the radio interference value of multi-split conductors. Table 6 shows the radio interference value on a 0.5 MHz sunny day. For the double 80% value, an additional 6~10 dB is required. It can be seen from Table 6 that all 4 conductor combinations can meet the 55 dB standard in good weather conditions. 5.3 Audible Noise Calculation In this paper, the audible noise prediction formula recommended by BPA is used to calculate the audible noise of various conductor splitting methods. The results are shown in Table 6. When the altitude is 500 m and below and the TP is less than 5000 MW, the audible noise level of the 4 kinds of conductors can meet the requirements of the regulations. When the power TP reaches 5500 MW, the audible noise level of the 4 kinds of conductors exceeds 55 dB. Increase the cross-section of the conductor to meet the requirements of the electromagnetic environment. It can be seen that the audible noise has a controlling effect on the selection of the conductor scheme.
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Table 6. The value of radio interference (0.5 MHz sunny) Conductor bundle
Split distance (mm)
Radio interference (dB) 4500 MW
5000 MW
5500 MW
Audible noise (dB) 4500 MW
5000 MW
5500 MW
8× JL/G1A-500/45
400
45.5
49.65
51.84
46.29
54.56
59.52
8× JL/G1A-630/45
400
44.73
49.29
51.84
44.21
52.49
57.45
8× JL/G1A-710/50
400
44.26
49.04
51.56
43.21
51.48
56.44
8× JL/G1A-800/55
400
43.68
48.71
51.37
42.12
50.39
55.35
6 Comparison of Mechanical Properties It can be seen from Table 7 that the sag characteristics of the four conductors are close. First of all, the sag characteristic of the JL/G1A-500/45 conductor is the best, and the sag characteristic of the JL/G1A-800/55 conductor is the worst. Secondly, as the crosssection of the sub-conductor increases, its ice-coating overload capacity also increases, and various conductors can meet the requirements of light and medium ice-coated areas in the design of this line. On the whole, these 4 kinds of conductors can meet the engineering requirements for the mechanical properties of the conductors. Table 7. Comparison of mechanical properties Conductor
JL/G1A-800/55 JL/G1A-710/50 JL/G1A-630/45 JL/G1A-500/45
Total Section (mm2 )
870.60
759
674
Aluminum to steel section ratio (m)
14.46
14.46
14.45
11.34
Conductor breaking force (kN)
192.22
169.56
150.45
127.31
2.50
2.50
2.50
2.50
Average service stress (MPa)
83.59
84.89
84.83
91.56
Average operating stress (MPa)
52.24
53.06
53.01
L p = 400
12.66
12.53
L p = 500
19.23
19.03
19.03
18.81
L p = 600
27.21
26.92
26.92
26.77
Overload capacity L p = 400 (mm) (10 mm Ice zone) L = 500 p
26.02
24.41
23.02
21.72
24.65
23.09
21.78
20.66
L p = 600
23.86
22.32
21.06
20.05
Safety factor
Maximum sag (m) (10 mm Ice zone)
12.53
531.68
57.22 12.28
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It can be seen from Table 8 that the string configuration is more economical when using JL/G1A -500/45 conductors. For wind loads, the phase conductors of various subconductor combinations are not much different, within 13.6%. Among the 4 conductor schemes, the maximum tension is 8-split JL/G1A-500/45 the smallest (84.6%), and the largest is 8-split JL/JLA1–800/55 (127.77%). Table 8. Load comparison of different conductor bundle Conductor structure
JL/G1A-800/55 JL/G1A-710/50 JL/G1A-630/45 JL/G1A-500/45
Split number
7
8
8
8
Maximum tension of phase conductor
584.35
515.46
457.37
287.02
Tension insulator string configuration (kN)
3 × 550
3 × 550
3 × 550
3 × 420
Maximum vertical load of phase conductor (N/m) (l v = 600 m, 10 mm Ice zone)
190.95
171.41
156.24
132.58
Suspension insulator 2 × 300 string configuration (kN)
2 × 300
2 × 300
2 × 210
Percentage of maximum load (%)
122.17
109.8
100
84.9
Horizontal 113.60 load under strong wind condition
106.2
100
88.8
Maximum 127.77 longitudinal load
112.7
100
84.6
Vertical load under icing condition
7 Economic Comparison The total cost of the transmission line project includes the initial investment in the construction of the project, that is, the project cost, as well as the annual operation, maintenance and loss costs. This section further selects the conductor combination 8split JL/G1A-630/45 and 8-split JL/G1A-710/50 for comprehensive comparison and selection.
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Table 9. Comparison of project investment per kilometer of different conductor (CNY/km) Conductor bundle Conductor investment
Tower investment
Foundation investment
Tension insulator string investment
Engineering ontology investment
8× JL/G1A-630/45
931500
2677300
929700
26900
4807400
8× JL/G1A-710/50
1049800
2785300
994900
26900
5099100
Table 9 shows the investment table of the conductor body. From the table, it can be seen that the body investment of 8-split JL/G1A-630/45 steel core aluminum stranded conductor can save about 525 thousand yuan per km compared with the 8-split JL/G1A710/50. Power loss includes resistance and corona loss. The corona loss is related to factors such as the electric field strength of the conductor surface, the surface roughness of the conductor, meteorological conditions, and altitude. The resistance loss calculation formula is I 2 R, which is calculated according to different TP. Table 10. Comparison of corona loss (kW/km) Conductor bundle
Loss type
8 × JL/G1A-630/45
Corona loss Resistance loss
8 × JL/G1A-710/50
4500 MW
5000 MW
23.67
31.22
111.35
175.35
5500 MW 39.06 254.7
Corona loss
22.17
29.72
37.56
Resistance loss
99.25
156.15
226.55
From Tables 10, it can be seen that using 8-split JL/G1A-710/50 steel core aluminum stranded conductor can reduce resistance and corona loss compared with 8-split JL/G1A630/45 steel core aluminum stranded conductor. The increase in power, the more obvious the energy-saving effect. The annual cost method will be used to evaluate the economics of the conductor, according to the actual situation of this project, the minimum annual cost calculation boundary conditions are as follows: 1) The economic service life is 30 years, and the construction period is calculated as 2 years. The investment in the first year is 60%, and in the second year is 40%. 2) The annual maximum load loss hours are calculated as 2700 h, 3200 h, and 3750 h respectively. 3) The equipment operation and maintenance rate is 1.4%. 4) The recovery rate of power engineering is calculated at 8%.
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5) The electricity price is calculated at 0.3 CNY/kWh, 0.4 CNY/kWh, 0.5 CNY/kWh.
Table 11. Comparison of Project construction and maintenance costs (CNY/km) Conductor
Project construction investment
First year investment
Second year investment
Total investment
Operation and maintenance fee
8× JL/G1A-630/45
4807400
2884400
1922900
5441200
67300
8× JL/G1A-710/50
5099100
3059400
2039600
5771300
71400
Table 12. Comparison of annual fee for different conductor (CNY/km) Conductor Annual loss hours (h) 2 × 4500 MW annual fee (104 CNY/km) 2 × 5000 MW annual fee (104 CNY/km) 2 × 5500 MW annual fee (104 CNY/km)
8 × JL/G1A-630/45
8 × JL/G1A-710/50
2700
3200
3750
2700
3200
3750
0.3 CNY/kWh
57.98
60.00
62.23
59.73
61.56
63.56
0.4 CNY/kWh
61.62
64.32
67.30
63.01
65.44
68.11
0.5 CNY/kWh
65.27
68.65
72.36
66.29
69.33
72.67
0.3 CNY/kWh
63.77
66.87
70.28
64.96
67.74
70.81
0.4 CNY/kWh
69.35
73.48
78.03
69.97
73.69
77.78
0.5 CNY/kWh
74.93
80.09
85.77
74.99
79.64
84.75
0.3 CNY/kWh
70.84
75.24
80.09
71.29
75.25
79.61
0.4 CNY/kWh
78.77
84.64
91.11
78.24
83.71
89.52
0.5 CNY/kWh
86.70
94.04
102.12
85.55
92.16
99.42
From the analysis of Tables 11 and 12, it can be seen that when the TP is 4500 MW and 5000 MW, and the electricity price is less than 0.5 CNY/KWh, the 8-split JL/G1A630/45 is basically superior. When the TP is 5500 MW and the electricity price is greater than 0.4 CNY/KWh, the 8-split JL/G1A-710/50 is better. As the TP increases, the corona loss increases accordingly, so it is more economical to use a large cross section.
8 Conclusion Based on the above studies, it can be concluded that from the consideration of electromagnetic environment and economy, if the AC UHV half-wavelength TP is 4500 MW, 8-split JL/G1A-630/45, JL/G1A-710/50 and JL/JLA1-800/55 can meet the requirements, and the economy of JL/G1A-630/45 is better. If the TP is increased to 5000 MW, 8-split
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JL/G1A-630/45 can be used at the start section and finish section of the line, and 8split JL/G1A-710/50 for the middle section of the line. When the TP is increased above 5500 MW, the cross-section of the conductor in the middle section of the line needs to be greater than 800 mm2 , and the line construction cost is high, and further economic analysis will be required.
References 1. Prabhakara, F.S., Parthasarathy, K., Rao, H.N.R.: Analysis of natural half-wave-length power transmission lines. IEEE Trans. Power Apparatus Syst. PAS 88(12), 1787–1794 (1969) 2. Wang, G., Lu, X., Qiuqin, S., et al.: Research status and prospects of AC half-wavelength transmission technology. Autom. Electr. Power Syst. 16, 13–18+68 (2010). (in Chinese) 3. Tavares, M.C., Portela, C.M.: Half-wave length line energization case test proposition of a real test, pp. 261–264. IEEE (2008) 4. Yi, J., Hu, W., Chen, X., et al.: Optimal configuration of arresters for suppressing power frequency overvoltage in half-wavelength transmission system. Power Syst. Technol. 43(6), 2227–2234 (2019). (in Chinese) 5. Han, X., Sun, X., Chen, H., et al.: The overview of development of UHV AC transmission technology in China. Proc. CSEE 40(14), 4371–4386 (2020). (in Chinese) 6. Albuquerque, F.P., Pereira, R.F.R., Marques Costa, E.C., Bartocci Liboni, L.H.: Temporary overvoltage suppression in half-wavelength transmission lines during asymmetric faults. Electr. Power Syst. Res. 178, 106028 (2020) 7. Tian, H., Liu, Y., Yang, D., Qin, X., Zhang, Y.: Voltage characteristic analysis of ultra-high voltage half-wavelength transmission system based on wave process method. J. Modem Power Syst. Clean Energy 8(1), 150–158 (2020). https://doi.org/10.35833/MPCE.2018.000470 8. Zhanchun, L., Lingtao, W., Xiang, C.: Preliminary study on energy extraction and power supply along UHV AC half-wavelength transmission line. Power Syst. Technol. 35(9), 37–41 (2011). (in Chinese) 9. Dias, O., Tavares, M.C., Magrin, F.: New mitigation method and analysis of the secondary arc current in half-wavelength transmission lines, vol. 182 (2020) 10. Dias, O., Tavares, M.C.: Single-phase auto-reclosing mitigation procedure for halfwavelength transmission line, vol. 93 (2019) 11. Hao, T.: Study on Operation Characteristics and Voltage Control of UHV Half Wavelength Transmission System. Shandong University (2020). (in Chinese) 12. Chen, C., Yang, H., Wang, W., Mandich, M., Yao, W., Li, Y.: Harmonic transmission characteristics for ultra-long distance AC transmission lines based on frequency-length factor. Electr. Power Syst. Res. 182, 106189. ISSN 0378-7796 (2020) 13. Guoxiao, L.: Analysis of over voltage characteristics and suppression methods of tuned halfwavelength AC transmission lines. Sanxia University (2020).https://doi.org/10.27270/d.cnki. gsxau.2020.000366 14. Northeast Electric Power Design Institute of State Power Corporation. Design Manual of High Voltage Transmission Lines for Electric Power Engineering. 2nd Edition. China Electric Power Press, Beijing (1999). (in Chinese)
Dynamic Reactive Power Optimization of Power System Considering Load Demand Side Response Jie Chen1
, Changchun Cai1(B) , Shuqin Wang2 , Zengmao Cheng1 , and Shenshen Zhuo1
1 College of the Internet of Things, Hohai University, Changzhou 213022, Jiangsu, China
[email protected] 2 Maanshan Power Supply Company, Anhui Electric Power Corp., Ltd., State Grid,
Xuancheng 243000, Anhui, China
Abstract. Reactive power optimization is the basis of the security and stability operation of power system. With the development and maturity of power market, how to fully tap the dispatching capacity of demand side resources to enhance the adjustable capacity of power grid becomes particularly important. According to the dynamic characteristics of load, a reactive power optimization model considering demand side response is proposed in this paper. Firstly, a model of flexible load under the price type demand response is constructed with the user’s load transfer cost and electricity price. Then, OLTC (On-Load Tap Changer, OLTC), SVC and the reactive power output of the distributed power generation were coordinated and optimized, and the dynamic reactive power optimization model was established with the objective function of minimizing the active power network loss in one day. Finally, the model was verified by particle swarm optimization algorithm on the improved IEEE 33-bus distribution network. The simulation results show that the model proposed in this paper is not only beneficial to the safe and stable operation of the power grid, but also to ensure the interests of users, and to achieve the function of peak clipping and valley filling. Keywords: Demand side resources · Flexible load · Operation scheduling · Reactive power optimization
1 Introduction In traditional power grid, demand-side resources can only participate as consumers of electric energy, and with the continuous progress of the reform of Chinese electricity market, the power transaction mode is becoming more and more diversified, and the status of demand side resources in power grid dispatching is becoming more and more important. The role of demand-side resources is also transformed from consumers to a virtual power generation resource, and it participates in the power system dispatching in the form of flexible load. The reactive power optimization of distribution network is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1316–1323, 2022. https://doi.org/10.1007/978-981-19-1528-4_133
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an important measure to ensure the economic operation of power system, so this paper studies the participation of flexible load in the reactive power optimization of distribution network. At present, researches on reactive power optimization of distribution network mainly include configuration of reactive power compensation equipment and grouping control strategy of capacitors, voltage control research using self-regulation ability, and evaluation of reactive power regulation reserve of large-scale wind farms. Considering the control ability of OLTC, capacitor bank and distributed power supply in voltage reactive power regulation, a coordinated optimization method of the three was proposed in [1, 2]. In [3], the output and capacitor bank switching of the distributed power supply were taken as the optimization objects, and the minimum network loss and voltage offset were taken as the objective functions. The multi-objective model based on Pareto optimal solution was adopted to optimize the power grid. In [4] studied the interaction between various voltage reactive power control methods, and proposed a voltage regulation strategy based on sensitivity analysis. In [5, 6] established a dynamic reactive power optimization mathematical model of distribution network considering OLTC, capacitor bank and energy storage, and solved the model through linear decreasing weight particle swarm optimization algorithm. In [7] proposes an intelligent real-time calibration system based on multi-step capacitor banks, and uses genetic optimization algorithm to determine the optimal capacitor size to optimize the reactive power. Under the background of smart grid, various demand-side response projects are being carried out, and the interaction of “source-grid-charge” has become the inevitable trend of the development of power grid. Flexible load response is an important part of DR. Compared with traditional scheduling methods, flexible load response is fast, low carbon, environmental friendly and less cost. With the continuous development and maturity of the power market, how to fully tap the scheduling potential of flexible loads to improve the adjustable capacity of the power grid has attracted extensive attention [8, 9]. From the above analysis, it can be seen that flexible loads are mostly optimized from the aspects of power grid economy and new energy consumption, while researches on demand-side resources’ participation in reactive power optimization are relatively few. So this article mainly research considering demand-side resources, traditional reactive compensation of reactive power equipment and distributed power distribution network dynamic reactive power optimization problem, the flexible load optimization model under the demand-side response is established. A mathematical model for reactive power optimization of distribution network is established, which takes the system active power loss as the objective function and OLTC, SVC and the reactive power output of distributed power generation as the optimization objective. Particle Swarm Optimization (PSO) algorithm was used to solve the problem. Finally, the improved IEEE 33-bus distribution network is taken as an example to simulate. The simulation results show that the reactive power optimization of distribution network based on flexible load is not only beneficial to the safe and stable operation of the network, but also can ensure the interests of users, so that the power network and users can achieve a win-win situation.
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2 Flexible Load Optimization Model Under Demand-Side Response 2.1 Demand-Side Response Demand-side response is a strategy to adjust the operating state of the power system by shifting the user side load or interrupting the user side load when the system fails or during the peak period of system power consumption by utilizing the flexibility of the demand side load. According to the difference in the nature of power grid demand-side user response, the demand-side response can be divided into two types of demandside response based on incentive mechanism and price. In this paper, the demand side response based on electricity price is used to achieve the purpose of load translation, to ensure the interests of the participants of the measure, the user side, so as to mobilize the enthusiasm of the load side resources to participate in the optimization of the power grid, make the load transfer play the maximum role, and improve the safety, reliability and economy of the power grid. 2.2 Mathematical Model Only when users respond to the TOU price according to their own interests can load shift be achieved. Therefore, it is necessary to ensure that after the implementation of TOU, the overall cost of electricity for users is lower than before the implementation. In this paper, the user load transfer cost and the user purchase cost are used to form the user integrated electricity cost model. Objective Function. In order to maximize user benefits, that is, the sum of user purchase cost and user response cost after load shift is minimum, as shown: f1 = min(SC (i − j) + Ctou )
(1)
Where SC (i − j) is the total electricity transfer cost at the time when i is transferred to j; Ctou is the total cost of electricity purchase after the response of price; The translation cost of user load is shown: ⎧ ⎨ SC (i − j) = SP (i − j) + ST (i − j) (2) SP (i − j) = a · xi−j + b ⎩ ST (i − j) = c · Ti−j + d Where SC (i −j) is the total electricity transfer cost at the time when i is transferred to j; SP (i −j) is the cost related to the electricity transferred from time i to time j; ST (i −j) is the cost related to the time interval from time i to time j; xi−j is the total load transferred from time i to time j;Ti−j is the interval time between time i to time j;a, b, c, d are cost coefficients, where a, c are coefficient of variable cost and b, d are coefficient of fixed cost. The specific value is determined by the type of user on the user side. The cost of electricity purchase for users is shown: Ce = Pe · Qe
(3)
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Where Ce is the total electricity purchase cost before the user responds to the TOU electricity price; Qe is the electricity purchase price when it does not participate in the TOU price; is the electricity purchased while not participating in the TOU price. Qtou.h +Ptou.u · Ttou.u Qtou.u +Ptou.l · Ttou.l Qtou.l Ctou = Ptou.h · Ttou.h t∈Ttou.h
t∈Ttou.u
t∈Ttou.l
(4) Where Ctou is the total electricity price after the user responds to the TOU electricity price; Ptou.h , Ptou.u , Ptou.l are respectively the electricity purchase prices of peak, flat and trough periods; Qtou.h , Qtou.u , Qtou.l are the electric quantity purchased in each period of peak, flat and valley; Ttou.h , Ttou.u , Ttou.l are the time of peak, flat and valley respectively.
3 Flexible Loads Participate in Reactive Power Optimization of Distribution Network Under Demand Side Response 3.1 Reactive Power Output Mathematical Model of Distributed Power Generation Wind power generation and photovoltaic power generation can not only provide active power output for the grid, but also support the load; Moreover, doubly-fed inductive wind unit and photovoltaic inverter can be used for reactive power decoupling control to provide reactive power output for the power grid, improve the voltage distribution of the power grid and reduce the network loss. Its reactive power output is expressed as follows: 2 − P 2 (t) (5) Qmax (t) = SDG out Where Qmax (t) is the maximum reactive power that can be emitted by wind turbine or photovoltaic inverter at time t; SDG is the rated apparent power of wind turbines or photovoltaic inverters; Pout is active power for the wind generator or photovoltaic inverter at time t. 3.2 Objective Function Taking the minimum active network loss as the objective function, node voltage constraint is added to the objective function in the form of penalty function, namely: Ploss (t) =
i,i∈Nl
gij (Ui2
+ Uj2
− 2Ui Uj cos θij ) + λ
NPQ i=1
(
Ui − Ui lim ) Ui max − Ui min
(6)
Where Ploss (t) is the total active power loss of the system at time t, gij , θij is the conductance and voltage phase Angle difference between them, Ui , Ui max , Ui min is the voltage amplitude of node i and its upper and lower limits, λ is the penalty factor for node voltage exceeding the limit, Ui lim is the set value when node voltage exceeds the limit, Nl is the collection of transmission lines of the distribution network, and f1 is the total sky network loss.
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3.3 The Constraints Trend of the Constraint ⎧ Pwt.i + Ppv.i − Pload .i Pi = ⎪ ⎨ i∈Nwt i∈Npv i∈Nload ⎪ Qwt.i + Qpv.i + Qc.i − Qload .i ⎩ Qi = i∈Nwt
i∈Npv
i∈Nc
(7)
i∈Nloda
Where Nwt , Npv , Nc , Nload are access nodes of wind power, photovoltaic and reactive power compensation equipment and load nodes respectively; Pi , Pwt.i , Ppv.i , Pload .i Are respectively the active power injected into node I, the active power injected into wind power and photovoltaic access nodes and the active power of load; Qi , Qwt.i , Qpv.i , Qc.i , Qload .i are respectively the injected reactive power of node I, the injected reactive power and load reactive power of the access node of wind power, photovoltaic and reactive power compensation equipment. Branch Current Constraints Iij. min < Iij < Iij. max
(8)
Where, Iij. min , Iij. max are the minimum and maximum allowable current through branch ij respectively. Node Voltage Constraint Ui. min < Uij < Ui. max
(9)
Where Ui. min , Ui. max are the minimum and maximum permissible voltage of node i respectively.
4 Experimental Analysis 4.1 Parameter Settings Dividing principle of peak and flat valley period: 8:00–11:00, from 18:00 to 21:00 is the peak period. 7:00, 12:00–17:00, 22:00 for the normal period, 23:00–6:00 for the grain period. After the implementation of the TOU electricity price, it is specific as follows: the peak electricity price is 1.0407 yuan/kWh; The usual electricity price is 0.6022 yuan/kWh; The hourly electricity price is 0.2561 yuan/kWh; The benchmark electricity price without TOU is 0.69 yuan/kWh. Transfer cost coefficient a = 0.01, b = 0.01, c = 0.6, d = 0.1.
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4.2 Results Analysis The curve changes significantly before and after load shift, after load shift, the peak load decreases from 445 kW to 434.93 kW, the valley load changes from 320 kW to 335 kW, and the peak-valley difference decreases from 0.281 to 0.230 before load shifting, which plays a good role in peak clipping-valley filling. In addition, the benefits of the user side are also guaranteed, and the user cost is reduced by ¥565.33 from ¥6358.35 to ¥5793.02, indicating that the implementation of load shifting can not only guarantee the benefits of the user side, but also can carry out peak load cutting and valley filling, which is conducive to the safe operation of the power grid and improve the economy of power generation (see in Fig. 1 and Table 1, Fig. 2).
Fig. 1. Residents load curve comparison before and after translation
Table 1. Residents load curve optimization results Peak load (kW)
Valley load (kW)
Peak valley rate
Cost (RMB)
Before translation
445
320
0.281
6358.35
After translation
434.93
335
0.230
5793.02
After flexible load, the peak load decreases from 763 kW to 684 kW, the valley load changes from 230 kW to 230 kW, and the peak-valley difference decreases from 0.700 to 0.663 before load shifting, which plays a good role in peak load cutting and valley filling. In addition, the benefits of the user side are also guaranteed. The user cost is reduced from ¥8,414.07 to ¥5,594.69, a reduction of ¥2,819.38. This indicates that the implementation of load shifting can not only guarantee the benefits of the user side, but also load cutting and valley filling, which is conducive to the safe operation of the power grid and improve the economy of power generation (see in Table 2). When the load is not shifted and optimized, after reactive power optimization of the distribution network, the network loss of the whole day is 1.1877 MW. The total amount of reactive power provided by SVC, PV and fan is 9.57 MVar, 7.60 MVar and 9.20 MVar respectively, and the total amount of the three is 26.37 MVar. Reactive power
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Fig. 2. Industrial load curve comparison before and after translation
Table 2. Industrial load curve optimization results Peak load(kW)
Valley load(kW)
Peak valley rate
Cost (RMB)
Before translation
763
230
0.700
8141.07
After translation
684
230
0.663
5594.69
Table 3. Results of reactive power optimization based on flexible load Ploss (MW)
SVC (MVar)
PV (MVar)
WT (MVar)
Before translation
1.1877
9.57
7.60
9.20
After translation
0.7959
9.53
6.98
6.27
optimization was carried out after load shifting, and the network loss of the whole day was 0.7959 MW. The total amount of reactive power provided by SVC, PV and fan was 9.53 MVar, 6.98 MVar and 6.27 MVar, respectively, and the total amount of the three was 22.78 MVar. After load translation optimization, the total reactive power supply of the network throughout the day decreased by 3.59 MVar compared with that before load optimization, and the total network loss throughout the day also decreased by 0.3918 MW. Experiments show that the reactive power optimization based on load translation can not only reduce the active power loss of the system, but also reduce the reactive power output of the distributed power supply and the reactive power compensation capacity of the SVC (see in Table 3).
5 Conclusion This paper mainly studies the demand-side resources participating in the reactive power optimization of the distribution network under the demand side response, A mathematical model for reactive power optimization of distribution network is established, which
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takes active power loss as the objective function and OLTC, SVC and the reactive power output of distributed power generation as the optimization objective. Particle Swarm Optimization (PSO) algorithm was used to solve the problem. The simulation results show that the participation of flexible load in the reactive power optimization of distribution network not only reduces the system’s active power loss and node voltage deviation, but also reduces the number of actions of OLTC connector, the reactive power output of distributed power generation and the reactive power compensation capacity of SVC. It is conducive to the safe and stable operation of the power grid, which not only guarantees the interests of users, but also achieves the function of peak clipping and valley filling, so that the power grid and users can achieve a win-win situation.
References 1. Gongbo, L., Wentao, Y., Wenbin, Z., et al.: Dynamic reactive power optimization scheduling method of distribution network with distributed power supply. Autom. Power Syst. 39(15), 49–54 (2015). (in Chinese) 2. Yuqi, J., Guangfei, G., Boying, W., et al.: Dynamic reactive power optimization of distribution network with DG based on optimal segmentation method. Power Network Technol. 41(08), 2585–2593 (2017). (in Chinese) 3. Yingjie, F., Feng, W., Yanghong, T., et al.: Reactive power optimization of distribution network with distributed power supply based on Pareto optimal solution. J. Power Syst. Autom. 29(01), 18–23 (2017). (in Chinese) 4. Zhuo, Y., Weirong, C., Chaohua, D.: Reactive power voltage control of active distribution network based on sensitivity analysis. J. Power Syst. Autom. 29(05), 21–27 (2017). (in Chinese) 5. Yanwei, M., Jianguang, Z., Jian, S., et al.: Research on dynamic reactive power optimization of distribution network considering distributed photovoltaic and energy storage. Electro Tech. Eng. 10, 56–59 (2020). (in Chinese) 6. Zhang, C., Chen, H., Shi, K., et al.: A multi-time reactive power optimization under interval uncertainty of renewable power generation by an interval sequential quadratic programming method. IEEE Trans. Sustain. Energy 3, 1 (2019). https://doi.org/10.1109/TSTE.2018.2860901 7. Abdelhady, S., Osama, A., Shaban, A., et al.: A real-time optimization of reactive power for an intelligent system using genetic algorithm. IEEE Access 8(1), 11991–12000 (2020) 8. Dongmei, Z., Yuan, S., Yunlong, W., et al.: Multi time scale coordinated dispatch model considering uncertainty of flexible load response. Power Syst. Autom. 43(22), 21–30 (2019) 9. Nan, Y., Bo, W., Dichen, L., et al.: Joint stochastic dispatch method for power system supply and demand side considering large-scale wind power and flexible load. Chin. J. Electr. Eng. 33(16), 63–69 + 17 (2013)
A LLC Soft-Start Control Strategy Based on PSM and PFM Yuxing Li1(B) , Jingkai Niu2 , Xiaomin Xin2 , Peng Liu1 , Yunfeng Guo3 , and Yu Lu1 1 State Grid Jilin Electric Power Supply Company Research Institute of Economics and
Technology, Changchun 130000, China [email protected] 2 Beijing Jiaotong University, Beijing 100044, China 3 State Grid Jilin Electric Power Supply Company, Changchun 130000, China
Abstract. The LLC resonant converter is widely used in various fields because of high frequency, high efficiency and high power density. However, the large current impact in the starting process of LLC resonant converter is a problem that cannot be ignored. A soft-start control strategy for LLC resonant converter based on PSM and PFM is presented in this paper. By comparing PSM methods with different power functions, the PSM with an appropriate power function is selected. Besides, PFM is used to improve the dynamic performance of soft start process. The soft start control strategy proposed in this paper is verified by experiments. Keywords: LLC resonant converter · Soft-start control strategy · PSM and PFM
1 Introduction In recent years, LLC resonant converters have been widely applied in photovoltaic, electric vehicles and other fields because of their characteristics of soft switching in the full load range. But the high voltage start-up of LLC converter will be accompanied by a large resonance current impact, which will exceed the current stress of the power device and cause damage to the devices. Many researchers have studied and optimized the traditional soft-start control strategy [1, 2], but the effect is not very obvious. Reference [3] proposed a method to switch the full bridge mode to half bridge mode during start-up, but the starting impulse current is still large. Reference [4] uses the look-up table method to realize the soft-start function by opening the loop to preset the initial value, but this method is too complex and the delay time is large. Reference [5] reduce the switching loss in the start-up process by reducing the duty cycle, and minimizes the start-up impulse current at a specific frequency by studying the optimal start-up duty cycle. In the method proposed in [5], the start-up process is divided into three stages and a specific duty cycle is given respectively. In [6], the traditional frequency modulation control is replaced by phase-shifting control, and the power function is used in phase-shifting control to reduce the impulse current in the process of soft start. Reference [7] presents a hybrid control strategy which combines PFM with PWM. Compared with the traditional frequency modulation control and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1324–1331, 2022. https://doi.org/10.1007/978-981-19-1528-4_134
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phase-shifted control soft-start methods, the hybrid soft-start strategy is more effective in suppressing the impact current. However, Reference [7] is based on the linear duty cycle change, and the frequency and duty cycle change curves are not discussed in detail. In addition, the trajectory control method [8, 9] is applied to the soft-start process. Nonlinear variables are calculated by collecting input and output voltage, resonance current and resonance capacitance voltage. This paper first analyses LLC resonant converter based on fundamental harmonic approximation (FHA) method, then compares the impact of soft-start of phase-shifting control with different power parameters on the impact current. Frequency-converting control is further added to suppress the impact current, compared with traditional frequency-converting control and PWM control. Finally, the proposed method is verified by experiments.
2 LLC Resonant Converter Topology The topology of LLC resonant converter in this paper is shown in Fig. 1. Where S1 –S4 are switches, D1 –D2 are diodes, L r is resonant inductor, C r is resonant capacitor, L m is excitation inductance of primary side of the transformer, turn ratio of transformer is N:1:1, C o is output filter capacitor, Rload is load resistance. The current of L r is I r , the input voltage is V i and the output voltage is V o .
S1
S3
Cr
Lr Ir
Vi
S2
D1
Co
–
Lm N :1:1
S4
+
Rload Vo
D2
Fig. 1. Full-bridge LLC resonant converter topology
+
Lr
vi
Lm Cr
–
+
R eq
vo –
Fig. 2. Fundamental harmonic equivalent circuit diagram
In order to facilitate the analysis, this paper uses fundamental harmonic approximation (FHA) method. The fundamental harmonic equivalent circuit diagram is shown in Fig. 2.
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vi is the fundamental harmonic components of the AC voltage input to the resonance cell, Req and vo are the equivalent load and output voltage. vi , Req and vo can be defined as follow: √ 2 2 ϕ Vi cos (1) vi = π 2 8N 2 Rload π2 √ 2 2 NVo vo = π
Req =
(2)
(3)
Where ϕ is the phase shift angle between the driving pulses of the two bridge arms. When the switching frequency is f s and the resonance frequency of L r and C r is f r , the normalized voltage gain M can be obtained from the fundamental harmonic equivalent circuit in Fig. 2: M = 1+
cos ϕ2 2 2 1 1 fn − + RZeqr Ln 1 − f 2 n
1 fn
2
(4)
Where inductance coefficient L n , normalized frequency f n and resonance impedance Z r can be defined as follows: Ln = Lm /Lr
(5)
fn = fs /fr
(6)
Zr =
Lr /Cr
(7)
By controlling normalized voltage gain M to change slowly, the shock current during start-up can be effectively reduced.
3 The Soft-Start Control Strategy Based on PSM and PFM 3.1 PSM Soft-Start with Power Function Compared with PWM soft-start, PSM soft-start can effectively avoid the possible loss of duty cycle in the early stage of PWM soft-start, and the EMI noise of PSM is less. In order to further reduce the start-up current shock, the curve of ϕ is studied, and a power function of ϕ with the start-up time is proposed. The expression for ϕ varying with time t is as follows: ⎧ k ⎨ π t < tr ϕ = trt−t r (8) ⎩ϕ = 0 t > tr
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Where t r is the soft start time and k is the exponent. The trend of power function curve is different when k > 1 and k < 1. When k > 1, the change rate of ϕ in the beginning of start-up is higher and the impact current is larger. But the change rate of ϕ decreases gradually during start-up. At the end of start-up, the change rate of ϕ is 0, which can realize the smooth transition between start-up process and steady-state process. When k < 1, the change rate of ϕ is lower and the impact current is smaller in the beginning of start-up. But the change rate of ϕ will gradually increase during the start-up process and the change rate will reach the maximum at the end of the soft start. Therefore, the change rate of ϕ will change abruptly at the transition time of the start-up process and the steady-state process, resulting in a certain transient impact current. The magnitude of transient shock current is related to the value of the exponent k. Figure 3 show the power function curves when k = 0.25, k = 0.5, k = 2, and k = 10, and when the phase shift changes linearly, that is, k = 1.
π
φ k =0.25 k =0.5 k=1 k=2 k=10
0
tr
t
Fig. 3. Comparison of PSM soft-start curves with different power functions
3.2 Soft-Start Control Strategy Based on PFM Conventional LLC soft-start control usually start with frequency reduction, which increases the equivalent impedance of the resonance cell and decreases the DC voltage gain. A higher switching frequency can achieve a smaller impact current in the beginning of start-up. During the start-up process, the switching frequency is gradually reduced until the rated switching frequency. The curve between normalized switching frequency f n and time with the soft-start control based on PFM is shown in Fig. 4. Where f nmax is maximum normalized frequency and f nset is the rated normalized frequency. This method can achieve soft start, but the voltage gain curve changes smoothly when f n > 1, and the setting of the highest switching frequency needs to be far enough away from the resonance frequency point. As a result, the start-up time is longer and it is difficult to achieve a very high switching frequency and the ideal current suppression effect due to the limitation of the switching devices.
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f nmax
f nset 0
tr
t
Fig. 4. The curve between normalized switching frequency and time with the soft-start control based on PFM
3.3 Hybrid Soft-Start Control Strategy Based on PSM and PFM A hybrid soft-start control strategy is presented by combining PSM with PFM control. By comparing the PSM soft-start process in simulation with different power functions, the appropriate power function can be selected. Then combining with PFM control, a more ideal soft start process can be realized. PSM control can achieve a smaller switch frequency in the beginning of PFM soft-start process. PFM control can achieve smoother mode-switching at the beginning and end of the soft start process, and make further reduction of impulse current.
4 Simulation and Experimental Verification 4.1 Related Parameters The related parameters used in the simulation and experiment in this paper are shown in Table 1. Table 1. The related parameters used in the simulation and experiment Parameter name
Unit
Value
Input voltage V i
V
375
Output voltage V o
V
48
Load resistance Rload
1.152
Output filter capacitor C o
µF
560
Resonant inductor L r
µH
12.6
Resonant capacitor C r
nF
8.1
Excitation inductance L m
µH
44
Turn ratio of transformer N
/
8:1:1
Maximum normalized frequency f nmax
/
2
Rated normalized frequency f nset
/
0.96
Soft start time t r
Ms
5
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4.2 Simulations
Ir /A(10A/div)
The comparison of resonance current during PSM soft-start control under different k values is shown in Fig. 5. Based on the comparison between current peaks of different waveforms in Fig. 5, when k = 2, the shock current during the start-up process can be effectively suppressed. k=10
I r,max =10.3A
Ir /A(10A/div)
Soft-start
k=2
Ir /A(10A/div)
Ir /A(10A/div)
Soft-start
5ms
t/ms (1ms/div)
Steady-state
5ms
t/ms (1ms/div)
Steady-state
I r,max =10.2A
Soft-start
Ir /A(10A/div)
Steady-state
I r,max =10.5A
k=1
k =0.25
t/ms (1ms/div)
I r,max =10A
Soft-start
k =0.5
5ms
5ms
t/ms (1ms/div)
Steady-state
I r,max =12.8A
Soft-start
5ms
t/ms (1ms/div)
Steady-state
Fig. 5. Comparison of starting impulse current under different phase-shift angle change curves
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Based on the above control, PFM control is introduced to further reduce the impact current during the start-up phase, and the change of resonance current is more gentle. The soft-start waveform under the hybrid soft-start control strategy is shown in Fig. 6.
Vo Ir
Vo /V(10V/div) Ir /A(10A/div)
Vo =48V
Ir,max =8.8A
Steady-state
Soft-start ：5ms
t/ms (1ms/div)
Fig. 6. Output voltage and resonant current waveforms under the hybrid soft-start control strategy
4.3 Experiments Using the above experimental prototypes, the following experimental comparisons are made: (a) PSM at k = 1; (b) PSM at k = 2; (c) hybrid control at k = 2. The resonance current and output voltage waveforms under different soft-start control is shown in Fig. 7. It still can be seen in Fig. 7 that the selected power function PSM soft-start control can reduce the impulse current during start-up process better than the linear PSM soft-start control. Besides, hybrid control further reduces the shock current during start-up process. The above experimental results are in agreement with the simulation results and theoretical analysis. V o (20V/div)
I r,max =13.2A I r (10A/div)
(2.5ms/div) (a)
V o (20V/div)
I r,max =11.6A I r (10A/div)
(2.5ms/div) (b)
V o (20V/div)
I r,max =11.2A
I r (10A/div)
(2.5ms/div) (c)
Fig. 7. The output voltage and resonant current waveforms under different soft-start control
5 Conclusion In this paper, the soft-start process of LLC is analyzed. On the basis of conventional PFM and PSM soft-start control, a hybrid soft-start control based on PSM and PFM is
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proposed. The curves of phase shift angle with different power functions are analyzed, and the most suitable power function curve is determined as the optimal result. The optimal soft-start control strategy is obtained, and the resonant current in the starting process is effectively suppressed.
References 1. Ruichang, Z., Bangyin, L., Shanxu, D.: Analysis and parameter optimization of start-up process for LLC resonant converter. IEEE Trans. Power Electron. 30(12), 7113–7122 (2015) 2. Dongdong, Y., Changsong, C., Shanxu, D., Jiuqing, C.: An improved start-up method for LLC series resonant converter based on state-plane analysis. In: 2014 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 2026–2030 (2014) 3. Wei, G., Kevin, B., Taylor, A., Patterson, J., Kane, J.: A novel soft starting strategy of an LLC resonant DC/DC converter for plug-in hybrid electric vehicles. In: 2013 Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2012–2015 (2013) 4. Chao, F., Lee Fred, C., Qiang, L.: Soft start-up for high frequency LLC resonant converter with optimal trajectory control. In: 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 609–615 (2015) 5. He, G.: Research on start-up and light load control of LLC resonant converter. Master Thesis, Zhejiang University, Hangzhou, Zhejiang (2019). (in Chinese) 6. Chen, Q., Wang, J., Ji, Y., Liang, S.: Soft starting strategy of bidirectional LLC resonant DCDC transformer based on phase-shift control. In: 2014 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, pp.318–322 (2014) 7. Tianwen, W., Zhizhong, L., Hui, Y., Xueyi, L.: Hybrid control strategy for full-bridge LLC resonant converter based on digital soft-start. Electr. Drive 49(3), 54–58+65 (2019). (in Chinese) 8. Weiyi, F., Lee, F.C.: Optimal trajectory vontrol of LLC resonant converters for soft start-up. IEEE Trans. Power Electron. 29(3), 1461–1468 (2014) 9. Wang, L., Zhu, Q., Yu, W., Huang, A.Q.: Full ZVS soft-start of a SiC medium voltage series resonant DC-DC converter using variable frequency variable duty cycle control. In: 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia), pp. 1855–1860 (2017)
Research and Design of 72.5 kV Environmental Protection Circuit Breaker Canjiang Yao(B) , Longyong Sun, Gang Xu, Yingying Liu, Panke Yuan, Guang Yang, Senran Li, and Wuyang Chen PingGao Group Co., Ltd., No. 22, South Ring Road East, Pingdingshan, Henan, China [email protected]
Abstract. 72.5 kV environment-friendly circuit breaker is a new type of clean, low-carbon, safe, reliable, technologically advanced and environment-friendly high-voltage electrical equipment to replace the traditional SF6 circuit breaker, it can realize the control, measurement, protection and switching of transmission lines. In this paper, the structural characteristics and performance parameters of 72.5 kV environmental protection circuit breaker are introduced, the arc extinguishing chamber and external insulation electric field of 72.5 kV environmental protection circuit breaker are simulated by simulation analysis software, and the magnetic field of the contact of the arc extinguishing chamber is simulated, it is determined that the contact structure of the arc extinguishing chamber is horseshoe type longitudinal magnetic contact. The calculation results show that the 72.5 kV environmental protection circuit breaker is safe and reliable, and the successful passing of various type tests verifies the rationality of its design and the correctness of the calculation results. Keywords: Circuit breaker · Vacuum interrupter · Electric field simulation
1 Introduction High voltage circuit breaker is the most important control and protection equipment in power system [1–3]. At present, almost all the circuit breakers of 72.5 kV and above used in power system are SF6 circuit breakers. However, SF6 gas is the strongest greenhouse gas restricted by international conventions. The development of environment-friendly switchgear to replace SF6 circuit breakers has become a research hotspot in the field of high-voltage switchgear at home and abroad [3–6]. As insulation and arc extinguishing medium, vacuum has the characteristics of high insulation strength, large breaking capacity and strong arc extinguishing capacity, and will not cause environmental pollution [7–10]. Vacuum circuit breaker has the advantages of small volume, light weight, simple structure and long service life. It is suitable for frequent operation and rapid disconnection [7, 8]. At present, vacuum air switch has occupied 70%~80% of the total output of switches in medium and low voltage fields in the world. It is widely used in electric power, metallurgy, chemical industry, coal, petroleum, mining, high-rise buildings, electrified railways and other fields [11]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1332–1339, 2022. https://doi.org/10.1007/978-981-19-1528-4_135
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Based on the key technology of isolation and breaking of vacuum interrupter, 72.5 kV single break vacuum interrupter and 72.5 kV environmental protection column circuit breaker products with vacuum interrupter as circuit breaker breaking unit are developed as the technical reserve of high-voltage vacuum switchgear in the future, so as to provide safe and reliable equipment support and strong technical support for the development of high-voltage transmission and distribution.
2 Structural Characteristics of 72.5 kV Environmental Protection Circuit Breaker 72.5 kV environment-friendly circuit breaker has the advantages of compact structure, small volume, small operation power, high reliability and convenient installation. The product mainly includes basic components such as vacuum interrupter, strut and spring mechanism. The three-dimensional model of 72.5 kV environmental protection circuit breaker is shown in Fig. 1, and the main technical parameters are shown in Table 1.
Fig. 1. Three-dimensional model of 72.5 kV environmental protection circuit breaker
Table 1. Main technical parameters of 72.5 kV environmental protection circuit breaker Main parameter
Parameter value
Rated voltage/kV
72.5
Rated current/A
3150
Rated short-time/peak withstand current/kA
40/100
Rated short circuit duration/s
3
1 min rated short-time power frequency withstand voltage (phase to phase/fracture)/kV
160/160(+42)
Rated lightning impulse withstand voltage (phase to phase/fracture)/kV
380/(+59)
Terminal dead load (longitudinal/transverse/vertical)/N
1250/750/1000
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3 Structural Design of 72.5 kV Vacuum Interrupter The core component of the vacuum circuit breaker is the vacuum arc extinguishing chamber, which is mainly composed of three parts: air tight insulating shell, contact and shielding system, as shown in Fig. 2. The function of air tight insulating shell is to form a vacuum sealed container, which is equipped with moving contact, static contact and shielding cover, and also serves as the support between moving contact and static contact. Contact is the most important component in vacuum interrupter, which plays a decisive role in breaking, temperature rise, insulation and other properties. The common shielding covers of vacuum arc extinguishing chamber include main shielding cover and bellows shielding cover. The main shielding cover is wrapped around the contact to prevent the contact from generating a large amount of metal vapor and droplet splashing during arcing, polluting the inner wall of the insulating shell, avoiding the decline of the insulation strength of the shell of the vacuum arc extinguishing chamber. At the same time, it can also make the metal vapor cool quickly and condense into solid, so as not to return to the arc gap, it is favorable for the rapid reduction of gas particle density in the arc gap and the rapid recovery of dielectric strength. The bellows is mainly responsible for the movement of the moving contact within a certain range and the function of long-term high vacuum, and ensures that the vacuum interrupter has a high mechanical life.
Fig. 2. Structure diagram of 72.5 kV vacuum interrupter
3.1 Analysis and Calculation of Insulation Performance of Vacuum Interrupter The 72.5 kV Vacuum interrupter is composed of dynamic and static conductive rods, contacts, shielding covers, bellows, static end covers and ceramic shells. When the dynamic and static contacts of the vacuum interrupter are separated, the current shrinks to the point where the contacts are just separated, and a very high electric field intensity is formed, resulting in extremely strong emission and gap breakdown, resulting in vacuum arc [12–14]. When the power frequency current is close to zero, with the increase of contact opening distance, the plasma of vacuum arc quickly diffuses around under the magnetic field generated by the special contact structure. After the arc current crosses zero, the medium in the contact gap quickly changes from conductor to insulator, so the current is disconnected. In this paper, the electric field strength of 72.5 kV vacuum interrupter is simulated and analyzed under two harsh working conditions. The two working conditions are as follows: (1) the lightning impulse voltage loaded at the static end is 380 kV, the reverse voltage loaded at the moving end is 59 kV, and the shield
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plus suspension potential is 0V. (2) The lightning impulse voltage loaded at the moving end is 380 kV, the reverse voltage loaded at the static end is 59 kV, and the suspension potential added to the shield is 0 V. The finite element analysis software ANSYS is used for electric field analysis and calculation. The cloud diagram of potential distribution of 72.5 kV vacuum interrupter under two working conditions is shown in Fig. 3.
Fig. 3. Cloud diagram of potential distribution of 72.5 kV vacuum interrupter
Through calculation and comparative analysis, the maximum field strength under the two calculation conditions is less than the engineering critical breakdown field strength, and has a certain margin, which meets the electrical insulation design requirements of 72.5 kV vacuum interrupter. 3.2 Magnetic Field Simulation Analysis of Vacuum Interrupter The vacuum interrupter can break large current, which is closely related to the contact structure. Different contact structures form two types of magnetic fields in space, so that large current can be broken. The first is to apply a magnetic field perpendicular to the axial direction of the arc column on the vacuum arc, that is, a transverse magnetic field; The second is to apply a magnetic field parallel to the user’s main axis on the arc, that is, the longitudinal magnetic field. Because the longitudinal magnetic field can improve the voltage and current value of the concentrated arc, reduce the appearance of anode spots, make the arc evenly distributed on the contact surface, and maintain a low arc voltage. Therefore, in order to improve the voltage level of vacuum circuit breaker and make the vacuum interrupter have high recovery speed of post arc dielectric strength, the longitudinal magnetic contact is generally selected in the high-voltage vacuum interrupter. The horseshoe shaped longitudinal magnetic contact overlaps the horseshoe shaped magnetic conducting material on the back of the flat contact, so that a magnetic circuit dominated by ferromagnetic material (small magnetic resistance) is formed between the two contacts, so as to enhance the magnetic field strength of the gap and increase the breaking capacity of the arc extinguishing chamber. The three-dimensional software is used to simplify the structure of the horseshoe type longitudinal magnetic contact. The static end contact applies a current of 3.15 kA. The magnetic field simulation analysis is carried out on the horseshoe type longitudinal magnetic contact. When the contact opening distance is 37 mm at the current peak, the magnetic field distribution on the central plane is shown in Fig. 4.
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Fig. 4. Longitudinal magnetic field distribution in contact center plane at current peak
It can be seen from Fig. 4 that the magnetic field in the contact gap is bipolar. Under the action of two pole magnetic field, double arcs will be generated between contacts when breaking current. With the increase of contact opening distance, the magnetic field on the central plane gradually weakens, and the two magnetic field concentration areas tend to close to and coincide with the center.
4 Simulation Analysis of External Insulation Performance The 72.5 kV environment-friendly circuit breaker adopts the vacuum arc extinguishing chamber as the breaking unit and is integrally assembled in the composite casing. The inner side is filled with nitrogen with rated 0.25 MPa as the external insulation medium. It adopts three-phase linkage structure and is equipped with spring mechanism. As the 72.5 kV environment-friendly circuit breaker is an open circuit breaker, the corresponding external insulation requirements are more stringent. A new design is made for the external insulation of the arc extinguishing component of the circuit breaker to meet the use requirements. The finite element simulation software is used to establish the simulation analysis model. The 72.5 kV environmental protection circuit breaker is in the opening state. According to the common technical requirements of DL/T593-2006 standard for high voltage switchgear and control equipment, the rated lightning impulse withstand voltage is 380 (+59) kV, positive 380 kV is applied at the static end side, negative 59 kV is applied at the moving end side, and 0 kV is applied at the peripheral surface of the air domain. According to the finite element simulation calculation, the cloud diagram of electric field intensity distribution of arc extinguishing components of 72.5 kV environmental protection circuit breaker is shown in Fig. 5. It can be seen from Fig. 5 that the maximum field strength of the arc extinguishing component of 72.5 kV environmental protection circuit breaker is 3.0 kV/mm, which is less than the criterion value of 6.73 kV/mm, which meets the insulation performance requirements of external leakage metal parts and has a large design margin.
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Fig. 5. Cloud diagram of electric field intensity distribution of arc extinguishing assembly
5 Type Test Verification According to the national standards and relevant technical conditions, the type test certification of 72.5 kV environmental protection circuit breaker is carried out, and the relevant type test proves that its performance meets the requirements and has a certain margin. 5.1 Insulation Test The 72.5 kV environment-friendly circuit breaker successfully passed all insulation tests required by the standard, including: the 1min power frequency withstand voltage to the ground is 160 kV, and the fracture is 160 (+42) kV; The rated lightning impulse withstand voltage to the ground is 380 kV, the fracture is 380 (+59) kV. 5.2 Mechanical Life Test Mechanical life operation test shall be conducted for 72.5 kV environmental protection circuit breaker. The service life test was carried out according to the requirements of GB28525-2008. After 10000 mechanical life tests were carried out on the circuit breaker body, no abnormality was found. In each opening and opening operation cycle, the circuit breaker shall be closed and opened correctly. 5.3 Short Circuit Making and Breaking Test The basic circuit breaker breaking test, near zone fault test and capacitive current switching test of 72.5 kV environmental protection circuit breaker were carried out according to relevant standards of JB/T3855-2008. After the test, the arc extinguishing chamber was disassembled, no damage to breaking parts was found, and the ablation was light during the test.
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6 Conclusion The structural design scheme of horseshoe type longitudinal magnetic contact is proposed, which effectively improves the breaking capacity of 72.5 kV environmental protection circuit breaker, ensures the insulation performance of the fracture after breaking, and lays a good verification foundation for the electrical life test of the circuit breaker. The insulation performance of exposed metal parts of 72.5 kV environmental protection circuit breaker meets the design requirements and has large design margin. The successful development of 72.5 kV environmental protection circuit breaker provides solid equipment support and guarantee for building a clean, low-carbon, safe and efficient modern power grid system. Acknowledgment. This work is supported by the science and technology project of PingGao Group Co., Ltd. (PGKJ2021-014).
References 1. Haibo, S., Jinwei, M., Lijun, W.: Cooperation of tank-type fast vacuum switch bel-lows and SF6 gas pressure based on bidirectional coupling method. High Voltage Eng. 47(5), 1634–1639 (2021). (in Chinese) 2. Yinghua, B., Xuxu, L., Yuanzhao, L.: Novel contact structure for 126 kV high voltage vacuum interrupter and its arcing characteristic. High Voltage Apparatus 57(6), 56–63 (2021). (in Chinese) 3. Zhang, Y., Liu, Z., Cheng, S., et al.: Influence of contact contour on breakdown behavior in vacuum under uniform field. IEEE Trans. Dielectr. Electr. Insul. 16(6), 1717–1723 (2009) 4. Bo, L., Yongquan, B., Zhendong, P.: Post-arc transient simulation and dielectric recovery analysis based on improved DC vacuum circuit breaker. Trans. China Electrotech. Soc. 36(8), 1752–1760 (2021). (in Chinese) 5. Yupeng, C., Lin, L., Qiao, W.: Fault diagnosis of high-voltage vacuum circuit breaker with a convolutional deep network. Power Syst. Prot. Control 49(3), 39–47 (2021). (in Chinese) 6. Kato, K., Kaneko, S., Okabe, S., et al.: Optimization technique for electrical insulation design of vacuum interrupters. IEEE Trans. Dielectr. Electr. Insul. 15(5), 1456–1463 (2008) 7. Xianghao, Z., Haichuan, Z., Minfu, L.: Restrictive asynchronous breaking strategy of multibreak VCB with fiber controlled module. High Voltage Apparatus 57(2), 1–6 (2021). (in Chinese) 8. Kato, K., Xhan, X., Okubo, H.: Insulation optimization by electrode contour modification based on breakdown area/volume effects. IEEE Trans. Dielectr. Electr. Insul. 8(2), 162–167 (2001) 9. Ning, X., Xingyu, G., Jiyan, Z.: Experimental research on the safe stroke of HVDC vacuum circuit breaker. High Voltage Eng. 46(5), 1823–1829 (2020). (in Chinese) 10. Shaogui, A., Lidong, Y., Xiaofei, Y.: Research on mechanical reliability of operating mechanisms of 126 kV vacuum circuit breakers. High Voltage Apparatus 56(7), 77–85 (2020). (in Chinese) 11. Kato, K., Han, X., Okubo, H.: Insulation optimization by electrode contour modification based on breakdown area/volume effects. IEEE Trans. Dielectr. Electr. Insul. 25(2), 162–167 (2018)
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12. Jia, Z., Zhiying, C., Lian, C.: Feature extraction of vacuum circuit breaker’s opening and closing coil current based on modified ensemble empirical mode decomposition. High Voltage Apparatus 56(12), 116–123 (2020). (in Chinese) 13. Hechong, C., Xuanshu, C., Bo, L.: Study on closing inrush current of double break vacuum circuit breaker with equalizing capacitors. High Voltage Apparatus 56(4), 29–34 (2020). (in Chinese) 14. Zhang, Y., Liu, Z., Geng, Y., et al.: Lightning impulse voltage breakdown characteristics of vacuum interrupters with contact gaps 10 to 50 mm. IEEE Trans. Dielectr. Electr. Insul. 18(6), 2123–2130 (2011)
Influence of Via Stubs on Signal Integrity of Multi-layer PCB Boards Xueyan Lin(B) , Yongsheng Zhou, and Xin An School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China {xylin,yongshengzhou,anxin}@bupt.edu.cn
Abstract. In this paper, the 13-layer PCB board with via stubs is taken as the research object, and the three-dimensional electromagnetic simulation software CST microwave studio is used to simulate the influence of the via stubs on the signal integrity of the PCB board at the frequency of 0–20 GHz. Firstly, the changes of insertion loss, return loss and TDR impedance of through holes, buried holes and blind holes are simulated and analyzed. It is concluded that the signal integrity of the buried holes is the best and that of the through hole is the worst. Then, on the basis of the blind holes, the length of the stubs is changed by adding backdrilling technology. The simulation results show that the back-drilling can reduce the length of the stubs and greatly improve the signal transmission performance. Finally, on the basis of blind hole, the number of spanning layers between signal lines is deliberately increased to achieve the purpose of increasing the length of inter-signal via holes and reducing the length of stubs. The feasibility of reducing the influence of stub on signal integrity by changing the wiring layer of signal lines is explored. The paper draws the following conclusions: adding back-drilling technology to through holes and blind holes, using buried holes and increasing the number of signal line wiring layers can reduce the length of via stubs and improve the high-speed transmission performance of signal on the multilayer PCB boards. Keywords: Stub · PCB transmission line · Via hole · Signal integrity · CST simulation
1 Introduction With the continuous improvement of the performance of electronic products, the transmission line frequency on the single board has reached 10 GHz or even higher, which puts forward more stringent requirements for the design of electronic products. In the topology of high-speed signal, traces at both ends of any device are connected to other devices, such as chips, connectors, etc. Then the short track part of the device is called stub [1]. In the low-speed era, the length of the stubs relative to the transmission lines is short, and the impact on the signal can be almost ignored. But in the high-speed era, the influence of stubs on signal has been an important issue to be considered in the design of electronic products [2]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1340–1352, 2022. https://doi.org/10.1007/978-981-19-1528-4_136
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The stub in Fig. 1 is caused by design error, which can be deleted by PCB design tool. In Fig. 2, there are transmission lines between the source end A and the far end B, including the transmission line from A to the via hole, the routing in the via hole, and the transmission line from the via hole to B. The red circle in the figure is the stub generated in the via hole due to technological reasons [3].
Fig. 1. Stub caused by design error
Fig. 2. Schematic diagram of stub in via hole
After the signal leaves the source end, the branch point is encountered in the transmission process. At this time, the signal encounters the parallel impedance of two transmission lines, which is low and leads to the impedance mismatch. Therefore, signal reflection is generated, some signals will return to the source end, while the other signals will continue to propagate along the two branches. When the signal on the stub reaches the end of the stub, it will be reflected back to the branch point, and then reflected from the branch point to the end of the stub, so it oscillates back and forth in the stub. The signal rise time and the length of the stub are two important factors that determine the influence of the stub on the signal quality. If the length of the stub is less than 20% of the spatial extension of the rising edge of the signal, the effect can be ignored; on the contrary, if its length is greater than 20% of the spatial extension of the rising edge of the signal, it will have a great impact on the signal quality [4]. The complexity and density requirements of PCB design are becoming more and more strict, which increases the number of PCB stacking layers [5]. As shown in Fig. 3, the high-speed multilayer PCB board needs to add via holes in order to realize cross layer signal transmission, and the addition of via holes will often produce redundant short columns, which is often called the via stub [6]. Therefore, high speed multi-layer PCB boards are the most important scene to form the stubs.
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Fig. 3. Via hole and via stub
This paper takes the strip transmission line with the via stubs on a 13-layer PCB board as the research object, and establishes the simulation model by using the threedimensional electromagnetic simulation software CST Microwave Studio. The influence of the length of the stubs on the insertion loss, return loss and impedance is analyzed. Specific suggestions are given to reduce the influence of the stubs from the aspects of the signal line wiring and the via hole manufacturing technology.
2 The Via Hole Model of a PCB Board 2.1 Types of the Via Hole
Fig. 4. Via hole types on PCB
Via holes are the path of signal transfer on PCB board [7]. As shown in Fig. 4, there are four types of via holes in a PCB board, including a through hole, a blind hole, a buried hole and a back-drilling hole.
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The through hole is to drill through the entire PCB board, which is easy to implement, but it often produces long stub. The blind hole is the electrical connection between the top or the bottom layer transmission line and the inner layer transmission line of the PCB board. It is more complex to realize than the through hole, but the stub of the blind hole is generally shorter than the through holes. The buried hole is the connection between transmission lines in the inner layers and the inner layer of PCB board. It is the most complicated to realize, but can completely eliminate the influence of the stub. Back-drilling is to drill the electroplated through hole again with the method of depth control drilling to the required depth, which can reduce the stub length of the original through hole [8] and reduce the resonance caused by the stub of the through hole [9]. 2.2 Establishing the Via Hole Model In order to transmit high-frequency and high-speed signals from the source end (input) to the far end (output) of the PCB board through the differential strip line, the models of the differential strip transmission lines with via holes in a 13-layer PCB are designed in this paper, as shown in Figs. 5, 6, 7 and 8. In order to simulate the situation that the middle part of the second layer of the PCB board does not allow routing, the via holes are designed for signal transmission between the transmission lines of different layers. The length, width and thickness of a single strip line are 15 mm, 0.68 mm and 0.04 mm respectively. The spacing within the differential strip lines is 1.24 mm. The length, width and thickness of the return (ground) plane are 15 mm, 10 mm and 0.04 mm respectively. The length, width and thickness of the dielectric layer are 15 mm, 10 mm and 0.46 mm respectively. The signal line and the return path are made of annealed copper metal. The dielectric layer material is Rogers RT5880 with the relative dielectric constant of 2.2 and the loss factor of 0. According to the calculation of Polar SI9000 software, the characteristic impedance of the well-distributed strip line is 103.64 . The transmission speed v of signal on the strip transmission line of the PCB board is shown in Eq. (1): 3 × 1011 mm/s c = 2.02 × 1011 mm/s v=√ = √ εr 2.2
(1)
When the interconnect length l is greater than 20% of the spatial extension ltr of the rising edge of the signal, as shown in Eq. (2), the interconnect path must be treated as a transmission line with distribution effect, and the signal integrity parameters must be considered. v (2) l ≥ 0.2ltr = 0.2 × v × Tr = 0.02 × v × T = 0.02 F In Eq. (2), Tr is the rise time of the signal (s); T is the signal period (s), in general, Tr = 0.1T; F is the signal frequency (Hz), F = 1/T. The lowest signal frequency on the transmission line can be derived from Eq. (2) and is shown in Eq. (3). F≥
2.02 × 1011 × 0.02 v × 0.02 = = 269 MHz l 15
(3)
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According to Eq. (3), when the signal frequency exceeds 269 MHz, the transmission line with via holes studied in this paper needs to evaluate the signal integrity parameters. Figure 5 is a three-dimensional model of a 13-layer PCB board with via holes. Figure 6 to Fig. 8 are the main views of the model of through holes, blind holes and buried holes respectively after hiding dielectric layer of the PCB board. Differential strip lines 1 and 2 are located on the second layer, and differential strip line 3 is located on the fourth layer. The signal is transferred from strip line 1 to strip line 3 and finally to strip line 2 through a pair of via hole. The length of the through hole in Fig. 6 is 7 mm, and the two sections in the blue circle are the redundant parts generated by the through hole, that is, the stubs to be studied in this paper, with a length of 6 mm. The via holes in Fig. 7 are in the form of blind holes, with the length of 6 mm and the length of the stubs of 5 mm. The via holes in Fig. 8 adopt the form of buried hole, eliminating the stub. In the models, the radius of blind holes, through holes and buried holes are both 0.16 mm, the radius of pads is 0.38 mm, and the radius of isolation holes (anti-pads) of ground plane is 0.9 mm.
Fig. 5. Three-dimensional model drawing of through holes
3 Simulation of Signal Integrity of Transmission Lines in the PCB Board 3.1 Simulation Steps In this paper, the simulation software CST Studio Suite 2019 is used to simulate the signal integrity parameters of transmission lines on the PCB board. The simulation steps are as follows: The first step is to build a new CST project, and set the unit, simulation frequency, boundary conditions and background materials, etc. In this paper, the length unit is “mm”, the frequency unit is “GHz”, and the time unit is “ns”. The simulation frequency
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Fig. 6. Model of through holes (total length is 7 mm, stub length is 6 mm)
Fig. 7. Model of blind holes (total length is 6 mm, stub length is 5 mm)
Fig. 8. Model of buried holes (total length is 1 mm, stub length is 0 mm)
range is 0–20 GHz, the boundary condition is the electric boundary “Electric (Et = 0)”, and the background material is air “Normal”. In the second step, the signal transmission line models with via holes in the 13 layer PCB board shown in Fig. 6 to Fig. 8 are drawn respectively. In addition, select the end face of the signal lines to add differential ports. The third step is to use the default meshing method and set the appropriate density to mesh the model. The fourth step selects the “TDR Analysis” option in the time domain solver to obtain the simulation results of the TDR impedance, sets the port normalized impedance “normalize to fixed impedance” to 103.64 , and conducts simulation.
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3.2 Simulation Results
Fig. 9. Insertion loss of PCB transmission lines with different types of via holes
Fig. 10. Return loss of PCB transmission lines with different types of via holes
Figure 9 and Fig. 10 show the simulation results of insertion loss and return loss of PCB transmission lines with different types of via holes respectively, which are summarized into Table 1. In terms of insertion loss, there is no resonance in the buried hole, which is represented by “/”; The blind hole resonates once and the through hole resonates twice. Generally, the insertion loss is required to be greater than -3dB, that is, more than 70% of the electromagnetic energy reaches the output. According to this criterion, the buried hole meets the requirement within 20 GHz, the blind hole meets the requirement within 7.1 GHz, and the through hole meets the requirement within 6.6 GHz. In terms of return loss, the buried hole resonates once, the blind hole resonates twice, and the through
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hole resonates thrice. In general, the return loss is required less than −20 dB, that is, the electromagnetic energy reflected back to the incident port is less than 10%, then the buried hole meets the requirement in 10.9 GHz, the blind hole meets the requirement in 1.3 GHz, and the through hole meets the requirement in 1.0 GHz. Table 1. Insertion loss and return loss of PCB with different types of via hole Via hole types
Stub length (mm)
Resonance of insertion loss Frequency (GHz)
Amplitude (dB)
Minimum frequency at −3 dB (GHz)
Buried hole
0
/
/
/
Blind hole
5.0
8.1
26.0
7.1
Through hole
5.0; 1.0
7.9; 19.3
25.4; 9.7
6.6
Via hole types
Stub length (mm)
Resonance of return loss Frequency (GHz)
Amplitude (dB)
Minimum frequency at −20 dB (GHz)
Buried hole
0
8.1
56.9
10.9
Blind hole
5.0
6.6; 12.8
35.3; 39.6
1.3
Through hole
5.0; 1.0
6.1; 14.7; 19.9
40.5; 57.4; 21.5
1.0
Fig. 11. TDR impedance of PCB transmission lines with different types of via holes
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According to the simulation results in Fig. 11, the impedance value of the buried hole is from 103 to 111 , and the change is relatively stable with small range. The minimum impedance of the through hole is 64 , the maximum is 122 , and the stable value of oscillation attenuation is 104 . The change rule of the impedance of the blind hole is similar to that of the through hole, both of which have great changes and large oscillations, but the change range of the blind hole is from 68 to 122 , which is slightly smaller than that of the through hole. 3.3 Influence of Stub Length on Signal The excitation source of CST software is the Gaussian signal with the frequency of 20GHz, and its rise time of 10%–90% is 0.03 ns. According to Eq. (2), the spatial extension ltr of the rising edge is 0.24 in. Therefore, when the stub length exceeds 0.048 in (1.22 mm), the influence of the stub on the signal should be considered. In this paper, the back-drilling technology is used to shorten the length of the stub for the blind hole with the initial stub length of 5 mm. Figure 12 shows the insertion loss and characteristic impedance when the stub length is 0.5 mm, 1.5 mm, 2.5 mm, 3 mm, 3.5 mm and 4 mm respectively.
Fig. 12. Insertion loss and TDR impedance of PCB with different stub lengths
In terms of insertion loss, when the stub length is 0.5 mm, the insertion loss decreases monotonically with the frequency without resonance. The minimum insertion loss is − 1.8 dB, which is basically consistent with the buried hole. When the length of the stub is greater than 1.5 mm, the parasitic capacitance effect increases with the increase of the stub length, and the resonant frequency of the insertion loss decreases and the variation amplitude increases [10]. In terms of impedance, when the stub length is less than 1.5 mm, the variation range of impedance is less than 10% of the stable value. When the stub length is greater than 1.5 mm,withtheincreaseofstublength,themaximumvalueandminimumvalueofimpedance become larger and smaller, the amplitude of impedance increases, and the performance of signal transmission becomes worse. The specific data are shown in Table 2.
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Table 2. Comparison of simulation results of different stub lengths Resonance of insertion loss
The change of TDR impedance
Stub Frequency Amplitude Minimum Stationary Minimum Maximum Impedance length (GHz) (dB) frequency value () value () value () variation (mm) at −3dB range () (GHz) 0.50
/
/
/
103.64
103.26
112.15
8.89
1.50
19.87
−14.07
17.54
103.84
99.40
108.12
8.72
2.50
14.92
−18.50
12.07
103.92
87.46
112.02
24.56
3.00
12.87
−21.12
10.53
103.69
82.15
116.45
34.30
3.50
11.43
−23.09
9.34
103.67
77.57
120.51
42.94
4.00
10.19
−24.51
8.42
103.49
73.71
121.71
48.00
4 Solutions of Stub Influence The essence of using back-drilling, blind hole, buried hole and other technologies to solve the impact of the via on signal integrity is to reduce the stub length, that is, the stub length is controlled to be less than 20% of the space extension of the signal rising edge. Three solutions to reduce the length of stub are given below. Scheme 1 is to optimize the laminated structure or consciously increase the number of layers crossed by via holes. When the signal layer in Fig. 13 is the second layer and the fourth layer, the length of the signal via hole and the stub are 1 mm and 5 mm respectively; In Fig. 14, the signal layer is changed into layer 2 and layer 8, and the length of the signal via hole and the stub are 3 mm and 3 mm respectively. Table 3 shows the corresponding via hole length and stub length of different signal routing layers under the condition of blind hole.
Fig. 13. The signal layer is the schematic diagram of the second and fourth layers
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Fig. 14. The signal layer is the schematic diagram of the second and eighth layers Table 3. Comparison table of via hole and stub length between different layer routing signals Layer of strip-line
Length of Stub length inter-signal via hole (mm) (mm)
2, 4
1
5
2, 6
2
4
2, 8
3
3
2, 10
4
2
2, 12
5
1
Firstly, the influence of via hole length on insertion loss is simulated in the form of buried hole, and the results are shown in Fig. 15. Since there is no stub in the buried hole, the length of the buried hole is the length of signal via hole. Then, the blind hole
Fig. 15. Insertion loss of buried hole wiring between different layers
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is used to simulate the comprehensive influence of via hole length and stub length on insertion loss, and the results are shown in Fig. 16.
Fig. 16. Insertion loss of blind hole wiring between different layers
Figure 15 shows the simulation results of insertion loss of different signal layers with the buried via hole. The longer the buried hole, the smaller the resonant frequency of insertion loss and the greater the amplitude attenuation. It shows that the increase of the signal via length will decrease the insertion loss and reduce the signal transmission performance. Figure 16 is the simulation result of insertion loss of different signal layers with the blind via hole. The signal via length of the blind hole is the same as that in case of the buried hole, but the influence of the via stub shall be considered in case of the blind hole. When the total length of the blind hole is the same, the shorter the signal via length, the longer the via stub, and vice versa. According to the simulation results, the resonant frequency of insertion loss increases with the decrease of the via stub length. When the via stub length is more than 3 mm, the minimum value of insertion loss is almost the same, which is −26 dB. When the via stub length is equal to 1mm, the minimum insertion loss is −5.68 db, which occurs at 16.7 GHz. Scheme 2 uses blind hole or buried hole. First press and drill the middle layer, then press the outermost layer after electroplating, and finally drill the outermost layer via hole with laser microporous process, which can reduce the excessive stub brought by through hole. This method solves the problem of stub from the origin, but it needs to go through secondary pressing, etching, drilling, copper sinking, electroplating and other processes, the production process is complex, and the production cost is high [1]. Scheme 3 uses the back-drilling technology. Select a bit larger than the via hole diameter to drill out the via hole that is not on the signal path from the back, so that this section of via hole loses its conductivity. Back-drilling technology can alleviate most of
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the adverse effects on high-speed signal integrity caused by via stub without increasing the production time and cost of PCB, and make it within an acceptable range. It has the advantages of simple design, low cost and wide range of application, so it should be preferred in this kind of design [11].
5 Conclusions In this paper, the signal integrity of the PCB transmission lines with via stubs is simulated, and the effects of via hole type, back drilling technology and signal wiring technology on signal integrity are analyzed. The paper draws the following conclusions: Only when the length of various types of the via stubs is less than 20% of the spatial extension of the rising edge of the signal, the signal transmission performance will not be affected. Although increasing the signal via length will degrade the signal transmission performance, the influence of the via stub length on the signal integrity is greater than the signal via length. Therefore, under the same blind hole structure, optimizing the wiring layer structure can greatly improve the signal transmission performance. Buried hole can eliminate the influence of stub and obtain the best signal transmission performance. However, due to the high manufacturing cost of buried holes, in practical application, through holes can be drilled first, and then back drilling can be added on the basis of through holes to reduce the impact of stubs.
References 1. Ye, F.: Influence of via stub on signal integrity and its solution. Aviat. Comput. Technol. 48(05), 257–260 (2018). (in Chinese) 2. Hu, J.D.: Research on the influence of hole on signal integrity in high speed circuit design. Nucl. Electron. Detect. Technol. 036(006), 596–601 (2016). (in Chinese) 3. Tian, Y.: Influence of high-speed backplane stub on signal quality and its improvement. Changjiang Inf. Communi. 34(01), 138–141 (2021). (in Chinese) 4. Li, Y.S., Liu, Y.: Signal Integrity and Power Integrity Analysis. Electronic Industry Press, Beijing (2019). (in Chinese) 5. Benjamin, D.: Signal integrity characterization of via stubs on high-speed DDR4 channels. In: DesignCon 2020, Santa Clara, California (2020) 6. Yuan, W.Q., Song, J.Y.: Research on influencing factors and optimization of differential hole impedance of high-speed PCB. Printed Circ. Inf. 8, 7–11 (2019). (in Chinese) 7. Lin, X.Y., An, X.: Influence of via holes on signal integrity of multi-layer PCB transmission lines. In: 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), pp. 862–865, Changsha, China (2021) 8. Yu, K., Hu, X.X., Liu, F.: Study on influencing factors of high-speed signal via on signal. Printed Circ. Inf. 06, 20–22 (2014). (in Chinese) 9. Han, K.J., Gu, X.: Modeling on-board via stubs and traces in high-speed channels for achieving higher data bandwidth. IEEE Trans. Compon. Packag. Manuf. Technol. 4(2), 268–278 (2014) 10. Ben, R.R., Hu, S.Q.: Signal integrity analysis for SMA via on the PCB. In: 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), Guangzhou, China (2017) 11. Du, H.B.: Experimental study on the influence of back-drilling stub on high-speed signal integrity. Electron. Devices 43(06), 1244 (2020). (in Chinese)
Predictive Fuzzy Control Using Particle Swarm Optimization for Magnetic Levitation System Fanqi Bu
and Jie Xu(B)
College of National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430033, China [email protected]
Abstract. The magnetic levitation system (MLS) has the characteristics of timedelay, open-loop unstable and non-linear. Considering the poor robustness and ability to tack ideal position of conventional algorithms, a predictive fuzzy proportional-integral-derivative (PID) with particle swarm optimization (PSOPFPID) controller was proposed. With the recursive least squares (RLS) algorithm, a controlled auto-regressive integrated moving-average (CARIMA) model is identified on line and it serves for the predictive model of the generalized predictive control (GPC). Then the predicted optimal control law in the future time is served as the input of fuzzy PID (FPID). To enhance the dynamic and steady performance of the predictive fuzzy PID (PFPID) controller at the same time, the softening coefficient α and forgetting factor μ of the PFPID controller are globally optimized offline with the particle swarm optimization (PSO) algorithm. Finally, the maglev ball system is employed as the controlled object of the simulation and experiment and the mathematical model in balance points at the equilibrium point. Compared with PID, cascade GPC PID(PPID), simple PFPID, the PSO-PFPID controller has better robustness and make the controlled object stable in the case of mismatch. It can effectively adapt to system parameters changes. Keywords: FPID controller · Generalized predictive PID control · Maglev system · Parameter identification · PSO algorithm · Robustness
1 Introduction As a typical mechatronic system, magnetic levitation technology can achieve high-speed and low-loss movement [1], and high-performance control is of great significance to its research. In the literature about magnetic levitation technology, the magnetic levitation ball system has the one degree of freedom characteristics, so it serves for a simplified model of complicated MLS. Its structure is simple and is easily implemented, so its research contributes to the study of multi-degree-of-freedom MLS [2, 3]. PID control is widely used in the industrial fields, but its parameters are determined by many factors equilibrium position and the controlled objected. The parameters cannot be easily adjusted after the determination. Therefore, when the controlled object changes, the system may change greatly, or even be unstable [4]. Literature [5] finds the suitable © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1353–1369, 2022. https://doi.org/10.1007/978-981-19-1528-4_137
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values of PID controller parameters by pole placement technique. But the parameters of the controllers needs are changed depending on equilibrium position. Literature [6] used iterative linear matrix inequality technique to derive mathematical modeling and designed a linear feedback PID controller. The method can precisely adjust the position of steel ball by linearization of feedback control system, but the complicated mathematical model is difficultly popularized in industrial promotion. FPID control is a kind of intelligent control and can realize the effective control of complex systems. The fuzzy controller can make the nonlinear system more robust than the PID controller [7, 8]. This intelligent controller can make the MLS stable and is better than the PID controller under parameters change and exogenous disturbance. But the error of the time delay cannot be ignored. Predictive control can realize setpoint stability control for nonlinear and its robustness and dynamic perform are worth researching [9]. Literature [10] applied the constrained GPC controller to regulate the temperature of an electric vehicle battery. The online self-tuning predictive model adopts CARIMA model whose parameter is calculated by the RLS algorithm. Because the prospective performance cannot be obtained by a single predictive control algorithm, literature [11] combines the sliding mode and the predictive control to control the MLS. But its computational time may be longer than the sampling time, which leads to the instability of the system. Fuzzy control has strong adaptability and robustness, but its performance relies on the instantaneous response of the system. Therefore, literature [12–14] combined PID control, fuzzy algorithm, and predictive control and moreover designed the predictive fuzzy control algorithm. The combination of these three control algorithms can able to good match the input curve and is attractive research. Literature [15] proposed a new PFPID control method. This controller can track dynamic set-point and reject disturbance better, but has the small chatter of output wave in the initial process. Literature [16] designed a model predictive fuzzy PID control algorithm with weights (WM-F-PID) applying into automatic train operation. The input curve is composed of two parts of ideal speed information and future according to a specific weight. The weight is online turned with a steepest descent optimization algorithm. This control algorithm has good robustness and performance in the term of train operation. However, model predictive control (MPC) needs high precision mathematical models and the computational process is cumbersome. Literature [17] proposed a fuzzy generalized predictive controller and the CARMA model is transformed by the T-S fuzzy model of the nonlinear systems. For fuzzy and generalized predictive control, there are obvious advantages and disadvantages. But the mathematical model of this controller is complex and difficult to adjust. To improve the performance of controller, the controller parameters often are turned to obtain the optimal value with some optimization. Literature [18] proposed the genetic algorithm for optimizing the prediction and control time domain length. However, the controller cannot deal with the change of system parameters well. Literature [19] proposes an automatic tuning method for predictive control using PSO. The optimization method adjusts the weight parameters of controllers according to the predictive control
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input and particle swarm state. The error of the controller optimized by the PSO algorithm is less than the other optimization algorithm. But the computational time is long and should be set within a reasonable range. In summary, the MLS has unstable and nonlinear characteristics. Its parameters are easily changed by environmental factors. Based on the existing researches, to remain the system sable in the case of mistake and achieve good static and dynamics performance, a novel magnetic levitation ball location control architecture was proposed in this paper. It is composed of three main parts. The GPC algorithm is the outer control, the FPID control is the inner control, and the PSO algorithm is the optimization algorithm. The input of FPID control algorithm is the future optimal control law predictive by GPC algorithm. The last layer is the PSO algorithm to optimize the parameters of PFPID. According to the above description, the essential part of this controller is generalized as follows: 1) To adapt to the change of maglev system parameters and simplify the complex mathematical process, a PFPID control algorithm was proposed. The control algorithm contains two layers. The GPC on the basis of the CARIMA model serves for the outer layer controller to gain the future optimal control law information. The number of CARIMA model parameters is not large and online turning by RLS algorithm. This control algorithm adopts multi-step prediction and dynamic optimization strategy. It can enhance the adaptive capacity for model mismatch. Then the FPID control is used as the inner layer control algorithm to weaken the influence of parameter changes. 2) Considering the static and dynamic performance, a performance index was proposed based on ITSE objective function, and the PSO algorithm offline optimizes the softening coefficient α and forgetting factor μ of PFPID. The rest of this article is organized as the following parts. Section 2 analyzes the circuit equations and dynamic equations for the magnetic levitation ball at the equilibrium point, and the mathematical model of controlled object is established. In Sect. 3, presenting a PFPID controller design for magnetic levitation ball position control and an offline PSO algorithm for controller softening coefficient and forgetting factor. In Sect. 4, compared with the PID, PPID, simple PFPID controller, the PSO-PFPID controller has better performance by analyzing data collected in simulation and experiment. In the end, conclusions are summarized in Sect. 5.
2 Analysis and Modeling of MLS The system device structure is displayed in Fig. 1. This system converts the change of the distance x between the steel ball center and the electromagnet pole monitored by the photoelectric sensor into an electrical signal. After the electrical signal is transmitted to computer, the digital control signal output by computer is converted into an analog signal by D/A converters. Then the analog signal can adjust the instantaneous current i in the electromagnetic coil so that the ball can suspend in the desired equilibrium position by balancing the electromagnetic force F and the steel ball gravity mg. The MLS is a complex system and its many parameters are uncertain affected by many environmental factors, and there is a complex analysis process for MLS. The experimental environment in this article is relatively stable. So, this paper assumes that
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some interference for the controller is ignored, such as wind force, the force generated by the sudden change of the power grid, etc. The ball moves only in the vertical direction, so the controlled object has one degree of freedom.
Fig. 1. MLS schematic diagram.
(1) The electromagnetic force exerted on the ball in vertical direction is: 2 i F(i, x) = K x
(1)
Where k is the force coefficient of the electromagnet. Because the instantaneous current i in the electromagnetic coil and air gap distance x are nonlinear variables and the process of solving this system is expected to use simple linear theory. The electromagnetic force F is linearized by Taylor expansion at the equilibrium point (i0 ,x 0 ). The linear electromagnetic force equation is obtained as: F(i, x) = Ki + Kx + F(i0 , x0 ) where
(2)
⎧ ⎨ Ki = Fi (i0 , x0 ) = ⎩ Kx =
2Ki0 x02 2Ki2 Fx (i0 , x0 ) = − x3 0 0
(3)
K i is the stiffness coefficient between the electromagnetic force and the coil current, and K x is the stiffness coefficient between the electromagnetic force and the air gap distance around the balance point. (2) The dynamic equation of the system m
d 2 x(t) = F(i, x) + mg dt 2
(4)
The (2) is substituted into (4), the expression is as follow: m
d 2x = Ki (i − i0 ) + Kx (x − x0 ) dt 2
(5)
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(3) Circuit equation In order to simplify the derivation process, the electromagnet coil is modeled as consisting of inductance and resistance, and the expression is: U (t) = Ri(t) + L
di dt
(6)
Where, R and is L are the resistance and the static inductance of the electromagnetic coil, respectively; U(t) is the voltage applied on the electromagnetic coil. (4) Boundary conditions When the steel ball is in an equilibrium position, based on Newton’s second law, the first type of expression of boundary condition can be given as: mg + F(i0 , x0 ) = 0
(7)
(5) System model The output voltage of the drive circuit, the control voltage U in (s), is defined as the input of the controlled object and is proportional to i(s). The output of controlled object position is expressed by the photoelectric sensor output measured voltage U out (s), which is proportional to x. The model of the controlled object can be expressed as: G(s) =
Ks x(s) −(Ks /Ka ) Uout (s) = = Uin (s) Ka i(s) (i0 /2g)s2 − i0 /x0
(8)
In the expression, K s is the sensor output voltage gain between the air gap distance and the photoelectric sensor output measured voltage, and K a is the power amplifier gain between the output control voltage of the external circuit and the instantaneous current in the electromagnetic coil.
3 Design of PSO-PFPID Controller
Fig. 2. The structure of PSO-PFPID controller.
The magnetic levitation ball system has one degree of freedom and is an unstable system with time delay and variable parameters. The design of the controller for this system
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needs to pay attention to the following two points: Firstly, the effective control methods make original unstable system stable. Secondly, the controller has strong robustness when the controlled object parameters change. This article adopts PFPID controller and optimizes the controller parameters with the particle swarm algorithm. This method can well make the MLS stable. The controller, combined by the predictive control and fuzzy control, can overcome each other’s shortcomings and improve the system performance. The structure of PSO-PFPID controller system is expressed in Fig. 2. 3.1 Generalized Predictive Control System Design The CARIMA model serves for the predictive model of GPC algorithm. Based on the input and output differential equations, the expression of CARIMA model is established at the equilibrium point by linearization and discretization. The mathematical expression is expressed as follow: 1− → − → −1 − → A (z )y(k) = B (z −1 )u(k − 1) + C (z −1 )ξ(k)
(9)
⎧− → −1 ⎪ A (z ) = ana z −na + ana −1 z −(na −1) + · · · + a1 z −1 + 1 ⎪ ⎪ ⎪ ⎪ → −1 ⎨− B (z ) = bnb z −nb + anb −1 z −(nb −1) + · · · + b1 z −1 + 1 − → −1 ⎪ ⎪ C (z ) = cnc z −nc + cnc −1 z −(nc −1) + · · · + c1 z −1 + 1 ⎪ ⎪ ⎪ ⎩ = −z −1 + 1
(10)
where,
y(k) and u(k) are the system input and output, respectively; is a differential operator; ξ (k) is a white noise sequence with the average of zero. When the system has a q-beat time delay, there is b0 ~ bq-1 = 0, (q ≤ nb ). Considering the model accuracy, na and nb are taken as 9 and 10, respectively. In order to simplify the calculation process, the article assumes C(z−1 ) = 1. The identification process of predictive model is the solution of the above polyno− → − → mials A (z −1 ) and B (z −1 ). Then (9) can be rewritten as: − → y(k) = − A(z −1 )y(k) + B (z −1 )u(k − 1) + ξ(k) (11) = ϕ(k)T θ + ξ(k) where, ⎧− → ⎪ H (k) = [u(k − 1), · · · , u(k − nb − 1), ⎪ ⎪
T ⎨ −y(k − 1), · · · , −y(k − na )
T ⎪ ⎪ 2 , · · · , ann , b0 , b1 , · · · , bnb ⎪ θ = a1 , a− ⎩ → A(z −1 ) = A (z −1 ) − 1
(12)
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The parameter vector θ is identified with the RLS method of fading memory. The recursive formula is expressed as the following formula: ⎧ ⎪ K(k) = ϕ(k)TQ(k−1)ϕ(k) ⎪ ⎪ Q(k−1)ϕ(k)+μ
⎨ Q(k) = μ−1 I − K(k)ϕ(k)T Q(k − 1) (13) ⎪ ⎪ ε(k) = y(k) − ϕ(k)T θˆ (k − 1) ⎪ ⎩ˆ ˆ − 1) + K(k)ε(k) θ (k) = θ(k In the expression, μ represents the forgetting factor and is generally 0.95 to 1; K(k) is the gain factor vector; Q(k) is the positive definite matrix; θˆ (k) is the model parameter error matrix; E(k) is the prediction error. According to the identified system CARIMA model parameters, the Diophantine equation is introduced to derive the variation of the optimal control law and predictive output value at the future time. The Diophantine expression is as follows: → − → − → − 1 = z −j F (j) + E (j) A (z −1 )
(14)
− → − → − → Where, polynomials E (j) and F (j) are derived by A (z −1 ) and the predictive length j, and are as follows: − → E (j) = ej,j−1 z −(j−1) + ej,j−2 z −(j−2) + · · · + ej,0 (15) − → F (j) = fj,na z −na + fj,na −1 z −(na −1) + · · · + fj,0 On the basis of the system input and output value in the current moment, the predictive system output value at the next j sampling time is obtained as the following: − → − → y(k + j|k) = E (j) B (j)u(k + j − 1|k) − → − → + F (j)y(k) + E (j)ξ(k + j)
(16)
According to (16), another Diophantine equation is introduced as: − → − → − → − → G (j) = E (j)B(j)= G (j) + z −1 T (j)
(17)
where, ⎧ → ⎨− G (j) = gj,j−1 z −(j−1) + gj,j−2 z −(j−2) + · · · + g → ⎩− T (j) = t
j,0
j,nb z
−nb
+ tj,nb −1 z
−nb −1
+ · · · + tj,1 z
−1
(18)
In order to achieve soft control, the system output does not directly track the set value, but the target value of the control tracks the reference trajectory R(k + j), which is expressed as the following expression:
R(k + j) = α j y(k) + 1 − α j yR (k + j), (19) (j = N1 , N1 + 1, . . . N2 )
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Where yR (k + j) is the expected output value in the future, that is, the output reference value. In the GPC controller, the output value is expected to track the ideal input value as much as possible, and then the performance index is defined as: Nμ min J (k) = E λ(j)[u(k + j − 1)]2 j=1 N2 (20) 2 + [y(k + j|k ) − R(k + j)] j=N1
Where E{·} is the mathematical expectation; N 1 is the starting time of the predictive time domain; N 2 is the ending time of the predictive time domain; N μ is the control time domain length, and the control law does not change after N μ sampling time; λ(j) is the control weighting factor, the control weighting factor is taken as 1. In order to make the calculation process easier to understand, the paper uses a matrix to describe the relatively lengthy calculation process. According to (16) and (20), taking the partial derivative of J(k) as 0 can obtain the following control law with the local optimal performance index expression as:
−1 − → (21) GT R (k) − C(k) U(k|k ) = GT G + λI where,
(22)
GPC control decision is based on predictive future system information, and the selection of predictive information affects the selection and design of the inner controller. To overcome the influence of the time lag on the system, the input of the inner controller adopts the value of the optimal control law in the future time. The variation of the optimal control law of N μ-1 steps at time k and the control law at the current time are accumulated to obtain the predicted optimal control law of inner controller. The predicted optimal control law expression is as follows: u(k) = u(k − 1) + [0 0 · · · 0 1] ∗ U(k|k)
(23)
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The online control steps of GPC are described as follows: 1) Initialization: Set the CARIMA model identification parameter range number na and nb , the prediction domain starting and ending time N 1 and N 2 , the control time domain length Nμ, and the RLS algorithm initial value θ (−1) = 0, P(−1) = α 2 I. 2) On the basis of the latest system input and output information, calculate the system − → − → predictive model parameters A (z −1 ), B (z −1 ) by (13). − → −1 − → 3) According to the obtained A (z ), B (z −1 ) and the Diophantine equation, − → − → − → − → recursively calculate E (j), F (j), G (j), T (j). 4) Calculate the optimal control law change rate U(k|k) by (21). 5) Calculate the predicted optimal control law u(k) by (23), and substitute u(k) into the control system. 6) Obtain system input and output information at the current time, take k = k + 1, and back to step 2). 3.2 Fuzzy PID Control Design In this article, the FPID controller is established on the two-dimensional fuzzy logic. There are two inputs and three outputs composing the FPID controller. The error e and the error change rate ec serve for the controller input variables. The controller output variables select the PID parameters change rate K p , K i , and K d . The controller gets input variables, then continuously turns the parameters of the PID controller to meet the control requirements in different situations. The adjustment expression of PID parameters are as follows: ⎧ ⎨ Kp = Kp0 + Kp (24) K = Ki0 + Ki ⎩ i Kd = Kd 0 + Kd Where K P0 , K i0 , K d0 are the initial setting values of K P , K i , K d , which are obtained by the normal PID tuning method. In FPID controller, the basic domains of the error e and the error change rate ec respectively are [−0.6, 0.6], [−0.3, 0.3]. The basic domains of output K p , K i , K d respectively are defined as [−2, 2], [−2, 2], [−0.02, 0.02]. The basic domain of input and output is the same as the fuzzy domain, that is, the quantization factor is 1. According to the control experience and requirement, the input and output are fuzzified to fuzzy subsets including seven levels. These levels are negative big, negative medium, negative small, zero, positive small, positive medium, and positive big in fuzzy subsets, simplified as [NB, NM, NS, Z, PS, PM, PB]. A triangular membership function serves for the membership function in this paper. Because its shape can be changed by only selecting the straight-line slope and it is suitable for online adjustment. In addition, Mamdani reasoning is adopted as the fuzzy reasoning method and the defuzzification method chooses the center of gravity method. The control rules of the fuzzy controller are obtained based on the basic rules of fuzzy rules, PID parameter self-adjustment rule and the generalized control experience of MLS control experts. The rules between the input variables and output variables are shown in Table 1.
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Kp /K i /K d
e
ec NB
NM
NS
PS
PM
NB
PB/NB/PS
PB/NB/NS
PM/NM/NB PM/NM/NB
ZO
PS/NS/NB
ZO/ZO/NM
PB ZO/ZO/PS
NM
PB/NB/PS
PB/NB/NS
PM/NM/NB PS/NS/NM
PS/NS/NM
ZO/ZO/NS
NS/ZO/ZO
NS
PM/NB/ZO
PM/NM/NS PM/NS/NM
PS/NS/NM
ZO/ZO/NS
NS/PS/NS
NS/PS/ZO
ZO
PM/NM/ZO PM/NM/NS PS/NS/NS
ZO/ZO/NS
NS/PS/NS
NM/PM/NS
NM/PM/ZO
PS
PS/NM/ZO
PS/NS/ZO
ZO/ZO/ZO
NS/PS/ZO
NS/PS/ZO
NM/PM/ZO NM/PB/ZO
PM
PS/ZO/PB
ZO/ZO/NS
NS/PS/PS
NM/PS/PS
NM/PM/PS NM/PB/PS
NB/PB/PB
PB
ZO/ZO/PB
ZO/ZO/PM
NM/PS/PM
NM/PM/PM NM/PM/PS NB/PB/PS
NB/PB/PB
3.3 Particle Swarm Optimization Algorithm Suppose the particle is modified in an M-dimensional space and there are N particles constituting a particle swarm. A M-dimensional vector X i = (x i1 ,x i2 ,x i3 , …,x iM ) can represent the position of the particle in the space; a M-dimensional vector V i = (vi1 ,vi2 , vi3 ,…,viM ) can also express the velocity of the particle; the optimal position of individual and global particle at the current instance can be recorded as P i = (pi1 ,pi2 , …,piM ) and Gbest= (g1 ,g2 …,gM ), respectively. Based on the optimal value, the coordinates of particles position and velocity are modified as the following expression: ⎧ ⎨ V i (k + 1) = w · V i (k) + c1 · r1 · (P i (k) − X i (k)) (25) +c2 · r2 (Gbest − X i (k)) ⎩ X i (k + 1) = X i (k) + V i (k + 1) In the formula, c1 and c2 represent learning factors; r 1 and r 2 are random numbers, which are generally from 0 to 1; k is the number of iterations; w is the inertia factor. Based on tracking trajectory information, the optimal control law is obtained with GPC algorithm, so the softening coefficient α of PFPID has an important influence on the tracking trajectory. According to (19), provided that α is reduced, w(k) would quickly approximate yr so that the celerity of tracking is increased and the robustness is reduced and vice versa. Therefore, the value α must be considered between dynamic quality and the robustness. It is generally 0 to 1. The forgetting factor μ is important for the CARIMA model parameters identification. If μ is small, the system has good tracking ability, but at the same time the influence of noise is increased and vice versa. A suitable forgetting factor μ can balance the estimation error and the tracking ability of the controller. It is generally 0.95 to 1. To weaken the system output chatter and reduce the overshoot of the step response, the change rate of system error ec is taken as the optimization target. At the same time, aiming to reduce the influence of the initial trial error and to weigh the error fluctuations that appear in the later stage of the response, the time t is taken as optimization goals. This paper combines ITSE indicator and then proposed a fitness function. The combination coefficient is determined based on researcher’s experience. The corresponding fitness
Predictive Fuzzy Control Using Particle Swarm Optimization
function is as follows:
J2 =
t × 1.5|ec (t)|2 + 0.1|e(t)| dt
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(26)
The optimization algorithm can be expressed via the following steps in Fig. 3.
Fig. 3. Flow chart of offline particle swarm optimization.
4 Simulation and Experiment Analysis To validate the availability of the PSO-PFPID controller in practice, the Googoltech GML2001 magnetic levitation ball experimental device is used as the experimental device, and it is a typical suction suspension maglev system and a simplified platform for magnetic levitation technology. The physical model parameters of the controlled object are shown in Table 2 by measurement. Table 2. Actual system parameters Notation Parameter
Value
M
quality of the steel ball
0.104 kg
G
gravitational acceleration 9.8 m/s2
Ka
gain of power amplifier
Ks
Voltage conversion factor −166.697 V/m
i0
Equilibrium current
0.54 A
x0
Equilibrium voltage
31.4 mm
5.8
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After the physical parameters are plugged into (8), the transfer function expression of the magnetic levitation ball system can be expressed as: G(s) =
s2
1043.19 − 623.956
(27)
4.1 Simulation Analysis To validate the availability and dynamic characteristics of the PSO-PFPID controller, MATLAB/Simulink is selected as the platform conducting simulation based on the above transfer function. Firstly, some parameters of the PFPID controller are set. The reasonable number of the prediction time domain is generally to have 10 to 25 sampling steps obtaining the transient system information. In general, the length of control steps is 20%–35% of the pre-diction domain length. For simplifying the large numbers of calculations, the start time step and the end time step of the prediction time domain are 1 and 12 respectively, and the number of control steps is 4. The PID controller is important to stabile the internal loop system. Based on the trial-and-error method, the initial setting values KP0, Ki0, Kd0 are 8.5, 10, 0.495. In the PSO algorithm, the particle number is 50; the iteration number is 50; the learning factors c1 and c2 are 0.6 and 0.7, respectively; the particle dimension is 2. The particle swarm optimization process is shown in Fig. 4 as follows.
Fig. 4. Particle swarm change curve.
The paper analyses that the performance indexes of PSO-PFPID controller and compares the controller with the PID, PPID, and simple PFPID, when these algorithms track a reference waveform (RW). The output waves comparison of the above controllers are displayed in Fig. 5.
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Fig. 5. Comparison of position-tracking wave based on different controllers.
As Fig. 5 shows, the PID controller has larger over-shoot and longer transient time. When the inner controller of the GPC adopts PID, the system has shorter transient time and no overshoot. The transient time of the PPID system is 1.2575 s, 2.1541 s, 2.340 s, 2.2173 s, respectively at sampling point 1, 2, 3 and 4. Although the GPC and fuzzy algorithm improve the dynamic performance and reduce overshoot, the performance indicator is relatively poor influenced by the controller parameters and the response has fluctuation in each initial time, so the parameters of PFPID need to be optimized globally. After the optimized softening coefficient α and forgetting factor μ are substituted into PFPID controller, the system by PSO-PFPID control algorithm improves the performance of rapidity and stability. The transient time of the PPID system is 1.4274 s, 1.581 s, 1.6166 s, 1.7636 s, respectively at sampling point 1, 2, 3 and 4. The control algorithm proposed in this article has excellent comprehensive control performance, especially in the matter of dynamic response. To compare the tracking performance of the PSO-PFPID controller for different system parameters, the transfer function of the controlled object is adjusted appropriately. The robustness of the algorithm is tested in the cases of model mismatch. The original and adjusted transfer functions of the controlled object are as follows: ⎧ 850 ⎪ ⎪ G(s) = s2 −600 (case1) ⎪ ⎨ G(s) = 950 (case2) s2 −500 (28) ⎪ G(s) = s21150 (case3) ⎪ −750 ⎪ ⎩ (origin) G(s) = s21043.19 −623.956 When the model parameters change, the tracking trajectory of the original system and the adjusted system is shown in Fig. 6.
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Fig. 6. Comparison of position-tracking wave based on different model parameters.
As Fig. 6 shows, when the parameters of the controlled object are adjusted appropriately, PSO-PFPID controller still can make different controlled objects stable and track input square signal well. These control systems have no overshoot and well dynamic and steady state performance. Therefore, the PSO-PFPID controller is a feasible and effective position-tracking controller and has strong robustness for the model mismatch. 4.2 Experiment Based on MATLAB/Real-Time Windows Target, the experiment of the magnetic levitation ball system is conducted and the system ability to track the signal is analyzed in the case of mismatch. Therefore, the following experimental process is designed: According to the relationship between the input voltage and the position, the distance between the steel ball and the bottom of the electromagnet is set to 1 cm by controlling the input signal. Then adding a step signal in 30 s makes the position of the magnetic levitation ball increase by 0.2 cm. To validate the trace ability of the PSO-PFPID controller for different objects, the clips are added on the steel ball. To adjusting the parameters of the controlled object, the numbers of clips are 4(case4), 6(case5) and 8(case6). One clip weighs 0.02 kg. In different situations, the position changes of the steel ball are shown in Fig. 7. From Fig. 7, the PSO-PFPID control algorithm not only makes the ball stably suspend, but also has good robustness. When the system model is mismatched, the controller still maintains better tracking performance. The PSO-PFPID control algorithm not only has the advantage of no overshoot, but also can well cope with the changes of system parameters.
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Fig. 7. Comparison of experimental results based on the different objects.
5 Conclusion Focusing on minimize the influence of the time lag and the uncertain parameters of the mathematical model, this paper proposed a cascaded PSO-PFPID controller and verified the feasibility of the algorithm. More specifically, the outer controller adopts GPC to minimize the error resulted from the time lag. The CARIMA model is used as the prediction model and its parameters are online identified by RLS algorithm, on the basis of the system input and output value at the present and past moments. The optimal control law in the next N μ -1 sampling time is predicted by GPC algorithm. The inner controller adopts a FPID controller. It can effectively deal with different parameter changes. Finally, to design the controller having the better static and dynamic performance, the softening coefficient α and forgetting factor μ of the predictive controller are globally optimized by PSO. The magnetic levitation ball system is chosen as the simulation and experiment object. The results imply that, compared with conventional control method PID, PPID and simple PFPID, the PSO-PFPID controller can relatively reduce response time and maintain good tracking performance even in the case of model mismatch. The controller has strong robustness. However, the offline optimization method was adopted, so the total operating time is long. In future work, the complex calculation will be improved to suit complex MLS. Future research in this field is worthwhile. Acknowledgment. This work was supported in part by the National Natural Science Foundation of China under Grants 51825703 and 51690181, and the Natural Science Foundation of Hubei Province under Grant ZRMS2020000185, and the National Defense Science and Industry Administration Stable Support Project under Grant 614221720200510.
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References 1. Barbosa, F.C.: High speed intercity and urban passenger transport maglev train technology review: a technical and operational assessment. In: Proceedings of the 2019 Joint Rail Conference (2019). https://doi.org/10.1115/JRC2019-1227 2. Borgohain, N., Buragohain, M.: Comparative study of optimal controller application on nonlinear systems. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds.) Modeling, Simulation and Optimization. SIST, vol. 206. Springer, Singapore (2021). https://doi.org/10. 1007/978-981-15-9829-6-32 3. Sain, D.: Real-Time implementation and performance analysis of robust 2-DOF PID controller for Maglev system using pole search technique. J. Ind. Inf. Integr. 15, 183–190 (2019). https:// doi.org/10.1016/j.jii.2018.11.003 4. Gilson, F.B.J., José, A.L.B.: PID control design for a maglev train system. Appl. Mech. Mater. 389, 425–429 (2013). https://doi.org/10.4028/www.scientific.net/AMM.389.425 5. Kishore, S., Laxmi, V.: Modeling, analysis and experimental evaluation of boundary threshold limits for Maglev system. Int. J. Dyn. Control 8(3), 707–716 (2020). https://doi.org/10.1007/ s40435-020-00619-w 6. Zheng, F., Wang, Q.-G., Lee, T.H.: On the design of multivariable PID controllers via LMI approach. Automatica 38(3), 517–526 (2002). https://doi.org/10.1016/S0005-1098(01)002 37-0 7. Yadav, S., Tiwari, J.P., Nagar, S.K.: Digital control of magnetic levitation system using fuzzy logic controller. Int. J. Comput. Appl. 41(21), 27–31 (2012). https://doi.org/10.5120/58268141 8. Shen, H., Yan, J.: Optimal control of rail transportation associated automatic train operation based on fuzzy control algorithm and PID algorithm. Autom. Control. Comput. Sci. 51(6), 435–441 (2018). https://doi.org/10.3103/s0146411617060086 9. Artal-Sevil, J.S., Bernal-Ruiz, C., Bono-Nuez, A., Peñas, M.S.: Design of a fuzzy-controller for a magnetic levitation system using hall-effect sensors. In: 2020 XIV Technologies Applied to Electronics Teaching Conference (TAEE), 8–10 July 2020, pp. 1–9 (2020). https://doi.org/ 10.1109/TAEE46915.2020.9163711 10. Liu, K., Li, K., Zhang, C.: Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model. J. Power Sour. 347, 145–158 (2017). https:// doi.org/10.1016/j.jpowsour.2017.02.039 11. Assis, P.A.Q., Galvao, R.K.H.: Sliding mode predictive control of a magnetic levitation system employing multi-parametric programming. IEEE Lat. Am. Trans. 15(2), 239–248 (2017). https://doi.org/10.1109/TLA.2017.7854618 12. Cao, Y., Ma, L., Zhang, Y.: Application of fuzzy predictive control technology in automatic train operation. Clust. Comput. 22(6), 14135–14144 (2018). https://doi.org/10.1007/s10586018-2258-0 13. Lima, N.M.N., Liñan, L.Z., Filho, R.M., Maciel, M.R.W., Embiruçu, M., Grácio, F.: Modeling and predictive control using fuzzy logic: application for a polymerization system. AIChE J. 56(4), 965–978 (2010). https://doi.org/10.1002/aic.12030 14. Yu, J.Z., Chen, Y.S.: Automatic speed control algorithm study based on fuzzy-predictive control logic. Appl. Mech. Mater. 195–196, 1163–1168 (2012). https://doi.org/10.4028/www. scientific.net/AMM.195-196.1163 15. Wang, Y., Jin, Q., Zhang, R.: Improved fuzzy PID controller design using predictive functional control structure. ISA Trans. 71(Pt 2), 354–363 (2017). https://doi.org/10.1016/j.isatra.2017. 09.005 16. Liu, Y., Fan, K., Ouyang, Q.: Intelligent traction control method based on model predictive fuzzy PID control and online optimization for permanent magnetic Maglev trains. IEEE Access 9, 29032–29046 (2021). https://doi.org/10.1109/access.2021.3059443
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17. Shi, K., Wang, B., Yang, L., Jian, S., Bi, J.: Takagi–Sugeno fuzzy generalized predictive control for a class of nonlinear systems. Nonlinear Dyn. 89(1), 169–177 (2017). https://doi. org/10.1007/s11071-017-3443-z 18. Filali, S., Wertz, V.: Using genetic algorithms to optimize the design parameters of generalized predictive controllers. Int. J. Syst. Sci. 32(4), 503–512 (2001). https://doi.org/10.1080/002077 20121237 19. Suzuki, R., Kawai, F., Nakazawa, C., Matsui, T., Aiyoshi, E.: Parameter optimization of model predictive control by PSO. Electr. Eng. Jpn. 178(1), 40–49 (2012). https://doi.org/10.1002/ eej.21188
Effect of Defect Location on Decomposition Components Detection in SF6 Gas Under Partial Discharge Yifan He1,2 , Xianjun Shao1 , Xiaoxin Chen1 , Yanliang He2(B) , Wei Ding2 , Yuancheng Liu1 , Chen Li1 , Anbang Sun2 , and Guanjun Zhang2 1 Research Institute of State Grid Zhejiang Electric Power Limited Company, Hangzhou, China 2 State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical
Engineering, Xi’an Jiaotong University, Xi’an, China [email protected]
Abstract. Partial discharge (PD) inside gas-insulated power equipment is an important cause of sudden failures such as flashover and breakdown in equipment. In order to study the effect of defect location on decomposition components detection in SF6 gas under partial discharge during the operational conditions of gas-insulated power equipment, a 252 kV full-scale gas-insulated switchgear (GIS) in-situ detection technology experimental platform was built. On this basis, a SF6 gas decomposition components in-situ detection system was constructed. Through continuous partial discharge experiments, the gas chromatograph was used to detect the characteristic components generated by the decomposition of SF6 gas, including CO, CO2 , CF4 , and C2 F6 changing with the defect location. The results show that under the conditions of continuous partial discharge monitoring on site, no obvious formation of sulfur compounds is detected. When organic insulating materials participate in partial discharge, the CF4 content increases with increasing discharge time, and CF4 is expected to be used as the judgment basis of surface discharge in GIS. The amount of C2 F6 produced is very large, the C2 F6 content increases with increasing discharge time, and the amount of C2 F6 produced decreases with increasing diffusion distance. C2 F6 is expected to be used as the judgment basis for partial discharge in GIS. The CO content and CO2 content show a decreasing trend and increasing trend respectively with the increase of discharge time, and the change of these two decomposition components decreases with the increase of diffusion distance. Keywords: SF6 gas · Decomposition component · Defect location · Diffusion distance · Partial discharge
1 Introduction With the development of power system and the improvement of voltage class, the gasinsulated power equipment has been widely used because of its advantages of convenient maintenance, long service life and small floor area. As the main filling gas in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1370–1380, 2022. https://doi.org/10.1007/978-981-19-1528-4_138
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gas-insulated power equipment, SF6 has stable physicochemical properties and is difficult to decompose under normal circumstances. However, during the transportation and installation of power equipment, different types of insulation defects may occur inside the equipment, and the equipment in operation will cause partial discharge (PD) at the defects [1–2]. SF6 gas will decompose to produce low fluorides under partial discharge, and with the participation of moisture or oxygen, these low fluorides can further undergo hydrolysis or oxidation reaction to produce a series of decomposition components, such as SOF2 , SO2 F2 , SO2 , CF4 , C2 F6 , CO2 , CO, and H2 S. These decomposition components will not only reduce the insulation performance of SF6 , but also cause corrosion to the internal elements of power equipment and shorten the service life of power equipment [3–4]. Therefore, by analyzing the decomposition components of SF6 to judge the latent faults in power equipment, the further expansion of faults can be effectively avoided. In the research on partial discharge of gas-insulated power equipment based on SF6 decomposition components analysis method, scholars have made some achievements. Van brunt et al. [5] proposed a three-zone reaction model of SF6 gas decomposition under partial discharge, obtained the main reaction processes in different zones, and explained the SF6 discharge decomposition mechanism. Wang et al. [6] studied the variation rules of SO2 F2 , SO2 and S2 OF10 with moisture content under tip defects, and obtained the difference between decomposition components under corona discharge and mild spark discharge. Tang et al. [7–10] investigated the effects of discharge type, discharge energy, moisture and oxygen on the type, formation rate and characteristic ratios of decomposition components respectively through the self-developed SF6 decomposition experimental equipment. Zhou et al. [11] jointly with Electric Power Research Institute of Guangdong Power Grid simulated the surface defects of epoxy resin spacers in SF6 through a 110 kV gas-insulated switchgear (GIS) experimental chamber, and detected CS2 gas through the gas chromatograph with special capillary column. They believed that CS2 can be used as a new characteristic decomposition component for the diagnosis of surface discharge. The existing researches have fully investigated the influencing factors of SF6 decomposition process. However, under the same discharge conditions, the diffusion speeds of different decomposition components are different. The change of defect location will change the gas diffusion distance, resulting in the hysteresis of detection level, which may not be able to make timely and effective judgments on insulation defects. Moreover, most of the current research stays in the laboratory simulation environment, and there is still a lack of research on the detection of defects under the operational conditions of power grid. In this paper, a 252 kV full-scale GIS in-situ detection technology experimental platform is built, and the typical tip model is selected as the discharge insulation defect. The detection of SF6 gas decomposition components under different defect locations is experimentally studied. The variation of typical decomposition components with discharge time and defect location is analyzed, including CF4 , C2 F6 , CO and CO2 . The judgment basis of characteristic components under partial discharge is given, which provides help for the diagnosis of partial discharge in GIS on site.
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2 Experimental Platform and Method 2.1 Experimental Platform Figure 1 shows the structure of 252 kV full-scale GIS in-situ detection technology experimental platform. The experimental platform consists of 252 kV GIS experimental chambers, voltage-regulating system, ultrahigh frequency-partial discharge (UHF-PD) monitoring device, SF6 gas in-situ detection device, PD check, vacuum pump and gas recovery device. The internal and external diameters of the GIS experimental chamber are 29.8 cm and 30.6 cm, respectively, the external diameter of the high-voltage conductor is 9 cm, and the thickness of the disc insulator is 5 cm. The GIS model includes eight experimental chambers, high-voltage bushing, voltage transformer, current transformer, charging coupling capacitor and isolation switches.
Fig. 1. Schematic diagram of the GIS in-situ detection technology experimental platform.
Gas chromatograph and moisture analyzer are added to the experimental platform to form the SF6 gas decomposition components in-situ detection system, as shown in Fig. 2. The purity of SF6 gas used in experiments is 99.999%, and the oxygen concentration is less than 4 × 10–4 . The volume of the discharge chamber is 70 L, and the side wall is equipped with a quartz window with a diameter of 10 cm to observe the discharge.
Fig. 2. Schematic diagram of SF6 gas decomposition components in-situ detection system.
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Figure 3 shows the tip defect location in the experimental chamber. The tip defect is set on the high-voltage conductor, and the distance from the gas sampling port to the defect are 0 cm, 25 cm, 50 cm and 70 cm, respectively. The needle electrode material is stainless steel, the needle length is 50 mm, the needle tip radius is 200 µm, and the needle tip is 53 mm from the chamber shell.
(a) External structure
(b) Internal structure Fig. 3. Schematic diagram of tip defect location.
2.2 SF6 Gas In-Situ Detection Device The SF6 gas in-situ detection device developed by ourselves is used in experiments. The photo of the device is shown in Fig. 4. The device can continuously and real-time monitor the gas pressure leakage of the experimental chamber without changing the state of the detected gas. It can supplement the gas pressure of the experimental chamber and use the gas in the experimental chamber. The design index of the device is 0.4 MPa for the normal experimental pressure and 0.8 MPa for the maximum experimental pressure.
Fig. 4. Photo of SF6 gas in-situ detection device.
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The measurement range of gas pressure leakage is 0–30 kPa, and the maximum pressure of pipeline is 1.6 MPa. The standby time of the power supply can be more than 100 days in full power state. 2.3 SF6 Gas Detection and Analysis Methods In the experiments, the gas chromatograph (HUAAI GC9760B) is used to detect and analyze the SF6 gas decomposition components, which is commonly used in the site measurement. The gas chromatograph is composed of five switching valves, six chromatographic columns and two pulsed discharge detectors (PDD). Four column furnaces and purifiers with constant temperature control are built in, and they are 60 °C, 120 °C, 60 °C, 43 °C and 132 °C, respectively. He gas with 99.999% mass fraction is used as carrier gas. The chromatograph adjusts the peak time of each component by adjusting the column furnace temperature, so as to realize the effective separation of each component. It can accurately detect and analyze H2 , O2 , N2 , CF4 , CO2 , SO2 F2 , SO2 , H2 S, CS2 and other gases, and output the results in channels A and B. Figure 5 shows the peak time of the main decomposition components in channel A and channel B under the standard gas sample, and Table 1 shows the peak time of the main decomposition components.
(a) Channel A
(b) Channel B
Fig. 5. Peak diagram of main decomposition components.
Table 1. Peak time of main decomposition components. Channel
Main decomposition components/Peak time (min)
A
SO2 F2 /2.34
H2 S/2.98
C3 F8 /3.26
COS/4.02
SO2 /7.68
CS2 /15.31
/
/
CO/2.57
CF4 /3.41
CO2 /5.81
C2 F6 /8.31
B
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2.4 Experimental Method The experimental steps were as follows: (1) After vacuumizing the experimental chamber, SF6 gas was fed into with purity of 99.999%, and this process was repeated twice to achieve the purpose of cleaning. (2) The chamber was filled with SF6 gas again to ensure the experimental pressure of 0.45 MPa which lasted for 2 h. (3) We used a moisture analyzer to measure and ensure that the moisture content in chamber was less than 3 × 10–4 . (4) The sampling port, SF6 gas in-situ detection device and gas chromatograph were connected and the gas component content in the chamber before discharge was analyzed and recorded through the chromatograph. (5) The step-by-step voltage application method was used to increase the applied voltage until a stable UHF discharge signal was obtained, the voltage was recorded as the PD onset voltage, U 0 , and the mean discharge capacity, Q0 , was recorded. The applied voltage was adjusted to 1.5U 0 , 8 h continuous partial discharge decomposition experiment was carried out at this voltage, and the discharge in the chamber was monitored through the UHF-PD monitoring device. (6) The SF6 gas decomposition components through the sample gas was quantitatively analyzed and recorded every 1 h.
3 Experimental Results and Discussion The PD onset voltage under this experimental model varies within 20–22 kV. In order to ensure uniform discharge energy, U 0 is taken as 21 kV, the experimental voltage is set to 32 kV, and the mean discharge capacity is 80 pC. During the experiments, the change of gas pressure in the experimental chamber is less than 1%. As shown in Fig. 6, the variation trend of CF4 content with discharge time under different defect locations is given. For the diffusion process of decomposition components under different defect locations, the variation of CF4 content with discharge time fluctuates slightly, and there is no obvious change rules. At defect locations 2, 3 and 4, the CF4 content is in relative equilibrium, and its production has almost no change. At defect location 1, because the needle electrode is close to the disc insulator, it is inevitable to form surface discharge. With the participation of organic insulating materials, the amount of CF4 produced increases significantly. Although the diffusion distance at defect location 1 is far, the CF4 content still increases significantly and remains at 3.5 × 10–6 after 6 h. CF4 is not formed by the one-step reaction of C atoms and F atoms, its formation needs to be through the following reactions, as shown in Eqs. (1)–(4). These intermediates have high reaction activity and are easy to react with free H atoms and O atoms produced by the impact of charged particles in gas environment to produce CO2 , HF and other components [12], as shown in Eqs. (5)–(8). These reactions lead to the consumption of intermediate components CF, CF2 and CF3 before combining with F atoms to form CF4 , resulting in a lower CF4 content than CO2 content. The organic insulating materials in GIS is mainly epoxy resin, which contains a large number of carbonaceous structures. Under the action of partial discharge, the carbonaceous perssad on the surface of materials decomposes, producing a variety of CHX perssad, and the CHX perssad provides a large number of initial components for the formation of CF4 . C + F → CF
(1)
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CF + F → CF2
(2)
CF2 + F → CF3
(3)
CF3 + F → CF4
(4)
CF2 + O → COF + F
(5)
COF + O → CO2 + F
(6)
CH3 + F → CH3 F
(7)
CH3 F + F → CH2 F + HF
(8)
Fig. 6. Variation trend of CF4 content with discharge time under different defect locations.
Figure 7 shows the variation trend of C2 F6 content with discharge time under different defect locations. It can be found that the C2 F6 content increases with increasing discharge time, and the amount of C2 F6 produced decreases with increasing diffusion distance. The formation of C2 F6 also involves intermediate components such as CF, CF2 and CF3 , and these intermediate components further combine with free high-energy F atoms to form C2 F6 . Thus, the change rules of CF4 is not obvious under these experimental conditions. In addition, the amount of C2 F6 produced is very large, and the overall increasing range is 400 × 10–6 –800 × 10–6 . Therefore, C2 F6 is expected to be used as the judgment basis for partial discharge in GIS.
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Fig. 7. Variation trend of C2 F6 content with discharge time under different defect locations.
Figure 8(a) shows the variation trend of CO content with discharge time under different defect locations. It can be found that the CO content decreases gradually with increasing discharge time, and the decreasing degree of CO becomes weaker with increasing diffusion distance. Taking defect location 4 as an example, the CO content is reduced by about 5.7 × 10–6 .
(a) CO content
(b) CO2 content
Fig. 8. Variation trends of CO content and CO2 content with discharge time under different defect locations.
Under the action of partial discharge, charged particles will impact the surface of needle electrode and release free C atoms. Since there exists a small amount of moisture and oxygen in the experimental chamber, these molecules will also be impacted by charged particles to produce free O atoms. Therefore, the main reaction processes of CO are shown in Eqs. (9)–(10). The variation trend of CO content shows that the reaction rate of Eq. (10) is greater than that of Eq. (9) under partial discharge. This is because CO2 is more stable than CO, the two electrons of CO exist on the anti-bond, their energy
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is higher, thus CO is easier to react further with free O atoms to produce CO2 . C + O → CO
(9)
CO + O → CO2
(10)
Figure 8(b) shows the variation trend of CO2 content with discharge time under different defect locations. It can be found that, similar to the change rules of C2 F6 content, CO2 content shows an overall increasing trend with the discharge time, and the increasing degree of CO2 decreases with increasing diffusion distance. Taking defect location 4 as an example, the CO2 content increases by about 4 × 10–6 after 8 h. CO2 can be produced both in moisture and oxygen environment, and the formation of CO2 and CO exists a competitive mechanism. In the environment with moisture, CO2 is mainly produced through the following reaction processes, as shown in Eqs. (11)–(13). However, because C atoms also participate in the formation of C2 F6 and CF4 , and O atoms are easy to react with highly active F atoms to produce oxygen-containing sulfur fluorides, the total production of CO2 is less. e + H2 O → H + OH + e
(11)
CO + OH → COOH
(12)
COOH → CO2 + H
(13)
The needle electrode used in the experiments is made of stainless steel. Under the action of partial discharge, the active Fe atoms vapor will be released from the electrode surface, and the evaporation of surface atoms will make the electrode surface activated and easy to react, so as to generate FeS2 and other metal compounds. These reactions will make some S atoms form solid powder or adhere to the electrode. Due to the large size of the full-scale experimental chamber, part of the decomposition components will be adsorbed by the side wall of chamber before diffusion to the sampling port. When the amount of sulfur compounds produced is less, this adsorption further reduces the content of sulfur compounds, making the detection difficult. Therefore, the experiments do not detect the common sulfur compounds in laboratory environment in the past [13–14].
4 Conclusion In this paper, a 252 kV full-scale GIS in-situ detection technology experimental platform was established, and a SF6 gas decomposition components in-situ detection system was constructed on this platform. The effect of defect location on decomposition components detection in SF6 gas was studied by continuous partial discharge experiments. Within the scope of experiments, the following conclusions can be drawn: (1) Under the conditions of continuous partial discharge monitoring on site, no obvious formation of sulfur compounds is detected, and four carbonaceous compounds including CF4 , C2 F6 , CO and CO2 could be detected.
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(2) When organic insulating materials participate in partial discharge, such as surface discharge, the CF4 content increases with increasing discharge time. Without the participation of organic insulating materials, the CF4 content is less and has no obvious variation trend. Therefore, CF4 is expected to be used as the judgment basis of surface discharge in GIS. (3) The C2 F6 content increases with increasing discharge time, and the amount of C2 F6 produced decreases with increasing diffusion distance. The amount of C2 F6 produced is very large and has no obvious relationship with the discharge type. Therefore, C2 F6 is expected to be used as the judgment basis for partial discharge in GIS. (4) The CO content and CO2 content show a decreasing trend and increasing trend respectively with increasing discharge time, and the change of these two decomposition components decreases with increasing diffusion distance. Since the contents of CO and CO2 are relatively less compared with C2 F6 , they are expected to be used as auxiliary judgment basis for partial discharge in GIS.
Acknowledgement. This work was supported by State Grid Zhejiang Electric Power Limited Company Technology Project (5211DS190030).
References 1. Pan, C., et al.: Understanding partial discharge behavior from the memory effect induced by residual charges: a review. IEEE Trans. Dielectr. Electr. Insul. 27(6), 1951–1965 (2020) 2. Ren, M., Zhou, J., Miao, J.: Adopting spectral analysis in partial discharge fault diagnosis of GIS with a micro built-in optical sensor. IEEE Trans. Power Delivery 36(2), 1237–1240 (2021) 3. Li, B., Zhou, Q., Peng, R., Liao, Y., Zeng, W.: Adsorption of SF6 decomposition gases (H2 S, SO2 , SOF2 and SO2 F2 ) on Sc-doped MoS2 surface: a DFT study. Appl. Surf. Sci. 549, 149271 (2021) 4. Zeng, F., et al.: SF6 fault decomposition feature component extraction and triangle fault diagnosis method. IEEE Trans. Dielectr. Electr. Insul. 27(2), 581–589 (2020) 5. Vanbrunt, R.J.: Production rates for oxyfluorides SOF2 , SO2 F2 , and SOF4 in SF6 corona discharges. J. Res. Natl. Bur. Stan. Caithersburg 90(3), 229–253 (1985) 6. Wang, Y., Ji, S., Zhang, Q., Ren, J., Li, J., Wang, W.: Experimental investigations on lowenergy discharge in SF6 under low-moisture conditions. IEEE Trans. Plasma Sci. 42(2), 307–314 (2014) 7. Tang, J., et al.: Correlation analysis between formation process of SF6 decomposed components and partial discharge qualities. IEEE Trans. Dielectr. Electr. Insul. 20(3), 864–875 (2013) 8. Wu, S., Zeng, F., Tang, J., Yao, Q., Miao, Y.: Triangle fault diagnosis method for SF6 gasinsulated equipment. IEEE Trans. Power Delivery 34(4), 1470–1477 (2019) 9. Rao, X., et al.: Mechanism of trace O2 on SF6 characteristic decomposed components under spark discharge. Plasma Chem. Plasma Process. 40(2), 469–481 (2020) 10. Zeng, F., Lei, Z., Yang, X., Tang, J., Yao, Q., Miao, Y.: Evaluating DC partial discharge with SF6 decomposition characteristics. IEEE Trans. Power Deliv. 34(4), 1383–1392 (2019)
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11. Chen, J., et al.: Insulation condition monitoring of epoxy spacers in GIS using a decomposed gas CS2 . IEEE Trans. Dielectr. Electr. Insul. 20(6), 2152–2157 (2013) 12. Plumb, I.C., Ryan, K.R.: Gas-phase reactions of CF3 and CF2 with atomic and molecular fluorine: their significance in plasma etching. Plasma Chem. Plasma Process. 6(1), 11–25 (1986) 13. Van Brunt, R.J., Herron, J.T.: Fundamental processes of SF6 decomposition and oxidation in glow and corona discharges. IEEE Trans. Electr. Insul. 25(1), 75–94 (1990) 14. Sima, W., et al.: Thermal damage process and failure mechanism of epoxy/SF6 composite insulation subjected to arc ablation. IEEE Trans. Dielectr. Electr. Insul. 27(6), 2014–2022 (2020)
Effect of DC Bias on the Operation Characteristics and Magnetic Circuit Difference of Single-Phase Transformer and Three-Phase Transformer Beibei Liang1(B) , Zhiwei Chen1 , Landong Liang2 , Fei Xia1 , Qipei Zhou1 , and Yingying Zhang1 1 School of Electrical Engineering and Automation, Hefei University of Technology, Hefei,
Anhui, China [email protected] 2 China Datang Corporation Science and Technology Research Institute Co., LTD., East China Electric Power Test and Research Institute, Hefei, China
Abstract. Aiming at the influence of operation characteristics of single-phase transformer and three-phase column transformer, the difference of magnetic circuit formed by DC bias, this paper takes 1 kV single-phase transformer and 1 kV three-phase column transformer as research objects. Firstly, the causes of DC bias are explained and the mechanism of DC bias is analyzed. Secondly, according to the magnetic circuit structure of single-phase transformer and three-phase transformer, the magnetic circuit is analyzed and the DC bias magnetic circuit is deduced. Finally, the quantitative DC bias is calculated by the formula, and the distribution of DC bias, magnetic density, distortion of excitation current and harmonic content are carried out. The DC bias tolerance of single-phase and threephase transformers is verified from theoretical simulation analysis and real test, and the influence of bias current on excitation current, harmonic distortion rate and electromagnetic transient is comprehensively analyzed. It can provide a reference for solving the influence of transformer under DC bias condition, and has important research significance. Keywords: Unipolar earth circuit · DC magnetic bias · Excitation current · Power transformer
1 Introduction Energy is an important strategic issue related to the overall development of the society and economy. China’s energy resources are characterized by the large total amount, small per capita and unbalanced regional distribution, which puts forward corresponding requirements for the development of China’s power resources. The development of energy resources and productivity is in reverse distribution, and energy reserves are scarce in economically developed areas, making it inevitable to construct long-distance © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1381–1389, 2022. https://doi.org/10.1007/978-981-19-1528-4_139
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and large-capacity DC transmission system. With the rapid development of HVDC transmission technology in China, up to now, a number of long-distance and large-capacity HVDC transmission projects including UHVDC have been put into operation in China [1]. In the normal operation state of DC transmission, the DC line works in the bipolar operation mode, and the transmission lines that the DC current passes through the two poles form a loop. However, in the case of system debugging, overhaul or failure, HVDC transmission will adopt the operation mode of unipolar earth loop [2, 3]. When unipolar ground loop is running, the current field in ground has great influence on the environment where the current flows through the range. Especially for the ac system grounded at neutral point, the substations with different DC potentials will form a DC loop through transmission lines and transformer windings, and the DC current will invade the transformer windings through the neutral point of the transformer, thus causing DC magnetic bias of the transformer [4]. After dc magnetic bias occurs in the transformer, noise increases, vibration intensifies, and overheating may also occur, resulting in increased harmonic distortion of the AC power grid [5–7]. The work of DC magnetic bias has been studied deeply in many aspects. But studies mainly focused on the content of the single structure of transformer DC bias current of tolerance. For the DC bias magnetic leakage flux, the traditional method of inhibition of DC bias current, etc., and most of the research is not enough in-depth, to different structure of transformer DC bias magnetic tape to run less impact and magnetic circuit analysis. Electric power system, the high voltage direct current transmission transformer structure is complex, expensive, high voltage grade. It is difficult to conduct the magnetic circuit and characteristic analysis about the different structure of the main transformer, so it is necessary through the simulation analysis, field current and harmonic magnetic field of the main substation and so on carries on the detailed study, and summarizes the characteristics of a DC bias differentiation.
2 Formation Mechanism of DC Bias in Transformers When the transformer is in the normal operation, its working range is within the range of line segment O-A, as shown in Fig. 1. When DC bias occurs, the current in the working power transformer winding is the superposition of DC component and AC components, so that it will form DC constant flux and AC magnetic flux. The main flux density of the transformer core is greatly increased in a direction consistent with the direction of the DC magnetic potential, and the flux density in a period opposite to the direction of the DC magnetic potential is greatly weakened, finally forming a positive and negative half period no longer symmetric tip wave [8]. It can be seen from the figure that after DC intrusion, serious distortion of transformer excitation current will cause great harm to the safe operation and service life of transformer. Figure 2 shows the distribution of basic magnetization curve of power transformer core under the conditions of no DC bias and DC bias invasion. In the linear region in the figure, when the magnetic field intensity is the same, the value of magnetic induction intensity corresponding to the core magnetization curve after DC bias is lower than that without DC magnetic bias, mainly due to the influence of DC magnetic flux. Corresponding to the nonlinear saturation region, due to the rapid decrease of the DC flux, the
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Fig. 1. Mechanism of transformer DC bias
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Fig. 2. B-H curve under the DC bias and without DC bias
final trend of the two magnetization curves tends to be the same. It can be clearly seen from the figure that the changing trend of magnetic field intensity H under the conditions of DC bias. The total excitation current if can be expressed as the sum of the three parts of the bias DC current ib , the normal fundamental frequency excitation current im and the magnetic saturation additional excitation current ib . Among them, the fundamental frequency excitation current im = I m sin(ωt). The magnetic saturation additional excitation current ih in the interval α ≤ ωt ≤ π -α can be expressed as: ih = (h − 1)(Im sin(ωt) − Im + Ib )
(1)
In other intervals from 0–2π, ih = 0. The DC component I contained in the additional excitation current ih of magnetic saturation is I=
(h − 1) 2 Ib (2Im − Ib ) − (π − 2α)(Im − Ib ) 2π
(2)
The total DC current in the excitation current is I = I + Ib
(3)
It is equivalent to the DC component forced to flow into the transformer winding through the neutral wire. It is also possible to derive the expressions of the peak value of the excitation current, the effective value of the excitation current, the fundamental frequency component and the high-order harmonic components. In addition, analyzing Fig. 1, it can be seen that when the core is in the unsaturated zone, A Le = L0 = 0.4π μ0 Ne2 l
(4)
Considering that the unsaturated zone generally has L0 =
die = Rie dt
(5)
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When the core enters the saturation zone, the inductance of the excitation winding is approximately L e = 0. So there is U0 = L0 didte (Unsaturated zone) (6) U0 = ie R (Saturated zone) Assuming that the even harmonic components in ie are iek (k = 2, 4, …), then U0k = Uek + iek R (k = 2, 4, · · · )
(7)
Since the superposition of even harmonics of sinusoidal voltage is equal to 0, there is U0k = −iek R (k = 2, 4, · · · )
(8)
Therefore, through the feedback of the loop resistance, an even harmonic voltage appears in the excitation winding, and an even harmonic magnetic flux is generated.
3 Magnetic Circuit Analysis of Single-Phase Transformer and Three-Phase Column Transformer Three-phase transformer is divided into three-phase group transformer, three-phase three-column transformer and three-phase five-column transformer. This paper takes single-phase transformer and three-phase three-column transformer as the research object, and its magnetic circuit diagram is shown in 3. The three windings of the threephase three-column transformer are respectively wound on the three core columns. The zero sequence flux has no separate path, but needs to flow through air and transformer oil. The high and low voltage windings of the single-phase transformer are wound around the middle iron core, and two side columns are also included. Similar to the three-phase group transformer, there are independent channels of zero sequence flux in both of them. The magnetic circuits of three-phase, three-column and single-phase transformers are shown in Fig. 3 respectively.
(a) Single-phase transformer
(b) Three-phase transformer
Fig. 3. Schematic diagram of magnetic circuit
Because the reluctance of air and transformer oil is much greater than that of magnetic conducting materials such as iron core and iron yoke, the three-phase three-column transformer has the strongest ability to withstand DC magnetic bias, and the single-phase and three-phase group transformers are the weakest. It can confirm this characteristic from the DC magnetic bias size, excitation current and internal magnetic field simulation.
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4 Simulation Analysis of Transformer DC Bias Current Firstly, the magnetization curve of the transformer iron core is established in the COMSOL software. The single-phase transformer and the three-phase three-column transformer are respectively connected with 0 A, 0.25 A and 0.75 A DC current to observe the excitation current and its harmonic changes. The finite element simulation software was used to analyze and compare the magnetic density of these three cases under the condition of DC bias, and the influence of other devices was ignored in this process. Since the transformer core and coil are the main solving areas, and the partition density is higher than other areas, the mesh division of the surrounding air model is relatively large, so as to reduce the memory occupied by data. Grid generation of single-phase transformer and three-phase three-column transformer is shown in Fig. 4.
(a) Single-phase transformer
(b) Three-phase transformer
Fig. 4. Schematic diagram of transformer grid subdivision
4.1 Analysis of Excitation Current and Harmonic Simulation Results of Single-Phase Transformer Figure 5 is the transformer no-load current waveform on the primary side and the Fast Fourier analysis (FFT) spectrum diagram, the figure shows that the field current enhances, explain the transformer core material has been saturated, but the waveform is relative to the horizontal axis symmetry. This is because there is no straight flow into, while the transformer works in the saturated zone. But there was no magnetic bias. It can be seen from FFT analysis that although the odd harmonic is generated due to the saturation of the transformer, the excitation current is still positive and negative half-cycle symmetry, so there is no even harmonic. Figure 6 is the excitation winding current waveform and fast Fourier analysis (FFT) spectrum diagram of the single-phase transformer no-load under 0.25 A DC current. The figure shows that when there is a 0.25 A DC current, field current waveforms obviously shift to the positive half axis, and along with the increase of current amplitude. It can be seen from the spectrum diagram that the content of odd harmonics in the excitation current decreases and even harmonics appear. According to the Principle of Fourier series, when the current waveform is no longer symmetric about the abscise. The spectrum contains not only odd components but also even components.
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Fig. 5. Simulation diagram of excitation current of single-phase transformer (I dc = 0 A)
Fig. 6. Simulation diagram of excitation current of single-phase transformer (I dc = 0.25 A)
Figure 7 is the excitation winding current waveform and fast Fourier analysis spectrum diagram of the single-phase transformer under 0.75 A DC current. The figure shows that when the ventilation with 0.75 A DC current, the field current waveform shift to the half shaft seriously, and current amplitude increase obviously. By spectrum diagram it can be seen that the exciting current harmonic content is reduced, three times five times harmonic disappeared, even order harmonic grew sharply at the same time. By the principle of Fourier series, the electric current waveform is not symmetrical about abscissa, spectrum contain not only the odd components at the same time. And this phenomenon becomes more obvious as the degree of deviation increases.
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Fig. 7. Simulation diagram of excitation current of single-phase transformer (I dc = 0.75 A)
4.2 Analysis of Excitation Current and Harmonic Simulation Results of Three-Phase Three-Column Transformer Figure 8 shows the primary side current waveform of the three-phase three-column transformer in no-load condition and the spectrum diagram obtained by FFT of the excitation current. It can be seen from the figure that the three-phase excitation current is close to sine wave. It can be seen from the spectrum analysis that the excitation current is mainly fundamental wave and contains a very small number of odd harmonics, which can be ignored.
Fig. 8. Simulation diagram of excitation current of three-phase three-column transformer (I dc = 0 A)
Figure 9 shows the excitation current waveform of the primary side of the winding and the spectrum obtained by FFT. When the 0.25 A DC current is entered, the positive half axis deviation of the excitation current direction is not obvious, and the current amplitude basically does not change. The harmonic content of excitation current is not different from that of no-load condition. This indicates that the transformer is in good working condition and can work in this environment for a long time.
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Fig. 9. Simulation diagram of excitation current of three-phase three-column transformer (I dc = 0.25 A)
Figure 10 shows the excitation current waveform of the primary side of the winding and the spectrum diagram obtained by FFT when the three-phase three-column transformer is unloaded and working at rated voltage and 0.75 A DC current is entered. It can be seen from the figure that when the 0.75 A DC current is entered, the excitation current wave begins to shift to the positive half axis of the coordinate axis in small amplitude. It can be seen from the spectrum diagram that the excitation current spectrum after 0.75 A does not change much with the no-load.
Phase A
0.84
0.8
Phase B Phase C
Phase A Phase B Phase C
0.8 0.6
i/A
FFT/%
0.76
0.4
0.72
0.68 0.64
0.2
0.22
0.24
0.26
0.28
t/s (a) Excitation current waveform
0.0
0
200 300 100 Frequency/Hz
400
(b) Spectrum of excitation current
Fig. 10. Simulation diagram of excitation current of three-phase three-column transformer (I dc = 0.75 A)
Through the above analysis, the conclusion is drawn that the three-phase threecolumn transformer does not produce a very strong DC magnetic bias phenomenon after the DC current. The reason is that the flux generated by the DC component exists in the form of zero sequence flux in the transformer. And three phase three column type transformer is not a separate zero sequence flux pathway. Zero sequence flux can only through
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the circulation of air and transformer oil, since this pathway magnetic resistance is very large, suppresses the dc current generated in a certain sense magnetic phenomenon. That is, under the same DC, the DC bias flux produced by three-phase three-column transformers is much smaller than that produced by single-phase transformers. Under different DC magnetic bias, the DC magnetic bias of single-phase transformer is relatively obvious with the increase of DC, and the increase of magnetic induction intensity amplitude is also prominent, so the capacity of single-phase transformer to withstand DC magnetic bias is worse than that of three-phase three-column transformer.
5 Conclusion In this paper, the single-phase and three-phase three-column transformers are analyzed respectively under the condition of no load and DC current intrusion. It can be seen that when there is DC intrusion in the transformer, the excitation current of the single-phase transformer has different degrees of positive deviation, while the three-phase threecolumn transformer has no obvious magnetic bias. Therefore, it can be concluded that the three-phase three-column transformer has better ability to withstand DC magnetic bias than other iron-core transformers. That is to say, under the same DC invasion, the three-phase three-column transformer can operate for a longer time without causing too much harm to the transformer itself and the power system. In addition, the DC bias will cause the transformer excitation current distortion, resulting in the increase of even harmonic content. However, due to the different structure of transformer cores, the threephase three-column transformer has the strongest ability to withstand DC magnetic bias, while the single-phase transformer has a relatively weak ability to withstand DC bias.
References 1. Yao, Y., Chang, S.K., Ni, G., et al.: 3-D nonlinear transient eddy current calculation of online power transformer under DC bias. IEEE Trans. Magn. 41(5), 1840–1843 (2005) 2. Li, X.P., Wen, X.S., Lan, L., et al.: Test and simulation for single-phase transformer under DC bias. Proc. CSEE 27(9), 33–40 (2007) 3. Han, D., Zheng, T., Zhu, X., Qi, H.: The modeling and excitation characteristics of the transformer under DC bias. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds.) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol. 238. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-4981-2_89 4. Wang, J., Gao, C., Xu, D., et al.: Multi-field coupling simulation and experimental study on transformer vibration caused by DC bias. J. Electr. Eng. Technol. 10(1), 176–187 (2015) 5. Kun, Y., Ni, Y., Zeng, X., Peng, P., Fan, X., Leng, Y.: Modeling and analysis of transformer dc bias current caused by metro stray current. IEEJ Trans. Electr. Electron. Eng. 15(10), 1507– 1519 (2020) 6. Ma, C., Sun, L., Zhao, S., et al.: Research on influence of converter transformer grouping and receiving end power grid structure on converter transformer DC bias. J. Phys. Conf. Ser. 1304, 012002 (2019) 7. Yan, L., Gao, Y., Li, L., et al.: Effects of DC magnetic bias on the magnetic and sound fields of transformer. Energy Power Eng. 05(4), 1097–1100 (2013) 8. Wang, F-H., et al.: Simulation and experiment research on the effects of DC-bias current on the 500kV power transformer. In: Electronics and Signal Processing (EEIC 2011 LNEE V4). Ed, pp. 245–252. Springer (2011)
Unify Control for Bidirectional Buck-Boost Converter Used in Supercapacitor Energy Storage System of Crane Qinghua Lin(B) Putian University, Putian 351100, Fujian, China [email protected]
Abstract. The supercapacitor tank is used to save and feedback energy which is conventionally wasted by a breaking resistor in crane. This paper proposes a bidirectional buck-boost converter to interface the difference in voltage level between the supercapacitor tank and DC bus in transducer. The control strategy of buck and boost model was unified, the current of inductance was used to demonstrate working condition. Theory analysis and simulation results also proved the feasibility and effective of the proposed topology. Simulation results show that the unify control for bidirectional buck-boost converter was suitable for supercapacitor energy storage system in crane. Keywords: Unify control · Bidirectional buck-boost converter · Supercapacitor tank · Energy saving · Crane
1 Introduction A crane can be used to lift and lower materials and to move them horizontally. As the crane is in the state of hoist-down, the mechanical energy of the load will be decreasing and regenerates energy. The regeneration energy is transported into transducer and consumed by a power resistor in the form of heat, which is a great waste of energy. The regeneration energy can also be feedback on the grid [1–5]. This will cause problems as fee counting, harmonic interference, disturbance on power distribution. Another effective way to utilize the regeneration energy is using energy storage tank to save the regeneration energy [6–10]. The collection energy is then feedback on the crane when it needs energy. For the energy storage tank, supercapacitor has two outstanding advantages [11]; the energy density is hundreds of times higher than that of traditional aluminum electrolytic capacitor, the power density is tens of times higher than that of battery. The supercapacitor also has a longer life-cycle than that of battery. Supercapacitor is fit for energy absorb and feedback in crane application. The voltage of supercapacitor tank varies a lot as it is in state of absorbing or feedback energy, and hard to control. This paper proposes bidirectional buck-boost converter to interface the difference in voltage level between the supercapacitor tank and DC bus in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1390–1397, 2022. https://doi.org/10.1007/978-981-19-1528-4_140
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transducer. When the crane works in hoist-down state and generates energy, the storage tank should absorb the regeneration energy. Since the voltage of supercapacitor is lower than that of DC bus, the bidirectional buck-boost converter will work at buck function, and the energy flows through crane to the supercapacitor tank and will be saved in it. When the crane works hoist-up state and needs energy, the bidirectional buck-boost converter will work at boost function. The energy flows through the supercapacitor tank to the crane and provides the energy to crane. The control strategy of buck and boost model was unified, the current of inductance was used to demonstrate working condition.
2 Topology Analysis When crane is in the state of hoist-up, the materials are lifted up and have more gravitational potential energy. In this process, energy flows from grid to transducer, motor, and then converts into mechanical energy of load. When crane is in the state of hoist-down, the materials are lifted down and have less gravitational potential energy. In this process, the mechanical energy of load converts into electrical energy, then flows into transducer and is consumed by the braking resistance in the form of heat. The energy flow direction in the process of rising and falling was shown as Fig. 1.
Grid
Resistance
Transducer Rectifier Inverter
M
(a) Rising process Transducer G Rectifier Rectifier
Mechanical drive
Mechanical drive
Load
Load
(b) Falling process Fig. 1. The energy flow direction of crane.
The crane hoist-up and hoist-down materials frequently when it works. The electrical energy is converted into mechanical energy of load in the state of hoist-up. Then the mechanical energy is converted into regeneration electrical energy in the state of hoistdown. From Fig. 1, the regeneration energy is consumed by the braking resistance in the form of heat and the total regeneration energy of load is huge. Using the breaking resistance to consume the regeneration power is a great waste of energy. This paper uses supercapacitor tank to save the regeneration energy and the storage energy is then feedback on the crane when it needs energy. As shown in Fig. 2, a bidirectional buckboost converter is proposed to interface the difference in voltage level between the storage tank and the DC bus in transducer. When the crane is in the state of hoist-down, it generates renewable energy. The regeneration energy is transported into transducer, but is blocked by the front rectifier of the transducer. The voltage of DC bus in transducer will increase, and the regeneration
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S
R
AC
DC M
CB DC
AC
DC chopper L S1 Ci
S2
Co
SC
Fig. 2. Proposed topology scheme.
energy should be moved from DC bus. This paper uses supercapacitor tank to save the regeneration energy. Since the voltage of supercapacitor tank is lower than that of DC bus in transducer, the bidirectional buck-boost converter will work in a standard buck regulator, as shown in Fig. 3(a). BD2 is the body diode of switch S2. When the converter works in continue current mode (CCM), the transfer function of the converter is: VSC = D1 ∗ VCB
(1)
D1 is the duty cycle of switch S1.
L
L
BD1
S1 CB
Ci
BD2
Co
SC
SC
Co
(a) Buck regulator
S2
Ci
CB
(b) Boost regulator
Fig. 3. Buck and Boost mode of proposed topology
When the crane is in the state of hoist-up, the electrical energy converts into mechanical energy of load. If supercapacitor tank has enough energy, the supercapacitor tank will supply the electrical energy. Since the voltage of supercapacitor is lower than that of DC bus in transducer, the bidirectional buck-boost converter will work in a standard boost regulator, as shown is Fig. 3(b). BD1 is the body diode of switch S1. When the converter works in CCM, the transfer function of the converter is: 1 ∗ VSC 1 − D2
(2)
VSC = (1 − D2) ∗ VCB
(3)
VCB = D2 is the duty cycle of switch S2. Equation (2) can be expressed as:
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From Eqs. (1) and (3), we can get: D1 = 1 − D2
(4)
The duty cycle of switch S1 and S2 can be unified as D1 or D2, and the other switch will work as synchronous rectification mode. The operation modes of bidirectional buckboost converter then can be shown as Fig. 4. IL
IL
L
L
S1 CB
Ci
S1 S2
Co
SC
CB
Ci
(a) Switch S1 is on
S2
Co
SC
(b) Switch S2 is on
Fig. 4. Two operation modes of proposed topology.
3 Control Strategy The duty cycle is unified, the work state of the bidirectional buck-boost converter can be decided by the average current of inductance L. As shown in Fig. 5, the average current is bigger than zero, the converter works in buck regulator. As shown in Fig. 6, the average current is lower than zero, the converter works in boost regulator. i
i
iL IL
iL IL t
(a) low power
t
(b) big power
Fig. 5. Current shape in Buck regulator.
The current of proposed topology can’t be predicted, since you never know the state of the crane and the power of it. The voltage of supercapacitor is variation all the time, and can’t be used to control the topology. This paper uses the voltage of DC bus in transducer to control the size and the direction of power. As shown in Fig. 7, when the crane works at electrical motor mode, the electrical power will be converted into mechanical energy of load. The voltage of DC bus will drop and lower than the reference voltage. Then PID will give out a higher voltage, which will decrease the width of S1 gate signal. The width of S2 gate signal will be increasing, make the current of inductance falling. The inductance current will be lower than zero.
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i
i
IL
iL
t
t IL
iL (a) low power
(b) big power
Fig. 6. Current shape in Boost regulator.
+ Vref
PID
-
Dead time
S1 gate signal
+
Dead time
S2 gate signal
Fig. 7. Control block diagram of the system.
The proposed topology works in boost regulator. At this time, the power will flow from supercapacitor tank to the crane and converter into mechanical energy of load. In this process, the supercapacitor tank works in energy feedback state. When the crane works at generator motor mode, the mechanical energy of load will be converted into electrical energy. The electrical energy flows into transducer and lift the voltage of DC bus. The voltage of DC bus will be higher than the reference voltage. Then PID will give out a lower voltage, which will increase the width of S1 gate signal, make the current of inductance rising. The inductance current will be higher than zero. AT this time, the proposed topology works in buck regulator. The power will flow from the crane to supercapacitor tank. In this process, the supercapacitor tank works in energy saving state.
4 Simulation Results Simulation is taken out to confirm the stability of the proposed topology and parameters is listed in Table 1. The control aim is to keep the voltage of DC bus at 600 V, the voltage of supercapacitor varying from 200 V to 450 V. To short the simulation time, the supercapacitor tank is set as 0.2 F. The duty cycle of the proposed topology is unified with switch S1, and switch S2 works in synchronous rectification mode. Two different power levels were designed to confirm the stability of the proposed topology. From Fig. 8(a), a power as 1000 W was injected in the proposed topology, and the voltage of supercapacitor tank varied from 246 V to 310 V. The voltage of DC bus was kept at 600 V. The power in Fig. 8(b) is 2400 W, and the voltage of supercapacitor tank varied from 305 V to 416 V. The voltage of DC bus was also kept at 600 V. The current in Fig. 9 shows that the proposed topology worked in buck regulator.
Unify Control for Bidirectional Buck-Boost Converter Table 1. Circuit parameters. DC bus voltage: VCB
600 V
Supercapacitor tank voltage: VSC
200 V–450 V
Switching frequency: f
80 kHz
inductance: L
200 uH
Supercapacitor tank
0.2 F
(a) 1000W
(b) 2400W
Fig. 8. Voltage and power shape of buck mode.
Fig. 9. Current of inductance and voltage of MOSFET in buck mode.
(a) 1000W
(b) 2400W
Fig. 10. Voltage and power shape of boost mode.
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From Fig. 10(a), a power as 1000 W was consumed from the proposed topology, and the voltage of supercapacitor tank varied from 395 V to 348 V. The voltage of DC bus was kept at 600 V. The power in Fig. 10(b) is 2400 W, and the voltage of supercapacitor tank varied from 385 V to 255 V. The voltage of DC bus was also kept at 600 V. The current in Fig. 11 shows that the proposed topology worked in boost regulator.
Fig. 11. Current of inductance and voltage of MOSFET in boost mode.
5 Conclusion Supercapacitor tank is fit for energy absorb and feedback in crane application. But the voltage of supercapacitor tank varies a lot when it works. This paper proposes a bidirectional buck-boost converter to interface the difference in voltage level between the supercapacitor tank and DC bus in transducer. The control strategy of buck and boost model was unified, the current of inductance was used to demonstrate the work condition. Theory analysis and simulation result both proved the feasibility and effective of the proposed topology.
References 1. Zhao, N., Schofield, N., Niu, W.: Energy storage system for a port crane hybrid power-train. IEEE Trans. Transp. Electr. 2(4), 480–492 (2016) 2. Nie, D., Zhou, D.: Energy-saving research on hydraulic system of lifting mechanism of truck crane. In: 2016 International Conference on Intelligent Transportation, Big Data & Smart City, Changsha, China, pp. 440–442 (2016) 3. Yoshihara, H.: Energy saving system trend for harbor crane with lithium-ion battery. In: 2018 International Power Electronics Conference, Niigata, Japan, pp. 219–226 (2018) 4. Anisa, E., Asmae, B., et al.: Modeling and performance evaluation of the dynamic behavior of gravity energy storage with a wire rope hoisting system. J. Energy Storage 33(2), 1–15 (2021) 5. Jabbour, N., Mademlis, C.: Improved control strategy of a supercapacitor-based energy recovery system for elevator applications. IEEE Trans. Power Electron. 31(12), 8398–8408 (2016)
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6. Shen, X., Cao, G.: Comparative analysis on configuration methods of supercapacitor array for braking energy recovery of urban rail transit. Trans. China Electrotech. Soc. 35(23), 4988–4997 (2020). (in Chinses with English Abstract) 7. Yang, Z., Yang, Z., et al.: Brake voltage following control of supercapacitor-based energy storage systems in metro considering train operation state. IEEE Trans. Industr. Electron. 65(8), 6751–6761 (2018) 8. Alejandro, C.-A., Vazquez-Castillo, J., et al.: An energy-saving data statistics-driven management technique for bio-powered indoor wireless sensor nodes. IEEE Trans. Instrum. Meas. 70, 1–1 (2021) 9. Li, B., Wan, J., et al.: Research on elevator drive device with supercapacitor for energy storage. In: 2011 International Conference on Power Electronics Systems and Applications, Hongkong, China, pp. 1–5 (2011) 10. Chang, C., Yang, J., et al.: Study on supercapacitor energy saving system for rubber-tyred gantry crane. In: 2010 Asia-Pacific Power and Energy Engineering Conference, Chendu, China, pp. 1–6 (2010) 11. Perumal, S., Jarvis, A.L.L., et al.: Effect of graphite precursor flake size on energy storage capabilities of graphene oxide supercapacitors. SAIEE Africa Res. J. 112(2), 67–76 (2021)
Parameter Modification Method and Influence Analysis of Double-Circuit Transmission Lines on the Same Tower Jingyuan Dong(B) , Xiaoming Li, Xiangyu Liu, Tengkai Yu, Tianying Chen, and Rui Zhang State Grid Hebei Electric Power Research Institute, Yu Hua District, Shijiazhuang, China [email protected]
Abstract. There is a big difference between transmission line state estimation results and actual measured values, which seriously affects the normal operation of power grid analysis and other links. As a kind of static basic data, the accuracy of transmission line parameters affects the result of state estimation. Power system state estimation is the basis of dispatch automation application functions. State estimation can not only provide more credible basic data for power grid analysis, but also the difference between state estimation results and actual measured values can reflect the quality of basic power grid data. In order to solve the low state estimation accuracy of the short-distance non-transposition double-circuit transmission line on the same tower with the power plant directly connected to the grid, simulate line and grid operation through PSCAD, phase split modeling of the line, calculate line parameters, analyze the influence of the self-inductance and mutual inductance of lines under different phase sequence arrangements and the reasons for the lower accuracy of the line reactive power. Keywords: Double-circuit transmission lines on the same tower · State estimation · PSCAD simulation
1 Introduction In the state estimation, the high-voltage AC line parameters strictly adopt the actual measured values, and the calculated values are used if there is no actual measured value. The high-voltage AC line uses the different frequency signal measurement method to apply positive sequence/zero sequence voltage, current, and active power to the head end of the line, and calculates the parameters of the double-circuit line on the same tower [1–3]. The measurement and calculation process considers that the transmission line is three-phase symmetrical, and does not consider the three-phase unbalance factors caused by the distance and phase sequence between the phases of the double circuit line [4, 5]. This article focuses on the difference between the estimated state of the transmission line and the actual measurement that appears in the operation and maintenance, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1398–1405, 2022. https://doi.org/10.1007/978-981-19-1528-4_141
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conducting split-phase model of double-circuit transmission lines on the same tower, considering the mutual influence between lines, calculating line parameters reflecting actual operating conditions which effectively improves the qualification rate of state estimation for double-circuit transmission lines on the same tower, analyzing the line parameter characteristics under different phase sequence arrangements and the influence of line parameters on state estimation results.
2 State Estimation and Actual Measurement Analysis Take the short-distance non-transposition double-circuit transmission line on the same tower with the power plant A as an example. We list the measured parameters of the line found in the dispatching OMS system in Table 1. Table 1. Transmission line parameters of power plant A The calculated values of line I
The actual measured value of line I
The calculated values of line I
The actual measured value of line II
Resistance R/
0.3906
0.4733
0.3906
0.4360
Reactance X/
2.9298
2.8585
2.9298
2.8497
There is a big difference between state estimation results and actual measured values, as shown in Table 2. Table 2. The comparison of state estimation and actual measurement results
Active power of line I/MW Reactive power of line I/MVar Active power of line II/MW Reactive power of line II/MVar
State estimation
Actual measurement
Deviation rate %
235.3
232
1.4
57.4
67
14.3
235.7
235
0.3
60.7
52
16.7
In the state estimation, the active power and reactive power transmitted by the doublecircuit line are basically the same. In actual measurement, the active power transmitted by the double-circuit line is basically the same, but the reactive power gap is large.
3 Parameter Correction Method and Analysis 3.1 Calculation of Parameters of Double-Circuit Transmission Lines on the Same Tower Phase split modeling of double circuit lines on the same tower, a 6 × 6 phase impedance parameter matrix Z can be obtained, that is, the self-impedance of the phase line and
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the mutual impedance of the other five-phase lines to the phase line. Divide the phaseseparated impedance parameter matrix into four 3 × 3 dimensional sub-matrices, as shown in formula (1). ZABC Z12 (1) Z= Z12 Zabc Among them, Z ABC is the self-impedance and mutual impedance between the phases are the mutual impedances between one line and the other line, of line I, Z 12 and Z12 Z abc is the self-impedance and mutual impedance between the phases of line II. Transform the A, B, and C three-phase system into a positive, negative, and zero sequence system through phase sequence transformation, the transformation matrix T is shown in formula (2). ⎤ ⎡ 1 1 1 (2) T = ⎣ α 2 α 1 ⎦, α = ej2π/3 α α2 1 Under stable operation of the power system, there are only positive sequence components. Calculate the mutual impedance between loops into the line impedance and correct the line impedance value [6], as shown in formula (3) and formula (4). ZI = T −1 ZABC T + T −1 Z12 T (3) ZII = T −1 Zabc T
1,1
1,1
+ T −1 Z12 T
1,1
1,1
(4)
3.2 Comparative Analysis of Different Phase Sequence Arrangements of Double-Circuit Transmission Lines on the Same Tower The current imbalance of double circuit transmission lines on the same tower is mainly due to the asymmetry of the parameters of each phase, short-distance non-transposition is the main reason for the asymmetry of line parameters. In compact arrangement, analyze the phase separation parameters of the same phase sequence, reverse phase sequence, and four out-of-phase sequences. Through phase sequence transformation of ZABC and Zabc , the diagonal elements of the matrix are the same, that is, the positive sequence self-impedance of the two lines are the same. are mutually transposed matrices, and Z is an asymmetric matrix. After Z12 and Z12 12 phase sequence transformation of Z12 and Z12 ’ , mutual inductance between lines is affected by line phase sequence. In the same phase sequence, considering the influence of line mutual inductance, line resistance increases. In the reverse phase sequence, line resistance decreases. In the out-of-phase sequences, the resistance of one increase and the other decrease. The reactance parameters have little effect in each arrangement. Through calculation of unbalance degree by different phase sequence arrangement, for power plant A, the length of the transmission line and the arrangement of towers, lines arranged in reverse phase sequence have the lowest three-phase unbalance.
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4 Simulation Analysis Take the short-distance non-transposition double-circuit transmission line on the same tower with the power plant A as an example, the length of the line is 9.767 km, the lines are arranged compactly in different phase sequence. 4.1 Line Modeling on the Double-Circuit Transmission Lines on the Same Tower The actual operating status of transmission lines are shown in the Fig. 1. 235
232
52
67 608
618
64 271
201 2M
1
2
1
272
2
1
2
231
1M
231
233
237
66
56 612 #1
598 #2
254
248
#1
#2
252
247
91
99
Fig. 1. Real-time data of transmission lines
Simulate transmission lines of Power plant A through PSCAD [7]. In the simulation, it is considered that the power supply and load of the power plant are all three-phase symmetrical components. Perform detailed modeling according to the actual length and phase sequence arrangement of transmission lines [8]. The model is shown in the Fig. 2.
Fig. 2. Simulation model of transmission lines
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The comparison of PSCAD simulation and actual measurement results is shown in Table 3. Table 3. The comparison of PSCAD simulation and actual measurement results PSCAD simulation Actual measurement Deviation rate % Active power of line I/MW Reactive power of line I/MVar Active power of line II/MW Reactive power of line II/MVar
233.4
232
0.6
67.8
67
1.2
233.1
235
0.8
50.1
52
3.6
4.2 Simulating Calculation of Double-Circuit Transmission Lines on the Same Tower We list the split phase impedance parameter matrix of double-circuit transmission lines on the same tower in Table 4. Table 4. Split phase impedance parameter matrix/ a1
b1
c1
a2
b2
c2
a1
0.9635 +j5.4902
0.8651 +j2.5313
0.8245 +j2.9777
0.8236 +j2.4856
0.8631 +j2.3195
0.7963 +j2.5688
b1
0.8651 +j2.5313
1.1173 +j5.4116
0.8995 +j2.9389
0.8958 +j2.4474
0.9421 +j2.4916
0.8631 +j2.3195
c1
0.8245 +j2.9777
0.8995 +j2.9389
1.0216 +j5.4600
0.8532 +j2.5389
0.8958 +j2.4474
0.8236 +j2.4856
a2
0.8236 +j2.4856
0.8958 +j2.4474
0.8532 +j2.5389
1.0216 +j5.4600
0.8995 +j2.9389
0.8245 +j2.9777
b2
0.8631 +j2.3195
0.9421 +j2.4917
0.8958 +j2.4474
0.8995 +j2.9389
1.1173 +j5.4416
0.8651 +j2.5313
c2
0.7963 +j2.5688
0.8631 +j2.3195
0.8236 +j2.4856
0.8245 +j2.9777
0.8651 +j2.5313
0.9635 +j5.4902
Through phase sequence transformation, sequence impedance parameter matrix of double-circuit transmission lines on the same tower is shown in Table 5. Impedance of transmission lines are shown in formula (5) and formula (6). ZI+ = 0.0905 + j 2.6878
(5)
ZII+ = 0.2555 + j 2.6829
(6)
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Table 5. Sequence impedance parameter matrix/ I+
I−
I0
II+
II−
II0
I+
0.1711 +j2.6380
0.2449 +j0.1369
0.0893 −j0.0040
−0.0806 +j0.0498
0.0986 −j0.0604
0.0103 +j0.0399
I−
−0.2426 +j0.1453
0.1711 +j2.6380
−0.1964 −j0.0827
−0.1016 −j0.0552
0.0843 +j0.0449
−0.1129 −j0.0342
I0
−0.1964 −j 0.0827
0.0893 −j 0.0040
2.7602 +j11.086
−0.0397 −j0.0110
0.0269 +j0.1148
2.5855 +j7.3681
II+
0.0844 +j0.0449
0.0986 −j0.0605
0.0268 +j0.1148
0.1711 +j2.6380
0.0041 −j0.2877
0.0179 +j0.2064
II−
−0.1017 −j 0.0552
−0.0806 +j0.0498
−0.0397 −j0.0110
−0.0126 −j0.2855
0.1711 +j2.6380
−0.0325 +j0.0743
II0
−0.1129 −j 0.0342
0.0103 +j0.0399
2.5855 +j7.3681
−0.0325 +j0.0743
0.0179 +j0.2064
2.7602 +j11.0859
The state estimation results of lumped parameters obtained by calculation are shown in Table 6. Table 6. The comparison of state estimation and results using corrected parameters State estimation
Actual measurement
Deviation rate %
Active power of line I/MW
234.6
232
1.1
Reactive power of line I/MVar
66.98
67
0.03
Active power of line II/MW
237.8
235
1.2
Reactive power of line II/MVar
51.57
52
0.83
Using the calculated line lumped parameters to verify the other operating status, it can accurately describe the real-time operating status of the double-circuit lines sent from the power plant on the same tower.
5 Analysis of the Influence of Resistance on Reactive Power The centralized parameter model of double-circuit transmission lines on the same tower is shown in the Fig. 3. The head and terminal voltages of double circuit transmission lines on the same tower are the same. Suppose the terminal voltage is U 2 0° , the head voltage is U 1 θ° ,
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R1+jX1
P1+jQ1
R2+jX2
P2+jQ2
U2
AC
P+jQ
Fig. 3. Concentrated parameter model of double-circuit transmission lines
the terminal active power is P, the terminal reactive power is Q, we can get formula (7). ⎧ R U1 U2 cos θ − U22 + XU1 U2 sin θ ⎪ ⎪ ⎨P = R2 + X 2 (7) 2 ⎪ ⎪ ⎩ Q = −RU1 U2 sin θ + X U1 U2 cos θ − U2 R2 + X 2 The double-circuit transmission lines on the same tower are mostly short lines, the phase angle difference between the two ends of the line is less than 1° . The value of sin θ is much larger than (cos θ – 1), the order of magnitude difference is nearly 100 times. Therefore, the modified resistance value R has a negligible effect on P and has a greater impact on Q which makes the big difference of the reactive power of the two lines [9]. Line parameters adopt measured values and modified parameters has little effect on the qualified rate of line active power state estimation, but seriously affects the qualified rate of reactive power state estimation.
6 Conclusion This article using PSCAD to model the double-circuit line on the same tower in detail, the concentrated parameters of the line are obtained through phase sequence transformation, which effectively improves the pass rate of the state estimation of the transmission line. By analyzing six different phase sequence arrangements of double-circuit lines on the same tower, it is found that the line adopts reverse phase sequence arrangement with the lowest current imbalance. According to the centralized parameter model of doublecircuit transmission lines on the same tower, it is concluded that inaccurate resistance parameters of double-circuit transmission lines on the same tower have a greater impact on the reactive power of the line.
References 1. Song, S., Shi, J., Huang, C., Gao, L., Bu, Q., Yuan, Y.: Method for estimating unbalanced currents in untransposed double-circuit lines on the same tower. In: International Conference on Renewable Power Generation (RPG 2015), pp. 1–6 (2015). https://doi.org/10.1049/cp.2015. 0321 2. Idoniboyeobu, D.C., Wokoma, B.A., Osegi, E.N.: Fault location prediction on double-circuit transmission lines based on the hierarchical temporal memory. In: 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), pp. 1100–1105 (2017). https://doi.org/10.1109/NIGERCON.2017.8281973
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3. Zhang, Y., Tang, Z., Yang, L., Wu, M., Bai, R.: Research on the influences of different phase sequence arrangement of common-tower double-circuit transmission lines. East China Electric Power 42(10), 2119–2122 (2014). (in Chinese) 4. Guo, Z., Cheng, C., Su, J., Lu, H., Wang, Z., Zhu, Z.: Analysis on oversize unbalanced currents in part of 500kV transmission lines of Shandong power gird and corresponding improvement measures. Power Syst. Technol. 21, 32–36 (2006). (in Chinese) 5. Wang, J., Li, Z., Cao, X., Pan, C., Lin, F.: Research on electric unbalance degree of 1000 kV AC double-circuit on the same tower. Southern Power Syst. Technol. 4(S1), 108–112 (2010). (in Chinese) 6. Chen, J., Li, Y., Zhang, H., Guo, Z.: Analysis of influencing mechanism of reactive power circulating current problems in high-voltage parallel transmission lines. Electric Power 49(06), 83–89 (2016) 7. Teng, H.: A Large-scale AC-DC Hybrid System Simulation and Modeling in PSCAD. North China Electric Power University (Beijing) (2010). (in Chinese) 8. Kuang, C.: Research on Line Parameter Error Hypersensitivity of Reactive Power Flow in the Complex Large-Scale Power Grids. South China University of Technology (2015). (in Chinese) 9. Wang, M., Qi, X.: Analysis of Transmission Line Resistance Parameter’s impacts on reactive power estimation results. Power Syst. Prot. Control 43(23), 143–147 (2015)
Analysis of Inverter Commutation Failure Caused by Background Harmonics Yue Wang1(B) , Jun Wen1 , Tian Chen1 , Zhiyong Yu2 , and Zhengang Lu2 1 School of Electrical Engineering, North China Electric Power University, No. 2 Bei Nong Lu,
Chang Ping District, Beijing, China [email protected] 2 Global Energy Interconnection Research Institute Ltd., Beijing, China
Abstract. Commutation failure is one of the most common faults in the operation of high voltage direct current transmission (HVDC). This paper analyzes the influence of voltage amplitude reduction, zero-crossing displacement, and voltage waveform distortion on the commutation process based on the phenomenon of commutation failure in HVDC transmission system and its principles. In view of the distortion of the commutation voltage waveform caused by the low-order background harmonics, the DC system is more prone to commutation failure, and the simulation verification is carried out to provide a reference for the prevention of commutation failure. Keywords: HVDC transmission · Commutation failure · Inverter · AC background harmonics
1 Introduction Line commutated-converter based high voltage direct current (LCC-HVDC) transmission, with its advantages of being suitable for large-capacity and long-distance transmission, has been widely used in power grids [1]. Since LCC-HVDC uses semi-controlled thyristor devices, the problem of commutation failure is inevitable. The commutation failure can be simply summarized as: the converter valve that has just exited conduction has not recovered its ability to block the forward voltage, or the commutation cannot be completed when the reverse voltage is received, so the converter valve is re-conducted and the commutation occurs. The converter valve of the rectifier stays in the reverse voltage for a long time after it is turned off, enough to restore the forward voltage blocking ability, and the commutation failure will only occur when the trigger circuit fails. The inverter is at the forward voltage most of the time after it is turned off, so most of the commutation failures in HVDC power transmission projects occur on the inverter side [2]. Failure of commutation will cause the reduction of DC transmission power, the decrease of DC voltage, the increase of DC current, the shortening of converter valve life, and the overvoltage of weak AC system on the inverter side. With the development of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1406–1415, 2022. https://doi.org/10.1007/978-981-19-1528-4_142
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HVDC power transmission projects, the mutual coupling between AC and DC has gradually deepened. The contradiction between “strong DC and fragile AC” is increasingly prominent in the power grid, and the power of DC transmission is gradually increasing. Therefore, the impact of commutation failure on the safe and stable operation of AC and DC systems is increasing [3]. Commutation failure is one of the most common faults in HVDC transmission. When analyzing its causes theoretically, two aspects are generally considered: (1) the voltage amplitude of the commutation bus is reduced, and (2) the voltage zero-crossing drift. How to accurately detect and predict commutation failures, and to improve the resistance of HVDC transmission to commutation failures based on these characteristics is one of the important research projects in the development of my country’s power grids. It is necessary to continue in-depth research on commutation failures. Accurately grasping the mechanism of commutation failure and using its boundary conditions as the basis for judging whether commutation failure occurs is the prerequisite for predicting and preventing commutation failure. There are many influencing factors of commutation failure. For this reason, domestic and foreign scholars have conducted a lot of research on the mechanism of commutation failure and its influencing factors. Literature [4] studied the influence of voltage distortion on the commutation failure through harmonic analysis, and the study found that low-order harmonics have a greater influence on the commutation process of the DC system. Literature [5] found that the AC voltage transient disturbance caused by the AC system failure has a strong correlation with whether the HVDC project has a commutation failure. Literature [6] found that the mutual inductance between two-pole DC lines in long-distance UHVDC transmission projects will increase the risk of commutation failure in normal operation. This paper analyzes the phenomenon and principle of commutation failure in HVDC transmission system, and analyzes the influence of voltage amplitude reduction, zerocrossing displacement, and voltage waveform distortion on the commutation process. In view of the distortion of the commutation voltage waveform caused by the loworder background harmonics, the DC system is more prone to commutation failure, and the simulation verification is carried out to provide a reference for the prevention of commutation failure.
2 Commutation Process and Commutation Failure Mechanism Current HVDC projects all use 12-pulse converters. The 12-pulse converters are made up of two 6-pulse converters in series on the DC side and parallel on the AC side. This article uses the 6-pulse converter to explain the commutation process. 2.1 Commutation Process The structure of the 6-pulse converter is shown in Fig. 1. In the figure, ua , ub , and uc are three equal value power supplies, L C is the equivalent commutation inductance, and the short-circuit current between the two phases of the isc . uab , uac , ucb , uba , ubc , uca are commutation voltages. Assuming that V5 and V6 are turned on in the initial state, the timing starts at the zero-crossing point of ua and uc (uac = 0). After an angle α, a trigger
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pulse is sent to turn on V1. After V1 is turned on, because ua > uc , V5 is reversed. The conduction valve is V1 and V6, and the DC output voltage is uab ; at the zero-crossing point of ub and uc (ubc = 0), V2 is turned on through the α angle. Since ub > uc , V6 is subjected to reverse voltage for turn-off, the conduction valves are V1 and V2, and the DC output voltage is uac ; in this way, each valve conduction cycle is 120°, and the shut-off cycle is 240°. There are always two valves conduction at any time, three-phase The AC voltage becomes 6 cycles of DC voltage. id
Ud
V5
V2
V3
V6
V1
ia ib ic
LC LC LC
ua ub uc
V4
Fig. 1. Equivalent circuit of three-phase full-wave bridge converter
Adjusting the trigger angle α can control the magnitude of the DC voltage and change the operating performance of the converter. When the converter operates as an inverter, α > 90°. The turn-on and turn-off of the converter valve do not happen instantaneously. Because of the commutation reactance X B in the loop, the rise and fall of the current will take a period of time. The angle corresponding to this period is called the commutation angle β. is the leading trigger angle of the inverter, β = π − α. 2.2 Commutation Failure Mechanism If the inverter fails two consecutive commutation times or more, it is called continuous commutation failure, and vice versa is called one commutation failure. The problem of commutation failure discussed in this article is a commutation failure. The converter valve is composed of semi-control device thyristors. The thyristors complete the carrier recombination, and it takes a certain time to restore the voltage blocking ability. When the commutation process between the two converter valves is over, the valve that is scheduled to be shut off fails to recover its voltage blocking capability within the time when the reverse voltage is applied, or the commutation process fails to end during the reverse voltage period, resulting in the valve that was supposed to be opened reverses phase to the valve that is scheduled to be closed. This phenomenon is called commutation failure. The judgment basis of commutation failure is mainly divided into three types: (1) Cut-off angle. The reduction of inverter cut-off angle γ is generally considered to be the essential cause of commutation failure. The calculation formula of the cut-off angle is as follows. √ (1) γ = arccos 2XB Id UL + cos β
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In the formula, I d is the direct current; U L is the effective value of the inverter busbar voltage. At present, the deionization recovery time of thyristors used in HVDC transmission is generally about 400 µs, and the corresponding electrical angle is about 7.2°. Considering factors such as series element errors, the minimum shut-off angle is set at about 12° in engineering. When the switch-off angle of the inverter station is less than this value, it is considered that a commutation failure has occurred, and min is usually set to 7°–10° [7]. (2) Commutation bus voltage. The voltage drop of the commutation bus at the receiving end is one of the direct causes of the drop in the switch-off angle of the inverter station. The commutation bus voltage amplitude of the inverter station is detected to determine whether the commutation failure occurs. At present, it is generally considered in engineering that the commutation failure occurs when the commutation voltage of the inverter station drops to 0.9 p.u. [8]. However, related simulation experiments show that the critical voltage amplitude drop level of DC commutation failure is different when the fault occurs at different times, locations, or types. Especially when an asymmetric fault occurs in the AC system, the voltage of the commutation bus is no longer symmetrical, and the effect of voltage phase shift on the commutation failure cannot be ignored, and the judgment accuracy of the commutation failure will be further reduced. (3) Direct current. When a fault causes a commutation failure in the DC system, the short circuit of the upper and lower arms of the inverter will cause a sudden increase in DC current. By detecting the change law of the DC current I d at the outlet of the rectifier, it can be diagnosed whether the converter has failed commutation. The wavelet transforms and energy statistics of the fault signal show that when the commutation failure of the DC line occurs, the I d has a higher component in the low frequency band (including the fundamental frequency and the double frequency) [9], through the analysis of the I d , the HVDC system can be distinguished Whether it is in normal operation or a commutation failure fault or DC line short-circuit fault occurs. In this paper, the turn-off angle is used as the basis for judging phase commutation failure. Due to the inherent characteristics of the simulation software, in many cases there is no commutation failure at the turn-off angle between 8° and 10°, which can be known from the valve voltage and valve current waveforms. Combined with the characteristics of the software itself, this article sets the critical cut-off angle to 7.2°.
3 Inducing Factors for Commutation Failure The reasons for the commutation failure mainly come from two aspects of the DC system and the AC system. The internal reasons of the DC system include: (1) Loss of trigger pulse or failure of the gate control circuit, which makes the commutation process impossible; (2) The valve is triggered by mistake, causing the converter valve to open by mistake; (3) The inverter has a valve short circuit. Short circuit on the DC side, bidirectional conduction, commutation failure will occur periodically. The reason for the AC system is mainly due to the AC system failure, which causes the commutation
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bus voltage to change and the commutation failure occurs. Traditionally, two reasons are considered when analyzing commutation failure: (1) When a symmetrical fault occurs, the time of commutation failure is mainly determined by the reduction of the commutation bus voltage amplitude; (2) When an asymmetrical fault occurs, the impact of the reduction of the amplitude. In addition, the zero-crossing point of the commutation voltage must be moved forward. But in the simulation, the voltage zero-crossing drift has no decisive effect on the commutation failure, and the commutation failure under many faults still depends on the degree of reduction of the commutation voltage. In fact, there is a more complicated reason that causes commutation failure, that is, voltage waveform distortion caused by asymmetric faults and harmonics. The voltage waveform distortion may be accompanied by zero-crossing drift, and there is no relevant rule to follow when the distortion occurs, so it is difficult to analyze it from a quantitative perspective [10]. 3.1 Commutation Voltage Amplitude Reduction The commutation process can be represented by voltage-time area, as shown in Fig. 2. In normal operation, the inverter side has α + μ + γ = π, and the commutation area is S1. In the event of a fault, the voltage amplitude of the commutation bus is reduced, and the commutation area is S1 . When S1 = S1 and the firing angle remains the same, the commutation time will be extended. As shown in Fig. 2, the commutation angle μ increases to μ, and the turn-off angle γ decreases to γ . UL
UL
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0
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μ
γ
μ′
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Fig. 2. Commutation failure caused by voltage amplitude drops
ωt
0
α
γ
μ μ′
ωt
γ′
Fig. 3. Commutation failure caused by forward movement of voltage zero-crossing point
3.2 Forward Movement of the Zero-Crossing Point of the Commutation Voltage When an asymmetric fault occurs, the voltage amplitude is reduced, and the zero-crossing point of the commutation voltage is advanced. Due to the constant commutation area, the converter cut-off angle g is also reduced and commutation failure occurs, as shown in Fig. 3. Currently, the calculation formula of the cut-off angle becomes √ (2) γ = arccos 2XB Id UL + cos β − ϕ Compared with formula (1), formula (2) has one more displacement angle ϕ, which reduces the converter cut-off angle and increases the possibility of commutation failure.
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3.3 Voltage Waveform Distortion When the AC system fails, the voltage waveform of the commutation bus will be distorted to a certain degree. This distortion includes both the voltage amplitude reduction and the voltage zero-crossing drift. Literature [10] found from many simulations that many faults move back after the zero-crossing point when the voltage waveform is distorted and the amplitude is reduced. This can theoretically reduce the possibility of commutation failure, but in fact, it can prevent commutation. The occurrence of failure did not contribute much. Because the voltage waveform distortion is irregular, formula (1) and formula (2) are no longer applicable, and it is necessary to judge from the perspective of harmonics. The following will simulate and verify the commutation failure process caused by the 3rd background harmonics.
4 Simulation Verification of Commutation Failure Caused by Harmonics This paper is based on the CIGRE HVDC benchmark model, and the detailed parameters of the model can be found in literature [11]. In normal operation, the rectifier side of the CIGRE HVDC system is controlled by constant current, and the inverter side is mainly controlled by a constant cut-off angle. At the same time, the system includes a voltage dependent current order limit (VDCOL) link. All controls in the CIGRE model use PI controllers. In normal operation, the AC side system contains 6n ± 1 harmonic components, and n is any positive integer. The 12-pulse converter bridge of the CIGRE model consists of two 6-pulse bridges, each with a Y-Y converter transformer and a Y- converter transformer. Because the odd harmonics of n cancel, the 12n ± 1 harmonics will be injected into the AC system. The amplitude of each harmonic current is inversely proportional to the harmonic order. At the same time, the reactive power compensation device of the CIGRE model contains two sets of high-pass filters, which can effectively suppress high-order harmonic components (above 11th). One of the characteristics of a weak grid is severe background harmonics, which can cause significant distortion of the grid current. In this paper, the third harmonic voltage source is added to the Thevenin equivalent power supply on the inverter side, and the influence of the waveform distortion caused by the background harmonics on the commutation failure is analyzed. 4.1 Normal Operation In normal operation, the Thevenin equivalent voltage source amplitude on the inverter side is 215.05 kV, the frequency is 50 Hz, and the converter valve current waveform is shown in Fig. 4.
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Fig. 4. Current of converter valves during normal operation
4.2 Failure Analysis A three-phase short-circuit fault lasting 0.1 s is set on the inverter side of the CIGRE model, the ground reactance is 424, and the fault is removed for 3.1 s. The system response in the event of a fault is shown in Fig. 5. It can be seen from Fig. 5 that during
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the fault period (2–2.1 s), although the DC voltage has decreased and the DC current has increased, it does not cause a commutation failure, which is a slight fault. 4.3 AC System Background Harmonics When the model is running for 1.5 s, the third harmonic is injected, and the peak value of the harmonic voltage injected into the DC system is about 2.64 kV. The three-phase grounding fault described in Sect. 3.2 is still put into 3 s, and the system response is shown in Fig. 6. It can be seen from the figure that a commutation failure occurred in the DC system after the addition of the third harmonic, the converter valve current waveform was significantly distorted, and the system DC current rose rapidly, up to 2.35 p.u. 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
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When the background harmonics were not increased, the failure did not cause the commutation failure. After the background harmonics were increased, the commutation
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failure occurred after the failure. Since the three-phase grounding fault is a symmetrical fault, it only has the effect of reducing the amplitude of the commutation voltage. Therefore, the waveform distortion is caused by the background harmonics. It can be concluded that under the influence of larger background harmonics, the DC system will be more prone to commutation failure.
5 Conclusion This paper analyzes the phenomenon and principle of the commutation failure of the HVDC transmission system, and analyzes the influence of the reduction of the commutation bus voltage amplitude, the advance of the zero-crossing point and the distortion of the voltage waveform on the commutation process. According to the background harmonics of the AC system, the commutation voltage waveform is distorted, and the DC system is more prone to commutation failure. It is concluded that when the background harmonics are large, the commutation voltage waveform is distorted and the DC system is Commutation failure will also occur under slight disturbances, so it is necessary to suppress the background harmonics.
References 1. Miesaeidi, S., Dong, X., Tzelepis, D., et al.: A predictive control strategy for mitigation of commutation failure in LCC-based HVDC systems. IEEE Trans. Power Electron. 34(1), 160–172 (2019) 2. Li, T., Zhao, T., Zou, L., et al.: The mechanism and solution of the anomalous commutation failure of multi-infeed HVDC transmission systems. Int. J. Electr. Power Energy Syst. 11(4), 146–152 (2020) 3. Shao, Y., Tang, Y.: Fast evaluation of commutation failure risk in multi-infeed HVDC systems. IEEE Trans. Power Syst. 33(1), 646–653 (2018) 4. Wang, F., Liu, T., Zhou, S., et al.: Mechanism and quantitative analysis method for HVDC commutation failure resulting from harmonics. J. Proc. CSEE 35(19), 4888–4894 (2015). (in Chinese) 5. Zhao, D., Han, Z., Huang, H., et al.: Commutation failure suppression strategy for HVDC system based on AC bus voltage disturbance. J. Proc. CSEE 40(19), 6173–6182 (2020). (in Chinese) 6. She, Z., Liu, K., Zhang, Q., et al.: Commutation failure mitigation based on detecting the direct voltage difference caused by mutual inductance of transmission lines in ±1100 kV HVDC systems. J. High Volt. Eng. 46(8), 2780–2790 (2020). (in Chinese) 7. Shao, Y., Tang, Y.: A commutation failure detection method for HVDC systems based on multi-infeed interaction factors. J. Proc. CSEE 32(4), 108–114 (2012). (in Chinese) 8. Chen, J., Wang, Q., et al.: Risk assessment of commutation failure for HVDC transmission due to harmonic voltage. J. High Volt. Appar. 56(5), 196–202 (2020). (in Chinese) 9. Lin, L., Zhang, Y., Zhong, Q., et al.: Fault diagnosis of commutation failures in the HVDC system based on a method of wavelet energy statistics. J. Autom. Electr. Power Syst. 31(23), 61–64 (2007). (in Chinese)
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10. Zhao, T., Lü, M., Lou, J., et al.: Analysis on potential anomalous commutation failure in multi-infeed HVDC transmission systems. J. Power Syst. Technol. 39(3), 705–711 (2015). (in Chinese) 11. Szechtman, M., Wess, T., Thio, C.V.: A benchmark model for HVDC system studies. In: International Conference on AC and DC Power Transmission, pp. 374–378 (1991). Author, F.: Article title. J. 2(5), 99–110 (2016)
A Novel AC/DC Residual Current Sensor for Power Electronic-Enabled Devices Yao Wang1,2(B)
, Tongtong Ma1,2 , Chenguang Hao1,2 and Yi Wu1,2
, Zhizhou Bao3
,
1 Electrical Engineering Department, Hebei University of Technology, Tianjin 300132, China
{wangyao,wuyi}@hebut.edu.cn
2 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei
University of Technology, Tianjin 300132, China 3 People Electrical Appliance Group, Zhejiang 325604, China
Abstract. Residual current protected devices (RCDs) are used worldwide to protect against electric shock and electrical fire hazards. With the application of power electronic-enabled devices, RCDs suffer from a higher risk of nuisance tripping. This paper proposes a novel AC/DC residual current (RC) sensor that combines a magnetic modulation sensor and an electromagnetic sensor. The structure and operation methodology of the proposed RC sensor is illustrated in detail. In order to balance the influence of high-frequency and high-peak AC noises on the magnetic modulated sensor, an active compensation method is proposed. Moreover, the outputs of the two types of sensors are superposed to achieve an extended dynamic range in both frequency bandwidth and measurement amplitude. In addition, the working principle of the proposed RC sensor is analyzed, and a corresponding simulation model is built to investigate the relationship between the measured current and output of the RC sensor. A prototype with active compensation is designed as well. Finally, experimental tests are conducted to verify the effectiveness and superiority of the proposed RC sensor. The results show that the frequency bandwidth of the proposed RC sensor can reach 40 kHz, and the maximum measurement range can be up to 1 A, which is consistent with the theoretical analysis and simulation results. Keywords: Current sensor · Magnetic modulated · Active compensation · Residual current measurement
1 Introduction Residual current protective devices (RCDs) are used in power distribution systems worldwide to protect against electric shock and electrical fire hazards. With the in-creasing requirement on environmental protection and low energy-consuming, more and more power electronic-enabled converters and inverters are used in power systems [1]. Due to the pulse width modulated (PWM) control strategy, these devices exhibit high performance and energy-efficient. It is expected that the application of power electronic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Yang et al. (Eds.): The proceedings of the 16th Annual Conference of China Electrotechnical Society, LNEE 889, pp. 1416–1429, 2022. https://doi.org/10.1007/978-981-19-1528-4_143
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devices will increase further with the demand for carbon neutrality and CO2 emissions peak. However, the residual current (RC) caused by such devices is no longer sinusoidal, which will include DC and AC components. Moreover, they will introduce serious common-mode noises to the residual current [2], resulting in nuisance tripping to RCDs. Since the switching frequencies of power electronic devices cover several to tens of kilohertz, the common-mode current is typically high-frequency components [3]. Because the traditional electromagnetic current sensor can only sense AC current, it cannot fulfill the measurement requirements anymore. Therefore, high-performance AC/DC residual current sensor becomes a popular re-search topic in recent years. Among the available AC/DC current measuring methods, Hall-effect current sensor is not suitable for weak current signal detection, and the magnetoresistive current sensor is easier to be affected by an external magnetic field [4, 5]. Fluxgate current sensors are one of the best ways to measure both DC and AC currents. An AC/DC earth leakage current sensor based on the fluxgate principle is proposed [6], which owns a wide measurement range because of the particular detection principle, including pulse width and pulse frequency detection. Another 16 kHz bandwidth fluxgate excitation system using a sinewave excitation source is presented in [7], which can effectively eliminate the high order harmonics caused by the square-waves excitation source. Current sensors based on magnetic modulation technology are proposed in [8] for nonlinear leakage current detection. A fluxgate current sensor with an amphitheater busbar is introduced in [9], which achieves a more excellent detection range than magnetoresistive sensors and better stability than Hall sensors. A compact and straightforward leakage current sensor based on magnetic modulation technology is proposed in [10] to measure current ranging from dc to several kilo-Hertz. Moreover, a fault leakage current detection method has been proposed to eliminate the dead-zone of the residual current protection [11]. However, the traditional fluxgate current sensor is not suitable for measuring highfrequency AC because of its inherent upper limit of the carrier frequency. In this paper, a novel AC/DC residual current sensor, which combines a magnetic modulated current sensor with the traditional electromagnetic current sensor, is proposed to realize accurate measurement for both DC and high-frequency AC. Furthermore, an AC feedback and compensation circuit is designed to balance the bias of high-amplitude AC noises in the magnetic modulated sensor. Finally, an accurate AC/DC residual current measurement is implemented by superimposing the corresponding output of two sensors.
2 The Operation Principle of the Proposed AC/DC Residual Current Sensor 2.1 The Topology of the Proposed AC/DC Residual Current Sensor It is challenging to measure the residual current over a wide frequency bandwidth from DC to tens of kilo-Hertz. The commonly used current measuring methods, such as the Hall effect-based current sensor, cannot function well. In order to fulfill the detection requirements of wide-bandwidth residual current, an AC/DC residual current measurement topology is proposed, which has a hybrid structure of two different types of current sensors. One is a magnetic modulated current sensor used to measure DC and lowfrequency AC current, while the other is an electromagnetic current sensor responsible
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for sensing high-frequency AC current. In addition, the output of the electromagnetic sensor is fed to the modulated current sensor to balance the high-frequency AC noises emitted from the power electronic systems. Finally, the outputs of the two sensors can be superposed together to obtain a signal proportional to the measured current. The detailed sensor structure is shown in Fig. 1, where ip1 represents the AC/DC measured current; I c is the compensation current; U L is the voltage across compensation winding and U Rc is the voltage across Rc ; U PA is the maximum output voltage of the operational amplifier. Magnetic Modulated sensor
Excitation circuit AC/DC measured current Ic
Where Ic=URc/Rc Comply with UL+URc