Advances in Smart Grid Automation and Industry 4.0: Select Proceedings of ICETSGAI4.0 (Lecture Notes in Electrical Engineering, 693) [1st ed. 2021] 9811576742, 9789811576744

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Lecture Notes in Electrical Engineering 693

M. Jaya Bharata Reddy Dusmanta Kr. Mohanta Deepak Kumar Debomita Ghosh   Editors

Advances in Smart Grid Automation and Industry 4.0 Select Proceedings of ICETSGAI4.0

Lecture Notes in Electrical Engineering Volume 693

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 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 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 Junjie James Zhang, Charlotte, NC, USA

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M. Jaya Bharata Reddy Dusmanta Kr. Mohanta Deepak Kumar Debomita Ghosh •

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Editors

Advances in Smart Grid Automation and Industry 4.0 Select Proceedings of ICETSGAI4.0

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Editors M. Jaya Bharata Reddy National Institute of Technology Tiruchirappalli Tiruchirappalli, Tamil Nadu, India Deepak Kumar Department of Electrical and Electronics Engineering Birla Institute of Technology, Mesra Ranchi, Jharkhand, India

Dusmanta Kr. Mohanta Department of Electrical and Electronics Engineering Birla Institute of Technology, Mesra Ranchi, Jharkhand, India Debomita Ghosh Department of Electrical and Electronics Engineering Birla Institute of Technology, Mesra Ranchi, Jharkhand, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-7674-4 ISBN 978-981-15-7675-1 (eBook) https://doi.org/10.1007/978-981-15-7675-1 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved 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

Preface

The conventional power system is going through a paradigm change toward smart grid, incorporating technological innovations for sensing, communicating, applying intelligence and exercising control through feedback. This enhancement in technology in the field of power is a very crucial part to the industries, and it can also be inferred that both are complementary to each other. No doubt, this booming knowledge in the field of Internet of Things, big data, robotics automation, artificial intelligence is becoming prerequisite for the Fourth Industrial Revolution. Furthermore, wide-area measurement in the power sector is adequately providing a way to the industry for achieving the credentials of Industry 4.0. All these technologies contributing to the manufacturing industry are called “Industry 4.0.” Due to the necessity of data exchange, acquisition and cost minimization, three cornerstones are “digitization,” “sensitization” and “optimization” which act as a foundation to support optimized technologies which are not only cost-effective but also reliable and resilient and ensures quality and safety. The title of the book Advances in Smart Grid Automation and Industry 4.0 is well justified with the content incorporating a total of 81 research papers. Broadly, the book is segregated into five parts, viz. “Data Analytic for Smart Grid Operation and Control,” “Integrated Power Generation Technologies,” “Green Technologies,” “Recent Trends in Smart Grid” and “Microgrid Operation and Planning” based on relevance of papers to particular tracks. This book can be recommended for researchers, faculty members and engineers to strengthen their knowledge in the area of smart grid and industry automation. Along with strengthening the subject knowledge, the readers can also take it as a reference for further research or existing practices. The first part entitled “Data Analytic for Smart Grid Operation and Control” constitutes eight research papers which address smart grid control through data analytics and computational methods, machine learning application in power systems, cybersecurity threat, challenges and opportunities and IoT-based monitoring and control of microgrids. Various optimization techniques and control strategies are presented by the authors for the operation and control of the modern grid.

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The second part entitled “Integrated Power Generation Technologies/Advanced Technology Developments” constitutes seventeen research papers which address the control and automation of nonlinear system, computational intelligence and optimization for smart energy systems, software tools, artificial intelligence control and their application in industrial automation and smart utilization of non-conventional energy sources. The third part entitled “Green Technologies: Driving the World to the Era of Renewable Sources of Energy” constitutes nineteen research papers which address the possibilities and challenges of more and more integration of renewable energy, i.e., wind, solar, fuel cells and distributed generation within microgrids, energy conversion systems, energy efficiency, energy storage technologies and systems, self-aware and self-configuring and self-healing power/energy systems, and focus on automation in the form of plug-in hybrid electric vehicle (PHEV) systems and clean transportation. The fourth part entitled “Recent Trends in Smart Grid Technology Application” constitutes twenty-three research papers which majorly deal with the advancement in the application of smart grid technology through grid modeling, simulation and data management, utilizing recent trends of soft computing techniques, applications of power electronic converters and drives, transient stability enhancement using FACTS, analyzing resilient/adaptive grid infrastructures design, planning, operation and management, grid protection, reliability, energy/power quality and maintenance. The fifth part entitled “Recent Trends in Microgrid Operation and Planning” constitutes eighteen research papers which majorly deal with microgrid planning, operation and control, smart energy management system in distribution side, demand response, smart buildings and home automation, smart metering, measurement, instrumentation and control, virtual power plant and virtual stability of microgrid. This book will certainly help in building a platform for future research enhancement. Faculties can utilize it as a reference for upgrading the current syllabus. Researchers can get state-of-the-art research topics/methodologies, which can be very vital toward their research. The editorial team is thankful to the authors as well as the reviewers for their valuable contributions for ensuring the quality of the papers. Tiruchirappalli, India Ranchi, India Ranchi, India Ranchi, India

M. Jaya Bharata Reddy Dusmanta Kr. Mohanta Deepak Kumar Debomita Ghosh Editors

Contents

Data Analytic for Smart Grid Operation and Control Reactive Power Control of Stand-Alone Hybrid Power System Using Genetic Algorithm and Particle Swarm Optimization . . . . . . . . . . . . . . Abhijeet Vijay Kotwal, Divya Rishi Shrivastava, and Priyank Singh Hada Synergistic Damping Operation of TCSC and CPSS Using PSO in a Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jayant Sharma, Shriram S. Rangarajan, C. K. Sundarabalan, D. Karthikaikannan, N. S. Srinath, D. P. Kothari, and Tomonobu Senjyu

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Enhancing Oscillation Damping in a Power Network Using EWOA Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ramesh Devarapalli, Biplab Bhattacharyya, and Archana Kumari

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Fault Detection of Smart Grid Equipment Using Machine Learning and Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kumari Sarita, Sachin Kumar, and R. K. Saket

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Analytical Modeling and Optimal Control of Cold Storage System with Large-Scale Implementation Using IoT . . . . . . . . . . . . . . . . . . . . . Bhargav Appasani, Amitkumar V. Jha, Abu Nasar Ghazali, and Deepak K. Gupta Challenges and Learnings During Integration of Phasor Measurement Unit Data with Supervisory Control and Data Acquisition System—An Indian Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pramod Singh, Santosh Kumar Jain, Devender Kumar, and P. K. Agarwal Cyber Intrusion Detection in Smart Grid Using KPCA and Novel Trust Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neeraj Kumar Singh, Praveen Gupta, Vasundhara Mahajan, Saurabh Pranjale, Tharun Balaji, and Syed Aamir Ahmed

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Analyzing Impacts of Energy Dissipation on Scalable IoT Architectures for Smart Grid Applications . . . . . . . . . . . . . . . . . . . . . . . Sakshi Gupta and Itu Snigdh

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Integrated Power Generation Technologies/Advanced Technology Developments A Novel CNFET-Based CCCDTA and Its Application as a Schmitt Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jyoti Sharma, Ritambhara, and Avireni Srinivasulu

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A New Current Mode Multiplier Using Single CCCII Without Passive Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Syed Zahiruddin, Avireni Srinivasulu, and Musala Sarada Optimal Capacitor Placement in Radial Distribution System Using Chicken Swarm Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 113 Saubhagya Ranjan Biswal and Gauri Shankar Optimal Controller Design for Automatic Generation Control Under Renewable Energy Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Mahmoud A. Attia, Mohamed Mokhtar, Almoataz Y. Abdelaziz, Suchetan Sasis, Sachin Kumar, and R. K. Saket Trapezoidal Triangular Carrier-Based PWM Scheme for Performance Enhanced in Multilevel Inverter . . . . . . . . . . . . . . . . . 133 Kishor Thakre, Kanungo Barada Mohanty, S. Shiva Kumar, and Somesh Krishna Thanvi Fractional Order Particle Swarm Optimization to Optimize a Fractional Order PID Controller for a Vertical Take-Off and Landing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 D. S. Acharya, S. K. Mishra, B. Sarkar, and D. Bharti Design of 2-Degree of Freedom Fractional Order PID Controller for Magnetic Levitation Plant with Time Delay . . . . . . . . . . . . . . . . . . . 157 D. S. Acharya, S. K. Mishra, Shudit Kumar, and Shubham Kumar Design of Salp Swarm Algorithm Tuned Cascade Proportional–Integral–Tilt-Derivative Controller for the Performance Study of Load Frequency Control . . . . . . . . . . . . . 167 Sandhya Kumari and Gauri Shankar A Multi-objective Integral Sliding Mode Controller for Magnetic Levitation Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 D. S. Acharya and Srihari Gude A Review Article on PWM Inverter and Z-Source Inverter for Induction Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Hempushpa Bharti and B. M. Prasad

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Artificial Neural Network-Based Prediction of Cutting Parameters from Tool Vibration and Forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Ejaz Ahamed, Prasanta Kumar Samal, B. S. Suresh, and H. R. Kemparaju Dynamic State Estimation of Synchronous Generator Rotor Angle Using PMU and EKF Under Stressed Conditions . . . . . . . . . . . . . . . . . 213 Rishabh Devagupta, K. T. Sai Akhil, N. Sai Suprabhath, P. Kritesh, B. Mallikarjuna, M. Jaya Bharata Reddy, D. K. Mohanta, and Diptendu Sinha Roy Review of the Parameter Estimation and Transient Analysis of Three-Phase Induction Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Trisha, G. S. Gupta, and S. Shiva Kumar Transient Analysis of a Standard IEEE-9 Bus Power System Using Power World Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 P. V. Rajesh Varma, Manoj Kumar Kar, and A. K. Singh DSA-Based Analysis for Capacitor Requirement of a Three-Phase Standalone Asynchronous Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Himadri Sekhar Chatterjee, Dipanjan Samajpati, Sambaran Ray, and Sankar Narayan Mahato Point-To-Point Low-Frequency AC Transmission of Offshore Wind Power Using Voltage Source-Based AC/AC Converter . . . . . . . . . . . . . . 253 Ajit Kumar Panda, Subhranshu Sekhar Pati, and Aurobindo Behera Green Technologies: Driving the World to the Era of Renewable Sources of Energy Robust Adaptive Observer Based Fault Tolerant Controller Design for Proton Exchange Membrane Fuel Cell Systems . . . . . . . . . . . . . . . . 263 Vikash Sinha and Sharifuddin Mondal Exergy and Energy Analysis of Hybrid Solar PV/T Collector . . . . . . . . 271 Om Prakash and Ankish Aman Experimental Analysis of Ignition Delay in Dual Fuel Diesel Engine with Secondary Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Chandra Bhushan Kumar, D. B. Lata, and Dhaneshwar Mahto Modelling of a Greenhouse Drying System Using COMSOL Multi-physics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Asim Ahmad and Om Prakash Comparison of 3-Leg, 4-Leg, and 6-Leg Topologies of Renewable Energy Fed Impedance Source Inverter-Based Dynamic Voltage Restorer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Miska Prasad

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Impact of Seasonal Variations on Generation Cost and Social Benefit of ISO in Presence of Renewable Energy Sources . . . . . . . . . . . . . . . . . 303 Ritu Jain, Praveen Kumar Gupta, and Vasundhara Mahajan Kinematic Control of an Autonomous Ground Vehicle Using Inverse Dynamics Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Praneeth Kumar Pedapati, Santanu Kumar Pradhan, and Surendra Kumar Enabling the Technologies for Incorporating Battery Energy Storage System with Standalone Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Sumit Kumar Jha and Deepak Kumar Wind–Diesel-Based Isolated Hybrid Power Systems with Cascaded PID Controller for Load Frequency Control . . . . . . . . . . . . . . . . . . . . . 335 Tarkeshwar Mahto, P. R. Thakura, and T. Ghose Automatic Generation Control Study with Plug-In Electric Vehicle Integrated Multi-area Renewable Power System . . . . . . . . . . . . . . . . . . 345 Aurobindo Behera, Subhranshu Sekhar Pati, Tapas Ku Panigrahi, and Subhankar Ghatak An Interleaved ZCS Supplied Switched Power Converter for Fuel Cell-Based Electric Vehicle Propulsion System . . . . . . . . . . . . . . . . . . . . 355 Neeraj Priyadarshi, Farooque Azam, Amarjeet Kumar Sharma, Pradeep Chhawchharia, and P. R. Thakura Feasibility Study and Analysis of Wind Power Generation Toward Achieving Renewable Powered Island . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Anik Goswami, Utpal Goswami, and Pradip Kumar Sadhu An Investigation on Effect of Parasitic Resistances on Grid Connected PV System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Malay Bhunia and Bidyadhar Subudhi Closed-Loop V-f Control Strategy for PV-Battery Energy Storage System in Stand-Alone Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 Sourav Chakraborty and Deepak Kumar A Review on Intelligent PID Controllers in Autonomous Vehicle . . . . . . 391 Vagisha Vartika, Swati Singh, Subhranil Das, Sudhansu Kumar Mishra, and Sitanshu Sekhar Sahu Analysis of Solar Fed Grid-Connected Single-Stage Single-Phase Boost Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Prabhat Kumar Ranjan, Piyush Kumar Ojha, P. R. Thakura, and Aditya Kumar Singh

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Two-Port Network-Based Modeling and Analysis of Three-Phase Self-excited Induction Generator Used in Renewable Energy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Sambaran Ray, Himadri Sekhar Chatterjee, Dipanjan Samajpati, Sankar Narayan Mahato, and Nirmal Kumar Roy Generalized Review on MPPT Methods Under Variant PSC . . . . . . . . . 419 Niteesha Kumari, S. Shiva Kumar, and V. Laxmi Recent Trends in Smart Grid Technology Application Novel Utilization of Phasor Measurement Units (PMU) in Smart Grid Restoration: A Brief Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Shriram S. Rangarajan, Jayant Sharma, D. P. Kothari, and Tomonobu Senjyu Solving an Optimal Control Problem of Fractional-Order Continuous-Time Singular System with Fixed Final Time by an Approximate Numerical Method . . . . . . . . . . . . . . . . . . . . . . . . . 443 Tirumalasetty Chiranjeevi and Raj Kumar Biswas Impact of Reconfiguration and Network Topology on Voltage Stability Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 V. V. S. N. Murty Vallem and Ashwani Kumar Power Quality Improvement Through Modulation Techniques . . . . . . . 461 Sonali Raj and Ramesh Kumar Economic Integration of Renewable Energy Sources for Optimal Operation of Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Abhinaw Kumar Rai, Sandip Chanda, and Abhinandan De Improved Moth Flame Optimization in Systematization of STATCOM and PSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Ramesh Devarapalli, Biplab Bhattacharyya, Vikash Kumar, and Sourav Kumar A Grid-Connected Solar Photovoltaic Hybrid System for Reliable Power and Water Supply in Modern Irrigation Application . . . . . . . . . 493 Sheshadri Shekhar Rauth and Banshidhari Samanta Transient Stability Enhancement Using FACTS Devices in a Distribution System Involving Distributed Generation Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Jayant Sharma, Shriram S. Rangarajan, V. S. S. Srikanth, C. K. Sundarabalan, D. P. Kothari, and Tomonobu Senjyu Transmission Congestion Management with FACTS Devices Using SOS Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Khushboo Verma, S. K. Gupta, S. Kumar, and Gaurav Singh

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A Schmitt Trigger by Means of a Voltage Differencing Transconductance Amplifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Mathur Anushree, Kumawat Mrinalini, Sharma Jyoti, and Avireni Srinivasulu Impact of Solar and Wind Energy on Reliability of Power System Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535 Soumya Mudgal, Atul Kumar Yadav, Praveen Gupta, and Vasundhara Mahajan Simulation and Analysis of Single-Phase Cascaded H-Bridge Multilevel Inverter for Solar PV Application . . . . . . . . . . . . . . . . . . . . . 543 Alok Kumar Acharya, Kantipudi V. V. S. R. Chowdary, Pradeep Kumar Sahu, and Kundan Kumar Petri Net Modeling for Reliability Assessment of Integrity Protection Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Nilesh Kumar Rajalwal and Debomita Ghosh EHV/UHV Transmission Line Protection Using Traveling Waves . . . . . 565 K. Jnaneswar, S. Devaraj, B. Mallikarjuna, M. Jaya Bharata Reddy, and D. K. Mohanata Microgrid Situational Awareness Using Micro-PMU . . . . . . . . . . . . . . . 573 Kunjabihari Swain, S. S. Mahato, S. K. Mandal, and Murthy Cherukuri Fault Classification Comparison in Power System Using Nearest Neighbor and Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581 Dhruba Kumar Closed-Loop V/f Control of Symmetrical 6-Phase Induction Motor Using Cascaded H-Bridge Multilevel Inverter . . . . . . . . . . . . . . . . . . . . 591 Spandan Dhamudia, Vishal Rathore, and K. B. Yadav Real Time Simulation of 3-u Grid-Connected Converter with Real and Reactive Power Control Under Different Grid Fault Conditions . . . 601 Gaurav Gautam and Sudipto Poddar Operational Resiliency Enhancement Using Synchrophasor Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Anant Milan Khalkho and Dusmanta Kumar Mohanta A Multi HIL-Based Approach for Real-Time Phasor Data Monitoring Using Phasor Measurement Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 Rohit Kumar, Sourav Kumar Sahu, Debomita Ghosh, and Sarbani Chakraborty Fault Detection Using Harmonic Analysis of Single Terminal DC Current Signal of HVDC Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631 Shobha Agarwal, Rakesh Kumar Singh, and Vimlesh Verma

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Resilience Trapezoid-Based Operational Reliability of Distribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Sonal, Debomita Ghosh, and Dusmanta Kumar Mohanta Spectral Kurtosis-Based Fault Detection for a Highly Penetrated Distributed Generation: A Real-Time Analysis . . . . . . . . . . . . . . . . . . . 649 Sourav Kumar Sahu and Debomita Ghosh Recent Trends in Microgrid Operation and Planning Energy Simulation and Modelling of Corporate Building: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 Om Prakash, Ankish Aman, Saurabh Ranjan, and Namrata Dhamankar MGWOSCACSA: A Novel Hybrid Algorithm for Energy Management of Microgrid Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Bishwajit Dey, Sourav Basak, and Biplab Bhattacharyya Design and Analysis of a 1.1 kW BIPV-Based Residential Grid-Connected System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679 Debayan Sarkar, Anand Kumar, and Pradip Kumar Sadhu Support Vector Machine-Based Fault Detection, Classification and Section Identification Scheme for DC Microgrid . . . . . . . . . . . . . . . 691 Shankarshan Prasad Tiwari, Ebha Koley, and Subhojit Ghosh Spectral Kurtosis-Based Island Detection Technique . . . . . . . . . . . . . . . 699 Sikander Singh, Soham Dutta, Sourav Kumar Sahu, and Pradip Kumar Sadhu Frequency Control of Wind Power Plant Assisted Hybrid Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707 Akshay Anand, Akshay Kumar, and Gauri Shankar Adapting the Control Strategies of Microgrid to Propel the Future Research Towards Demand Side Management: A Review . . . . . . . . . . . 719 Sumit Kumar Jha and Deepak Kumar A New Approach Based on Indicators for Demand Side Management in Multiobjective Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 Mayank Singh and Rakesh Chandra Jha Techno-economic Benefits of Grid Penetrated 1 MW PV System in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 G. Swaminathan, Shriram S. Rangarajan, Jayant Sharma, D. P. Kothari, and Tomonobu Senjyu State-of-the-Art Review on Microgrid Control Strategies and Power Management with Distributed Energy Resources . . . . . . . . . . . . . . . . . . 749 Nada Ali and Deepak Kumar

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An Energy Storage Planning and Analysis of Microgrid: A College Campus Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757 Jeet Dhoriyani, Renison Macwan, and Chetan Upadhyay Effect of Modulation Index of Nonlinearly Decreasing Inertia Weight on the Performance of PSO Algorithm for Solving ELD Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 Nimish Kumar, Promit Kumar Saha, Nitai Pal, and Neha Kumari Sensitivity Factor Analysis of Hybrid AC–DC Distribution System and Overload Alleviation Using Modified Generator Shift Distribution Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777 Abhinandan Kumar and T. Ghose Study of Different Passive Islanding Detection Techniques and Verification by MATLAB Simulation . . . . . . . . . . . . . . . . . . . . . . . 787 Jayshree, Jai Prakash Sharma, and Om Hari Gupta Operation of Virtual Droop Resistance-Based Parallel Connected Boost Converters for Power Sharing and Minimization of Circulating Current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797 Shalini Sinha and T. Ghose Evaluation of the Performance of Droop Control Scheme for Interlinking Converter in Stand-Alone Hybrid Microgrid . . . . . . . . 807 Shivaditya Chatterjee, Aftab Alam, and T Ghose

About the Editors

Dr. M. Jaya Bharata Reddy received his B.Tech. Degree in Electrical and Electronics Engineering from Nagarjuna University, Guntur, Andhra Pradesh, India. He received his M.E. and Ph.D. degree from Birla Institute of Technology (BIT), Ranchi, India. He is currently working as a Professor in the Department of Electrical and Electronics Engineering, National Institute of Technology (NIT), Tiruchirappalli, India. He has a total experience of 16 years in teaching and research. In 2010, he received the prestigious national level IEI Young Engineer’s Award in the field of Electrical Engineering in recognition of his research. Subsequently in 2013, he received the SERC Fast Track Young Scientist Award from Department of Science and Technology (DST), Government of India. He holds two patents. He has published more than 100 journal and conference papers. His current research interests include smart grid, substation automation, wide-area protection, digital relaying, soft computing applications in power system and power system protection. He is a Senior Member of IEEE (USA), Member of IEEE Power and energy Society. He is an Editor of Power Components & Systems (Taylor & Francis); Associate Editor of IET High Voltage; Associate Editor of IEEE Access as well as of IET Generation, Transmission Distribution.

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About the Editors

Prof. Dusmanta Kr. Mohanta received his B. Sc. Engg. (Electrical) degree from the College of Engineering and Technology, Bhubaneswar, India. He received his M.E. degree from Birla Institute of Technology, Ranchi, India, and Ph. D. (Engg.) from Jadavpur University, Kolkata, India. He is an IEEE senior member. He has a total experience of 26 years in teaching and the industry. He was an electrical engineer in the captive power plant of the National Aluminum Company (NALCO), Angul, India. His current research interests include the power quality analysis, soft computing applications, power system planning, phasor measurement unit placement and reliability analysis. He is an editor of Electric Power Components and Systems journal, Associate Editor of IEEE Access; Subject Editor of IET Generation, Transmission Distribution; guest editor-in-chief, special issue in IET Generation, Transmission Distribution on emerging trends in system integrity protection scheme (SIPS) for improving the performance of smart grid; Editor, ‘Synchronized Phasor Measurements for Smart Grids’. Dr. Deepak Kumar received B.Eng. in Electrical and Electronics Engineering (EEE) and M.Eng. in Electrical Engineering (EE) from Birla Institute of Technology (BIT), Ranchi, India, in 2007 and 2011, respectively. He received his Ph.D. (Power Systems) from the School of Electrical Sciences (SES), Indian Institute of Technology (IIT), Bhubaneswar, Odisha, India in 2016. Currently, he is an Assistant Professor in the Department of Electrical and Electronics Engineering, Birla Institute of Technology (BIT), Ranchi, India, working since June 2015. He has more than 5 years of experience in teaching and research. In 2018, he visited Department of Electrical Engineering and Automation, Aalto University, Finland as a Postdoctoral Research Fellow. Dr. Deepak was the recipient of the prestigious national level POSOCO Power System Award under Doctoral category in recognition for his research contribution in power system field in 2016. He has published several research papers in a reputed quality journals such as IEEE Systems, IET Generation, Transmission Distribution, IJEPES (Elsevier) etc.

About the Editors

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His major research interests include microgrid control with distributed generation, distributed energy storage systems, demand side management, distribution system design and planning, and applications of intelligent computational techniques to power systems. Dr. Debomita Ghosh received her B.Tech. degree in Electrical Engineering in 2006 from WBUT, M.E degree in Electrical Engineering (Power Systems), and Ph.D. in Engineering from Birla Institute of Technology Mesra, Ranchi, India, in 2008 and 2014, respectively. Currently, she is Assistant Professor in the Electrical & Electronics Engineering Department at BIT, Mesra, Ranchi, India. She joined the Institute in July 2008 and has 11 years experience in teaching and research. She has published in several journals of repute such as IEEE Industrial Informatics, IEEE Systems Journal, IEEE Sensors journal, IJEPES, EPCS. She is Member of IEEE and member of ISTE. Her current research activities are concentrated on protection and reliability analysis of power system network, wide area measurement system, phasor measurement units, and smart grids.

Data Analytic for Smart Grid Operation and Control

Reactive Power Control of Stand-Alone Hybrid Power System Using Genetic Algorithm and Particle Swarm Optimization Abhijeet Vijay Kotwal, Divya Rishi Shrivastava, and Priyank Singh Hada

Abstract This paper presents automatic reactive power stabilization of a standalone wind–diesel hybrid power system. Reactive power flow equation is used to formulate a mathematical model of system. The voltage is controlled by evaluating small-signal analysis for a static VAR compensator (SVC) and an IEEE Type-1 excitation system. SVC is used to obtain a healthy voltage profile with minimum voltage oscillations to achieve the changing demand of reactive power required by the induction generator and the load. The amplifier gain of the Type-1 SVC is then optimized by employing optimization technique. A comparative study on usage of two optimization algorithms namely genetic algorithm and particle swarm algorithm is shown. Keywords Stand-alone wind–diesel hybrid power system (WDHPS) · Genetic algorithm (GA) · Particle swarm optimization (PSO) · Synchronous generator (SG) and static VAR compensator(SVC)

1 Introduction The present world population scenario is at its all-time highest peaking with 7.3 billion people, and a further estimated growth till 9.7 billion people in the next thirty years (till 2050) [1]. Our conventional sources of power (e.g., petrol, diesel, etc.) will not service much longer, and it is now vital for mankind to focus on non-exhaustible and renewable sources of energy. Traditionally setup power systems are constrained A. V. Kotwal (B) · D. R. Shrivastava Department of Electrical Engineering, Manipal University Jaipur, Jaipur 303007, India e-mail: [email protected] D. R. Shrivastava e-mail: [email protected] P. S. Hada Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur 303007, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_1

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A. V. Kotwal et al.

by various factors like cost of setting up additional transmission lines, environmental reasons, geographical reasons, etc., which leads to a rapidly growing gap between the load demand and power supply, further accelerated by the expanding population [2]. However, this difference between the supply and demand can be reduced by hybrid power systems. Such systems employ a conventional power source (typically diesel), along with one or more non-conventional renewable sources of energy. Renewable Energy Systems are clean, non-exhaustible, and sustainable. Diesel power generators are typically used for remote locations and are able to supply power independent of the grid, and these systems when integrated with a renewable power source become capable of working independently. Such systems are called stand-alone or isolated systems. For grid-dependent hybrid power systems (HPS), the grid supplies the VAR requirement, whereas a stand-alone HPS usually requires additional reactive power support. This is accomplished by the introduction of FACTS devices like STATCOM and an SVC [3]. The system studied in this paper is a stand-alone WDHPS. The FACTS device employed in this paper is a (SVC). Without a large reactive power control device, the hybrid system could be subject to large voltage fluctuations that are undesirable. By regulating the reactive power in such a power system, certain parameters like power factor, voltage stability, system efficiency, energy cost, etc., can be controlled [4]. In general, hybrid energy systems can comprise of one or more generators, typically for such cases (though not necessary) a synchronous generator is paired with diesel engine and an IG is paired with the wind energy conversion system. [5] Induction generators provide many advantages over synchronous generators, since these generators are more rugged, are easier to maintain, do not need a separate DC source, brushless, cheaper, etc. Though these have one major disadvantage, they need reactive power for operation. Induction motor is preferred due to the intermittent nature of wind.

2 SVC An SVC provides a continuous range of control by employing a combination of inductors and capacitors which provide fast-switching capability, lesser than a 1/60TH of a second, thus allowing a continuous range of control. This is then used to absorb and generate reactive power, thus giving the operator a control of reactive power; the controls can provide instantaneous and effective reactive power support and voltage control [6]. SVCs also do not overload as quickly as generators and synchronous condensers.

2.1 SVC Characteristics The above graph is a representation of the voltage and current characteristics of a static VAR compensator, the range from I Cr to I Lr is known as the linear range of

Reactive Power Control of Stand-Alone Hybrid …

5

Fig. 1 Voltage–current(V-I) characteristics of an SVC [10]

control, basically it spans the area through which the thyristors firing angle is varied, and the flow of reactive power is regulated. The capacitive region is represented to the left part of the graph and the inductive portion is represented by the portion on the right side (Fig. 1). An SVC is not the same as a voltage source, it just regulates and adjusts the system voltage where its connected by varying the reactive power drawn or supplied to the system. It acts as a variable reactive load which keeps the AC voltage nearly constant.

3 System Setup and Modeling This paper analyzes a stand-alone WDHPS. The basic layout of the system is as shown below in Fig. 2. The system comprises of an induction generator working in combination with WECS, a synchronous generator working in combination with the diesel engine, and SVC. The active power demand is fulfilled by the SG while the reactive power requirement is fulfilled by the SVC. The excitation system in the synchronous generator used in the diesel-powered generator is an IEEE Type-1 excitation system with an automatic voltage regulator. The SVC used here is Type-1 as shown in Fig. 3.

3.1 Mathematical Modeling Under steady-state conditions, the system should be governed by two equations given as: PIG + PSG = PL

(1)

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Fig. 2 Layout of a stand-alone WDHPS [11]

Fig. 3 SVC scheme [5]

QSG + QSVC = QL + QIG

(2)

Equations (1) and (2) govern the reactive power balance in the system. PIG in Eq. (1) represents the real power output of the wind turbine, PSG represents the real power output of the SG, lastly PL denotes the real power requirement of the connected load. For active power balance in system, the supply should meet the demand as shown in Eq. (1). Similarly, in the case of Eq. (2), QSG and QSVC denote the reactive power produced by the SG and the SVC, respectively, and this should in turn meet the demand of the load and the IG to maintain a reactive power balance in the system. Now, if we assume the system undergoes a slight rise in reactive power load denoted as QL pu MVAR [7]. Then, the AVR and SVC controllers would cause the systems reactive power generation to also increase by QSG + QSVC pu MVAR. Then, the reactive power requirement would also vary because of the change in system voltage by an amount of V. Thus, total reactive power required in system will then be given by “QSG + QSVC − QL − QIG ” pu MVAR. This incremented power would then be absorbed in the system by: (1) A higher consumption of load in system arising from an increase in voltage. (2) A rise in the rate of electromagnetic energy absorption, denoted as (d/dt)E M .

Reactive Power Control of Stand-Alone Hybrid …

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Fig. 4 Block diagram of the system [5]

Mathematically, this can be denoted as: Q SG + Q SVC − Q L − Q I G =

d E M + DV V dt

(3)

Taking Laplace transform and simplifying: Q SG (s) = K 3 E q  (s) + K 4 V (s)

(4)

Now for SVC we have: Q SVC = V 2 BSVC Taking Laplace transform and simplifying:

(5)

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Q SVC (s) = K 6 V (s) + K 7 BSVC (s)

(6)

where K 7 = V 2 and K 6 = 2VBSVC

4 Genetic Algorithm (GA) GA is a metaheuristic algorithm and an optimization technique that mirrors the principal of natural selection. This principal states that in a group of individuals, those who are the strongest have the highest probability of survival in a competitive environment. Genetic algorithm also assumes that an individual is the possible solution to a problem and thus could be given by certain parameters. One such parameter that represents the strength or goodness value of an individual’s chromosome is the “fitness value” [8]. Through the GA the fittest individual has the ability to produce an even better offspring, and this repetition continues until the best desired solution is reached. Figure 5 gives the workflow of GA. The crossover operator swaps the sequence of the top two chromosomes that were chosen for creating two new offspring. Then another function called the mutation operator will randomly select and flip single bits into new chromosomes. Selection and crossover preserve the genetic information of the healthier and better suited chromosomes. The mutation operator prevents the algorithm from converging at a local optimum, by maintaining a diversity in the population, it does this by randomly changing a bit (Fig. 4). The standard objective functions used here are ITSE and ITAE: 1. Integral time square error criterion [ITSE]: TSIM T (V )2 dt

(7)

0

2. Integral time absolute error criterion [ITAE]: TSIM T |V |dT

(8)

0

5 Particle Swarm Optimization (PSO) The PSO algorithm is developed using the concept of swarm intelligence, and this algorithm studies the social behavior of animals like a school of fish or a flock of birds. It is a population-based stochastic algorithm, which highlights the intuitive

Reactive Power Control of Stand-Alone Hybrid …

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Fig. 5 GA workflow [8]

group behavior of animals. It follows the way swarms of animals conform in a cooperative manner in search of food, where every member of the swarm constantly changes its search pattern in accordance with the learning experience of its own and of other members of the group [9]. Inspired by the cornfield model, Eberhart and Kennedy devised the initial PSO algorithm. vx = vx + 2 ∗ rand ∗ ( pbest x−−x) + 2 ∗ rand ∗ (gbest x − x)(11); x = x + vx

(9)

The basic PSO is given as—let the swarm size be “N”, each particles position vector in a X i = (x i1 , x i2, …x id , … x iD ), velocity vector be V i = (vi1 , vi2 ,…, vid ,…viD ), individuals optimum location, Pi = (pi1 , pi2 , … pid , …piD ) and swarms’ optimal location be Pg = (pg1, pg2 , … pgd, … pgD ). The equation for each individuals position is:  d xi,t+1 , f (X i,t+1 ) < f (Pi,t ) d pi,t+1 = (10) d pi,t otherwise ,

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Fig. 6 Iteration scheme in PSO [9]

Figure 6 shows the iteration scheme of the particles. Revise equation of velocity and position is given by (Fig. 7): d d d d d d d = vi,t + c1 ∗ rand∗ ( pi,t − xi,t ) + c2 ∗ rand∗ ( pg,t − xi,t )xi,t+1 vi,t+1 d d = xi,t + vi,t +1

(11)

6 Results The system is simulated on MATLAB Version 7.10.0.499 2018b. The simulation and control system bock designing were done on Simulink. Figures 8 and 9 show the ITAE and ITSE function used and designed in Simulink. Figure 9 shows the system constructed in MATLAB for ITSE and ITAE. The parameters were then changed to suit system data-I, II, and III. Hence, the objective of supplying deficit reactive power in order to provide voltage stability and reduce fluctuations, when there is a step increase of reactive power load by 1% or 0.01 pu, is satisfied. Optimization was carried out using G.A and P.S.O, the error was tabulated and the response characteristics of the same are shown graphically. The transient responses of the signals as the reactive power load increases by 0.01 pu are shown. It is observed that a FACTS device like an SVC is necessary in order to satisfy the frequently changing demand of reactive power by the IG and the connected loads. It has been shown that in the absence of the SVC, the error in voltage deviation signal is high and the reactive power instead of being supplied is being consumed by it (negative value) and the SG is supplying the reactive power. Hence, there is a reactive power mismatch in this system as the need for reactive power is not fulfilled. With the optimized value of the gain, the SVC fulfills approximately the entire reactive power requirement, and very little is met by synchronous generator. There is also minimal steady-state error

Reactive Power Control of Stand-Alone Hybrid …

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Fig. 7 Flowchart for PSO [9]

and the peak deviation decreases. The condition [QL + QIG = QSVC + QSG ] was met and satisfied (Fig. 10).

7 Conclusion The available results show the contribution by the SVC though consuming reactive power has increased, thereby the contribution by SG decreases. The error in voltage deviation signal also reduces. With the optimized value of the controller gain is used, the SVC fulfills the requirement of reactive power, and only a little is contributed by the SG. GA and PSO were employed in a comparative manner to find the optimum

12 Fig. 8 ITAE function

Fig. 9 ITSE function

A. V. Kotwal et al.

Reactive Power Control of Stand-Alone Hybrid …

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Fig. 10 ITSE (system data-I) function

solution. Particle swarm optimization has performed better than genetic algorithm (Tables 1, 2, 3, 4, 5 and 6).

Table 1 Value of controller gain K R

Wind–diesel system data

Objective function

System data-I

ITSE ITAE

996.5248

1731.2

System data-II

ITSE

975.8132

1965.8

ITAE

962.5372

2065.5

ITSE

981.9761

1964.7

ITAE

993.4656

3251.1

System data-III

KR GA

PSO

1000

1932.5

Table 2 Maximum deviations of parameters (V and Qsvc ) of the system for 1% step increase in the reactive power load for ITSE function Wind–diesel system data

V (Pu) GA

Qsvc (Pu Kvar) PSO

GA

PSO

Objective function

System data-I −2.154 × 10−5

−1.016 × 9.874 × 10−3 10−5

9.944 × 10−3

ITSE

System data-II

−2.502 × 10−5

−1.325 × 9.833 × 10−3 10−5

9.909 × 10−3

ITSE

System data-III

−3.246 × 10−5

9.73 × 10−3

9.864 × 10−3

ITSE

1.619 × 10−5

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Table 3 Maximum deviations of parameters(V and Qsvc ) of the system for 1% step increase in the reactive power load for ITSE function Wind–diesel system data

QSG (Pu)

System data-I

9.063 × 10−5

3.97 × 10−5

−2.715 × 10−6

−1.28 × 10−6

ITSE

System data-II

1.267 × 10−4

6.971 × 10−5

−2.671 × 10−6

−1.415 × 10−6

ITSE

System data-III

2.195 × 10−4

1.109 × 10−4

−1.694 × 10−6

−8.447 × 10−7

ITSE

GA

QIG (Pu Kvar) PSO

GA

Objective function

PSO

Table 4 Maximum deviations of parameters(V and Qsvc ) of the system for 1% step increase in the reactive power load for ITAE function Wind–diesel system data GA

V (Pu)

QSVC (Pu Kvar)

PSO

GA

Objective function PSO

10−3

9.921 × 10−3

ITAE

System data-I

−2.162 × 10−5

−1.315 × 10−5

9.874 ×

System data-II

−2.535 × 10−5

−1.22 × 10−5

9.831 × 10−3

9.917 × 10−3

ITAE

System data-III

−3.195 × 10−5

−1.034 × 10−5

9.735 × 10−3

9.912 × 10−3

ITAE

Table 5 Maximum deviations of parameters (V and Qsvc ) of the system for 1% step increase in the reactive power load for ITAE function Wind–diesel system data

QIG Pu Kvar

QSG Pu GA

PSO

Objective function

GA

PSO 10−6

−1.657 × 10−6

ITAE

System data-I

9.103 × 10−5

5.766 × 10−5

−2.726 ×

System data-II

1.283 × 10−4

6.312 × 10−5

−2.707 × 10−6

−1.303 × 10−6

ITAE

System data-III

2.159 × 10−4

7.201 × 10−5

−1.667 × 10−6

−5.394 × 10−7

ITAE

System data- II (pu)

System data-III

Table 6 Non-optimized values for the three system data

System data

System data-I (pu)

V

−0.001414

−0.001299

−0.001092

QSVC

−0.002014

−0.001673

−0.001048

QSG

0.009716

0.009586

0.009353

QIG

−0.0001782

−0.0001387

−0.00005697

QL

0.01

0.01

0.01

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Appendix Parameters for SD-I, II, III and gains functions K 1, K 2 , K 3 , etc., were taken from reference [5].

References 1. https://www.un.org/en/development/desa/news/population/2015-report.html 2. Shiraliyan M, Sharma P, Sharma C (2018) Automatic reactive power control of isolated wind– diesel hybrid power system using artificial bee colony and gray wolf optimization. Int J Green Energy 15(14–15):889–904. https://doi.org/10.1080/15435075.2018.1529584 3. Bansal RC, Bhatti TS, Kothari DP (2002) Reactive power control of isolated wind-diesel hybrid power systems for variable slip. In: National power systems conference, NPSC 2002, Indian Institute of Technology, Kharagpur 4. https://electrical-engineering-portal.com/how-reactive-power-is-helpful-to-maintain-a-sys tem-healthy/ 5. Bansal RC (2006) Automatic reactive-power control of isolated wind–diesel hybrid power systems. IEEE Trans Indus Electr 53(4):1116–1126. https://doi.org/10.1109/TIE.2006.878322 6. https://ieee.org/role-of-reactive-power-in-power-systems-reactive-power-is-both-the-pro blem-and-thesolution-to-the-power-system/ 7. Bansal RC Bhatti TS, Small signal analysis of isolated hybrid power systems. Reactive power and frequency control analysis, Narosa Publication, ISBN: 978-81-7319-799-4 8. Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications. IEEE Trans Indus Electr 43(5). https://doi.org/10.1109/41.538609 9. Wang D, Tan D, Liu L (2017) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408. https://doi.org/10.1007/s00500-016-2474-6 10. Jain S, Thakur SS (2012) Voltage control of transmission system using static var compensator. Int J Sci Eng Appl (IJSEA) 1(2). ISSN: 2319-7560 11. Yang MI, Chao MA, Wang S, Chunyan BI, Zhu Y, Zhang H (2016) Reactive power control of an isolated wind-diesel hybrid power system based on SVC by using sliding mode control. In: Proceedings of the 35th Chinese control conference, 27–29 July 2016, Chengdu, China

Synergistic Damping Operation of TCSC and CPSS Using PSO in a Power System Jayant Sharma, Shriram S. Rangarajan, C. K. Sundarabalan, D. Karthikaikannan, N. S. Srinath, D. P. Kothari, and Tomonobu Senjyu

Abstract Damping of power system oscillations is highly important. Considering the response speed of the stabilizer, the design and choice of power system stabilizer (PSS) are vital. Traditional power systems employ power system stabilizers that were sluggish. As the grid is becoming smarter, flexible AC transmission system (FACTS) devices started playing a major role. Series compensation devices like Thyristor controlled series compensator (TCSC) are fast and have good dynamic response to damp the oscillations. This paper demonstrates the efficacy of TCSC for enhancing the power system transient stability in a single machine infinite bus system (SMIB). The controller parameters of TCSC are fine-tuned based on particle swarm optimization (PSO) technique. The synergistic operation of PSS and TCSC approach presented in this paper serves to an efficient technique to enhance the transient stability compared to conventional stabilizers. Keywords Power system stabilizer (PSS) · Particle swarm optimization (PSO) · Small signal oscillations · SMIB · Thyristor controlled series compensator (TCSC)

S. S. Rangarajan (B) Clemson University, Clemson, SC, USA e-mail: [email protected] J. Sharma · S. S. Rangarajan · C. K. Sundarabalan · D. Karthikaikannan · N. S. Srinath SASTRA Deemed To Be University, Thanjavur, Tamilnadu, India e-mail: [email protected] D. Karthikaikannan e-mail: [email protected] N. S. Srinath e-mail: [email protected] D. P. Kothari Former Director, Indian Institute of Technology, Delhi, India T. Senjyu University of the Rykyus, Rykyus, Japan © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_2

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Fig. 1 A typical power system stabilizer

1 Introduction The major problem associated with the power system today is inter-area oscillations ranging from a frequency of 0.2–2 Hz that could be long lasting [1]. This can further hamper the stability of the system resulting in larger disturbances following the smaller oscillations. Damping such oscillations within a short span of time is really critical. Several devices are installed in the power system network to damp such oscillations. Automatic voltage regulators (AVRs) are one among such devices installed in the system to regulate the voltage and provide damping. But, the response of AVR has not been satisfactory. With the introduction of power system stabilizers (PSSs), additional damping torque could be introduced in the system to damp these oscillations.

2 Power System Stabilizer Power system stabilizers have been used for a long time in US and Canada while PSS have been employed in UK to damp the oscillations in tie lines. In case of weak tie lines in a long distance transmission system, PSS play a vital role. Although the application of PSS during steady state is limited, it possesses excellent damping capability. As the grid is becoming smarter, the role of FACTS devices started playing a major to provide an enhanced level of oscillation damping in the system. A typical block diagram of PSS is shown in Fig. 1.

3 Implementation of TCSC TCSC-based FACTS device has a Thyristor controlled reactor (TCR) in parallel with a fixed capacitor (FC). The inductance in TCR could be varied by altering the firing angle from 0 to 90° for achieving a smooth variation of inductance. Along with the FC, the required rating of net TCSC reactance could be achieved [2–5].

Synergistic Damping Operation of TCSC …

19

4 FACTS-Based Stabilizer A SMIB system is considered for evaluating the efficacy of TCSC. The complexity in case of SMIB is less. So, for the analysis purpose in reducing the complexity, testing of the TCSC device on a SMIB system is carried out. A bus, whose frequency and voltage (in both magnitude and angle) are constant, is considered to be an infinite bus. The dynamic response of the synchronous generator is a little difficult in analyzing considering the non-linearity complexities associated with it. It is a well-known fact that the non-linearity is due to the magnetic saturation. The dynamic generator system involving the third order classical model is considered along with the nonlinearities. FACTS devices like TCSC possesses excellent capabilities to improve the small signal stability of the power system. When there is a sudden increase or decrease in loading, the generator slows down or speeds up correspondingly. As a result the frequency also decreases and increases, respectively. When subjected to such a situation, the oscillations occur in the synchronous generator. Further, with normal capacitive compensation with a simple series capacitor, it can also lead to a phenomenon of sub synchronous resonance (SSR). With the utilization of TCSC, small signal oscillations and SSR could be mitigated easily considering the fact that TCSC has an enhanced capability to damp out the oscillations and also provide series capacitive compensation. Stabilizers are implemented in the system to damp the oscillations during a transient condition. TCSC has primarily been employed to increase the power transfer capacity in a transmission line. This is done by varying the firing angle of the TCR to provide the necessary series compensation in capacitive mode. Apart from providing series compensation, TCSC can play a major in oscillation damping. During abnormal conditions, a conventional power system stabilizer (CPSS) considers the generator power output and the speed of the rotor or both as an input signal to generate a voltage signal. This signal is compared with a reference voltage signal by the CPSS. Although AVR has been used as a stabilizer, the response of the AVR is slow. So, an enhanced and quick control action was needed to overcome the cons associated with the conventional-based stabilizers. TCSC-based stabilizer on the other hand, generates a reactance-based signal in comparison with the transmission line reactance that is continuously altered by suitable gating pulses to the Thyristors [6–8]. In this paper, the efficacious nature of TCSC involving its synergism with PSS for small signal oscillation damping is studied. The controller parameters of TCSC are optimized and designed using particle swarm optimization (PSO) technique [7]. Figure 2 presents the controller block of TCSC-based PSS.

5 Proposed Methodology Figure 2 presents the block diagram of TCSC-based controller PSS that is designed and optimized using PSO. Figure 3 presents the excitation system with PSS. Figure 4

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Fig. 2 TCSC-based PSS design

Fig. 3 Control block diagram of excitation system

Fig. 4 Excitation system of synchronous generator with PSS

presents an AVR that compares the voltage at the output against a reference voltage to produce an error signal that is fed to the proportional-based controller to maintain the output voltage as constant. A suitable limiter is used to keep E fd value within the limits. If this limit is exceeded, then E fd is fixed. The per unit torque equation of generator in d-q reference frame is given as   2H dw = Tm d i d − q i q

(1)

ωB dt

(2)

E d = − sin(d) E q = cos(d)

(3)

Synergistic Damping Operation of TCSC …

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Fig. 5 Single machine system with AVR and PSS

θ = ω0 t + δ dδ/dt = ω − ωo

(4)

State space equations of TCSC and excitation model are given as E fd = 1/TA K A (Vref + U P SS − V ) − E fd 

(5)

X TCSC = 1/TS K S (X TCSC − UXTCSC ) − X TCSC 

(6)

Figure 5 depicts the schematic diagram of SMIB system with TCSC. The variation in the reactance of TCSC could be achieved by changing the firing angle in order to achieve the sufficient damping during abnormal conditions. When the rotor speed varies, it is given as an input in this case. PSS generates a voltage that is added with the reference voltage. At the terminals of the generator, a 3-phase fault is created and the clearance is achieved after 5 cycles for a 50 Hz system. There are three conditions considered during the fault. One is the lightly loaded condition, normal loaded, and heavily loaded conditions. A fault is created at 1 s. and cleared at 1.1 s. As a result, the oscillations in the response of the system are observed with AVR and CPSS in place to check for their dynamic response in damping the oscillations. The same system is then tested with TCSC in place in synergism with CPSS and AVR. It could be well witnessed from the results that the TCSC-based stabilizer improves stability of the system by damping the oscillations very quickly in comparison with system with CPSS and without any controller. Machine parameters are: Ra = 0.003, X d = 1.8, X d  = 0.3, Td0 = 5, Tq0 = 1, TA = 0.015s,

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K S = 1, TS = 0.05 s, X q = 1.7, Tq0 = 0.07, TC = 1.0 s, H = 3, K A = 200, TA = 0.02, X d  = 0.23, M = 9.26 p.u, V = 1.0 s, X q  = 0.65, X q  = 0.25, Td0 = 0.03, D = 0, X = 0.997 p.u., Tdo = 7.76, K A = 200, TB = 10 s. Particle swarm optimization (PSO)-based control parameters are: K w = 3.1278, T3TCSC = 0.3457, Td = 0.02s, Tm = 0.05 s, T2 = T4 = 0.1 s, K pe = 0.0121, T3CPSS = 0.2391, UTCSC max = UTCSC min = 0.1 p.u, T1CPSS = 0.1753, T1TCSC = 0.4385, Tw = 5 s.

6 Simulation Results The TCSC-based PSS for generator model is simulated by creating a fault. Since the existence of non-linearities are evident due to the generator model, performing a linearized analysis of the system around the equilibrium point is highly essential. The analysis has been carried for different levels of loading during different fault conditions. Three loading conditions are. (A) Mechanical input Pm = 0.3 p.u—Lightly loaded conditions (B) Mechanical input Pm = 0.6 p.u—Normally loaded conditions (C) Mechanical input Pm = 0.85 p.u—Heavily loaded conditions. The synergistic operation of CPSS and TCSC-based stabilizer is carried out using PSO with various time constants and gains of CPSS and TCSC-based stabilizer. A three-phase fault is created for 5 cycles lasting from t = 1 s. to t = 1.1 at an operating frequency of 50 Hz. The rotor angle, rotor speed, and power output variation are simulated using Euler’s method for three loading conditions. For heavy loading conditions, it is carried out for 20 s, for normal loading it is done for 12 and 6 s for lightly loaded conditions (Figs. 6, 7, 8, 9, 10, 11, 12, 13 and 14). Fig. 6 Response of rotor angle (Pm = 0.3)—light loading

Synergistic Damping Operation of TCSC … Fig. 7 Response of rotor speed (Pm = 0.3)—light loading

Fig. 8 Power output-light loading (Pm = 0.3)

Fig. 9 Power output-normal loading (Pm = 0.6)

Fig. 10 Rotor speed-normal loading (Pm = 0.6)

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24 Fig. 11 Power-normal loading (Pm = 0.6)

Fig. 12 Rotor angle-heavy loading (Pm = 0.85)

Fig. 13 Rotor angle-heavy loading (Pm = 0.85)

Fig. 14 Power-heavy loading (Pm = 0.85)

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7 Further Insight Further, in a smart grid environment when more renewable energy resources and plug in electric vehicles are integrated into a sub transmission and distribution network, the stability of the system becomes a vital aspect and FACTS devices are playing a major role in damping such oscillations. With the introduction of smart inverters into the system as FACTS devices, such damping can be easily performed by them by means an auxiliary control associated with the smart inverters [9–13].

8 Conclusion It could be well witnessed from the studies that TCSC-based PSS that has been employed in a single machine infinite bus power system has a greater edge compared to the CPSS in improving the dynamic performance of the system under unbalanced conditions. The system stability, speed of the rotor, torque angle (angle of the rotor), and power output with TCSC-based PSS presented an improved response with fast damping compared to the system without the conventional PSS. Acknowledgements Authors are highly grateful and thankful to SASTRA Deemed to be University for providing the support to carry performing the research.

References 1. Kundur P, Balu NJ, Lauby MG (1994) Power system stability and control, vol 7. McGraw-Hill, New York 2. Hingorani NG, Gyugyi L (2000) Understanding FACTS: concepts and technology of flexible AC transmission systems. IEEE Press, New York 3. Varma RK, Rangarajan SS, Axente I, Sharma V (2011) Novel application of a PV solar plant as STATCOM during night and day in a distribution utility network. In: 2011 IEEE/PES power systems conference and exposition, Phoenix, AZ, pp 1–8 4. Berge J, Rangarajan SS, Varma RK, Litzenberger WH (2011) Bibliography of FACTS 2009– 2010: part IV IEEE working group report. 2011 IEEE power and energy society general meeting. Detroit, MI, USA, pp 1–10 5. Berge J, Rangarajan SS, Varma RK, Litzenberger WH (2011) Bibliography of FACTS 2009– 2010: part III, IEEE working group report. In: Proceedings of IEEE PES general meeting, Detroit 6. Mozumder S, Dhar A, Rangarajan SS, Karthikeyan SP. Coordinated operation of multiple inverter based renewable distributed generators as an active power injector and reactive power compensator. 2014 international conference on computation of power, energy, information and communication (ICCPEIC), Chennai, 2014, pp 298–303 7. Sunkara SK, Narne R, Panda PC (2013) Co-ordinated tuning of PSS with TCSC damping controller through advanced adaptive PSO for a multi-machine power system. 2013 international conference on energy efficient technologies for sustainability. IEEE, Nagercoil, pp 1097–1102

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8. Koohi I, Groza VZ (2014) Optimizing particle swarm optimization algorithm. In2014 IEEE 27th Canadian conference on electrical and computer engineering (CCECE), Toronto, ON, pp 1–5 9. Poyyamani Sunddararaj S, Rangarajan SS, Gopalan S (2019) Neoteric fuzzy control stratagem and design of chopper fed multilevel inverter for enhanced voltage output involving plug-in electric vehicle (PEV) applications. Electronics 8:1092 10. Rangarajan SS, Collins ER, Fox JC (2017) Detuning of harmonic resonant modes in accordance with IEEE 519 Standard in an exemplary North American distribution system with PV and Wind. In: 6th IEEE international conference on renewable energy research and applications, San Diego 11. Rangarajan SS, Collins ER, Fox JC (2018) Smart PV and SmartPark Inverters as suppressors of Temporary Over-Voltage (TOV) phenomenon in distribution systems. IET Gener Trans Distrib J 12. Rangarajan SS, Collins ER, Fox JC (2019) Efficacy of Smart PV inverter as a virtual detuner in mitigating network harmonic resonances. Elsevier Electr Power Syst Res J 13. Rangarajan SS, Collins ER, Fox JC, Kothari DP (2018) Consolidated compendium of PV interconnection standards across the globe in a smart grid environment. J Energy Technol Res UK

Enhancing Oscillation Damping in a Power Network Using EWOA Technique Ramesh Devarapalli, Biplab Bhattacharyya, and Archana Kumari

Abstract Small oscillations with low frequency are ubiquitous in the power system, and it needs to be damp out to preserve the stability of the system. This paper deals with a power system stabilizer (PSS), including STATCOM for damping oscillations of small frequency. An enhanced whale optimization algorithm (EWOA) is proposed to tune the parameter of the controllers. This paper describes the significance of the FACTS controller and proper coordination between the controllers through novel soft computing technique; a sample power network is considered and modeled with system dynamics. Keywords Enhanced whale optimization algorithm · Power system stabilizer · STATCOM

1 Introduction The power system network is inherently non-linear interconnected and can get influenced by small disturbance. These disturbances excite the system into state oscillation. Some of the oscillation dies out with time are called stable oscillation, and some of the oscillation does not die with time are called unstable oscillation such sort of disturbances are liable for the loss of synchronism [1]. Small disturbance can be caused by the control device, field winding, damper winding, exciter, turbine loads, automatic voltage regulator (AVR), etc. In the case of AVR, it is intended to maintain constant terminal voltage with fast response and significant gain that can make some dynamics unstable [2]. Any control system that R. Devarapalli (B) · A. Kumari B. I. T. Sindri, Dhanbad, Jharkhand 828123, India e-mail: [email protected] A. Kumari e-mail: [email protected] B. Bhattacharyya IIT (ISM), Dhanbad, Jharkhand 826004, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_3

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is poorly designed can result in a particular pole or eigenvalue becoming unstable and excite the system into the state of oscillation. These oscillations are of order 0.1–2 Hz [3, 4]. To damp out such oscillation power system stabilizer (PSS) and static synchronous compensator (STATCOM) are used in [5]. The applications of STATCOM are numerous in damping oscillations that are generated by various system conditions and weak converter terminals of HVDC [6–8]. PSS provides conditional damping torque in the system to minimize electromechanical oscillation or provide an auxiliary control signal to generator excitation input. Dynamic stabilization is a big challenge for power system researchers. Different approaches are taken to set parameters of PSS to get optimal design. PSS parameters tunning is done via different methods which uses eigenvalues and damping ratios. PSS design using WOA is developed based on the location of lightly damped electromechanical modes os system to enhance damping performance. ˙In this paper, whale optimization algorithm (WOA) and its enhanced version are adopted to tune the controller parameters. The paper is presented in sections with introduction in Sects. 1 and 2 deals with the test system design. Section 3 explains the proposed EWOA algorithm, and the system performance with the proposed method is presented in Sect. 4. And finally, the conclusion in Sect. 5 followed by the references.

2 Test System Design The test system embedded with a STATCOM is shown in Fig. 1. The constants of system components considered in the test system are mentioned in figure corresponding to each component. The dynamics of generator and excitation system for test power system connected to STATCOM as shown in Fig. 1 is expressed by Eqs. (1)–(4). δ˙ = ω − ω O

Fig. 1 Test system with VSC-based STATCOM

(1)

Enhancing Oscillation Damping in a Power Network Using EWOA …

29

1 [ Pm − Pe − D(ω − ωO ) ] M

(2)

 1   E fd − eq Td0

(3)

ω˙ = −

e˙q =

1 KA E˙ fd = − (E fd − E fd0 ) + (Vto − Vt ) TA TA

(4)

The output power, terminal voltage (V t ), internal voltages behind transient (E q ) of the generator are given in Eqs. (5)–(7). Pe = E  q Itq + (xq − x  d )Itd Itq

(5)

E q = E  q + (xd − x  d )Itd

(6)

Vt =



(E  q − x  d Itd )2 + (xq Itq )2

(7)

The current magnitude and direction from the STATCOM at utility bus depends on the voltages at both the ends based on the capacitance value which is controlled by the PWM controller of voltage source converter, and the magnitude is found by Eqs. (8)–(10). IL0 = IL0d + j IL0q

(8)

dVdc Idc = dt Cdc

(9)

where IL0d and IL0q are the direct and quadrature axes components of STATCOM current,IL0 . The control action of STATCOM is represented by means of modulation index me and phase de of VSC, as in Eq. (10), V0 = meVdc ∠de V0 = meVdc (cos de + j sin de)

(10)

The conventional power system stabilizer (CPSS) with single stage compensation is represented as shown in Fig. 2. It produces a control signal depending on the speed deviation of the alternator rotor within the reset and compensation blocks (Fig. 3). Fig. 2 Single stage compensation CPSS

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Fig. 3 Encircling mechanism of Whale

Time constants [9]. The parameters of the damping devices can be tuned for the coordinated operation of PSS and STATCOM by selecting within the limits as given in Eq. (11). ⎫ T1,min  T1  T1,max ⎪ ⎪ ⎪ ⎪ ⎪ T2,min  T2  T2,max ⎪ ⎪ ⎬ K c ,min  K c  K c, max ⎪ ⎪ ⎪ m e,min  m e  m e,max ⎪ ⎪ ⎪ ⎪ δe,min  δe  δe,max ⎭

(11)

where T 1 , T 2 , K c are the time constants and gain of PSS of synchronous generator and me and de are the PWM modulation index and phase angle of VSC-based STATCOM. The control parameter limits are given in Table 1, For the mathematically modeled power network with STATCOM, the proposed EWOA algorithm employed and the performance of damping further improved by considering the enhancements in the algorithm. The parameter tuning considers the objective function given in Eq. (12). J = max min (ξ ) p

(12)

n

Table 1 Limits of control parameters PSS 1

STATCOM

T1

T2

Min. limit

0.01

0.01

Max. limit

2

2

Kc 0.1 50

me

de

0

0

1

1

Enhancing Oscillation Damping in a Power Network Using EWOA …

31

Equation (12) represents to strengthen the weakly damped system states through which overall system stability can be improved. The proposed algorithm identifies the system states with lesser damping ratios and maximizes by the proper tuning of the controller parameter settings. The detailed algorithm is explained in the next section.

3 Enhanced Whale Optimization Algorithm (EWOA) Different techniques are used to find the best solution for the variable for a particular function under constraints to maximize or minimize it. WOA is one of them. The involvement of fewer control parameters makes the evolution process faster. This technique was inspired by a unique hunting technique of humpback whales called bubble-net feeding. This technique starts with attacking the prey, then encircle them by forming bubble-net around them in a spiral manner (exploitation phase) and then search for the prey (exploration phase) [10]. In the encircling mechanism, the humpback whales locate the position of prey and circularly enclose them [10]. WOA assumes the best location that is target prey in search space, and other agents try to modify their location concerning the best search agent. This mechanism is mathematically formulated as D = C.X ∗ (t) − X (t)

(13)

X (t + 1) = X ∗ (t) − A.D

(14)

where t is the current iteration, X is the position vector represents the current position, X* is the position vector of best position, D is the difference vector, A and C are the coefficient vectors value of A is random value in [a, −a] and the value of ‘a’ decreases as iteration increases. A = 2a.r1 .a

(15)

C = 2.r

(16)

2

where r is a random vector in the interval [0,1] where a is linearly decreased from 2 to 0 given by Eq. (17) [10].

abase = 2 − t ∗

2 Max_iter

 (17)

The value of ‘a’ denotes the exploration and exploitation phase of the proposed algorithm w. r. t. the number of iterations. However, the characteristics of these

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Fig. 4 Variation of ‘a’ w. r. t. the number of iterations

phases can be increased by the increase in the exploration ability and convergence rate with the modified value of ‘a’ as given in Eq. (18) [11]. The nature of functions in Eqs. (17) and (18) can be observed by Fig. 4. amodified

= 1 + cos π ∗

t Max_iter

 (18)

In bubble-net attacking method, two approaches are designed and describes as, Shrinking Mechanism: The value of a is decreased from 2 to 0 over course of iteration that sets the random value of A in the interval [−1, 1] which gives a new position of whale anywhere between original and current location of agent. Spiral Updating Mechanism: Difference in whale and prey position is given by D  = X ∗ (t) − X (t)

(19)

where D represents the best solution of the distance of ith whale and prey, and then, the spiral equation is created for helix-shaped movement X (t + 1) = D  ebl cos(2πl) + X ∗ (t)

(20)

There is 50% probability to choose between shrinking mechanism and spiral model so, X (t + 1) =

if p < 0.5 X ∗ (t) − A.D D  ebl cos(2πl) + X ∗ (t) if p > 0.5

(21)

The last step involves the hunt for prey in which Whales randomly explores the position of prey with positions relative to each other so, the position of search agent is modified with value of A > 1 or < 1.

Enhancing Oscillation Damping in a Power Network Using EWOA …

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D = |C · X rand − X |

(22)

X(t) = X rand − A · D where X is the random position vector [10].

4 System Performance Characteristics with EWOA The proposed algorithm is implemented on the modeled power network with the objective function provided in (12). The resulted tuned parameters with the given inequality constraints, as explained in (11) and Table 1 are listed in Table 2: The damping performance of system states with the tuned controller parameters are observed for a perturbation of 10% at t = 0 s. A system is stable δ(t) perform =0 oscillation whose amplitude decreases with time and becomes constant, i.e., dδ(t) dt > 0 for long time [1], so oscillations and the system can become unstable if dδ(t) dt in the system must be damped rapidly. The given plot shows the comparison of different techniques at t = 0 under 10% disturbance. Figure 5 shows the torque angle deviation w.r.t time it is observed that conventional power system stabilizer (CPSS) makes large oscillation before it reaches to steady state, while whale optimization algorithm (WOA) and enhanced whale optimization algorithm (EWOA) reaches the Table 2 The obtained results are tabulated as follows T1

T2

Kc

me

de

WOA

0.329064

0.0908106

14.2278

0.72867

0.863901

EWOA

0.275734

0.0123519

24.1804

0.991855

0.000303693

Fig. 5 Torque angle δ (rad) deviation with time

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steady-state rapidly with less oscillation. Figure 6 shows the angular speed deviation with time. It is observed that the EWOA is more steady and stable than any other optimization methods and takes less settling time to mitigate the system oscillations. Figure 7 shows the alternator internal voltage deviation with time, as compared to CPSS and WOA EWOA removes the oscillation rapidly and settles with some steady state error. Figure 8 shows the internal voltage deviation with w.r.t time, and it is observed that in the case of EWOA, it settles down earlier as compared to CPSS and WOA. Fig. 6 Angular speed deviation with time

Fig. 7 Alternator internal voltage deviation with time

Enhancing Oscillation Damping in a Power Network Using EWOA …

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Fig. 8 Excitation voltage deviation with time

5 Conclusion When the parameters of PSS are appropriately evaluated, it can enhance the dynamic performance. The best result is attained by EWOA in this paper. It is seen that parameters tunned by EWOA damps out the oscillation rapidly as compared to CPSS and WOA for the designed system parameters. It is also observed that the coordination between the damping devises achieved with the proposed algorithm. From the results, it is conferred that the proposed soft computing method is further recommended for the multi-machine system and complex networks.

References 1. Kothari DP, Nagrath IJ (2003) Modern power system analysis. Tata McGraw-Hill Educationn, New York 2. Dudgeon GJW, Leithead WE, Dysko A, O’Reilly J, McDonald JR (2007) The effective role of AVR and PSS in power systems: frequency response analysis. IEEE Trans Power Syst 22(4):1986–1994 3. Rogers G (2000) Power system oscillations. Springer, US 4. Divya B, Devarapalli R (2014) Estimation of sensitive node for IEEE-30 bus system by load variation. In: 2014 international conference on green computing communication and electrical engineering (ICGCCEE), pp 1–4 5. Panda S, Padhy NP (2008) “Optimal location and controller design of STATCOM for power system stability improvement using PSO. J Franklin Inst 345(2):166–181 6. Devarapalli R, Pandey RK (2012) Analysis of weak AC system interface with multi-infeed HVDC. In: 2012 international conference on computing, electronics and electrical technologies (ICCEET), pp 138–144 7. Devarapalli R, Pandey RK (2012) HVDC converter control performance during faults. In: IEEEinternational conference on advances in engineering, science and management (ICAESM2012), pp 386–392

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8. Devarapalli R, Pandey RK (2012) Performance evaluation of HVDC system with ESCR variation. In: 2012 students conference on engineering and systems, pp 1–6 9. Devarapalli R, Bhattacharyya B (2019) A framework for H2 = H ¥ synthesis in damping power network oscillations with STATCOM. Iranian J Sci Technol 22 10. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67 11. Fu W, Wang K, Li C, Tan J (2019) Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Convers Manag 187:356–377

Fault Detection of Smart Grid Equipment Using Machine Learning and Data Analytics Kumari Sarita, Sachin Kumar, and R. K. Saket

Abstract The high vibration, temperature, and pressure issues cause the failure of the rotating electrical equipments. The failures become considerable when these equipments are used in industries and in smart grid. The more common failures are because of high vibrations, and sometimes, it may lead to complete shutdown of the system. The condition monitoring system must be reliable and detects the future fault conditions of the electrical equipment. The condition monitoring (CM) system is reliable and predictive when machine learning and data analytics are implemented. There are various machine learning techniques that help to detect the fault in minimum time using the historical data of the equipment and data analytics. It also helps to avoid the permanent failure of the electrical rotating equipment. Therefore, this paper focuses on health monitoring and remaining useful life (RUL) estimation of the electrical equipment connected to the grid using principal component analysis (PCA). PCA is an unsupervised machine learning technique that is proposed in this paper for case study of high-speed wind turbine bearing. Keywords Smart grid · Machine learning · Predictive maintenance · Condition monitoring · Industry 4.0 · Fault detection · PCA

1 Introduction The sensors’ data from different locations of the grid are collected. These data are stored and used in analytical machine learning-based algorithms for making the system smart and reliable. The system becomes reliable and smart when appropriate information from the collected datasets are extracted. The system is made automatic K. Sarita (B) · S. Kumar · R. K. Saket Department of Electrical Engineering, IIT(BHU), Varanasi, UP, India e-mail: [email protected] S. Kumar e-mail: [email protected] R. K. Saket e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_4

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for its operation, decision making, and controlling the analytical procedure. A smart grid network is integrated with different equipment, feeders, and substations. Hence, there are variety of data coming from each location of the grid. These collected data are used to predict future states, load demands, weather conditions, fault in the equipment. These data are also helpful in forecasting future states. The rotating parts of the electrical equipment of the grid generally degrade due to high vibration, temperature, and pressure. To avoid or reduce this degradation rate of these equipments, CM is implemented. In industrial smart grid equipment, the maintenance is done in three ways are as follows. (i)

Reactive Maintenance- This type of maintenance is done during the post-fault condition. (ii) Preventive Maintenance- The maintenance is done in a regular interval of time like once in a week, month, or year (iii) Predictive Maintenance- In this type of maintenance, the future coming fault is predicted and maintenance is done during the pre-fault condition. Predictive maintenance is more advantageous than the other two types of maintenance because it avoids the occurrence of faults, increases the lifetime of the equipment, and it is cost-effective too. The recent developments and facilities in data collection, data analysis, cloud storage, data computation, and the Internet of Things (IoT) are helpful to move the smart grid performance towards the industry 4.0 revolution. Data collection is the first step for data-driven maintenance in predictive maintenance. The collected data are further used in different predictive algorithms using statistics and machine learning techniques. The digital transformation makes the grids smarter and more reliable [1, 2]. For CM of the industrial equipment, vibration-based CM is commonly implemented. The era of acoustic emission (AE) has taken attention for the researchers and industries towards AE for the diagnosis of the equipment because of the advancements in machine learnings and data science. The analysis of data of the equipment is done in time and frequency domain for AE based techniques. The authors in [3] have explained the AE based approach for bearing fault diagnosis. The three methodologies were demonstrated in the previous research work for the fault diagnosis. These methodologies are given as follows. (i)

Frequency reduction technique based on heterodyne: This is used to reduce the frequency of the AE signal from a range of MegaHertz to a fewer range to Hertz. (ii) Synchronous time sampling (iii) Spectral frequency-domain analysis: This is used to find the condition indicators for the fault diagnosis. The above-proposed techniques are useful in the diagnosis of all four types bearing faults. These bearing faults are as follows [4, 5]. (i)

Inner race fault,

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(ii) Outer race fault, (iii) Faults in the ball, and (iv) Faults in the cage. The mathematical expressions and equations have been explained in [4, 5]. In the time-domain analysis, kurtosis, peak-to-peak value, crest factor, mean value, standard deviation are important indicators for feature extraction of the dataset and in frequency-domain analysis, spectral kurtosis is a better indicator for extracting information from the vibration signal [6, 7]. Fault detection is crucial than the fault diagnosis in predictive maintenance. So, the important step is to detect the fault in minimum time so that fault can be diagnosed [8]. Machine learning is the method of learning and extracting information from historical data. Machine learning techniques are classified into two groups. (i) Supervised type machine learning: In this machine learning technique, both pre-fault and post-fault data are needed to make the prediction algorithm. (ii) Unsupervised type machine learning: In this learning technique, if the fault data is not available, then it can be useful to detect the fault condition but it cannot classify the fault that occurred. The most common supervised machine learning techniques are regression and classification-based machine learnings. Clustering and PCA are commonly used unsupervised machine learning techniques [9–15]. PCA is also a data reduction technique, which is used to reduce the high dimensional dataset into a low dimensional dataset. The number of principal components (PCs) is less than or equal to the number of attributes available in the dataset. Among the principal components, the first principal component (PC1) is given the highest priority because it covers the highest variance percentage of the dataset and goes on decreasing for PC2, PC3, … where PC2 and PC3 are the second and third principal components, respectively. The most important properties of principal components are that they are independent and orthogonal to each other [16–20]. The authors in [18, 21] have explained the PCA technique in detail and they have used this unsupervised machine learning technique for early fault detection algorithms for equipment based on vibration sensor data. The author in [18] has observed that clustering is a better tool for the fast fault detection of the equipment. The author in [21] has observed that along with the PCA technique, frequency-domain analysis is a better method for fault detection. Thus, the fast fourier transform (FFT) technique is implemented to detect the fault as the frequency and amplitude of faulty vibration signal will be different from the normal trend. It is not recommended to use this FFT technique for fault detection in minimum time. It is recommended to diagnose the fault after detection. During unbalance fault condition of bearing, the FFT signal of the vibration signal has a peak value at a frequency double of its original frequency. It does not show the health degradation. Therefore, the detection system should be pro-active and must detect the fault in minimal possible time. This paper mainly focuses on the health condition monitoring of smart grid equipment like rotating electrical machines and high-speed wind turbine bearings.

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The frequency-domain analysis for feature extraction of the datasets using spectral kurtosis is discussed. In this paper, the PCA technique is used to monitor the health status of the high-speed wind turbine bearing and for RUL estimation. The exponential degradation model is chosen for RUL estimation which is explained in Sect. 2. The computation procedure starts from collection of data and then pre-processing of data, post-processing, PCA algorithm, RUL estimation are carried out in Sect. 3 and the results obtained from PCA algorithm are discussed briefly in Sect. 4. The work done in this paper is concluded in Sect. 5 with some scopes for future work.

2 PCA and RUL Estimation PCA is used to reduce the dimension of the dataset. Before applying PCA to the dataset, the data is standardized to remove the noise and smoothen the dataset. The first principal component is used as the health indicator and also helpful in estimating RUL of the equipment. For estimating RUL, a model is developed based upon the statistical properties of condition indicator values. The degradation model gives the probability distribution of the RUL of the selected equipment. There are various types of RUL models discussed in the literature and also implemented in industries. Some of them are exponential degradation model, linear degradation model, hash similarity model, residual similarity model, reliability survival model. The most commonly used model is the degradation model which is of two types linear degradation model and exponential degradation model. The objective function of the linear degradation model is as given in Eq. (1) [11] X (t) = ∅ + θ (t) + ε(t)

(1)

where φ is the model intercept, which is constant. One can initialize φ as the nominal value of the degradation variable using Phi. θ (t) is the model slope and is modeled as a random variable with a normal distribution with mean Theta and variance ThetaVariance. ε(t) is the model additive noise and is modeled as a normal distribution with zero mean and variance NoiseVariance. Exponential degradation model is defined as Eq. (2) [11].   σ2 h(t) = ∅ + θ exp βt +  − 2

(2)

where h(t) is the health indicator as a function of time, ∅ is the intercept term considered as a constant. θ and β are random parameters determining the slope of the model, where θ is lognormal-distributed and β .is gaussian-distributed. At each time step t, the distribution of θ and β is updated to the posterior based on the latest   2 observation of h(t).  is a gaussian white noise yielding to N 0, σ 2 . The − σ2 term in the exponential is to make the expectation of h(t) satisfy the Eq. (3) [11].

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E[h(t)|θ, β] = ∅ + θ exp(βt)

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(3)

In this paper, the exponential degradation model is used as a health indicator. The selection of threshold value is very important in degradation model which is generally done based on the historical data of the equipment or sometimes the last value of the health indicator is itself chosen as threshold value.

3 Computational Procedure The algorithm starts with the collection of data on wind turbine bearing. The parameters in rotating equipment which can indicate the health status of the equipment are vibration, temperature, lubrication, pressure, etc. Out of these parameters, vibration is an important parameter to be used as an indicator of health status. So, in this paper, vibration data has been selected for early fault detection and estimation of RUL.

3.1 Collection of Data The full datasets analyzed in this paper are available in the data repository, http://dataacoustics.com/measurements/bearing-faults/bearing-3/, which have been collected from a 2 MW wind turbine high-speed shaft driven by a 20-tooth pinion gear [22]. A vibration signal had been measured each day of 6 s and this had been done for the next 50 consecutive days. In this dataset, there is fault data of inner race fault occurred in the bearing which caused failure. The collected data set is imported in the first step of the algorithm which contains the vibration sensor data and tachometer data. The information can be extracted from the data signals when it is analyzed both in time and frequency domains. The vibration signal in time domain is shown in Fig. 1. The vibration measurements are in acceleration unit (g = m/s2 ) and time is in seconds. In Fig. 1, the vibration signal is showing an increasing trend of the signal impulsiveness. Therefore, kurtosis, peak-to-peak value, crest factors would be good indicators for the prognostic features of this dataset in the time-domain analysis. In the frequency domain, spectral kurtosis is a good technique for the prognosis of this case study.

3.2 Data Pre-processing and Features Extraction The features extracted from time-domain analysis and frequency-domain analysis are (i) time-domain features: mean, standard deviation, skewness, kurtosis, peak-topeak, RMS, Crest Factor, shape factor, impulse factor, margin factor, energy, etc. (ii) frequency-domain (Spectral Kurtosis) features: spectral kurtosis mean, standard

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Fig. 1 Vibration signals in the time domain

deviation, skewness, kurtosis, etc. These are helpful in the prognosis of the bearing of wind turbines.

3.3 Data Post-processing The signals being selected for prediction generally contain noise which can affect the prediction result and also the RUL value would not be accurate. To avoid this problem, a casual moving mean filter can be used with a lag window of some steps (4, 5, or 6). With this filter, it is meant that no future value is used in the filtering. The removal or filtering of noise from the features and signals is also called as smoothening. One selected feature, for example, spectral kurtosis means is shown in Fig. 2 before smoothing and after smoothing. After ranking the features in increasing order, the features with a score larger than 0.3 are selected for the further steps of the algorithm which are mean, standard deviation, kurtosis, and skewness. After the selection of features, the next step is to normalize the dataset into a common scale. For this, a dimensional reduction technique is used which is PCA. The number of principal components can be less than or equal to the number of variables. The highest data variance is covered by the first principal component and the value goes decreasing as the numbering of the component is increased. The two principal components have been selected and the dataset has been scattered in the space formed by the first two components as shown in Fig. 3a. In Fig. 3a, the bar chart is showing the time of measurement of the data. The first principal component is very important as it indicates the health of the equipment showing the direction of movement of the data as shown in Fig. 3b. Therefore, PC1 is used as a health indicator. Figure 4 is showing the percentage of data variance covered by each principal component individually and cumulatively. It can be observed from Fig. 4 that the

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Fig. 2 Spectral kurtosis feature (mean) before smoothening and after smoothening

first two principal components are covering approximately 65% and 22% of the total data variance individually, respectively. When first two principal components are considered cumulatively, they will cover approximately 87% of the data variance. Here, first two principal components will be used for further procedure of data analysis and fault detecting alarm system.

3.4 RUL Estimation The confidence interval boundary in the exponential degradation model is taken 95%. Due to a lack of historical data, the last health indicator value is chosen as the threshold value means the threshold value will be updated as the health indicator is updated. The model gives a probability density function of the health indicator (RUL) which shows the probability of the fault to occur. The 50 days duration has been scaled into a small scale of 10 units.

4 Results and Discussion The spectral kurtosis in the frequency domain of the taken dataset has been shown in Fig. 5 plotted as a function of frequency (Hz) and time (day). The bar chart in Fig. 5 shows the severity of the fault indicating ‘0’ as no-fault and ‘1’ as faulty condition.

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Fig. 3 a Scattered dataset into a space of principal components, b health indicator: PC1

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Fig. 4 Percentage of data variance covered by individual PC and cumulatively

Fig. 5 Spectral Kurtosis as a function of time of measurement and frequency

From Fig. 5, it can be observed that the spectral kurtosis value is high near 10 kHz frequency, and it is increasing day by day as the machine health status is degrading. Various statistical features of the spectral kurtosis which are commonly used as good indicators of the bearing degradation are a mean and standard deviation. The Probability Density Function (PDF) shown in Fig. 6 is of day 9 of the 50 days data.

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Fig. 6 The output of an exponential degradation model of RUL estimation

In this paper, an unsupervised machine learning technique, PCA, is used for fault detection of rotating electrical equipment and high-speed wind turbine bearing is taken as case study. A very important part of predictive maintenance and automatic system is condition and data visualization. As the first principal component indicates the health status of the equipment, the score values are helpful in indicating the boundary for the normal and fault region and also show the degradation direction, therefore, it is easier to visualize the health condition of the equipment directly on the monitoring system screen. Hence, an alarm system and visualization can be found using the PCA technique for the early fault detection of equipment as shown in Fig. 7. Data points lying in the green region shows the normal operation of the equipment; if the data points move in the orange range, then there is something wrong with the equipment and the system automatically give alarm, and if the data points are lying in the red region, the monitoring system would give warning that the equipment is not in normal operating condition and some problem has occurred in the equipment. The PCA technique is also helpful in estimating the uptime gained by doing maintenance of the equipment upon first alarm or first warning, which helps the maintenance team in forecasting and scheduling the maintenance of the equipment.

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Fig. 7 Alarm system for early fault detection of bearing using PCA technique

5 Conclusion and Scopes for Future Work The previous condition monitoring method is not reliable. In these methods, the maintenance of the equipment is done on pre-scheduled date in a fixed interval or during the post fault. The proposed technique of CM for the equipment using machine learning algorithms and data analytics is more reliable which detects the future faults and supports to avoid the electrical equipment shutdown. The maintenance schedule can be forecasted before the occurrence of the equipment fault. It saves the maintenance cost by avoiding unnecessary maintenance. The proposed technique in this paper is applicable in other smart grid electrical equipment which is one of the future scopes of this paperwork. The proposed method of this paper differs in all applications in the selection of appropriate predictors for the algorithm. The time domain and frequency-domain analysis give important information about features of dataset. These features are useful in machine learning-based algorithm for the prediction of future states. Hence, the involvement of machine learning and data analytics in the CM of smart grid equipment makes the grid compatible with the digital transformation of the system. This digital transformation helps the grid to move towards the revolution of industry 4.0. The first step in predictive maintenance of equipment is the fault detection. The maintenance can be scheduled after the detection of the fault. There are a few suggestions which can be scoped for future work:

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(i)

The proposed technique of fault detection is based on the vibration sensor data. This can also be done using other parameters like temperature, pressure, lubrication. These parameters are also very important indicators of the health status of the smart grid electrical equipment. (ii) The proposed algorithm can be implemented in the smart grid online condition monitoring system. Also, the output of the algorithm can be used as health condition monitoring of the electrical equipment. (iii) A supervised machine learning-based algorithm can be useful for the classification of faults. (iv) Time-domain analysis can also be integrated with frequency-domain analysis for electrical equipment fault detection.

References 1. Wan J, Tang S, Li D, Wang S, Liu C, Abbas H, Vasilakos AV (2017) A manufacturing big data solution for active preventive maintenance. IEEE Trans Indus Inf 13(4):2039–2047 2. Kumar S, Kumar KA, Kumar S, Bharti OP, Varshney L, Saket RK, Vishwakarma DN (2019) Probabilistic evaluation and design aspects for reliability enhancement of induction motor. Int J Reliab Saf 13(4):267–290 3. Van Hecke B, He D, Qu Y (2014) On the use of spectral averaging of acoustic emission signals for bearing fault diagnostics. J Vibr Acoust 136(6):061009 4. Van Hecke B, Qu Y, He D (2015) Bearing fault diagnosis based on a new acoustic emission sensor technique. Proc Inst Mech Eng Part O J Risk Reliab 229(2):105–118 5. ZhAng X, Kang J, Bechhoefer E, Zhao J (2014) A new feature extraction method for gear fault diagnosis and prognosis. Eksploatacja i Niezawodno´sc´ 16(2):295–300 6. Saidi L, Ali JB, Bechhoefer E, Benbouzid M (2017) Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Appl Acoust 120:1–8 7. Ali JB, Saidi L, Harrath S, Bechhoefer E, Benbouzid M (2018) Online automatic diagnosis of wind turbine bearings progressive degradations under real experimental conditions based on unsupervised machine learning. Appl Acoust 132:167–181 8. Kumar S, Mukherjee D, Guchhait PK, Banerjee R, Srivastava AK, Vishwakarma DN, Saket RK (2019) A comprehensive review of condition based prognostic maintenance (CBPM) for induction motor. IEEE Access. 3(7):90690–90704 9. Nair PS, Rao KR, Nair MS (2019) A machine learning approach for fast mode decision in HEVC intra prediction based on statistical features. J Intell Fuzzy Syst 36(3):2095–2106 10. O’Dwyer H, Csadi S, Bates E, Boland FM (2019) A study in machine learning applications for sound source localization with regards to distance. In Audio engineering society convention 146 11. Henri G, Lu N (2019) A supervised machine learning approach to control energy storage devices. IEEE Trans Smart Grid 12. Wang Y, Silva-Saravia HD, Pulgar-Painemal HA (2019) Actuator placement for enhanced grid dynamic performance: a machine learning approach. IEEE Trans Power Syst 13. Cui M, Wang J, Yue M (2019) Machine learning based anomaly detection for load forecasting under cyberattacks. IEEE Trans Smart Grid 14. Shahsavari A, Farajollahi M, Stewart E, Cortez E, Mohsenian-Rad H (2019) Situational awareness in distribution grid using micro-PMU data: a machine learning approach. IEEE Trans Smart Grid

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15. Arendt D, Saldanha E, Wesslen R, Volkova S, Dou W (2019) Towards rapid interactive machine learning: evaluating tradeoffs of classification without representation. In Proceedings of the 24th international conference on intelligent user interfaces, pp 591–602 16. Wold S (2012) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics 17. Li W, Shi T, Liao G, Yang S (2003) Feature extraction and classification of gear faults using principal component analysis. J Qual Main Eng 18. Amruthnath N, Gupta T (2018) A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In: 5th ınternational conference on ındustrial engineering and applications (ICIEA), pp 355–361 19. Wang Y, Ma X, Qian P (2018) Wind turbine fault detection and identification through PCAbased optimal variable selection. IEEE Trans Sustain Energy 9(4):1627–1635 20. Yellapu VS, Vajpayee V, Tiwari AP (2019) Online fault detection and ısolation in advanced heavy water reactor using multiscale principal component analysis. IEEE Trans Nucl Sci 66(7):1790–1803 21. Bachir A, Hafaifa A, Guemana M, Hadroug N (2018) Application of principal component analysis approach in gas turbine defect diagnosis. In: Iin ınternational conference on applied smart systems (ICASS), pp. 1–6 22. High speed bearing data | Acoustics and Vibration Database, http://data-acoustics.com/measur ements/bearing-faults/bearing-3/

Analytical Modeling and Optimal Control of Cold Storage System with Large-Scale Implementation Using IoT Bhargav Appasani, Amitkumar V. Jha, Abu Nasar Ghazali, and Deepak K. Gupta Abstract Warehouses and cold storage mechanisms are integral and important components for the storage of the agricultural products beyond their intended lifeperiod. Configurability and self-adaptability can greatly enhance the functional efficiency of these systems and minimize wastage of resources. This paper explores the technical glitches faced by the cold storage system and proposes a novel design for a new and advanced automated cold storage system (ACSS). To analyze the performance of ACSS, a mathematical representation of the model is presented. Two performance metrics, namely, temperature and humidity, are considered which play a vital role in any cold storage system. This paper also proposes efficient methods to monitor and control these performance metrics of the cold storage system. The selfadaptable capabilities are provided by using proportional-integral-derivative (PID) controller whose parameter are tuned using particle swarm optimization (PSO) algorithm. The performance of the designed cold storages system is evaluated using MATLAB simulation. Also, to provide the intelligence capability, the ubiquitous Internet of Things (IoT) implementation of ACSS has been proposed in this paper along with the deployment strategy which further improves the efficacy of cold storage system due to its seamless reconfigurable and self-adaptable capabilities. Keywords Cold storage system · Controllers · IoT · Optimization algorithm

1 Introduction Inefficient cold storage systems have caused immense loss of agricultural products particularly in agrarian countries like India. Ineffectiveness and improper management of cold storage systems have resulted in wastage of more than 30% of the food grains, fruits, and vegetables every year. Thus, there is a lot of research that is directed B. Appasani (B) · A. V. Jha · A. N. Ghazali School of Electronics Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, India e-mail: [email protected] D. K. Gupta School of Electrical Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, India © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_5

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towards technical upgradation of existing cold storage systems [1]. The motivation of this work is the design of advanced cold storage system (ACSS) equipped with IoT technology to provide configurability and self-adaptability to the existing systems. Recently, new technologies have been discovered in the milieu of cooling systems [2, 3]. In [4], Li et al. have studied the materials used in a cool storage system and presented a comparative analysis. In [5], a cold storage system has been equipped with sensor nodes to monitor its internal environmental parameters to enhance the lifetime of agricultural produce. Further, a monitoring system (based on microcontrollers) has been discussed in [6] capable of monitoring the temperature between −20 and 60 °C with a relative humidity of 100%. Moreover, the research study by Deng et al. in [7] shows that the door of a cold storage system has a great impact on the overall performance of the system and they propose the automatic door control of cold storage system based on frequency conversion mechanism. A unique architecture of the multiple echelon inventory has been proposed in the context of cold chain logistics to facilitate the random market demands in [8]. A fuzzy logic controller based on sensor nodes is proposed in [9] with an intention to monitor and control the internal parameters of a cold storage system. Despite of having a plethora of work related to the Internet of Things (IoT) in the literature ranging from the discussion of application-specific architecture [10–12] to domain-specific architecture [13, 14], none of the works have explored the efficacy of IoT in the context of the cold storage system. In this article, we model a ACSS by completely describing its transfer function, considering various vital factors involved in this. Further, to control the two most important parameters of the cold storage system, namely, temperature and relative humidity; the proportional-integral derivative (PID) controller is used by combining IoT architecture to it to achieve configurability and self-adaptability [15, 16]. Further, the optimum values of controller parameters are estimated using the particle swam optimization (PSO) algorithm. The organization of the rest of the paper is as follows: Sect. 2 starts with the analytical modeling ACSS and reviews the PID controller. In Sect. 2, the optimization for various attributes of analytical model is presented. The optimal tuning of control parameters is achieved using BPSO algorithm. The validation of proposed model is performed using simulation in Sect. 3. The large-scale implementation of ACSS using IoT architecture is given in Sect. 4. At the last, Sect. 5 concludes the research work with a way forward in the context of the cold storage system.

2 System Modeling The section starts with a conceptual representation of the cold storage system, which is further mathematically modeled using state space analysis.

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2.1 A Generic Cold Storage System A generic cold storage system typically consists of the following blocks: a warehouse, temperature controller, humidity controller, sensors, and feedback mechanism as shown in Fig. 1a with its block diagram representation Fig. 1b. The warehouse consists of temperature and humidity regulator to regulate the temperature and humidity of the warehouse. This is worth of mentioning that the outside temperature due to the sun will have a great impact on the internal atmosphere of the warehouse which is also depicted in Fig. 1a. The complete cold storage system is automated by providing the intelligent mechanism using PID controller which is integrated with the temperature and humidity regulator which automatically sets the internal atmosphere to the desired level (Fig. 2).

External Temperature and Humidity

Temperature and Humidity Regulator

(a) A Generic Representation

Internal Temperature and Humidity of ACSS

Sensor and IoT Integration

(b) Block Diagram Representation

Fig. 1 Proposed automated cold storage system

Fig. 2 Block diagram representation of ACSS using transfer function

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2.2 Mathematical Modeling of ACSS Without loss of generality (W.L.O.G), we focus on the temperature and relative humidity (simply referred to as humidity) of the internal atmosphere of a warehouse. Let us say that the initial internal temperature and relative humidity of the warehouse is Ti and Hi . If the desired temperature and humidity of the warehouse be Td and Hd then, the error (also known as deviation) in temperature and humidity is given as T and H , respectively. W.L.O.G, we notice that the internal atmosphere of warehouse is said to be at the desired level, if and only if the deviation in temperature as well as humidity becomes zero. Any linear system can be approximately modeled using first order transfer function for mathematical analysis [17]. Consequently, block diagram of the ACSS using transfer functions of each blocks involved is as shown in Fig. 2 for mathematical analysis, where T1 and T2 are deviation in the temperature and humidity of the ACSS. Moreover, T2 is the temperature equivalent of the relative humidity; and T A1 and T A2 are used which models the effect of external temperature due to the sun. From the analysis of the transfer functions, we can have the following equations. K1 T1 (s) =  T A1 (s) (sτ1 + 1) − (L − s − P1 ) K P1 + s K D1 +

KI1 s

 − B1 F

(1)

MB4 P1 T1 (s) T2 (s) = × T A2 (s) (1 + MB4 )(s + P1 ) T A1 (s)

(2)

MB4 P1 T2 (s) T1 (s) = × T A2 (s) (1 + MB4 )(s + P1 ) T A1 (s)

(3)

NB2 P1 T1 (s) T2 (s) = × T A2 (s) (1 + NB2 )(s + P1 ) T A1 (s)

(4)

where, MB4 P12 K1 K6 K5 ,F = , + MB4 )(s + P1 ) (1 + sτ5 )(1 + sτ6 ) NB2 P12 J= 2 } s (1 + NB2 )(s + P1 )

L=

s 2 (1

K2 K3 K4 (1 + sτ2 )(1 + sτ3 )  K2 K3 K4 M= × K P2 + s K D2 + (1 + sτ2 )(1 + sτ3 )(1 + sτ4 ) + K 2 K 3 K 4  K1 K6 K5 N= × K P1 + s K D1 + (1 + sτ1 )(1 + sτ5 )(1 + sτ6 ) + K 1 K 6 K 5

(5)

H=

(6) KI2 s KI1 s

 (7)  (8)

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Therefore, by solving above equations, the temperature T1 and humidity equivalent temperature T2 of the ACSS can be written, respectively, as, T1 =

T1 T1 T A1 + T A2 T A1 T A2

(9)

T2 =

T2 T2 T A1 + T A2 T A1 T A2

(10)

2.3 Controller and Optimization The PID controller is used which includes advantages of all (i.e., proportional, integral, and derivative) controllers by mitigating the disadvantages of all three. The transfer function of a typical PID controller is given by Eq. (11) where, K P , K I and K D are the parameters of PID controller, and the input error e(t) is related to output u(t) by Eq. (12). KI U (s) = KP + + s KD E(s) s  d u(t) = K P e(t) + K I e(t)dt + K D e(t) dt H (s) =

(11) (12)

The parameters of the controller are to tuned using particle swarm optimization (PSO) with ITAE as an objective function which is given by Eq. (13). ∞ t × |e(t)|dt

ITAE =

(13)

o

3 Simulation Results The proposed cold storage system is implemented in MATLAB by using the analytical equations from (1 to 10). This is worth of mentioning that these analytical equations sufficiently (but not necessarily) model the cold storage system under consideration using transfer function approach of dynamic system. The temperature and humidity of the internal atmosphere of automated cold storage system are plotted using the MATLAB, and these plots are depicted in Fig. 3. From Fig. 3a, it can be observed that the initial deviation in the temperature is automatically adjusted to the desired level irrespective of the initial temperature of the

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Fig. 3 Self-adaptation of ACSS to desired temperature a temperature deviation b relative humidity deviation

cold storage system. Moreover, the fascinating result is that the time taken by the system to adjust to the desired temperature is only 20 s approximately. Further, the relative humidity deviation of ACSS is shown in Fig. 3b indicates that initially, there is some error observed in relative humidity, which is approximately 0.12%. However, the relative humidity reaches to the desired level within very short span of time (approximately 4 s). A negligible damping is found which converges quickly to the desired relative humidity. Therefore, from Fig. 3, it can be observed that the proposed design is able to adjust the temperature and relative humidity of the cold storage system automatically. The more applauding observation of the proposed design is that the time taken to maintain the internal atmosphere of cold storage system is very minimum, which is approximately 20 s.

4 Smart Monitoring and Control of ACSS Using IoT In this section, we propose a generic and efficient way to provide the intelligence capabilities in automated cold storage system.

4.1 Design Perspective A comprehensive pictorial representation of the ACSS with smart connectivity based on ubiquitous Internet of Things concept is presented in Fig. 4. As shown in Fig. 4, the ACSS are integrated with the several sensors such as temperature and humidity sensors which are used to collect the temperature and humidity of the ACSS at different interval of time. These data are then need to be

Analytical Modeling and Optimal Control … Fig. 4 IoT enabled ACSS with reconfigurable and self-adaptatable capabilities

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IoT Cloud

SUN Cold Storage

IoT Gateway

IP Network

AP ED

ED

Router

ED Central Monitoring Station

WLAN

communicated to the central monitoring station (CMS) which monitors and requests to set the desired atmosphere of the cold storage system depending upon the several factors such as external weather condition, current internal atmosphere of the storage system, the products to be stored inside the warehouse, etc. This is worth of noting that the central condition monitoring center may not necessarily be located at the same place where a cold storage system is established. Consequently, the ACSS is connected to a local area network (LAN) which also host some of the other devices which are referred as end-devices (ED). Among the available choices-wired or wireless, the wireless connectivity for LAN (WLAN) is preferred for establishing seamless connectivity.

4.2 IoT Deployment The architecture of the IoT for automated ACSS is depicted in Fig. 5. The components of IoT enabled ACSS are briefly discussed below with its deployment strategy. Fig. 5 IoT deployment strategy of ACSS

Cloud Service

3G/4G Sensors/ actuators Wired sensor/actuators based on RS232, USB, etc connectivity

IoT Server BLE Gateway CMS

IP Network

BLE Sensors/ actuators

Non-IoT Sensors/actuators LPWAN-IoT Gateway

Concentrator

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Sensors and actuators: The sensors and actuators are responsible to directly establish a connection with physical layer. The various sensors such as temperature, humidity, CO2, etc. Sensors collect the data which needs to be sent to the IoT server for processing. On the basis of the processed data, commanding instruction is sent to actuators through IoT servers to perform the desired task. Interconnectivity: The sensors and actuators may have IP-based communication capabilities (they are referred to IoT sensors and actuators) or may not have IP-based communication capabilities (referred to as non-IoT sensors and actuators). Application layer Protocols: The widely accepted application layer protocols include: message queuing telemetry transport (MQTT), hyper text transfer protocol (HTTP), constrained application protocol (CoAP), advanced message qeuing protocol (AMQP), data distributed service (DDS), extensible messaging and presence protocol (XMPP), etc. The choice of these protocols completely depends upon the required quality of service (QoS) and security levels. For the proposed ACSS, CoAP (an application layer protocol) would be a wise choice as it works on user datagram protocol (UDP) having less overhead.

5 Conclusion and Future Scope A novel large-scale automated cold storage system with an analytical modeling is proposed. The design of ACSS is implemented in MATLAB, which shows excellent self-adaptable capabilities of the system. The performance metric: temperature and relative humidity can be easily set to the desired level yielding almost 0% deviation from the targeted values. Moreover, the deployment strategy of the system considering various implementation challenges is presented based on burgeoning IoT, which further improves the efficacy of the system. A complete IoT protocol stack for the ACSS and its implementation is the future work, which authors wish to explore.

References 1. Satiti D, Rusdiansyah A, Dewi RS (2018) Review of refrigerated inventory control system for perishable products. In: 2018 IEEE international conference on industrial engineering and engineering management (IEEM), Bangkok, pp 36–40 2. Lee J, Jeong S, Han YH, Park BJ (2011) Concept of cold energy storage for superconducting flywheel energy storage system. IEEE Trans Appl Supercond 21(3):2221–2224 3. Nejad AR, Nejad AR, Abedi ME, Nejad AR (2017) Production of electrical power in very extreme-temperature environmental conditions: a new implementation of thermoelectric generators. In: 2017 IEEE 6th international conference on renewable energy research and applications (ICRERA), San Diego, CA, pp 468–472 4. Xiaoyan L, Shuting Y, Dong Z (2010) Study of new cool storage materials for refrigerated vehicle in cold chain. In: 2010 international conference on logistics systems and intelligent management (ICLSIM), Harbin, pp 637–640

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5. Nagpal SK, Sudha S (2015) Design and development of a sensor node to monitor and detect change in internal parameters of a cold storage system. In: 2015 global conference on communication technologies (GCCT), Thuckalay, pp 745–748 6. Sousa PJ, Quintas M, Abreu P (2015) Monitoring system for cold storages. In: 2015 3rd experiment international conference (exp.at’15), Ponta Delgada, pp 139–140 7. Deng Z, Zhang Y (2011) Research on the cold-storage door control based on frequency conversion technology. In: Proceedings of 2011 international conference on electronic and mechanical engineering and information technology, Harbin, pp 4512–4515 8. S Gui, W Feng, Z Zhang (2010) Research on multi-echelon inventory model of cold-chain logistics on random demand. In: 2010 international conference on logistics systems and intelligent management (ICLSIM), Harbin, pp 1454–1457 9. AS Kumar, Sudha S (2016) Design of wireless sensor network based fuzzy logic controller for a cold storage system. In: 2016 IEEE 7th power india international conference (PIICON), Bikaner, pp 1–6 10. Daponte P, Lamonaca F, Picariello F, De Vito L, Mazzilli G, Tudosa I (2018) A survey of measurement applications based on IoT. In: 2018 workshop on metrology for industry 4.0 and IoT, Brescia, pp 1–6 11. Coelho C, Coelho D, Wolf M (2015) An IoT smart home architecture for long-term care of people with special needs. In: 2015 IEEE 2nd world forum on Internet of Things (WF-IoT), Milan, pp 626–627 12. Du R, Santi P, Xiao M, Vasilakos AV, Fischione C (2018) The sensable city: a survey on the deployment and management for smart city monitoring. IEEE Commun Surv Tutor 21(2):1533– 1560 13. Chen S, Xu H, Liu D, Hu B, Wang H (2014) A vision of IoT: applications, challenges, and opportunities with China perspective. IEEE IoT J 1(4):349–359 14. Abdelsamea MHA, Zorkany M, Abdelkader N (2016) Real time operating systems for the Internet of Things, vision, architecture and research directions. In: 2016 world symposium on computer applications and research (WSCAR), Cairo, pp 72–77 15. Gupta DK, Naresh R, Jha AV (2018) Automatic generation control for hybrid hydro-thermal system using soft computing techniques. In: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), Gorakhpur, India, pp 1–6 16. Jha AKV, Verma VK, Prince P, Priyadarshini B, Ranjan RK (2018) PSO based design of current CCII-PID controller for the speed control of BLDC motor. In: 2018 international conference on power, energy, control and transmission systems (ICPECTS), Chennai, pp 245–248 17. Nagrath IJ, Gopal M (2005) Control systems engineering, 4th edn, ch. 2. Wiley Eastern Ltd, p 23

Challenges and Learnings During Integration of Phasor Measurement Unit Data with Supervisory Control and Data Acquisition System—An Indian Experience Pramod Singh, Santosh Kumar Jain, Devender Kumar, and P. K. Agarwal Abstract With gradual expansion of the grid, the conventional Supervisory Control and Data Acquisition (SCADA) required additional visualization tools to operate the grid in more efficient manner. Due to low situational awareness, it became difficult for the operator to control the grid in safe and secure manner. This also resulted in isolated blackouts in the grid. In order to enhance the situational awareness, Phasor Measurement Units (PMUs) were installed initially at critical locations identified on the basis of system studies. Upon installation of PMUs, the resolution of data increased from 2 to 4 (SCADA) to 40–60 samples/cycle (PMU). India being geographically diverse and large, the number of PMUs requirement was very high. This large-scale integration of PMUs with existing SCADA posed many technological, geographical, financial and adaptability problems (electricity being concurrent subject of state and centre). This paper elaborates all such integration issues and challenges faced during integration of SCADA-PMU pan India. Keywords ICCP · PMU · PDC · RTUs · SCADA · Situational awareness · URTDSM · WAMS

1 Introduction Supervisory Control and Data Acquisition (SCADA) system facilitates smooth grid operation in real time by the system operator. Data updation is the key requirement in the SCADA system for operator to take suitable action in real time. Over the period of time, it was observed that despite taking all measures to get uninterrupted and accurate data in SCADA, few data (analog, status, etc.) became erratic/nonreporting at times due to various reasons. Non-reporting of data may be on account of communication failures, RTU failures, errors in conversions, transducer errors, P. Singh · S. K. Jain (B) · D. Kumar · P. K. Agarwal Power System Operation Corporation, New Delhi, India e-mail: [email protected] P. Singh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_6

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etc. Since resolution of these issues take some time and non-availability of real-time data during this period may result in various operational and commercial issues. To overcome such issues, synchro-phasors were envisaged and commissioned that helped in providing redundancy of data in case of SCADA failure and that too at a higher speed. Unified Real-Time Dynamic State Measurements (URTDSM) scheme by POWERGRID (Central Transmission Utility of India) involved in installation of PMUs to establish wide area measurement system (WAMS) in Indian power system.

1.1 SCADA System at Load Despatch Centres Primary functions of Load Despatch Centres is to ensure the integrated operation of the power system in the respective region, monitoring of system parameters and system security along with many other allied functions. India is being demarcated into five regions, viz., Western, Eastern, Southern, Northern and NorthEastern. These regions control the states coming under their jurisdiction through State Load Despatch Centres (SLDCs). For performing these functions, the state-of-the-art SCADA systems were implemented at all control centres. SCADA is also called eyes and ears of the operator, as without SCADA it becomes very difficult to comprehend the state of the power system which is absolutely necessary for the system operator in real time. Load dispatch and communication facilities are available at State Load Despatch Centres (SLDCs), Regional Load Despatch Centres (RLDCs), National Load Despatch Centres (NLDC). ULDC scheme has been established in the hierarchical order which includes Regional Load Despatch Centre at the apex level and the Remote Terminal Units (RTUs)/Substation Automation Systems (SAS) at power station/substation at the lowest level as shown in Fig. 1. The data of RTU at substation (lowest level) is routed through SLDC (Intermediary level) in addition to direct link to RLDC(Apex level). The RTU/SAS acquire and forward parameters like voltage, frequency, MW, MVAR, breaker and isolator positions, etc. to SLDC/RLDC in real time. The central sector data is directly transmitted to RLDC and the state sector data is transmitted to SLDC in the respective state. For data exchange between control centres, ICCP protocol has been used. Subsequently, all SCADA system at Regional Load Despatch Centres (RLDCs) are reporting to SCADA system of main National Load Despatch Centre (NLDC), whereas back up SCADA system of RLDCs reports to SCADA system of back up NLDC (Fig. 2). Further data from SCADA system of all the State Load Despatch Centre reports to SCADA system of Regional Load Despatch Centre. All the SCADA system of Regional Load Despatch Centre reports data to SCADA system of NLDC through ICCP protocol (Fig. 3).

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Fig. 1 RTU reporting to control centre Fig. 2 Data reporting to main and back up control centre

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Fig. 3 Hierarchical set up of data reporting

1.2 WAMS at Load Despatch Centres WAMS is based on the new data acquisition technology of phasor measurement. Recognizing the need of wide area monitoring systems application in Indian power system, PMUs have been installed on substations at 400 kV level and above in the state and central grids, all generating stations at 220 kV level and above, high voltage direct current (HVDC) terminals, important inter-regional connection points, international connection points, etc., provision of phasor data concentrators (PDC) at all SLDCs, RLDCs and NLDC along with visualization aids in the first phase. This has been done as part of URTDSM project towards improved system operation. Architecture of URTDSM is as given in Fig. 4.

Fig. 4 Architecture of URTDSM system

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Normally, the basic data, i.e. voltage, MW, MVAR, connection status of elements of power system, etc. which is required for real-time grid operation is available in SCADA at control centre. With implementation of URTDSM project, lot of data is available from many substations where PMU has been installed. Although data available from URTDSM is subset of data available from SCADA in terms of geographic spread, it is very important as a redundant data source. Additionally, data available from PMUs is more accurate with high refresh rate. With implementation of URTDSM, data redundancy has been increased as the data is available from multiple sources and from diverse communication channels. After integration of PMU data with the SCADA system, data availability from PMU can be used for operation, in case data from RTU is not reporting or faulty, thereby improving the real-time data availability.

2 Need of Integration of PMU Data in SCADA 2.1 Angular Difference Between Different Buses The Phase angle difference of voltage across different nodes (buses) is an indication of stress across the grid and needs to be monitored with respect to the stability limit for secure grid operation. SCADA being asynchronous measurement, angular difference cannot be measured. Angular difference between two buses in interconnected system is very important from real-time operation point of view. In WAMS, phase angle of each bus can be computed with respect to reference bus. This has been integrated in GRID display helping real-time operator to get angular data of each bus and hence determine angular separation (Fig. 5).

Fig. 5 Displaying angular separation in grid display

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Fig. 6 Real-time drawl computation using PMU data

2.2 Real-Time Drawl Computation of State from the Grid (Central Sector) Real-time drawl computation is a key calculation used to find out drawl from central sector for each state/control centre in real time. It is always desirable to have drawl equal to schedule for the state. For a state, schedule remains same for at least 15 min (1 block), whereas drawl keeps on changing. Accurate computation of drawl can be ensured with the availability of telemetry of all interface points. In case anyone interface telemetry is not available, drawl calculation becomes erroneous. resulting in operational and commercial issue. In this type of problem of erratic interface, telemetry data can be obtained from WAMS. Calculation using PMU data for drawl computation has been shown in Fig. 6. It appears with an @ A (shown in red circle in Fig. 6) symbol against the source in display. When the value of a particular telemetry is not updating or has wrong value due to transducer issues or RTU errors, PMU data serves as ideal replacement.

2.3 Availability of All Type of Data from PMU Several points from PMU report to SCADA from the URTDSM PMUs. Some of them are: • • • • •

Voltage magnitude (+ve seq) Voltage angle (+ve seq) Frequency MW MVAR.

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2.4 State Estimation Usage of PMU data in state estimation gives better and consistent results. It has been observed that cost factor (index used for quality of state estimation) reduces when PMU data is used in state estimation. While configuring data base, higher weightage can be given to telemetered data with WAMS as source.

3 Integration Philosophy of WAMS (URTDSM) to SCADA System at Regional Control Centre-Indian Perspective As mentioned, WAMS has been deployed pan India under URTDSM WAMS. The selected real-time data is being exchanged from URTDSM WAMS to existing SCADA/EMS (Energy Management System) of respective control centre in each region over IEC-60870-5-104 protocol at all RLDCs/SLDCs, respectively. Architecture of same is shown in Figs. 7 and 8.

Fig. 7 URTDSM WAMS integration with existing SCADA/EMS at RLDCs and SLDCs

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Fig. 8 URTDSM WAMS integration with existing SCADA/EMS at RLDCs and SLDCs

4 Challenges and Mitigation in Integration of PMU Data to SCADA Following are the key challenges and its solutions adopted for integration of PMU data to SCADA system.

4.1 Protocol Compatibility Issue PMU data reports to phasor data concentrator (PDC) which can be integrated to SCADA. This integration can be done on 60870-5-104 protocol. Some SCADA systems did not support integration of data on 60870-5-104 protocol. To resolve this problem, a protocol converter was used for conversion of 608705-101 to 60870-5-104 between WAMS and SCADA. Following screen shot shows integration of WAMS with SCADA system using protocol converter (Figs. 9 and 10).

4.2 Data Acquisition Rate Refresh rate of data in case of WAMS is very high as compared to SCADA system. Sampling rate in WAMS may be in the range of 10–50 samples per second. Considering average sampling rate to be 25 cycles per sec, in terms of time scale, it comes out to one sample of data at an interval of 40 ms. Whereas in case of SCADA normally, for analog data, generally periodic scan is in the range of 2–10 s. Due to this, many samples may be missed while transferring PMU data to SCADA. This problem was solved to a large extent by configuring lowest periodic scan in the available SCADA system. Data very critical from operation point of view was

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Fig. 9 Integration of PDC to SCADA system using protocol converter

Fig. 10 Integration of PDC to SCADA system using protocol converter

configured at higher scan rate, whereas other less critical data was configured to scan rate similar to data available from RTUs.

4.3 High Volume of Data Due to very high sampling rate, volume of data was huge in case of WAMS system. After integration of PMU data to SCADA system, this data is available in SCADA system also. SCADA system is designed to have its own archival system to store data. In case data volume increases in SCADA system because of PMU integration,

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it may result into scarcity of resources in historian of SCADA system in terms of storage space and performance. To mitigate this problem, proper design of SCADA archival system was done envisaging volume of PMU data to be integrated with SCADA. Alternatively, data archival from WAMS was configured selectively.

4.4 Data Base Size Generally, SCADA data base sizing is designed considering the number of substations reporting through RTU or ICCP. Due to integration of PMU data to SCADA, additional data points (tags) were required to be configured in SCADA. This may create problem in terms of data base sizing beyond the limit of designed data base. For resolution of this issue and to have smooth integration of PMU data, the SCADA data base size needs to be considered at design stage itself. In the Indian context, the existing PMU data was integrated smoothly as the design of existing data was sufficient. However, for upcoming advancement of SCADA system and PMUs, the data base size was not sufficient. So, in all upcoming SCADA systems, enhanced data base size specifications were incorporated.

4.5 Data Base-Related Issues Data which is being integrated into SCADA is already available from RTUs. In SCADA data base design, provision should be there to integrate data with multiple sources. For example, bus voltage of a substation may be reporting in SCADA from RTU. In SCADA, feature should be available to integrate same data as a separate tag or data base point. There should be “alternate data source” (ADS) feature in SCADA system for optimal use of PMU data. This problem was resolved by designing the data base in such a fashion that was able to take data from multiple input sources automatically.

4.6 Hardware Issues For integration of WAMS data to SCADA system, additional hardware may be required, which are not part of implemented SCADA and WAMS system. This may include switches, protocol converter, routers, etc. It has been observed that many times these hardware devices create problem in smooth integration of WAMS system to SCADA because of non-standard design, difference in data transfer rate, unavailability of proper interface, etc.

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To avoid this problem, detail engineering needs to be done after study of both the WAMS and SCADA system. All technical aspects such as design, speed and configuration, etc. of additional hardware devices should be clearly defined to avoid any issue during integration. Additionally, it will ensure stable data transfer between WAMS and SCADA system, post-integration

5 Recent Trends of PMU Data in SCADA With increase in complexity in real-time grid operation, importance of situational awareness has increased manifold. WAMS data availability in SCADA system increases the observability of the system. In view of importance of measurement in real-time grid operation, various alternatives are being explored to get accurate data with high refresh rate. While PMU is one of the very good option for the same, the cost involved has to be factored. With proper system study, same can be reduced while deploying PMU at various buses. While placing the PMU at various nodes in electrical grid, not only numbers but coordinates also play a key role for getting optimum information from PMU data.

6 Conclusion and Way Ahead SCADA and WAMS are the enabling technologies currently available for power system operation and analysis. However, applications developed on SCADA systems are much more matured than those developed for PMUs. Present phase is witnessing a gradual transition from traditional SCADA systems to the high-resolution data technology-based WAMS. Future SCADA systems will surely be based on PMUs as de facto system for data acquisition for Load Despatch Centres. In the intervening period, integration of WAMS data with SCADA systems offers lot of advantages. Further, this will pave the way for development of SCADA application with most of the data acquired through PMUs. Indian grid being larger in size and geographically diverse posed many challenges and the same were resolved by meticulous planning and real-time adaptations. The lessons learnt during said integration might be useful to other grids across the globe to design their SCADA and PMU system in a smooth and economical manner. Acknowledgements Authors would like to acknowledge POSOCO management for their encouragement and valuable inputs during the course of presenting this paper.

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References 1. Synchrophasor initiative in India, annual report of Power System Operation Corporation Limited, December 2013 2. Synchrophasor Initiative in India, annual report of Power System Operation Corporation Limited, June, 2012 3. URTDSM (Unified Real Time Dynamic State Measurement) Report by POWERGRID, 2012 4. Soonee SK, Narasimhan SR, Porwal RK, Kumar S, Kumar R, Pandey V (2008) Application of phase angle measurement for real time security monitoring of Indian Electric Power SystemAn Experience CIGRE 2008, C2–107

Cyber Intrusion Detection in Smart Grid Using KPCA and Novel Trust Method Neeraj Kumar Singh, Praveen Gupta, Vasundhara Mahajan, Saurabh Pranjale, Tharun Balaji, and Syed Aamir Ahmed

Abstract Smart grid network consists of large number of sensors interacting with each other for data transfer. These sensors are highly sensitive in order to detect minute changes. Intruders can inject false data after compromising the wireless sensor nodes, changing system status to abnormal and activating false alarm. This research proposes a new approach to detect false data from the pool of large data collected through different measuring units in wireless network of smart grid. In this innovative technique, the Kernel Principal Component Analysis (KPCA) with Novel Trust Method (NTM) is implemented to detect cyber intrusion. Clustered data obtained from wireless sensors is processed through NTM and then analyzed through trained KPCA to monitor possible fake data injection. T 2 is used to identify different abnormal data sample and Q of KPCA highlight the data sample manipulated through cyber-attack. MATLAB 2019 is used to evaluate proposed method on IEEE-9 test system to analyze severity of intrusion. Keywords Smart grid · Cyber intrusion · Kernel Principal Component Analysis (KPCA) · Novel Trust Method (NTM) · False data · Wireless sensor (WS)

1 Introduction Modern smart grid network consists of networked digital resources with increase connectivity using different wireless networks. Increased connectivity has greatly increased the potential damage that can be caused by attacks launched against different digital components of smart grid from remote areas [1, 2]. The security of a wireless sensor network (WSN) is compromised when a cyber intrusion takes place. A cyber intrusion detection system (CIDS) is an important aspect that attempts to detect any malicious activities which may compromise power grid security [3]. In general intrusion detection, technique in power system is classified in two types:

N. K. Singh (B) · P. Gupta · V. Mahajan · S. Pranjale · T. Balaji · S. A. Ahmed Electrical Department, SVNIT Surat, Gujarat 395007, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_7

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Expert detection technique and anomaly detection technique. Expert detection technique is a method which expert code to protect the system from known cyber-attacks. Different cyber-attack pattern is studied and using expert knowledge prevention technique is draft. This technique leads to low false alarm rate because of it nature. Anomaly detection technique can detect new and unknown intrusion [4]. Different concepts have been developed for the detection of anomaly based detection techniques, such as data mining, statistical method and machining learning. All these methods have been applied on real-time data with high dimension variations without dimension reduction techniques. The main idea behind this work is to reduce the data magnitude while classifying the data in normal and abnormal category through NTM and then applying KPCA method to detect the sample and time of abnormal detection. Principal component analysis extracts the relevant information from the set of data which can be used for detection of abnormality in the data sets [5]. However, PCA allows only a linear dimensionality reduction making it non-suitable for smart grid. But if data has more number of nonlinear elements, stated method will be not so effective. This drawback is overcome through KPCA for extracting principal component which show satisfactory results in the field of cyber intrusion detection [6].

2 Methodology Using NTM and KPCA This section deals with modeling of the system and theoretical analysis of NTM and KPCA.

2.1 Novel Trust Method (NTM) NTM is a very new concept and easy to understand that a measured value is trusted vale or manipulated vale. Suppose a WS measured any electrical value (voltage, frequency, etc.) which can be categorized as most trusted, trusted and malicious data using the formula:   · ·  Ti = E/ Dir − Dih

(1)

E is threshold value, depends on upper bound value of real-time data (D) and historical data. r and h indicate present and historical data. Trust range is described in Table 1. Trust value is calculated for each data collected from WS of smart grid network and categorized as stated above. After getting the trust value of each measured data, it is used by KPCA using equation: Ti = xi

(2)

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Table 1 NTM range with description Ti

Range

Description

Most trusted

∞ (Infinity)

No change in the measurement

Trusted

>1

Variance in the measurement is low within the healthy limits of operation

Malicious data

0–1

Measured data has high deviation/unhealthy operating conditions

2.2 Kernel Principal Component Analysis (KPCA) PCA is powerful mathematical tool used to transform correlated data into uncorrelated data known as principal components (PC) [7–9]. The main aim of PCA is to reduce dimensionality of the initial measured data of WS, while retaining as much as possible the variance present in these data samples. Now suppose if training set of M vectors x 1 , x 2 , x 3 ,…, x M each containing f features. To determine the PC of this training set, following procedure must followed. In the first step, calculate the average σ of the set using equation:  σ =

1 M

 M

xi

(3)

i=1

Using the above equation, ρ can be obtained as follow: ρi = xi − σ

(4)

Now the covariance matrix C is computed as follow:  Cf×f =

1 M

 M

ρi ρiT

(5)

i=1

For better explanation, Eq. (3) is reduced to C f × f = B BT

(6)

  where B is stated as B f ×M = √1M ρi . Let U k be the kth eigenvector of C corresponding to the λk , we can conclude the relation of covariance matrix with eigenvalues as follow CUk = λk Uk

(7)

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Now sort the eigenvalues in decreasing order and choosing the first value as PC. Using PCA T 2 and Q monitoring chart is used to identify abnormality in the measurements due to false data injection through cyber-attack [8]. The T 2 statistic monitoring is modeled using the PC as follow: T2 =

p  ti2 λ i=1 i

(8)

where λi is the ith eigenvalue of the covariance matrix of original data set. T 2 statistic monitoring indicates the variation from healthy data set to unhealthy data set which may occur due to physical fault in the system or damaged measuring equipment. To identify the false data injected by attacker which cannot be calibration with historical data set, such data can be detected by computing the squared prediction error or Q statistic. The predicted form can be expressed as: Q = B BT

(9)

But PCA system fails if nonlinearity is high in the measured data set. To overcome this, KPCA come into existence. It converts PCA system to KPCA using nonlinear mapping ∅ with arbitrary large dimension space F [10, 11]. The mapped covariance is given as  Cf×f =

1 M

 M

ϕ(ρi )ϕ(ρiT )

(10)

i=1

The proposed method is described using the flow chart highlighted in Fig. 1. The real-time data from WS network is collected and the magnitude of all these data is minimized and categorized using NTM. The system is provided with training data such that normal and abnormal condition for detection can be trained. After this testing, data subset is selected and both subset is passed through KPCA. Using T 2 and Q statistic monitoring the system identify the physical anomaly and cyber anomaly.

3 Result and Discussion This section investigates the accuracy and efficiency of the proposed method to identify the cyber intrusion in smart grid WS network. In order to highlight the effectiveness of the method, cyber intruders try to attack on IEEE-9 bus by compromising the WS of bus 6 by intruding false measurement data. Two results are discussed in this portion, i.e., normal operation without cyber intrusion and operation with cyber intrusion. During normal operation, any physical fault may lead to alarm to alert

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Fig. 1 Flow chart of proposed method

operator for possible fault using T 2 statistic monitoring as shown in Fig. 2. Test sample 172–175 of WS of bus 6 shows that there is an abnormal condition. But Q statistic monitoring shows that none of the test data crosses the threshold level, which

Fig. 2 Monitoring chart under abnormal condition excluding anomaly cyber intrusion at bus 6

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Fig. 3 Monitoring chart under abnormal condition including anomaly cyber intrusion at bus 6

indicate there is no cyber intrusion and the abnormal condition is due to physical layer fault. Figure 3 shows the monitoring chart during cyber intrusion. T 2 statistic monitoring shows that data sample like 43, 76, 148–163, etc. crosses the threshold value indication abnormal conditions. But this number of abnormal condition will trigger alarm. But the actual cyber intrusion can be indicated by Q statistic monitoring showing sample data 87 and 163 crosses the threshold level. Means out of different data the system is able to highlight the false data injected by cyber intruders to make the smart grid operation unhealthy.

4 Conclusion In this paper, a mixed strategy of NTM and KPCA is used to identify the false data injected by cyber intruders to make smart grid operation unreliable. NTM is a new concept which can be easily apply for smart grid system to categorize the measured data in terms of trusted or non-trusted measurement with reduced magnitude of original data. Results show that KPCA is a suitable tool to differentiate the physical layer fault and cyber intrusion using T 2 and Q statistic monitoring. The system can be improved if more number of training data set is available for different conditions. The work can be extended on the basis of detection time and combining KPCA with other machine learning techniques.

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References 1. Qiao Y et al (2002) Anomaly intrusion detection method based on HMM. Electron Lett 38(13):663–664 2. Wang X et al (2015) Deep data fusion model for risk perception and coordinated control of smart grid. In: 2015 international conference on estimation, detection and information fusion (ICEDIF). IEEE 3. Yong H, Feng ZX (2010) Expert system based intrusion detection system. In: 2010 3rd international conference on information management, innovation management and industrial engineering. IEEE 4. Gao H-H, Yang H-H, Wang X-Y (2005) Kernel PCA based network intrusion feature extraction and detection using SVM. In: International conference on natural computation. Springer 5. Heba FE et al (2010) Principle components analysis and support vector machine based intrusion detection system. In: 2010 10th international conference on intelligent systems design and applications. IEEE 6. Kim B-J, Kim I-K (2005) Kernel based intrusion detection system. In: 4th annual ACIS international conference on computer and information science (ICIS’05). IEEE 7. Elkhadir Z, Chougdali K, Benattou M (2016) Intrusion detection system using pca and kernel pca methods. In: Proceedings of the mediterranean conference on information and communication technologies 2015. Springer 8. Yin S et al (2016) PCA and KPCA integrated support vector machine for multi-fault classification. In: IECON 2016–42nd annual conference of the IEEE industrial electronics society. IEEE 9. Chen Z, Ren H, Du X (2008) Minimax probability machine classifier with feature extraction by kernel PCA for intrusion detection. In: 2008 4th international conference on wireless communications, networking and mobile computing. IEEE 10. Ikram ST, Cherukuri AK (2016) Improving accuracy of intrusion detection model using PCA and optimized SVM. J Comput Inf Technol 24(2):133–148 11. Kuang F, Xu W, Zhang S (2014) A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 18:178–184

Analyzing Impacts of Energy Dissipation on Scalable IoT Architectures for Smart Grid Applications Sakshi Gupta and Itu Snigdh

Abstract The large volume of data generated by vast number of smart meters necessitates intelligent methods and solutions for their real time exploitation for the necessary decision making. They also call for minimal human interaction and provide intelligence to devices so that they can sense the situation and act accordingly. One of the underlying technologies for realizing this requirement is IoT that employs wireless sensor networks wherein sensors work as observational devices for the environment. Also, physical devices tend to add up every day and hence we need to prepare for a scalable IoT architecture. The need of a big network, to develop a scalable architecture for handling massive data generated by sensor devices is the main focus of our article. We propose an architecture that would help data collection from a smart grid network that can dynamically grow and study the impact on energy consumptions for such networks. Keywords Smart grid · IoT · MANET · Energy consumption · Scalable architecture · AMI

1 Introduction Advanced metering infrastructure (AMI) and demand response algorithms are solutions that facilitate the full integration of high speed and two way communication technologies with numerous power equipments as envisioned by smart grid technology. As compared with legacy power systems, the Smart Grid establishes a dynamic and interactive infrastructure with new energy management capabilities [1, 2]. The technologies that assist to optimize operations of such systems are robust data analytics, cloud computing infrastructures and efficient data network management

S. Gupta · I. Snigdh (B) Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, India e-mail: [email protected] S. Gupta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_8

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architectures. Still, potential vulnerabilities exist associated with the communications and networking systems on which the successes of smart grids heavily depend. Some of the essential requirements of smart grids may be summarized as: • • • •

A communications network that is highly distributed and hierarchical A network of infrastructure as well as ad hoc devices Need for both wired and wireless technologies A network capable of influencing the methods and modes of electricity generation, transmission and distribution. • Provide powerful data processing in distributed computing systems and clusters for real time processing. A solution to efficiently map the requirements of a smart grid to a viable solution is ˙Internet of Things applications. IoT has been defined as the interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications [3]. This is achieved by seamless ubiquitous sensing, data analytics and information representation with cloud computing as the unifying framework [4]. IoT as infrastructure, consists of networks of sensors attached to objects and communication devices, providing data that can be analyzed and used to initiate automated actions. In event of sensor based real time decision aiding monitoring, a huge volume of data is generated which needs to be collected efficiently and processed for effective decisions. Our article focuses on leveraging the concept of edge nodes, clustering hierarchy in communication of data that is generated from the sensors, or metering devices. We emphasize that the recurrent data generation and transmission may successfully be captured by our proposed framework and is energy efficient. We present forth an IoT based framework that employs both static devices like meters, infrastructure objects namely aggregators, transmission grids and ad hoc nodes like meters, sensors or intelligent electronic devices installed on the power infrastructure.

2 Related Work Typical communication architecture of a smart grid is two tiered. The first set of communication happens between utility servers, smart meters and hardware. The second set of communications is between the power plants utility meters and the power plant to households via transmission lines. The advance metering architecture [5] studies the design of a large-scale IoT system for smartgrid application, which constitutes large number of home users and has the requirement of a fast response time. The authors particularly focus on the messaging protocol of a universal IoT home gateway. A detailed discussion on smart metering and energy management applications in IoT systems and their comparison for ICTs utilized in these applications have been outlined in reference [6]. Literatures also present the delineate of network communications in smart home systems, which may be conducted through

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Fig. 1 AMI Communication in smart grid. Source NIST

neighborhood area network (NAN), which contains smart home groups and data aggregators in a specified area. To suit such varied requirements of a smart grid or DER infrastructure, the layered architecture of IoT applications incorporates four layers [2, 7].The basic layer is the sensory input layer that includes different types of sensor devices. The second layer is the network layer which facilitates heterogeneous network connections among Zigbee, WLAN, RFID, Bluetooth LoRA, through gateways. At the data processing layer, acquisition, validation, storage and processing of stored data are done (Fig. 1).

3 Proposed Architecture Our proposed architecture is capable of blending capacity of mobile ad hoc network to the IoT infrastructure. Figure 2 depicts the proposed architecture with four layers, where base layer of existing architecture is divided in two layers. The communication in the proposed architecture happens in the following manner. Data is generated by sensor nodes or member nodes (energy meters). Data is send to coordinator node (intelligent relays) by all its member nodes. The coordinator node preprocesses and transfers the data to a data sink which is essentially mobile.

4 Algorithm for Small Scale MANET-IoT Architecture We assume that there are ‘M’ homogeneous smart meters (M 1 , M 2 … M n ) in a N × N residential area which are fixed and receive constant power. There are ‘P’ intelligent relay devices that are called coordinators and are responsible for the connectivity among the ‘M’ meters. These relays are connected to ‘Q’ gateways that may be mobile and serve as the data collection points in the region of interest.

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Fig. 2 Small scale architecture for IoT-AMI systems

Step 1: Advertisement phase: The communication network creation starts with the advertisement phase where the gateway nodes transmit their node ID and respective coordinates. Step 2: Connection Phase: Based on communication range of all nodes (Node range) in network, the meters nodes connect to relay nodes in their range. Step 3: Implementation of K-Means to identify coordinator nodes: Node with minimum distance from meter nodes computed is declared as coordinator nodes. Figure 3 depicts the probable devices that may serve as relay devices or coordinator nodes. Figure 4 depicts the actual coordinator nodes that are chosen by implementing the K means clustering algorithm. Fig. 3 Image with centroids

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Fig. 4 Image with coordinator points

Step-4: Communication establishment of Coordinator node CN: The final step is connecting the coordinator nodes to the gateway devices. Here we assume a wireless network wherein the communication is dependent on the individual range of the devices. Step-5: Energy Computation: Computation of energy dissipated at coordinator packets from member nodes as well as calculated by n node in receiving 2 K ∗ L ∗ d ; Where K = Number of Bits; L = Length of bits and d = distance i=1 between neighbor node and CN) Computation of energy dissipated at coordinator node in transmitting packets to mobile node is n K ∗ L ∗ d 2 ; where n is the number of nodes with 1 coordinator node and d is the distance between coordinator node and gateway node.

5 Performance Analysis of Proposed Architecture Table 1 presents the simulation parameters that have been considered for analyzing the communication framework. We analyze our proposed IoT infrastructure by varying the parameters as shown in table 1, and observing their impact on the energy expedited in communicating data to wither the relay nodes or gateway devices. Table 1 Values of parameters affecting volume of network traffic

S. No

Parameters

Values

1

Node density

15,25,35,45,55

2

Node range

20 m,25 m,30 m,35 m

3

Number of coordinator node 2,3,4,5,6,7

4

Coordinator node range

20 m, 25 m, 30 m

5

ROI

100*100

S. Gupta and I. Snigdh Energy Consumption(nJ)

86 Energy Consumption vs Node Range 60000 50000 40000 30000 20000 10000 0

Energy consumption by CN in receiving data

15N

20N

25N

35N

45N

55N

Energy consumption by CN in transmitting data to MN

Node Density

Fig. 5 Effect of increasing node density on energy dissipated

Fig. 6 Effect of increasing node range in network for fixed node density, CN, CN range

Figure 5 depicts that increasing node density in defined network gradually effects an increase in energy consumption by the coordinator node in receiving data packets from its member nodes as there is a single mobile node and all the coordinator nodes send data to that single mobile node. From Fig. 6, analysis for 25 nodes and 35 nodes respectively illustrates that when range of every node is increased, more number of nodes are able to connect to the coordinator node that results in an overhead increase and after a particular range the network stabilizes. Figure 7 depicts that increasing number of coordinator nodes in architecture for fixed node density, node range and communication range, impacts reception energy dissipation but does not effect much the energy dissipation by coordinator node in transmitting them to mobile node. Figure 8 shows energy consumption at coordinator nodes in receiving and transmitting data for 25 and 35 nodes respectively when the coordinator nodes are increased. Figures 9 and 10 shows that while receiving and transmitting data by Coordinator node when increasing range of coordinator node while fixed node density, node range and fixed number of coordinator node as more number of nodes are connected with coordinator nodes for transmitting data to mobile node.

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Energy Consumption (nJ)

Energy consumption(nJ) Vs Number of Coordinator nodes 25000 20000 15000 10000 5000 0

Energy consumption by CN in receiving data Energy consumption by CN in transmitting data to MN 2c

3c

4c

5c

6c

Number of Coordinator Nodes

Fig. 7 Effect of increasing number of coordinator nodes in network

Fig. 8 Energy consumption at coordinator nodes in receiving and transmitting data

Fig. 9 Effect of increasing range of coordinator node

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Fig. 10 Effect of increasing range of coordinator node

6 Conclusion We have analyzed the scalability of proposed architecture by increasing the number of devices, their capacities in the AMI architecture. Our article confirms that the twotiered overlay architecture gives better consumption efficiency than the conventional communication between the meters and gateways. Hence adoption of our scalable architecture would yield better network longevity than the conventional methods of communication in the AMI applications. The inferences drawn and verified from our analysis are: • Increasing the node density in network; energy consumption increases gradually due to receiving and transmitting data packets. • When range of network nodes increase, a greater number of nodes connects; hence energy dissipation increases. • Increasing the number of CNs gradually decreases overhead for individual CN because connected nodes get distributed. • Increasing the CN range results in increasing energy consumption enabling a greater number of nodes to connect.

References 1. Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Industr Inf 7(4):529–539 2. Celik B, Roche R, Suryanarayanan S, Bouquain D, Miraoui A (2017) Electric energy management in residential areas through coordination of multiple smart homes. Renew Sustain Energy Rev 80:260–275 3. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

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4. Reina DG, Toral SL, Barrero F, Bessis N, Asimakopoulou E (2013) The role of ad hoc networks in the internet of things: A case scenario for smart environments. In: Internet of things and inter-cooperative computational technologies for collective intelligence. Springer, Berlin, pp 89–113 5. Kabalci Y, Kabalci E, Padmanaban S, Holm-Nielsen JB, Blaabjerg F (2019) Internet of Things applications as energy internet in smart grids and smart environments. Electronics 8(9):972 6. Anvari-Moghaddam A, Monsef H, Rahimi-Kian A (2014) Optimal smart home energy management considering energy saving and a comfortable lifestyle. IEEE Trans Smart Grid 6(1):324–332 7. Corson S, Macker J (1999) Mobile ad hoc networking (manet): routing protocol performance issues and evaluation considerations. rfc 2501

Integrated Power Generation Technologies/Advanced Technology Developments

A Novel CNFET-Based CCCDTA and Its Application as a Schmitt Trigger Jyoti Sharma, Ritambhara, and Avireni Srinivasulu

Abstract The current paper presents the design of CNFET-based Current Controlled Current Differencing Transconductance Amplifier (CNCCCDTA) Current Mode Building Block (CMBB). The proposed CMBB is realized in 32 nm CNFET technology and its performance has been examined using HSPICE. The Schmitt trigger circuit proposed in this paper is comprised of a single CNCCCDTA CMBB and no passive components. The HSPICE software is used to evaluate the function of the proposed Schmitt trigger circuit. The simulated results are presented. The simulated temperature stability of CNCCCDTA Schmitt trigger is 0.0000003%. Keywords CNFET · CCCDTA · CNCCCDTA · Schmitt trigger

1 Introduction Over the past few decades, the electronic circuit-related industries have undergone a sudden miniaturization of circuits with a number of transistors on integrated circuits that being doubled every two years approximately. However, as the device dimensions are scaled into sub-10 nm regime, MOSFETs face insurmountable fundamental challenges such as drain to source tunneling, further reduction of V th not possible with the scaling of power supply, and other unavoidable short-channel effects [1]. Thus, several new channel structures have been explored in order to provide (i) improve the electrostatcis and (ii) improved carrier transport properties at nanoscale. Carbon Nanotube Field-Effect Transistor (CNFET) has been emerged as a viable alternative in which the channel is comprised of carbon nanotubes (CNTs). CNFET-based devices provide enhanced bandwidth and reduced power dissipation as compared to CMOS due to its near-ballistic transport phenomenon. J. Sharma Birla Institute of Technology, Jaipur Campus, Mesra, Jharkhand, India e-mail: [email protected] Ritambhara · A. Srinivasulu (B) JECRC University, Jaipur 303905, Rajasthan, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_9

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Analog input signals can be converted to the digital output signals with the help of a Schmitt trigger circuit. It can be used to convert any type of input waveform into a square wave output waveform; thus, it is also named as squaring circuit [2–6]. Signal noise in digital signals is eliminated by using Schmitt trigger circuits. Relaxation oscillators can also be designed using Schmitt trigger circuit for closedloop negative response designs that can be used in function generators and switching power supplies [7–12]. Recently, Current Mode Building Blocks have drawn considerable attention in the development and design of current mode devices due to their advantages, such as higher slew rate, enhanced bandwidth and lower power consumption [12, 13]. Among several CMBBs, Current Controlled Current Differencing Transconductance Amplifier is a very promising device for current mode (CM) signal processing. Several circuits such as CM biquad filters, quadrature oscillators, analog multipliers/divider have been reported in the literature using this CMBB [13–36]. A novel current mode Schmitt trigger has been proposed in this paper. The proposed circuit is designed using single CNCCCDTA block without any passive components. Since no external passive components are used, the proposed Schmitt trigger circuit is suitable for IC implementation. The circuit operation is ideally temperature-insensitive. HSPICE simulation results have been included which verify the working capacity of the proposed Schmitt trigger circuit. The paper has been arranged as follows. The brief overview of Carbon Nanotube Field-Effect Transistors is given in Sect. 2. Section 3 includes the CNCCCDTA fundamentals and proposed design. Simulation results are reported in Sect. 4. Finally, conclusion is discussed in Sect. 5.

2 Carbon Nanotube Field-Effect Transistor (CNFET) Carbon nanotubes (CNTs) are graphene sheets rolled up to form hollow cynlinders. They manifest the properties of both metals and semiconductors. CNFETs have better electrostatics, higher mobility, near-ballistic transport capability, small size, and large current density, thus replacing the CMOS in analog and digital electronic devices. Also, high-k dielectric such as HfO2 is used for gate insulation so that the high gate capacitance controls the channel potential, thus reducing the short-channel effects. The carbon nanotube is uniquely defned by a chiral vector given by: ∧

∧

∧

Ch = n a 1 + m a 2

(1)

Carbon nanotubes are described by wrapping indices (n, m), i.e., if wrapping indices are (n, n) and (n, 0), the structure is called Armchair and Zigzag, respectively. The threshold voltage (V th ) is the voltage required to switch on a transistor and is calculated as follows:

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aVπ Vth = √ 3q DCNT

(2)

where q = electric charge, a = 2.49 Å is the lattice constant and V π = 3.033 eV is the carbon π –π bond energy in the tight bonding model. The diameter of the CNT (DCNT ) is given by: DCNT

 a (n 2 + m 2 + nm) = π

(3)

3 CNCCCDTA Fundamentals and Proposed Design The device-level circuit diagram of CNCCCDTA is shown in Fig. 1. Rp and Rn are the finite input resistances at the two input ports that can be electronically tuned by the bias current I B1 . The current that flows from the port Z is the difference between the currents I p and I n . The voltage V z is developed on Z terminal. The current I X is obtained at the output terminal X with the help of the voltage at Z terminal and the transconductance gm . The transconductance gm is controlled by bias current I B2 . The input and output of the ideal CNCCCDTA relations can be given in the form of a hybrid matrix given by (4). ⎤ ⎡ Rp Vp ⎢ Vn ⎥ ⎢ 0 ⎢ ⎥=⎢ ⎣ Iz ⎦ ⎣ 1 Ix 0 ⎡

0 Rn −1 0

0 0 0 0

⎤⎡ ⎤ Ip 0 ⎥ ⎢ 0 ⎥⎢ I n ⎥ ⎥ 0 ⎦⎣ Vx ⎦ Vz gm

(4)

Figure 2 shows the proposed circuit that employs a single CNCCCDTA block. No passive elements have been used in this circuit. +Vdd C14

C4 IB1 C7

C1 C8

C9

C5

n C2 C10

C19 C24

C17 C18

C16

C15

p

C6 C3

C21

C11 C12

Fig. 1 Transistor-level circuit diagramof CNCCCDTA

C22

C20

C25 C23

Z C13

IB2 -Vss

IX

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Fig. 2 Proposed CNCCCDTA Schmitt trigger circuit

Iz

Z

n Iin

I0 = I x = Iin =

gm Rn Iin 2

2 I0 gm Rn

p

CNTCCCDTA (gm)

I0 X

(5) (6)

By using (4)–(6), the output current expression of Schmitt trigger can be obtained as given in (7) and (8). IUT =

2 (+Isat ) gm Rn

(7)

ILT =

2 (−Isat ) gm Rn

(8)

4 Simulation Results The CNCCCDTA circuit in Fig. 1 is simulated with Standford 32 nm CNFET technology using HSPICE [37]. DC, transient, and AC analyses of the proposed CNCCCDTA are performed which shows the correct operation of the device. Transient analysis has been done by giving the sinusoidal input of 100 kHz frequency. The current relationship equation of the proposed CNCCCDTA circuit has been verified, i.e., I z = I p − I n as shown in Fig. 3. To show the conformity between input current (I p ) and output currents (I z ) and (I x ) of CNCCCDTA circuit, AC analysis is carried out and the results are shown in Fig. 4. For performing DC analysis of the CNCCCDTA, the current at p terminal is sweeped and the result is shown in Fig. 5. The proposed Schmitt trigger circuit shown in Fig. 2 is simulated in HSPICE using only one CNCCCDTA. The bias current I B2 is set at 50 µA and V DD = ± 0.6 V for the simulation. Figure 6 shows the transient response of the proposed Schmitt trigger. The hysteresis analysis is shown in Fig. 7. The temperature sensitivity of the circuit is shown in Fig. 8. The temperature stability of CNCCCDTASchmitt trigger is found to be 0.0000003%. The results confirm the functionality of the circuit as a Schmitt trigger. The detailed comparison

A Novel CNFET-Based CCCDTA and Its Application … Fig. 3 Transiant analysis of CNCCCDTA

Fig. 4 AC analysis of CNCCCDTA

Fig. 5 DC transfer characteristics of the CNCCCDTA

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Fig. 6 Simulated input and output waveforms of Fig. 2

Fig. 7 Hysteresis analysis of Fig. 2

and the advantages of the proposed circuit in Fig. 2 with the circuits designed in previous works are highlighted in Table 1.

5 Conclusion A new Schmitt trigger circuit using a sinlge CNCCCDTA is presented in this paper. The operation and analysis of the proposed circuit have been verified by the computer

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Fig. 8 Simulated temperature stability of Fig. 2

Table 1 Comparison of the proposed circuit with conventional circuits References

No. of active blocks

No. of passive components

Supply voltage/current

[5]

DVCC + → 1

2

± 1.5 V

[6]

DDCC → 1

2

± 2.5 V

[12]

MO-CTTA → 1

0

±5V

[14]

OpAmp → 1

3

± 12 V

[15] [16] [21] [22] [27] [34] [35] Proposed Circuitof Fig. 2

CCII → 2 CCDDCCCTA → 1 CCII → 1 OTA → 2 OTRA → 1 ZC-CDTA → 1 CDTA → 1 CNCCCDTA → 1

4 0 3 2 1 2 2 0

±6V ± 1.25 V ±5V ± 10 V ± 10 V ± 0.85 V/20 µA ± 2.5 V/85 µA ± 0.6 V/50 µA

simulations. The circuit having no passive elements, thus, has an advantage in integrated circuit (IC) implementation. The proposed Schmitt trigger circuit has an excellent temperature stability from −150 to +150 °C. The CNCCCDTA Schmitt trigger has broader applications such as waveform generators, instrumentation, and analog communication systems.

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References 1. Wang CS, Yuan SY, Kuo SY (1997) Full-swing BiCMOS Schmitt trigger. IEE Proc Circ Dev Syst 144(5):303–308 2. Cornmercon JC, Badard R (2002) Schmitt trigger oscillator and its synchronisation by an external square oscillator. IEEE Proc Circ Dev Syst 149(4):221–226 3. Schmıtt OH (1938) A thermionic trigger. J Sci Instrum 15(1):24–26 4. Atul K, Chaturvedı B, Sudhanshu M (2017) A fully electronically controllable Schmitt trigger and duty cycle-modulated waveform generator. Int J Circuit Theory Appl. https://doi.org/10. 1002/cta.2307 5. Mınaeı S, Yuce E (2012) A simple schmitt trigger circuit with grounded passive elements and its application to square/triangular generator. Circ Syst Sign Process 31:877–888 6. Das R, Banerjeeb K, Chakraborthy A, Mondal L (2016) Differential difference current conveyor based Schmitt trigger circuit and its application. Int J Recent Innov Trends Comput Commun 4(8):69–72 7. Kım H, Kım HJ, Chung WS (2007) Pulse width modulation circuits using CMOS OTAs. IEEE Trans Circ Syst-I 54(9):1869–1878 8. Sırıpruchyannun M, Wardkeın P (2003) A fully independently adjustable, integrable simple current controlled oscillator and derivative PWM signal generator. IEICE Trans Fund Electron Commun Comput Sci 86:3119–3126 9. Srınıvasulu A, Rukmını MSS, Sarada M, Ram MP, Prasad S (2014) Pulse width modulator based on second generation current conveyor. ˙In: Proceedings of the IEEE ınternational conference on devices, circuits and communications, Ranchi, India, pp 1–4. doi: https://doi.org/10.1109/ ICDCCom.2014.7024740 10. Srınıvasulu A (2009) Current conveyor-based square-wave generator with tunable grounded resistor/capacitor. In: International conference on applied electronics (IEEE AETC-09), Pilsen, Czech Republic, pp 233–236 11. Kar SK, Sen S (2011) Tunable square-wave generator for integrated sensor applications. IEEE Trans Instrum Meas 60(10):3369–3375 12. Sılapan P, Sırıpruchyanun M (2009) A simple current-mode Schmitt trigger employing only single MO-CTTA. ˙In: Proceedings of ınternational conference on electrical engineering/electronics, computer, telecommunications and ınformation technology (IEEE-ECTI), Pattaya, Thailand 13. Sonı GS, Ansarı MS (2014) Current-mode electronically-tunable Schmitt trigger using single 65 nm ±0.75V CMOS CDTA. ˙In: Proceedings of IEEE ınternational conference on signal propagation and computer technology (ICSPCT), pp137–141, Ajmer, India 14. Adel S, Smith KC (1998) Microelectronics circuits, 4th edn. Oxford University Press, Oxford, pp 1002–1005 15. Avırenı S (2011) Current conveyor based Schmitt trigger and its applications as a relaxation oscillator. Int J Circ Theo Appl 39(6):679–686. https://doi.org/10.1002/cta.669 16. Das R, Banerjeeb K (2016) A simple current mode schmitt trigger circuit based on single CCDDCCTA without employing any passive components. Int J Innov Emerg Res Eng 3(8):17– 21 17. Ferrı G, Guerrını NC (2003) Low-voltage low-power CMOS current conveyors. Kluwer Academic, London, UK 18. Maghami MH, Sodagar AM (2011) Fully-integrated, large-time-constant, low-pass, GmC filter based on current conveyors. ˙In: Proceedings of IEEE ınternational conference on electronics, circuits and systems (ICECS), pp 281–284, Beirut 19. Mısurec J, Koton J (2012) Schmitt trigger with controllable hysteresis using current conveyors. Int J Adv Telecommun Electrotech Sign Syst 1(1):26–30 20. Srinivasulu A (2012) Current conveyor based relaxation oscillator with tunable grounded resistor/capacitor. Int J Des Anal Tools Circ Syst 3(2):1–7 21. Cataldo GD, Palumbo G, Pennısı S (1995) A Schmitt trigger by means of a CCII+. Int J Circuit Theory Appl 23:161–165

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22. Kım K, Cha HW, Chung WS (1997) OTA-R Schmitt trigger with independently controllable threshold and output voltage levels. Electron Lett 33(13):1103–1105 23. Kumngern M (2012) Realization of electronically tunable first-order all-pass filter using singleended OTA. In: Proceedings of IEEE symposium on industrial electronics and applications (ISIEA), Bandung, Indonesia, pp 100–103 24. Lo YK, Chıen HC (2007) Switch controllable OTRA based square/triangular waveform generator. IEEE Trans Circ Syst-II-II 54(12):1110–1114 25. Shaker, P. C., Srınıvasulu, A (2014) Quadrature oscillator using operational transresistance amplifier. ˙In Proceedings of the IEEE ınternational conference on applied electronics (IEEE AEIC-14), pp 117–120, Pilsen, Czech Republic. Doi: https://doi.org/10.1109/AE.2014.701 1681 26. Srinivasulu A, Pıttala CS (2014) Grounded resistance/capacitance-controlled sinusoidal oscillators using operational transresistance amplifier. WSEAS Trans Circ Syst 13:145–152 27. Lo YK, Chıen HC, Chıu HJ (2009) Current-input OTRA Schmitt trigger with dual hysteresis modes. Int J Circ Theo Appl 38(7):739–746 28. Oner. S. E, Koksal. M, Sagbas. M (2006) Electronically controllable biquads using single CDBA. ˙In: Proceedings of the IEEE ınternational symposium in circuits and systems (ISCAS), Island of Kos, pp 3333–3336 29. Srivyshnavi, T., Srinivasulu. A (2015) A current mode Schmitt trigger based on current differencing transconductance amplifier. ˙In: Proceedings of 3rd IEEE ınternational conference on signal processing, communication and networking (ICSCN), pp 1–4. Doi: https://doi.org/10. 1109/ICSCN.2015.7219884 30. Keskin AU, Biolek D (2006) Current mode quadrature oscillator using current differencing transconductance amplifiers (CDTA). IEE Proc Circ Dev Syst 153(3):214–218 31. Bıolek, D (2003) CDTA–building block for current-mode analog signal processing. ˙In: Proceedings of the European conference on circuit theory and design, pp 397–400 32. AK Ersi, KH Hakan (2016) A new CMOS ZC-CDTA realization and its filter applications. Turk J Electr Eng Comput Sci 24:746–761 33. Pandey N, Paul SK (2011) Single CDTA-based current mode all-pass filter and its applications. J Electr Comput Eng Article ID 897631:5p. https://doi.org/10.1155/2011/897631 34. Suma M, Reddy VV, Srinivasulu A (2016) Current mode Schmitt trigger based on ZC-current differencing transconductance amplifier. ˙In: Proceedings of IEEE ınternational conference on ınventive computation technologies, Coimbatore, pp 1–5. Doi:https://doi.org/10.1109/INV ENTIVE.2016.7823226 35. Nagalakshmi K, Srinivasulu A, Cristian Ravariu C, Vijay V, Krishna VVSV (2018) A novel simple schmitt trigger circuit using CDTA and its application as a square-triangular waveform generator. J Modern Technol Eng 3(3):205–216 36. Siripruchyanuna M, Jaiklab W (2008) CMOScurrent-controlled current differencing transconductance amplifier and applications to analog signal processing. Int J Electron Commun (AEÜ) 62:277–287 37. https://www.circuitstoday.com

A New Current Mode Multiplier Using Single CCCII Without Passive Components Syed Zahiruddin, Avireni Srinivasulu, and Musala Sarada

Abstract A current mode multiplier is a valid structured block in many signal processing operations, such as modulation and demodulation schemes in communications, in development of bio-medical equipments, in analog computations, and fuzzy logic controllers. A single second generation current controlled conveyor (CCCII) based current mode multiplier without passive components is designed. CCCII has the eccentricity of intrinsic resistance in between the two input ports which are tunable by the external bias current. PSPICE simulator is utilized to examine the proposed design. The topology yields a simple structure and highly suitable for monolithic IC fabrication. Simulation results are confirmed with a voltage requirement of ± 2.5 V, the non linearities, total harmonic distortion (THD), and power dissipation are anticipated. Comparison of the proposed design with the existing methods is also performed. The parametric sweep and temperature analysis are also determined. The simulated results very well agree with the theoretical expectancy. Keywords Multiplier · Current conveyor · CCCII · Total harmonic distortion · Parametric sweep

1 Introduction Analog multipliers ascertain in various fields like signal processing, communication systems, instrumentation, and measurement systems. Several multiplier configurations are proposed previously using translinear bipolar transistors, MOS square law S. Zahiruddin · M. Sarada Department of Electronics and Communication Engineering, Vignan’s Foundation for Science, Technology and Research (Deemed to Be University), Guntur 522 213, A.P, India e-mail: [email protected] M. Sarada e-mail: [email protected] A. Srinivasulu (B) Department of Electronics and Communication Engineering, JECRC University, Jaipur 303905, Rajasthan, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_10

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characteristics, and switching capacitors. However, they are bounded to voltage mode operations and therefore not suitable for current mode. The current mode designs have several advantages such as large dynamic range, greater linearity, large signal bandwidth, simple configuration, and low power consumption [1–8]. In contemporary world, the analog VLSI has arised as a exceptional technology for the imminent demands of large bandwidth and less power requisites. A number of multiplier topologies as such is designed based on voltage mode. The eminent fact is that the dynamic range of voltage dependent devices is uttered by the frequency dependent gain. This glitch can also be elucidated by the current mode devices. Many voltage mode and current mode multipliers based on different active building modules have been proposed in the literature, but they involve two or more active devices, having the drawback of large area constraint and dissipation of more power. Abuelma’atti [2] has developed the current mode multiplier design possessing two CCCIIs without external passive components. The configuration proposed by Erkan Yuce [3] implies a single CCCII for realization along with a current controlled grounded resistor (CCGR), which is then realized with four-bipolar junction transistors (BJTs). Many such techniques using various active devices exist in the literature that uses operational transconductance amplifier (OTA), current controlled current differencing buffered amplifier (CCCDBA), current differencing transconductance amplifier (CDTA), current controlled current differencing transconductance amplifier (CCCDTA), current follower transconductance amplifier (CFTA), current controlled current conveyor transconductance amplifier (CCCCTA), digitally programmable current conveyor (DPCCII) [9–19]. In this manuscript, current mode CCCII-based multiplier utilizing only one CCCII without passive components is proposed with two input current signals and one controllable bias current. Compared with the existing current mode multipliers, the main advantage of the proposed topology is that it occupies less area and low power dissipation because of having only one CCCII with no passive components for realization.

2 Second Generation Current Controlled Conveyor Basically, CCCII is a mixed translinear loop, current mode active structured block for several analog applications. It has the additional advantage of intrinsic resistance (RB ) that can be observed at input terminal X that can be varied by tuning the external bias current and thus avoids the usage of external resistor which is more preferred for IC fabrication. The matrix relationship between input and output currents, by considering the intrinsic resistance RB into account of an basic CCCII is given in matrix form (1). ⎤ ⎡ ⎤⎡ ⎤ 0 0 0 IY VY ⎣ VX ⎦ = ⎣ 1 RB 0 ⎦⎣ I X ⎦ IZ VZ 0 ±1 0 ⎡

(1)

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105

Conventionally, using (1), the plus and minus sign signify the direction of current between input port X and output port Z. If the direction of current is same, then it is a positive current conveyor (CCCII+). If the direction of current is in opposite direction, then it is a negative current conveyor (CCCII-). At port X, RB denotes the input intrinsic resistance, tuned with bias current, (RB = V T / 2I B ), where V T ≈ 26 mV is the voltage equivalent of temperature, and I B ≥ 0 is the external bias current of the CCCII. At node Y, the current I Y is zero since the impedance at the input of node Y is infinite. Therefore, the current applied at input node X is transformed to output node Z. Several applications and other methods of realizing a multiplier topology are available by applying bias current to the CCCII [9–14]. Figure 1 shows the symbolic depiction of CCCII. Transistor model of CCCII is shown in Fig. 2 in which the translinear loop is realized using bipolar junction transistors to achieve a wide frequency range. The transistor loop (M 1 –M 4 ) forms a mixed translinear, DC biased by means of current mirrors (M 5 –M 6 , and M 9 –M 10 ). The transistors (M 11 –M 12 , and M 13 –M 14 ) are helpful to produce output current Z+ in response to the X terminal input current. The output Z- terminal is realized using additional current mirrors (M 15 –M 17 , and M 18 –M 20 ), and the transconductance gain can be varied by I B . Fig. 1 Symbolic representation of the CCCII±

IB

IZ+

IX

VX

X

VZ+

Z+

CCCII± VY IY

IZ-

M16 M5

M6

M11

+Vdd

M2

M1

X

Y

M7

M17

M15

M12 IB

VZ-

Z-

Y

M3

Z-

Z+

M4 M13

M8 M9

M10

Fig. 2 Internal structure of CCCII±

M14

M18

M19 M20

-Vss

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Fig. 3 Proposed multiplier circuit using CCCII

Y

CCCII+ Z I1

X

I Z+

IB

IX I2

3 Proposed Current Mode Multiplier The work is organized to design a new current-based multiplier using a single CCCII, with no passive components and has various applications in current mode signal processing circuits. The proposed design of Fig. 3 can multiply two current signals and is able to operate at a broad range of frequencies. The design is insensible to temperature variations, and the output current (I Z ) can be inhibited by varying the input bias current. The main advantage of the developed circuit is that it uses only one CCCII for construction which ultimately requires less area and low power. The internal structure of CCCII is as shown in Fig. 2. Using routine design analysis for the multiplier configuration of Fig. 3, the simplified output current expression is: I Z (t) =

I1 (t) × I2 (t) IB

(2)

Considering non-idealities, the above expression is denoted by I Z (t) =

α0 × β0 × I1 (t) × I2 (t) IB

(3)

where α 0 is the DC gain with current tracking error, and β 0 is the voltage gain with voltage tracking error, which are ideally equal to unity.

4 Simulation Results The proposed design of Fig. 3 has been simulated by using PSPICE simulator. Figure 4 shows the simulated results with the specifications shown as under. The CCCII was realized by the transistor schematic shown in Fig. 2; PR100N for PNP and NP100N for NPN transistors of bipolar group ALA400 of AT and T are utilized. The supply voltage is ± V cc = 2.5 V, and the value of bias current is I B = 50 µA (RX = 260 Ω). The input signal frequencies are I 1 = 10 Sin (2 × π × 10 × 103 t) µA and I 2 = 10 Sin (2 × π × 100 × 103 t) µA. The total harmonic distortion (THD) of the proposed multiplier circuit is 1.876%, and the total power dissipation is 3.74 mW.

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Fig. 4 The simulated outcome of Fig. 3 represents the multiplication of two sinusoidal currents

The harmonic distortion and power dissipation values are quite low and reasonably good. Figure 5 is the frequency spectrum of the proposed design, and Fig. 6 shows the variation of bias current versus the output current; the input signal frequencies are I 1 = 10 Sin (2 × π × 10 × 103 t) µA, and I 2 = 10 Sin (2 × π × 100 × 103 t) µA are preserved constant. The graph imitates the closeness of theoretical and simulated results for the variation of bias current over a range of 50–250 µA. Figure 7 is the graphical representation of total harmonic distortion versus the bias current. The bias current is varied over a range of 50–250 µA, and the observed distortion is in the range of 3.912 × 10−3 to 0.7824 × 10−3 . The THD is 1.876%, which is very low. Figures 8 and 9 are also the graphical interpretation confirming

Fig. 5 Simulated output frequency spectrum of the proposed circuit

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Fig. 6 The simulation results of output current versus bias current

Theoritical Simulated

Output Current (μA)

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4

50

100

150

200

250

200

250

Bias Current (μA) 4

Fig. 7 Simulated outcome of the total harmonic distortion versus bias current

3.5

THD (in milli)

3 2.5 2 1.5 1 0.5 50

100

150

Bias current Ib (uA)

Fig. 8 Simulated outcome of the multiplier circuit for the variation of temperatures from 0 to 150 °C

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Fig. 9 Simulated outcome of the multiplier circuit showing the parametric sweep over the temperatures from 0 to 150 °C

the variation of amplitudes of output current over the variation of temperature from 0 °C to a maximum of 150 °C. It reflects the insensitiveness over the temperature variations, keeping the input signal frequencies I 1 = 10 µA Sin (2 × π × 10 × 103 t), and I 2 = 10 µA Sin (2 × π ×100 × 103 t) unaltered (Table 1). The proposed multiplier employs a single CCCII with no passive components for the realization. The design of [3] consists of a single CCCII and one current controlled grounded resistor (CCGR), which is then realized with four BJTs and is able to produce a total harmonic distortion up to 5%. The design of [9] has been involved with 3 CCDDCCs along with a capacitor, and its distortion is 0.8% for a 0.6 V PP . The configuration of [10] has the advantage of using a single CFCTA with no passive components, but suffers a distortion of 4% which is less preferred. Whereas the design of [12] has the advantage of having less distortion of 1.21%, but at a cost of 3 op-amps, 6 resistors, and 2 CMOS transistors for its construction. It requires 1 CDBA and 6 MOS transistors for the implementation of the design of Table 1 Comparison of proposed method with the existing methods Topology (ref.)

Number of active elements

Type of active element

Number of passive components

[3]

1

CCCII

1 resistor

5%

[9]

3

CCDDCC

1 capacitor

0.8% within 0.6 Vpp

[10]

1

CFCTA

0

4%

[12]

3

OP-AMPs

6 resistors and 2 CMOS transistors

[13]

1

CDBA

6 MOS transistors

This work

1

CCCII

0

THD

1.21% NA 1.876%

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[13]. Referring to all the above designs, the advantages of the proposed design are that it requires only one CCCII with no passive components and offers low distortion and also highly suitable for monolithic IC fabrication.

5 Conclusion A current mode multiplier design is developed, having no capacitors or resistors involved which is suitable for IC fabrication. The total harmonic distortion and power dissipation are evaluated that are 1.876% and 3.74 mW, respectively. To improve the potency of the design, the non-linearity, frequency spectrum, and temperature insensitivity are determined. The simulation results for the variation of bias current with respect to the THD are verified and found satisfactory. The proposed design is compared with the existing methods and tabulated. Thus, the reported multiplier design has a simple structure that requires small area and dissipates low power over the other existing technologies.

References 1. Sedra S, Roberts GW, Gohh F (1990) The current conveyor: history, progress and new results. IEE Proc Part G Circ Dev Syst 137(2):78–87. https://doi.org/10.1049/jp-g-2.1990.0015 2. Abuelma’atti MT, Al-Qahtani MA (1998) A current-mode current controlled current conveyor based analogue multiplier/divider. Int J Electron 85(1):71–77. https://doi.org/10.1080/002072 198134364 3. Yuce E (2008) Design of simple current mode multiplier topology using a single CCCII+. IEEE Trans Instrum Measur 57(3):631–637. https://doi.org/10.1109/tim.2007.910112 4. Srinivasulu A, Pal D (2016) CCII + based novel waveform generator with grounded resistor/capacitor for tuning. In: Proceedings of IEEE applied electronics 2016 international conference, Czech Republic, pp 247–251. https://doi.org/10.1109/ae.2016.7577283 5. Mahendranath B, Srinivasulu A (2017) An output buffer for +3.3 V applications in A 180 nm + 1.8 V CMOS technology. Radioelectr Commun Syst 60(11):512–518. https://doi.org/10.3103/ s0735272717110061 6. Krishna PVSM, Srinivasulu A, Lal RK (2015) Two new first order all pass filters based on differential difference complementary current conveyor. Int J Cont Theo Appl 8(1):140–148 7. Linitha R, Srinivasulu A, Reddy, VV (2015) A Schmitt trigger based on DDCCTA without any passive components. In Proceedings of IEEE international conference on communication and signal processing, India, April 2–4, pp 1695–1698. https://doi.org/10.1109/iccsp.2015.732 2808 8. Sivakumari K, Srinivasulu A, Reddy VV (2014) A high slew rate, low voltage CMOS class-AB amplifier. In: Proceedings of IEEE applied electronics 2014 international conference, Czech Republic, pp 267–270. https://doi.org/10.1109/ae.2014.7011717 9. Prommee P, Somdunyakanok M (2009) CMOS based current controlled DDCC and its applications to capacitance multiplier and universal filter. Int J Electron Commun:1–8. https://doi. org/10.1016/j.aeue.2009.12.002 10. Tuntrakool S, Suwanjan P, Jaikla W (2017) Temperature insensitive current-mode four quadrant multiplier using single CFCTA. MATEC Web Conf 95:1–4. https://doi.org/10.1051/matecconf/ 20179514001

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11. Sawigun C, Serdijn WA (2009) Ultra low power, class-AB, CMOS four quadrant current multiplier. Electron Lett 45(10):483–484. https://doi.org/10.1049/el.2009.3311 12. Roy S, Paul TK, Pal RR (2017) A new method of realization of four quadrant analog multiplier using operational amplifiers and MOSFETs. J Phy Sci 22:163–173 13. Pathak JK, Singh AK, Senani R (2014) New multiplier/divider using a single CDBA. Am J Elect Electron Eng 2(3):98–102. https://doi.org/10.12691/ajeee-2-3-7 14. Saatlo AN, Ozoguz IS (2016) Design of a high-linear, high-precision analog multiplier, free from body effect. Turk J Electr Eng Comput Sci 24:820–832. https://doi.org/10.3906/elk-130 7-159 15. Srınıvasulu A, Rukmini MSS, Sarada M, Ram MP, Prasad S (2014) Pulse width modulator based on second generation current conveyor. In: Proceedings of the IEEE international conference on devices, circuits and communications, pp 1–4, Ranchi, India. https://doi.org/10.1109/icd ccom.2014.7024740 16. Avırenı S (2011) Current conveyor based Schmitt trigger and its applications as a relaxation oscillator. Int J Circ Theo Appl 39(6):679–686. https://doi.org/10.1002/cta.669 17. Bhargav A, Srinivasulu A, Pal D (2018) An operational transconductance amplifiers based sinusoidal oscillator using CNTFETs. In: Proceedings of the 23rd IEEE international conference on applied electronics, Pilsen, Czech Republic, 6p. https://doi.org/10.23919/ae.2018.850 1428 18. Suma M, Reddy VV, Srinivasulu, A (2016) Current mode Schmitt trigger based on ZC-current differencing transconductance amplifier. In: Proceedings of IEEE international conference on inventive computation technologies, Coimbatore, pp 1–5. https://doi.org/10.1109/invent ive.2016.7823226 19. Srivyshnavi T, Srinivasulu A (2015) A current mode Schmitt trigger based on current differencing transconductance amplifier. In: Proceedings of 3rd IEEE international conference on signal processing, communication and networking, pp 1–4. https://doi.org/10.1109/icscn.2015. 7219884

Optimal Capacitor Placement in Radial Distribution System Using Chicken Swarm Optimization Algorithm Saubhagya Ranjan Biswal and Gauri Shankar

Abstract This article presented an approach for incorporating shunt capacitor banks in radial distribution system with an aim to reduce the line losses and the total system cost. Along with the above, the optimal allocation of capacitor banks helps in improving the bus voltages and the power factor of the system. The above goal is realized by chicken swarm optimization algorithm on standard 85 and 118 bus systems. Before optimization, the possible candidate buses are obtained using power loss index, which helps in reducing search space during the algorithmic process. The obtained results are compared with other recent literatures for showing the efficacy and supremacy of the studied approach. Keywords Chicken swarm optimization algorithm · Optimal capacitor placement · Power loss index

1 Introduction The ever-increasing load demand causes the increase in the requirement of reactive power at load, as majority of the load have lagging power factor due to inductive nature. This causes problems like higher line losses and weak the voltage profile. For addressing the above issues, optimal capacitor placement (OCP) is considered as a concurrent topic of research in the field of electrical distribution system. The capacitor banks mainly placed near to the load and served as a reactive power source, which in result reduces the magnitude of current in distribution lines. This results in benefits such as less line losses, healthy voltage profile and improved power factor. The capacitor banks are required to be optimally positioned in terms of location, size and number for achieving the maximum benefits [1]. S. R. Biswal (B) · G. Shankar Department of Electrical Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, India e-mail: [email protected] G. Shankar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_11

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Recently, a number of optimization techniques have been projected by various authors based on analytical, numerical and artificial intelligence approaches. At first, due to expensiveness of the computational tools, many authors solve the OCP problem by using analytical approaches. Later on, easy and economical availability of computing resources inspires many authors to solve the OCP problem utilizing numerical analysis methods. Afterwards, many researchers have found the application of nature inspired algorithms highly efficient in solving OCP problem as it easily converges to global optima point [2]. However, as stated in “no free lunch” theorem, a particular optimization method may not be specified as best suited for solving a specific problem, which encourages many authors to apply different approaches for solving OCP problem and evaluate the effectiveness for the same. In recent times, many approaches like cuckoo search algorithm (CSA) [3], sine cosine algorithm (SCA) [4], bacterial foraging algorithm (BFA) [5], teaching learning-based optimization (TLBO) [6], modified monkey search (MMS) algorithm [7], flower pollination algorithm (FPA) [8], artificial bee colony (ABC) optimization [9] and so on have been implemented for solving OCP problem. Chicken swarm optimization (CSO) algorithm is a simple and efficient bio-inspired algorithm proposed by Meng et al. [10] has been utilized by many researchers efficiently in solving various problems. From bibliographic review, it has been observed that the capacity of CSO algorithm has not evaluated yet for solving OCP, which motivates the authors for evaluating the efficacy of CSO algorithm for solving OCP. Furthermore, by utilizing a sensitivity analysis termed as power loss index (PLI), the weak and vulnerable buses are identified. These buses take part in the algorithm for obtaining the optimized location for capacitor banks. Discrete capacitor banks are considered in the studied optimization approach and evaluated on standard 85 and 118 bus systems.

2 Problem Formulation For solving OCP, the objective is taken as total system cost. The total cost of the system is calculated by using Eq. (1):  Cost = PP ∗ APL ∗ T + D PI ∗ NC + PC ∗

NC 

 RCi

+ PO ∗ NC

(1)

i

where PP represents average energy cost, APL is taken as active power line losses,PC , PI , PO represents cost of capacitor banks in term of purchase, installation and operation, respectively, D represents depreciation factor, NC represents number of capacitors, RCi is the compensated reactive power at ith bus and T is taken as per year operating hours. The values of different parameters used in this equation are adopted from the article [3].

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115

2.1 Operational Constraints 2.1.1

Bus Voltage Constraints Vmin ≤ Vi ≤ Vmax

(2)

where Vi represents the ith bus voltage. Vmin and Vmax represents the minimum and the maximum bus voltage limit, respectively.

2.1.2

Reactive Power Compensation Constraints RCmin ≤ RCi ≤ RCmax

(3)

where RCmin and RCmax are the minimum and maximum value of allowable compensation, respectively.

2.2 Power Loss Index Some of the buses in the distribution system are more vulnerable than others and essentially needs reactive power compensation. PLI is introduced in order to sort those buses accordingly, from which some of top buses are considered for the optimization process and form which the optimized location and size of capacitor banks are found [3]. The PLI for all the system buses is calculated by utilizing Eq. (4). PLI =

LRi − LRmin LRmax − LRmin

(4)

where LRi represents the loss reduction at ith bus. LRmin and L Rmax represents the minimum and the maximum loss reduction obtained.

2.3 Load Flow In this studied work, a direct method of load flow approach is utilized for obtaining the system parameters such as line losses and bus voltages [11].

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3 Overview of CSO Algorithm CSO is a bio-inspired algorithm that imitates the behaviour of chicken swarms [10]. CSO utilizes the following approaches for finding the optimal solution. Each group of chickens consists of a rooster, few hens and chicks, in which the rooster is the dominant one and reflects the global optimal value during algorithmic process. The hens, which are considered as less fit to rooster, may follow the rooster in their subgroup for searching food. The hens may steal food found by other hens which is associated by random function. The chicks are considered as the weaker group among them and search around their mother hens for food. The equations associated with the several phases of algorithm are explained below.

3.1 Rooster Movement The equation for obtaining the rooster position at each iteration is explained by Eq. (5):    t 2 Yi,t+1 j = Yi, j ∗ 1 + randn 0, σ

σ = 2

⎧ ⎪ ⎨ 1, ⎪ ⎩ exp





(5)

if f ti ≤ f tl

f tl − f ti , | f ti | + ε

otherwise

(6)

  where Yi,t j is the position of solution at iteration t, randn 0, σ 2 represents Gaussian distribution with zero mean and standard deviation of σ 2 , ε represents a small constant, l is the random rooster index and f ti is the fitness value of Yi .

3.2 Hen Movement The positions of hens are updated by utilizing Eq. (7).  

t t t t t Yi,t+1 j = Yi, j + S1 ∗ randn ∗ Yr1,j − Yi,j + S2 ∗ randn ∗ Yr2,j − Yi,j  S1 = exp

f ti − f tr 1 | f ti | + ε

(7)



S2 = exp( f tr 2 − f t1 )

(8) (9)

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117

3.3 Chick Movement The positions of chicks are obtained at each iteration by using Eq. (10).   t t t Yi,t+1 j = Yi, j + F L ∗ Ym, j − Yi, j

(10)

where m ∈ (1, number of population) and F L ∈ (0, 2).

4 Result and Discussion The considered approach has been realized on 85 and 118 bus systems and the efficacy of the studied approach has been evaluated by comparing the results with different other algorithms. The compared values (line losses, bus voltages, total system cost) are obtained utilizing the load flow approach taken in this work by considering the results obtained by the respective literatures for avoiding unnecessary incongruities. The bus voltage constraint range is taken as 0.9–1.1 p.u. while the capacitor banks capacity varies within 0 to 1500 kVAr. The capacitor values are considered in discrete having step size of 50 kVAr within the allowable range. In the studied load flow approach, the voltage base value is taken 11 kV, whereas the base value of power is taken as 100 MVA. The number of iterations is taken as 100 during optimization for the present work. The important results in the tables are highlighted in bold.

4.1 Case Study 1: 85 Bus Test System The required values of data regarding the test system are taken from the literature presented in [12]. The total active load is 2570.28 kW, whereas the total reactive load of the system taken is 2622.2 kVAr. At first, the PLI value is obtained for all the buses and sorted accordingly. For this case study, the buses are organized as 54, 55, 51, 76, 69, 74, 39, 72, 66, 28, 62, 38, 61, 60, 59, 82, 37 …. The top 16 buses (user defined) are participated in the optimization process as possible location for capacitor placement. After executing the CSO algorithm, the optimal result is obtained as given in Table 1 and is consists of 5 different capacitor locations with total shunt compensation of 2000 kVAr. The size and location of each capacitor banks along with the respective bus voltage and line loss reduction are stated in Table 1. It may be observed form Table 1 that the total profit obtained using the proposed algorithm is better than the other compared approaches, which is taken as the optimization objective. The other parameters like line losses and bus voltages are comparable with other presented approaches (Fig. 1).

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Table 1 Summaries of optimal results for IEEE 85 bus test system Items Capacitor location (size in kVAr)

Un-compensated Compensated -

Total kVAr

-

APL (kW)

316.135

% Loss reduction RPL (kVAr)

BFA [5]

TLBO [6] MMS [7]

CSO (studied)

9 (840) 34 (660) 60 (650)

15 (150) 23 (300) 26 (300) 32 (150) 36 (150) 38 (150) 45 (150) 52 (150) 57 (300) 61 (150) 64 (300) 73 (150) 82 (150)

28 (600) 55 (350) 60 (550) 69 (200) 82 (300)

10 (150) 14 (300) 17 (150) 20 (300) 26 (300) 28 (150) 32 (150) 45 (150) 49 (300) 60 (450) 68 (150) 73 (150) 84 (150)

2150

2550

2850

2000

152.903

143.244

145.231

150.036

-

51.63

54.69

54.06

52.54

198.613

94.728

89.694

90.981

93.281

V min (p.u.)

0.871

0.919

0.924

0.927

0.919

Net savings ($)

-

73,184.74

70,061.51

67,517.14 74,201.39

% saving

-

44.04

42.16

40.63

44.66

Fig. 1 Results obtained for case study 1. a Convergence profile of objective function and b Improvement in voltage profile after OCP

4.2 Case Study 2: 118 Bus Test System The required values of data regarding the test system are taken from the literature presented in [13]. The total active load is 22,709.71 kW, whereas the total reactive load of the system taken is 17,040.97 kVAr. After carrying out the power loss index

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119

Table 2 Summaries of optimal results for IEEE 118 bus test system Items

Un-compensated Compensated

Capacitor location (size in kVAr)

-

Total kVAr APL (kW)

1297.413

% Loss reduction RPL (kVAr)

978.715

FPA [8]

ABC [9]

CSA [3]

CSO (studied)

39 (1500) 43 (600) 70 (500) 74 (1050) 86 (900) 91 (1500) 107 (700) 109 (500) 118 (1050)

32 (850) 35 (1050) 40 (1300) 50 (800) 70 (550) 73 (1300) 79 (1200) 105 (700) 106 (250) 109 (800) 110 (1200)

32 (1500) 39 (1500) 40 (550) 70 (950) 74 (750) 86 (1050) 108 (1500) 118 (1200)

32 (1000) 39 (950) 40 (650) 43 (300) 72 (1350) 76 (200) 86 (800) 91 (1300) 107 (950) 118 (1500)

8300

10,000

9000

9000

855.690

856.548

862.031

847.001

34.05

33.98

33.56

34.72

646.636

641.96

647.969

635.637

V min (p.u.)

0.869

0.907

0.909

0.906

0.906

Net savings ($)

-

185,089.61

174,898.64 178,876.78 185,536.758

% saving

-

27.14

25.65

26.23

27.21

calculation, the buses are arranged as 118, 39, 74, 70, 109, 107, 71, 111, 43, 110, 86, 76, 32, 91, 108, 40, 85, 72, 31 …. The top 18 buses are selected as the potential location for OCP and take part in the optimization for finding the best location. Thereafter, the CSO algorithm yields ten different locations for capacitor allocation with overall compensation of 9000 kVAr. The obtained result for CSO algorithm is given in detail in Table 2 and compared with the other algorithms. It may be observed from Table 2 that along with the total annual profit, the obtained result produces the maximum reduction in line losses and shows good improvement in voltage profile. This proves the superior efficacy of the proposed approach over the other compared algorithms (Fig. 2).

5 Conclusion In the current study, the optimum allocation and sizing of shunt capacitor banks are obtained by realizing CSO algorithm for 85 and 118 bus test systems. By considering the total system cost as the objective, the optimization process is carried out. The effectiveness and supremacy of the studied approach is demonstrated by obtaining the maximum benefits in terms of cost which is found better than some other recent optimization technique outputs.

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S. R. Biswal and G. Shankar

Fig. 2 Results obtained for case study 2. a Convergence profile of objective function and b improvement in voltage profile after OCP

References 1. El-Ela AA, El-Sehiemy RA, Kinawy AM et al (2016) Optimal capacitor placement in distribution systems for power loss reduction and voltage profile improvement. IET Gener Trans Distrib 10(5):1209–1221 2. Aman MM, Jasmon GB, Bakar AHA, Mokhli H, Karimi M (2014) Optimum shunt capacitor placement in distribution system—a review and comparative study. Renew Sustain Energy Rev 30:429–439 3. El-Fergany AA, Abdelaziz AY (2014) Capacitor allocation in radial distribution networks using cuckoo search algorithm. IET Gener Trans Distrib 8(2):223–232 4. Biswal SR, Shankar G (2018) Optimal sizing and allocation of capacitors in radial distribution system using sine cosine algorithm. In: IEEE international conference power electronics drives energy system (PEDES), IEEE, pp. 1–4 5. Devabalaji KR, Ravi K, Kothari DP (2015) Optimal location and sizing of capacitor placement in radial distribution system using bacterial foraging optimization algorithm. Int J Electr Power Energy Syst 71:383–390 6. Sultana S, Roy PK (2014) Optimal capacitor placement in radial distribution systems using teaching learning based optimization. Int J Electr Power Energy Syst 54:387–398 7. Duque FG, Oliveira LW, Oliveira EJ et al (2015) Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique. Int J Electr Power Energy Syst 73:420–432 8. Abdelaziz AY, Ali ES, Elazim SMA (2016) Optimal sizing and locations of capacitors in radial distribution systems via flower pollination optimization algorithm and power loss index. Int J Eng Sci Technol 19(1):610–618 9. El-Fergany AA, Abdelaziz AY (2014b) Artificial bee colony algorithm to allocate fixed and switched static shunt capacitors in radial distribution networks. Electr Power Comp Syst 42(5):427–438 10. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-ınspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence, pp. 86–94 11. Teng JH (2003) A direct approach for distribution system load flow solution. IEEE Trans Power Deliv 18(3):882–887 12. Das D, Kothari DP, Kalam A (1995) Simple and efficient method for load flow solution of radial distribution networks. Int J Electr Power Energy Syst 17(5):335–346 13. Zhang D, Fu Z, Zhang L (2007) An improved Tabu search algorithm for loss minimum reconfiguration in large-scale distribution systems. Electr Power Syst Res 77:685–694

Optimal Controller Design for Automatic Generation Control Under Renewable Energy Disturbance Mahmoud A. Attia, Mohamed Mokhtar, Almoataz Y. Abdelaziz, Suchetan Sasis, Sachin Kumar, and R. K. Saket

Abstract This paper presents a comparison between four types of optimization algorithms to design a suitable controller optimally for automatic generation control (AGC) under disturbance due to wind generator variation. To select the proper optimization technique, two case studies are considered. The first one is a single-area AGC without disruption due to wind generators, and the other research is carried out with the wind generator disturbance. In both cases, the system had a time delay and a small disruption at a specific time. In each case, four optimization techniques are carried out, gravitational search algorithm, genetic algorithm, the crow search algorithm, and the harmony search algorithm. The simulation results are performed using the MATLAB program. Also, a comparison between the techniques is carried out. Finally, the paper suggested a suitable method to design the controller of AGC according to the cases studied. Keywords Automatic generation control (AGC) · Crow search algorithm (CSA) · Genetic algorithm (GA) · Gravitational search algorithm (GSA) · Harmony search algorithm (HSA) M. A. Attia · M. Mokhtar Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt e-mail: [email protected] M. Mokhtar e-mail: [email protected] A. Y. Abdelaziz Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt e-mail: [email protected] S. Sasis · S. Kumar (B) · R. K. Saket Department of Electrical Engineering, IIT(BHU), Varanasi, UP, India e-mail: [email protected] S. Sasis e-mail: [email protected] R. K. Saket e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_12

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1 Introduction Power system utilities are connected through tie-lines in order to exchange power. AGC provides a means to achieve accepted operating conditions by regulating the tie-line flow and system frequency; multiple parameters can be used to control the frequency. The governor droop (R) is one of the parameters which can reduce the steady-state error in frequency [1–4] defined limits for selection of R. Another parameter, according to [5, 6], is the governor frequency bias setting (B), which should be not less than the area frequency response. According to Sahu et al. [7], numerous researches have presented different optimization techniques to design a controller for AGC, as in [8–10] with proper selection of the droop and governor frequency bias setting, the problem now is to design of a suitable controller [11, 12]. The most frequently used in industries is the Proportional–Integral (PI) controller. The challenge is to optimize the gains of the PI controller. Authors of [7] found that a controller for AGC can be designed by tuning the controller gains through suitable optimization algorithms. According to Gozde and Taplamacioglu [13], crazinessbased PSO is used to obtain the gain values of the PI controllers. Ali and AbdElazim [14] obtained the PI controller gain values by bacteria foraging technique. Differential evolution (DE) algorithm is used in [15] to select the PI gain values. Sahu et al. [7] explained another method to design controller gain using neural network and fuzzy logic to adopt self-tuning as in [16–18]. Although, the importance of renewable energy studies, which were carried out worldwide [19–22], a lot of previous studies have not considered wind generators in AGC controller design. In this paper, a comparison between several optimization techniques is carried out in order to optimize the parameter of a PI controller for AGC. In this paper, a comparison is carried out for AGC without wind generator participation followed by comparison for complicated cases considering disturbance due to wind generators. Finally, the paper proposed a suitable method to optimize the controller gain values.

2 System Understudy A single-area AGC system dynamic model with wind generator disturbances is shown in Fig. 1. The transfer function of the generator, turbine, and governor is modeled as the linear first order. The PI controller transfer function (TF) is: TF = K p + K i ∗

  1 S

(1)

The time delay is modeled as an exponential function with time constant (2 s) as explained in [23] and the gain values represent the droop and governor frequency bias.

Optimal Controller Design for Automatic Generation …

B

1/R

Delay

PI Controller

123

Wind Generator

Governor

Turbine

Generator and Load

df

P

Fig. 1 Single-area dynamic model of AGC with wind generator disturbance, including a time delay

Governor model = 1/(1 + sT g ), turbine model = 1/(1 + sT ch ), generator and load model = 1/(Ms + D), droop = 1/R, governor frequency bias = B. The data of the system are presented as follows [23]. T ch = 0.3 s, T g = 0.1 s, R = 0.05, D = 1, B = 21, M = 10 s. Wind generator data with swept area 5538.96 m2 and assume C p = 0.5 is shown in Fig. 2. From the data in Table 1, power can be calculated as follows: Power =

1 ∗ ρ ∗ Cp ∗ A ∗ V 3 2

(2)

where, ρ = density of air 1.225 (kg/m3 ), C p = power coefficient, A = swept area (m2 ), V = wind speed (m/s). 12

Wind Speed (m/s)

10 8 6 4 2 0

0

6

12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 Time (s)

Fig. 2 Variation of wind speed data with time

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Table 1 PI controller parameters and ISE using GA, GSA, crow, and harmony search methods without wind generator Parameter

GA

GSA

Crow

Crow with GA Harmony and GSA results as the initial point

Kp

0.4218

0.432

0.33544

0.4275

0.4430

Ki

0.2928

0.299

0.34935

0.2982

0.3043

ISE

6.81 × 10–5

6.80 × 10–5

7.02 × 10–5

6.81 × 10–5

6.81 × 10–5

Maximum overshoot

0.0004874

0.0005723

0.001067

0.0005612

0.0006705

3 Problem Formulation and Results with Discussion 3.1 Problem Formulation In this paper, the objective function is to minimize the integral of squared error (ISE), which can be calculated as follows: t

ISE = ∫ d f 2 .dt

(3)

0

where ‘df ’ is the deviation in system frequency from the desired value of frequency. The techniques such as GA, GSA, and crow search algorithms are presented in this paper. These techniques will aid in the selection of optimized parameters for a PI controller in order to minimize the ISE and reduce the integration of frequency error. There are two parts to this study, the first considers an AGC without any wind disturbance or its effects, and the second part will consider the effects of wind disturbance on the system.

3.2 Results and Discussion The single-area AGC model shown in Fig. 1 is used to optimize the gain of the PI controller using MATLAB with and without wind generator disturbances. The variation in both cases has been plotted with step size of 10 s.

3.2.1

Case Study 1

An AGC without wind generator disturbance and any type of control system has been considered. The error in frequency can be observed in Fig. 3. The steady-state error is high, around 5 × 10–3 .

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Fig. 3 Frequency deviation with time without wind generator

After applying GA, GSA, crow with random initial point, crow with GA and GSA results as the initial value in order to improve the crow output and finally the harmony optimization technique. All methods have succeeded in minimizing the steady-state error, as shown in Figs. 4, 5, 6, 7 and 8. A comparison between gain values of all the mentioned methods is shown in Table 1. It is to be noted that the excellent initial point for CSA can improve its results. Figure 9 illustrates df after the application of each technique to optimize the PI controller gain. Finally, it can be concluded that all optimization algorithms succeeded in improving system performance. Optimization with wind generator disturbances is considered henceforth.

Fig. 4 ‘df ’ after using GA to optimize the PI controller gain

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Fig. 5 ‘df ’ after using GSA to optimize the PI controller gain

Fig. 6 ‘df ’ after using CSA to optimize the PI controller gain

3.2.2

Case Study 2

AGC with disturbance due to wind generator variation is considered. The error in frequency with disturbance due to wind generator variation is shown in Fig. 10 in the absence of any controllers. It is clear that the steady-state error is high around 0.03. After applying GA, GSA, c with random initial point, crow with GA and GSA results as the initial value in order to improve the crow output and finally the harmony optimization technique. The steady-state error and the maximum overshoot are improved, as shown in Figs. 11, 12, 13, 14 and 15. The values of PI controller gains and comparison between the techniques are shown in Table 2. Also, the proper selection of the initial CSA changes its output from unstable to a stable condition. Figure 16 shows the response of df due to each technique.

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Fig. 7 ‘df ’ after using crow with initials are considered as GA and GSA results to optimize the PI controller gain

Fig. 8 ‘df ’ after using harmony to optimize the PI controller gain

4 Conclusion It is evident that the best method to optimize the PI controller in the cases presented is the GSA, which gave the best value of error and maximum overshoot. The result obtained by harmony was better than that of GA; it was observed that the crow method is highly reliant on the starting point as it gave excellent results when the initial point was assumed from the outcomes of GA and GSA methods. This paper has applied optimization techniques to evaluate the AGC performance. The paper has considered the application of wind energy as a disturbance to the system, which is considered being an essential issue with the ever-rising penetration of renewable

128

Fig. 9 ‘df ’ after using each technique to optimize the PI controller gain

Fig. 10 ‘df ’ without control under disturbance due to wind generator variation

Fig. 11 ‘df ’ after using GA to optimize the PI controller gain

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Fig. 12 ‘df ’ after using GSA to optimize the PI controller gain

Fig. 13 ‘df ’ after using CSA to optimize the PI controller gain

Fig. 14 ‘df ’ after using crow with initials are considered as GA and GSA results to optimize the PI controller gain

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Fig. 15 ‘df ’ after using harmony to optimize the PI controller gain

Table 2 PI controller parameters and ISE using GA, GSA, crow, and harmony search methods with the wind generator Parameter

GA

GSA

Crow

Crow with GA and GSA results as the initial point

Harmony

Kp

0.5469

0.4846

Unstable

0.5364

0.5085

Ki

0.3922

0.3321

0.3886

0.3532

ISE

0.0025

0.0023

0.00257

0.0024

Maximum overshoot

0.01024

0.005751

0.009823

0.0075

Fig. 16 ‘df ’ after using each technique to optimize the PI controller gain

energy. Finally, the paper presents the dependence of the crow search optimization method on the initialization parameters.

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References 1. Nanda J, Mishra, S, Saikia, LC (2009) Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control. IEEE Trans Power Syst 24(2) 2. Nanda J, Kaul BL (1978) Automatic generation control of an interconnected power system. Proc Inst Electr Eng 125(5):385–390 3. Hari L, Kothari ML, Nanda J (1991) Optimum selection of speed regulation parameters for automatic generation control in discrete mode considering generation rate constraints. IEE Proc C—Gener Transm Distrib 138(5):401–406 4. Nanda J, Mangla A, Suri S (2006) Some new findings on automatic generation control of an interconnected hydrothermal system with conventional controllers. IEEE Trans Energy Convers 21(1):187–194 5. Arya Y, Kumar N (2017) Optimal control strategy-based AGC of electrical power systems: a comparative performance analysis. Optimal Control Appl Methods 38(6):982–992 6. Fosha CE, Elgerd OI (1970) The megawatt-frequency control problem-a new approach via optimal control theory. IEEE Trans Power Apparatus Syst PAS-89(4):563–577 7. Sahu BK, Pati S, Panda S (2014) Hybrid differential evolution particle swarm optimization optimized fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system. IET Gener Transm Distrib 8:1789–1800 8. Arya Y, Kumar N (2017b) Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems. Soft Comput 21:6435– 6452 9. Arya Y, Kumar N (2016) Fuzzy gain scheduling controllers for AGC of two-area interconnected electrical power systems. Electr Power Compon Syst 44:737–751 10. Nanda J, Mangla A, Suri S (2006b) Some findings on automatic generation control of an interconnected hydrothermal system with conventional controllers. IEEE Trans Energy Convers 21:187–193 11. Vorobev P, Greenwood DM, Bell JH (2019) Deadbands, droop, and ınertia ımpact on power system frequency distribution. IEEE Trans Power Syst 34 12. Afshar Z, Bathaee ST, Bina MT (2019) A novel accurate power sharing method versus droop control in autonomous microgrid with critical loads. IEEE Access 7:89466–89474 13. Gozde H, Taplamacioglu MC (2011) Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int J Electr Power Energy Syst 33:8–16 14. Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Electr Power Energy Syst 33:633–638 15. Rout UK, Sahu RK, Panda S (2013) Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Eng J 4:409– 421 16. Yesil E, Guzelkaya M, Eksin I (2004) Self tuning fuzzy PID type load and frequency controller. Energy Convers Manage 45:377–390 17. Khuntia SR, Panda S (2012) Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Appl Soft Comput 12:333–341 18. Ghosal SP (2004) Optimization of PID gains by particle swarm optimization in fuzzy based automatic generation control. Electr Power Syst Res 72:203–212 19. Kumar N, Chelliah TR, Srivastava SP (2016) Analysis of doubly-fed induction machine operating at motoring mode subjected to voltage sag. Eng Sci Technol Int J 19:1117–1131 20. Kaundal V, Mondal AK, Sharma P, Bansal K (2015) Tracing of shading effect on underachieving SPV cell of an SPV grid using wireless sensor network. Eng Sci Technol Int J 18:475–484 21. Nayanar V, Kumaresan N, Gounden NGA (2016) Wind-driven SEIG supplying DC microgrid through a single-stage power converter. Eng Sci Technol Int J 19:1600–1607

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22. Mahmoud AA, Hany MH, Abdelaziz AY (2016) Performance enhancement of power systems with wave energy using gravitational search algorithm based TCSC devices. Eng Sci Technol Int J 19:1661–1667 23. Jiang L, Yao W, Wu QH, Wen JY, Cheng SJ (2012) Delay-dependent stability for load frequency control with constant and time-varying delays. IEEE Trans Power Syst 27:932–941

Trapezoidal Triangular Carrier-Based PWM Scheme for Performance Enhanced in Multilevel Inverter Kishor Thakre, Kanungo Barada Mohanty, S. Shiva Kumar, and Somesh Krishna Thanvi

Abstract In this study, a novel PWM scheme based on a trapezoidal triangular carrier (TTC) is proposed. A 17-level H-bridge inverter is implemented using TTC PWM and compared with other different kinds of carrier-based pulse width modulation scheme for enhancing the performance. Different carriers-based PWM scheme such as level-shift carrier-based (LS) PWM scheme, phase shift carrierbased (PSC) PWM, and variable switching frequency-based schemes are compared with TTC PWM scheme for symmetric source and asymmetric source configuration of cascaded H-bridge inverter. 17-level H-bridge inverter has been simulated in MATLAB/Simulink platform. The output voltage waveform and their harmonic spectrum for different PWM schemes are analysed and compared. The variations of total harmonics distortion with modulation indices for output voltages are also analysed. The simulation results validate through experimental implementation. Keywords Multilevel inverter · Carrier-based sinusoidal PWM · Trapezoidal triangular carrier-based PWM (TTC PWM) · THD

1 Introduction In last ten years, multilevel inverters (MLI) have gained tremendous response in high power and medium voltage applications. Nowadays, multilevel converters are widely used as STATCOM, renewable energy, electric vehicle and in machine drives. The merits of MLI are high quality output voltage waveform, better electromagnetic K. Thakre (B) Department of Electrical Engineering, Government Engineering College Jhalawar, Jhalawar, Rajasthan 326023, India e-mail: [email protected] K. B. Mohanty Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008, India S. S. Kumar · S. K. Thanvi Department of Electrical Engineering, Birla Institute of Technology Mesra, Ranchi, Jharkhand, India © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_13

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compatibility and less dv/dt stress on switches [1–3]. There are three kinds of MLIs: the diode clamped or neutral point clamped, flying capacitor and cascaded H-bridge inverters [4–7]. The cascaded H-bridge (CHB) multilevel inverter (MLI) is widely used because it requires less number of components, easy switching and modular structure. A 17-level cascaded H-bridge inverter is illustrated in Fig. 1. Each Hbridge synthesis three voltage levels, +V dc , 0 and –V dc using various additive and subtractive combinations. With eight H-bridges with symmetric source configuration, 17-levels output voltage can generate (+8V dc – +V dc , −8V dc –−V dc, and zero level). The overall output voltage of 17-level cascaded H-bridge inverter is given by: Vo = V1 + V2 + V3 · · · + V8 The number of voltage levels or steps in conventional cascaded H-bridge inverter is obtained by L = 2n + 1, where n is the number of isolated dc voltage supplies and L is the number of steps in staircase waveform. Thus, with 8 H-bridges in series, 2n + 1 = 17-level output voltage can be generated. The CHB inverter is divided in to two classes: symmetric source configuration and asymmetric source configuration. It is based on the magnitude of the separate dc supplies voltages. If magnitude of dc sources is equal, then MLI said to be symmetric source configuration, whereas magnitude of dc sources is unequal them MLI called asymmetric source configuration cascaded H-bridge inverter. Cascaded H-bridge inverter can be modulated using various carrier-based PWM schemes [8–17]. In this work, 17-level inverter is modulated using level shift, phase shift carrierbased PWM schemes and compared among different carrier-based PWM schemes. A new PWM scheme called trapezoidal triangular carrier (TTC) PWM is carried out and correlated with other PWM schemes. The proposed TTC PWM schemes disposed improvement for MLI and enhanced the performance in harmonic distortion and quality of staircase output voltage waveform.

2 Constant Switching Frequency Carrier-Based PWM 2.1 Level Shift Carrier-Based PWM The level-shift carrier-based PWM scheme is popular and convenient modulation scheme for MLI. For L number of inverter, (L − 1) carrier signals are required with the equal frequency f c and equal magnitude Ac are arranged such that they are in cantinas. The modulating signal has maximum magnitude Am , and the frequency f m which is zero centred in the middle of the carrier wave. The modulating signal is analysed with carrier signals. If the modulating signal is higher than any carrier signal, then it generates cutting pulse for corresponding switches. The frequency modulation index M f and amplitude modulation index M a are given as follows:

Trapezoidal Triangular Carrier-Based PWM Scheme … Fig. 1 Circuit diagram of 17-level cascaded H-bridge inverter

135

S11

S12

S13

S14

H-bridge-1

V1

+ -

Vo H-bridge-2

H-bridge-3

H-bridge-4

H-bridge-5

H-bridge-6

H-bridge-7

H-bridge-8

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Ma =

Am (L − 1)Ac

(1)

fc fm

(2)

Mf =

In level-shift carrier-based PWM, on the basis of carrier waves arrangement, there are three cases can be distinguished. Figure 2a illustrates the phase disposition (PD) carrier-based PWM, where all the carrier signals are in phase. Figure 2b illustrates the phase opposition disposition (POD) arrangement-based PWM, where the carrier signals above reference value are in phase, but shifted by 180° from those carrier signals below the reference value. Figure 2c shows the alternative phase opposition disposition (APOD) arrangement-based PWM where each carrier signal is shifted by 180° from the neighbouring carriers.

2.2 Phase Shift Carrier-Based PWM Phase shift carrier-based (PSC) PWM [18], in this methods, the multicarrier signals with the equal frequency and equal peak magnitude, but there is a phase shift by an angle between two neighbouring signals expressed by: ∅cr =

360◦ (L − 1)

(3)

For L number of level inverter, it required (L − 1) carrier waves and they are phase shifted by an angle of 22.5° from the neighbouring carrier signals as depicted in Fig. 3.

3 Variable Switching Frequency Carrier-Based PWM This scheme is almost similar as constant switching frequency carrier-based PWM except the frequency of carrier is unequal. There are 16 different carrier signals with unequal frequency and equal magnitude. This modulation scheme signifies that harmonic energy is concentrated at the carrier signal frequency. Carrier signals frequencies of the 17-level inverter lies between 2000–5000 Hz are depicted in Fig. 4.

Trapezoidal Triangular Carrier-Based PWM Scheme …

(a) Phase disposition

(b) Phase opposition disposition

(c) Alternate Phase opposition disposition Fig. 2 Level shift constant switching frequency PWM

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Fig. 3 Phase shift carrier-based PWM

Fig. 4 Variable switching frequency-based PWM

4 Proposed Trapezoidal Triangular Carrier-Based (TTC) PWM This paper proposed a novel carrier, i.e. trapezoidal triangular carrier (TTC) PWM. TTC as illustrate in Figs. 5 and 6. It contains two halves, the bottom half is trapezoidal and top half is a triangular. The amplitude and frequency modulation index will be identical as that of the conventional triangular carrier-based PWM as discussed in Eqs. (1) and (2) in that order.

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139

Fig. 5 A single trapezoidal triangular carrier

Fig. 6 TTC with modulating signals for 17-level inverter

5 Results and Discussions To verify the efficacy of the proposed TTC-based PWM scheme, a single phase cascaded H-bridge 17-level inverter is implemented. The block diagrams of Simulink model for one case with their subsystem are shown in Fig. 7a and b. In 17-level inverter, 8 single phase H-bridges are connected in series, which consists of 8 dc sources with magnitude of 12 V for symmetric source configuration and 4 dc sources with unequal magnitude are used for the asymmetric source configuration. As concluded from Fig. 7b, if modulating signal is greater than carrier signals, then switches (S1 , S4 ) are turned-on otherwise switches (S2 , S3 ) turned-on. When the switches (S1 , S4 ) are turned-on, then the output voltage +12 V switches (S2 , S3 ) are ON; then, the output voltage −12 V and switches either (S1 , S2 ) or (S3 , S4 ) are

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Fig. 7 a Main Simulink block diagram. b Subsystems of Simulink model

turned-on the output voltage will be zero. Similar operation for other 7 H-bridges to determine output voltage levels ±24, ±36, ±48, ±60, ±72, ±84 and ±96 V. The voltage and current waveforms obtained using TTC PWM scheme with their harmonic spectrum of output voltages for symmetrical source configuration and asymmetrical source configuration of 17-level inverters are illustrated in Figs. 8 and 9, in that order.

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100 80

Output voltage(V)

60 40 20 0 -20 -40 -60 -80 -100

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Time(s) Fundamental (50Hz) = 96.93 , THD= 3.04% Mag (% of Fundamental)

20

15

10

5

0

0

5

10

15 20 25 Harmonic order

30

35

40

Fig. 8 Output voltage waveform for symmetrical MLI using TTC with harmonic spectrum of output voltage

The total harmonic distortion in percentage of the output voltage can be calculated by:  THD =

∞ n=1,2,3,...

V1

Vn2

(4)

where V 1 and n are the fundamental voltage component and order of the harmonics, in that order. The comparison between harmonic distortion, fundamental voltage and order of harmonics using different PWM scheme for symmetrical source configuration and asymmetrical source configuration inverter. Table 1 and Table 2 are shown based on simulation results, respectively (Figs. 10 and 11).

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Output voltage (V)

60 40 20 0 -20 -40 -60 -80 -100

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

Time (s)

Mag (% of Fundamental)

Fundamental (50Hz) = 96.82 , THD= 4.32% 20

15

10

5

0

0

5

10

15

20

25

30

35

40

Harmonic order

Fig. 9 Output voltage waveform for asymmetrical MLI using TTC with harmonics spectrum of output voltage Table 1 Different order of harmonics and their % THD for symmetrical 17-level inverter PWM schemes

Fundamental voltage

THD in %

Different order of harmonic in % 3rd

5th

7th

11th

13th

PD

95.8

3.55

0.45

0.5

1.0

0.2

0.5

POD

96.1

3.51

0.08

0.08

0.04

0.15

0.03

APOD

96.0

3.52

0.03

0.09

0.05

0.13

0.06

PSC

97.7

6.09

0.59

1.19

1.46

0.4

3.05

VSF

95.9

4.78

1.63

1.39

1.01

0.41

0.11

TTM

96.9

3.04

0.36

0.22

0.25

0.37

0.22

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Table 2 Different order of harmonics and their % THD for asymmetrical 17-level inverter PWM schemes

Fundamental voltage

THD in %

Different order of harmonic in % 3rd

5th

7th

11th

13th

PD

98.77

4.89

2.48

0.23

1.3

0.57

0.39

POD

96.12

4.55

0.13

0.16

0.44

0.2

0.02

APOD

96.02

4.52

0.09

0.12

0.08

0.9

0.06

PS

97.76

6.77

0.64

1.14

1.49

0.84

3.04

VSF

95.86

5.72

1.7

1.32

1.1

0.56

0.17

TTM

96.82

4.32

0.37

0.21

0.13

0.46

0.12

Voltage: 30V/div.

Time: 10ms/div.

Voltage: 30V/div.

Time: 5ms/div.

Fig. 10 Experimental output voltage waveform for 17-level inverter

(a) symmetrical MLI

(b) asymmetrical MLI

Fig. 11 Voltage harmonic spectrum using TTM

The output voltage and their THD for different modulation index are shown in Fig. 12. To validate the simulation results, FPGA module-based a single phase 17level inverter has built in the laboratory. 17-level inverter composed of 8 H-bridges connected in series. An analogue expansion board is used to interface between the FPGA and an inverter module. FPGA generated the gate signals for inverter switches

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THD %

90 80

PD

70

POD

60

APOD PSC TTMC

50

VSF

40 30 20 10 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Modulation Index

Fig. 12 Graphical representation between percentage THD and different modulation indices for different carrier-based PWM scheme

using VHDL code. The output voltage waveform and their harmonic spectrums are depicted in Fig. 10 and Fig. 11, respectively.

6 Conclusion This paper presents a 17-level inverter with symmetric source configuration and asymmetric source configuration using proposed TTC PWM. Also, different kinds of PWM like level-shift carrier-based (PD, POD, APOD) PWM, phase shift carrierbased PWM and variable switching frequency carrier-based scheme, are implemented and compared with proposed TTC scheme. The THD values are low in symmetric source configuration compared those in asymmetric source configurationbased inverter, but output voltage is little bit higher in asymmetric source configuration inverter. The suggested TTC signal as novel carrier signal for sinusoidal PWM scheme gave lowest THD of 3.04%, whereas similar values are higher in triangular level-shift carrier-based PWM, phase shift carrier-based PWM for both symmetrical source configuration and asymmetrical source configuration. So, the TTC PWM is the preferable for least possible THD and largest output voltage. The proposed multicarrier-based PWM scheme validated through simulation results and hardware prototype.

Trapezoidal Triangular Carrier-Based PWM Scheme …

145

References 1. Lai JS, Peng FZ (1996) Multilevel converters—a new breed of converters. IEEE Trans Ind Appl 32:509–517 2. Teodorescu R, Blaabjerg F, Pedersen JK, Cengelci E, Enjeti PN (2002) Multilevel inverter by cascading industrial VSI. IEEE Trans Ind Electron 49:832–838 3. Palanivel P, Dash SS (2010) Analysis of THD and output voltage performance for cascaded multilevel inverter using carrier pulse width modulation technique. IET Power Electron 4(8):951–958 4. Rodriguez J, Lai JS, Peng FZ (2002) Multilevel inverters; a survey of topologies, controls, and applications. IEEE Trans Ind Electron 49:724–738 5. Tolber LM, Habetler TG (1999) Novel multilevel inverter carrier based PWM method. IEEE Ind Appl 35:1098–1108 6. McGrath BP, Holmes DG, Meynard T (2006) Reduced PWM harmonic distortion for multilevel inverter operating over a wide modulation range. IEEE Trans Power Electron 21:941–949 7. Sirisukpraserl S, Lai JS, Liu TH (2002) Optimum harmonic reduction with a wide range of modulation indices for multilevel converter. IEEE Trans Ind Electron 49:875–881 8. Jimenez OL, Vargas RA, Aguayo J (2011) THD in cascade multilevel inverters symmetric and asymmetric. ERAMC IEEE Computer Society, pp. 289–295 9. Wu B (2006) High-power converters and AC drives. IEEE, Wiley, Canada 10. Thakre K, Mohanty KB (2015) Performance improvement of multilevel inverter through trapezoidal triangular carrier based PWM. In: Proceedings of IEEE international conference on energy, power and environment: towards sustainable growth, pp. 1–6 11. Thakre K, Mohanty KB (2015) Comparative analysis of THD for symmetrical and asymmetrical 17 level cascaded H-bridge inverter using carrier based PWM techniques. In: Proceedings of IEEE international conference on industrial instrumentation and control, pp. 306–310 12. Thakre K, Mohanty KB, Kommukuri VS, Chatterjee A (2016) Optimal configuration for cascaded voltage source multilevel inverter based on series connection sub-multilevel inverter. Cogent Eng 3(1):1261470 13. Thakre K, Mohanty KB, Kommukuri VS, Chatterjee A (2016) Experimental validation of a modular multilevel inverter with less number of switches. In: Proceedings of 19th national power systems conference (NPSC), IIT Bhubaneswar, India, pp. 1–5 14. Thakre K, Mohanty KB, Kommukuri VS, Chatterjee A (2018) New topology for asymmetrical multilevel inverter: an effort to reduced device count. J Circ Syst Comput 27(4):1850055 15. Thakre K, Mohanty KB, Kommukuri VS, Chatterjee A (2019) A modified circuit for symmetric and symmetric multilevel inverter with reduced components count. Int Trans Elect Energy Syst 29(6):e12011 16. Siddique MD, Mekhilef S, Shah NM, Memon MA (2019) Optimal design of a new cascaded multilevel inverter topology with reduced switch count. IEEE Access 7:24498–24510 17. Mohanty KB, Thakre K, Kommukuri VS, Chatterjee A, Nayak AK (2019) Reduction in components using modified topology for asymmetrical multilevel inverter. World J Eng 16(1):71–77 18. Naderi R, Rahmati A (2008) Phase-shifted carrier PWM technique for general cascaded inverters. IEEE Trans Power Electron 23:1257–1269

Fractional Order Particle Swarm Optimization to Optimize a Fractional Order PID Controller for a Vertical Take-Off and Landing System D. S. Acharya, S. K. Mishra, B. Sarkar, and D. Bharti

Abstract A Vertical Take-Off and Landing (VTOL) system is highly unstable and hence a robust controller is needed to stabilize it in finite time. This work presents the design of a Fractional Order Proportional-Integral-Derivative (FOPID) controller to stabilize the VTOL system. The applied FOPID controller has been tuned, to achieve the desired performance and robustness, by Fractional Order Particle Swarm Optimization (FOPSO), which is a modified form of the original PSO obtained by generalizing the order of the velocity derivative to a real number 0 ≤ α ≤ 1. This helps in smoother transitions, better exploration and longer memory of the particles. The results obtained using the FOPID controller has been compared with the conventional PID controller. It has been found that the FOPID controller exhibits superior performance and robustness. Keywords Particle swarm optimization · Fractional particle swarm optimization · PID · Fractional order · Vertical take-off and landing

1 Introduction Since the last few decades, Fractional Order PID (FOPID) controller design and analysis has attracted the attention of researchers, worldwide. FOPID controller was first proposed by Podlubny [1]. It has numerous applications in the field of material science, electro-chemistry, biological systems, identification and modeling of the thermal systems as mentioned in literature. It has also proved effective in areas such as robotics, bio-engineering, control theory, etc. [2]. The reason for increased research in the field of FOPID is its advantages over conventional or integer order PID controllers [2]. In FOPID controller, the integral and derivative actions, being D. S. Acharya (B) · B. Sarkar · D. Bharti Department of EEE, BIT Mesra, Off-Campus Deoghar, Jharkhand, India e-mail: [email protected] S. K. Mishra Departmenr of EEE, BIT Mesra, Ranchi, Jharkhand, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_14

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non-integer, provide broad scope for design. Intuitively, besides determining the P, I, D gains, the orders of the derivative and control actions also need to be properly decided [3]. Analytical approach for estimating these five parameters becomes very cumbersome because of existence of non-integer ordered equations. Numerical techniques or optimization algorithms are needed to find the best values of these five parameters, so as to optimize the system performance. There are a plethora of research in literature that discuss the application of evolutionary algorithms for tuning an FOPID controller. An encapsulation of several applications and approximation techniques for FOPID controller design and tuning using Scilab, may be found in [4]. In [5], authors employ Symbiotic Organisms Search to tune an FOPID controller. An FOPID controller for synchronous generator exciattion system is presented in [6], Tuning of a decentralized FOPID controller is presented in [7]. This work presents the application of Fractional Order Particle Swarm Optimization (FOPSO) for tuning an FOPID controller for stabilizing a VTOL system.

2 Vertical Take-Off and Landing (VTOL) Modeling Figure 1 illustrates the free body diagram of the VTOL system. The torque-current equation of the plant may be expressed as [8]: Kt Im + m2 gl2 cos(θ (t)) − m1 gl1 cos(θ (t)) − m3 gl3 cos(θ (t)) = 0

(1)

where m1 , m2 , m3 are masses of the motor-fan, counterweight and the beam respectively. l1 , l2 , l3 are lengths of the sections shown in Figure 1. Kt , Im and θ (t) are the torque constant, motor current and pitch angle respectively. Equation of the motion may be expressed as:

Fig. 1 Free body diagram of VTOL system

Fractional Order Particle Swarm Optimization … Table 1 Value of specifications of the VTOL plant [8] Parameter Value J B Ks Kt

3.47 × 10−3 × 10−3

2 3.73 × 10−2 1.08 × 10−2

149

Unit kg m2 Nm s/rad Nm/rad Nm/A

J θ¨ + Bθ˙ + Ks θ = Kt Im

(2)

where J ,B and Ks are the moment of inertia, coefficient of damping and stiffness respectively. Assuming θ as the first state variable z1 , θ˙ as the second state variable z2 and Im as the input u to the system, the state-space model of VTOL may be expressed as:        0 1 0 z˙1 z1 = −Ks −B + Kt u z˙2 ( J ) ( J ) z2 (J )

(3)

    z1 y= 10 z2

(4)

Substituting the values from Table 1, Eqs. (3)–(4) may be written as:        z˙1 0 1 0 z1 = + u z˙2 −10.7 −0.576 z2 3.112

(5)

    z1 y= 10 z2

(6)

3 Fractional Order PID Controller The Fractional Order PID controller (FOPID) has proved itself to be superior than the classical PID controller, also referred to as integer order PID (IOPID), whether it is used for integer order or fractional order plants. The FOPID controller provides greater flexibility in the design process. For non-linear and complex systems, FOPID has proved to be very effective. They are more robust and less sensitive to parametric uncertainties. FOPID controllers are, therefore, capable of achieving the iso-damping

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Fig. 2 1-DOF FOPID controller

Fig. 3 2-DOF FOPID controller

property. The transfer function of an FOPID controller is given by (7) G c (s) = Kp +

Kd s(λ+μ) + Kp sλ + Ki Ki μ + K S = d Sλ sλ

(7)

where λ is the non-integer integral order and μ is the non-integer differential order, such that 0 < λ, μ < 2. The block diagrams of 1-DOF and 2-DOF FOPID controllers are illustrated in Figs. 2 and 3 respectively. The 2-DOF configuration exhibits superior performance due to the absence of controller zeros in the forward path. Also, the PD controller q2 s + q1 helps to achieve faster speed of response.

4 Fractional Order Particle Swarm Optimization The dynamics of Particle Swarm Optimization (PSO) are defined by (8): vt+1 = vt + θ1 (lb − x) + θ2 (gb − x) xt+1 = xt + vt+1

(8)

where, the current position of the particle is denoted by x, global best by gb and local best by lb. The self-learning and social learning factors are scaled by uniformly generated random factors θ1 and θ2 respectively.

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Traditional PSO algorithm tends to suffer from slow convergence and trapping into local optima. Therefore, to accelerate the convergence speed and to avoid the local optimal solutions are the two important issues in the PSO research. Different types of PSO have been used so far to overcome the above issues. Some of them are Comprehensive learning PSO (CLPSO), Orthogonal learning strategy (OLPSO), Hybrid PSO (HPSOM) and Fractional order PSO (FPSO). The fractional-order PSO is used for controlling the convergence rate by using fractional order calculus. This approach was tested for several well-known functions and the results showed that it contributes to improve the convergence rate of PSO. This article aims to use the Fractional-order PSO algorithm for designing the FOPID controller for the magnetic levitation plant (maglev). The velocity update equation in (8) may be re-arranged as: vt+1 − vt = θ1 (l − x) + θ2 (gb − x)

(9)

The left side of (9) is the discrete form of derivative. It may be expressed in fractional form as (10) [9] Dη [vt+1 ] = θ1 (l − x) + θ2 (gb − x)

(10)

where 0 ≤ η ≤ 1 is a real number. Employing the Grunwald-Letnikov definition of η-th order differ-integral [5, 10] and considering first five terms, Eq. (10) may be expanded as in equation (11).

Dη [vt+1 ] =

(η + 1)v(t + 1) (η + 1)v(t) (η + 1)v(t − 1) − + − (1)(η + 1) (2)(η) (3)(η − 1) (η + 1)v(t − 2) (η + 1)v(t − 3) + (4)(η − 2) (5)(η − 3)

(11)

Using the property of Gamma function: (η + 1) = η(η) = η(η − 1)(η − 1)(η + 1) = η(η − 1)(η − 2)(η − 2) = · · ·

and (1) = 1, (2) = 1, (3) = 2, (4) = 6, (5) = 24 The final fractional order velocity equation is obtained as: 1 1 1 vt+1 = ηvt + ηvt−1 + η(1 − η)vt−2 + η(1 − η)(2 − η)vt−3 + 2 6 24 θ1 (l − x) + θ2 (gb − x)

(12)

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5 Simulation and Results Applying Routh-Hurwitz criteria, the search space for the algorithm is found to be: Kp ≥ −3.438, Kd ≥ −0.1850, Ki ≥ 0 and 0 ≤ λ, μ ≤ 2. The parameter setting for FOPSO is: η = 0.1 [9] and θ1 = θ2 = 2.05. The population size and maximum iteration count has been set to 50 and 500, respectively. Both the FOPSO and PSO have been executed for 30 independent runs. The objective function proposed for the purpose is defined in (13). J (Kp , Ki , Kd , λ, μ) = ω1 ISE + ω2 Mp + ω3 ts

(13)

subject to ||S||∞ < 2 & ||T ||∞ < 2 where ISE, Mp and ts are the integral square error, peak overshoot and settling time, respectively and ω1 + ω2 + ω3 = 1. ||S||∞ and ||T ||∞ are the infinity norms of sensitivity and complementary sensitivity respectively. The two constraints imposed on the objective function ensure good robustness to external disturbances and parameter uncertainties [10]. The best results of both PSO and FOPSO, for 2-DOF FOPID controller tuning, are summarized in Table 2. The associated time domain specifications of the system response are also given in the table. It’s evident that FOPSO performs better and exhibits significant reduction in peak overshoot, settling time and ISE. The corresponding time responses are shown in Fig. 4. The elimination of overshoot is clearly observed in the figure. Comparison of the time domain performance of the system with 1-DOF and 2DOF FOPID controllers is summarized in Table 3. The associated time responses are illustrated in Fig. 5. It can be inferred that the 1-DOF structure provides a faster response. The 2-DOF structure surpasses 1-DOF in reducing peak overshoot and ISE. Moreover, both the controller structures are robust enough, which is confirmed from the sensitivity and complementary sensitivity values being < 2. Table 4 summarizes the frequency domain specifications of the compensated system. The associated bode plots are shown in Fig. 6. Looking closely, it is observed that the phase plot, of both 1-DOF and 2-DOF, are flat near the gain crossover frequencies. This means that both 1-DOF and 2-DOF FOPID structures, exhibit the iso-damping property, making the system robust to gain or parameter variations, as illustrated in Fig. 7, which shows the system response under an external periodic disturbance of 0.05 amplitude. The compensated system attains a better phase margin with 2-DOF structure. Also, the compensated system achieves better gain cross-over

Table 2 Comparison of FOPSO and PSO Algorithm

Kp

Kd 33.98

μ

q2

q1

Mp (%)

ts (s)

||S||∞

||T ||∞

26.53

Ki 19.96

λ

PSO

0.987

1.06

0

19.96

11.06

10.5081 6.1337

1.0

1.53

FOPSO

44

20

35

1.003

1.103

0

20

0

1.116

1.0

1.48

ISE

0.0034

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Fig. 4 Comparison of FOPSO and PSO Table 3 Result of tuning FOPID controller using FOPSO Controller Kp

Ki

Kd

λ

μ

q1

Mp (%) ts (s)

ISE

||S||∞

1-DOF

95

94

93

0.93

1.0008 –

–

8.1206 1.014

4.103

1.0

2-DOF

44

20

35

1.003

1.103

20

0

0.0034 1.0

q2

0

1.116

||T ||∞

1.0 1.48

Fig. 5 Comparison of VTOL responses with 1-DOF and 2-DOF FOPID

frequency, with 1-DOF FOPID. This also confirms the lower settling time in Table 3. Also, the higher gain margin with 2-DOF FOPID confirms the lower peak overshoot in Table 3.

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Table 4 Frequency domain specifications System ωg (rad/s) GM (dB) Uncompensated With 1-DOF FOPID With 2-DOF FOPID

PM (deg)

iso-damping

3.61 203

∞ ∞

41.9 90

No Yes

186

∞

98.1

Yes

Fig. 6 Comparison of frequency response

Fig. 7 VTOL response under external disturbance

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6 Conclusion Fractional Order Particle Swarm Optimization has been applied for tuning 1-DOF and 2-DOF FOPID controllers to stabilize a VTOL system. Performance of the algorithm has been compared with the original PSO. The FOPSO surpasses PSO in tuning the controllers. This work also presented a comparison between the performance of 1-DOF and 2-DOF FOPID controllers, in stabilizing the VTOL system. The 2DOF FOPID controller is found to exhibit superior performance, compared to its 1DOF counterpart. When properly tuned, the 2-DOF FOPID controller improves both time and frequency domain specifications of the compensated system. Moreover, the iso-damping property exhibited by the controller, makes the system robust against external disturbance and gain/parameter uncertainties. Extension of the present work to plants with time-delay and/or uncertainty needs to be explored.

References 1. Podlubny I (1999) Fractional-order systems and piλdμ controller. IEEE Trans Autom Control 44:208–214 2. Shah P, Agashe S (2016) Review of fractional PID controller. Mechatronics 38:29–41 3. Chen Y, Petras I, Xue D (2009) Fractional order control-a tutorial. In: 2009 American control conference, pp 1397–1411. IEEE 4. Bingi K (2020) Fractional-order systems and PID controllers: using Scilab and curve fitting based approximation techniques. Springer Nature, Berlin 5. Acharya DS, Mishra SK, Ranjan PK, Misra S, Pallavi S (2018) Design of optimally tuned two degree of freedom fractional order pid controller for magnetic levitation plant. In: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), pp 1–6. IEEE 6. Kumar L, Narang D et al (2018) Tuning of fractional order piλ dμ controllers using evolutionary optimization for PID tuned synchronous generator excitation system. IFAC-PapersOnLine 51(4):859–864 7. Lakshmanaprabu S, Elhoseny M, Shankar K (2019) Optimal tuning of decentralized fractional order PID controllers for TITO process using equivalent transfer function. Cogn Syst Res 58:292–303 8. Anoop S, Sharma KR (2018) Model predictive control: simulation studies for the implementation on vertical take-off and landing lab prototype. Proc Comput Scie 143:663–670 9. Pires ES, Machado JT, de Moura Oliveira P, Cunha JB, Mendes L (2010) Particle swarm optimization with fractional-order velocity. Nonlinear Dyn 61(1–2), pp 295–301 10. Swain SK, Sain D, Mishra SK, Ghosh S (2017) Real time implementation of fractional order pid controllers for a magnetic levitation plant. AEU-Int J Electron Commun 78:141–156

Design of 2-Degree of Freedom Fractional Order PID Controller for Magnetic Levitation Plant with Time Delay D. S. Acharya, S. K. Mishra, Shudit Kumar, and Shubham Kumar

Abstract This work presents the design of a two Degree of Freedom fractional order PID (2-DOF FOPID) controller to stabilize a second order unstable magnetic levitation plant having time delay. To achieve the optimum performance of the system, the controller gains have been tuned using Monarch Butterfly Optimization (MBO), a recently developed evolutionary algorithm. The performance of the 2-DOF FOPID controller has been compared with its 1-DOF counterpart. The obtained results validate that the 2-DOF FOPID enhances the performance of the system in both frequency and time domains and also exhibits superior robustness to external disturbances and parameter uncertainties. Keywords Fractional pid · Monarch butterfly optimization · 2-DOF · Time delay · Magnetic levitation

1 Introduction Time delay is one of the major causes of instability and degraded performance of real time plants. Several industrial processes, such as stirred tanks, bio-reactors, polymerization reactors, etc have time delays and are also open loop unstable. Designing a controller for an open loop unstable system with time delay is a challenging task, which is the reason it has attracted the attention of researchers worldwide. Many researchers are focused on formulating Proportional-Integral Derivative (PID) control schemes for unstable plants with time delay. Due to its simple structure, ease of realization and availability of simple tuning methods, PID controller is being used in more than 90% of industrial closed loops D. S. Acharya (B) · S. Kumar · S. Kumar Department of EEE, B.I.T. Mesra, Off-Campus Deoghar, Deoghar, India e-mail: [email protected] S. K. Mishra Department of EEE, B.I.T. Mesra, Ranchi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_15

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[1]. However, it has also been reported in literature that PID control proves to be inefficient for nonlinear, time-delay and uncertain systems [1, 2]. Since few decades, researchers have done significant advancement in the modeling and applications of Fractional PID controller (FOPID) [3]. In an FOPID controller, the derivative and integral actions are of non-integer order, the values of which may vary from 0 to 2. It has been confirmed in literature, that FOPID performs better than PID, whether used with an integer order or a fractional order plant. A comprehensive review of the applications of FOPID controller can be found in [4, 5]. Recently, several works report the design or parameter tuning of FOPID controller using recently proposed meta-heuristics such as Symbiotic Organisms Search (SOS) [6], Monarch Butterfly Optimization (MBO) [7], Teaching Learning based Optimization (TLBO) [8], Chaotic Atom Search Optimization Algorithm (CASO) [9]. A multivariable multiobjective genetic algorithm (MMGA) has also been applied to tune FOPID controller in [10]. This work illustrates the development of a 2-DOF FOPID controller to stabilize a second order unstable magnetic levitation system having time delay. The controller parameters have been tuned using MBO, after formulating an objective function. The obtained results are then, compared with those of 1-DOF FOPID controller.

2 Magnetic Levitation System (Maglev) The basic illustration of the magnetic levitation plant is shown in Fig. 1. The maglev system considered for this work is an experimental hardware-in-loop setup from Feedback Instruments Ltd. [11]. The setup consists of a metallic ball suspended in air using an electromagnet, which is excited through an input control voltage. The real time kit works in conjunction with MATLAB Simulink. The controller signal to the real time maglev system and the real time system response from the maglev system are sent/received through an A/D board which handles all the communications between MATLAB Simulink and the real time maglev kit. The dynamics of the maglev system may be expressed as [6, 12]: m x¨ = mg − k

i2 x2

(1)

Since (1) is a nonlinear plant, it needs to be linearized to obtain the transfer function and proceed with further analysis. The linearization of (1) may be done by: x¨ = − 2

  ∂ f (x, i)  ∂ f (x, i)  x − i ∂ x i0 ,x0 ∂i i0 ,x0

(2)

where f (x, i) = g − k xi 2 . x and i are the negligibly small deviations from x0 and i 0 , the equilibrium value of position and current. After calculating the partial derivatives and applying Laplace Transform to (2), we get:

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Fig. 1 Basic block diagram of maglev

−kc x = 2 i s − kp

(3)

where kc = 2g and k p = 2g [6, 12]. Substituting the values of all the constants from i0 x0 [11], we get the plant transfer function G p (s) =

−3518.85 s 2 − 2180

(4)

It is evident that (4) is an open loop unstable system. In this work, we add time delay into the system. The delay transfer function is defined by: G p (s) =

−3518.85e−sTd s 2 − 2180

(5)

where Td is time delay.

3 Fractional Order PID Controller (FOPID) The control law of an FOPID controller is defined by: −ρ

u fopid (t) = K p e(t) + K i [0 Dt e(t)] + K d [0 Dtσ e(t)]

(6)

where ρ, σ , K p , K i , K d are the non-integer integral order, non-integer derivative order, proportional gain, integral gain and derivative gain, respectively. The transfer function may, thus, be expressed as:

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Fig. 2 Block diagram of 1-DOF FOPID configuration

Fig. 3 Block diagram of 2-DOF FOPID configuration

Ufopid (s) = K p +

Ki + Kd sσ sρ

(7)

where 0 < ρ, σ < 2. By looking at (7) it becomes evident that the performance of FOPID controller depends on five parameters. The increased number of tunable parameters has its own advantage and disadvantage. Advantage: More number of system specifications, both from time and frequency domain, can be controlled simultaneously, giving the designer more control over the plant. Disadvantage: Tuning the FOPID is complex compared to PID. The FOPID controller, just like PID, may be implemented in different configurations. This work focuses on the 1-degree of freedom (DOF) and 2-DOF configurations. The schematics of the 1-DOF FOPID and 2-DOF FOPID configurations are illustrated in Figs. 2 and 3, respectively. It becomes evident from Fig. 2, that in 1-DOF configuration, the controller zeros appear in the forward path. These zeros introduce excess overshoot in the closed loop system response. This problem is overcome by using the 2-DOF configuration. It is clear from Fig. 3, that in a 2-DOF configuration, the controller zeros appear in the feedback path; while the integral term appears in the forward path. Elimination of zeros from the forward path reduces the overshoot in the closed loop response [12]. The PD controller q2 s + q1 , further, helps in improving the speed of response [13]. Note that the loop transfer function for both the configurations remain same.

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4 Monarch Butterfly Optimization Wang et. al. proposed a meta-heuristic in 2015, namely, Monarch Butterfly Optimization (MBO) [14]. This optimization algorithm is inspired from the southward migration, spanning thousands of kilometers, of North American monarch butterflies during summer/autumn. Due to its minimal dependency on control parameters/constants, it has been employed to solve optimization problems in several fields like controller tuning, optimal power flow and more. The MBO consists of two operators, namely, (1) Migration operator and (2) Butterfly adjusting operator. Initially, a population is generated, wherein each butterfly represent a candidate solution to the problem. The total population is split into two sub-populations, namely, Subpopulation-1 and Subpopulation-1. The migration operator generates a new Subpopulation-1, whereas, the butterfly adjusting operator generates a new Subpopulation-2, for the upcoming generation.

4.1 Migration Operator Let the total number of butterflies be N B. If p percent of butterflies are assumed to be located in Land-1, then the number of butterflies in Subpopulation-1 will be NB1 = p × NB and that in Subpopulation-2 will be NB2 = NB − NB1. The position of a butterfly in Subpopulation-1, in the next generation is given by [14]:  t+1 xi,k

=

xrt1 ,k , if r ≤ p xrt2 ,k , if r > p

(8)

t+1 is the kth dimension of the ith where, t represents the present generation; xi,k t butterfly in t + 1th generation; xr1 ,k is the kth dimension of the r 1-th butterfly randomly chosen from Subpopulation-1 and xrt2 ,k is the kth dimension of the r 2-th butterfly randomly chosen from Subpopulation-2. Note that, r1 ∈ {1, 2 . . . , NB1 } and r2 ∈ {1, 2 . . . , NB2 }. So, the butterflies of the Subpopulation-1 in the next generation will be the off-springs of butterflies from Subpopulation-1 and Subpopulation-2. The factor r is found by r = rand × peri, where peri indicates migration period and has been set to 1.2 [14] and rand is a random number uniformly distributed between [0,1].

4.2 Butterfly Adjusting Operator The location of the butterflies is also updated using this operator. This operator is applied only to the butterflies in Subpopulation-2. The position of a butterfly of Subpopulation-2, in the next generation is given by [14]:

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x t+1 j,k

⎧ t ⎪ ⎨xbest,k , if rand ≤ p = xrt3 ,k , if rand > p ⎪ ⎩ t x j,k + α × (d xk − 0.5),

(9) if rand > B A R

The terms in (9) are defined as: dx = Levy(x tj ) is the step-size of the random walk t is the kth dimentaken by a monarch butterfly; BAR is butterfly adjusting rate; xbest,k t sion of the best monarch butterfly; xr3 ,k is the kth dimension of the r3 th monarch butterfly randomly chosen from Subpopulation-2. Note that r3 ∈ {1, 2, . . . , NB2}. rand is a random number uniformly distributed between [0, 1]; α = Stmax 2 , where Smax is the maximum step-size that a monarch butterfly may take and t is the present generation. After completion of both the operators, the two sub-populations are merged and sorted as per the fitness value. The resulting new population is again divided into two sub-populations. This procedure is repeated until the termination condition is achieved or the algorithm reaches the maximum number of generations.

5 Simulation For tuning the FOPID controllers using the MBO algorithm, it is important to define the search space, i.e. the lower limit and upper limit of the parameters. Note that the plant transfer function has negative dc-gain [refer to Eq. (5)]. To stabilize the plant, controller gains K p , K i and K d must be negative. The search space considered in this work is −5 < K p , K i , K d < 0, 0 < ρ < 2 and 0 < σ < 2. To tune a 2-DOF controller, the controller gains are, first tuned, keeping q2 =0 and q1 =ki. q2 may then be tuned to achieve the desired speed of response. Population size and maximum iteration count of MBO are kept at 50 and 1000, respectively. Other required parameters are set as: p = 0.45, peri = 1.2 and BAR = 5/12, Smax = 1.0. The algorithm is executed for 30 independent runs. Th objective function proposed for the purpose is given in (10) J (K p , K i , K d , ρ, σ ) = ω1 ISE + ω2 ts + ω3 M p subject to ||S||∞ < 2 and ||T ||∞ < 2

(10)

where J (.) is the objective function, signifying the minimization of integal square error (ISE), settling time (ts ) and peak overshoot (M p ) of the system response. ω1 , ω2 and ω3 are weighing factors, such that ω1 + ω2 + ω3 = 1. ||S||∞ and ||T ||∞ represent the infinity norms of sensitivity and complementary sensitivity of the closed loop system. The constraints have been imposed to achieve satisfactory robustness to external disturbances and parameter uncertainties. The best results obtained from MBO, over 30 runs, is summarized in Table 1. The time domain specifications ts , M p , rise time (tr ), ISE, J , ||S||∞ and ||T ||∞ of the

Design of 2-Degree of Freedom Fractional Order … Table 1 Best results of MBO, over 30 independent runs Controller K p Ki Kd ρ 1-DOF 2-DOF

−1 −0.8623

−0.3 −0.85

−0.014 −0.0232

1.03 1.014

163

σ

q1

q2

1.2 1.051

– −0.85

– 0

Table 2 Time domain specifications for the optimum solution Controller ts (s) M p (%) ISE J tr (s) 1-DOF 2-DOF

4.171 0.769

214.71 0.442

1.8267 0.1810

56.22 0.54025

0.0054 0.4512

||S||∞

||T ||∞

1.016 1.1

1.182 1.23

Fig. 4 Comparison between responses of the compensated system for 1-DOF and 2-DOF FOPID controllers

compensated system are given in Table 2. Results summarized in the table reveal the superior performance of 2-DOF FOPID configuration. The system responses for 1-DOF and 2-DOF FOPID controllers are shown in Fig. 4. The excessively prominent overshoot in the response with 1-DOF controller is because of the existence of controller-zeros along with zeros of the delay term (e−sTd ), in the forward path. It is clear, that the 2-DOF configuration mitigates the overshoot occurring in the system response of the time-delay plant. Also, the values of sensitivity and complementary sensitivity being less than 2 signify satisfactory robustness. For examine the robustness of the closed loop system to external disturbances, it is subjected to a periodic output disturbance signal of magnitude 0.02 and time period 10 s. The output of the system, in presence of output disturbance, is shown in Fig. 5. It is clear that the system exhibits satisfactory robustness to external disturbance. Figure 6 reveals that the phase plot of the compensated system is flat in the vicinity of gain

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Fig. 5 Response of the system subject to periodic output disturbance

Fig. 6 Bode plot of compensated system

cross-over frequency. This means that the system exhibits iso-damping behaviour, which verifies that the closed loop system will remain robust to gain and parameter variations/uncertainties.

6 Conclusion A 2-DOF FOPID controller has been designed to stabilize a second order unstable magnetic levitation plant, having time delay. The parameters of the controller have been tuned using Monarch Butterfly Optimization algorithm by to minimizing the

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proposed objective function. The results and responses of the 2-DOF FOPID controller have been compared with its 1-DOF counterpart and it has been verified that the 2-DOF FOPID controller exhibits better performance bringing significant reduction in settling time and peak overshoot. The system, compensated with 2-DOF FOPID controller exhibits iso-damping property, thereby displaying good robustness to external disturbance and parameter uncertainties. Control of systems having fractional-order time delay, uncertain plant models and more are some of the areas which are open to be explored.

References 1. Ang KH, Chong G, Li Y (2005) PID control system analysis, design, and technology. IEEE Trans Control Syst Technol 13(4):559–576 2. Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning, vol 2. Instrument society of America Research, Triangle Park 3. Polubny I (1999) Fractional-order systems and piλdμ controller. IEEE Trans Autom Control 44:208–214 4. Shah P, Agashe S (2016) Review of fractional PID controller. Mechatronics 38:29–41 5. Bingi K (2020) Fractional-order systems and PID controllers: using Scilab and curve fitting based approximation techniques. Springer Nature, Berlin 6. Acharya DS, Mishra SK, Ranjan PK, Misra S, Pallavi S (2018) Design of optimally tuned two degree of freedom fractional order PID controller for magnetic levitation plant. In: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), pp 1–6. IEEE 7. D. Sambariya and T. Gupta, “Optimal design of pid controller for an avr system using monarch butterfly optimization,” in 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp. 1–6, IEEE, 2017 8. Gorripotu TS, Samalla H, Rao CJM, Azar AT, Pelusi D (2019) Tlbo algorithm optimized fractional-order PID controller for AGC of interconnected power system. In: Soft computing in data analytics. Springer, Berlin, pp 847–855 9. Hekimo˘glu B (2019) Optimal tuning of fractional order pid controller for dc motor speed control via chaotic atom search optimization algorithm. IEEE Access 7:38100–38114 10. Ren H-P, Fan J-T, Kaynak O (2018) Optimal design of a fractional-order proportionalinteger-differential controller for a pneumatic position servo system. IEEE Trans Ind Electron 66(8):6220–6229 11. Magnetic levitation: control experiments feedback instruments limited (2011) 12. Swain SK, Sain D, Mishra SK, Ghosh S (2017) Real time implementation of fractional order PID controllers for a magnetic levitation plant. AEU-Int J Electron Commun 78:141–156 13. Ghosh A, Krishnan TR, Tejaswy P, Mandal A, Pradhan JK, Ranasingh S (2014) Design and implementation of a 2-dof PID compensation for magnetic levitation systems. ISA Trans 53(4):1216–1222 14. Wang G-G, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31(7):1995–2014

Design of Salp Swarm Algorithm Tuned Cascade Proportional–Integral–Tilt-Derivative Controller for the Performance Study of Load Frequency Control Sandhya Kumari and Gauri Shankar Abstract In the present article, a fractional calculus-based cascade proportional– integral–tilt-derivative (CC-PI-TD) controller is designed for the performance scrutiny of four-area interlinked thermal-hydro generating power system in load frequency control (LFC). PI controller introduced as master controller whereas TD controller is used as a slave controller in the designed CC-PI-TD controller. The gain parameters of controller are optimized by recently developed meta-heuristic salp swarm algorithm (SSA). To show the effectiveness of designed SSA tuned CC-PI-TD controller, the simulated results are matched with the results obtained by employing SSA tuned PID controller. The comparative analysis reveals that the designed CC-PI-TD controller performs superior than PID controller in terms of minimum settling time, peak deviation of overshoot, undershoot, and performance indices. Keywords Fractional order cascade controller · Load frequency control · Salp swarm algorithm

1 Introduction The complexity and size of electrical power framework are continously rising. consequently, the adequate and stable operation of electrical power network against the sudden fluctuation in load is an important factor for the utility houses. For the steady operation, large number of generating units are interlinked via tie-line. The main target of load frequency control (LFC) is to maintain the nominal frequency and specified value of tie-line power flow between interlinked generating units within the tolerable limits [1]. Hitherto, distinct control strategy is already discussed in the litrature to figure out the LFC problem. The comparative study of conventional S. Kumari (B) · G. Shankar Electrical Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, India e-mail: [email protected] G. Shankar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_16

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controllers like integral (I), proportional–integral (PI), integral-double-derivative (IDD), and proportional–integral–derivative (PID) is found in [2]. ˙In their scrutiny, it is observed that IDD controller improved the transient response over other conventional controllers. The efficiency of above mentioned controllers is deteriorated by applying large load demand. Hence, cascade controller with two feedback loop is implemented for enriching the system performance. Lee et al. [3] have proposed cascade control system. The scrutiny of mutually connected multi-area power system model (IPSM) consisting thermal generating unit using PD-PID cascade controller is discussed in [4]. Padhy et al. [5] have introduced cascade PI-PD controller in LFC for performance investigation of multi-area IPSM. In the current trends, researchers have paid a significant attention toward the study of fractional order calculus (FOC)-based cascade controllers due to its superior performance over conventional-based cascade controllers. Nayak and Shaw [6] have examined the performance of cascaded PDFOPID controller in LFC study. Saha and Saikia [7] have presented the comparative analysis of cascaded PIDN-FOI and two-degree of freedom PIDN-FOI controllers for LFC study. Kumari and Shankar [8], have outlined the effectivness of designed FOCbased integral–tilt-derivative (I-TD) controller over the PID and TID controllers in the LFC study. Recently, Guha et al. [9] have proposed the cascade TID controller for solving the LFC issues in two- and four-area IPSM. From literature survey, it is found that there is still a lot of scope to design the various FOC-based cascade controller structure for the study of LFC issue. Hence, the present paper is mainly centered on utilization of cascade proportional–integral–tilt-derivative (CC-PI-TD) controller for LFC of considered four-area IPSM. Satisfactory function of controller can be obtained when controller gain parameters are well tuned. In the recent past, distinct optimization techniques such as bacterial foraging [2], bat algorithm [4], hybrid stochastic fractal search and pattern search [5], group hunting search [6], water cycle algorithm [8], etc., have been used to tuned the gain values of controllers in LFC. Kumari and Shankar [10] have presented a comparative study on LFC using various algorithms. The comparative analysis confirms that salp swarm algorithm (SSA) performs better than other optimization techniques. Therefore, SSA is used for getting the optimal gain values of desinged CC-PI-TD controller in the present work.

2 Power System Modeling and Adopted Control Methods 2.1 Studied Power System Modeling In the current paper, four-area thermal-hydro IPSM which is created in MATLAB/SIMULINK environment is considered for the performance evaluation of desinged SSA tuned CC-PI-TD controller. It may be seen from the Fig. 1, area-1, area-2, and area-3 are thermal generating unit consisting of reheat turbine, where as area-4 is hydro generating unit. The nominal values of studied system and transfer

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Fig. 1 Layout of four-area IPSM

function representation of each components of thermal-hydro generating units are found in [10].

2.2 Adopted Control Methods PID Controller: It is highly prefered controller for industry aplications and its structure is simple and robust. The transfer function (TF) of PID controller is referred by Eq. (1): TF P I D (s) = K P +

Ns KI + KD s N+s

(1)

whereN , K D , K I and K P are referred as the filter coefficients, derivative, integral, and proportional, respectively. Area control error (ACE) signal is fed as input to the PID controller in the current study which is presented by Eq. (2): AC E j = B j F j ± PT ie j

(2)

where PT ie j , B j , and F j are known as tie-line fluctuation, frequency bias constant, and change in frequency of jth area, respectively. The expression of fitness functions namely integral time absolute error (ITAE), integral time square error (ITSE), integral square error (ISE), and integral absolute error (IAE) are written as a function of ACE and constraints of fitness function are available in published work of [10]. The term FOD known as figure of demerit which is same as ISE in the present paper. CC-PI-TD Controller: The merits of both cascade control method and FOC are used to construct the CC-PI-TD controller. For better disturbance rejection and obtaining

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Fig. 2 Structure of CC-PI-TD controller

good set-point, two single control loops are cascaded in cascade controller diagram. In Fig. 2, TD controller is termed as slave controller and PI controller is called as master controller. TF of master controller (G M (s)) and slave controller (G S (s)) are defined by Eqs. (3) and (4), respectively. G M (s) = K P +

KI s

G S (s) = K T s −1/n + K D

(3)

sN s+N

(4)

where K T is termed as tilt gain. The value of n is lies between 2 and 3.

3 Salp Swarm Algorithm (SSA) SSA is a newly proposed meta-heuristic optimizer, which is motivated by swarming nature of salps in ocean [11]. Its barrel-shaped body is transparent just like jelly fishes. Salps form swarm within the ocean which is named as salp chain. The population of salp is break into two groups, one is named as leader and other is follower on the basis of mathematical modeling of salp chains. The front position of salp chain is captured by leader and remaning of other salps follow the leader. The location of leader is updated by Eq. (5) with respect to food position [9–11].  Z 1j

=

   F j + C1 Z max, j − Z min, j C2 + Z min, j    F j − C1 Z max, j − Z min, j C2 + Z min, j

if C3 ≥ 0.50 if C3 < 0.50

(5)

where Z 1j , F j , Z min, j and Z max, j are the current location of the first salp, food source location, minimum and maximum limit of jth dimension, respectively. C1 is a most important coefficient in SSA because it maintains the balance between

Design of Salp Swarm Algorithm Tuned Cascade …

171

exploration and exploitation phase. C1 , C2 and C3 are calculated by Eq. (6) [10]: 4c C1 = 2 exp−( M ) C2 , C3 ∈ [0, 1]

2

 (6)

where c and M are the present and the maximum cycle, respectively. Now, the follower update its position by Eq. (7) [9]: Z ij =

  1 v f inal 2 t + vo t 2 vo

(7)

where i ≥ 2 and Z ij termed as updated place of jth follower in ith dimension. o t,v f inal = Z −Z and vo are the time, final and initial speed, respectively. Here, in t optimization process, time is the iteration and the difference between two continuous iteration is one. So, the Eq. (7) can be drafted as Eq. (8) by considering vo = 0. Z ij =

1 i Z j + Z i−1 j 2

(8)

Using Eqs. (5) and (8), salp chain may be simulated.

4 Simulated Results and Observation In the present paper, an analysis of transient responses of four-area thermal-hydro IPSM have been performed to verify the effectiveness of designed SSA tuned CCPI-TD controller. The best value of SSA parameters is population size = 60 and iteration number = 100 which is obtained by 25 trial run. In the present section, two case studies are taken.

4.1 Case Study 1: When a Step Load Perturbation (SLP) of 0.01 p.u. is Employed in Area-1 of Studied System at Time t = 0 second (s) The optimized gain of PID controller (taken from [10]) and designed CC-PI-TD controller is listed in Table 1. The comparable frequency responses are displayed in Fig. 3 and its transient characteristics are described in Table 2. A significant improvment is observed in the value of settling time (Ts ), peak time (Tp ), undershoot (Us ), and overshoot (Os ) under the action of designed CC-PI-TD controller. It can

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Table 1 The best gain values of controllers getting under the case study 1

Parameter

PID [10]

CC-PI-TD

Area-1 The gain values of controller

K P = 9.980

K P = 9.9947

K T = 9.9980

K I = 9.940

K I = 9.9780

n = 2.0132

K D = 8.950

K D = 9.4960

Area-2 K P = 4.175

K P = 9.0575

K T = 2.1626

K I = 3.605

K I = 7.1080

n = 2.2605

K D = 10.00

K D = 7.4645

Area-3 K P = 9.997

K P = 0.1169

K T = 9.5368

K I = 0.812

K I = 9.7257

n = 2.3279

K D = 4.643

K D = 4.4496

Area-4 K P = 0.011

K P = 0.8381

K T = 0.8584

K I = 0.012

K I = 0.0101

n = 2.4606

K D = 10.00

K D = 9.9790

(a)

(c) Fig. 3 Dynamic response profiles of a F1 , b F2 , c F3 , d F4

(b)

(d)

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Table 2 Transient performance parameters obtained under case study 1 Signals Transient performance parameters PID [10]

CC-PI-TD

Us (×10  f1

Ts

Os −4

)

(×10

64.000

−4

Tp

Us

)

9.2770

(×10

Ts

Os −4

)

9.5927 0.1695 41.000

(×10

−4

Tp

)

5.1409

9.6133 0.1033

 f2

6.3175

1.1889

21.343

1.5769

2.8973

0.2593

31.061

2.7557

 f3

6.1872

1.4638

21.342

1.5070

1.8241

0.3242

33.901

2.9400

21.000

24.315

2.4466 10.000

5.5882

13.653

2.0325

 f4

22.000

Ptie12

2.2823

0.4375

26.720

1.5398

4.1889

0.4460

26.065

1.6343

Ptie23

0.0495

0.0495

45.795

7.4447

1.4861

0

29.934

1.7107

Ptie31

2.2721

0

27.178

1.5398

1.6245

0

27.947

1.9852

Ptie41

2.1432

2.1432

26.763

1.3616

1.1738

1.1738

13.826

1.3023

The bold values are result of interest

Table 3 Performance indices parameters obtained under case study 1 Methodology

Performance indices value FOD (×10−4 )

ITSE (×10−4 )

ITAE

IAE

PID [10]

0.0787

0.2251

0.0729

0.0113

CC-PI-TD

0.0189

0.0268

0.0271

0.0049

be seen from Table 3, the designed CC-PI-TD controller offers better FOD and other performance indices perameters over PID controller which is reported in [10].

4.2 Case Study 2: Load Sensitivity Analysis of Studied System In the present case study, 0.01 p.u. of SLP is employed in area-3 at time t = 100 s whereas SLP employed in area-1 is similar to case study 1. It can be viewed from Fig. 4 that the gain values of proposed CC-PI-TD controller (see in Table 1) are robust and gives improved results than the PID controller against multiple SLP at distinct time interval.

5 Conclusion In the current study, four-area IPSM is considered to analyze the performance of designed SSA tuned CC-PI-TD controller. The comparative analysis reveals that the

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(a)

(b)

(c)

(d)

Fig. 4 Dynamic response profiles for multiple SLP at distinct time interval of a F1 , b F2 , c F3 , d F4

designed CC-PI-TD controller offers better transient responses than PID controller. The performance indices perameters are also enhanced in presence of designed CCPI-TD controller. Load sensitivity analysis demonstrates that the proposed CC-PI-TD controller delivered stable performance and robust.

References 1. Kundur, P (1994) Power system stability and control. McGraw-Hill 2. Saikia LC, Nanda J, Mishra S (2011) Performance comparison of several classical controllers in agc for multi-area interconnected thermal system. Int J Electr Power Energy Syst 33(3):394–401 3. Lee Y, Park S (1998) PID controller tuning to obtain desired closed loop responses for cascade control systems. Ind Eng Chem Res 37:1859–1865 4. Dash P, Saikia LC, Sinha N (2015) Automatic generation control of multi-area thermal system using bat algorithm optimized PD–PID cascade controller. Int J Electr Power Energy Syst 68:364–372 5. Padhy S, Panda S (2017) A hybrid stochastic fractal search and pattern search technique based cascade PI PD controller for automatic generation control of multisource power systems in presence of plug in electric vehicles. CAAI Trans Intell Tech 2(1):12–25 6. Nayak JR, Shaw B (2018) Application of group hunting search optimized cascade PD-fractional order PID controller in interconnected thermal power system. Trends Renew Energy 4(3):22–33 7. Saha A, Saikia LC (2018) Performance analysis of combination of ultra-capacitor and superconducting magnetic energy storage in a thermal-gas AGC system with utilization of whale optimization algorithm optimized cascade controller. J Renew Sustain Energy 10(1):014103

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8. Kumari S, Shankar G (2018) Novel application of integral–tilt-derivative controller for performance evaluation of load frequency control of interconnected power system. IET Gener Trans Distrib 12(14):3550–3560 9. Guha D, Roy PK, Banerjee S (2018) Maiden application of SSA-optimised CC-TID controller for load frequency control of power systems. IET Gener Trans Distrib 13(7):1110–1120 10. Kumari S, Shankar G (2018) A novel application of salp swarm algorithm in load frequency control of multi-area power system. In: IEEE international conference on power electronics and drives energy systems (PEDES), pp. 1–5, December 2018 11. Mirjalili S, Gandomi AH et al (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

A Multi-objective Integral Sliding Mode Controller for Magnetic Levitation Plant D. S. Acharya and Srihari Gude

Abstract This work proposes the design of an Integral Sliding Mode Controller (ISMC) to stabilize a second order unstable magnetic levitation plant (MLP). The sliding surface is employed to drive the system towards equilibrium and thus, stability. Parameters of the ISMC are obtained by solving a multi-objective optimization problem (MOOP), formulated for the purpose. The MOOP has been solved using NonDominated Sorting Genetic ALgorithm-II (NSGA-II). Simulations have been carried out for reference signal tracking and disturbance rejection. The performance of the proposed ISMC has been compared with that of a proportional-integral-derivative (PID) controller. The obtained results are promising and confirm that SMC exhibits superior performance than PID controller. Keywords Sliding mode control · Magnetic levitation · PID · Sliding surface · NSGA-II · Multi objective optimization

1 Introduction Sliding mode control (SMC) has attracted a lot of interest due to system order reduction, large signal stability, easy implementation, robustness to model uncertainties and disturbances [1]. SMC is a nonlinear control strategy based on the variable structure theory. In a variable structure control system, the structure of the controller changes from one form to another. By doing so, it is possible to combine useful properties of different control structures and can even be made to attain new properties not presented in any of the single structures utilized [2].

D. S. Acharya (B) · S. Gude Department of Electrical and Electronics Engineering, Birla Institute of Technology Mesra, Off-Campus Deoghar, Jharkhand, India e-mail: [email protected] S. Gude e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_17

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Magnetic levitation plant (MLP) is an open loop unstable system. In general, classical controllers such as PID controller or lead-lag compensator are used to maintain stability and thus, to attain desired performance. These classical controllers are designed by linearizing the MLP around a certain equilibrium point. Hence, robustness issues due to modeling uncertainties and external disturbance may remain intact and they need to be addressed to get accurate performance from MLP. In [3], SMC is designed to stabilize and commanding a magnetic levitation system. To control magnetic levitation system, SMC of the static and dynamic types are proposed in [4]. In [5], a SMC is designed to get desired levitation against model uncertainties and non-linearities. In this paper, an Integral sliding mode controller (ISMC) is implemented to get improved robustness, due to fact that adding integral term to sliding law increases the order of the sliding surface which in turn is equal to the order of the plant. In addition, ISMC also improves the steady state performance from MLP in comparison with conventional SMC based MLP [6].

2 Magnetic Levitation Plant (MLP) Magnetic levitation is the process of suspending an object in air, without any support or physical contact. It is quite intuitive that in such a process, enough electromagnetic force has to be created to balance the gravitational force, which pulls down the object towards ground. Since the friction and other mechanical losses are significantly reduced, the MLP system has found several applications in areas such as non-contact melting, magnetic bearings, transportation, wireless charging. The MLP system considered in this work is an experimental setup provided by the Feedback Instruments Ltd. having model number 33-210 [7]. The basic schematics of the MLP is shown in Fig. 1. This setup is, basically, a Hardware-in-Loop simulation system. The simulation model, created in MATLAB Simulink® controls the real-time MLP through A/D interface. The A/D interface handles the communication between the computer and the real time plant. The dynamics of the MLP may be expressed as [8–10]: m x¨ = mg − k

i2 x2

(1)

where, m represents mass the metal ball, g is the acceleration due to gravity, x is the position of the ball from the electromagnet, k is the plant constant which depends on coil parameters, i is the excitation current and x is the distance between the ball and the electromagnet. It is evident from (1) that the MLP is a nonlinear system. To obtain a model for analysis purpose, (1) is linearized using Taylor’s series as mentioned in (2)

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Fig. 1 Schematic representation of magnetic levitation plant

 ∂ f (x, i)  x¨ = − ∂x 

i 0 ,x0

 ∂ f (x, i)  x − ∂i 

i 0 ,x0

i

(2)

After re-arranging (2) and using the parameters of MLP as in [7], the transfer function of position (expressed as voltage) to the input/control voltage, the transfer function is found to be: G P (s) =

s2

Kp −3518.85 = 2 + a0 s − 2180

(3)

Looking at (3) it becomes obvious that MLP is an open loop unstable system with negative gain. Assuming distance x of the ball from the electromagnet to be the first state variable z 1 and its derivative as the second state variable z 2 , the state space model of MLP may be expressed as:    z˙1 0 = z˙2 2180  y = −3518.85

    0 z1 + u z2 1      z1 0 + 0 u z2

1 0

(4)

3 Sliding Mode Controller Design As per [5, 11], the sliding surface employed in this work is expressed as:  s(t) =

d +α dt

n−1 e(t)

(5)

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where n is the order of the system and α is a tuning parameter. From (4) we have: z˙1 = z 2 z˙2 = −a0 z 1 + u

(6) (7)

y = K p z1

(8)

It is known that error=reference-output. So, e(t) = r (t) − y(t) = r (t) − K p z 1

(9)

Dropping the time variable t for simplicity and differentiating (9) twice, we obtain: e˙ = r˙ − K p z˙1 = r˙ − K p z 2 e¨ = r¨ − K p z˙2 e¨ = r¨ − K p (−a0 z 1 + u) = r¨ + a0 K p z 1 − K p u

(10)

Defining the integral sliding surface: s = e˙ + αe + ki

edt

(11)

where, ki is a constant. Differentiating (11), we get: s˙ = e¨ + α e˙ + ki e

(12)

s˙ = r¨ + a0 K p z 1 − K p u + α e˙ + ki e

(13)

Using (10), To estimated the control law put s˙ = 0, which yields: s˙ = 0 r¨ + a0 K p z 1 − K p u eq + α e˙ + ki e = 0   u eq = K1p r¨ + a0 K p z 1 + α e˙ + ki e

(14)

The control law is chosen to be:

u(t) =

1 Kp

u(t) =

1 Kp

u(t) = u eq + u switch   r¨ + a0 K p z 1 + α e˙ + ki e + ASat (s)   r¨ + a0 K p z 1 + α e˙ + ki e + γ Sat (s)

(15)

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where u(t) is the total control law comprising of the estimated equivalent control u eq and switching control u switch . Sat(.) is saturation function defined in (16), A is a constant and γ = AK p is the upper limit of the saturation function.

10 if s > 0 Sat(s) = (16) −10 if s < 0 Check s s˙ < 0:   s s˙ = s r¨ + a0 K p z 1 − K p u(t) + α e˙ + ki e

(17)

Substituting u(t) from (15) into (17), we obtain:   s s˙ = s r¨ + a0 K p z 1 − r¨ − a0 K p z 1 − α e˙ − ki e − γ Sat(s) + α e˙ + ki e s s˙ = −γ Sat(s) = −AK p Sat(s) (18) From (18) it can be inferred: Case-1: If s > 0 For s > 0, Sat(s) > 0. Since K p < 0, s s˙ < 0 only if A < 0 Case-2: If s < 0 For s > 0, Sat(s) < 0. Since K p < 0, s s˙ < 0 only if A < 0 The two cases may be summarized by s s˙ = −AK p |s|

(19)

=⇒ s s˙ < 0 iff A < 0 because K p < 0.

4 Simulation and Results It is clear from the analysis presented in the previous section that the efficiency of the proposed ISMC depends on proper choice of the parameters α, A and ki . A multiobjective problem, defined in (20), is formulated to minimize the settling time (ts ) and the integral square error (ISE). The first objective signifies the improvement in speed of system response; whereas the second objective represents the achievement of desired accuracy of the system. Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) [12], a multi-objective optimization algorithm, has been applied to solve the problem.

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J = minimize{F1 , F2 } F1 = ts F2 = I S E α>0

subject to :

(20)

A 0 A block diagram of the proposed control scheme is shown in Fig. 2. The performance of the proposed ISMC has been compared with that of PID controller. The objective function is same in both the cases. Population size and maximum iteration count for NSGA-II has been set to 50 and 1000, respectively. The algorithm was executed for 30 independent runs. The final result of optimization is given in Table 1. The associated Pareto fronts are shown in Fig. 3. The corresponding time domain specifications are summarized in Table 2. Application of ISMC brings a 100%, 26% and 78% reduction in peak overshoot, settling

Fig. 2 Block diagram of the proposed control scheme Table 1 Result of NSGA-II Controller Kp Ki PID ISMC

-1.25 –

-4.2681 –

Kd

α

A

ki

-0.0253 –

– 22

– -21

– 0.0244

Fig. 3 Pareto fronts of tuning PID and ISMC, respectively

A Multi-objective Integral Sliding Mode Controller for Magnetic Levitation Plant Table 2 Time domain specifications Controller M p (%) PID ISMC

87.48 0

ts (s)

tr (s)

I SE

1.49 1.1786

0.023 0.1

0.1097 0.0234

183

Fig. 4 Comparison of maglev response

Fig. 5 Phase plot of the proposed SMC

time and ISE, respectively, which is a significant improvement in system response speed, oscillations and accuracy. It is therefore, verified that ISMC exhibits a superior performance than PID. A comparison of the system responses, for the parameter values of Table 1, is illustrated in Fig. 4. Elimination of overshoot is distinctly noticeable in the figure. The phase portrait of sliding mode control is shown in Fig. 5, with the reaching and sliding modes clearly indicated. A close look into the figure reveals minor chattering prior to sliding mode (see inset in Fig. 5).

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Fig. 6 Response of maglev system subject to periodic disturbance

The system has been tested for disturbance elimination also. A periodic signal of amplitude 0.01 was added to the system output as a disturbance. This disturbance may also represent the presence of possible measurement errors. The system response subject to the periodic disturbance is shown in Fig. 6. The ISMC was able to reject the effects of the disturbance very efficiently and quickly. Looking closely (insets), it is found that the transients due to the disturbance are eliminated in nearly 0.03 s and the system resumes tracking the reference signal.

5 Conclusion A multi-objective Integral Sliding Mode Controller (ISMC) has been proposed for a magnetic levitation plant. The control law for the proposed SMC has been formulated and it has been proved that the proposed ISMC satisfies the existence criteria s s˙ < 0. The variable constants in the control law have been tuned using a multi-objective problem, which has been solved using NSGA-II. The results, thus obtained, have been compared with PID controller. It is found the ISMC exhibits superior performance. When the system response is mixed with external disturbance or measurement errors, the ISMC is able to eliminate the disturbance/error and track the reference signal effectively. Real time implementation of the proposed control scheme is among the many future scopes of the work. The application of the proposed ISMC to plants with time delay, model/parameter uncertainties are few of the other areas to be explored.

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References 1. Yu X, Kaynak O (2009) Sliding-mode control with soft computing: a survey. IEEE Trans Ind Electron 56(9):3275–3285 2. Utkin VI (1993) Sliding mode control design principles and applications to electric drives. IEEE Trans Ind Electron 40(1):23–36 3. Cho D, Kato Y, Spilman D (1993) Sliding mode and classical controllers in magnetic levitation systems. IEEE Control Syste Mag 13(1):42–48 4. Al-Muthairi N, Zribi M (2004) Sliding mode control of a magnetic levitation system. Math Probl Eng 2004(2):93–107 5. Sathiyavathi S et al (2019) Design of sliding mode controller for magnetic levitation system. Comput Electr Eng 78:184–203 6. Utkin V, Shi J (1996) Integral sliding mode in systems operating under uncertainty conditions. In: Proceedings of 35th IEEE conference on decision and control, vol 4. IEEE, pp 4591–4596 7. Magnetic levitation: Control experiments feedback instruments limited (2011) 8. Ghosh A, Krishnan TR, Tejaswy P, Mandal A, Pradhan JK, Ranasingh S (2014) Design and implementation of a 2-dof PID compensation for magnetic levitation systems. ISA Trans 53(4):1216–1222 9. Swain SK, Sain D, Mishra SK, Ghosh S (2017) Real time implementation of fractional order PID controllers for a magnetic levitation plant. AEU-Int J Electron Commun 78:141–156 10. Acharya DS, Mishra SK, Ranjan PK, Misra S, Pallavi S (2018) Design of optimally tuned two degree of freedom fractional order PID controller for magnetic levitation plant. In: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON). IEEE, pp 1–6 11. Slotine J-JE, Li W et al (1991) Applied nonlinear control, vol 199. Prentice Hall, Englewood Cliffs 12. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolution Comput 6(2):182–197

A Review Article on PWM Inverter and Z-Source Inverter for Induction Motor Hempushpa Bharti and B. M. Prasad

Abstract This paper presents the study and analysis of the development of an inverter specifically PWM-based voltage source inverter and Z-source inverter that supplies variable frequency and variable voltage and is used for speed control in an induction motor. In this paper, the review work of the already presented paper has been done. The sinusoidal PWM method and space vector PWM method used in VSI fed induction motor are studied, and also, the comparison between them is studied and analyzed. These methods have advantages in terms of harmonic reduction. The space vector PWM method reduces current and voltage harmonics more than the sinusoidal PWM method. Also, using a Z-source inverter increases the efficiency which has also been described and studied in this paper. Utilizing this method with a maximum boost control switching method results in a faster rate of achieving a steady speed. Keywords Induction motor · MBS-ZSI · SPWM · SVPWM · VSI

1 Introduction Induction motor (IM) has been playing an important role in running of industry since a long time due to its robustness, high efficiency, low cost and less maintenance. As the frequency voltage supply was not available, the induction motors were mainly for constant speed applications. With the progress of power electronics, the frequency of the voltage supplies can be varied, and thus, the induction motor can also be utilized in variable speed drive applications [1–3]. The drive units are being designed with voltage source inverters together with induction motors in various fields [4–6]. Variable speed AC motor drives are also being designed using highspeed power semiconductor technology utilizing the three-phase inverters. Although the basic circuit for an inverter may seem simple, but these devices incur lots of challenges during switching [7–10]. Three-phase VSI will supply Variable frequency and H. Bharti (B) · B. M. Prasad Department of EEE, BIT, Mesra, Ranchi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_18

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variable voltage for adjustable speed drives (ASD). To control the speed of the load, the required AC signal for the load is obtained from converting the DC currents to AC through different converter topology. For these converters, the switching pulses can be obtained from different PWM techniques. Hence, different advanced PWM techniques have been developed to generate the switching pulse [11–13]. Due to the low power losses in the switching devices, this method gives higher efficiency which is a major advantage. PWM switching strategy also has the advantages like less total harmonic distortion, effective dc bus utilization and also takes care of problems like electromagnetic interference (EMI) reduction, better distribution of harmonics over spectrum [14, 15]. Many research works are being done towards the development of the efficient control algorithms for high-performance variable speed induction motor (IM) drives. While constructing IM drives, six switches three-phase inverters (SSTP) have been normally used [16]. But, it results in the increased losses and increased complexity of the control algorithms and interface circuits. The disadvantage of using the six switches three stage inverter is that the harmonic content is high. Researchers focused on the improvement of new control calculations to deal with this issue. The main objective of the research work was to design a simple, cost-effective and efficient high-performance IM drive. There is no single PWM method that suits to all our requirements as far as the optimal performance of induction motor is concerned. The two popular approaches for the implementation of PWM algorithms are sinusoidal PWM (SPWM) and space vector PWM (SVPWM) [17–26]. SPWM is used for harmonic reduction and better performance at output of induction motor. It is simple method to implement and hence is widely used. But SVPWM gives better output voltage and is more capable of reducing the harmonics. Space vector PWM (SVPWM) is a complex method as regarding the switching time of individual switching devices, it comprises minute calculations. SVPWM gives more output voltage as compared to other techniques and has fewer ripples in output waveforms that give smooth torque characteristics in industrial appliances such as induction motors [27]. Also, to enhance the efficiency and increase DC output voltage from the battery, a dual stage boost voltage source inverter (VSI) has been proposed, but it has low efficiency because of high switching losses, and gate drivers must be specifically designed so that the effect of shoot through zero state on VSI topology does not damage the switching component. Thus, a maximum boost control Z-source inverter (MBC-ZSI) topology method is developed which increases voltage with single stage or only one circuit topology. This topology can increase the efficiency of the inverter for induction motor drives [28–31]. This paper presents the review of the work done towards the development of SPWM and SVPWM techniques used for the VSI fed induction motor. The comparison among both is studied. A Z-source inverter has also been studied and analyzed in this paper for improvement in terms of efficiency.

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2 Voltage Source Inverter At the desired voltage and frequency output, to translate power in DC form to AC form, an inverter is used. Voltage source inverter (VSI) accepts the DC input from a voltage source. Whereas when the current source provides input to the circuit, then it is known as a current source inverter (CSI) [31–33]. The VSI circuit directly controls AC output voltage, whereas the CSI is capable of controlling AC output current. Output voltage waveforms of an ideal VSI should be independent of load connected at the output. According to a number of phases, inverters are classified into two types: Single-phase voltage source inverter and three-phase voltage source inverter. Single-phase VSI can deal with only low power applications. With controlled magnitude, phase and frequency of voltages, three-phase VSI are favoured to provide three-phase voltage source for high power applications. Figure 1 shows a typical structure of a three-phase power inverter, where Vdc is the continuous inverter input voltage, and a, b and c are the voltage output points that are supplied to the star connected motor windings. Power electronic devices such as IGBT, GTO and power BJT can be used as switches. The order of making the devices ON and OFF must follow certain conditions: out of six, three switches must always be ON, keeping the other three always OFF, and also, two complementary pulsed signals drive the upper and the lower switches of the same leg. Thus, in this way, the overlap in the power switch transitions is avoided while making no vertical conduction possible.

2.1 Sinusoidal PWM-VSI Whenever any waveform controls the duty ratio of any pulsating waveform, the technique is called as pulse width modulation (PWM). The sinusoidal wave is compared Fig. 1 Voltage source inverter

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Fig. 2 Sinusoidal pulse width modulation [18, p.1]

with the triangular wave in SPWM method. Whenever the magnitude of the sinusoidal wave is more than triangular wave, then the trigger pulse is generated which is fed to the VSI. In SPWM, the triangular signal is imposed upon the sinusoidal reference wave. A comparison is done between the two waves, and a gate pulse is generated where the sinusoidal wave amplitude is more than the triangular carrier wave as shown in Fig. 2. Three sinusoidal reference waves are there to obtain three-phase system which results in gate pulses of three types that will be fed to VSI using suitable logic.

2.2 Space Vector PWM-VSI In SVPWM method, the reference voltage obtained from the rotating reference vector is used to generate the triggering pulse. This strategy uses the DC link voltage more efficiently, and the harmonic content that is present in the three-phase VSI output voltage is very low. In SVPWM, based on the discrete voltage vectors of eight switching states in three-phase VSI system, the vector position is traced and shown in Fig. 3. The two adjacent basic vectors are having binary representations that differ Fig. 3. Switching vectors and sectors [18, p.2]

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Fig. 4 Three-phase Z-source inverter [29, p. 381]

in only one bit. Therefore, when the movement of switching pattern is from one vector to the adjacent one, only one of the upper transistors switches.

2.3 Z-Source Inverter The Z-source inverter is an impedance network operating in two modes, buck mode and boost mode and which consist of two inductors and two capacitors. It can also fulfil the purpose of a second order filter to decrease voltage ripple and reduce harmonics. The Z-source inverter can be connected to a voltage source in the form of ia photovoltaic or battery. The Z-source inverter can be seen in Fig. 4. One of the switching methods of Z-source inverter is maximum boost control. This method is an amendment of sinusoidal PWM (SPWM) where there are extra conditions for shoot through zero state. When the carrier wave is larger than the sine wave in maximum boost control Z-source inverter (MBC-ZSI), the condition of the shoot through zero state occurs as the switch at the gate gets activated. Whereas when the carrier wave is smaller than the sine wave, a normal PWM wave is produced [34].

3 Results and Discussion A simulink block model is designed in MATLAB for the SPWM VSI fed to induction motor shown in Fig. 5. MATLAB simulation for SPWM VSI fed to induction motor is done, and Fig. 6 shows the corresponding results. Stator current for all three phases, electrical torque, angular frequency and line voltage is plotted. To calculate the harmonic content in voltage and current, FFT analysis is done. MATLAB simulink

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Fig. 5. Modelling of SPWM [18, p.2]

model is developed for SVPWM method as shown in Fig. 7. MATLAB simulation results for SVPWM VSI fed to induction motor are shown in Fig. 8. From the FFT analysis, the THD for current and voltage for sinusoidal PWM and SVPWM is measured. Based on the MATLAB simulation, it can be inferred that THD for voltage is 33.08%, and THD for current in SPWM is 19.25%. Meanwhile, in SVPWM, the THD for voltage is 14.15%, and THD for current in sinusoidal PWM is 16.65% [18]. This concludes that the harmonic reduction in SVPWM technique is better done than sinusoidal PWM. i The MBC-ZSI system’s performance is tested by simulation. Modelling of the MBC-ZSI system can be seen in Figure 9. Testing has been carried out with different loads for induction motor. With a load of 1 Nm, the MBC-ZSI system can achieve steady speeds of 0.2 s faster than SPWM VSI. Then, at 4 Nm load, MBC-ZSI also has a faster time of 0.27 s. Then, at 8 Nm load, the time needed is 0.33 s faster. So that the average time needed to achieve steady speeds on the MBC-ZSI system is 0.26 s faster than SPWM VSI [29].

4 Conclusion Thus, from the above discussion, it can be concluded that SPWM and SVPWM methods for VSI fed induction motor have several advantages which can be utilized in the application of induction motor. Also, comparatively, the SVPWM method results in lesser harmonics for current and voltage. A Z-source inverter also increases the efficiency of the induction motor. Based on the results of testing that has been

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Fig. 6. Waveforms for SPWM [18, p.3]

Fig. 7 Modelling of SVPWM [18, p.3]

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Fig. 8. Waveforms for SVPWM [18, p.3]

done, in achieving a steady speed, the MBC-ZSI system gave a better response. As future scope of work, utilizing the ZSI along with the aforementioned PWM methods can give better performance. Also, the closed loop system can be developed using a controller-like fuzzy controller.

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Fig. 9 . Modelling of MBC-ZSI [29, p. 382]

References 1. Hughes A, Drury B (2013) In: Electric motors and drives 2. Koreboina VB, Magajikondi SJ, Raju AB (2011) Modeling simulation and PC based implementation of a closed loop speed control of VSI fed induction motor drive. In: India international conference power electronics IICPE 2010, pp 1–5 3. Lorenz RD, Lipo TA, Novotny DW (1994) Motion control with induction motors. In: Proceedings IEEE 4. Kalitjuka T (2011) Control of voltage source converters for power system applications. Master Sci Electr Power Eng 5. Bellil A, Meroufel A (2012) VSI PWM inverter feed induction machine using volts per hertz control scheme. Prz Elektrotechniczny 88:128–131 6. Uma D, Vijayarekha K (2017) Modeling and simulation of VSI fed induction motor drive in Matlab/Simulink. Int J Electr Comput Eng 7:584–595 7. Rashid MH (2007) In: Power electronics handbook 8. Naveena GJ, Dodakundi M, Layadgundi A (2015) Fault diagnosis of VSI fed induction motor drive using fuzzy logic approach. In: Proceedings 2015 IEEE international conference power advanced control engineering ICPACE 2015, pp 315–321 9. Bandyopadhyay I, Purkait P, Koley C (2019) A SVM based classifier for identification of spurious resistance fault involving IGBTs in VSI based induction motor drive. In: 2018 IEEE application signal processing conference, pp 9–13

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10. Yong Y, Jinyang H, Ziyuan W, Dianguo X (2014) IGBT open circuit fault diagnosis in VSI fed induction motor drives based on modified average current method. In: Proceedings 2014 9th IEEE conference industrial electronics application ICIEA 2014, pp 1334–1338 11. Holtz J (1992) Pulsewidth modulation-a survey. In: PESC record—IEEE annual power electronics specialists conference 12. Bajjuri NK, Jain AK (2019) Minimization of current ripple and switching losses in doubleinverter fed wound rotor induction machine drive using PWM techniques. IEEE Trans Ind Electron 1 13. Swamy PS (2018) Control of real power flow in the transmission line using PWM based voltage source inverter. In: 2018 National power engineering conference, pp 1–4 14. Chitra UC, Rajendran A (2015) Bus clamping PWM for three level neutral point clamped inverters. In: Proceedings of IEEE international conference on technological advancements in power and energy, TAP Energy 2015 15. Paterakis F (2017) Comparison of a PWM inverter and a multilevel inverter using the switching function analysis for harmonic content and efficiency 16. Abbas MA, Christen R, Jahns TM (1984) Six-phase voltage source inverter driven induction motor. IEEE Trans Indus Appl 17. Reddy TB, Ishwarya K (2012) Simple and efficient generalized scalar PWM algorithm for VSI fed induction motor drives. In: PEDES 2012—IEEE international conference power electronics drives energy systems, pp 1–6 18. Jena SP, Rout KC (2018) Comparative analysis of harmonic reduction of VSI Fed induction motor using SVPWM and sinusoidal PWM. In: Proceedings 2nd international conference trends electronics informatics, ICOEI 2018. 1, pp 1–5 19. Klima J (2002) Analytical closed-form solution of a space-vector modulated VSI feeding an induction motor drive. IEEE Trans Energy Convers 17:191–196 20. Gupta A, Gupta H, Tiwari AK (2002) A comparative study of sine-triangular and space vector PWM inverter fed induction motor drive, pp 833–836 21. Shafi kp M, Peter J, Ramchand R (2017) Space vector based synchronized PWM strategies for field oriented control of VSI fed induction motor. In: IEEE International conference power electronics drives energy systems PEDES 2016. 2016-January, pp 1–5 22. Mishra I, Singh M, Roy D (2018) Comparative evaluation of different pulse width modulation techniques on VSI fed three phase induction motor. In: IEEE International conference 2018 recent advanced engineering technology computer science RAETCS 2018, pp 1–5 23. Ishwarya K, Rashmi MR (2014) Space vector based PWM strategies for VSI fed induction motor drives with reduced complexity. In: 2014 Power energy system conference toward sustainable energy, PESTSE 2014, pp 1–8 24. Kwon YA, Kim SK (2007) A high-performance strategy for sensorless induction motor drive using variable link voltage. IEEE Trans Power Electron 22:329–332 25. Deb M (2013) Control of voltage source inverter for adjustable speed drive- a study report. 1:1–3 26. Lopatkin NN (2017) Voltage quality comparison of space vector PWM voltage source multilevel inverter under symmetric and nonsymmetric switching sequence variants : voltage harmonics integr factors 27. Datta A, Poddar G (2019) Improved low frequency operation of hybrid inverter for medium voltage induction motor drive under V/ f and vector control mode of operation. IEEE J Emerg Sel Top Power Electron 1 28. Peng FZ (2003) Z-source inverter. IEEE Trans Ind Appl 39:504–510 29. Ferdiansyah I, Sutedjo S, Yanaratri DS, Raharja LPS (2019) Comparative study of maximum boost control Z-source inverter with SPWM-VSI for induction motor drive. In: Proceedings— 2018 3rd international conference information technology information systems electronics engineering ICITISEE 2018, pp 380–384 30. Ellabban O, Van Mierlo J, Lataire P (2010) A new closed loop speed control of induction motor fed by high performance Z-source inverter. In: EVS 2010—sustainable mobile revolution 25th world battery, hybrid and fuel cell electric vehicle symposium and exhibition, pp 1–6

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31. Gambhir A, Member S, Mishra SK, Member S, Joshi A (2019) Control approach to enhance the performance of a current-fed switched inverter. IEEE J Emerg Sel Top Power Electron 1 32. Hatua K, Ranganathan VT (2011) A novel VSI- and CSI-fed active-reactive induction motor drive with sinusoidal voltages and currents. IEEE Trans Power Electron 26:3936–3947 33. Ali US (2011) A novel space vector PWM for Z-source inverter. 2011 1st International conference electrical energy systems, pp 82–85. https://doi.org/10.1109/ICEES.2011.5725307 34. Ibtissam C, Asma B, Zahra HF, Ouarda B, El-Madjid B (2017) Comparative study between diffirent control strategy of the z-source inverter. In: 2017 5th International conference on electrical engineering—boumerdes, ICEE-B 2017

Artificial Neural Network-Based Prediction of Cutting Parameters from Tool Vibration and Forces Ejaz Ahamed, Prasanta Kumar Samal, B. S. Suresh, and H. R. Kemparaju

Abstract Automation in machining process becomes crucial in successfully maintaining high-quality and low-cost production. In automated machining process, the tool condition and the cutting parameters affect the quality of product. So it is required to monitor cutting parameters without interruption of machining operation. This can be done by monitoring the tool vibration continuously and predicting the cutting parameters using artificial neural network (ANN). This work aims at monitoring vibration of tool and feeding it as input to ANN for predicting the cutting parameters. This will help in reducing the production time by not interrupting the operation. Experiments were conducted to monitor tool vibration in conventional lathe for machining mild steel using HSS tool based on design of experiments. Tri-axial accelerometer was mounted on the tool to acquire vibration data using National Instruments Data Acquisition systems (NIDAQ). The force data is measured using a dynamometer attached to the lathe. The experimental values were used for developing a feed-forward backpropagation ANN model. The cutting parameters were predicted using the trained network, compared and found to be very close with the experimental values. It is concluded that the proposed ANN model is able to predict the cutting parameters which helps in monitoring the tool condition for good product quality. Keywords Prediction of cutting parameters · Tool vibration · Monitoring tool vibration · Data acquisition system(DAQ) · Artificial neural network(ANN)

E. Ahamed · P. K. Samal (B) · B. S. Suresh · H. R. Kemparaju Department of Mechanical Engineering, NIE, Mysuru, Karnataka, India e-mail: [email protected] B. S. Suresh e-mail: [email protected] H. R. Kemparaju e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_19

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1 Introduction Tool condition is greatly affected by the voids and other manufacturing defects in the workpiece which creates undesirable vibration. Various authors have used ANN to predict the surface roughness and tool wear with respect to input parameters like speed, feed and depth of cut. This is done by interrupting the machining operation. Most of the machining operations in an industry are automated. Interruption of machining operation leads to higher production time. It is ideal to predict cutting parameters without interruption of the machining operation. This can be done by monitoring the tool vibration and predicting the cutting parameters using ANN. The objective of the condition monitoring is early detection of the disturbances, presence of wear and crack on the structures. Monitoring refers to the way of identifying the changes in characteristics of a process from the process signatures with normal operating condition [1]. Lin and Chang showed the correlation between vibrations and surface roughness. Accordingly, surface quality is estimated from various features of vibration. In this work, authors studied the effects of tool vibration on surface quality and tool wear [2]. Yongjin and Fischer developed tool life model to analyze tool material loss and tool wear index (TWI) to analyze tool wear. They proposed optimal control strategy to minimize production cost. In this, the machining parameters are adjusted, and thereby, the tool usage is enhanced [3]. Mehrotra et al. explained that a neural network is a combination of set of nodes and branches. Nodes contribute certain function and branches transfer the data [4]. Kalidas et al. studied the effects of cutting parameters on the tool life prediction in milling process using ANN. The model was capable to predict flank wear of the tool within the range in which it was trained [5]. Marimuthu and Chandrasekaran stated that tool wear and surface roughness are considered the main cause of poor quality machining of stainless steel products [6]. Benardos and ClVosniakos presented an ANN approach to predict the surface roughness in CNC milling. According to Taguchi design of experiments (DoE), the experiments were conducted and the data was used to train and check the performance of the network. The trained network was able to predict the surface roughness with 1.86% mean squared error and found to be consistent for the entire range of values [7]. Umit et al. used L8 orthogonal design of experiments, trained an artificial neural network and investigated the effect of cutting parameters on the response [8]. From the literature studies, it can be concluded that the detail study of vibrations, forces induced in machining operation, is very important to control the machining operation and their effect on the workpiece and cutting tool. Vibrations of machine tool need to be mitigated to ensure good surface finish, avoid tool wear and noise. Most of the researchers have taken cutting parameters as input and predicted the vibration amplitude from the ANN. But, in an automated industry, it is required to know the process parameters continuously simply sitting in the control room without visiting the actual machine tool. In this work, an attempt has been made to predict the cutting parameters using vibration amplitude and cutting forces as input to ANN.

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2 Monitoring Tool Vibration and Predicting Cutting Parameters The experiments were conducted on the conventional lathe as shown in Fig. 1. The workpiece was mild steel with a length of 150 mm, diameter of 25 mm, and the cutting tool was high-speed steel. A tri-axial accelerometer is mounted on the tool as shown in Fig. 2. This is connected to data acquisition card 9234 mounted on cDAQ 9178 chassis. The graphical program used to acquire data in LABVIEW is shown in Fig. 3.

Fig. 1 Experimental setup

Fig. 2 Mounting of accelerometer

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Fig. 3 Graphical program in labvIEW

2.1 L8 Orthogonal Design of Experiments Number of levels = 2. Number of variables = 3 (speed, depth of cut, feed).Design of experiment is 23 = 8 Trials. The L8 orthogonal array was prepared as shown in Table 1. In accordance with this, the vibration data was acquired and is given in Table 2. The experimental data thus obtained was used for training artificial neural network (ANN)in MATLAB using a feed-forward backpropagation network and multilayer perceptron algorithm. Tool vibration amplitudes in X, Y, Z directions obtained were used as input with two hidden layers of 12 neurons each, and cutting parameters, i.e., speed, depth of cut and feed, were taken as the target as shown in Fig. 4. The training is continued until a good performance curve with the goal, i.e., 0.001, which means 0.1% error is achieved. The training performance of the proposed network is presented in Figs. 5, 6. It is seen that the error of 0.1% is achieved at 2026 epochs. Once the network is trained, cutting parameters were predicted using desired tool vibration amplitude as input. The percentage of error was calculated and is given in Table 3. Mean error percentage was found to be 0.74% for speed, 1.62% for feed and 2.5% for depth of cut. Table 1 Design of experiments Trials

Speed (m/min)

Depth of cut (mm)

Feed (m/rev)

Trials

Speed (m/min)

Depth of cut (mm)

Feed (m/rev)

1

360

0.15

0.03

5

220

0.15

0.03

2

360

0.25

0.09

6

220

0.25

0.09

3

360

0.15

0.03

7

220

0.15

0.03

4

360

0.25

0.09

8

220

0.25

0.09

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Table 2 Vibration data obtained from experiment Trails

Speed (m/min)

Depth of cut (mm)

Feed (m/rev)

Vibration V x

Vibration V y

Vibration V z

1

360

0.15

0.03

0.3192

0.3986

0.1006

2

360

0.25

0.09

0.3873

0.4451

0.1175

3

360

0.15

0.03

0.1205

0.1635

0.0304

4

360

0.25

0.09

0.2737

0.3457

0.0759

5

220

0.15

0.03

0.2335

0.1972

0.062

6

220

0.25

0.09

0.1721

0.1513

0.044

7

220

0.15

0.03

0.2304

0.1905

0.0601

8

220

0.25

0.09

0.2394

0.2134

0.0615

Fig. 4 ANN architecture

Fig. 5 Performance curve

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Fig. 6 Regression curve

Figure 7 indicates good correlation between the predicted and experimental values indicating the capability of the proposed ANN model. Hence, the trained network can predict the cutting parameters from real-time tool vibration data.

3 Monitoring Tool Vibration and Cutting Forces, and Predicting Cutting Parameters 3.1 L27 Orthogonal Design of Experiments Figure 8 shows the experimental setup to measure the tool forces. Number of levels = 3. Number of variables = 3 (speed, depth of cut, feed). Design of experiment is 33 = 27 Trails. The L27 orthogonal array or DoE was prepared with 27 trials as shown in Table 4 consisting of three levels of cutting speed, depth of cut and feed. The vibration and force data obtained from the experiment for above L27 orthogonal DoE is given in Table 5. The experimental data thus obtained was used for training the network. Tool vibration in X, Y, Z directions and cutting force and feed force were used as input. The cutting parameters, namely speed, depth of cut and feed, were taken as the target. The ANN architecture is shown in Fig. 9. The training was continued till a good performance curve and the goal of 0.001, i.e., 0.1% error, are achieved as shown in Figs. 10, 11. It is clearly seen that an error of 0.13% is achieved at 62792 epochs. Once the network was trained, cutting

360

360

360

220

220

1

2

3

4

5

Mean error (%)

Exp. speed

Trial No.

220.0037

220.0088

359.9842

359.9992

360.0079

Pred. Speed

0.74

0.37

0.88

1.58

0.08

0.79

Error (%)

Table 3 Comparison of predicted and experimental data

0.09

0.03

0.09

0.03

0.09

Exp. feed

0.0600

0.0450

0.0921

0.0315

0.0710

Pred. feed

1.62

3

1.5

0.21

1.5

1.9

Error (%)

0.2500

0.1500

0.2500

0.1500

0.2500

Exp. depth of cut

0.200003

0.160004

0.216662

0.150016

0.216665

Pred. Depth of cut

2.5

4.99

1.004

3.334

0

3.333

Error (%)

Artificial Neural Network-Based Prediction of Cutting Parameters… 205

E. Ahamed et al. EXPERIMENTAL DATA

EXPERIMENTAL DATA

PREDICTED DATA

PREDICTED DATA

PREDICTED DATA

500

0.1

0 1

2

3

4

5

0 1

2

3

4

5

TRIALS

TRIALS

DEPTH OF CUT

EXPERIMENTAL DATA

FEED

SPEED

206

0.5 0 1

2

3

4

TRIALS

Fig. 7 Comparisons of experimental and predicted values a speed, b feed, c depth of cut Fig. 8 Experimental setup for tool condition monitoring using dynamometer

Table 4 Design of experiments Trials

Speed (m/min)

Depth of cut (mm)

Feed (m/rev)

Trials

Speed (m/min)

Depth of cut (mm)

Feed (m/rev)

1

220

0.15

0.03

15

360

0.25

0.12

2

220

0.15

0.09

16

360

0.35

0.03

3

220

0.15

0.12

17

360

0.35

0.09

4

220

0.25

0.03

18

360

0.35

0.12

5

220

0.25

0.09

19

560

0.15

0.03

6

220

0.25

0.12

20

560

0.15

0.09

7

220

0.35

0.03

21

560

0.15

0.12

8

220

0.35

0.09

22

560

0.25

0.03

9

220

0.35

0.12

23

560

0.25

0.09

10

360

0.15

0.03

24

560

0.25

0.12

11

360

0.15

0.09

25

560

0.35

0.03

12

360

0.15

0.12

26

560

0.35

0.09

13

360

0.25

0.03

27

560

0.35

0.12

14

360

0.25

0.09

5

Artificial Neural Network-Based Prediction of Cutting Parameters…

207

Table 5 Experimental data containing tool vibration and forces induced Trials Speed Depth of Feed Vibration Vibration Vibration Force F C Force F f (m/min) cut (mm) (m/rev) amplitude amplitude amplitude (N) (N) V x (mm) V y (mm) V z (mm) 1

220

0.15

0.03

0.1513

0.0898

0.177

9

2

2

220

0.15

0.09

0.207

0.1113

0.2443

11

2

3

220

0.15

0.12

0.2118

0.1142

0.2562

11

2

4

220

0.25

0.03

0.2378

0.1164

0.284

14

3

5

220

0.25

0.09

0.2355

0.118

0.2791

16

3

6

220

0.25

0.12

0.1823

0.0978

0.2195

14

3

7

220

0.35

0.03

0.2452

0.1225

0.288

15

3

8

220

0.35

0.09

0.2582

0.1246

0.298

16

4

9

220

0.35

0.12

0.294

0.1349

0.3322

21

5

10

360

0.15

0.03

0.1987

0.1149

0.2444

3

1

11

360

0.15

0.09

0.6779

0.496

0.8436

15

4

12

360

0.15

0.12

0.4367

0.2231

0.6059

14

4

13

360

0.25

0.03

0.2609

0.1489

0.3717

9

1

14

360

0.25

0.09

0.2437

0.1328

0.3098

6

1

15

360

0.25

0.12

0.4348

0.216

0.5621

14

3

16

360

0.35

0.03

0.3174

0.1677

0.4148

10

2

17

360

0.35

0.09

0.3752

0.1901

0.5169

12

3

18

360

0.35

0.12

0.3704

0.1784

0.4862

14

4

19

560

0.15

0.03

0.3804

1781

5195

6

2

20

560

0.15

0.09

0.246

0.1292

0.3365

6

2

21

560

0.15

0.12

0.4237

0.1856

0.594

5

2

22

560

0.25

0.03

0.4751

0.2244

0.658

12

5

23

560

0.25

0.09

3.2651

1.5094

2.8543

22

8

24

560

0.25

0.12

3.1613

1.5802

2.6491

12

3

25

560

0.35

0.03

1.0706

0.3926

1.4387

20

7

26

560

0.35

0.09

3.6594

3.6416

3.4615

18

6

27

560

0.35

0.12

4.318

2.5085

3.7958

21

6

parameters were predicted using tool vibration and tool forces. The percentage of error was calculated and is given in Table 6. Mean error was found as 0.036% for speed, 0.965% for feed and 2.80%, for depth of cut (Fig. 12). Hence, from the trained network, we can monitor the tool condition by predicting the cutting parameters from real-time monitored tool vibration data and force data. This helps in avoiding physical interruption of the machining process to monitor cutting tool condition. By controlling these parameters, we can avoid cutting tool failure to achieve a good surface finish.

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Fig. 9 ANN architecture

Fig. 10 Performance curve

4 Conclusions In this work, tool vibrations and cutting forces were monitored in a conventional lathe while machining mild steel with HSS tool. The experiments were designed based on the Taguchi methodology of DoE. The experimental values of tool vibration data and cutting force data corresponding to the values of cutting parameters were used to train a proposed ANN model. The trained network was used to predict the values of cutting parameters. The predicted values of cutting parameters obtained from trained ANN with respect to tool vibration and the cutting forces were compared with the actual values. It is found that the values predicted by the network are very close to the actual values. Mean percentage of error was found to be 0.74, 1.62 and 2.5% for speed, feed and depth of cut, respectively, when input was only tool vibration.

Artificial Neural Network-Based Prediction of Cutting Parameters…

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Fig. 11 Regression curve

Mean percentage of error were found to be 0.036, 0.965 and 2.8% for speed, feed and depth of cut, respectively, when inputs were tool vibration and cutting forces. The proposed network was capable of predicting cutting parameters within the specified range. This helps in monitoring the tool condition for good quality product. From this trained network, we can predict the cutting parameters from real-time monitored tool vibration data and cutting forces data. This plays a vital role in tool condition monitoring and assurance of quality production.

220

220

220

220

220

220

360

360

560

560

1

2

3

4

5

6

7

8

9

10

Mean error (%)

Exp. speed

Trial No.

560

559.9998

360.0001

359.999

219.9998

219.9998

219.9998

219.9998

220.0016

219.9991

Pred. speed

0.036

0

0.002

0.01

0.02

0.02

0.02

0.02

0.02

0.16

0.09

Error (%)

Table 6 Comparison of predicted and experimental data

0.09

0.12

0.09

0.09

0.12

0.12

0.09

0.03

0.12

0.09

Exp. feed

0.105

0.12

0.08

0.079

0.1135

0.1121

0.1029

0.0158

0.101

0.0891

Pred. feed

0.965

1.5

0

1

1.1

0.65

0.79

1.29

1.42

1.9

0.009

Error (%)

0.3500

0.2500

0.3500

0.1500

0.3500

0.2500

0.2500

0.2500

0.1500

0.1500

Exp. depth of cut

0.3500

0.2500

0.2500

0.17800

0.2858

0.2872

0.2863

0.2383

0.1341

0.1486

Pred. depth of cut

2.80

0

0

8.75

2.8

6.42

3.72

3.63

1.17

1.59

0.14

Error (%)

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Fig. 12 Comparisons of experimental and predicted values a speed, b feed, c depth of cut

References 1. Elbestawi MA, Wu SM, Du R (1995) Automated monitoring of manufacturing processes, part 1: monitoring methods. J Eng Indus 117(2):121–132 2. Lin SC, Chang MF (1998) A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning. Int J Machine Tools Manuf 38(7):763–782 3. Kwon Y, Fischer GW (2003) A novel approach to quantifying tool wear and tool life measurements for optimal tool management. Int J Machine Tools Manuf 43:359–368 4. Mehrotra K, Mohan CK, Ranka S (1997) Elements of artificial neural networks. MIT press 5. Kalidass S, Palanisamy P, Muthukumaran V (2012) Prediction of tool wear using regression and artificial neural network models in end milling of AISI 304austenitistainless steel. Int J Eng Innov Technol 1(2):29–35 6. Marimuthu P, Chandrasekaran K (2011) Experimental study on stainless steel for optimal setting of machining parameters using Taguchi and neural network. ARPN J Eng Appl Sci 6(10):119– 127 7. Benardos PG, ClVosniakos G (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput-Integr Manuf 18(5):343– 354 8. Yalcin U, Karaoglan AD, Korkut I (2013) Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. Int J Prod Res 51(11):3404–3414

Dynamic State Estimation of Synchronous Generator Rotor Angle Using PMU and EKF Under Stressed Conditions Rishabh Devagupta, K. T. Sai Akhil, N. Sai Suprabhath, P. Kritesh, B. Mallikarjuna, M. Jaya Bharata Reddy, D. K. Mohanta, and Diptendu Sinha Roy Abstract This paper deals with monitoring the synchronous generator’s rotor angle under healthy and faulty conditions in the power system. The method used to estimate the synchronous generator rotor angle by extended Kalman filter (EKF) technique and phasor measurement unit (PMU). The proposed algorithm helps us to understand the stability of the system by observing the rotor angle dynamically. Apart from just monitoring, it is proposed to be used as a backup protection in case, the relay fails to operate and issue trip signal under stressed conditions in transmission line. Thus, it helps to protect the generator to go out of synchronism under any kind of mal-operation in the transmission line. Robustness of the proposed algorithm is validated on the single-machine infinite-bus and Western System Coordinating Council (WSCC)-9 bus system. The obtained results are quite accurate and therefore can be relied upon to perform dynamic-state estimation in real time and help to protect the system to maintain stability. Keywords Phasor measurement unit · Extended Kalman filter · Dynamic-state estimation · Synchronous generator

R. Devagupta (B) · K. T. S. Akhil · N. S. Suprabhath · P. Kritesh · M. J. B. Reddy Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, India e-mail: [email protected] B. Mallikarjuna Department of Electrical and Electronics Engineering, Vishnu Institute of Technology, Bhimavaram, India D. K. Mohanta Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India D. S. Roy Department of Computer Science and Engineering, National Institute of Technology, Meghalaya, Shillong, India © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_20

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1 Introduction Early power system-state estimation and monitoring has been done using supervisory control and data acquisition (SCADA) systems. However, these systems are proven to be inefficient for measuring dynamic changes in the power network since the load angle and rotor angle of synchronous generator were estimated using load flow analysis. For accurate state estimation with minimum measurements, Jaya et al. [1] have proposed weighted least square method. The performance of this method has been validated on IEEE-6 bus model. However, a lot of iterations may be performed to estimate the state variables accurately. The load flow analysis is not feasible if the system is dynamic. In the recent years, this very reason incited utilization of widearea measurement systems (WAMS) using PMU for dynamic-state estimation and monitoring as the system dynamics are estimated with higher accuracy [2]. Jha et al. [3] has proposed a rotor angle estimation technique using PMU measurements. These measurements are used to design damper currents of the observer. Further, during transient condition, the generator’s stator reactance is estimated from the known machine parameters, stator reactance, PMU measurements, and observed damper currents. Angel et al. [4] have proposed investigated ways for rotor angle estimation in a time frame of transient stability. Then, they have proposed a technique for estimation of power system dynamic state using measurements (voltage and current phasors) from PMUs. In addition, two neural networks have been employed to estimate sin(δ) and cos(δ) of the angle as results obtained from the intuitive approach may not be satisfactory for to mapping phasor measurement inputs to the neural network directly. However, synchronous machine reactance changes owing to change in flux linkages and magnetic saturation followed by a disturbance. These changes may not properly reflect in machine parameters which are specified by the manufacturer. Hence, the estimated rotor angle from the stated parameters and available measurements may be inaccurate. Further in real power system, most of the complex systems are nonlinear, such as the synchronous generator. The various techniques used for rotor angle estimation are the unscented Kalman filter [5] and extended Kalman filter (EKF) techniques. In general, the estimation of rotor angle is done using EKF with few predetermined inputs. The state estimation can also be done in case of unknown inputs using EKF—unknown inputs algorithm [6]. Relay also has a certain probability of its unsuccessful operation, so the reliability of proper relay operation needs to be enhanced. Various problems arise due to aging effect [10]. Versatile distribution function is used to calculate failure rate in protective relays. A mean graph is constructed to predict the failure rate, but the exact location is still difficult to find. Reliability analysis of relays during fault time gives a clearer picture [11]. Markov probability models are also used to study such cases. Due to long routine test involved in such models, the dynamic failure prediction in real systems becomes very difficult. In this paper, the performance of the EKF algorithm has been verified on a singlemachine infinite-bus system (SMIB) and on WSCC-9 bus system. The state estimator at generator terminal is used to estimate the dynamic rotor angle and also helps to

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act as an instrument in providing a backup protection device for the failure of relay operations in transmission line during fault conditions. The results of both the systems (SMIB and WSCC-9 bus) convey that the proposed EKF algorithm is accurate and effective under different fault conditions including process and measurement noise. The proposed analysis can help to improve the performance on protection of the power system under stressed condition.

2 Development of EKF and Backup Protection Scheme Synchronous generator rotor angle is estimated dynamically with the help of PMU and EKF technique under healthy as well as faulty conditions in transmission system. Initially, the synchronous generator is mathematically modeled to apply EKF for obtaining rotor angle. A detailed methodology to develop EKF system is presented by Ghahremani and Kamva [6]. PMU estimates the voltage and current phasors using recursive algorithm since it has lesser computational stress along with the fact that a constant phase angle is maintained [7]. The phasor values obtained are then used to find the terminal voltage and real power of the generator that is connected to load via transmission line, in PMU as shown in Fig. 1. These values are then passed on to the generator’s state estimator where EKF algorithm has been employed to find the synchronous generator rotor angle. State estimator with backup protection will

Fig. 1 Overall scheme for protection

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be present in phasor data concentrator (PDC). In the event that the relay fails to operate, a backup protection scheme is developed in PDC which will give the trip signal to PMU which will be further passed to relay. This backup protection scheme is connected at the state estimator output.

2.1 Mathematical Modeling of Synchronous Generator The synchronous generator’s mathematical modeling is discussed by Kundur [8]. The state variables and input variables of the generator are given by Eqs. (1) to (2). However, state space modeling of synchronous machine is already present in Ghahremani and Kamva [6]. Equation (3) gives electrical power (Pe ) at generator terminal bus which is the output’s final equation. T    x = δωeq ed

y1 =

= [x1 x2 x3 x4 ]T

(1)

 T u = Tm E f d Vt = [u 1 u 2 u 3 ]T

(2)

  Vt 1 Vt2 1 −  sin(2x1 )  (x 3 ) sin(x 1 ) + 2 xq xd xd

(3)

where ω is the rotor speed (p.u); Tm is the mechanical input torque (p.u); E f d is the exciter output voltage or the field voltage (p.u); Vt is the terminal voltage (p.u); δ is the rotor angle in (elec. rad); y1 denotes output electrical power (Pe ) at generator terminal bus.

2.2 Implementation of EKF The synchronous machine state space modeling in present in Ghahremani and Kamva [9]. The final equations for a nonlinear system in discrete time system can be represented as follows xk = f k−1 (xk−1 , u k−1 , wk−1 )

(4)

Dynamic State Estimation of Synchronous Generator Rotor Angle …

yk = h k (xk , u k , vk )

217

(5)

where xk is state vector of the system; u k is the known input vector of the system; wk is the process noise; yk is the measured variable; vk is the measurement noise. Thus, the generator state space which are differential in nature is converted into discrete time system equations using Eq. 4. Eqs. (4) and (5) are used in EKF to obtain rotor angle and other states [9]. At each time step, the states are estimated using prediction and updation steps of EKF. The mean of wk−1 and vk−1 is assumed to be zero.

2.3 Algorithm for Proposed Methodology The following steps showing the proposed methodology used for checking the stability of the system is discussed below. Step 1: V t , P from PMU and Tm , E f d from generator are given as inputs to EKF. Step 2: Calculate Jacobians of prediction step using posteriori estimates of previous time step. Step 3: Prediction of priori estimate of rotor angle. Step 4: Calculate Jacobians of updation step using priori estimates of present time step. Step 5: Updation of priori estimate to obtain posteriori estimate of rotor angle present time step. Step 6: Check whether generator’s rotor angle δ is at verge of instability. Step 7: Trip, if YES. Step 8: Otherwise, continue for the duration of interest.

2.4 Proposed Backup Protection Scheme Using State Estimator To explain the proposed methodology, the single generator connected to load through transmission line is considered as shown in Fig. 1. The state estimator will get the inputs from generator and PMU connected at generator terminal. It estimates the rotor angle very precisely. The state estimator will estimate the rotor angle and will be checking whether the estimated value is within the threshold value or not. This process is done in generator’s phasor data concentrator (PDC). In a stressed condition like fault across the transmission line, the relay is designed to operate correctly. In

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case the relay fails to operate due to mechanical failure [10], backup protection will see the rotor angle going out of synchronism by the state estimation at PDC. Once a threshold is reached whereby instability probability is high, the backup protection will give trip signal to the PMU which further trips the relay and isolate the generator from the system before its instability is reached. The latter signal will be issued only if the primary protection is failed, thus making it as a backup. So, the circuit breaker will operate, and thus, its reliability is increased with the use of state estimation as backup protection.

3 Results and Discussions The aim of the proposed algorithm is to use this state estimator with PMU for monitoring the dynamic rotor angle and also used as a backup protection for relays in transmission line from its mal-operation which may be due to its aging, mechanical failure, etc.[10]. So, the estimation of rotor angle precisely is the foremost step. The performance of synchronous generator rotor angle dynamic-state estimation under various operating conditions i.e. normal and faulty conditions in transmission line is verified on a single-machine infinite-bus and WSCC-9 bus systems as shown in Fig. 2a and Fig. 2b, respectively, in MATLAB Simulink platform.

3.1 Performance of EKF Algorithm for SMIB System The performance of the EKF algorithm under normal condition is depicted in Fig. 3 from 0 to 1.5 s for the system shown in Fig. 2a. Sustained asymmetrical and symmetrical faults are applied at 1.5 secs. The obtained rotor angle and performance of the EKF algorithm are as shown in Fig. 3 under different fault conditions. As the expected

Fig. 2 a Single-machine infinite-bus system (SMIB) and b WSCC-9 bus system

Dynamic State Estimation of Synchronous Generator Rotor Angle …

219

Fig. 3 Performance of EKF algorithm for a LG fault b LL fault c LLG fault d LLLG fault with FR of 10  at 10 km for SMIB system

angle and obtained result values are nearly same, it is evident that the implemented EKF algorithm is efficient in estimating rotor angle correctly.

3.2 Performance of EKF Algorithm for WSCC-9 Bus System. To check the validity of the algorithm under dynamic conditions further, the rotor angle has been estimated for various transmission line faults simulated on WSCC9 bus system shown in Fig. 2b. The performance of state estimator under normal condition, particularly, is depicted in Fig. 4 and Fig. 5c, only during 0–1 secs and in Fig. 5a, b, d, only during 0–2 secs. Sustained asymmetrical and symmetrical faults are applied. The results in Figs. 4a–d and 5a-d prove that the implemented EKF algorithm is estimating rotor angle also under different faulty conditions accurately. In WSCC-9 bus system, nonlinearity of the system increases. So, the convergence rate when compared to SMIB system is less. But as expected, the obtained result converges with expected result as shown in Figs. 4 and 5. To check the validity of the algorithm under dynamic phases of power system like pre- and post-faults scenarios, such cases are simulated in Figs. 4 and 5. In case the relays connected between Bus6 and Bus-9 or Bus-5 and Bus-7 fails to operate, the backup protection will guide the respective relay to trip before instability is reached with the help of the state estimator in the generator’s PDC via PMU. Thus, the EKF algorithm is effective tool for dynamic-state estimation of generator rotor angle and backup protection

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Fig. 4 Performance of EKF algorithm for a LG fault b LL fault c LLG fault d LLLG faults with FR of 10  at 100 km from Bus-5 between Bus-5 and 7 in WSCC-9 bus system

Fig. 5 Performance of EKF algorithm for a LG fault b LL fault c LLG fault d LLLG faults with FR of 10  at 100 km from Bus-6 between Bus-9 and 6 in WSCC-9 bus system

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for transmission line. This state estimator is reliable in predicting the stability of the generator in various stressed conditions. Using these results, internal state of machines can be predicted further, and the design of generators can be improved which is an added advantage.

4 Conclusion In this paper, synchronous generator’s rotor angle dynamic estimation using PMU and EKF under healthy and faulty conditions is presented. The proposed algorithm is verified firstly on a single machine connected to an infinite-bus system in MATLAB/Simulink platform then performed on WSCC 9 bus system which proofs its adaptability to systems. From the results, algorithm implemented is determining the rotor angle efficiently in both systems. If the protective relay fails to operate in transmission line due to aging effect or mechanical misfunction, then the rotor angle is observed to be stepping into instability. The proposed backup protection will give trip signal to circuit breaker and protects the transmission line through PDC, PMU, and relays connected to transmission line. Acknowledgements The authors would like to thank the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), India, for financially supporting this work through the sanction order no. CRG/2018/000302 and EMR/2017/001508.

References 1. Jaya CK, Sunitha R, Mathew AT (2015) State estimation using weighted least square method with minimum measurement data. In: 2015 international conference on control communication and computing India (ICCC), Trivandrum, pp 275–279 2. Zhao J, G´omez-Exp´osito A, Netto M, Mili L, Abur A, Terzija V, Kamwa I, Pal B, Singh AK, Qi J, Huang Z, Meliopoulos APS (2019) Power system dynamic state ‘estimation: motivations, definitions, methodologies, and future work. IEEE Trans Power Syst 34(4):3188-3198 3. Jha M, Chakrabarti S, Kyriakides, E (2015) Estimation of the rotor angle of a synchronous generator by using PMU measurements. In: 2015 IEEE Eindhoven PowerTech, Eindhoven, pp 1–6 4. Angel AD, Geurts P, Ernst D, Glavic M, Wehenkel L (2007) Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. Neurocomputing. 70(16–18):2668–2678 5. Valverde G, Terzija V (2011) Unscented Kalman filter for power system dynamic state estimation. IET Gener Transm Distrib 5(1):29–37 6. Ghahremani E, Kamwa I (2011) Dynamic state estimation in power system by applying the extended kalman filter with unknown inputs to phasor measurements. IEEE Trans Power Syst 26(4):2556–2566 7. Phadke AG, Thorp JS (2008) In: Synchronized phasor measurements and their applications. Springer 8. Kundur P (1993) In: Power system stability and control. McGraw-Hill, Inc

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9. Ghahremani E, Kamwa I (2011) Simultaneous state and input estimation of a synchronous machine using the Extended Kalman Filter with unknown inputs. In: 2011 IEEE International electric machines and drives conference (IEMDC), Niagara Falls, ON, pp 1468–1473 10. Jun-hao W, An-cheng X, Qi J, Zhen Z, Shao-feng H (2015) Research on aging failure rate estimation of protective relay based on versatile distribution. In: 34th Chinese control conference, pp 6 0–6324 11. Wang C, Xu Z, Zheng S (2006) Reliability analysis of protective relays in fault information processing system in China. In: IEEE Power engineering society general meeting, pp 5

Review of the Parameter Estimation and Transient Analysis of Three-Phase Induction Motor Trisha, G. S. Gupta, and S. Shiva Kumar

Abstract Induction motor is the most abundantly utilized motor in the industry, and along these lines, the study and analysis of its transient performance has become a major area of interest. After the introduction of power electronic components, the modern equipment has started portraying nonlinear characteristic, especially under starting transients and unbalanced voltage conditions. Estimation of circuit parameters is necessary in order to improve the performance of induction motor drives. This paper aims to review and analyze different techniques investigated for the transient characteristics of a three-phase induction motor under varied conditions. Thus, this work gives an understanding on the different modeling techniques used to deduce the equivalent circuit parameters of induction motors. Keywords Gravitational search algorithm · Degree of unbalance · Indirect field-oriented control · Finite element analysis · Finite element model · Particle swarm optimization · Reference frame

1 Introduction Induction motor is the most widely used asynchronous, self-starting motor in the world today. It finds application in more than 85% of the motors in the industry and is a significant part taker of the domestic applications [1–4]. Since the power range of induction motor varies from a fraction of kilowatt to even hundreds of kilowatts, the versatility in the implementation of induction motor can be found. Induction motors finds its usage in elevators, pumps, compressors, conveyors, shredders, variable speed Trisha (B) · G. S. Gupta · S. Shiva Kumar Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India e-mail: [email protected] G. S. Gupta e-mail: [email protected] S. Shiva Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_21

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drives, crushing machines, presses, fans [5, 6], and even in the equipment required for petrochemicals and coal handling. Induction motor is so popular because of its ruggedness, robustness, reliability, efficiency, and inexpensiveness [7–11]. The performance of an induction motor plays a crucial role in the stability as well as control of power system [12]. The control of induction motor significantly depends upon the accurate knowledge of the parameters of the motor. If the motor parameter is not estimated correctly, it will lead to the deterioration of both transient- and steady-state responses, which would ultimately affect the motor performance. The induction motor parameter changes under various circumstances. Therefore, it is very essential to estimate the parameters of the induction motor. Incorrect identification of the parameters of an induction motor can sell out the operation of the device. If the parameter values are unavailable, an accurate model cannot be constructed. If these parameters are accurately calculated, it will lead to reduction in the power consumed by the motors which will ultimately lead to significant decrease in the total power generated, thereby saving a considerable amount of energy, which was earlier wasted because of inaccurate models [13]. Thus, the identification of motor parameters has become a primary area of research. This paper has, therefore, tried to provide an insight into the various modeling techniques utilized to estimate the equivalent circuit parameters of an induction motor. It has also attempted to give a review on the transient behavior of three-phase induction motor under various conditions like unbalanced supply, unbalanced voltage, under the influence of skin effect and during starting.

2 Parameter Estimation Different researchers have used various methods to calculate the motor parameters. A detailed analysis of these methods has been delineated in the following section. Also, the importance of estimating the parameters of induction motor has been presented.

2.1 Methods Used for Parameter Estimation The various methods and models used for estimating the motor parameters are delineated below. (a) Estimation of parameters by conventional methods: The researcher has utilized conventional methods like DC test, no-load test, and blocked rotor test in order to calculate the circuit parameters [14]. The parameters obtained were then simulated by MATLAB/Simulink and power system blockset (PSB). This simulation model was again used to find out the equivalent parameters. The results obtained from both simulation and experiment were compared to check the accuracy. But

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this model [14] has limitations because the research was conducted only for two induction motors. So, it cannot be confirmed whether it can accurately estimate the parameters for all the induction motor. Therefore, there is a requirement to go for more advanced techniques for detection of motor parameters. (b) Estimation of parameters under the effect of saturation and deep bar effect: Due to the congestion of core caused because of starting current, the rotor and stator reactance value changes [15]. Saturation and deep bar effect depends upon the current and speed of rotation. Serious damage might be caused to the motor whenever ground fault occurs at the terminals of the motor. So, it is essential to study the transient behavior under such a situation. In the earlier studies, d-q axis-based models were used in order to take deep bar effect and saturation into account [16]. But this approach had certain limitations like fixed rotor resistance and inductance along with balanced voltage. In this model, the simulation of induction machine was done using direct axis method under the influence of both saturation and deep bar effect. This model is more precise than the previous models because here the leakage reactance and resistances are changed according to the change in permeance of the slots and according to the deep bar effect. Therefore, the author concluded that this model [15] is efficient in studying the transient phenomenon under the effect of saturation and deep bar effect, and the various parameters could be estimated precisely. Additionally, the ground faults were also calculated accurately. The proposed model is analyzed by the differential Eq. (1), where L ∗ is the self and mutual inductance of stator and rotor windings and in R # , the diagonal elements show resistances of stator and rotor, whereas the non-diagonal elements indicate the induced voltages by the rotor rotation.

E∗ =

dL dI dI d(L ∗ I ) + R I = L∗ + (R + )I = L ∗ + R# I dt dt dt dt

(1)

(c) Estimation of parameter using ELSMO: An extended Luenberger sliding mode observer has been suggested in order to calculate the precise values of resistance and flux of rotor [2, 8]. Under ELSMO, a nonlinear induction machine model is converted into a linear induction machine model for further calculations. If the rotor flux or stator current is not properly calculated, then it will affect the performance of induction motor by incorrectly identifying the flux level and making the current demand more. Lyapunov’s stability concept is used in order to estimate rotor resistance. A parameter adaptation unit, along with ELSMO, is used in order to take care of rotor flux and stator current under differing conditions. Optimization of Luenberger gain matrix by hit and trial method of placement of pole and setting of sliding gain matrix is done, so that the controller is indifferent to parameter variation. This model can thus estimate the rotor resistance and rotor flux accurately and therefore can be used to minimize the error, so that the model [11] can be utilized wherever there is a requirement

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Fig. 1 Schematic diagram of ELSMO model

of an efficient and fast dynamic response machine. The schematic of ELSMO model is illustrated in Fig. 1. (d) Estimation of Parameter using GSA and IFOC: This method utilizes the gravitational search algorithm (GSA) and indirect field-oriented control (IFOC) in order to calculate the time variant inductances, rotor resistance, and other dynamic parameters. Here, the concept of vector control is incorporated whereby a threephase system is converted into a two-phase invariant system for easy analysis, and this method is quite advantageous since the induction motor can be controlled as a DC machine, so that there can be independent control of each component. Vector control can be again divided into two parts: I. Direct fieldoriented control II. Indirect field-oriented control (IFOC). In this model [6, 17], the author has proposed to utilize the IFOC (which is based on phase transformation) and GSA (where the solution of the problem will be given by the position of the mass, and here, each particle will be considered as an object). The calculation in GSA is performed using (2).

F(t) = G(t) ×

M1 (t) × M2 (t) × (x2 (t) − x1 (t)) R1 (t) + e

(2)

where F: gravity, G: gravitational constant, M 1 and M 2 : the inertial mass of the first and second particle, and R: Euclid distance between the first and second particle. In this model [9], four steps were involved: I. Rotor and stator model development. II. Parameter estimation at steady state. III. Dynamic parameter estimation. IV. Construction of algorithm using GSA. Thus, the various parameters are efficiently calculated by using this method, and the main advantages of this technique are the reduced number of iterations required and the improved transient response. (e) Parameter Estimation of multi-winding Iiduction machine: Finite element analysis (FEA) has been used in order to predict the parameters in this method [18]. It specifically deals with the induction motor having multiple windings

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and whose rotors are same while stators are different. This method also utilizes independent field-oriented controls in order to take care of multiple directional flow of power. In order to prove the accuracy of this model, the parameters were estimated both by FEA and experimental tests [14], and then, the result was compared, and thus, it can be seen that this model [18] is quite accurate. Under the FEA and experimental test, both no-load test and locked rotor analysis has been carried out. Therefore, this method can efficiently and effectively calculate all the sub-motor parameters. The equalities used in this model are given by (3)

vsi∗ = Rsi∗ i si∗ +

k 

L ∗sisl pi si∗ + ( pL ∗sir )ir∗ + L ∗sir pir∗ ;

l=1

vr∗ = Rr∗ ir∗ + L r∗ pir∗ +

k 

pL ∗slr Tisl∗ + L ∗slr Tpi∗sl

(3)

l=1

= diag(Rsi∗ , Rsi∗ , Rsi∗ ), Rr∗ = diag(Rr∗ , Rr∗ , Rr∗ ), i si∗ where Rsi∗ T ∗ T ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ]T [ i asi i bsi i csi ] vsi = [ vasi vbsi vcsi ] , ir = [ i ar i br i cr ]T , vr∗ = [ var vbr vcr

=

(f) Estimation of parameters using PSO and H-G diagram: The author has suggested the usage of the H-G diagram and the particle swarm optimization technique (PSO) in order to estimate the parameters under the running condition in method [19, 20]. Also, motor parameters were calculated under different loading conditions in order to identify the effect of loading on the motor parameters. It was observed that, as the loading was increased from 40 to 100%, the efficiency of the estimated parameter also increased. The advantage of this methodology [4] is that, only with a slight modification, the application of this technique can be extended to motors other than induction motor as well. (g) Estimation of parameters using FEM: The parameters were estimated either by DC test, no-load test, and blocked rotor test or by using feedback system conventionally [14]. But these methods had limitations like excessive time consumption, more complexity, regular mounting, and unmounting of the motors in order to perform the test. These limitations forced the researchers to go for a method that could overcome these limitations. Thus, finite element eodel (FEM) was approached. FEM is quite versatile and can be used for the calculation of electrical loss, fault detection, and parameters could be more accurately detected, determination of short circuit faults by using harmonics of the stator, calculation of stray losses, and complex magnetic flux. In this model [10], circuit parameters were calculated by using the physical dimension of the magnetic model. Parameters were calculated by using finite element method magnetics (FEMM) software, and then, the calculated parameters were sent to the dynamic model constructed by MATLAB/Simulink, and here, the motor performance can be studied rigorously, and then, the motor response can be achieved as the

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output of the study. The response curve is verified using power analysis equipment. In this method, the differential equation is obtained by using Clarke–Park transformation. The Clarke–Park transformation is given by (4).



i α1 i β1



 =

1 0

1 − √2 3 2

−√21 − 23

⎡ i ⎤      a1 Id1 cos θ sin θ i α1 ⎣ i b1 ⎦ = Iq1 − sin θ cos θ i β1 i c1

(4)

(h) Numerical estimation of parameters (NE): Nonlinearity up to second degree is taken into consideration in this model [12]. Numerical solution techniques are used in order to estimate the parameters. This numerical solution technique is based on nonlinear least square estimation techniques (basically Gauss–Newton method is used). This method utilizes the slip-torque characteristic, but the sliptorque characteristics alone cannot possibly give the proper output. Therefore, slip-power factor characteristic and slip-current characteristic are also incorporated for the analysis. Thus, this method has basically used analytically based identification procedure for quasi-steady-state and steady-state analysis of three-phase induction motor. Also, this model [12] gives future scope for more detailed numerical analysis and calculation of robustness using both noisy as well as actual measurements. The author used (5) in order to incorporate the mathematical modeling.

q k+1 = q k − (H (q k )T × W × H (q k ))−1 × H (q k )T × W × r (q k )

(5)

where ‘q,’ ‘r,’ ‘W,’ ‘k,’ and ‘H’ symbolizes vector parameter to be calculated, residual vector, weight matrix, iteration index, and Jacobian matrix of measurement equations, respectively, taking the assumption that current and torque measurements are used. (i) Estimation of copper and iron losses using FEA: In order to improve the efficiency of induction motor, its losses must be reduced, and proper ways for reducing the losses can be found only if we can clearly estimate the copper and iron losses [13]. Many researchers have therefore tried to estimate the losses, but all the methods adopted by them were quite complex, and the accuracy can be questionable. By using finite element software, copper and iron losses can be calculated in an easy and straightforward manner. In this model [21], modeling was done using Maxwell 2D software along with RMxprt software. (j) Determination of parameters using neural networks (NN): The FEM method [10], which was used earlier, provides high accuracy but at the cost of time because it needed large number of iterations, whereas equivalent circuit model (ECM) has always been popular because of its easy computation. This gave rise to the requirement of a method that had the advantage of both FEM and convenience of ECM, and this combination could be provided by neural network.

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Table 1 Different methods of parameter estimation Method Conventional

Error (%)

Drawback

10

In conventional method, error increases if rating is increased [14]

ELSMO

1

Error becomes zero only when sampling time is greater than response time. And the response time depends on the placement of poles [11]

GSA

1

In GSA, motor rating increases, error increases [17]

FEA

3

Error is larger compared to GSA, ELSMO [18]

FEM

0.5 More number of iterations compared to neural network [10]

NE

1

PSO & H-G

1.9 Error dependent upon loading [20]

Needs at least 4 iterations to give 0% error [12]

NN

0.5 Almost negligible drawback [22]

In this method, the author has suggested the usage of self-organized distributed network (SODN) [22], in which the neural network was trained with FEM data. The neural network had to be trained offline by supplementary computational data, as well. The time taken for offline training is approximately 320 s, which is quite less, and therefore, this method can be considered as an efficient technique for evaluation of motor parameters.

3 Comparison Between Different Methods of Parameter Estimation A comparative analysis of the proposed techniques has been given in Table 1 in order to judge which of the methodologies is best when compared with others. ELSMO method, GSA method, and numerical estimation method have same error percentage and have the limitations of pole placement, motor rating, and number of iterations, respectively. The settling time of ELSMO is higher than that of GSA. Thus, GSA is better technique than ELSMO. It is found that neural network method gives faster response than FEM. Therefore, neural network is the best method based on the error, settling time, and number of iterations.

4 Transient Analysis The transient performance of induction motor plays a major role in motor design and control and thereby affecting the overall performance of the motor. Table 2 summarizes different methods and their respective results. The determination of the motor parameter is essential for improving the accuracy and performance of three-phase induction motor. This paper has provided an insight

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Table 2 Transient analysis under various conditions Transient behavior analysis

State and platform

Aim

Model

Result

SYMNAP

Skin effect

To establish motor performance and parameter variation due to skin effect at higher frequencies

Park–Blondel equation [16]

Skin effect is considered in calculating the parameters accurately, improving transient stability

MATLAB

At starting

Analysis of the d–q reference performance of frame equation three-phase [23] induction motor with interruption in any one phase at the time of starting

Induction motor’s starting performance depends on the nature of supply as well as load. Transient torque is found to be higher for a small duration at the time of starting

Ddifferent sizes machine

To estimate the d–q influence of transformation variation in technique [24] parameter values and motor size on the performance of induction machine

Analysis of two different sized induction motor (2250 and 3 hp) shows that 3 hp machine has better transient performance while 2250hp machine has better steady state performance

Voltage imbalance

Investigation of Phase frame variation in analysis [25] voltage on the performance of induction motor and energy loss

It is concluded that voltage unbalance is a hazard for the performance of induction motor and degrades the transient performance (continued)

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Table 2 (continued) State and platform Uunbalanced supply

Aim

Model

Result

Analysis of the effect of source impedance on induction motor with both balanced and unbalanced supply

d–q axis-based transient modeling [26, 27]

It is found that under an unbalanced supply condition, oscillating torque is produced. This degrades the transient performance of induction motor

on the various methods used in order to estimate the parameters of a three-phase induction motor. It has also provided a concise comparison of the different parameter estimation techniques. This paper has also efficiently showcased the transient performance of three-phase induction motor during starting, under the influence of skin effect, by taking different sized induction machine, under unbalanced supply conditions.

References 1. Usha S, Subramani C, Padmanaban S (2019) Neural network-based model reference adaptive system for torque ripple reduction in sensorless poly phase induction motor drive. Energies (5):920 2. Cherifi D, Miloud Y (2019) Online stator and rotor resistance estimation scheme using sliding mode observer for indirect vector controlled speed sensorless induction motor. Am J Comput Sci Technol 2(1):1–8 3. Pereira LA, Perin M, Pereira LF (2019) A new method to estimate induction machine parameters from the no-load startup transient. J Control Autom Electr Syst 30(1):41–53 4. Bhowmick D, Manna M, Chowdhury SK (2018) Estimation of equivalent circuit parameters of transformer and induction motor from load data. IEEE Trans Indus Appl 54(3):2784–2791 5. Guedes JJ, Castoldi MF, Goedtel A, Agulhari CM, Sanches DS (2018) Parameters estimation of three-phase induction motors using differential evolution. Electric Power Syst Res 154:204–212 6. Bijan MG, Al-Badri M, Pillay P, Angers P (2018) Induction machine parameter range constraints in genetic algorithm based efficiency estimation techniques. IEEE Trans Indus Appl 54(5):4186–4197 7. Alonge F, Cirrincione M, Pucci M, Sferlazza A (2015) Input–output feedback linearization control with on-line MRAS-based inductor resistance estimation of linear induction motors including the dynamic end effects. IEEE Trans Indus Appl 52(1):254–266 8. Kong W, Huang J, Li B, Kang M, Zhao L (2013) Improved sliding-mode observer for sensorless control of five-phase induction motor. In: 2013 International conference on electrical machines and systems (ICEMS) Oct 26, IEEE, pp 2024–2027 9. Tavner P, Ran L, Penman J, Sedding H (2008) Condition monitoring of rotating electrical machines. IET

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10. Gouda E, El-Hayes M, Abdelkader S, El-Salam GA (2017) Estimation of equivalent circuit parameters of induction motor based on FEM with modelling and experimental verification. In: 2017 nineteenth international middle east power systems conference (MEPCON) Dec 19, IEEE, pp 1440–1446 11. Kim J, Ko J, Lee J, Lee Y (2017) Rotor flux and rotor resistance estimation using extended luenberger-sliding mode observer (ELSMO) for three phase induction motor control. Canad J Electri Comput Eng 40(3):181–188 12. Stefopoulos GK, Meliopoulos AS (2007) Numerical parameter estimation procedure for three phase induction motor models. In: 2007 IEEE lausanne power tech Jul 1, IEEE, pp 1111–1116 13. Saheb AN, Ali AM (2018) Estimation of copper and iron losses in a three-phase induction motor using finite element analysis. In: 2018 2nd International conference for engineering, technology and Sciences of Al-Kitab (ICETS) Dec 4, IEEE, pp 6–10 14. Salimin RH, Kadir SH, Mohd SR, Baki S, Ismail F (2013) Parameter identification of threephase induction motor using MATLAB-simulink. In: 2013 IEEE 7th international power engineering and optimization conference (PEOCO) Jun 3, IEEE, pp 647–651 15. Ikeda M, Hiyama T (2007) Simulation studies of the transients of squirrel-cage induction motors. IEEE Trans Energy Convers 22(2):233–239 16. Calin M, Rezmerita F, Ileana C, Iordache M, Galan N (2012) Performance analysis of three phase squirrel cage induction motor with deep rotor bars in transient behavior. Electri Electron Eng 2(2):11–17 17. Banerjee T, Bera J, Sarkar G (2015) Parameter estimation of three phase induction motor using gravitational search algorithm for IFOC 18. Caruso D, Scelba G, Bianchi N, Alberti L, Scarcella G (2016) Parameters identification of multiwindings induction machines. In: 2016 XXII International conference on electrical machines (ICEM) Sep 4, IEEE, pp 519–525 19. Bhowmick D, Manna M, Chowdhury SK (2017) Improved equivalent circuit parameter estimation of induction motor using HG diagram and PSO. In: 2017 IEEE calcutta conference (CALCON) Dec 2, IEEE, pp 443–447 20. Bhowmick D, Chowdhury SK (2018) Parameter and loss estimation of three phase induction motor from dynamic model using H-G diagram and particle swarm optimization. In: 2018 IEEE 8th power india international conference (PIICON) Dec 10, IEEE, pp 1–5 21. Liu Y, Bazzi AM (2017) A general analytical three-phase induction machine core loss model in the arbitrary reference frame. IEEE Trans Indus Appl 53(5):4210–4220 22. Bae D, Kim D, Jung HK, Hahn SY, Koh CS (1997) Determination of induction motor parameters by using neural network based on FEM results. IEEE Trans Magnet 33(2):1924–1927 23. Basak B (2002) Transient behaviour of three phase induction motor under supply interruption of one phase during starting 24. Patel HK (2009) Steady state and transient performance analysis of three phase induction machine using MATLAB simulations. Int J Recent Trends Eng 1(3):266 25. Aderibigbe A, Ogunjuyigbe A, Ayodele R, Samuel I (2017) The performance of a 3-phase induction machine under unbalance voltage regime. J Eng Sci Technol Rev 10(5):136–143 26. Sandhu KS, Praneeth AV, Pradeep AV (2013) Transient analysis of three phase induction machine with unbalanced supply 27. Madawala UK, Baguley CA (2008) Transient modeling and parameter estimation of field aligned starting. IEEE Trans Energy Convers 23(1):15–24

Transient Analysis of a Standard IEEE-9 Bus Power System Using Power World Simulator P. V. Rajesh Varma, Manoj Kumar Kar, and A. K. Singh

Abstract Study of transient analysis and its behavior is very important aspect for power system planning and its design for reliable operation. The paper’s main objective is to analyze the system for different fault conditions such as three phase balanced, line to ground, line to line, and double line to ground fault. As a test system, the standard IEEE 9 bus system is selected because of its simplicity of smart grid which facilitates to study all the concepts. Due to abnormal conditions (fault conditions), power system may become unstable thereby causing severe damage to protection equipment. Therefore, to ensure reliable power system operation, fault analysis study is necessary. Here, power world simulator software is used to analyze system. The pre-fault conditions are determined by the Newton Raphson method of load flow analysis. In this paper, the effects of generator outage and bus outage are studied and how the system retains its stability after outage has been discussed. Further, transient analysis of power system network will facilitate the design of better smart grid. Keywords Fault analysis · Power world simulator · Transient stability analysis

1 Introduction Nowadays, the efficiency and performance of the electrical power network are very critical as the system grows day after day. The increased demand for load results in large machines is mounted. Stability of the power system is mentioned as system’s ability to recover its initial stable state value or to achieve the new stable state value after the breakdown of the system. The possibility of system failure cannot be P. V. Rajesh Varma (B) · M. K. Kar · A. K. Singh Department of Electrical Engineering, National Institute of Technology Jamshedpur, Jharkhand 831014, India e-mail: [email protected] M. K. Kar e-mail: [email protected] A. K. Singh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_22

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prevented. The main concern of the operation of the transmission system is stability and security of the power system. The assessment capacity of electrical system in defense plan in terms of transient behavior and the description of the fundamental definition for transient stability are discussed in [1]. Stability of the power system and various categories of stability behavior linking reliability, security system are discussed in [2].The basic importance, application of power world simulator is discussed in [3] for more realistic design projects using analysis package for commercial grade power systems. The different types of stability are discussed in [4]. The variation of generator parameters are discussed in [5] with three phase balanced fault condition. The IEEE 9 bus model is modeled in real time using RT lab software [6]. The advantages and disadvantages of excitation system is discussed in [7]. The transient stability analysis using the Runga method is addressed in three phase fault [8].

2 Power World Simulator It is an immersive power system software simulated with three generators, nine buses, transformer and load consuming real, and reactive power. Fault analysis is observed by power world simulator, a simulation software package designed on high voltage. The main features were optimal power flow, available transfer capacity, contingency analysis, area generation control. Since symmetrical component method includes many matrix operations, computers are used to perform fault analysis in well-organized manner. Therefore, power world simulator is best tool for performing fault analysis of larger system. In addition, any code can be edited or modified using command edit window.

3 Power System Modeling (Figs. 1, 2).

4 Problem Formulation In power flow studies, pre-fault voltage magnitudes are required. Newton Raphson method can obtain pre-fault values by the load flow analysis. It has faster solution. This method has a faster solution for load flow analysis with the optimized technoeconomical and saving the stable system. The Newton Raphson method requires an initial guess. The selection of slack buses does not affect convergence. Also, apart from a slack bus, we find the existence of PQ buses. For an lth bus,

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Fig. 1 Single line diagram of IEEE 9 bus system

Fig. 2 Schematic design of IEEE 9 bus system in power world simulator

Pl =

n  |Vl ||Vm ||Ylm | cos(θlm + δm − δl )

(1)

k=1

on observing above, it is obvious that Pl is a function of |V| and δ, i.e., Pl (|V |, δ) Ql =

n k=1

|Vl ||Vm ||Ylm | sin(θlm + δm − δl )

(2)

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on observing above, it is obvious that Ql is a function of |V| and δ, i.e., Q l (|V |, δ). active and reactive powers given in terms of (|V |, δ). Therefore, |V | = (|V1 |, . . . . . . ., |Vx |)T δ = (δ1 , . . . . . . . . . ., δx )T , Pi (|V |, δ) = Pi (a) Q i (|V |, δ) = Q i (a) where a = δ/|V | Let Pl and Ql be the expected load bus power will tend to value in the cycle of iteration “a” which makes Pi − Pi (a) = 0

(3)

Q i − Q i (a) = 0

(4)

We get its matrix form when we write equations for all load buses  f (x) =

   P(scheduled) − P(a) P(a) = = 0. Q(scheduled) − Q(a) Q(a)

Active and reactive powers are unspecified values given by slack bus. We can write      δ P(a) −J11 −J12 f (x) = = −J21 −J22 [V ] Q(a) where δ = (δ2 . . . ..δx )T |V | = (|V2 | . . . . . . .|Vx |)T J is Jacobian matrix and elements are J 11 , J 12 , J 21 , J 22 ∂P ∂δ ∂P = ∂|V | ∂Q = ∂|V |

− J11 = − J12 − J22

(5)

(6)

Transient Analysis of a Standard IEEE-9 Bus Power System …

− J21 =

237

∂Q ∂δ

∂ Ql ∂ Pl ∂ Pl The elements of −J 11 , −J 12 , −J 21 , −J 22 are ∂δ ; ∂|V ; ∂∂δQml ; ∂|V . m m| m| Where l = 2 . . . . . . ..n; m = 2 . . . . . . . . . . . . .n Using Eq. (1) and (2), we obtain

∂ Pl = −|Vl ||Vm ||Ylm | sin(θlm + δm − δl )(l = m) ∂δm n  |Vl ||Vm ||Ylm | sin(θlm + δm − δl )(l = m) =

(7)

m=1 m = i ∂ Pl = |Vl ||Ylm | cos(θlm + δm − δl )(l = m) ∂|Vm | n  ∂ Pl |Vm ||Ylm | cos(θlm + δm − δl )(l = m) (8) = 2|Vl ||Ylm | cos(θlm ) + ∂Vm  m=1 m = l

∂ Ql = |Vl ||Vm ||Ylm | sin(θlm + δm − δl )(l = m) ∂δm n  |Vl ||Vm ||Ylm | cos(θlm + δm − δl )(l = m) =

(9)

m=1 m = l ∂ Ql = |Vl ||Ylm | sin(θlm + δm − δl )(l = m) ∂|Vm | n  |Vm ||Ylm | cos(θlm + δm − δl )(l = m) (10) = 2|Vl ||Ylm | sin(θll ) + m=1 m = l If there is no link between each other lth bus and mth bus, then Y lm = 0. This process goes on until a condition of stoppage is reached.

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5 Fault Analysis In this paper, effects of different types of faults, such as three phase balanced, line to ground (L–G), double line to line(L–L), and double line to ground (L–L–G), have been studied by applying the fault at bus 6. The different phase voltages and phase angles for various types of faults are given in Tables 1, 2, 3 and 4. The change in magnitude of voltage and phase of angle is observed during occurrence of fault from following tables. Table 1 System with three phase balanced fault (fault current magnitude = 3.140 p.u., IfI f angle = −48.76 degree) Bus

phase A voltage

phase B voltage

phase C voltage

Phase of angle A

Phase of angle B

Phase of angle C

1

0.247

0.247

0.247

26.20

−93.80

146.20

2

0.45101

0.45101

0.45101

45.80

−74.20

165.80

3

0.31988

0.31988

0.31988

37.52

−82.48

157.52

4

0.17747

0.17747

0.17747

25.38

−94.62

145.38

5

0.23315

0.23315

0.23315

29.79

−90.21

149.79

6

0.0000

0.0000

0.0000

178.73

58.73

-61.27

7

0.36742

0.36742

0.36742

39.66

−80.34

159.66

8

0.32149

0.32149

0.32149

35.82

−84.18

155.82

9

0.26727

0.26727

0.26727

35.03

−84.97

155.05

Table 2 System with line to ground (L–G) fault (I f magnitude = 6.319 p.u., I f angle = 17.17 degree) Bus number

phase A voltage

phase B voltage

phase C voltage

Phase of angle A

Phase of angle B

Phase of angle C

1

1.00292

0.53093

1.35773

−59.81

−118.36

100.71

2

0.72466

0.61327

1.20930

−48.32

−99.17

105.52

3

0.88688

0.55241

1.28999

−55

−109.26

104.66

4

0.43242

1.21161

2.82918

88.33

126.07

103.17

5

0.67206

1.32582

2.82249

97.44

123.22

102.09

6

0.00000

1.12527

2.69734

12.44

128.32

102.53

7

0.97392

1.28996

2.98651

91.04

120.32

104.87

8

0.89236

1.32582

2.98107

92.32

120.92

103.65

9

0.72201

1.25334

2.93943

87.26

122.21

104.37

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Table 3 System with line to line (L–L) fault (If magnitude = 2.983 p.u., If angle = −138.74 degree) Bus Number

phase A voltage

phase B voltage

phase C voltage

Phase of Angle A

1

1.04

2

1.025

3 4

Phase of Angle B

Phase of Angle C

0.46681

0.64425

0

−155.63

162.60

0.42066

0.80837

9.28

−122.46

166.43

1.025

0.43052

0.70246

4.67

−142.62

165.32

1.02579

0.46222

0.59979

−2.22

−165.08

164.66

5

0.99564

0.42050

0.63275

−3.99

−160.47

160.63

6

1.01266

0.50633

0.50633

−3.69

176.31

176.31

7

1.02577

0.41563

0.74555

3.72

−137.97

163.50

8

1.01589

0.41586

0.70577

0.73

−146.05

161.90

9

1.03236

0.43548

0.67111

1.97

−151.58

165.17

Table 4 System with double line to ground (L–L–G) fault (If magnitude = 3.10 p.u., I f angle = −109.16 degree) Bus Number

phase A voltage

phase B voltage

phase C voltage

Phase of angle A

Phase of Angle B

Phase of Angle C

1

1.15294

0.47736

0.72727

−11.26

−170.34

155.19

2

1.07303

0.36096

0.86632

−1.92

−135.21

160.42

3

1.11218

0.41376

0.77508

−6.67

−157.99

158.48

4

1.84885

0.27450

0.06733

−15.59

−53.01

60.25

5

1.86574

0.40282

0.11694

−17.14

−43.53

6

1.78250

0.0000

0.0000

−16.28

124

7

1.90882

0.62643

0.04201

−13.06

−46.64

55.21

8

1.90762

0.55923

0.07665

−14.80

−46.31

37.97

9

1.89430

0.46102

0.03616

−13.76

−50.49

40.28

38.42 117.38

6 Simulation Results and Discussions Results of simulation of one generator outage are given in Figs. 3, 4 and 5. Figure 3 shows that outage occur for generator 2. Generator bus-3 magnitude is decreased because it is nearer to the outage generator, whereas the generator bus 1 magnitude is not that much reduced. In Fig. 4, it is observed that generator rotor angle for outage generator is zero degree, whereas for other two it is higher because of increased local power. Figure 7 displays the current magnitude of the generator in the event of single generator failure. The magnitude of current change will be higher for outage generator and the current magnitude close to the unity. Figure 6 shows the change in bus frequency w.r.t time. Due to the outage, the frequency shift in bus 2 is higher. The remaining bus frequencies are not being affected.

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Fig. 3 Generator voltage during generator outage

Fig. 4 Rotor angle during generator outage

P. V. Rajesh Varma et al.

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Fig. 5 Generator current magnitude during generator outage

Fig. 6 Bus frequency during bus outage

Figure 7 shows fall in bus voltage due to single bus outage. Since bus 7 is closer to bus 2 (i.e., outage bus), the decrease in magnitude is higher in bus 7 whereas the other buses remain unaffected.

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Fig. 7 Bus voltage during bus outage

7 Conclusion This paper explains consequences of different types of faults using power world simulator on a small smart grid based on the IEEE 9 bus test model. Based on observations, the most probable fault will be single line to ground (L–G) as high deviation in the phase voltage and angle of phase compared to all other faults. Simulation result shows that the power system can hold on generator outage and bus outage and hence stability may be retained. The load demand can be satisfied by other generators, buses if outage taken place. It is observed that the most serious failure for the transient stability of the generator is the critical clearing time value for the fault on the nearer bus from the outage generator. These simulation results show that how the system responds to sudden change, if it exists in real-time domain. Further, scope of this simulation can explore us to improve the system stability by using flexible ac transmission system devices and various controllers in conjunction with power world simulator.

References 1. Eleschova Z, Smitkova M, Belan A (2010) Evaluation of power system transient stability and defination of the basic criterion. Int J Energy 4(1) 2. Kundur P, Paserba J, Ajjarapu V, Anderson G, Bose A, Canizares C, Hatziargyriou N, Hill D, Stankovic A, Taylor C, Cutsem TV, Vittal V (2004) Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions. IEEE Trans Power Syst 19(2)

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3. Duncan Glover J, Sarma MS, Thomas J (2011) Overbye, power system analysis and design, 5th edition, 2011 4. Kundur P (1994) In: Power system stability and control. USA, EPRI 5. Kaur G, Bhangu NS (2016) Transient stability analysis of IEEE 9 bus sytem using power world simulator. Int J Adv Res Electri Electron Instrum Eng 5(4) 6. Singh AK, Thakur R (2018) Real time simulation of IEEE 9 bus system for fault analysis using transient response. In: International conference on advaced informatics for computing research 2018, pp 117–126 7. Jolevski D (2009) Excitation system of synchronous generator. University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split 8. Kaur R, Kumar D (2016) Transient Stability Improvement of IEEE 9 Bus System Using Power World Simulator. Int J Electri Comput Eng Syst 6:36–39

DSA-Based Analysis for Capacitor Requirement of a Three-Phase Standalone Asynchronous Generator Himadri Sekhar Chatterjee, Dipanjan Samajpati, Sambaran Ray, and Sankar Narayan Mahato

Abstract This paper proposes an efficient algorithm for finding the lowest capacitance value needed to initiate no-load voltage establishment of a three-phase Standalone Asynchronous Generator (SAG) operating under variable speed. Additionally, variation of capacitance value with change in load power under constant voltagespeed operation has been observed. Differential Search Algorithm (DSA) has been enforced to find an optimal capacitance value required for the aforesaid mentioned operations. The results obtained using DSA have been compared with experimental results. ˙It is observed that there is close agreement between the simulated results with the experimental values. Keywords DSA · SAG · Minimum capacitance

1 Introduction Fast reduction of fossil fuels and expansion of individual natives and their demand for electricity have forced the investigators in search for an alternate substitution of traditional way of energy generation. Three-phase Standalone Asynchronous Generator (SAG) due to its inherent properties of self-protection against short-circuit, no maintenance, brushless rotor, and rugged construction have given an important solution to the aforesaid mentioned problem. Three-phase SAG lags the ability to supply reactive power to the system. Therefore, a suitable three-phase capacitor bank when connected in parallel with stator load terminals initiates voltage establishment and sequentially manages a fixed terminal voltage under loaded conditions. Various types of approach have been carried out by researchers in order to select the optimum capacitance value for self-excitation of SAG. A simple and direct method have been proposed by AlJabri and Alolah [1] for calculation of excitation capacitance requirement of SAG feeding an R-L load. The authors also derived a threshold value of speed below which voltage build up is not feasible. Chan [2] presented a H. S. Chatterjee · D. Samajpati · S. Ray (B) · S. N. Mahato Department of Electrical Engineering, National Institute of Technology, Durgapur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_23

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simple iterative procedure for computing the minimum capacitance value for voltage triggering of induction generator. Sequential unconstrained minimization technique (SUMT) have been recommended by Mahato et al. [3] for resolving of optimal capacitors for maximum power output. Singaravelu and Velusami [4] have used fuzzy logic approach for solution of the unknown variables while Mahato et al. [5] have purposed eigen value sensitivity analysis approach for determination of the maximum and minimum capacitance value required to feed a single phase load from a three-phase machine. “fsolve” technique has been used for solving the non-linear equation for short-shunt induction generator [6]. “Fmincon” optimizer has been effectively used for solution of six-phase induction generator [7] while “Internal technique” has been suggested to calculate the least excitation capacitance requirement for three-phase machine [8]. “Eigen value computation” approach have been effectively applied to calculate unknown variables [9] and Palival et al. [10] have used “Gravitational Search Algorithm” for finding the optimal capacitance of a short-shunt induction generator. In this paper, Differential Search Algorithm (DSA) has been effectively used for observing the change in capacitance value with respect to speed of the rotor of the three-phase SAG under no-load condition. Also variation of capacitance along with change in load impedance to retain fixed voltage across the generator terminals under constant speed operation has been investigated. Additionally, for voltage ignition in the circuit under no-load condition, the rotor speed should be above certain threshold value, which has also been calculated in this research work.

2 Problem Formulation Three-phase standalone asynchronous machine running at a certain speed has the capability to generate power, if a three-phase reactive power source is connected in parallel to the stator ends and its equivalent schematic diagram has been presented in Fig. 1. Figure 2 presents the steady-state circuit of a three-phase SAG. Considering loop impedance method, we get (Z 1 + Z 2 + Z 3 ) ∗ I S = 0

(1)

  where I S is the stator current shown in Fig. 2, Z 1 = Ra1 + j X 1 , Z 2 =  R2      + j X 2 ||( j X m ) and Z 3 = RaL + j X L || − j Xa 2C . a−b Now, stator current flowing through the circuit cannot be zero. Therefore, (Z 1 + Z 2 + Z 3 ) = 0

(2)

After complex mathematical calculations and separating real and imaginary parts, we get,

DSA-Based Analysis for Capacitor Requirement …

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Fig. 1 Schematic diagram of standalone asynchronous generator

Fig. 2 Equivalent steady-state circuit of SAG

M(X C , a) = −m 1 ∗ a 3 + m 2 ∗ a 2 + (m 3 ∗ X C + m 4 ) ∗ a − m 5 ∗ X C = 0

(3)

N (X C , a) = −n 1 ∗ a 4 + n 2 ∗ a 3 + (n 3 ∗ X C + n 4 ) ∗ a 2 − (n 5 ∗ X C + n 6 ) ∗ a − n 7 ∗ X C = 0

(4)

Differential Search Algorithm (DSA) proposed by Civicioglu [11] has been used by chatterjee et al. [12] for solving steady-state variables, i.e., magnetizing reactance (X m ) and generated frequency (a). In this paper, DSA has been used for solving the two state variables by taking into consideration either Eq. (3) or (4). The important equations involved in DSA are stated below.   P(a,b) = Lowb + rand∗ Upb − Lowb

(5)

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where rand is the random number between 0 and 1, Lowb , and Upb are the lower and upper boundary limit of the system, respectively. stopover site = superorganism + scale∗ (donor − superorganism)

(6)

 ∗ scale = rand∗g 2∗ rand 1 (rand2 − rand3)

(7)

The objective function considered is M(X C , a) = −m 1 ∗ a 3 + m 2 ∗ a 2 + (m 3 ∗ X C + m 4 ) ∗ a − m 5 ∗ X C = 0

(8)

The major problem formulation till now has been concentrated on the capacitor and its capacitance value but less priority have been given to the rotor speed of the generator. AlJabri and Alolah [1] have derived a minimum threshold speed value beyond which self-excitation is not possible. The mathematical formulae has been defined below.    2 ∗ R1 R2 X2 2 + 1+ (9) vc = Xm R1 Xm where vc is the critical speed of the machine beyond which self-ignition is not possible, R1 is the stator resistance, R2 and X 2 their rotor resistance and reactance, respectively, and X m their maximum magnetizing reactance of the machine.

3 Results and Discussions The machine considered for our study is a 3 − ϕ, 2.2kW, 415V, 4.6A, 4 − pole, star-connected asynchronous machine and its circuit parameters are: R1 = 3.71, R2 = 2.846, X1 = 4.98, X2 = 4.98. The magnetization curve after employing curve fitting technique is obtained as: for X m < 76.5 E g = 249.1 = 0.000229X m3 − 0.104X m2 + 11.6X m −134 for 76.5 < X m < 141.65 = 0 =0 for X m > 141.65

(10)

Figure 3 demonstrates the experimental setup of a three-phase SAG. The hardware set mainly consists of a constant speed prime mover, an autotransformer, SAG, threephase lamp load and measuring instruments. Considering Eq. (9) for calculating the critical speed and putting the known values, we get

DSA-Based Analysis for Capacitor Requirement …

249

Fig. 3 Experimental setup of three-phase SAG

   2 ∗ R1 R2 X2 2 vc = + 1+ Xm R1 Xm    4.98 2 2 ∗ 3.71 2.846 + 1+ = = 105.77 rpm 141.65 3.71 141.65 Therefore, beyond 105.77 rpm initial voltage establishment of the machine is not possible. Figure 4 demonstrates the variance in excitation capacitance with diversification in rotor speed to initiate voltage establishment of the SAG. It can be clearly noticed that with hike in rotor speed of the machine, the required rate of capacitance value decreases. In order to operate the machine under constant voltage and constant speed operation, there is a need of switching of capacitors of different values under various loading conditions which have been shown in Fig. 5. With increase in output power, the var requirement of the machine increases and in order to provide a constant Fig. 4 Deviation of capacitance with speed at unloading condition

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Fig. 5 Alteration of capacitance value with output power under constant voltage operation

voltage across the load terminals, its corresponding capacitance value have to be increased.

4 Conclusion DSA approach has been effectively applied to study the necessity of capacitor of SAG at steady-state. The deviation of capacitance value with variation in rotor speed and change in output power has been analyzed using DSA technique and compared with the experimental results. It has been seen that with an increased rotor speed, the capacitance value recommended for voltage triggering decreases. Also, to operate the generator under constant voltage operation, the capacitance amount rises with increase in consumer load demand. The closeness of the simulated results obtained by using DSA technique with the experimental results indicates the efficiency of the proposed algorithm.

Appendix x = X1 + Xm = X2 + Xm X1 ∗ Xm y = X1 + X1 + Xm m 1 = R L ∗ x ∗ y + X L ∗ x ∗ (R1 + R2 ) m 2 = b ∗ x ∗ (R L ∗ y + R1 ∗ X L ) m 3 = R2 ∗ (X L + x) + x ∗ (R1 + R L ) m 4 = R L ∗ R1 ∗ R2 m 5 = b ∗ x ∗ (R L + R1 ) n1 = X L ∗ x ∗ y n2 = b ∗ n1

DSA-Based Analysis for Capacitor Requirement …

251

n 3 = x ∗ (X L + y) n 4 = R L ∗ x ∗ (R1 + R2 ) + R1 ∗ R2 ∗ X L n5 = b ∗ n3 n 6 = b ∗ x ∗ R1 ∗ R L n 7 = R2 ∗ (R1 + R L )

References 1. AlJabri AK, Alolah AI (1990) Capacitance requirement for isolated self-excited induction generator. IEEE Proceedings 137(Pt. B 3) 2. Chan TF (1993) Capacitance requirements of self-excited induction generators. IEEE Trans Energy Convers 8(2):304–311 3. Mahato SN, Sharma MP, Singh SP (2007) Selection of optimal capacitors for maximum power output of a single-phase self excited induction generator using a three-phase machine. Electric Power Components Syst 35:857–870 4. Singaravelu S, Velusami S (2007) Capacitive VAr requirements for wind driven self-excited induction generators. Energy Convers Manage 48:1367–1382 5. Mahato SN, Sharma MP, Singh SP (2008) Excitation capacitance required for self excited single phase induction generator using three phase machine. Energy Convers Manage 49:1126–1133 6. Haque MH (2009) Selection of capacitors to regulate voltage of a short-shunt induction generator. IET Gener Transm Distrib 3(3):257–265 7. Singh GK, Kumar AS, Saini RP (2010) Selection of capacitance for self-excited six-phase induction generator for stand-alone renewable energy generation. Energy 35:3273–3283 8. Thakur RK, Agarwal V, Nataraj PS (2011) A reliable and accurate calculation of excitation capacitance value for an ınduction generator based on ınterval computation technique. Inte J Autom Comput 429–436 9. Goyal SK, Palwalia DK (2016) Analysis of performance parameters and estimation of optimum capacitance for asynchronous generator. Eng Sci Technol 19:1753–1762 10. Paliwal S, Sinha SK, Chauhan YK (2019) Gravitational search algorithm based optimization technique for enhancing the performance of self excited induction generator. Int J Syst Assur Eng Manag 1082–1090 11. Civicioglu P (2012) Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46(15):229-247 12. Chatterjee HS, Samajpati D, Ray S, Mahato SN (2020) An efficient algorithm for performance analysis of ısolated asynchronous generator: In: Proceedings of IEEE international conference PESGRE , January (2020)

Point-To-Point Low-Frequency AC Transmission of Offshore Wind Power Using Voltage Source-Based AC/AC Converter Ajit Kumar Panda, Subhranshu Sekhar Pati, and Aurobindo Behera

Abstract This paper approaches an alternative way of transmitting wind power at low-frequency AC (LFAC) mode from the offshore grid to the onshore grid. At the onshore station, a voltage source converter (VSC)-based AC/AC converter is implemented in this study. Replacing the cyclo-converter and connecting wind power to a weak grid or even to a passive network. Whereas the cyclo-converter has a limitation of connecting to the only stiff grid. This approach gives the competitive options for VSC high voltage DC (HVDC) with regards to the multi-terminal HVDC network problem as the transmission distance and power transfer improves by using LFAC (1/3 of nominal frequency) approach. It also facilitates to independently controls the AC voltage and active power requirement. The requirement of filter will also be decreased which is normally more in line commutated converter (LCC) -based cyclo-converter. So, the wind power with LFAC can be interfaced with any type of AC network by using VSC-based AC/AC converter. Keywords Wind energy · Grid · Low-frequency AC · Voltage source-based AC/AC converter

1 Introduction The installation capacity of wind energy has been increasing to fulfill the growing energy needs; hence, the growing trends for establishment of Offshore wind farm

A. K. Panda Dept. Of Electrical Engineering, AIET, Bhubaneswar, Odisha 752050, India e-mail: [email protected] S. S. Pati Dept. Of Electrical Engineering, VIT, Balasore, Odisha 756043, India e-mail: [email protected] A. Behera (B) Dept. Of Electrical and Electronics Engineering, CIT, Ranchi, Jharkhand 835103, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_24

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have given opportunity to think engineers to provide the alternative solution of transmission system for integration of wind farm to grid. High voltage AC (HVAC) with 50/60 Hz is the cheapest and most attractive solution for integration of wind energy to grid [1]. However, it is limited to shorter distance and its efficiency of power transmission decreases as the distance increases because of high charging current in 50/60 Hz HVAC submarine cables.Another common solution is HVDC [2–4]. Two popular options are LCC- and VSC-based HVDC transmission systems. Both have the advantages of no restriction of transmission distance and also active power transmission can be increased because of no charging current problem. But it suffers from serious technical drawback of multi-terminal HVDC network such as DC breaker, relay protection, and DC transformer. And also there is space charge accumulation problem in XLPE seashore cable due to DC current. Another way of transmission system for wind energy grid integration has been introduced fractional frequency transmission system (FFTS) in 1992 [5]. In [6–8], a lot of research has taken place along with its application. Here, the drawback is that there is need of redesign of wind turbine to give low-frequency AC power that needs costlier and magnetic components. So the establishment of medium voltage DC (MVDC) grid has given the options to avoid the redesign of wind turbine. The design of MVDC grid is art of the state today which is out of the scope of this paper. As an alternative solution to get rid from above-stated problem, recently, a LFAC transmission technology has been proposed in paper [9–11]. These paper highlights to make MVDC grid in place of commonly used AC grid in offshore station and also special emphasis has given on use of Cycloconverter as AC/AC converter on the onshore station to interface wind energy to grid. The low frequency has been used in electrification of rail track system [12]. LFAC transmission technology is used to export the wind power to onshore grid in this paper. On the onshore grid, a voltage source-based AC/AC frequency converter is implemented in this paper replacing cyclo-converter as it faces the grid code compliance problem and also there is need of strong AC grid for cyclo-converter. By using voltage source-based AC/AC converter station, large filter requirement can be decreased and also it facilitates independent control of active and reactive power. Finally, we can interface wind energy to weak grid even also passive network. Then, the transmission configuration adopted in this paper has discussed. Then the control method for LFAC transmission configuration, and finally simulation results have outlined.

2 LFAC Connection Principle The real power (P) transmitting over the AC transmission lines cable for transmission of offshore wind power is expressed by: P=

Vs Vr × sin δ X

(1)

Point-To-Point Low-Frequency AC Transmission of Offshore …

255

Fig. 1 Configuration of LFAC transmission network

where V s and V r are sending side voltage and receiving side voltage of the grid, respectively, X is the line reactance and δ is the transmitting angle. For a certain transmission angle, power transmission capacity from one side to other side can be improved by increasing voltage level and decreasing the transmission line reactance value. Since the sending side voltage and receiving side voltage are fixed, so the power transmission capacity can now be increased by decreasing the value of line reactance. As the reactance is dominantly depended on electrical frequency; hence, by reducing this nominal frequency, power transmission capacity can be increased. In this project, a 60/3 Hz frequency is taken for designing LFAC transmission system.

2.1 LFAC Transmission Network Configuration The configuration of transmission of LFAC power network is shown in Fig. 1. A 30 kV DC bus is established in offshore station, where DC voltage is converted to LFAC power by 12 pulse Gate Turn Off (GTO)-based converter. Then a phase shifting transformer is taken to increase the voltage level of LFAC power from 62.5 to 132 kV. Frequency of the AC voltage is 20 Hz considered. Here, the power transmitting from offshore wind farm is taken 200 MW. Then power is transmitting over 160 km long distance. At the receiving side bus, a frequency converter station (i.e., voltage source technology-based AC/AC converter) is used to integrate the 20 Hz frequency AC power to AC grid of 132 kV, 60 Hz frequency capacity. The power factor is chosen as 0.9 lagging.

2.2 Importance of VSC Technology The LCC has problem of commutation failure as well as requirement of nore reactive power which can affect the performance of whole system. Thus, in recent days voltage source converter (VSC)-based technology is being adopted for system performance from technical to economical point of view. This technology needs device of IGBT, GTO. The VSC technology has the advantages such as no commutation failure, reduce filter capacity, require no more additional reactive power. The independent real and reactive power control can provide to passive network and hence improved

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voltage stability. The frequency converter station is an AC-AC converter station, where LFAC wind power at 20 Hz frequency is transmitted from offshore station to interface with 60 Hz ac grid. The frequency converter used is the voltage sourcebased technology. The components used are DC capacitor, power transformer, phase reactor, voltage source converter with PWM technique, AC filters for eliminating high switching harmonics created by PWM switching technique.

3 Control Strategy of LFAC Transmission Configuration Power angle control, also called as voltage angle control scheme is implemented in this paper because of most straight forward controller. This principle of power control is given by equation Vvsc Vac × sin δ X

(2)

Vac2 − Vvsc Vac cos δ X

(3)

P= Q=

where P and Q are the real and reactive power between two electrical nodes in an AC systems with voltage magnitudes of V vsc and V ac . The terms δ and X are the phase angle differences and line reactance between two nodes. As shown in Fig. 2, it is evident that active power mainly relates on δ while reactive power is regulated by voltage magnitudes of two nodes. In VSC, active power is goverened by phase angle. If, VSC voltage is phase advanced from AC then, real power transfers from converter to AC system and vice versa. But, reactive power is dependent on voltage magnitude. If, voltage magnitude is less than the voltage of AC system, reactive power flows from system to converter and vice versa. The VSC needs three variables which are magnitude, phase angle, and frequency for generation of three phase AC voltages which are given by phase locked oscillator.

Fig. 2 Phasor diagram

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4 Simulation Results PWM switching technique uses carrier signal which is triangular signal of magnitude −1 and + 1 and modulated sine wave signal. The carrier signal of PWM is compared with modulated signal of sine wave for both turn off process and turn on process of switches by giving pulses. Figure 3 shows model for carrier wave generation where phase locked oscillator synchronized to three phase AC system with one ramp output synchronized to phase A. The phase A ramp signal is converted to PWM frequency, then to carrier signal of magnitude between −1 and + 1 and finally firing pulses are sent to ınterpolated switching turn on and turn off. Figure 4 shows the model for generating sinewave signal. Here, the phase locked oscillator synchronized to three-phase AC system which gives output of six ramps, 600 apart between 0–3600 over one cycle of fundamental frequency. Ramp array signals are phase shifted by 300 for star-delta transformer configuration. The ramps are converted to sine wave where magnitude can control. By varying magnitude and phase shift control, we can independently control the variable like DC voltage, AC voltage, and active power at each converter station. In sending end converter station, AC voltage is controlled by phase shift control whereas DC voltage is controlled by magnitude control. But at frequency converter station, one 6 pulse VSC acts as rectifier and other 6 pulse converters act as inverter. At rectifier end VSC, DC link voltage is regulated by phase shift control and AC system voltage is regulated by magnitude control. At inverter end, DC link power is regulated by phase shift control whereas AC system voltage is regulated by magnitude control. In each case, PI controller is used to get the desired output while comparing with Reference signal. In Fig. 5, sending end DC voltage is shown, that is of 30 kV. ˙In

Fig. 3 Model for carrier wave generation

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Fig. 4 Model for generating sine wave signal

Fig. 5 Sending end DC voltage

Fig. 6, sending end AC voltage that is of 1 p.u (Base value is of 132 kV line-to-line R.M.S). Frequency is 20 Hz which is one third of grid frequency. Figure 7 shows the real power sending at inverter station which is of 200 MW. In Fig. 8, it shows the grid end AC Voltage which is of 1 p.u (Base value is of 132 kV line-to-line R.M.S) and frequency is 60 Hz.

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Fig. 6 Sending end AC voltage

Fig. 7 Sending end real power

Fig. 8 Grid end AC voltage

5 Conclusion In this paper, VSC technology is implemented and voltage source-based AC/AC converter is introduced in place of cyclo-converter. In this simulation process, LFAC transmission with voltage source-based AC/AC converter is successfully implemented. So it is possible to transmit the wind power over long distance with increased

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transmission capacity by LFAC transmission system. The result evident that, the proposed methodlogy can not only transfer the power over long distance but also helps the system to remain in synchonism with the grid. The possibility of using existing AC circuit breaker is also there. There are more options to further develop like multi-terminal transmission is possible, and wind power with MVDC grid can be coordinated to this work. And also there is need of designing LCL grid filter parameter.

References 1. Kabsha MM, Rather ZH (2019) A new control scheme for fast frequency support from HVDC connected offshore wind farm in low ınertia system. IEEE Trans Sustain Energy 2. Korompili A, Wu Q, Zhao H (2016) Review of VSC HVDC connection for offshore wind power integration. Renew Sustain Energy Rev 59:1405–1414 3. van der Meer AA, Mario N, Bos JA, Gibescu M, van der Meijden MAMM , Kling WL (2015) Stability assessment of VSC-HVDC connected large-scale offshore wind power: A North-Sea region case study. In: 2015 IEEE eindhoven powertech, IEEE, pp 1–6 4. Pinto RT, Bauer P, Rodrigues SF, Wiggelinkhuizen EJ, Pierik J, Ferreira B (2013) A novel distributed direct-voltage control strategy for grid integration of offshore wind energy systems through MTDC network. IEEE Trans Indus Electron 60(6):2429–2441 5. Ruddy J, Meere R, O’Donnell T (2016) Low frequency AC transmission for offshore wind power: a review. Renew Sustain Energy Rev 56:75–86 6. L iu S, Wang X, Meng Y, Sun P, Luo H, Wang B (2017) A decoupled control strategy of modular multilevel matrix converter for fractional frequency transmission system. IEEE Trans Power Del 32(4):2111–2121 7. Xifan W, Chengjun C, Zhichao Z (2006) Experiment on fractional frequency transmission system. IEEE Trans Power Syst 21(1):372-377 8. Li J, Zhang X-P (2016) Small signal stability of fractional frequency transmission system with offshore wind farms. IEEE Trans Sustain Energy 7:1538–1546 9. Chen H, Johnson MH, Aliprantis DC (2013) Low-frequency ac transmission for offshore wind power. IEEE Trans Power Delivery 28(4):2236–2244 10. Luo J, Zhang XP, Xue Y (2019) Small signal model of modular multilevel matrix converter for fractional frequency transmission system. IEEE Access 7:110187–110196 11. Ruddy J, Chen J, Meere R, O’Loughlin C, O’Donnell T (2018) Harmonic stability of VSC connected low frequency AC offshore transmission with long HVAC cables. Electric Power Syst Res 162:220–232 12. Stackler C, Evans N, Bourserie L, Wallart F, Morel F, Ladoux P (2019) 25 kV–50 Hz railway power supply system emulation for power-hardware-in-the-loop testings. IET Electri Syst Transp 9(2):86–92

Green Technologies: Driving the World to the Era of Renewable Sources of Energy

Robust Adaptive Observer Based Fault Tolerant Controller Design for Proton Exchange Membrane Fuel Cell Systems Vikash Sinha and Sharifuddin Mondal

Abstract This work designs the FTC system based robust adaptive observer approach for PEMFC systems. A robust adaptive estimation algorithm is presented to improve speed and accurate fault estimation. It is derived by LMI technique which is used to enhance the efficiency of PEMFC system using fault estimation strategy. A robust adaptive fault estimation observer that is based on state feedback FTC strategy is designed that ensures stability and also improves system performance. A type of actuator fault, stuck fault, is considered where the inputs delivered by the faulty actuators are blocked to constant value. Finally, simulation results of the cathode pressure model are presented in order to show efficacies of the proposed robust adaptive fault estimation algorithm and fault tolerant control design method. Keywords PEMFC · FTC · RAFEA · RAFEO

1 Introduction Energy technologies based on the renewable sources are of a new era. For many countries and regions, these are responsible for fulfilling substantial energy needs. Their quick and substantial progress in coming years has been propelled by local, national and regional policies with the business community, as well as innovative technology and cost cutting in energy generated with these sources. Proton exchange membrane fuel cell (PEMFC) systems are considered as very efficient and environmental friendly systems for power generation due to their good efficiency and less emission of greenhouse gasses. The entire process involves several scientific phenomena resulting in strongly interdependent parameters which makes these systems very complex and tough to control and hence enhances the chances V. Sinha (B) · S. Mondal Department of Mechanical Engineering, National Institute of Technology Patna, Bihar 800005, India e-mail: [email protected] S. Mondal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_25

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of fault occurrence. Short lifetime and low reliability are still the main challenges for a wide scale use of these systems. With the integrated system complexity, the probability of actuator, sensor, and component faults increases that rapidly alter the system behaviour resulting in performance degradation. So, reliability and safety are very important measures of performance of the PEMFC systems [1–3]. Fault tolerant control (FTC) has emerged as an effective method to enhance reliability and safety of the system. This has caught the attention of the research community in recent years. It plays a crucial role in the fields of advanced control and research methodology in last few decades. FTC is classified into two types, one is passive FTC [4] and another is active FTC [5, 6]. The passive FTC applies control algorithms for making the system robust in response to probable faults. These probable faults are modelled as uncertainties. On the other hand, active FTC works on real time fault scenario supplied by fault estimator. Based on the faulty scenario, an additional control algorithm is designed for the stability of the system to preserve an acceptable performance of the faulty system. Fault estimation is a very important step in the design of active FTC. Different techniques have been devised for fault estimation like sliding mode observer, robust observer, unknown input observer, and adaptive observer [7–9]. Many researchers have worked on fault estimation using adaptive observer. Adaptive design based observer and FTC designs for nonlinear systems have become dominant and important research areas over the past few decades [7–9]. The key advantage of an adaptive observer lies in the fact that satisfies the standard conditions of simple unknown input observer and also it estimates both state variables and parameters at the same time thus can be used for robust adaptive control and fault diagnosis. An actuator based fault diagnosis scheme is implemented for PEMFC systems in this study. The estimation design for PEMFC systems has not been explored much in the literature [10]. Schultze and Horn [10] developed a state estimation technique that uses the Unscented Kalman Filter (UKF) combined with prediction, which compensates the effect of time-varying delay on the estimated true PEMFC system state variables. The demerit of above method is that a linear state feedback based control is used. But in literature, a robust framework of FTC based adaptive design observer for PEMFC systems is still lacking. In this paper, a robust adaptive fault estimation algorithm (RAFEA) is presented for PEMFC system. The RAFEA is constructed based on a robust adaptive observer with multi-objective to reduce the effect of disturbances on the fault estimation. The RAFEA, then, uses a fault estimation strategy playing a major role in enhancing the speed as well as accuracy. The LMI technique based method [11] and rate of convergence are used to obtain the robust adaptive fault estimation observer (RAFEO) gain matrices and unknown parameter. The obtained online state as well as fault information is used to design RAFEO based FTC to develop the closed-loop system stability in faulty scenario. Remaining paper is organized in this sequence. The PEMFC system is presented in Sect. 2. Section 3 introduce RAFEA design and Sect. 4 illustrates the RAFEA based design of the FTC. Simulation results of the cathode pressure model of PEMFC

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system are shown in Sect. 5 to demonstrate the effect of the algorithms proposed with some conclusions in Sect. 6.

2 PEMFC System Consider a PEMFC system as x(t) ˙ = (A + A)x(t) + Blu(t) + Bu(t) + E f (t, u, x) + Dv(t)

(1)

y(t) = C x(t)

(2)

In (1) and (2), x(t) ∈ R n , u(t) ∈ R m , and y(t) ∈ R p are state, input, and output vectors respectively and v(t) ∈ R q is disturbance vector including uncertainties i.e., Ax(t) + Bu(t); f (t, u, x) ∈ R r is actuator stuck fault; the matrices A, B,  being known and u is the unknown parameter. In(1), l =  C, D and E diag l1 , l2 , . . . l p , l = 0 correlates to stuck fault, and l = 1 gives the fault free situation in an actuator. The value of u is (i) zero (no fault, stuck or effectiveness loss); (ii) u = constant (stuck fault). For the RAFEO design problem by (1)(2), following assumptions are as follows: Assumption 1 The function f (t, u, x) and its derivative is bounded. Assumption 2 The pair of matrices (A, B) and (A, C) are, controllable and observable, respectively. Assumption 3 The function f (t) satisfies the Lipschitz criterion such that.       f (t, u, x) − f t, u, ˆ x  ≤  u(t) − u(t) ˆ 

(3)

where  is the Lipschitz constant. Now the RAFEO design for PEMFC system (1–2) is defined as follows:     ˙ˆ = A x(t) x(t) ˆ + Bu(t) + E f t, u, ˆ x + L y(t) − yˆ (t)

(4)

where x(t) ˆ ∈ R n , yˆ (t) ∈ R p , and f (t, u, x) ∈ R r are estimated state, output, and fault vectors respectively and L ∈ R n× p is gain matrix to be designed. The state, fault, and output errors are given by   ˆ x , and e y (t) = y(t) − yˆ (t) ˆ e f (t) = f (t, u, x) − f t, u, ex (t) = x(t) − x(t), (5) The error dynamics is written as

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e˙ x (t) = (A − LC)ex (t) + Ee f (t) + Dv(t)

(6)

e y (t) = Cex (t)

(7)

where ex (t) ∈ R n , e y (t) ∈ R p , and e f (t) ∈ R r are state, output, and fault estimation errors respectively. The method of robust adaptive observer based fault estimation algorithm design is discussed in next section.

3 RAFEA Design In this section, the main idea of RAFEA is presented in a theorem (i.e. Theorem 1). A fault estimator model is given below.       ˙ˆ x = S1 f t, u, ˆ x + S2 e˙ y (t) + ρe y (t) f t, u,

(8)

where S1 ∈ R r ×r and S2 ∈ R r × p are the matrices to be designed with a scalar ρ. There exist symmetric matrices P, L and S2 to satisfy these conditions: P( A − LC) + (A − LC)T P < 0

(9)

E T P = S2 C

(10)

Using [9], the following relation holds: 2a T b ≤

1 T a a + βb T b β

(11)

The e f (t) derivative is given by using RAFEA as follows using (8) as       ˙ˆ x = f˙(t, u, x) − S1 f t, u, e˙ f (t) = f˙(t, u, x) − f t, u, ˆ x − S2 e˙ y (t) + ρe y (t)     ˆ x − S2 e˙ y (t) + ρe y (t) − S1 f (t, u, x) + f˙(t, u, x) = S1 f (t, u, x) − S1 f t, u,   = S1 e f (t) − S2 e˙ y (t) + ρe y (t) − S1 f (t, u, x) + f˙(t, u, x) (12) According to (6) and (12), except for the matrices L , S1 , and S2 , other parameter matrices are known. Therefore, the RAFEO design calculates the gain matrices L , S1 , and S2 to stabilize error systems (6) and (12) and gives minimum value of ex (t) and e f (t. Now the main result of this work is expressed in the following Theorem 1.

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Theorem 1 The eigen values of a given matrix A ∈ R n×n in a closed circular region D(λ, τ )with center λ + j0 and radius τ , H∞ performance level γ > 0and a known scalar ρ > 0there exists symmetric matrices P, S1 , and a matrix Y such that the following conditions hold.

⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣

ϕ11 ∗ ∗ ∗ ∗ ∗ ∗

−P P A − Y C − λP 0, the solution G = G T > 0 of the Lyapunov equation is given below. (A − B K )T G + G(A − B K ) = −Z

(16)

As can be seen from (16), if A − B K is stable, an FTC ensures the faulty system to be stable. Thus, for a pair of closed loop poles in the L.H.S of the complex plane, K can be obtained. The computational complexity of the FTC algorithm can be described in such a way that it takes some time to compute the result but the robust adaptive observer quickly calculates the gain matrices in continuous time domain.

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5 Simulation Results The cathode pressure model of a PEMFC system [1, 2] is given in state space formulation as to illustrate the efficacy of the design method. The state matrices and vectors of T T   the system are found out considering x(t) = x1 x2 x3 and u(t) = u 1 u 2 u 3 . The system matrices are as follows: ⎡

⎤ −22.9610 −22.9610 −22.9610 A = ⎣ −46.4930 −46.4930 −46.4930 ⎦, −0.3295 −0.3295 −0.3295 ⎡ ⎤ 367.5 7.7390 −0.2296 B = ⎣ 367.5 29.104 −0.4649 ⎦, 367.5 0.1180 −942.225 ⎡ ⎤ 101  T C = ⎣ 0 1 0 ⎦ and D = 0.1 0.1 0.1 001 Using Assumption 2 here, stuck fault in an actuator is considered in such a way that they occur in the input assuming E = B. The unknown parameter is chosen as u = 1.5. The input is taken as u(t) = sin(t). The initial conditions of the observer states and the RAFEA algorithm are taken as zero. With these matrices, a robust adaptive observer is designed. The system output y(t) in fault-free and FTC scenario is shown in Fig. 1. Using the RAFEO in comparison to the system outputs in fault-free case, the designed FTC algorithm ensures the stability of the closed loop PEMFC system and also keeps a desired performance in faulty scenario. Here, a single slow-varying stuck fault as a half step input is considered at t = 50 s in the stack current actuator signal. Using Theorem  1, by choosing ρ = 1, which increases the convergence rate of ex (t), e f (t) according to [7], and solving conditions (13) and (14) gives γ = 0.01. Now solving the LMI relations [11], stated in Theorem 1, other matrices are calculated. The FTC gain matrices are calculated using (15, 16) as by choosing Z = 0.5I3 . The simulation results can be compared with the previous results from [7].

6 Conclusion In this paper, a FTC strategy based on RAFEO for PEMFC system has been proposed. A robust adaptive FTC algorithm is derived using the fault estimator model. The algorithm is formulated based on LMI technique that improves the accuracy and speed of estimation. By using the estimated states and fault information, FTC algorithm is designed in order to stabilize the faulty system. An application for cathode pressure model of a PEMFC system shows that the proposed technique is very efficient in

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Fig. 1 y(t) in fault-free and FTC scenario (dotted and solid lines)

estimating actuator stuck faults and FTC ensures that the faulty system recovers to its normal working condition within a finite time interval.

References 1. Pukrushpan JT, Peng H, Stefanopoulou AG (2004) Control-oriented modelling and analysis for automotive fuel cell systems. transactions of the ASME. J Dyn Syst Meas Contr 126:14–25 2. Kim ES, Kim CJ, Eom KS (2007) Nonlinear observer design for PEM fuel cell systems. In: Proceedings of international conference on electrical machines and systems. Seoul, Korea, pp 1835–1839 3. Sinha V, Mondal S (2018) Recent development on performance modelling and fault diagnosis of fuel cell systems. Int J Dyn Control 6:511–528 4. Fan LL, Song YD (2010) On fault-tolerant control of dynamic systems with actuator failures and external disturbances. Acta Automatica Sinica 36:1620–1625

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5. Patton RJ, Putra D, Klinkhieo S (2010) Friction compensation as a fault-tolerant control problem. Int J Syst Sci 41:987–1001 6. Alwi H, Edwards C (2008) Fault tolerant control using sliding modes with on-line control allocation. Automatica 44:1859–1866 7. Qian H, Peng Y, Cui M (2015) Adaptive observer-based fault-tolerant control design for uncertain systems. Math Problems Eng 429361:16 8. Mondal S, Chakraborty G, Bhattacharyya K (2008) Robust unknown input observer for nonlinear systems and its application to fault detection and isolation. J Dyn Syst Measur Control Trans ASME 130:0445031–0445035 9. Zemouche A, Boutayeb M (2009) A unified H∞ adaptive observer synthesis method for a class of systems with both Lipschitz and monotone nonlinearities. Syst Control Lett 58:282–288 10. Schultze M, Horn J (2013) State estimation for PEM fuel cell system with time delay by an unscented kalman filter and predictor strategy. In: Control and automation (MED) 21st mediterranean conference on IEEE, pp 104–112 11. Gahinet P, Nemirovsky A, Laum AJ, Chilali M (1995) LMI control toolbox: for use with MATLAB. The Mathworks Inc., USA

Exergy and Energy Analysis of Hybrid Solar PV/T Collector Om Prakash and Ankish Aman

Abstract This paper deals with the exergy analysis and energy analysis of hybrid solar photovoltaic/thermal (PV/T) collector in the composite climate zone in central India. Experiment was conducted in the month of June. In the energy analysis, the following parameters are calculated: energy loss, energy efficiency, and maximum electrical power output. Various heat transfer coefficients (HTC) such as the radiative heat transfer, convective heat transfer, and overall heat transfer are also determined. In the exergy analysis section, the following parameters are being calculated: exergy loss, exergy efficiency, thermal exergy, and electrical exergy. The error analysis is also calculated. The result obtained from the computation is being validated with previously published results. Keywords Energy efficiency · Hybrid solar PV/T collector · Exergy analysis

1 Introduction Over the past few decades, efforts have been made toward the development of renewable energy technologies to replace conventional energy sources [1, 2]. Due to the natural resource depletion, there is growth of fuel prices throughout the world, and the increase in pollution due to emissions from conventional energy sources and the development of non-conventional energy technologies have become important. According to statistics, 96% of villages in India have been electrified till May 2015, and as the stats speak, about 31% household’s people get electricity less than 11 h of day. Moreover, it is observed that there has been a hike in demand for electricity from 2010 to 2016 by 27% and further rise by 1.3% rate per year. Seasons and weather, locations, and surface orientation affect solar radiation intensity. An average of 1000 W/m2 of solar radiation’s intensity is received as perpendicular rays when the O. Prakash (B) · A. Aman Mechanical Department, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] A. Aman e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_26

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sky is clear. 5–8 kWh/m2 day variation in average solar radiation intensity annually [3]. The electric power about 46.8 MWh/year generation done if about 1–2% of land usage to harness with 10% efficiency solar. In the last 25 years, both experimental with comparison of theoretical studies done on solar energy owing to the fact that it is environment-friendly, readily available, and cheap source of alternative resources [4]. The distribution purpose of power generation, consideration solar PV technology is done. There is an increment in the reliability of the systems connected to PV/T collector systems when the electricity generation consumption starts, reducing loss in distribution and transmission [5]. The radiation beam and minimal diffuse radiation component are utilized by solar photovoltaic systems. However, the contribution of diffuse solar radiation is about 20%, even on a bright and sunny day [6]. The received solar radiation when the sun rays direction is directed on a normal surface. As the solar energy strikes a solar cell, the electron move to p-layer thus holes remain in the n-layer, which in turn are extracted by an electric circuit. According to the first law of thermodynamics, validation is done with the energy efficiency. It is characterized as proportion of the force yield of sunlight-based solar cell to the sun oriented energy conveyed to the sun-powered board solar panel while contrasted with exergy examination, which depends on the second law of thermodynamics. The amount of energy association interaction during a procedure is dictated by energy examination by proper analyzing, while the nature of energy accessible for changing over to work in a controlled process by exergy investigation. The efficiency of a framework can be improved by exergy examination by assessing the sources and extent of irreversibility’s. Exergy is the most extreme helpful work which can be gotten from input energy [7, 8]. An elective technique for assessing and contrasting the exhibition of the PV framework is acquired from exergy investigation [9]. Inferable from its points of interest and advantages over energy examination, the investigation exergy has been progressively done in the course of the most recent year. For the utilization of the energy and exergy balance on the sun-based photovoltaic framework, Geng [11] assessed the exergy execution examination of sun-powered water solar heating framework tentatively, and the exergy and energy efficiencies are thought about. Exergy examination and appraisal of sustainable power source assets are setup [12] for a supportable future. A model of a sun-powered photovoltaic thermal system as far as exergy examination has been portrayed by Joshi [13]. The target of this paper is to apply an exergy and energy equilibrium for the sun-based PV/T hybrid system and with the assistance of energy and exergy investigation, a sensible model was created for foreseeing the presentation of sun-powered PV/T hybrid collector to various climate conditions (Fig. 1).

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Fig. 1 Experimental setup [10]

2 Methodology 2.1 Solar Panel Energy Efficiency A sunlight-based solar cell’s efficiency energy transformation of consumed sunlight to electrical energy when a sun-based cell is mounted with an electrical circuit. Hence, energy efficiency of the sunlight based is the proportion of intensity yield to energy contribution of the sun-oriented solar cell. The yield force and energy effectiveness of the PV framework fluctuation are relying upon surface temperature and sun-oriented insolation. The energy transformation efficiency of the sun-based PV (ηenergy ) is determined from the accompanying condition. η energy =

Voc × Isc × F F ; A×G

The output PV power. Pel = I × V ; The Output max power. Pmax = Voc × Isc × F F = Vmp × Imp ;

2.2 Exergy Solar Panel Efficiency The greatest measure of valuable work acquired during a procedure when a framework system is brought to balance is called exergy. It is a consolidated property of both the framework system and encompassing. Exergy investigation of a procedure

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can be completed to decide the energy quality for best use. The exergy parity of the sun-powered solar cell under the consistent state stream process, i.e.: Input Exergy = Output Exergy + Loss of Exergy + Irreversibility. Exergy misfortune is the nature of energy debased when experiencing the procedure. The loss of exergy is called irreversibility. The occurrence of sun-powered radiation is changed over into electrical and thermal energies. The electrical energy is used, and the warm energy is scattered to the encompassing as a warm or heat misfortune cause exergy obliteration. The exergy productivity of the sun-powered PV can be characterized as the proportion of the yield exergy by the sun-powered pv module to the information exergy of the sun-based radiation ηex =

Exout ; Exin

Exergy input of sun-oriented PV module incorporates sun-oriented solar radiation force exergy occurrence on the module and is given by: 4 Ta 1 Ta Exin = A × G[1 − ( ) + ( )4 ]; 3 Ts 3 Ts The output exergy of the solar PV module is given as: Exout = Exthermal + Exelectrical , Thermal exergy is given by Exthermal = Q(1 − TTma ) where Q = U × A(Tm − Ta ); Electrical exergy is given by Exelectrical = V oc × I sc × FF; Radiation losses as well a convection loss contribute to the overall HTC of a solar pv module represented as U = hconv + hrad ;Convective HTC is given by hconv = 2.8 + 3Vw; Radiation HTC between sun-oriented solar module and its surroundings is given by hrad = εσ (T sky + T m )(T sky 2 + T m 2 ); Sky effective temperature is given by T sky = T a – 6; Module NOCT temperature can be given as Tm = Ta (NOCT − 20) ×

G ; 800

2.3 Experimental Study The trial was acted in the Central locale of India. The scope and longitude of the area are 23°251 N and 77°422 E. The encompassing temperature of the area fluctuates in the scope of 6–49 °C during a year. The half and half sun-powered PV/T authority was tried, and the parameters, for example, Voc, Isc, wind speed, the surrounding

Exergy and Energy Analysis of Hybrid Solar PV/T Collector Table 1 Input parameter value

Table 2 PV module parameter value

275

Input parameter

Value

Emissivity of the panel (ε)

0.9

Nominal operating cell temperature (NOCT)

45 °C

Sun temperature

5780 K

Stefan Boltzmann constant (σ )

5.67 × 〖10〗ˆ(−8) W/m2 K

Collector parameter

Value

Open-circuit voltage

18 V

Maximum power

36 W

Dimensions

500 × 500 × 35mm

Fill factor

0.761

Short-circuit current

2A

temperature, and so forth., required for the assessment of the frameworks were estimated at an interim of one hour somewhere in the range of 10–17 h. Tables 1 and 2 rundown the info parameters and specification of the authority framework for the examination.

2.4 Module Input Parameter 3 Result and Discussion From the data collected during the experiment, the exergy and energy analysis was carried out. The parameters calculated during the analysis were: (a) Convective, radiative, (b) Overall HTC (c) Overall heat transfer rate (HTR) (d) Thermal, electrical, and output exergy e) Exergy efficiency (f) Maximum power output (g) Energy efficiency. First day or day 1 of the experiment was performed in the clear sky weather time, and on the subsequent day 2, the climate condition was incompletely shady. The variety of the above parameters was seen on and per hour premise during the analysis and are displayed in the accompanying figures. Figure 2 delineates the variety of various warmth move coefficients of HTR concerning time. It shows that the greatest by and large HTC was 16.7 for day 1 and 17.2 W/m2 K for day 2 examination time. Figure 3 shows the variation of overall HTR on both days of experimentation. On the day of partly cloudy sky, which is day 2, the overall HTC is found to be always higher than that the overall HTC on the day of clear sky.

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Fig. 2 Variation of HTC

Hrad-Day 1

15.000

Hrad-Day 2

10.000

Hconv-Day 1

5.000 17.00

15.00

16.00

14.00

12.00

10.00

13.00

Hconv-Day 2

0.000

11.00

H (W/m²K)

20.000

Overall HTC- Day 1 Overall HTC- Day 2

Fig. 3 Variation of overall rate of heat transfer

Q (W)

Experimenta on Time (Hrs)

200.000 150.000 100.000 50.000 0.000

Day 1 Day 2

80.000

Thermal ExergyDay1

60.000 40.000

Electrical exergyDay 1 16.00

17.00

15.00

14.00

12.00

13.00

0.000

10.00

20.000 11.00

Fig. 4 Variation of exergy output

Exergy (W)

Experimentaion Time(hrs)

Exergy OutputDay 1

Expreimentaion Time (hrs)

Fig. 5 Variation of exergy efficiency

ηex (%)

Figure 4 Exergy output of day 1 and day 2 variation. The maximum exergy output for day 1 and day 2 is 51.3 W and 57.4 W. On day 2, it is determined that has high exergy output as compared to day 1 examination Figure 5 represents the efficiency of exergy. On day 2 of experimentation found that exergy efficiency is high in comparison to day 1. The exergy efficiency on day 1 is 13.2–16.3 (%) and 15.1–18.1(%) for day 2, respectively. Figures 6 and 7 show the variety of the maximum power and energy productivity efficiency of the hybrid breed sun-oriented PV/T authority, separately. Because of 20.000 15.000 10.000 5.000 0.000

Day 1 Day 2

Experimena on Time (hrs)

Exergy and Energy Analysis of Hybrid Solar PV/T Collector 40.000

Pmax (W)

Fig. 6 Variation of maximum power

277

30.000 20.000

Day 1

10.000

Day 2

0.000

Experimentaion Time (Hrs)

ηenergy(%)

Fig. 7 Variation of energy efficiency

12.000 10.000 8.000 6.000 4.000 2.000 0.000

Day 1 Day 2 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00

Experimentaion Time (Hrs)

clear sky weather scenario on day 1, the temperature of cell was higher than that on day 2. The higher cell temperature diminishes the most max power created and the energy effectiveness of the authority collector.

4 Conclusion The results analysis show that there is an increement in the voltage and current in shady climate conditions when contrasted with clear sky weather scenario because of low encompassing temperature. The performance parameters such as overall HTR, output exergy, exergy efficiency, energy efficiency, and maximum electrical power developed are found to increase substantially in cloudy weather conditions. Thus, to improve the efficiency of the collector, proper cooling has to be provided via suitable modifications.

References 1. Solangi KH, Islam MR, Saidur R, Rahim NA, Fayaz H (2011) A review on global solar energy policy. Renew Sustain Energy Rev 15(4):2149–63 2. Mekhilef S, Saidur R, Safari A (2011) A review on solar energy use in industries. Renew Sustain Energy Rev 15(4):1777–1790 3. Sudhakar K, Rajesh M, Premalatha M (2012) A Mathematical model to assess the potential of algal bio-fuels in India energy sources. Part A: Recovery Utilisation Environ Effects 34(12):1114–1120

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4. Basunia MA, Abe T (2001) Thin-layer solar drying characteristics of rough rice under natural convection. Food Eng 47:295–301 5. Singh SN, Singh B, Ostergaard J (2009) renewable energy generation in india: present scenario and future prospects. In: IEEE power and energy society general meeting, pp 1–8 6. Wyman C, Castle J, Kreith F (1980) A review of collector and energy storage technology for intermediate temperature applications. Sol Energy 24:517–540 7. Bejan A (1988) Advanced engineering thermodynamics. Wiley Interscience Publishers, New York 8. Cengel YA, Boles MA (2006) Thermodynamics: an engineering approach, 5th edn. Tata McGraw Hill, New York 9. Rosen MA (1999) Second law analysis: approaches and implications. Int J Energy Res 23(5):415–429 10. Qureshi U, Baredar P, Kumar A (2014) Effect of weather conditions on the hybrid solar PV/T collector in variation of voltage and current. Maulana Azad National Institute of Technology, Bhopal, India 11. Geng L, Cengel YA, Turner RH (1995) Exergy analysis of a solar heating system. J SolEnergy Eng 117(3):249–251 12. Hepabsli A (2008) A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future. Renew Sustain Energy Rev[J] 12:593–661 13. Joshi AS, Dincer I, Reddy BV (2009) Development of solar exergy maps. Int J Energy Res 33:709-718

Experimental Analysis of Ignition Delay in Dual Fuel Diesel Engine with Secondary Fuel Chandra Bhushan Kumar, D. B. Lata, and Dhaneshwar Mahto

Abstract In the present research work, experiments were conducted on four cylinder water-cooled DI compression ignition diesel engine with diesel as base fuel, LPG and hydrogen gas as substitute fuel. For the experimental work, diesel engine was modified to run on base fuel diesel and secondary fuel gas. The experiments were conducted to measure the variation of pressure, ID period at varied conditions of load and different substitution of diesel with LPG and hydrogen fuel with and without EGR at rated speed. For the comparative study, the experimental result of ID was compared and contrasted with Hadenberge-Hase equation for the diesel engine and DFD diesel. Keywords Dual fuel · Dual fuel compression ignition diesel engine · Alternative fuel · Ignition delay · Exhaust gas recirculation

Nomenclature DFD DF ID CNG NG NGL

Dual fuel diesel Duel fuel Ignition delay Compressed natural gas Natural gas Natural gas liquid

C. B. Kumar (B) · D. Mahto Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] D. Mahto e-mail: [email protected] D. B. Lata Department of Energy, Central University of Jharkhand, Ranchi 835205, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_27

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MMCFD CI PM DPF CN HRR BTDC EGR Ea

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Million cubic sustain everyday Compression ignition Particulate matter Diesel particulate filter Cetane number Heat release rate Before top dead center Exhaust gas recirculation Activation energy

1 Introduction In conventional diesel engines, overall, just around 82% fuel burns completely. When gaseous fuel like H2 or LPG inducted in the dual fuel diesel engine by intake manifold, 98% of fuel can be burnt. It is starting at now comprehended that emissions issues like are PM,CO and NOX are, as it were, associated with the diesel engine. By using gaseous fuel like H2,LPG or mixture of H2 and LPG, pollution can be reduced [1]. Saket verma et al. [2] experimented on dual fuel diesel engine using jatropha as a biodiesel and hydrogen gas as secondary fuel. They were used up to 80.7% and 24.5% of hydrogen (by energy basis) in the DFD engine at high and low loads conditions. Brarake thermal efficiency was increased and exhaust emissions like CO,NOx smoke were decreases as compared to parent diesel engine. Exhaust emission NOx was reduced with addition of EGR in the DF with bio-diesel as primary source of fuel and hydrogen as substitute fuel. Utilization of hydrogen improves at the comparable brake thermal efficieny and NOx emissions [2]. Karthica et al. [3] have experimented on DFD single cylinder diesel engine at constant speed to study the effective utilization of madhuca longifolia oil (ML) in a diesel engine. Experimental work were done by utilizing primary fuel hydrogen. Hydrogen was inducted in the engine with air by the help of intake manifold where as ML was injected directly into the engine cylinder for ignition source. They were claimed that the performance of DFD engine increased from 25 to 28% with ML used as pilot fuel and hydrogen as base fuel for diesel engine. However,they were facing the knocking in the DFD engine when hydrogen used as base fuel and as a result brake thermal efiiciency decreases. In their expriment, they used water injection to increase the knocking limit so that more percentage of ML can be replaced. Further, they claimed that BTE exhuast gas emissions were increases upto 18 to 76 with 5% of water addition along with hydrogen fuel. Pavlos Dimitriou et al. [4] were tested hydrogen gas under different conditions of load on a HD DFD engine. They used different hydrogen energy share ration to substitute diesel fuel under different conditions such as diesel injection pressure, EGR, and injection pattern to compared the performance and exhaust emissions of dual fuel diesel engine with parent diesel engine. Engine operation was smooth over

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98% use of hydrogen on the energy basis. Further, it was observed that 90% reduction in NOx and carbon monoxide while soot emissions was reduced upto 85% compared to the base diesel engine. Appeared differently concerning natural gas LPG has the advantage of lowpressure storage, straightforwardness of transportation, and more secure managing. Natural gas liquids (NGL), available in different gas fields in India, are a potential wellspring of hydrocarbons from which LPG can be conveyed locally. LPG can be use as the substitute fuel for diesel fuel in the DFD engines and a choice fuel with diesel for CI engines in DF operation [5–7]. The usage of liquid petroleum gas (LPG), as an alternative fuel, is a promising course of action. The higher autoignition temperature, higher calorific value, and likewise, easy mobility make LPG a good alternative fuel for diesel replacement [8, 9]. The reasonably high auto-ignition temperature of LPG is used, allowing the compression extent of conventional diesel engines to be kept up and making minimum engine adjustments. It has the capacity of significant diesel save supports and extended vitality to the system [9]. Engine response is smoother and snappier when gas is incorporated, and substitution is high [10]. Modifying timetable can be connected in view of fumigation of gaseous fuel, lessened injector updating, and longer oil change between times in light of less contamination of engine oil. Furthermore, the engine can continue running on diesel as it did before change, paying little heed to the likelihood that it misses the mark on LPG [11]. The present work introducing LPG using gas fumigation technique is a way for thinking the distinct parts of using LPG as a DF with diesel in a conventional CI ignition engine [12]. From the above literature, it was conclude that LPG and hydrogen gas can be used as base fuel with some modification in the diesel engine. Use of LPG and hydrogen in the diesel engine not only increases the performance but also reduces the exhaust emissions. The objective of this paper is to study the ID of DFD diesel engine using base fuel diesel with LPG and hydrogen gas with and without EGR.

2 Methodology 2.1 Experimental Setup The experimental works were done on four-cylinder turbocharged, intercooler with 62.5 KW diesel engine with generator, which was modified to work on DFD mode. The system was fitted with arrangements for measurements of air-flow, fuel flow (diesel LPG & hydrogen), various temperatures, engine speed, torque, etc.

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Fig. 1 Diagram of experiment

2.2 Testing The trials were performed under three cases. (i) Case I: DFD engine runs on diesel only. (ii) Case II: DFD engine runs on blend of diesel and hydrogen gas. (iii) Case III: DFD engine runs on blend of diesel and LPG gas (Fig. 1)

3 Experimental Results The experimentals works were performed on four-cylinder turbocharged diesel engine at speed with different load conditions. The experimental results of ID were comparing and contrasting with Hadenberge–Hase equation. The consequences are talked about beneath (Table 1). It is found that at light load, ignition delay is less as compared to heavy load. When the load increases on the engine, the mean temperature of the charge also increases. At higher load, vapor pressure increases, which increases the temperature and reducing the ID period. This might be the reason of decreasing ID with increasing load. When the hydrogen gas is used in the diesel engine, the ID increases. With utilization Table 1 Ignition delay at various fuel substitutions

Load (%)

LPG (CA)

Hydrogen (CA)

Diesel(CA)

2

14

13

10

20

12

11

9

40

9

8

6

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Fig. 2 Mean pressure versus crank angle

of hydrogen in diesl engine, the partial pressure of O2 decreases and specific heat of charge increases as a result ID increases. Also, it is found that pre-ignition is responsible for ID. LPG has higher ID as compared to other cases of the experiment. This may be because of the low specific heat of LPG; hence, the ID is more. We had also found that as the EGR% increases, the ID increase, this is because, in the case with EGR, the space covered by the combustion phenomena in the cylinder of the engine is smaller than in the case without recirculation (Fig. 2). The experimental results of ignition delay have been compared with Hadenberge– Hase equation for diesel engine [13, 14]. τid = Ap−n e( RT ) Ea

The linear regression has been done to find the empirical constant A,n & E a by putting the experimental value of τid , p, R and T. By doing this we get the value as follow: A = 0.072309 n = −0.46347 E a = 15741.75 Hence, the formula with an empirical constant of ignition delay will be

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τid = 0.072309 p −0.46347 e(

15741.75 RT

)

4 Conclusions From this research work, a few conclusions can be drawn that the ID of DFD engine depends on the nature and type of the fuel used in the engine. The ID of DFD dual fuel is the function of mean temperature, mean pressure, and oxygen concentration of the changes. The ID of DFD engine running on hydrogen & LPG decreases with the increase in loads. The ID of DFD engine increases with increase in the percentage EGR. The presence of hydrogen, LPG, or blend of LPG and hydrogen before combustion might be influences energy release rate, pre-ignition reaction rate, and as a results the ID increases.

References 1. Tomita E, Kawahara N, Hamamoto Y, Piao Z-Y, Fujita S (2000) A Study on hydrogen combustion ıgnited with light oil ın a dual-fuel engine In: 4th Jsme-Ksme thermal engineering conference, October 1-6, Kobe, Japan 2. Verma S, Suman A, Das LM, Kaushik SC, Tyagi SK (2018) An experimental investigation of exergetic performance and emission characteristics of hydrogen supplemented biogas-diesel dual-fuel engine. Int J Hydrog Energy 43:2452–2468 3. Karthica SV, Senthil Kumara M, Pradeepa P, Vinoth Kumar S (2020) Assessment of hydrogenbased dual fuel engine on extending knocks limiting combustion. Fuel 260:116342 4. Dimitriou P, Kumar M, Tsujimura T, Suzuki Y (2018) Combustion and emission characteristics of a hydrogen-diesel dual-fuel engine. Int J Hydrog Energy 43:13605–13617 5. Das LM (1996) Hydrogen-oxygen reaction mechanism and ıts implication to hydrogen engine combustion. Int J Hydrog Energy 21(8):70–715, International Association For Hydrogen Energy Elsevier Science Ltd 6. Verhelst S, Sierens R (2001) Hydrogen Eng-Specif Prope. Int J Hydrogen Energy 26:987–990 7. Amendola SC, Sharp-Goldman SL, Janjua MS, Spencer NC, Kelly MT, Petillo PJ, Michael (2000) Safe, portable, hydrogen gas generator using aqueous borohydride solution and ru catalyst. Int J Hydrogen Energy 25:969–975 8. Orhan Akansu S, Dulger Z, Kahraman N, Nejat Veziroglu T (2004) Internal combustion engines fueled by natural gas—hydrogen mixtures. Int J Hydrogen Energy 29:1527–1539 9. Mohammadi A, Shioji M, Nakai Y, Ishikura W, Tabo E (2007) Performance and combustion characteristics of a direct ınjection si hydrogen engine. Int J Hydrogen Energy 32:296–304 10. Wang W, Herreros JM, Tsolakis A, York APE (2015) Increased No2 concentration ın the diesel engine exhaust for ımproved Ag/Al2o3 catalyst Nh3-Scr activity. Chem Eng J 270:582–589 11. Misdariis A, Vermorel O, Poinsot T (2015) A methodology based on reduced schemes to compute autoignition and propagation ın ınternal combustion engines. In: Proceedings of the combustion ınstitute, vol 35. pp 3001–3008 12. Tsolakis A, Hernandez JJ, Megaritis A, Crampton M (2005) Dual fuel diesel engine operation using h2. effect on particulate emissions. Energy Fuels 19:418–425 13. Jabbr AI, Koylu UO (2019) Influence of operating parameters on performance and emissions for a compression-ignition engine fueled by hydrogen/diesel mixtures. Int J Hydrogen Energy 44(26):13964–13973

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14. Juknelevicius R, Szwaja S, Pyrc M, Gruca M (2019) Influence of hydrogen co-combustion with diesel fuel on performance, smoke and combustion phases in the compression ignition engine. Int J Hydrogen Energy 44(34):19026

Modelling of a Greenhouse Drying System Using COMSOL Multi-physics Asim Ahmad and Om Prakash

Abstract In this paper simulation of greenhouse, drying system is done using COMSOL Multi-Physics software 5.3a. This software allows fast and reliable numerical simulations of variables of solar dryer. COMSOL Multi-Physics shows itself to be a handy tool for the simulation of concentration, flow and temperature profile, and surface profile fields. COMSOL is a flexible tool for users that need to model complex phenomena quickly and reliably to produce results for analysis and interpretation. Hence, this software is highly recommended for simulations. Keywords Greenhouse dryer · Simulations · Temperature variations · Insulated north wall · Natural convection

1 Introduction Energy is an excellent way for the existence as well as this development of humankind also this way to issue in international politics, military preparedness, the economy additionally diplomacy. To decrease the impact of conventional energy sources on the environment, besides, to be paid to the development of new resource of energy also renewable energy resources [1, 2]. Solar energy is an economy-free environment additionally is recognised as one of the best promising alternative energy recourses options. In future, the large-scale solar energy system is directly converting solar radiation into heat. Solar energy is intermittent by its nature; also, there is no sun at night. It’s total available value is dependent on the meteorological conditions of the location [3, 4]. Drying is a method which can be used to preserve food items for more extended periods. The food for preservation of dry product to the heat source with the sun coupled for many thousand years. In solar drying, the product is made exposed to the sun in the open air since it is necessary for moisture removal [5]. A. Ahmad · O. Prakash (B) Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_28

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The design of a solar dryer depends on the temperature of the air, solar radiation, moisture content of a product, the relative humidity of the air, amount of product to be dried, time required availability of auxiliary energy, material of a construction of a dryer as well as a resource availability [6]. There are various types of solar dryers which have been developed, designed, and got tested in the various regions of the tropical and subtropical zones. There are mainly two categories of solar dryers [7]. They are natural convection solar dryers and the forced convection solar dryers. In natural convection, the solar dryers use the principle of buoyancy of the airflow induced whereas in forced convection, solar dryers are based on the usage of airflow on the fan which runs on electricity or the solar module. Now, the solar dryer designed can be developed for besides used in tropics also subtropics. Solar technologies are mainly of two types. They are passive solar or active solar [8]. In various forms of solar technology, applications have been used in energy conversion devices, cooking, drying, central heating, and even refrigeration [9]. Drying is a vital role to operate in any industrial processing and daily needs requiring substantial conventional energy. Hence, conventional dryers have been proved to be less beneficial than solar renewable energy only. It can be proved to be one of the most efficient sources of energy when every other sources gets exhausted in the coming future. Prakash et al. (2016) analysed the various technique for solar drying including efficiency, variation in temperature moisture in the crop, rate of drying, varied colours, and variety in crop. ANFIS software is used for the CFD analysis. Mathematical modelling suggests accurate results [10]. Sahdev et al. (2016) designed Quonset shape greenhouse that is extensively used for agricultural purposes. As west–east direction is open to the sun, hence, it is the most suitable direction. The results obtained are excellent as it provides protection from dirt, insects, rains, etc. [11]. Chauhan et al. (2015) studied the software that are used in the solar drying processes. Dynamics of fluid can be done FLUENT and ANSYS. Mathematical modelling can be solved by FORTRAN and MATLAB. SPSS can be used for statistical analysis [12]. Aghbashlo et al. (2015) analysed a greenhouse for chamomile flowers, and they used TRNSYS software. The root normalised mean square error was less than 8.3%. They found that TRNSYS is an excellent tool for the simulation of solar dryers’ designs. The solar greenhouse dryer can design and develop with an extensive range of materials at a variety of conditions of climate without requirement of the practical demonstration project [13]. From the above literature, it was found that operation in solar dryer is time dependent due to daylight factor. It is necessary to enhance the operational time of the dryer. Proper heat transfer analysis and thermal analysis are still not done. Numerical modelling may help to improve the thermal execution of the system. The objective of this paper is to study the heat transfer analysis for solar dryer system and to develop modelling of heat transfer analysis using COMSOL software.

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2 Methodology 2.1 Use of COMSOL Multi-physics The COMSOL is a cross-platform finite element analysis and solver. It provides a unified workflow for mechanical and fluid properties. It contains simulation tools to study the mechanism of heat transfer such as conduction, convection, and radiation. COMSOL offers two options for the solution of the proposed geometry. For a heat transfer analysis of solar drying system to be a model of greenhouse dryer or modelled using the PDE for study time-dependent problems application. It is using the heat transfer analysis of surface-to-surface radiation. In module can be faster and simpler than inputting specific PDE’s into COMSOL Multi-Physics, the latter seems to be more convenient for our future goal of modelling of greenhouse dryer using COMSOL Multi-Physics. The model of greenhouse dryer is the Radiative Transfer Equation (RTE) and is used for solving surface-to-surface radiation heat transfer [14, 15].

2.2 Application Areas There are a few material science explicit modules in COMSOL Multi-Physics. The most common physics are fluid flow, AC/DC module, heat transfer module, electrochemistry, chemical species transport, and acoustics.

3 Simulation Procedure The simulation procedures are: Create Geometric Object—Specify Material Properties—Define Physics Boundary Condition—Create the Mesh—Run Simulation.

3.1 Using Surface-To-Surface Radiation In COMSOL Multi-Physics, heat transfer module to surface radiation that the geometry of three-dimension is present, either with or without physics modules, meshes, and studies. Some steps of the module can be skipped, depending on the heat transfer. To enter radioactive heat transfer to the model in COMSOL Multi-Physics 5.3 a, the necessary steps should be taken: like modules, studies, and properties, initial values, boundary conditions, meshing, and simulation.

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3.2 Design of the Dryer The greenhouse dryer consisted of a simple box design. The specifications for the dryer are as follows: 1 (W) × 1.5 (D) × 1 (H) cm. The radiation is captured via an acrylic plastic cover configured at the top of the dryer. For cost-effectiveness, aluminium was chosen as our structural material. Material used in greenhouse dryer are acrylic plastic, concrete, and black paint.

4 Result and Discussion The following result and discussion have been achieved by the experimentation:

4.1 Messing Messing was a significant phenomenon of simulation of any software. There were various types of messing, such as: Extremely coarse, extra coarse, coarse, normal, extremely fine, extra fine, finer, and fine (as shown in Fig. 1). It was found that maximum temperature at 1 P.M for the whole simulation (as shown in Fig. 2.) It can also show that maximum heats are available in dryer. Solar dryer of greenhouse depends on many ambient parameters. Hence, it is necessary to experiment accurately.

Fig. 1 Messing of solar greenhouse dryer

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Fig. 2 Surface temperature of middle phase at 1 PM

Based on the temperature profiles observed to incorporate phase change materials, the material must possess a phase transition temperature of 56 °C. A 3D model for a solar greenhouse dryer using COMSOL Multi-Physics 5.3a software was developed to simulate the temperature profiles of a transparent acrylic material with varies of different temperature with different time (as shown in Fig. 3.). Solar dryer of greenhouse depends on many ambient temperature parameters. Hence, it is necessary to experiment in an accurate manner. At 1 P.M, the modelling was simulated for a greenhouse dryer for the contour, and numerical iteration was achieved within this period. The drying chamber means the temperature of about 337.6 K was recorded, which is suitable for drying of no-load condition. The surface temperature shows the distribution of temperature within the modelled system. The modelling and simulation of greenhouse dryer to be cut at 1 P.M have been done with a heat transfer module of COMSOL Multi-Physics. This result indicates that contour temperature is maximum at 1 P.M. Solar dryer of greenhouse depends on many ambient temperature parameters. Hence, it is necessary to experiment in an accurate manner. Contour temperature varies depending on the time. It was found that the maximum radiation occurs in the temperature profile. It can be also be observed that at 1 P.M, maximum hear transfer occurred. When no-load condition of solar greenhouse to reduce the efficiency.

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Fig. 3 Total surface temperature and contour temperature of profile

4.2 Validation The variation of experimental temperature, simulated temperature, and ambient temperature of the air inside the greenhouse dryer with time from 10.00 am to 5.00 pm is shown in the graphical form for one hour. The average temperature difference between the simulated values and the experimental values is 0.60 °C.

5 Conclusions The present study is a discussion of some numerical simulations and experimental methods for measuring hard-to-see variables within the channel and variable profile. The thermal model developed in Solidworks 2018 for the simulation of the greenhouse dryer is efficient in the prediction of the air temperature inside the dryer. The developed model explained the greenhouse effect very conclusively and visualised the distribution of temperature due to natural convection. The results obtained were

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analysed and validated by using computational fluid dynamics software Solidworks 2018 SP.0. The simulated results of inside air temperature were in good agreement with experimental results during peak sunshine hours (11 AM to 3 PM). Thus, various shape and structure of solar greenhouse dryer can be simulated. Hence, a lot of time and energy can be saved in making solar dryer for different purposes. This proposed solar greenhouse dryer can be effectively developed and designed for various industrial purposes at different climatic conditions. The thermal models have been developed for each greenhouse dryer and are validated and evaluated experimentally. The solar tunnel-type greenhouse dryers were selected based on thermal need of the resincoated marble, of raising the temperature of the air inside the dryer above 450 °C but not more than 600 °C. Evaluated based on thermal need of the resin-coated marble, if the temperature exceeds the upper limit or falls below the lower limit then south wall and north wall of the dryer were insulated respectively. Acknowledgements The Author acknowledge BIT Mesra, Ranchi Jharkhand, India for supporting this work.

References 1. Sharma N, Garcha S, Singh S (2013) Potential of Lactococcus lactis subsp. lactis MTCC 3041 as a biopreservative Ascus Conidia. 3(2):168–171 2. Singh V, Zaman P, Meher J (2014) Review article JPHT postharvest technology of fruits and vegetables : an overview JPHT. 2(02):124–135 3. Singh S, Kumar S (2012) Testing method for thermal performance based rating of various solar dryer designs. Sol Energy 86(1):87–98 4. Jin W, Mujumdar AS, Zhang M, Shi W (2018) Novel drying techniques for spices and herbs : a review. 34–45 5. Kucuk H, Midilli A, Kilic A, Dincer I (2014) A review on thin-layer drying-curve equations. Dry. Technol. 32(7):757–773 6. Ekechukwu OV Norton B (1990) Review of solar-energy drying systems II : an overview of solar drying technology. 40 7. Singh P, Kumar A, Tekasakul P (2015) Applications of software in solar drying systems: a review. Renew Sustain Energy Rev 51:1326–1337 8. Kumar A, Singh R, Prakash O (2015) Review on global solar drying status 9. Kumar A, Tiwari GN, Energy B (2006) Thermal modeling and parametric study of a forced convection greenhouse drying system for jaggery : an experimental validation 10. Prakash O, Laguri V, Pandey A, Kumar A, Kumar A (2016) Review on various modelling techniques for the solar dryers. Renew Sustain Energy Rev 62:396–417 11. Sahdev RK, Kumar M, Dhingra AK (2016) A review on applications of greenhouse drying and its performance. Agricul Eng Int CIGR J 18(2):395–412 12. Chauhan PS, Kumar A, Tekasakul P (2015) Applications of software in solar drying systems: a review. Renew Sustain Energy Rev 51:1326–1337 13. Aghbashlo M, Hosseinpour S, Mujumdar AS (2015) Application of artificial neural networks (ANNs) in drying technology: a comprehensive review. Drying Technol 33(12):1397–1462 14. Multiphysics, C.O.M.S.O.L. introduction to COMSOL Multiphysics®. COMSOL Multiphysics, Burlington, MA. Accessed 9 Feb, 9 2018

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15. Vintil˘a M, Ghiaus, AG, F˘atu V (2014) Prediction of air flow and temperature profiles inside convective solar dryer. Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Food Sci Technol 71(2):188–194 (2014)

Comparison of 3-Leg, 4-Leg, and 6-Leg Topologies of Renewable Energy Fed Impedance Source Inverter-Based Dynamic Voltage Restorer Miska Prasad

Abstract This paper discusses the renewable energy (RE) fed different topologies such as, Three-Leg (3-L), Four-Leg (4-L), and Six-Leg (6-L) dynamic voltage restorer (DVR) for control the voltage swell. Perturb & Observe (P&O) technique is also utilized to get maximum power solar system. The most popular topologies are validated using MATLAB/SIMULINK software for minimizing voltage swell. The acquired results are equivalent however even marginally better in six-leg PV-ZSI-DVR contrasted with three-leg and four-leg PV-ZSI-based DVRs. Keywords Three-leg · Four-leg · Six-leg · Perturb and observe

1 Introduction To improve the exhibition of the power system, various topologies of D-FACTS devices like three-phase three-leg, three-phase four-leg and three-phase six-leg-based dynamic voltage restorers are utilized [1, 2]. The three-leg topology is only here and there favored inferable from less number of switching devices and lower switching losses contrasted with the four-leg and six-leg topologies [3]. Anyway, higher-order harmonics produced in the 4-leg and 6-leg topologies because of regular exchanging of semi-conductor gadgets can be wiped out by the utilization of RL high-pass channels [4–7] and exchanging misfortunes happening in the IGBT switches can likewise be dispensed with by the utilization of DC-interface voltage controller [8, 9]. The impedance or Z-source inverter has a emerging inverter and has a capacity to work as a step-down and step-up inverter like voltage and current source inverters [10–13]. Since ZSI is considered as a another kind of inverter, a great deal of research around there has been expanded. However, these methods could not function admirably if the perturb and observe algorithm calculation is not been adjusted to get more power from the solar system and furthermore to keep up the voltage at the DC connection of the inverter input [14]. This paper discusses the solar photovoltaic M. Prasad (B) Department of Electrical & Electronics Engineering, ACE Engineering College, Secunderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_29

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Fig. 1 Three-leg 3-leg and four-leg 4-leg ZSI-DVR

(PV) fed different topologies such as, Three-Leg (3-L), Four-Leg (4-L), and SixLeg (6-L) ZSI-DVR. For getting a maximum output power from the solar panel under change of irradiation of the sun as well as ambient temperature of the solar panel Perturb and Observe (P&O) is used. To show the performance comparison of different topologies, the topologies are simulated in a MATLAB/SIMULINK platform. The obtained simulation results are comparable but even slightly better in six-leg ZSI-based DVR compared to three-leg, and four-Leg ZSI-based DVRs.

2 Different Topologies of Z-Source Inverter-Based DVR 2.1 Three-Phase Three-Leg (3-l) ZSI-DVR The three-leg 3-leg and 4-leg four-leg ZSI-based DVR configuration is shown in Fig. 1. The advantage of the isolated ZSI-based DVR topology is that the voltage rating of the ZSI can be optimally designed as this is an interfacing transformer. The three single-phase 3-leg three-Leg ZSI-based DVR requires six switches and the three single-phase 4-leg four-leg ZSI DVR requires eight switches.

2.2 Three-Phase Six-Leg (6-leg) ZSI DVR The schematic diagram of a three-phase six-leg 6-leg ZSI-based DVR connected to a three-phase four-wire power distribution system is shown in Fig. 2. The three single-phase six-leg 6-leg ZSI DVR requires twelve switches.

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Fig. 2 Six-leg 6-Leg ZSI-DVR

3 Perturb and Observe Technique [14–18] The P&O technique is commonly embraced for hunting down the MPPT in light of fact that it is straight forward and requires just estimation of the voltage (V pv ) and current (I pv ) of the PV system. P&O works by perturbing (increasing or decreasing) the measured photovoltaic voltage (V pv ) and comparing the instantaneous power previously, then after the perturbation [18]. The algorithm of the P&O method is depicted in Fig. 3.

4 Simulation Results and Discussion Figures 1 and 2 are simulated using MATLAB/Simulink software to compare the performance of solar photovoltaic-based 3-Leg,4-Leg, and 6-Leg DVRs. Figure 4 shows the capacity of P&O technique to get maximum power.

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Measure: Vpv and Ipv

Calculate: Ppv (m)= Vpv (m)*I

pv(m)

yes

Ppv (m)-Ppv(m-1)=0

no

no

yes

Ppv (m)-Ppv(m-1) > 0

no yes

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Vref = Vref - ΔV

no

Vref = Vref + ΔV

Vpv (m)-Vpv(m-1) > 0

Vref = Vref - ΔV

yes

Vref = Vref + ΔV

Update history Ppv (m-1) = Ppv (m) Vpv (m-1) = Vpv (m)

Fig. 3 Flowchart of the perturbation and observation MPPT technique

Fig. 4 Maximum power point tracking by perturb and observe strategy

4.1 Performance of Three-Leg (3-L) ZSI-DVR The performance of 3-leg DVR is highlighted in Fig. 5a–d for minimizing the voltage swell. A voltage swell of magnitude 31% is noted as shown in Fig. 5a–b. To minimize this problem, solar photovoltaic-based 3-leg DVR comes into action and inject missing voltage as shown in Fig. 5c. The injected voltage is added to supply voltage and, therefore, load voltage is free from voltage swell as shown in Fig. 5d.

Comparison of 3-Leg, 4-Leg, and 6-Leg Topologies of Renewable …

299

Fig. 5 Swell and its mitigation by three-leg 3-leg DVR

4.2 Performance of Four-Leg (4-L) ZSI-DVR A swell of magnitude 31% observed as shown in Fig. 6a–b. To limit this voltage swell, sun-based photovoltaic-based 4-leg DVR is associated and produce required measure voltage as appeared in Fig. 6c. To limit this problem, solar photovoltaicbased 3-leg DVR comes into action and in infuse required voltage as highlighted in Fig. 5c. The injected voltage is added to supply voltage and, therefore, load voltage is free from voltage swell as shown in Fig. 5d.

Fig. 6 Voltage swell mitigation by four-leg4-leg DVR

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Fig. 7 Voltage swell mitigation by six-leg 6-leg DVR

4.3 Performance of Six-Leg (6-L) ZSI-DVR The capacity of 6-leg DVR is highlighted in Fig. 7a–d for mitigation of voltage swell. Without DVR, a voltage swell of magnitude 31% is noted as shown in Fig. 5a–b. To reduce the effect of voltage swell, solar photovoltaic-based 6-leg DVR comes into action and injects infuse required voltage as highlighted in Fig. 7c. The DVR injected voltage is added to source voltage and, therefore, load voltage is free from voltage swell as shown in Fig. 7d.

5 Injected Voltage Comparison of 3-L, 4-L and 6-L ZSI Based DVRs Without 3-L, 4-L, and 6-L ZSI-based DVRs, a voltage swell of 31% (98 V) magnitude is observed to the supply voltage but at t = 0.15 s, solar photovoltaic-based three-leg, four-leg, and six-leg DVRs are connected and inject a voltage of 132 V, 200 V, and 95 V as shown in Fig. 8. It is noted that six-leg ZSI DVR shows excelent capacity compare to three-leg and four-leg DVRs. Table 1 shows the comparison of fed 3-L, 4-L, and 6-L ZSI-based DVRs.

6 Conclusions This paper discusses the renewable energy (RE) fed different topologies of ZSI-based DVRs such as Three-Leg (3-L), Four-Leg (4-L), and Six-Leg (6-L) for minimizing

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Fig. 8 Comparison of injected voltages

Table 1 Comparison of 3-Leg, 4-Leg, and 6-Leg ZSI-based DVR Items

3-Leg VSI-DVR

4-Leg VSI-DVR

6-Leg VSI-DVR

3-Leg ZSI-DVR

4-Leg ZSI-DVR

6-Leg ZSI-DVR

Active states

6

14

40

6

14

40

Null states

2

2

2

2

2

2

Shoot-through states

0

0

0

7

15

41

No of switches

08

16

42

15

31

83

No of legs

3

4

6

3

4

6

Energy stored element

Capacitor

Capacitor

Capacitor

Capacitor and inductor

Capacitor and inductor

Capacitor and inductor

the swell. The three topologies such as three-leg, four-leg, and six-leg-based PVZSI-DVRs are considered, analyzed, and simulated using MATLAB/SIMULINK. The three topologies proved its effectiveness by injecting correct amount of injected voltage during voltage swell. The obtained simulation results are comparable but even slightly better in six-leg PV-ZSI-based DVR compared to three-leg, and fourLeg PV-ZSI-based DVRs. Apart from this most well-known MPPT algorithm such as P&O is considered for extracting the maximum power of the solar panel in the presence of sudden variations of the illumination and temperature.

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References 1. Alam MR, Muttaqi KM, Bouzerdoum A (2015) Characterizing voltage sags and swells using three-phase voltage ellipse parameters. IEEE Trans Ind Appl 51:2780–2790 2. Sadigh AK, Smedley KM (2016) Fast and precise voltage sag detection method for dynamic voltage restorer (DVR) application. Electric Power Syst Res 130:192–207 3. Nittala R, Parimi AM, Rao KU (2015) Comparing the performance of IDVR for mitigating voltage sag and harmonics with VSI and CSI as ıts building blocks. In: IEEE ınternational conference on signal processing, ınformatics, communication and energy systems (SPICES). Kozhikode, pp 1–5 4. Banaei MR, Dehghanzadeh AR (2010) DVR based cascaded multilevel Z source ınverter. In: IEEE ınternational conference on power and energy (PECON). Kuala Lumpur, Malaysia, 51–56, (2010) 5. Rajkumar M, Usha RP, Reddy SR (2011) Modeling and simulation of ZSI based DVR for voltage compensation. In: International conference on computer, communication and electrical technology—ICCCET 2011, pp 346–351 6. Balamurugan M, Sivakumaran TS, Devi MA (2013) Voltage sag/swell compensation using Zsource inverter DVR based on FUZZY controller. In IEEE ınternational conference on emerging trends in computing, communication and nanotechnology (ICECCN), pp 648–653 7. Tajuddin MFN, Arif MS, Ayob SM, Salam Z (2015) Perturbative methods for maximum power point tracking (MPPT) of photovoltaic (PV) systems: a review. Int J Energy Res 39(9):1153– 1178 8. Wu JC, Jou HL, Tsai JH (2017) A flexible grid interface for a PV power system. Sol Energy 144:540–547 9. Ali US (2018) Impedance source converter for photovoltaic stand-alone system with vanadium redox flow battery storage. Mater Today Proc 5:241–247 10. Ahmed J, Salam Z (2016) A modified P&O maximum power point tracking method with reduced steady state oscillation and improved tracking efficiency. IEEE Trans Sustain Energy. 7:1506–1515 11. Hanif M, Basu M, Gaughan K (2011) Understanding the operation of a Z-source inverter for photovoltaic application with a design example. IET Power Electron 4:278–287 12. Zope PH, Somkuwar A (2012) Design and Simualtion of single-phase Z-source inverter for utility interface. Int J Electri Eng Technol 1:127–143 13. Pilehvar MS, Mardaneh, Rajaei MA (2015) An analysis on the main formulas of Z-source inverter. Scientia Iranica Trans D Comput Sci Eng and Electri Eng 22(3):1077–1084 14. Alik R, Jusoh A (2018) An enhanced P&O checking algorithm MPPT for high tracking efficiency of partially shaded PV module. Sol Energy 163:570–580 15. Gheib A, Mohammad SMA, Farsangi MM (2016) A proposed maximum power point tracking by using adaptive fuzzy logic controller for photovoltaic systems. Scientia Iranica Trans D Comput Sci Eng Electri Eng 23:272–1281 16. Islam FR, Prakash K, Mamun KA, Lallu A, Mudliar R (2016) Design of an optimum MPPT Controller for solar energy system. Indonesian J Electri Eng Comput Sci 2:545–553 17. Bangarraju J, Rajagopal V, Jayalaxmi A (2014) Implementation of three-leg VSI based DVR using IRPT control algorithm. In: IEEE International Conference on Power Electronics (IICPE) 18. Jiang P, Zhang W, Jin Z (2018) Photovoltaic array MPPT based on improved perturbation observation method. In: Chinese Automation Congress (CAC), pp 1206–1210

Impact of Seasonal Variations on Generation Cost and Social Benefit of ISO in Presence of Renewable Energy Sources Ritu Jain, Praveen Kumar Gupta, and Vasundhara Mahajan

Abstract This paper presents the consequences of seasonal variation on the generation cost as well as on the social benefit of independent system operator (ISO). This paper also elaborates the influence of seasonal variation on the generated power output of wind turbine. It also shows how the impact of seasonal imbalance affects the voltage profile at all the buses. The seasonal load forecast modeling for 24 h is done by averaging the data for last 5 years. The wind power uncertainty is modeled by Weibull probability distribution function (WPDF). The study is performed on IEEE 9-bus system. The variation on locational marginal price at buses due to seasonal variation and variation on wind power is also observed. Keywords Independent system operator · Weibull probability density function · Locational marginal price · Social benefit

1 Introduction The deregulation of power system provides transmission open access (TOA) to all the market participants. Due to this open access, congestion in the transmission line has emerged as an issue. It becomes difficult for the power system operators to maintain reliable supply and secure operation. Congestion in the line occurs when the power is transferred from generating end to the consumer end by violating the thermal limit of the transmission line[1, 2]. There are different methods for managing congestion, i.e., generator rescheduling, installation of FACTS devices, load shedding, and integration of distributed generation (DG). The locational marginal price (LMP) helps in locating and managing congestion[3]. DG is defined as the small generating station that is placed near the load centers. Ref. [4] states that the main reasons for DG integration are environmental concerns, constraints in installing the new transmission line, and to supply uninterrupted power supply. Ref. [5] states that there are four types of DGs: Type1 feeds both real and reactive powers like synchronous generator, Type2 R. Jain (B) · P. K. Gupta · V. Mahajan Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_30

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DG feeds in real power but takes reactive power, i.e., induction generator, Type3 injects real power only, i.e., PV cell, and Type4 injects reactive power only like synchronous condenser and capacitor bank. The most commonly preferred nonconventional energy sources are wind and solar energies. These sources have zero operational cost but high initial investment cost. But due to intermittent nature of these sources, their integration with the main grid will affect the reliable operation of the system. Thus, it is necessary to predict the wind or solar power accurately. DG’s penetration into the deregulated power grid helps in reducing transmission line congestion because DG is near to the load. With the increasing demand of energy, it is difficult to rely on conventional energy sources for electrical power generation. Thus, the deployment of renewable energy based DG is increasing by each day. This work identifies the prime location of DG based on highest LMP approach. The size of DG is taken as 10% of the peak load. The advantage of placing renewable DG over conventional DG is explained. The impact of seasonal variation on social benefit and generation cost is also elaborated. The wind uncertainty for all seasons is modeled by Weibull probability distribution function (PDF). And the load forecasting is done by averaging approach.

2 Modeling of Load and Wind 2.1 Load Forecast Modeling In this paper, load forecasting is done for 24 h a day for each season. The averaging approach is used to forecast load for each day. The data of last 5 years (from 2013 to 2017) considered for averaging is taken from official site of PJM ISO. From the available data, it is observed that peak load occurs in summer. The load data is normalized as given in Eq. (1) in order to model it in the test system. Loadscaled =

L actual ∗ L t LM

(1)

where L actual = Actual system load, L t = Load for tth hr from load profile, and L M = peak load from the load profile.

2.2 Wind Uncertainity Modeling Wind power is the most popular and commonly used renewable energy and it is widely developing in recent years. The various advantages of wind energy are zero pollution, short gestation period, and low capital cost. The power output of wind turbine generator (WTG) depends on the wind speed[6]. Thus, the power generated

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from WTG is defined as ⎛

0, vvin , vvout ⎜ in ) , vin < vi < vr PW = ⎝ Pr × (vvri −v −vin Pr , vr < v < vout

(2)

where ν in , ν r , and ν out are the cut-in, rated, cut-out speed of wind in m/s, respectivley, and Pr is the rated power output of WTG. Wind speed is intermittent in nature and it follows a weibull distribution. Thus, to model the stochastic nature of wind speed, WPDF is used. The WPDF for wind speed v m/sec is given in Eq. (3). k  v k−1 −( vc )k e c c 

f (v) =



(3)

where c is the scale factor and k is the shape factor. For Rayleigh PDF, the value of k is 2. This is preferred PDF as it has periods of both low and high wind speeds.

3 Problem Formulation and System Data 3.1 Objective Function The objective of this work is to minimize the total generation cost including the DG cost. The auction is single sided, i.e., from the generator side. The cost of real power generation is considered only. The objective function is given in Eq. (4); min C =

N

N



 C j PG j + CDG j PDG j

j=1

j=1

(4)

where j = Number of buses, PGj the generation by conventional generator at jth bus in MW, PDGj is the generation by DG at jth bus in MW, C represents total generation cost in $/h, C j is the generation cost of conventional generators, and C DGj is the generating cost of DG in $/hr. The cost function for conventional generator and conventional DG is quadratic in nature. Whereas the cost function bid for wind based DG (WDG) is taken as linear. The above objective function is subjected to equality and inequality constraints.

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3.2 Equality and Inequality Constraints These are mandatory constraints and should be enforced, i.e., reactive and real power balances of the system. Inequality constraints may or may not be binding. The equations for equality and inequality constraints are given in Eqs. (5) to (6) and from Eq. (7) to Eq. (9), respectively. P j = PG j + PDG j − Ploss

(5)

Q j = Q G j + Q DG j − Q loss

(6)

Si j min ≤ Si j ≤ Si j

max

(7)

max

(8)

PG

min

≤ PG ≤ PG

QG

min

≤ QG ≤ QG

max

(9)

where S ij represents the power flow in the branch connecting ith bus from jth bus, PG is the real power generation in MW, QG is the reactive power generation in MVar, and V j is the voltage at the jth bus. DG constraints: The real power generated from DG at jth bus should be within minimum and maximum limits. The minimum generation is taken as zero and maximum generation is 63 MW.

3.3 System Data The study is conducted on IEEE 9-bus test system. It is composed of three loads, three generators, and nine transmission lines. The data for the test system is taken from MATPOWER. The data is modified for simplification in analysis. The load profile for 24 h of a day for each season is given in Fig. 1. The power output bid by wind plant owner for different season is given in Fig. 2. The process flow for this study is given in Fig. 3. In this case, the wind farm with 30 turbines of rated capacity of 2.1 MW is considered. The cut-in, rated and cut-out speed are 2 m/s, 12.5 m/s, and 25 m/s, respectively.

Load in MW

Impact of Seasonal Variations on Generation Cost and Social … 700 600 500 400 300 200 100 0

307

Spring Summer Fall Winter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Real Power in MW

Fig. 1 Load profile for different seasons 70 60 50 40 30 20 10 0

Spring Summer Fall Winter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour Fig. 2 Wind power available for different seasons

4 Results and Discussions In this paper, the influence of seasonal variation on the generation cost, load payment, and social benefit is analyzed. It is observed that the maximum load occurs during summer season. The work is divided into following five cases: Case1: No DG in peak load period or basecase. Case2: System with conventional or renewable DG in spring season. Case3: System with conventional DG or renewable DG in summer season. Case4: System with conventional or renewable DG in fall season. Case5: System with conventional or renewable DG in winter season. Case1: In case1, OPF is done at peak load when DG is not present in the system. It is analyzed that due to heavy demand, congestion occurs in the system which results in higher LMP. Due to this, the generation cost and the load payment are very high. The cost of generation is 193,812.8 $/day and the load payment is 272,583.66 $/day. Thus, in order to reduce the load payment and generation cost, the DG is allocated in the system. The prime location for DG is bus 9 because it has highest LMP. The DG size is 63 MW, i.e., 10% of the peak load.

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R. Jain et al. Start Read the system data for IEEE 9 bus system Read the load data of last five years for a winter day, spring day, summer day and fall day

Forecast the load for each of the above mentioned day by averaging approach Run OPF for the base case without DG in the system for each day and obtain the LMP at buses and total generation cost

Run OPF by implementing WTG and obtain the LMP and total generation cost

Run OPF by implementing conventional DG in the system and obtain the LMP and generation cost

Compare the results obtain by implementing different types of DG Print the result Stop

Fig. 3 Simulation process

Case2: In case2, the analysis is done when DG is penetrated in the system during spring season. The DG type considered is wind-based DG and conventional DG. The variation in power generated by the wind plant during spring will affect the cost of the system. The comparison is done for both the cases, i.e., when conventional DG is present, i.e., Case2(a) and when WTG is present in the system, i.e., case2(b). The Table1 shows the generation cost, load payment, and social benefit earned by ISO Table1 Comparison for different types of DG in spring season Case

Generation cost ($/day) Load payment ($/day) Social benefit ($/day)

2(a): Conventional DG 132,449.44 2(b): WTG

170,147.94

37,698.49

131,111.32

165,751.18

34,639.86

3(a): Conventional DG 190,843.94

252,114.86

61,270.93

3(b): WTG

189,510.39

259,022.29

69,511.90

4(a): Conventional DG 134,017.15

174,790.52

40,773.37

4(b): WTG

169,623.43

37,005.28

5(a): Conventional DG 172,031.2

227,068.2

55,036.93

5(b): WTG

223,395.3

54,608.87

132,618.15 168,786.4

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after market clearance. The social benefit is the difference between the generator bid and the revenue obtained from consumers. In deregulated market, there are mainly two types of payment policies: pay-as-bid policy and payment as per LMP. In pay-asbid policy, the Gencos (Generation Company) are paid as per their bidding and discos (Distribution Company) have to pay as per their bidding regardless of the marginal price. But in LMP-based pricing, the Gencos are paid at generator bus LMP and load has to pay load bus LMP to the ISO. In this paper, LMP-based pricing approach is used. In case2(a), the real power generation is from conventional generators due to their low generation cost. These conventional generators are enough to supply the total system demand. The conventional DG with high cost is not even dispatched. In Case2(b), the power available from the wind generator near to the load will affect the overall generation from conventional generators present away from the load. Since wind power is cheap and taken as a non-dispatchable generator; thus, the power generated from wind plant is fully consumed. Case3: In this case, the comparison is made between both the types of DGs but during summer season. From Fig. 3, it is observed that maximum demand occurs in this season. Due to limitation in availability of wind power plant, the highest cost generator is also dispatched. Thus, there is increase in the load bus LMP. Thus, the payment made by load is high when wind plant is present in the system as compared to the case when conventional DG is placed. In case3(a), due to high system demand, the conventional DG is dispatched. Since conventional DG is available all day and is near to the load, it is dispatched to its maximum rating during peak load and the rest of the required demand is supplied from highest cost. In Case3(b), due to uncertainity and less availability of wind power, the highest cost conventional generators are dispatched. Case4: In this case, comparison is made between different DG types during fall season. In Case4(a), the conventional DG is not dispatched because cheaper conventional generators are sufficient to supply loads. These generators are away from loads thus increasing the congestion in the transmission line which results in increase in LMP. Whereas in Case4(b), since wind power is non-dispatchable, cheap, and near to the load, thus, the LMP in this case will be low. Case5: In this case, the generation cost, load payment, and social benefit are more when conventional DG is present in the system. Table1 shows the cost comparison between different cases. In winter day, maximum wind generation is available which results in reduction in generation cost and LMPs. From all the above cases, it is analyzed that generation cost is minimum when WTG is present in the system. Therefore, it is better to install renewable-based DG as compared to conventional DG because the cost of fossil fuel is high. The social benefit received by ISO is maximum during the summer season and minimum in spring season. The social benefit received by ISO and Genco profit for all the season is shown in Fig. 4 and Fig. 5, respectively. The Gencos profit is minimum in spring and maximum in summer. The voltage at all the buses for Case2(b),Case3(b), Case4(b), and Case5(b) is shown in Fig. 6, Fig. 7, Fig. 8, and Fig. 9, respectively. From the figures, it is observed that minimum voltage in spring day is 0.95 pu at bus 9 during 7th and 14th h; in summer day, the voltage at bus 9 is 0.92 pu at 14th and 22th h; in

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Fig. 5 Genco profit in different seasons

80000 70000 60000 50000 40000 30000 20000 10000 0

30000 Genco profit $/MW

Fig. 4 Social Benefit in different seasons

Social Benefit $/day

310

25000 20000 15000 10000 5000

voltage in pu

0

1.01 1 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92

Bus1 Bus2 Bus3 Bus4 Bus5 Bus6 Bus7 Bus8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hours Fig. 6 Voltages at buses in presence of WTG for a day in spring season

Bus9

Impact of Seasonal Variations on Generation Cost and Social … 1.02

Voltage in pu

1 0.98 0.96 0.94 0.92 0.9 0.88 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

311 Bus1 Bus2 Bus3 Bus4 Bus5 Bus6 Bus7 Bus8 Bus9

Hours Fig. 7 Voltages at buses in presence of WTG in summer season

1.02

Voltage in pu

1 0.98 0.96 0.94 0.92 0.9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Bus1 Bus2 Bus3 Bus4 Bus5 Bus6 Bus7 Bus8 Bus9

Hour

Voltage in pu

Fig. 8 Voltages at buses in presence of WTG in fall season

1.01 1 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91

Bus1 Bus2 Bus3 Bus4 Bus5 Bus6 Bus7 Bus8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour Fig. 9 Voltages at buses in presence of WTG in winter season

Bus9

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fall day, minimum voltage is 0.94 pu in 16th h; and in winter day, it is 0.94 pu at 8th and 23rd h.

5 Conclusion In this study, the effect of seasonal variation on the wind power generation is shown. The available wind power for a day is maximum during winter and minimum during summer. Due to seasonal variation, the load profile of the system also varies. The influence of seasonal variation on the energy market is also observed. The generation cost is low in spring season and high in the summer season. The social benefit earned by ISO and Genco profit is high during summer and low during spring. In this paper, the impact of seasonal variation on the voltage profile of the system is also shown. The minimum voltage is observed at bus 9 in summer day.

References 1. Shahidehpour M, Yamin H, Li Z (2003) Market operations in electric power systems: forecasting, scheduling, and risk management. Wiley 2. Bhattacharya K, Bollen M, Daalder JE (2012) Operation of restructured power systems. Springer Science & Business Media 3. Sood YR, Padhy N, Gupta H (2007) Deregulated model and locational marginal pricing. Electric Power Syst Res 77:574–582 4. Ackermann T, Andersson G, Söder L (2001) Distributed generation: a definition. Electric Power Syst Res 57:195–204 5. Gautam D, Mithulananthan N (2007) Optimal DG placement in deregulated electricity market. Electric Power Syst Resssss 77:1627–1636 6. Gupta N (2016) A review on the inclusion of wind generation in power system studies. Renew Sustain Energy Rev 59:530–543

Kinematic Control of an Autonomous Ground Vehicle Using Inverse Dynamics Controller Praneeth Kumar Pedapati, Santanu Kumar Pradhan, and Surendra Kumar

Abstract In this paper, the kinematics of two-wheel differential drive mobile robot is analyzed and simulated for tracking a reference trajectory. The AGV is controlled with a combination of PID and a proposed controller depicted from the inverse of actuator dynamics in continuous domain. This inverse dynamics controller helps in compensating the unmodeled nonlinearities in the model, thereby keeping the rotor speed of the DC motor much accurate as required for the mobile robot to follow the reference trajectory. The simulation of the trajectory tracking of a differential drive mobile robot using proposed controller is performed in MATLAB and the results were shown. The results were found to be acceptable and applicable for multi-purpose AGVs for trajectory tracking applications. Keywords Mobile robot · Actuator dynamics · Differential drive · Kinematics of mobile robot · Inverse of dynamics · Matlab/simulink

1 Introduction Autonomous ground vehicle (AGV) is a wire area topic which has its applications in air, land, and underwater that is not confined to a single coordinate in the eco-system. Past decade has brought a greater advancement in the development of mobile vehicles in different fields of study. Its applications like medical assistance, space, defense, mining, farming, chemical industries, transportation, and many more have helped it to gain such importance. The research regarding AGVs has been undergoing an evolution since many years and has now reached a level where it still is an area of P. K. Pedapati (B) · S. Kumar Electrical & Electronics Engineering, BIT Mesra, Mersa, Jharkhand, India e-mail: [email protected] S. Kumar e-mail: [email protected] S. K. Pradhan School of Electrical Sciences, NIST, Berhampur, Odisha, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_31

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interest for many international reasearchers. Among many types of mobile robot, differential drive mobile robot (DDMR) is one of the simplest and most used structures. Trajectory tracking is one of the essential topics in controlling a mobile robot. It has been gaining its importance due to its easy, yet effective way of moving a mobile robot. Many such path tracking algorithms have been implemented successfully but with a few drawbacks. Kanayama and Yuta have worked on tracking a straight line reference trajectory by a mobile robot instead of setting a sequence of points [1]. The mobile robot is controlled including some non-holonomic constraints on the reference path in [2]. Also, in the trajectory tracking area of research, many control solutions were proposed like neuro-fuzzy controllers, fuzzy controllers, sliding mode controllers, adaptive controllers, etc. Out of all the controllers designed for the navigation of DDMR, PID controller is the controller that is most widely being used. In addition, there are few methods where a combination of PID and another controller is implemented for the specified task [3]. shows such method where a fuzzy controller is combined with PID to successfully track the trajectory of the mobile robot. In this paper, a discussion about one such combination of controllers, where the PID controller is tuned to minimize the error criteria required for the kinematic control and an actuator inverse dynamics modeled controller to improve the performance under nonlinear uncertainities of the motor, making the whole system more efficient and effective in trajectory tracking. The combination of both these controllers gives some acceptable results and shows that they can be applicable for multi-purpose AGVs in trajectory tracking applications.

2 Modeling of an AGV This paper considers a three-wheeled mobile robot where one of them is a castor wheel used only for balancing purpose. It helps the AGV to maintain the same plane through out its operation. The rest two wheels are the once that are to be controlled, which are identical to each other and are connected to each other through a shaft. These two wheels are run by DC motors seperately. Considering the slip of wheels to be negligible, the robot is assumed to be navigating in x–y plane. From the literature available on AGVs, the kinematics of the vehicle can be represented as (1) and (2). The navigation of AGV works as per the wheel velocities obtained from the kinematics. These equations that help in navigating the AGV are used for trajectory tracking purpose with the help of some basic equations that include the linear velocities of right (vl ) and left wheels (vr ). The angular and linear velocities of the robot are linked using the following equations. v=

vr + vl 2

(1)

ω=

vl − vr 2L

(2)

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315

Figure 1 shows the schematic of WMR in which 2L represents the distance between center of wheels. Assuming the postion and orientation of robot to be, X T = [x, y, θ ] where x and y are coordinates of the AGV along horizontal and vertical axes, respectively, θ is the direction of movement of AGV in the plane of navigation. Let the desired position and orientation coordinates of the robot, represented as a vector, X dT = [xd , yd , θd ], then the AGV kinematics can be shown as ⎡ ⎤ ⎡ ⎤ ⎡ cos θ cos θ ⎤   x˙ v cos θ 2 2 sin θ sin θ ⎦ vr ˙ ⎣ ⎦ ⎣ ⎦ ⎣ (3) X = y˙ = v sin θ = 2 2 vl 1 1 − θ˙ ω 2L 2L Similarly, the kinematics of the desired states can be written as ⎡

⎤ ⎡ ⎤ ⎡ cos θd cos θd ⎤   x˙d vd cos θd 2 2 v X˙ d = ⎣ y˙d ⎦ = ⎣ vd sin θd ⎦ = ⎣ sin2θd sin2θd ⎦ r d vld 1 1 θ˙d ωd − 2L 2L

(4)

where vd , wd and θd are the desired linear velocity, angular velocity, and the desired orientation of the robot according to the reference path generated. For the above said scenario, the error kinematics of the robot can be written as 

Fig. 1 WMR diagram





cos θ sin θ = − sin θ cos θ



xd − x yd − y

 (5)

Y-axis

x Xe = e ye

vl

ω

v

ϴ

y

l

2L

vr

x

X-axis

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Based on the error states and its dynamics, the position error (Pe ) and orientation error (θe ) can be written as Pe =



xe2 + ye2 and θe = tan−1



yd − y xd − x

(6)

The acutator dynamics play a vital role in controling the navigation of mobile robot. The kinematics helps in tracking the trajectory, whereas the actuator dynamics are the real game changers while navigating the AGV. A small uncertainity in the system performance can lead to a greater deviation from the original trajectory. This makes it more important to deal with the uncertain behavior that comes as a part of uncertainities in the environment. To compensate these uncertain characteristics of actuator dynamics, a controller is proposed which is designed using the inverse of actuator dynamics. This controller particularly works to eliminate the uncertain or unwanted disturbances that occur in the behavior of the actuator; thereby, making the system free from such unwanted dynamic characteristics while operating with motors. For this purpose, the DC motor drive system has been modeled using the equations can be written as: va (t) = i(t).R + L .

Tm (t) = J.

di a (t) + eb (t) dt

(7)

eb (t) = K b .ω(t)

(8)

Tm (t) = K t i a (t)

(9)

dω(t) + B.ω(t) + T f + Tl (t) dt

(10)

where R is the resistance of armature coil, moment of inertia represented with J, K t is the motor torque constant, K b is the motor back emf constant, L is the inductance, eb (t) is the back emf generated by the motor, ω(t) is the motor angular velocity, va (t) is the applied voltage to drive the motor, i(t) is the motor armature current, B is the viscous force, T m (t) is the torque developed mechanically by the shaft of the motor, T l (t) is the torque applied on the motor shaft as load, and T f is the frictional torque on the shaft of motor [4]. Few models have been studied in [5, 6] regarding various representation of friction. But, it is understood that there is no exact model structure for representing friction mathematically. Hence, on the basis of a well-known fact, that the frictional torque of any motor depends on its angular velocity, the mathematical model of friction is done. There is one model where friction opposes the motion when ω = 0 in the coulomb friction models. For a velocity which is close to zero, the striction opposes all motions where the torque magnitude is smaller than striction torque. Such nonlinear load torque in-terms of angular speed is considered here for the purpose of study and

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is represented as Tl (t) = vc ω2 (t)[sgn{ω(t)}]

(11)

where vc is the modeling constant used to represent the nonlinear mechanical load. The load torque shown in Eq. (11) is completely uncertain due to the presence of signum function. For making the control of DC motor much easier, let us consider the system as a SISO system taking applied voltage as input and angular velocity as the output of motor. Hence, using the equations from (7) to (11), we can get the single input single output representation of the actuator as [7]. LJ

dω(t) d 2 ω(t) + (R B + K e K t )ω(t) + (R J + L B) dt 2 dt  dTl (t) + R. T f + Tl (t) − K t va (t) = 0 + L. dt

(12)

3 Proposed Controller The purpose of proposing a controller with inverse of actuator dynamics is to compensate the nonlinearities that arise while the motor is operating. This has an added advantage of implementing any linear controller with gains that were calculated for a linear system. Implementing such compensating controller helps the system to be linearized externally and the nonlinearities will be taken care by the controller introduced in the feedback loop [8]. This concept of actuator inverse dynamics is shown in Fig. 2. Application of input–output linearization technique to any continuous time nonlinear model can be done as shown in [9]. This input–output linearization technique is taken as an inspiration to develop the actuator inverse dynamics. With the help of the SISO, Eq. (12) can be represented in a one degree of freedom equation format, i.e., x˙ = g(x).u + f (x) where Fig. 2 Concept of ınverse dynamics control of a dc motor drive

(13)

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 1 dTl (t) dω(t) + (R B + K e K t )ω(t) + L + R Tl (t) + T f (R J + L B) LJ dt dt (14) g(x) =

Kt LJ

and u = va (t)

(15)

For the above dynamic Eq. (13), the inverse can be written as. u =

1 [− f (x) + u c ] g(x)

(16)

or u  = G(x).u c + F(x)

(17)

F(x) = − f (x).g −1 (x) and G(x) = −g −1 (x)

(18)

where,

and uc is the linear control signal from PID.

4 Simulation Results and Discussion The proposed controller designed based on the inverse dynamics of the DC motor has been simulated in MATLAB/Simulink and is compared with the conventional controller. The hierarchical control of the kinematics and linearization of AGV is given in Fig. 3. Here, the kinematic control is done by minimizing two errors. First one being the position error which is driving the mobile robot coordinates toward the reference trajectory and the second one being the angle error which helps the mobile

Fig. 3 Hierarchical control of kinematics of AGV

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robot to maintain the orientation in which it needs to move to maintain the path as per the reference trajectory. The results of the same have been shown in this section. Figure 4 shows the comparison of step response of DC motor considered for the study for the conventional PID controller and the proposed combination of PID and inverse dynamics controller. It is clearly evident that the settling time and the rise time of the motor with conventional controller are greater than that with the proposed controller. A comparison of such performance parameters with both conventional and proposed controller is shown in Table 1. Figure 5 shows the plot of errors of DC motors with conventional and proposed controllers. It can be observed that the proposed controller reduces the error at a faster rate compared to conventional controller. Figures 6 and 7 show the circular and straightline trajectory trackings of AGV, respectively, with reference and the actual path of the AGV. Figures 8 and 9 show the position and angle error considered for successful tracking of circular trajectory using inverse dynamics of mobile robot and kinematic control of mobile robot. The reference and the actual path of the AGV were shown

Fig. 4 Step response of DC motor with and without ınverse dynamics

Table 1 Simulated performance of different controllers

PID Settling time (s)

0.04

Rising time (s)

0.0252

Overshoot (%)

0

PID + ID 0.02 0.007 16.58

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Fig. 5 Error minimization of DC motor with and without ınverse dynamics

Fig. 6 Circle trajectory tracking of AGV using ınverse dynamics controller

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Fig. 7 Straight-line trajectory tracking of AGV using inverse dynamics controller

Fig. 8 Position error obtained in kinematic control of AGV for circular trajectory

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Fig. 9 Angular error obtained in kinematic control of AGV for circular trajectory

in the figure. Figures 10 and 11 show the position and angle error considered for successful tracking of straight-line trajectory using inverse dynamics of mobile robot and kinematic control of mobile robot.

5 Conclusion This paper describes about the control of an AGV using kinematics and also about the linearization of DC motor in making the differential drive mobile robot follow the given reference trajectory in the presence of nonlinearities. A controller was designed based on the inverse dynamics of DC motor as a part of linearization which helps the motor to track the reference velocity with much accuracy in tracking the reference path. The simulation results were shown and the same has been compared with a simple PID controller. The proposed inverse dynamics controller was found to be performing better than a conventional PID controller.

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Fig. 10 Position error obtained in kinematic control of AGV for straight-line trajectory

Fig. 11 Angular error obtained in kinematic control of AGV for straight line trajectory

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Acknowledgements This work is a contribution to project funded by Govt. of India through DSTSERB with reference number SERB/ECR/2017/000667.

References 1. Kanayama Y, Yuta S (1988) Vehicle path specification by a sequence of straight lines. IEEE J Robot Automat 4(3):265–276 2. Kolmanovsky I, Harris McClamroch N (1995) Developments in nonholonomic control problems. IEEE Cont Syst 15(6):20–36 3. Wu S-F, Mei J-S, Niu P-Y (2001) Path guidance and control of a guided wheeled mobile robot. Cont Eng Pract 9(1):97–105 4. Pedapati PK, Pradhan SK, Kumar S (2019) Nonlinear adaptive control of an autonomous ground vehicle in obstacle rich environment: some experimental results. In: TENCON 2019–2019 IEEE region 10 conference (TENCON), pp 614–619 5. Dahl PR (1976) Solid friction damping of mechanical vibrations. AIAA J 14(12):1675–1682 6. Walrath CD (1984) Adaptive bearing friction compensation based on recent knowledge of dynamic friction. Automatica 20(6):717–727 7. Rahman MA, Ashraful Hoque M (1997) On-line self-tuning ANN-based speed control of a PM DC motor. IEEE ASME Trans Mechatron 2(3):169–178 8. Su Z, Khorasani K (2001) A neural-network-based controller for a single-link flexible manipulator using the inverse dynamics approach. IEEE Trans Industr Electron 48(6):1074–1086 9. Tang Y-G, Sun F-C, Sun Z-Q, Ting-Liang Hu (2006) Tip position control of a flexible-link manipulator with neural networks. Int J Control Autom Syst 4(3):308–317

Enabling the Technologies for Incorporating Battery Energy Storage System with Standalone Microgrid Sumit Kumar Jha and Deepak Kumar

Abstract The incorporation of energy storage system with distributed generation supports the islanded microgrid in remote location. The chapter is intended to develop control framework for battery storage system and incorporate it with the standalone microgrid to present the concept of microgrid as a potential provider of electricity to gridless society. The regulation of the voltage and frequency is the two key aspects of the standalone microgrid. The abrupt load changing scenario directly affects voltage and frequency parameters and it gets deviated from their nominal value results to inaccurate sharing of the apparent power. The energy storage system assists the islanded microgrid and regulates the voltage during change in load condition. The cascaded control mechanism achieves the desired value of voltage with voltage–current, P– f and QV droop. The paper presents the utilization of three droop mechanism to establish the nominal voltage. The robustness of the designed proposed strategy is established through simulation result of MATLAB. Keywords Microgrid · Islanded · Battery energy storage system · Voltage–current droop control · Voltage control

1 Introduction Extensive burden on coal-based energy and increasing rate of population exacerbate the problem and hence, the rise of renewable sources of energy which incorporate photovoltaic, fuel cells, wind power paves the way for adopting new era of emerging technology termed as microgrid. The concept is devised in the year 1998 by Consortium for Electric Reliability Technology Solutions (CERTS) and integration of different kind of renewable sources with precise control mechanism is possible with the adoption of this technology [1–3]. The availability of renewable S. K. Jha (B) · D. Kumar Department of EEE, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected]; [email protected] D. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_32

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resources is very irregular, and its intermittent behavior is regulated by the incorporation of battery energy storage system. Energy storage system incorporate features of high energy density and amalgamation of battery with the new era of Microgrid technology contributes power to the society that is not connected to the grid [4–7]. Microgrid is capable to disseminate power in grid forming and grid following mode. The system-level dynamics in case of connection with the grid is mainly taken care by the main grid whereas microsources in the standalone microgrid controls the system-level dynamics. The voltage and frequency are the two parameters which need to be taken care by the islanded mode of operation of the microgrid whereas in grid connected mode, microgrid needs to have proper power control mechanism. The islanded mode characterizes droop mechanism to control power flow among distributed energy resources (DER) [8–10]. Different kinds of distributed generation such as microturbines, fuel cells, photovoltaic need to be interfaced with the help of power electronics converters. The process of interfacing facilitates the flexible control of sources and provides sophisticated control. The technology of microgrid carries negligible inertia; hence, it is subjected to oscillation and further, it causes disturbances in the network [11–13]. The paper investigates robust control mechanism for BES system that incorporates the features of voltage stability of microgrid as well as it develops the charging/discharging phenomena for the battery. The BES system is advantageous at the time of sudden load demand and it efficaciously assists the regulation of the parameters of the microgrid network. The work in the paper presents integration of the BES system with the microgrid. The characteristic of three droop mechanism is being considered to mutually establish the desired voltage of the microgrid network. Hence, the current work is focused toward developing a robust control strategy with the coordination of BES system of the stand-alone microgrid.

2 Control Aspect of Standalone Microgrid The standalone microgrid comprises of four fundamental control blocks named as inner control loop, outer control loop, voltage–current droop mechanism, and frequency control through P–f droop mechanism as depicted in Fig. 1. The battery storage system is integrated with microgrid to provide power during the condition of variation of load and it prevents further plunging of voltage. A sophisticated control strategy has been developed to regulate the flow of current from the battery. The reactive power–voltage droop and V –I droop mutually coordinates to maintain the nominal system voltage. The active power–frequency is intended to produce ‘θ ’ and it helps to synchronize the frequency of the overall system.

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Fig. 1 Proposed control mechanism

3 Proposed Control Mechanism of Energy Storage System The battery module in Fig. 2 is consolidated with the standalone microgrid to balance the consumption of power between renewable sources of energy and load. The status of SOC decides the working of the primary and secondary controller. The interfacing of battery with the hybrid renewable resources is shown in Fig. 3. The developed control strategy follows these conditions as described below:

Fig. 2 Battery energy storage system

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Fig. 3 Connection diagram of BES module with hybrid renewable resources

(a) The BES module is in power injecting mode if the SOC lies in the range of 20–80%. In this mode, the battery is considered to be in safe conditions, and it is operated in conjunction with the microgrid network. (b) The battery module is considered to be overcharged if the status of SOC reaches 80%. In this condition, the battery does not recharge itself and if SOC falls below 80%, then the process of recharge will start again. (c) The BES module is said to be in unsafe condition if SOC ≤ 20%. The battery prohibits the supply of power and hence, microgrid independently has to support the load demand until it reaches the safe mode.

3.1 Primary Control of Battery Storage System The BES system constitutes primary controller exhibited in Fig. 4 is generating two pulse signal S1 and S2 and it cuts the recharging mechanism of the battery at the time of state of charge (SOC) reaching 80%. The primary controller incorporates a PI controller and its role is to maintain the desired voltage by reducing the error term contributed by DC voltage established by the hybrid renewable resources with the islanded microgrid. The resultant output from PI controller is further go with subtraction operation with battery current to generate the pulse signal S2. The relay switch produces output in the form of pulses if it lies in the region of −2 ≤ 0 ≤ 2. Moreover, the signal S1 is 180° phase shifted from the signal S2. The S1 signal is further operated to multiply with the control mechanism that cuts down the path of current to the battery for the purpose of recharging if SOC reaches 80%.

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Fig. 4 Primary controller of BES module

3.2 Secondary Control of BES System The battery energy storage incorporates secondary controller shown in Fig. 5 that prevents the battery from deep discharge. The status of SOC lies near 20% then secondary control get actuated and it allows the current to recharge the BES system. The secondary controller comprises PI controller and it acts to establish the DC voltage maintained by renewable resources. The AND gate is the amalgamation of two signal. If the output of PI controller becomes greater than ramp signal, then one signal for AND gets satisfied. Moreover, the AND gate is activated if and only if both the signals get true. Hence, secondary controller includes the control mechanism if SOC reaches 20% as shown in Fig. 5. If the two signals get true, then AND gate gets

Fig. 5 Secondary controller of BES module

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triggered and it activates the IGBT that allows the flow of current to the battery to recharge itself.

4 Establishment of Voltage Through Outer Loop and Voltage–Current Droop Control The constituting of the desired voltage is possible through collective coordination of reactive power–voltage droop and voltage–current droop mechanism. The generation of voltage from reactive power–voltage droop is directed to V –I droop that maintains the desired voltage at the load side. The outer loop comprises two PI controller and voltage and current from LCL filter is converted from three-axis frame to two-axis frames. The paper considered direct axis voltage to be aligned with the output voltage; hence, the quadrature axis would be zero. Figure 1 depict the control strategy. 





Id axis = K pd Vdr _axis − Vd axis + K id   Iq axis = K pq Vqr _axis − Vq axis + K iq





 Vdr _axis − Vd axis dt − ωC f d Voq + Hd i od (1)



 Vqr _axis − Vq axis dt − ωC f q Vod + Hq i oq (2)

The K pd , K id , K pq and K iq in Eqs. (1) and (2) are the gains of the PI controller, filter capacitance is depicted by C fd and C fq , and H d and H q represents feed-forward constant in dq frame.

5 Inner Loop The output generated from outer loop is sent to inner loop that consists of two PI controllers. The outer loop is 10 times or more slower than inner loop to achieve the constant output desired voltage. It generates V id and V iq signal and it is again converted to three reference frames.      Vd axis = K pd Idr _axis − Id axis + K id (3) Idr _axis − Id axis dt − ωL f d Iq   Vq axis = K pq Iqr _axis − Iq axis + K iq





 Iqr _axis − Iq axis dt + ωL f q Id

(4)

The K pd , K id , K pq and K iq in Eq. (1) and (2) are the controller parametric gains and L fd, L fq are the filter inductances in dq frame.

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6 Results The developed control strategy for microgrid network interlinked with BES module is validated on the MATLAB platform. Initially, the load of 12 kVA is being given and a circuit breaker connects the load at 0.5s and disconnects the load at 1.0s to test the efficacy of the control mechanism. The Q/V droop strategy sets the desired voltage of 450 V and it is provided to voltage–current droop mechanism. Further, the V –I droop strategy computes the total voltage after considering the line parameters value (Z WS ) and sent to slower dynamics outer loop. The battery energy storage system constituting primary controller is directed to maintain the nominal voltage of 450 V. Hence, the cumulative effort of Q/V droop, V –I droop, and primary controller of BES module maintains the desired voltage. The signals S1 and S2 are 180° phase shifted signal and it goes as the input to the IGBT switch of BES module. The phenomena of discharging and recharging take place according to the designed control mechanism. Figure 6a shows the establishment of desired voltage with the support of three droop controllers, i.e., primary controller of battery, Q/V droop and V –I droop mechanism. The renewable sources of energy constituting photovoltaic and wind power providing the power to the microgrid network as depicted in Fig. 6b. The current at the output side and power of BES module are increased at the time interval of 0.5s shown in Fig. 6c and d to compensate the sudden rise in load demand. The voltage and SOC of the battery are depicted in Fig. 7a and b and it is interesting to note that at 0.5s, the voltage and SOC of battery are falling to compensate the demand and once the microgrid is capable to generate enough power, the BES module regains its voltage as depicted in Fig. 7a and if the SOC falls below nominal range, i.e., SOC ≤ 20%, then recharging mechanism get started by the controller. Figure 8a and b shows the validation of primary controller

Fig. 6 DC side output a Desired voltage b Power of hybrid renewable resources c Output current at the DC side d Power of BES module

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Fig. 7 Signals generated by battery a Voltage developed by battery b State of charge of battery

of BES module and it depicts that as the SOC reaches 80%, the voltage and SOC of battery become constant.

7 Conclusion The control strategy for battery integrated standalone microgrid is successfully validated and tested. The microgrid network with BES system retains the desired voltage with the help of established control strategy that includes voltage–current droop, active power–frequency droop, reactive power–voltage droop, and primary controller of BES system. The outer control loop characterizes slower dynamics than inner loop and provides the desired value of voltage at the demand side.

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Fig. 8 Voltage and SOC of battery at 80%

The work can be extended to maintain the nominal voltage by adopting the V – I droop mechanism as an self-sufficient control mechanism to retain the system voltage at the distribution side of the standalone microgrid. The voltage–current droop mechanism is a viable droop controller that can direct the future research toward power saving strategy by employing voltage reduction technique.

References 1. Lasseter R, Akhil A, Marnay C, Stephens J, Dagle J, GuttromsonR, MeliopoulousA, Yinger R, Eto J (2002): The CERTS microgrid concept. White Paper for Transmission Reliability Program Office of Power Technologies US Department of Energy 2. Bevrani H, Watanabe M, Mitani Y (2012) Microgrid controls. In: Standard handbook for electrical engeneers. McGraw Hill, New York 3. Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids. IEEE Power Energy Mag 5(4):78–94 4. Jha SK, Kumar D (2019) Demand side management for stand-alone microgrid using coordinated control of battery energy storage system and hybrid renewable energy sources. Electr Pow Compo Syst 47(14–15):1261–1273. https://doi.org/10.1080/15325008.2019.1661544 5. George RP, Ghosh S, Kamalasadan S, Joshi M, Chandran S (2016) A hybrid battery optimal power dispatch for grid connected micro grid. In: 2016 IEEE international conference on power electronics, drives and energy systems (PEDES). Trivadrum, pp 1–6 6. Kerdphol T, Fuji K, Mitani Y, Watanabe M, Qudaih Y (2016) Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids. Int J Electr Power Energy Syst 81:32–39 7. Jha SK, Kumar D (2020) Assessment of battery energy storage system with hybrid renewable energy sources to voltage control of Islanded microgrid considering demand-side management capability. Iran J Sci Technol Trans Electr Eng 44:861–877. https://doi.org/10.1007/s40998019-00273-9 8. Bevrani H, Shokoohi S (2013) An ıntelligent droop control for simultaneous voltage and frequency regulation in ıslanded microgrids. IEEE Trans Smart Grid 4(3):1505–1513

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9. Golsorkhi MS, Lu DDC (2015) A control method for inverter-based islanded microgrids based on V-I droop characteristics. IEEE Trans Power Deliv 30(3):1196–1204 10. Pasha AM, Zeineldin HH, Al-Sumaiti AS, Moursi MSE, Sadaany EFE (2017) Conservation voltage reduction for autonomous microgrids based on V–I droop characteristics. IEEE Trans Sustain. Energy 8(3):1076–1085 11. Jha S, Kumar D, Kamwa I (2018) Smart demand response management of Islanded microgrid using voltage-current droop mechanism. Int J Emerg Electr Pow Sys 19(1):20170238. https:// doi.org/10.1515/ijeeps-2017-0238 12. Jha SK, Kumar D (2018) A Power saving approach to self-sustained microgrid. In: 2018 ınternational conference on power energy, environment and ıntelligent control (PEEIC). Greater Noida, India, pp 430–436 13. Pogaku N, Prodanovic M, Green T (2007) Modeling analysis and testing of autonomous operation of an inverter-based microgrid. IEEE Trans Power Electron 22(2):613–625

Wind–Diesel-Based Isolated Hybrid Power Systems with Cascaded PID Controller for Load Frequency Control Tarkeshwar Mahto, P. R. Thakura, and T. Ghose

Abstract In the present work, the undertaken power system (PS) is isolated hybrid wind–diesel PS (IHWDPS) consists of a generator based on wind turbine (GWT), a generator based on diesel engine (GDE) and an energy storing device (ESD) (for instance, capacitive energy and storage). In the present paper, an indicative analysis of frequency control also with power control for the considered IHWDPS using proportional–integral–derivative (PID) controller and cascaded PID controller aims to govern the pitch of GWT and to govern the speed governor of GDE. The controller gains have been tuned using quasi-oppositional harmony search (QOHS). The dynamic simulation reaction compression indicates that superior enactment may be recorded with cascaded PID controller over the PID controller while exposed to dissimilar perturbation. The results obtained disclose that the optimized gains of the cascaded PID controller offered with QOHS algorithm are robust in nature and do not requires resetting for extensive range for perturbations in the system. Keywords IHWDPS · PID controller · Cascaded PID controller · QOHS

1 Introduction Putting up and providing electric energy from grid to the rural and lightly populated locality pretenses intense logistics problems in the developing nations. This challenges the researcher pool to carry out research work in the area of stand-alone renewable energy source (RESs) also hybrid power systems (HPSs) which syndicates at least one RESs (like full cells, wind turbine, microturbine, photovoltaic, etc.) [1] alongside faint carbon alternators (such as GDE) alongside energy storage devices. T. Mahto (B) · P. R. Thakura · T. Ghose Birla Instituite of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] P. R. Thakura e-mail: [email protected] T. Ghose e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_33

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Additional essential factors are reduction in reserve of fossil fuels (like oil and coal), greenhouse gases causing global warming, and massive gain of population globally and, hence, growing demand of energy [2]. Energy from sun and wind is abundantly present globally from the set of RESs.But, GWTs based on wind energy are in a state of higher technical advancement than photovoltaic module technology in terms of cost-effective production of and more efficiently. Thus, GWT has received more attention of the researchers’ pool. Irregularity in the output power from GWT may source the supply of fluctuation in power supply, and it may also lead to a major condition of fluctuation in system frequency [3]. To enhance the characteristic of the power generated and for maintaining uninterrupted supply of power, a GDE is coupled to GWT with the energy storage device like capacitive energy storage (CES) in present study. This category HPS may be labeled as isolated hybrid wind—diesel power system (IHWDPS). An IHWDPS may act as a source to quality power for the consumers provided, and it is supported via an optimally tune controller. In spite of the advancements in the area for metaheuristic approaches of optimization, there are probably various additional opportunities for improving their searching competences, constancies, and convergence nature. With the same vision, an intelligent optimization practice, named harmony search (HS), has been established by Geem et al. [4]. The HS algorithm has been practiced in number of successful operations of real-world challenges [5, 6] in last decade. In [7], the HS algorithm has been practiced on solving the economic load dispatch issue. Though HS has the ability to recognize the better exploitation states in the solution space, it lacks in local searching competence [8] and moreover involves more number of parameters for designing each specific problem. This drawback of HS algorithm has been overcome by different modifications on HS algorithm which has been projected on various works to boost its satisfactory regulation potential and to diminish the count of parameters design. The strategy of adaptive for setting variables range namely improved HS was familiarized in [8]. In [9], the concept of global best of particle swarm optimization (PSO) integrating with HS was introduced. The idea of global best self-adaptive HS was projected in [10] which associations the aforementioned two improvements. The HS algorithm’s power of exploration was inspected in [11]. Additionally, the concept of opposition learning has been introduction to HS algorithm in [7] and noted as opposition-based HS (OHS) algorithm. All these variants’ HS algorithm has revealed their dominance in various applications and fields. In the current work, the concept of quasi-opposition-based learning (QOBL) that has been used for population initialization has been introduced in the existing HS algorithm. It neither generates the population with random initialization nor with random, number and their opposite number but it practices random number and its quasi-opposite number population with an aim of (a) performance enhancement, (b) overcoming the immature convergence, (c) enhancing the solutions heterogeneity, and (d) accelerating the HS algorithm speed of convergence. So, in the present work, quasi-oppositional HS (QOHS) algorithm has been used in order to resolve frequency and power deviation issue of, the considered IHWDPS, model. The noteworthy objectives of the current paper are

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(a) to craft a model of IHWDPS in order to deliver quality power to the end user, (b) to engage QOHS algorithm in, order to optimize the, controller gains of proportional–integral–derivative (PID) and cascaded PID controller mounted in the, considered IHWDPS, model, undertaking only one, at a particular, time, (c) to evaluate the dynamic enactments presented by the two considered controllers and find out the effective one among them for this explicit problem, The remaining paper is organized in following manner. Description of the model under study is carried in Sect. 2, trailed by methodology of controller and design in Sect. 3. Performance indices for the considered work are defined in Sect. 4. In Sect. 5, focus on QOHS algorithm has been given. Simulations results are demonstrated and analyzed in Sect. 6, and conclusion is followed in Sect. 7.

2 System Under Study The considered IHWDPS model consists of a GDE, a GWT and a storage device (for this work CES) for providing consistent quality supply of power in the connected load. In order to model the IHWDPS, for simplification, the considered system has been undertaken as linear first order for simulation studies [12, 13]. The considered IHWDPS model is having a gross rating of 300 kW [14] (the GWT and GDE, each having a rated capacity of 150 kW, respectively). Figure 1 displays the design of studied IHWDPS model with the transfer function blocks. The, studied IHWDPS, model carries two controllers, (one for GDE speed governing, i.e., controller I and another for GWT in order to control the pitch angle of the blades, i.e., controller 2). The details for modeling of GWT, GDE, and CES are described in [14, 15]. In Fig. 2, transfer function model for CES device has been presented. The power flow from GWT, GDE, and CES device is optimally managed in order to increase the utilization of renewable energy and also to avoid frequent start–stop of GDE. The equations for the power flow of the studied IHWDPS may be represented as in (1)

Fig. 1 Studied IHWDPS’s transfer function model

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Fig. 2 CES, subsystem block of Fig. 1

PTotal = PGDE + PGWT − PCES − PL

(1)

where PTotal indicates deviation in total demand load, PGDE is deviation in GDE power generation, PGWT is deviation in generated power by GWT, PCES is deviation in power output of CES, and PL is deviation in demand load.

3 Controller The undertaken IHWDPS embraces two controllers (i.e., controller 1 at GDE for speed governor and controller 2 at GWT for pitch angle control). The controller 1 input signal is provided from the system frequency deviation signal and controller 2 input signal may be described as in Eq. (2). PCO = PGW (max) − PGW

(2)

3.1 PID Controller In industries, the most commonly used controller is a PID controller with a closedloop control strategy. The controller tries to regulate the error inputs signal in order to diminish the error at the outputs. In PID controller, there are three distinct factors that are intricate (i.e., P, I, and D) to control the error signal. The gain parameters of PID may be incidental with respect to time (viz. present error is being controlled by P, built up of past errors that assist in defining I, and the forecast of future errors is notified by D). The gain values of P, I, and D weighted, sum is utilized for controlling the, practice via regulating methods like supply power for a heater or for positing a damper or for valve control, etc. The design of PID controller used in this paper is presented in Fig. 3 and it may be defined by (3) [16].

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Fig. 3 Structure of PID controller

PC1 = PC2 = G PID (s) = K P +

KI + sKD s

(3)

3.2 Cascaded PID Controller In general, classical controllers (like PI or PID controller) are mostly ineffective on the system with higher order or nonlinear or unstable. The cascade controllers may be one of the solution methods which are used for improving the enactment of the system. With cascading of controllers, number of regulating nubs increases in compression to than of a non-cascade controller which improves the system enactment. The tuning of PID controller is liable to the models process and the cascade PID controller for control systems as presented in literature [17]. The cascade PID controller presented in Fig. 4 is preferred for the current work of controlling frequency and power in the undertaken IHWDPS. The controller output signals are the orientation for the 1 power settings of each generator.

4 Performance Indices As indicated formerly, the key emphasis of present work is to diminish the deviation in power and frequency, after any load changes to zero as soon as possible. To realize this, the controller designing is performed by first defining a function to meet its

Fig. 4 Structure of cascaded PID controller

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objective and the performance measures generally undertaken for controller design are Integral, absolute, error − (IAE) =

∞ |F|dt

(4)

0

Integral − square, error−(ISE) =

∞ |F|2 dt

(5)

0

∞ Integral − time, absolute−error − (ITAE) =

t|F|dt

(6)

t|F|2 dt

(7)

0

∞ Integral − time, square−error − (ITSE) = 0

In this work, for optimization of controller gains for the considered IHPS, minimization function of ISE has been undertaken as the function for objective function.

5 The QOHS Algorithm The QOHS algorithm’s pseudocode has been offered in, Algorithm 4 of [18], Step 3 form algorithm highlights about the quasi-oppositional HM, initialization, while Step 6 emphases on quasi-oppositional generation jumping application in QOHS algorithm [19].

6 Results and Analysis Dynamic performance of cascaded PID-controlled IHWDPS is being equated to the PID-controlled IHWDPS. The cascaded PID-controlled configuration of IHWDPS model is labeled as “PI-PD” and, for PID-controlled model is labeled as “PID” for simulation work. The comparative study for the two models, two cases have been undertaken. These cases are: • Case 1: step increase of 1% at load demand and • Case 2: continuous alteration in load demand

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Fig. 5 Different types of p.u. input perturbation used a I % step increases in dead load and b continues change is demand load

Fig. 6 A dynamic response compression of IHWDPS for step increase of load demand a F(H Z ) and b P( p.u.)

6.1 Case 1: Step increase of 1% at Load demand Both configurations (i.e., “PID” and “PI-PD”) of IHWDPS are subjected to a step increase of 1% in load demand at t = 1s., the 1% step increase in demand load is depicted in Fig. 5a. The deviation profile for frequency and power generated by both configurations is shown in Fig. 6. Figure 6 clearly illustrates that the cascaded PID controller (i.e., “PI-PD” configuration) regulates the frequency and power deviation more effectively than that of PID controller-based lHWDPS (i.e., “PID” configuration).

6.2 Continuous Alteration in Load Demand Figure 7 presents the deviation profile of frequency and power for IHWPS subjected to condition of continues alteration in demand load. The signal used for simulating continuously alteration in demand load is portrayed in Fig. 5b. The continuously alteration in demand load signals alters its level at t = 0, 10, 20, and 30s in the time axis. It may be concluded from Fig. 7 that the cascaded PID controller-based IHWDPS (i.e., “PI-PD”) is notable than that of PID-controlled configuration (i.e.,

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Fig. 7 A dynamic response compression of IHWDPS for continuous alteration in load demand of a F(H Z ) and b P( p.u.)

“PID”) in dipping the profile of frequency and power deviation alongside diminishing the generation and demand load unbalance.

7 Conclusion From the current work, deviation profile of power and frequency for IHWDPS owing to variation at demanded load has been undertaken. The considered IHWDPS embraces a unit of GDE, a unit of GWT, and also a unit of CES device, and the considered controller is a cascaded PID controller. In order to optimize regulating variables of IHWDPS, CES devise, and cascaded PID controller, QOHS algorithm is undertaken. The results from simulation illustrate that the cascaded PID controller is superior to that of PID controller in regulating the deviation nature for power and frequency at the condition of sudden change in demand load of IHWDPS.

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References 1. Gabler H (1998) Autonomous power supply with photovoltaics: photovoltaics for rural electrification-reality and vision. Renew Energy 15:512–518 2. De Soto W, Klein SA, Beckman WA (2006) Improvement and validation of a model for photovoltaic array performance. Sol. Energy 80(1):78–88 3. Gouveia EM, Matos MA (2009) Evaluating operational risk in a power system with a large amount of wind power. J Electr Power Syst Res 79(5):734–739 4. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68 5. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82(9–10):781–798 6. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization, harmony search theory and practice. Comput Meth Appl Mech Eng 194(36– 38):3902–3933 7. Chatterjee A, Ghoshal SP, Mukherjee V (2012) Solution of combined economic and emission dispatch problems of power systems by an opposition based harmony search algorithm. Int J Electr Power Energy Syst 39(1):9–20 8. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. J Appl Math Comput 188(2):1567–1579 9. Omran MGH, Mahdavi M (2008) Global-best harmony search. J Appl Math Comput 198(2):643–656 10. Pan QK, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. J Appl Math Comput 216(3):830–848 11. Das S, Mukhopadhyay A, Roy A, Abraham A, Panigrahi BK (2011) Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization, IEEE Trans. Syst Man Cybern Part B Cybern 41(1):89–106 12. Lee OJ, Wang L (2008) Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system. Part I: Time domain simulations. IEEE Trans Energy Convers 23:311–320 13. Shaw B, Banetee B, Ghoshal SP, Mukhetee V (2011) Comparative seeker and bioinsired fzzy logic controllers for power system stabilizers. J Electr Power Energy Syst 33:1728–1738 14. Bhatti TS, AI-Ademi AAF, Bansal NK (1997) Load frequency control of isolated wind diesel hybrid power systems. Energy Converso Manag 38:829–837 15. Tripaty SC, Mishra LP (1996) Dynamic performance of wind-diesel power syst. With capacitive energy storage. Energy Converso Maag. 37:1787–1798 16. Zhong J (2003)PI controller tuning: a short tutorial. Springer 17. Dash P, Saikia LC, Sinha N (2016) Flower pollination algorithm optimized PI-PD cascade controller in automatic generation control of a multi-area power system. Int J Electr Power Energy Syst 82:19–28 18. Tarkeshwar MV (2015) A novel quasi-oppositional harmony search algorithm and fuzzy logic controller for frequency stabilization of an isolated hybrid power system. Int J Electr Power Energy Syst 66:247e61 19. Mahto T, Mukherjee V. Quasi-oppositional harmony search algorithm and fuzzy logic controller for load frequency stabilisation of an isolated hybrid power system. IET Gen Trans Distri 9(5):427e44

Automatic Generation Control Study with Plug-In Electric Vehicle Integrated Multi-area Renewable Power System Aurobindo Behera, Subhranshu Sekhar Pati, Tapas Ku Panigrahi, and Subhankar Ghatak

Abstract The concept of a smart grid is an integration of efficient storage system plug-in electric vehicle (PEV) to the existing grids. The application of any storage technique increases the dimension and complication of the conventional grid. Thus, to realize a smart grid scenario, the renewable energy and PEVs are incorporated and tested in this study. Here, assisting the AGC for maintaining the frequency of a Jaya optimized controller, additional filter integrated proportional-integral-derivative (nPID) is employed. A two-area interrelated network has been analyzed with hydel, thermal, gas power units and PEV in both areas. A load deviation of 10% has been considered in each area. The system is verified with various controllers such as PI, PID, and nPID, and it is noteworthy that the reaction period of the system has improved, thus enhancing the stability when introduced with PEVs as a storage medium. Keywords Automatic generation control (AGC) · Gas power plant · Jaya algorithm · Plug-in electric vehicle (PEV)

A. Behera (B) Department of Electrical Engineering, Cambridge Institute of Technology, Ranchi, Jharkhand 835103, India e-mail: [email protected] S. S. Pati Department of Electrical Engineering, International Institute of Information Technology, Bhubaneswar, Odisha 751003, India e-mail: [email protected] T. K. Panigrahi Department of Electrical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha 761003, India e-mail: [email protected] S. Ghatak Department of Computer Science Engineering, Sarla Birla University, Ranchi, Jharkhand 835103, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_34

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1 Introduction In recent years, most of the power is derived from renewable-based power generating unit and various distributed generations (DG) predominately. Power generating from gas plant is one of the effective DGs owing to numerous benefits such as quick state, easily transportation of power and less impact on global environment over the traditional power sources. Now, with advancement of science and technology, the cost associated with maintenance in gas plant and operational efficiency increases significantly. Additionally, to deliver the power uninterruptedly under critical condition, storage medium can also be clubbed with gas power plant. In spite of the power generation from renewable and DGs, rest amount of power is still generated from traditional power plant such as thermal or hydro. Hence, in such a complex interlinked power structure, frequency deviation and traction of power among several areas through tie line to be scheduled within a limit are one of the vital tasks and should be maintained within the permissible boundary. In such a scenario, AGC plays an important role for maintaining the deviation in tie line power and frequency within the prescribed interval discussed in [1, 2]. Moreover, recent AGC study shows that apart from traditional power plant design, most of the researchers implemented aggregate model of PEVs and analyzed its effect on the overall test system [3, 4]. The PEV can supply the power to the grid in generator operating condition or storing the power in its respective batteries in motoring mode. However, the survey reveals that the examination of PEVs in AGC needs to be extended, and its impact should be calculated more princely. Hence, the authors decided to study the dynamic interaction of PEVs with the test model by implementing two thousand numbers of PEV in either areas. The power rating of an individual PEV is ±5 KW. It is observed from the study that to get a satisfactory performance, optimal controller’s selection must be done. Thus, few common controllers applied are fractional order PID (FOPID), nPID and PID controller. Normally, controller constraints had to be optimized through suitable optimization algorithm. Some of optimization algorithms are ant colony optimization (ACO), intelligent water drops (IWD), gravitational search optimization (GSA) and Jaya optimization technique [5–8]. In this study, Jaya optimization algorithm is used for effective optimization of the gain parameters owing to its effectiveness and simplicity.

2 System Study Due to adverse effect of fossil fuel on environment and government support, use of PEVs increases rapidly across the world. Hence, to investigate the system performance with PEVs, two-area system comprising of sources such as hydel, thermal and gas shown in Fig. 1 has been considered. The thermal power plant considered in this study has a nonlinearity called boiler dynamics (BD) which is used for production of high-pressure steam. Moreover, other associate non-liberalities like generation rate

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Fig. 1 Diagrammatic illustration of (a) PEV design; (b) System considered

constant (GRC) and governor dead-band (GDB) is used in the thermal as well as hydel plant to simulate the working of practical functioning unit. In this study, 5% and 2% GDB is implemented in thermal and hydel plant, respectively. Similarly, 3% GRC is taken in thermal plant and 270 to 360% GRC used in hydro plant for lowering or rising the generations [9].

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Gas power plant is regarded as a distributed generation (DG), one of the effective powering units where there is abundant available of agricultural or industrial operations by products such as sugarcane husk, straw and hay; here, in gas plant, various system constraints which are discussed in [10] have been successfully incorporated to simulate the originality of the study. However, DGs are producing clean power and can be synchronized easily with the grid, but create an enormous challenges and difficulty in the existing conventional power structure. In recent scenario, most of the country used a large number of PEVs in their automobile sector. Hence, to analyze the effect of PEV in the system, 2000 numbers of PEVs are included in either areas as shown in Fig. 1. The complete structure of an individual PEV is shown in Fig. 1b. Detailed system constraints of an individual PEV and other parameters are considered as in [3, 4, 6]. To obtain the desired system response in the proposed study, a Jaya-tuned nPID controller is used.

3 Controller and Objective Function In this study, to control the system, a simple nPID controller is utilized. The controller comprises proportional gain term (K P ), integral gain term (K i ) and derivative gain term (K D ) with an extra filter coefficient (N). The block diagram of nPID controller is depicted in Fig. 2. Area control error (ACE) which should be minimized is the input signal of the controller, whereas the output controlling signal is fed to the system which under the consideration. Equation (1) discusses the controller transfer function used in the work.      1 Ns + KD (1) T FnPID = K P + K i s s+N It is worth to mention that, for every optimization problem, there are certain set of problem definition known as cost function or objective function that should be maximized or minimized depending upon system constraints. However, in our study, diverse objective function such as integral square error (ISE), integral absolute error Fig. 2 Diagram of the discussed controller

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(IAE) and integral time absolute error (ITAE) can be used. Due to efficacy and suitability of ITAE for achieving targeted control action, the same is used as objective function and depicted in Eq. 2 [11, 12]. tsim J = ITAE = (|F1 | + |F2 | + |Ptie |) × t × dt

(2)

0

In above equation, F 1 , F 2 and Ptie are termed as fluctuations of frequency in the first area, second area and variation of power in tie line, respectively. Apart from the design of the cost function, the optimized controller constraints are also included for the study.

4 JAYA Optimization Algorithm To solve constrained and unconstrained equations, Rao et al. proposed a simple yet influential algorithm and named it as Jaya optimization algorithm [7, 8]. The steps of the Jaya algorithm guided the given set of equation in such a way that it always travels in the direction of the finest solution and simultaneously escapes the worst solution. One of the other characteristics of the algorithm is that it does not have any algorithm-oriented factors. Hence, rate of the convergence increases. Jaya algorithm is compared with swarm intelligence and evolutionary-based techniques, and it is observed the working mechanism to be superior to the other algorithms. The phases involved in this technique are very simple and have high accuracy. X j, k, i are referred as the existing value, while a range of 0–1 is considered for obtaining the constant value for r 1 and r 2 . Using Eq. (3), revised position value X j,k,i has been obtained.       X  j,k,i = X j,k,i +r 1, j,i X j,best,i − X j,k,i −r 2, j .i X j,worst,i − X j,k,i

(3) Here, ‘i’ denotes population space (in our case, population = 50), ‘j’ denotes No’s of controller parameters (i.e., 4 in this modeling) and ‘k’ denotes the completed cycle. The basic operating principle of Jaya has been discussed in Algorithm 1. The application for controlling of the controller action is discussed in Fig. 3.

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System parameters are initialized and controller parameters are obtained by Jaya algorithm The ITAE value is calculated by applying the controller parameter obtained in previous step and using sim (‘model’, config.set) command .

Yes Current Solution retained

Is the obtained value of ITAE better?

No Previous solution retained

Reached the max value of iteration counter No Yes Stop Fig. 3 Flowchart discussing the application of Jaya algorithm to the working of AGC

5 Result and Discussion The system under study considers hydel, thermal and gas power plant in each control area. In addition to that, PEV is also incorporated in each area to make the analysis realistic. For optimal controlling of the system, an effective controller termed as nPID controller is employed. The upper and lower limits of the controller are as follows: 3 > K D , K i , K P > 0; 500 > n > 0 [6]. The superiority of the nPID controller is evaluated

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Table 1 Jaya adjusted constraints for PI, nPID and PID Controller

Kp

Ki

Kd

n

PI

1.2858

1.0154

-

-

PID

1.6184

1.0227

0.0987

-

nPID

1.3185

1.1044

0.5436

442

by comparative assessment of projected controller with PID and PI controller. In Table 1, the Jaya optimized parameters of PI, nPID and PID are presented. The graphical representation of the result derived from the system with application of 10% step load perturbation is presented in Fig. 4 and the requisite mathematical data for settling time (T s ) and under shoot (US) is depicted in Table 2. The figures

(a)

(b)

(c) Fig. 4 System response for a 10% step load variation in load

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Table 2 Mathematical values for F 1 , F 2 and Ptie with a variation of 10% in PD Controller

Under shoot (US) (×10–3 )

Settling period (2%) T s (s)

F 1

F 2

Ptie

F 1

F 2

Ptie

No controller

8.534

4.840

9.418

21.89

22.79

28.15

PI

4.535

4.860

10.50

21.15

20.72

–

PID

3.738

4.265

5.887

19.88

20.30

–

nPID

4.818

3.634

4.036

4.348

7.758

7.467

showcase the performance of diverse controller, and a relative analysis of suggested controller to that of traditional controllers are also displayed in the same figures. For better understanding of the figure, mathematical parameters of the result can be referred in Table 2.

6 Conclusion The study presents a brood discussion regarding the PEV system integrated in multiarea multi-source hybrid power system controlled by Jaya algorithm optimized nPID controller. With the optimal tuning of the suggested controller, the response of the system is stabilized as quickly as possible even with large loading which can be inferred from the result analysis. However, the major finding of the nonlinear and complex system can be highlighted in the following points. 1. It can be observed from the result that the Jaya-tuned nPID controller is effective when subjected to small as well as lager disturbances. 2. The optimal result of efficient Jaya algorithm is computed with minimum time because of its parameter-free operation, thus reducing the time delay of the system.

References 1. Bevrani H (2009) Robust power system frequency control. Springer 2. Elgerd OI (2000) Electric energy systems theory an introduction. McGraw-Hill Book Company, New York, NY 3. Debbarma S, Dutta A (2017) Utilizing electric vehicles for LFC in restructured power systems using fractional order controller. IEEE Trans Smart Grid 8(6):2554–2564 4. Izadkhast S, Garcia-Gonzalez P, Fras P, Bauer P (2017) Design of plug-in electric vehicle’s frequency-droop controller for primary frequency control and performance assessment. IEEE Trans Power Syst 32(6):4241–4254 5. Farahani M, Ganjefar S, Alizadeh M (2012) PID controller adjustment using chaotic optimization algorithm for multi-area load frequency control. IET Control Theory Appl 6(13):1984–1992

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6. Pati SS, Behera A, Panigrahi TK (2018) Plug in electric vehicle-wind integrated multi-area automatic generation control tuned by intelligent water drops algorithm. In: International conference on intelligent systems design and applications. Springer, Cham, pp 240–250 7. Rao RV (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34 8. Rao RV, Saroj A (2017) A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm Evolut Comput 37:1–26 9. Sahu RK, Gorripotu TS, Panda S (2016) Automatic generation control of multi-area power systems with diverse energy sources using teaching learning based optimization algorithm. Eng Sci Technol Int J 19:113–134 10. Pathak N, Bhatti TS, Verma A (2017) Accurate modeling of discrete AGC controllers for interconnected power systems. IET Gener Trans Distrib 11(8):2102–2114 11. Behera A, Panigrahi TK, Sahoo AK, Ray PK (2018) Hybrid ITLBO-DE optimized fuzzy pi controller for multi-area automatic generation control with generation rate constraint. In: Smart Computing and Informatics. Smart Innovation, Systems and Technologies 77. Springer 12. Panigrahi TK, Behera A, Sahoo AK (2017) Novel approach to automatic generation control with various non-linearities using 2-degree-of-freedom PID controller. Energy Procedia 138:464– 469

An Interleaved ZCS Supplied Switched Power Converter for Fuel Cell-Based Electric Vehicle Propulsion System Neeraj Priyadarshi, Farooque Azam, Amarjeet Kumar Sharma, Pradeep Chhawchharia, and P. R. Thakura

Abstract This research paper explains the modeling and simulated responses of the proposed interleaved zero current switching (ZCS) employed full bridged switched converter considering fuel cell vehicle application using MATLAB/Simulink simulation software. The aim of the proposed model is to reduce ripple in input current as well as reduction in dimension of used passive devices. It also minimizes the voltage/current ratings of the operated components with diminished switching/conduction losses. Moreover, an interleaved bidirected snubberless DC– DC as a voltage doubler circuit system is employed as energy repository for fuel cell-based electric propulsion system. The bidirected DC-DC converter allows higher switched frequency action. Keywords DC/DC converter · Electric vehicle · Fuel cell · Interleaved bidirected snubberless · MATLAB/Simulink

1 Introduction Compared to classical internal combustion engines, the modern electric vehicle is most promising technology because of higher pursuance, lower fuel cost as well as beneath radiation [1–3]. In recent years, by employing three-phase motor, the N. Priyadarshi (B) · A. K. Sharma Department of Electrical Engineering, Birsa Institute of Technology (Trust), Ranchi 835217, India e-mail: [email protected] F. Azam School of Computing & Information Technology, REVA University, Bangalore 560064, India e-mail: [email protected] P. Chhawchharia Department of Electronics and Communication Engineering, Techno India NJR Institute of Technology, Udaipur 313003, India P. R. Thakura Department of Electrical Engineering, Birla Institute of Technology, Mesra 835215, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_35

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Fig. 1 Overall structure of fuel cell vehicle

Fig. 2 Execution diagram of fuel cell vehicle scheme

fuel cell-based system becomes more accepted electric propulsion technology [4, 5]. Figure 1 presents the typical structure of fuel cell occupying electrical impulsive scheme. The fuel cell stack performs oxidation using hydrogen and air compressor which results electricity to drive the vehicle. However, an alternative energy storage system is employed to provide extra power during dynamic operations [6, 7]. Figure 2 describes the typical architecture of fuel cell-based electric propulsion system in which high voltage DC bus acts as a prime feed. A bidirected DCDC converter is linked with high voltage bus and working as a main supply. The DC–DC converter acts as an interface to main and auxiliary power supply. Moreover, in forward and converse orientation, bidirected converter operated as a current source and voltage source half-bridge converter, respectively. The snubber less circuit provides low conducting losses at higher frequency.

2 Simulation Model of Fuel Cell Propulsion System Figure 3 depicts simulated illustrative of the fuel cell propulsion scheme which is based on the soft-switching technique. Fuel cell modeling as well as bidirectional

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Fig. 3 Simulated fuel cell-based design

DC/DC converter is carried out using MATLAB/Simulink context which could find the behavior of structure and enhance the performance. Figure 4 showed fuel cell propulsion system, in which it showed the stack output voltage, bidirectional DC/DC converter, V-I measurement and rotor speed or three-phase rotor current of induction motor. Figures 3 and 4 show the PEMFC stack model; the fuel cell outcome is feeding to switched converter. Here, port 1 and port 2 are connection port block for subsystem. Figure 4 presents fuel cell equivalent circuit. Figure 5 based on the simplified and detailed model is of PEMFC stack. Figure 6 describes simulation of circuit which models have formal fuel voltage and power has magnitude 24 VDC and 1.26 kW, respectively. The output has observed under nominal fuel utilization condition and maximum fuel utilization condition. Designed subsystem of pulse generator model

Fig. 4 Equivalent circuit of fuel cell model

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Fig. 5 Simulated model of fuel cell subsystem

Fig. 6 Simulated subsystem of fuel cell stack model

shown in Fig. 7 generates the pulses for switched power converter as well as inverter. The gating pulses of switches S 1 –S 4 and S 2 –S 3 can be generated with 180° phase shift. Moreover, duty ratio is maintained more than 0.5. Furthermore, switching gating pulses can be produced for switches S 5 –S 8 and S 6 –S 7 with phase shift of 90°, and for three-phase inverter, here we are using PWM generator. The designed bidirected DC-DC converter simulated model using MATLAB. It consists of IGBT’s, capacitor, filters, transformers and a control PWM strategy. The specification of converter has

Fig. 7 Simulated model of pulse generator for switched converter and inverter

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supply voltage 12 V, switched frequency 100 kHz, output power 500 W with output voltage 300 V. In this proposed implementation, a bidirectional DC/DC converter permits higher switching frequency operation, bidirectional flow of power by owing zero current switching and zero voltage switching of fundamental and auxiliary switching, reciprocally which results reduction in switching losses. It eliminates the application of snubber components while allows lower voltage rated switching devices. The output voltage attributed to DC/DC converter given to the three-phase inverter, which transforms DC to AC and given to the induction motor that drives the vehicle.

3 Simulation Results All the Simulink responses were taken and waveforms are for fuel cell propulsion system. By the application of MATLAB simulation, the system behavior as well as design parameters can be optimized. As shown in Fig. 8, we have given the constant input to the fuel cell equivalent circuit. Figure 9 depicts the simulation result of fuel flow rate in which during startup situation, hydrogen is utilized constantly through regulation of fuel cell flow. And after some interval, the rate of fuel cell is getting increased and reached peak value which influences the afficacy, consumption of fuel as well as air. Figures 10 and 11 describe simulated fuel cell current/voltage signals are available on the scope 1 and scope 2, respectively, which is found constant. Simulation responses reveal that the fuel cell output current is having constant magnitude 12 A, after t = 0.03s as

Fig. 8 Scope result of constant

Fig. 9 Representing fuel flow rate

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Fig. 10 Generated current from fuel cell

Fig. 11 Fuel cell output voltage

shown in Fig. 10. Also, simulated response of fuel cell output voltage has magnitude 45.8 V, as shown in Fig. 11. The complete procedure of ZCS is described, and prior to tuned off the diagonally switches at primary side, the other primary position switches can be turned on with V 0 /4n magnitude of volt can be obtained across primary side of transformer. The current flowing through the switches of one pair has been diverted to another pair and becomes equal to zero. Moreover, the feedback diodes start conducting and switched pulses get eliminated so that ZCS becomes turned off. Moreover, the switch voltage increases and becomes clamped to reflected output. Simulated responses depicted in Fig. 12 reveal that the switched converter and nominal voltage output became V 0 = 200 V and 300 V, respectively. From simulated response of switched converter, it evident that after t = 0.3s, DC/DC

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Fig. 12 Simulated response of switched power converter

Table 1 Employed specifications

S. No

Parameters

1

Partial pressure of hydrogen 4 atm

Values

2

Partial pressure of oxygen

1 atm

3

Active cell area

300 cm2

4

Faraday constant

96,500,000 CKmol−1

5

Stator resistance (Induction motor)

5.6 

6

Rotor resistance

4.4 

7

No. of poles

2 pole pair

8

Inertia constant

47 × 10–4 kg m2

output has magnitude, V 0 = 200 V, whereas the nominal output voltage is 300 V. Table 1 depicts the employed specifications of fuel cell-based systems.

4 Conclusion In this research work, an interleaved ZCS employed switched power converter for the fuel cell vehicle application has been realized using MATLAB/Simulink. Through soft-switching methods, the bidirected switched converter outputs are maintained constant and continuous fuel cell input is preserved. Employed electric motor propels the drive system accurately, efficiently and smoothly operated. Moreover, switching converter output fed to the three-phase inverter circuitry and inverter transforms the DC voltage into AC voltage and this AC voltage given to the induction motor that drives the vehicle.

References 1. Lindahl PA, Shaw SR, Leeb SB (2018) Fuel cell stack emulation for cell and hardware-in-theloop testing. IEEE Trans Instr Meas 67(9):2143–2152

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2. Prajof P, Agarwal V (2015) Novel solar PV-fuel cell fed dual-input-dual-output dc-dc converter for DC microgrid applications. In: Proceedings of ieee 42nd photovoltaic specialist conference. IEEE, pp 1–6 3. Priyadarshi N, Sharma AK, Azam F (2017) A hybrid firefly-asymmetrical fuzzy logic controller based MPPT for PV-wind-fuel grid integration. Int J Renew Energy Res 7(4) 4. Padmanaban S, Priyadarshi N, Bhaskar MS, Holm-Nielsen JB, Hossain E, Azam F (2019) A hybrid photovoltaic-fuel cell for grid integration with jaya-based maximum power point tracking: experimental performance evaluation. IEEE Access 7:82978–82990 5. Coelho RF, Schimtz L, Martins DC (2011) Grid-connected PV-Wind-fuel cell hybrid system employing a super capacitor bank as storage device to supply a critical DC load. IEEE, pp 1–10 6. Egiziano L, Giustiniani A, Petrone G, Spagnuolo G, Vitelli M (2019) Optimization of perturb and observe control of grid connected PEM Fuel cells. In: Proceedings of international conference on clean electrical power, pp 775–781 7. Giustiniani A, Petrone G, Spagnuolo G, Vitelli M (2010) Low-frequency current oscillations and maximum power point tracking in grid-connected fuel-cell-based systems. IEEE Trans Ind Elec 57(6):2042–2053

Feasibility Study and Analysis of Wind Power Generation Toward Achieving Renewable Powered Island Anik Goswami, Utpal Goswami, and Pradip Kumar Sadhu

Abstract The world’s largest delta, the Sunderban delta, located on the eastern coastal region of India faces acute power crisis. The geographical position of the delta is a major hindrance in providing reliable grid power supply to the area. Sagar Island, one of the largest island in the delta, has very unreliable grid power supply and is plagued by frequent power cuts. Long-term studies have showed that wind energy potential of Sagar Island is very high. This paper discusses the feasibility of developing grid-connected wind power plant in Sagar Island to provide reliable and uninterrupted power supply. The wind power plant will also reduce the load on the grid power supply. The results show that renewable energy fraction is 6.39%, and the demand supply fraction is 25%. The results indicate the potential of Sagar Island to become a majorly sustainable energy powered island. Keywords Renewable energy · Wind power plant · Feasibility study · Power generation

1 Introduction India being one of the fastest developing countries has a huge demand for energy. Considering depletion of fossil fuels, adverse environmental impacts and rise in the cost of energy, the gap between the demand and supply of energy can be bridged by developing renewable energy sources. Among various renewable energy sources, solar energy and wind energy are the most widely used sources of generation. This is due to the fact that generation from both solar energy and wind energy are economically feasible. Wind energy is becoming one of the attractive renewable power A. Goswami (B) · U. Goswami · P. K. Sadhu Indian Institute of Technology (Indian School of Mines), Dhanbad, India e-mail: [email protected] U. Goswami e-mail: [email protected] P. K. Sadhu e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_36

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generation system. Wind energy has the advantage over the other sources of generation as the installation cost and maintenance cost is less, turbines have long life and the cost to power generation is low [1]. From, 2017 to 2025, it is expected that the wind power will grow from 600 to 1750 GW globally [2]. Mostafaeipour et al. [3], assessed the feasibility of electricity generation in Iran. The results showed that the wind turbines produced 5515.325 MW of electricity. A study by Chancham et al., [4] showed that the offshore wind speed of Thailand is 5.5–6.5 m/s and the wind potential is 6000–8000 MW. This will also save about 40 million tons of CO2 per year. India has a coastline of about 7800 km and as per National Institute of Wind Energy (NIWE), in India wind energy generation is mostly feasible in the coastal areas and has a potential of generating 302 GW of energy at 100 m hub height [5]. The high wind potential areas of India are the Gulf of Kutch, Sunderban delta, and the southern peninsula. Nagababu et al. [6] presented a feasibility study of wind power potential in the west coast of India. The results showed that the total available area is 67,622 km2 , and the annual wind power potential is 477 TWh. In the southern peninsula, coasts of Tamil Nadu have the highest wind energy potential. The wind power density in the eastern coast of India is 63–393 W/m2 and the wind energy potential is 107–1117 MWh/km2 annually [7]. In this paper, a feasibility study is done to find the wind energy potential of Sagar Island, located in the Sunderban delta using probabilistic seasonal-based model. As per NIWE, Sagar Island is located in the wind energy potential zone of India. Development of wind power plant in the island will reduce the dependence on the grid power supply which is unreliable. A major fraction of load can be supplied using renewable power. This will not only improve the power quality but also help the area to become a green power-fed island.

2 Site Selection The site selected for the feasibility study is Sagar Island, India, and the meteorological data is given in Table 1. The site is predominantly coastal region, and the terrestrial is predominantly agricultural. The soil is mainly delta clay. As the land has numerous water bodies and shelterbelts, the total contiguous usable land for wind power plant is nearly 16,895 m2 . Table 1 Meteorological data of the site

Project name

Sagar Island

Latitude

21°38 33.15 N

Longitude

88°4 26.67 E

Elevation

4 m above mean sea level

Mean wind speed

4.59 m/s

Feasibility Study and Analysis of Wind Power Generation Toward …

365

3 Wind Speed Prediction To accurately predict the wind speed, actual wind speed data was taken from 25 m mast located at Sagar Island. The measurement was done by NIWE between the period of February 1995 and November 1997. From Fig. 1, it can be seen that the wind speed values range between 2.25 and 7.50 m/s. For long-term measurement of wind data, Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA 2) [8] data is used. The MERRA 2 analysis is conducted with GEOS-5 Atmospheric Data Assimilation System (ADAS). The spatial resolution of the model grid is 0.5 degree latitude and 0.625 degree longitude. This reanalysis of data is helpful in understanding the long-term variability of wind speed in the region of interest. The nearby MERRA 2 grid point of latitude: 21.50 N and longitude: 88.125 E has been considered for the analysis. MERRA 2 is available for 50 m mast height, so the wind speed data at 25 m is extrapolated to 50 m using power-law index (PLI) of) 0.14 and shown in Fig. 2. A correlation analysis was performed between the actual wind data at 50 m and the extrapolated wind data at 50 m to find the accuracy of extrapolation. From Fig. 3, it is seen that extrapolated and measured data have a linear relationship with each other, and the points are very close to the y = x line, with a slope of 0.9047 and intercept of 1.0852. The R2 value is 0.933, which again represents a very close match between the results. Using the regression equation in Fig. 3, the long-term wind speed for 27 years is calculated at 50 m mast height and presented in Fig. 4. The wind power density of the location can be expressed using Weibull power density function [9, 10] and is given in Eq. (1).   1 P = ρc3  1 + 3 k A 2

(1)

8 6 4

Time Period

Jun-97

Feb-97

Oct-96

Jun-96

Feb-96

Oct-95

0

Jun-95

2

Feb-95

Wind Speed (m/s)

Fig. 1 Wind speed at Sagar Island from February 1995–November 1997

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Extrapolated data MERRA 2 data

8 6 4

Aug-97

Mar-97

May-96

Feb-95

0

Dec-95

2

Jul-95

Wind Speed (m/s)

10

Oct-96

Fig. 2 Comparison of wind speed at 50 m and MERRA 2 data

Time Period 12

Fig. 3 Correlation plot of wind speed at 50 m and MERRA 2 data

Extrapolated data

10 8 6 4

y = 0.9047x + 1.0852

2

R² = 0.9333

0 0

5

10

15

MERRA 2 data 10

Wind Speed (m/s)

Fig. 4 Predicted wind speed data for 27 years

5

0 1992

2000

Min

2008

Year Max

2016

Average

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367

where A is the blade sweep area, P is the wind power, and ρ is the mean air density.  is the gamma function. k and c are Weibull coefficients and given in Eq. (2) and Eq. (3), respectively.  −1.086 k = σ/v c=

(2)

v    1+1 k

(3)

where σ is the standard deviation of wind speed and v is the wind speed.

4 Results To determine the energy produced by the wind, micrositing of the site was done. Micrositing helps to maximize the wind energy yield. Micrositing was done using Wind Atlas Analysis and Application Program (WAsP) 11.5 model with its wind atlas methodology. Considering the current location, three wind turbine generators (WTG) are proposed for effective harnessing wind potential. Case-1: Development of 250 kW WTG. Case-2: Development of 600 kW WTG. Case-3: Development of 2 MW WTG. For all the cases, the air density is taken as 1.164 kg/m3 , which is the air density at the location. The losses and assumptions considered while calculating the electricity generated by the proposed system are given in Table 2. Case 1: 250 kW WTG (Shriram SEPC 250 T): For this case, the rotor diameter is taken as 28.5 m and hub height is 41.5 m. The power curve is given in Fig. 5. Table 3 represents the total electricity generated. Case 2: 600 kW WTG (Suzlon S.52): For this case, the rotor diameter is taken as 47 m and hub height is 50 m. The power curve is given in Fig. 6. The annual electricity produced is given in Table 4. Case 3: 2 MW WTG (Senvion 2.3M130): For this case, the rotor diameter is taken as 100 m and hub height is 100 m. The power curve is given in Fig. 7. The annual electricity produced is given in Table 5. Table 2 Assumptions and losses

Type

Value (%)

Electricity transmission loss

3

Turbine availability

95 (Standard procedure)

Utility down time

95

Weibull discrepancy

1

Internal losses

3

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Power (kW)

300

200 0.5

Ct

Fig. 5 Power generated and Ct for 250 kW WTG

100

0

1

5

9

13

17

21

0

Wind Speed (m/s) Power

Ct

Table 3 Annual electricity production by 250 kW WTG WTG (kW)

Elevation (m)

Wake loss (%)

P50 (MWH)

P75 (MWH)

P90 (MWH)

P95 (MWH)

250

5

3.63

258.14

234.18

212.62

199.71

1

Power (kW)

1000

0.5

500

0

1

5

9

13

17

21

Ct

Fig. 6 Power generated and Ct for 600 kW WTG

0

Wind Speed (m/s) Power

Ct

Table 4 Annual electricity production by 600 kW WTG WTG (kW)

Elevation (m)

Wake loss (%)

P50 (GWH)

P75 (GWH)

P90 (GWH)

P95 (GWH)

600

5

11.8

0.59

0.55

0.50

0.48

4.1 Performance Assessment of WTG The performance assessment of the WTG is done using two parameters. The first parameter is fraction of renewable energy served (F r ), and the second parameter is demand–Supply fraction (DSF) [11], the parameters are given in Eq. (4) and Eq. (5), respectively.

Feasibility Study and Analysis of Wind Power Generation Toward …

369 1

2000 0.5

Ct

Power (kW)

3000

1000

0

1

3

5

7

9

11

13

15

17

19

21

0

Wind Speed (m/s) Power

Ct

Fig. 7 Power generated and Ct for 2 MW WTG

Table 5 Annual electricity production by 2 MW WTG WTG (MW)

Elevation (m)

Wake loss (%)

P50 (GWH)

P75 (GWH)

P90 (GWH)

P95 (GWH)

2

5

0.45

4.05

3.67

3.33

3.13

 Fr = Dr D DSF =

(4)

H 24 × 365

(5)

where Dr is the demand supplied by renewable sources and D is the total demand. H is the total hours where demand is met completely by WTG. For the year 2017–2018, the load demand of Sagar Island is 9399 kW. The location being a rural area, it is assumed that 100% of the load is active from 6 am to 4 pm, 50% of the load is active from 4 pm to 12 am, and 20% of the load is active from 12 to 6 am. The performance of the WTG is presented in Table 6. Table 6 Performance of WTG

WTG (kW)

Level

Fr (%)

DSF(%)

250

P-90

0.41

0

600

P-90

0.96

0

2000

P-90

6.39

25

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Table 7 Financial assumptions

Type

Value

Type of WTG

Vertical axis

Life of project operation

25 years

Plant load factor (PLF)

13.5% (Standard procedure)

Capacity utilization factor (CUF) 21% (Average for Eastern India) Operation & Maintenance cost (O&M)

5.6 $/kWh/year

O & M escalation

3%/year

Degradation

1%

Discounting factor

11%

4.2 Economic Assessment of WTG Economic assessment of the WTG is required to determine the practical feasibility of implementing the wind power plant in the island. The main aim of setting up the wind power plant is to increase the reliability of electricity while decreasing the levelized cost of electricity (LCOE). Lowering LCOE will also encourage people from economically weaker background to take electricity connection in their homes. This will help Sagar Island to achieve the goal of 100% rural electrical. The assumptions to calculate the project cost is given in Table 7. The LCOE is calculated using accelerated depreciation method. In this method, the deductions are taken higher in the present years, thus reducing the current income tax and hence increasing profits. From Table 8, it can be observed that the LCOE decreases with increasing the capacity of WTGs. This is due to the fact that the return on investment (ROI) for larger power plants is better than smaller power plants, and also the miscellaneous cost proportion for smaller power plants are higher than larger power plants. Considering a gross metering tariff of 0.063$, the profit of the utility is calculated. The 2 MW WTG has an estimated profit of 63.8% over the life of the pant. This will also encourage private parties to invest in the project. The involvement of private parties will help in faster commissioning of the project. From Fig. 8, it is seen that the payback period for the 250 and 600 kW WTG is 14 years while for the 2000 kW WTG it is 13 years. Table 8 Cost estimates WTG (kW)

Cost ($)

LCOE ($)

Profit ($)

Profit (%)

250

367,150

0.055

120,181

32.7

600

853,160

0.053

349,156

40.9

2000

2,643,200

0.049

1,686,743

63.8

Feasibility Study and Analysis of Wind Power Generation Toward … 5000000

250 kW

4000000

600 kW

3000000

Revenue ($)

371

2000 kW

2000000 1000000 0 -1000000 -2000000 -3000000

1

4

7

10

13

16

19

22

25

Years

Fig. 8 Revenue generated by the WTGs

5 Conclusion Sagar Island being located in the wind rich zone of India has high potential for wind power generation. In this paper, feasibility analysis of setting up WTG is presented. The location being an island, transportation of large WTG is difficult as the present barge capacity is limited to 10 tons. But, from the study, it is seen that the 2000 kW WTG is capable of generating 3.33 GWH of energy annually with 6.39% Fr and 25% DSF. The economic assessment of the WTGs reveal that the 2000 kW WTG is capable of producing electricity at a LCOE of 0.049 $. This will help the residents of the Island to get reliable power supply at a cheaper rate. This will also help the island to achieve green island status as the renewable power is enough to supply the demand during 25% of the time annually.

References 1. Rafique M, Rehman S, Alam M, Alhems L (2018) Feasibility of a 100 MW installed capacity wind farm for different climatic conditions. Energies 11(8):2147 2. Al-Nassar WK, Neelamani S, Al-Salem KA, Al-Dashti HA (2019) Feasibility of offshore wind energy as an alternative source for the state of Kuwait. Energy 169:783–796 3. Mostafaeipour A, Qolipour M, Goudarzi H (2019) Feasibility of using wind turbines for renewable hydrogen production in Firuzkuh. Iran Front Energy 13(3):494–505 4. Chancham C, Waewsak J, Gagnon Y (2017) Offshore wind resource assessment and wind power plant optimization in the Gulf of Thailand. Energy 139:706–731 5. NIWE Annual Report 2017–2018 https://niwe.res.in/annual_report/Annual_Report_20172018_English 6. Nagababu G, Kachhwaha SS, Savsani V, Banerjee R (2017) Evaluation of offshore wind power potential in the western coast of India: a preliminary study. Curr Sci 113891:01 7. Nagababu G, Kachhwaha SS, Savsani V (2017) Estimation of technical and economic potential of offshore wind along the coast of India. Energy 138:79–91 8. MERRA-2—Global Modeling and Assimilation Office—Nasa. https://gmao.gsfc.nasa.gov/rea nalysis/MERRA-2

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9. Islam A, Rahman MM, Islam MS, Bhattacharya SS, Kim KH (2015) Comparative analysis of wind power energy potential at two coastal locations in Bangladesh. Asian J Atmosph Environ (AJAE) 9(4) 10. Daratha N, Agustian I, Suryadi D, Suandi A, Gupta N (2019) Development of low-cost wind power estimation system in Enggano Island Indonesia. In: Advances in interdisciplinary engineering. Springer, Singapore, pp 173–181 11. Al-Ghussain L, Taylan O, Baker DK (2019) An investigation of optimum PV and wind energy system capacities for alternate short and long-term energy storage sizing methodologies. Int J Energy Res 43(1):204–218

An Investigation on Effect of Parasitic Resistances on Grid Connected PV System Malay Bhunia and Bidyadhar Subudhi

Abstract This paper investigates the variation of eigenvalues and dynamic properties of different control loops in a grid connected PV inverter (GCPVI) with variation in dynamic resistance of PV source and internal resistance of associated passive components. Because of the nonlinearities of GCPVI, small signal modeling (SSM) is necessary to investigate the dynamics of GCPVI. Dynamics of the GCPVI depend upon the nature of the input source as well as passive components of the system, i.e., interfacing filter and DC-link capacitor. The dynamic resistance of the PV input source is varied with its operating point. Also, internal resistance of passive components in a GCPVI varies with time. By pursuing a detailed study on the frequency responses and eigenvalue analysis, it is observed that the GCPVI dynamics vary significantly with the aforesaid perturbation in different resistances. This further deteriorates the maximum power point tracking (MPPT) efficiency. Due to the increase of internal resistance of DC-link capacitor from 0 to 1.5 , the MPPT efficiency decreases from 99.97 to 99.4%. This analysis will help the designer in ensuring the satisfactory performance of GCPVI in the event of perturbation owing to the aging effect in the passive components. Keywords Small signal modeling · ESR effect · Parasitic resistance · Stability

1 Introduction Power quality and stability are the main concerns when power electronics (PE) converters are employed in GCPVI [1, 2]. Active filter [3] as well as passive filters (made by inductor, capacitor and resistor) [4] are used to improve the power quality. M. Bhunia (B) Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela, India e-mail: [email protected] B. Subudhi School of Electrical Sciences, Indian Institute of Technology Goa, Goa, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_37

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Due to aging, equivalent series resistance (ESR) of capacitor [5, 6] and the quality factor of inductor [7] vary. To extract the maximum power from a PV module, the PV operating point is perturbed from constant current region (CCR) to maximum power point (MPP) region, and constant voltage region (CVR) to MPP region by employing different PE converters [8]. Dynamic resistance of PV source varies with variation of the operating point [9]. To analyze the effect of PV dynamic resistance on GCPVI, a SSM is developed in a synchronous rotating (dq) frame where the direct axis is synchronous with grid voltage [8, 10]. From the above analysis, it is found that PV dynamic resistance affects the dynamics of the different control loops [8, 10]. However, the analyses in [8] and [10] are limited in perturbation of PV dynamic resistance only, perturbation of the internal resistances of DC-link capacitor and grid side filter are not considered. Also, the analysis in [8] and [10] is limited to frequency response of different control loops only. In this paper, the effect of perturbation in different resistances (i,e. ESR of DC-link capacitor, series resistance of inductor and dynamic resistance of the PV) on dynamics of different control loops of GCPVI system is investigated from frequency responses of different transfer functions (control to DC-link voltage (G ci−q = Vdc (s)/Sq (s)) and control to current transfer function (G co−q = Iq (s)/Sq (s))) as well as eigenvalues of the GCPVI. For the purpose of analysis, a quadrature axis small signal model (QASSM) of GCPVI in dq frame is developed where q-axis is synchronized with grid. The primary contributions of the paper are (a) a SSM of a GCPVI is derived by taking all internal resistances of GCPVI in dq frame where q-axis is synchronized with grid, (b) in addition to [8], this work analyzed the variation of system dynamics (i.e., eigenvalues) due to perturbation of different internal resistance for grid connected inverter (GCI) fed from DC battery as well as PV modules, and (c) in addition to [11], this paper evaluates efficacy of MPPT algorithm in presence of parametric uncertainty of DC-link capacitor. The rest of the paper is organized as follows. Section-2 presents the QASSM of GCPVI in dq frame. Then, effect of perturbation in different resistances on control dynamics of GCPVI is analyzed in Sect. 3. Finally, Sect. 4 concludes the paper.

2 Small Signal Modeling of GCPVI System The circuit diagram of a single stage GCI with PV source is shown in Fig. 1. The average valued model of GCI is established in Eqs. (4)–(7) of [10]. By using the dq0 Park’s transformation matrix (Tabc ) as described in Eq. (11) of [12], the time varying averaged model of GCI can be transformed into a time invariant synchronous rotating frame(d-q) under balanced symmetric grid as:

An Investigation on Effect of Parasitic Resistances … Inverter

icap

Ipv

S3

S1

Coupling filter

S5

D Rc

ia

E

ib

+ -

F

Vc

S4

L

R

L

eb N ec

ea

+ + -+ + +

ic

-

ωt ω

PLL ωt

Iq Vrd Id current controller ( PI) Id

abc-dq

ib

MPPT (IC)

Eq Ed L

Eq Ed

Iq -

ωt

ia

Vdcref I*q

ωt

ec

ipv

S6 SPWM

Vrq Id Iq current controller ( PI)

Grid ea

S6

Vdc

DC-link voltage controller (PI) +

R

S1

dq-abc

-Fdc

L

eb

V dc

S2

R

ic

+

C

+ + + +

PV

Vdc

375

Id Iq

abc-dq

I*d =0

Fig. 1 Single stage GCPVI with controller

⎫   R U Sd dId Sd Sd Rc Ed ⎪ ⎪ Id − (ω + U ) Iq + Vc + =− + Ipv − ⎪ ⎪ dt L Sq L L L⎪ ⎪ ⎪   ⎬ Sq Sq Rc Eq dIq R U Sq Iq + Vc + = (ω − U ) Id − + Ipv − ⎪ dt L Sd L L L ⎪ ⎪ ⎪ ⎪ ⎪ 1 3 Sd 3 Sq dVc ⎪ ⎭ = Ipv − Id − Iq dt C 2C 2 c dq0

(1)

dq0

where ω= grid voltage (eabc ) frequency in rad/s, Idq0 = Tabc iabc , Edq0 = Tabc eabc , dq0 Sdq0 = Tabc S135 , S135 are the switching signals of upper switches of inverter stack, 3R S S and U = c2Ld q . By linearizing the Eq.1 around the steady state operating point as: ⎫ 2 Ipv ⎪ ⎪ Ed = 0; Eq = E; Vdc = Vc ; Id = 0; Iq = ⎪ 3 Sq ⎪ ⎬  ⎪ Eq + Eq 2 + 83 RVdc Ipv ⎪ 2 ωL Ipv ⎪ ⎪ Sd = ; Sq = ⎭ 3 Sq Vdc 2Vdc

(2)

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one can obtain the QASSM of GCI with a DC source as: ⎡ ⎢ ⎣

d(ˆid ) dt d(ˆiq ) dt d(vˆc ) dt

⎡

⎤





⎤

⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

⎡

⎤ ˆ ⎥ id ⎥ ⎢ ⎥ ⎣ ˆi ⎦ ⎦=⎢ ⎣ ω − M3 ⎦ q vˆc −3Sq −3Sd 0 2C 2C ⎡ ⎤ ˆipv ⎡ R S −1 −M3 Iq ⎤ 1 c d ⎢ 0 M 1 eˆ d ⎥ L L L Sq ⎥ ⎥⎢ ⎢ −M3 Id 1 ⎢ ⎥ + ⎣ RcLSq 0 −1 e ˆ ⎦ M q 2 ⎢ ⎥ L Sd L ⎣ sˆd ⎦ −3Iq −3Id 1 0 0 C 2C 2C sˆq −

+

R L

⎡

⎤ ⎡ −3S R d c vˆdc 2 ⎣ ˆid ⎦ = ⎣ 1 ˆiq 0

M3 Sd Sq

−3Sq Rc 2

0 1

− (ω + M3 )   M S − RL + S3d q

Sd L Sq L

⎤⎡ ⎤ ⎡ 3R I ˆid Rc 0 0 − 3R2c Id − 2c q 1 ⎦ ⎣ ⎦ ⎣ ˆ 0 0 iq + 0 0 0 0 0 00 0 0 0 vˆc

⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎡ ⎤⎪ ⎪ ⎪ ˆipv ⎪ ⎪ ⎤ ⎪ ⎢ eˆ d ⎥⎪ ⎪ ⎢ ⎥⎪ ⎪ ⎦ ⎢ eˆ q ⎥⎪ ⎪ ⎢ ⎥⎪ ⎪ ⎣ sˆd ⎦⎪ ⎪ ⎪ ⎪ ⎭ sˆq

(3)

3Rc 3Rc (2Sd Id + Sq Iq ) + Vc + Rc Ipv ; M2 = − (Sd Id + 2Sq Iq ) + Vc + Rc Ipv 2 2 3Rc Sd Sq . M3 = 2L

where, M1 = −

2.1 Small Signal Model of GCI with PV Source The single diode model and Norton equivalent circuit of a solar cell are shown in Fig. 2(a) and 2(b) respectively. By using vˆdc from (3), the ˆipv can be obtained from Fig. 2b as

Iph

D

I shr c sh

R s i pv+ R sh

iˆpv + iˆsh

Vdc

T

I G Id shc

Y pv

Fig. 2 a PV cell, and b Norton equivalent circuit of PV cell

vˆdc

-

-

(a)

1 r pv

(b)

An Investigation on Effect of Parasitic Resistances …

377

   ˆipv = ˆish − Ypv Vˆc − 3Rc Sd ˆid + Sq ˆiq + Id sˆd + Iq sˆq . 2

(4)

 −1 where  = 1 + Ypv Rc . On substituting ˆipv from (4), in (3), the following SSM for three phase GCI with PV source can be obtained as: ⎫ x˙ˆ = Aˆx(t) + Bˆu(t); yˆ (t) = C xˆ (t) + Duˆ (t). ⎪ ⎪ ⎪ T T T ⎪    ⎪ where, xˆ (t) = ˆid ˆiq vˆc ; uˆ (t) = ˆish eˆ d eˆ q sˆd sˆq ; yˆ (t) = vˆdc ˆid ˆiq ;⎪ ⎪ ⎪ ⎪ ⎤ ⎡ R Sd  ⎪ ⎡ ⎤ ⎪  Sd (1−Rc Ys ) I  ⎪ q I  − L + LSq −ω + L ⎪ Rc 0 0 Sdd Sq Sd Sq L ⎪ ⎪ ⎥ ⎢ S  S Y (1−R ) ⎬ q q c s ⎣ ⎦ ; D = A = ⎣ ω + L − RL + LS ; ⎦ 0 0 0 0 0 L d −Ys   ⎪ 0 00 0 0 ⎪ CSq Rc CSd Rc C ⎪ ⎪ ⎪ ⎤ ⎡ Rc Sd  −1 I  ⎪ ⎤ ⎡ q M1  ⎪   0 + ⎪ Y − R (1 ) L L L Sq Iq LSq ⎪ c s Sq Sd ⎪ ⎪ ⎥ ⎢ Rc Sq  M2 Id  −1  ⎪ ⎦ ⎣ . ; C = B=⎣ L 0 L + ⎪ ⎦ 1 0 0 ⎪ LSd L Sd Id ⎪ ⎭ Iq  Id   0 1 0 0 0 C CRc Sd Sq CRc Sd Sq (5) 3R S S 3R2 S S I I  The constant φ and  in (5) is defined as  = c2d q (Rc − 1) ;  = c d2Lq d q . The model described in Eq.5 is called the QASSM of GCPVI.

3 Effect of Perturbation in Parasitic Resistances on the Inverter Control Dynamics In Fig. 2 of [8], the relationships between static resistance (rsta =

Vdc ) Ipv

and dynamic

resistances (rpv = are given in Eq. (8) of [8]. As shown in Fig. 1, a cascaded control scheme is adopted for GCPVI where DC-link voltage control loop as an outer loop and inverter current control loop as an inner loop [13]. The incremental conductance (IC) MPPT algorithm is used to generate the DC-link voltage reference (Vdcref ) for outer loop. For this study, the different parameters of GCPVI are shown in Table 1. The effects due to the perturbation of different resistances (Rc , R and rpv ) of GCPVI are discussed next. The deviation in frequency responses and MPPT tracking performance are shown under the perturbation of Rc only as it is more prone to be affected by aging. dVdc ) dIpv

Table 1 Parameters of GCPVI Vd c Ipv C 500 V

4.9 A

1100 µF

Rc

L

R

e(a,b,c)

0–1.5 

10 mH

0–6 

122 Vpeak, 50 Hz

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Table 2 Eigenvalues of GCI connected to DC battery Rc = 0  Rc = 0.5  Rc = 1  R=0 R=2 R=4 R=6

0, 0 ± 328.14i −16.8, −191.6 ± 328i −23.4, −388.3 ± 323i −23.6, −588.23 ± 320i

0, −2.5 ± 328i −16.7, −195 ± 328i −23.4, −392 ± 323i −23.2, −593 ± 320i

0, −5 ± 328i −16.5, −198 ± 328i −22.8, −397 ± 323i −23, −598 ± 320i

Rc = 1.5  0, −7.5 ± 328i −16.3, −201 ± 328i −22.4, −401 ± 323i −22.7, −603 ± 320i

3.1 System Dynamics of the GCI Fed by Constant DC Battery: From Eq.3, the variations of eigenvalues with variations in R and Rc of a GCI with DC battery are shown in Table 2. It is observed that all the eigenvalues lie in left hand side (LHS) in the s-plane and are shifted more to LHS with the increase of resistances. Thus, stability is improved due to increase of R. Also, imaginary part of eigenvalues decreases due to increase of R. Hence, increment of R improves the damping in the system. With perturbations in Rc , the frequency responses of G ci−q and G co−q are shown in Fig.3a, b, respectively.

3.2 System Dynamics of the GCI Fed by PV Source: Equation (5) describes the dynamics of GCI with a PV source. At different operating points, the variation in eigenvalues of the GCPVI with different R and Rc is tabulated in Table 3. The unstable eigenvalues in Table 3 are marked through bold-red color. The variation of system dynamics with and without the perturbation in R and Rc is discussed next. (A) Without perturbation in Rc and R: The variations of eigenvalues without perturbations in resistances (i.e., Rc = R = 0) are shown in Table 3. From Table 3, stable eigenvalues are observed in PV operating point at CCR and MPP region. However, the eigenvalues are shifted to right hand side (RHS) when PV operating point shifted CVR. Thus, operating point of PV source affects the stability of GCPVI. (B) With perturbation in Rc and R: It is observed from Table 3 that the stability of the GCPVI is degraded with the increase of Rc in both CCR and MPP regions as poles are shifted to RHS. But, stability is improved in CVR with the increase of Rc . It is also observed from Table 3 that the stability of the GCPVI is improved with an increase of R in all the PV operating regions as poles are shifted further from the jω axis toward the LHS. Under different perturbation in Rc , the frequency responses of

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379

Table 3 Eigenvalues of GCI connected to PV source

R= 0Ω CCR

R= 2Ω R= 4Ω R= 6Ω R= 0Ω

MPP

R= 2Ω R= 4Ω R= 6Ω R= 0Ω

CVR

R= 2Ω R= 4Ω R= 6Ω

Rc= 0 Ω –0.9, –0.04±328.1i –17.7, –191.6±328i –24.4, –388±323.4i –24.6, –588±320.2i –8.16, –0.4±328.1i –25.27, –192±328.3i –32.4, –388±323.6i –32.7, –588±321i 939 , 4.35±315.4i 938 , –195±315.3i 937.2 , –395±315.2i 937 , –595 315i

Rc= 0.5 Ω Rc= 1 Ω Rc=1.5Ω –0.96, 1, –1, –1.3±321.2i –.01±314i 3.8±306.7i –9.5, –1, 8, –197.4±321i –200±314i –200±307i –12.5, –1, 10.6 , –396.3±319i –400±314i –400±310i –12.6, –1, 10.5 , –596.6±317i –600 ±314i –599±311i –8.48, –8.8, –9.18, –1.45±321i –0.05±314i 3.78±307i –17.2, –9, –0.5, –198±321i –200±314i –199±307i –20.4, –9, 2.27 , –396±318.7i –400±314i –400±310i –20.5, –9, 2.25 , –596.6±317i –600±314i –599±311i 1980 , –22508, –17153, –0.1±314i –5±312i –10±311i 1980 , –22588, –1733, –200±314i –205±312i –211±309i 1980.4 , –22665, –1753, –400±314i –405±311i –409±305i 1980.4 , –22740, –1780, –600 314i –603 311i –604 301i

G ci−q and G co−q in CCR are shown in Fig.4a, b, respectively. It is observed from Fig.4a, b that stability margin changes due to change in ESR of DC-link capacitor. By analyzing Figs. 3 and 4, it is seen that the open loop transfer function of both G ci−q and G co−q are unstable. However, due to the PV, the stability margins in Fig. 4 are more affected than Fig. 3. Variation in Rc also affects the MPPT performance of GCPVI. It is observed from Fig. 5a that due to the increment of Rc , MPPT algorithm unable to converge the Vdcref to the true MPP voltage at different irradiation. As a result, the MPPT efficiency (shown in Fig. 5b) varies with the variation of Rc . It is

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Magnitude (dB)

Magnitude (dB)

100 50 0

Phase (deg)

Phase (deg)

Rc=1 Ω, R=0 Ω

50

Rc=1.5 Ω, R=0 Ω

0 180

360 180 Rc=0.5 Ω, R=0 Ω

0

Rc=1 Ω, R=0 Ω

-180 -360 100

Rc=0.5 Ω, R=0 Ω

100

Rc=1.5 Ω, R=0 Ω

102

104

0 -180 -360 100

106

102

104

106

Frequency (Hz)

Frequency (Hz)

(b)

(a) Fig. 3 Frequency responses of GCI with battery a G ci−q , and b G co−q 150

Magnitude (dB)

Magnitude (dB)

150 Rc=0.5 Ω, R=0 Ω, CCR

100

Rc=1 Ω, R=0 Ω, CCR Rc=1.5 Ω, R=0 Ω, CCR

50 0

Phase (deg)

Phase (deg)

Rc=1 Ω, R=0 Ω, CCR Rc=1.5 Ω, R=0 Ω, CCR

50 0 -50 180

-50 180 0 -180 -360 100

Rc=0.5 Ω, R=0 Ω, CCR

100

102

104

Frequency (Hz)

(a)

106

0 -180 -360 100

102

104

106

Frequency (Hz)

(b)

Fig. 4 Frequency responses of GCI with PV in CCR a G ci−q , and b G co−q

found that due to the increment of Rc from 0 to 1.5 , MPPT tracking efficiency is decreased from 99.97 to 99.4% at 1000 W/m2 irradiation. During the simulation, irradiation changed from 1000 W/m2 to 700 W/m2 at 2.5 s and again changed to 200 W/m2 at 4 s.

4 Conclusions In this paper, small signal model of GCPVI in quadrature axis is presented with more realistic situation. Variation of eigenvalues due to perturbation in ESR of DC-link capacitor and inductor quality factor is analyzed in different operating regions of PV. It is found that the increase in filter resistance improves the stability of GCPVI as eigenvalues are shifted further away from jω axis in LHS. However, the increase in

An Investigation on Effect of Parasitic Resistances … 530

100

490

Efficiency (%)

510

Vdcref (V)

381

MPP voltage Rc=0 Ω, R=0 Ω Rc=0.5 Ω, R=0 Ω

470

98 Rc=0 Ω, R=0 Ω Rc=0.5 Ω, R=0 Ω

96

Rc=1 Ω, R=0 Ω Rc=1.5 Ω, R=0 Ω

Rc=1 Ω, R=0 Ω Rc=1.5 Ω, R=0 Ω

450

0

1

2

3

4

Time (sec)

(a)

5

6

7

94

0

1

2

3

4

5

6

7

Time (sec)

(b)

Fig. 5 MPPT performance a generation of Vdcref and b MPPT efficiency

ESR (i.e., Rc ) of the DC-link capacitor degrades the stability of GCPVI as eigenvalues move toward the RHS from LHS in s-plane. Variation in Rc also affects the convergence capability of the MPPT algorithm to the true MPP point. As a result, MPPT efficiency is decreased from 99.97 to 99.4% due to increase of Rc from 0 to 1.5 . As eigenvalues are affected greatly by perturbation of different internal resistances, an adaptive controller may give performance guarantee in the long term as compared to the fixed gain controller.

References 1. Messo T, Jokipii J, Aapro A, Suntio T (2014) Time and frequency-domain evidence on power quality issues caused by grid-connected three-phase photovoltaic inverters. In: 2014 16th European conference on power electronics and applications, Aug 2014, pp 1–9 2. Asiminoaei L, Teodorescu R, Blaabjerg F, Borup U (2005) Implementation and test of an online embedded grid impedance estimation technique for pv inverters. IEEE Trans Ind Electron 52(4):1136–1144 Aug 3. Bajaj M, Singh, AK (2019) Grid integrated renewable dg systems: a review of power quality challenges and state-of-the-art mitigation techniques. Int J Energy Res 4. Ahmed KH, Finney SJ, Williams BW (2007) Passive filter design for three-phase inverter interfacing in distributed generation. In: 2007 compatibility in power electronics. May 2007, pp 1–9 5. Abdennadher K, Venet P, Rojat G, Retif J, Rosset C (2010) A real-time predictive-maintenance system of aluminum electrolytic capacitors used in uninterrupted power supplies. IEEE Trans Ind Appl 46(4):1644–1652 July 6. Agarwal N, Arya A, Ahmad MW, Anand S (2016) Lifetime monitoring of electrolytic capacitor to maximize earnings from grid-feeding pv system. IEEE Trans Ind. Electron 63(11):7049– 7058 Nov 7. Mukherjee S, Chowdhury VR, Shamsi P, Ferdowsi M (2017) Model reference adaptive control based estimation of equivalent resistance and reactance in grid-connected inverters. IEEE Trans Energy Convers 32(4):1407–1417 Dec

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8. Nousiainen L, Puukko J, Mäki A, Messo T, Huusari J, Jokipii J, Viinamäki J, Lobera DT, Valkealahti S, Suntio T (2013) Photovoltaic generator as an input source for power electronic converters. IEEE Trans Power Electron 28(6):3028–3038 June 9. Amer E, Kuperman A, Suntio T (2019) Direct fixed-step maximum power point tracking algorithms with adaptive perturbation frequency. Energies 12(3):399 10. Puukko J, Messo T, Suntio T (2011) Effect of photovoltaic generator on a typical vsi-based three-phase grid-connected photovoltaic inverter dynamics. In: IET conference on renewable power generation (RPG 2011), Sept 2011, pp 1–6 11. Azizi A, Logerais P-O, Omeiri A, Amiar A, Charki A, Riou O, Delaleux F, Durastanti J-F (2018) Impact of the aging of a photovoltaic module on the performance of a grid-connected system. Solar Energy 174:445–454 12. Mahmud MA, Pota HR, Hossain MJ, Roy NK (2014) Robust partial feedback linearizing stabilization scheme for three-phase grid-connected photovoltaic systems. IEEE J Photovoltaics 4(1):423–431 Jan 13. Kadri R, Gaubert JP, Champenois G (2011) An improved maximum power point tracking for photovoltaic grid-connected inverter based on voltage-oriented control. IEEE Trans Ind Electron 58(1):66–75 Jan

Closed-Loop V-f Control Strategy for PV-Battery Energy Storage System in Stand-Alone Microgrid Sourav Chakraborty and Deepak Kumar

Abstract The colossal increase in energy consumption owing to modern day’s lifestyle has led to the need for penetration of alternative sources of energy. Due to low inertia, microgrid may prone to several disturbances due to various unforseen conditions, which results in unstability. A glimpse of this paper encapsulates the twostage integration of P–V array in stand-alone microgrid with integrated V-f controller to generate a pulse width modulation (PWM) signal to inverter’s gate. It is basically a close loop strategy to stabilize the system under several malicious content caused due to increase in demand or due to faults with less oscillation, an effective battery energy storage system is also designed to enhance the efficiency of the system. The overall model is simulated in MATLAB Simulink, and the results clearly validate the overall strategy. Keywords Solar photovoltaics · PI controller · State of charge (SOC) · V-f controller · LC tuned filter

1 Introduction Non-conventional energy source plays a crucial role in supplying today’s energy demand, and the high penetration of DG (distributed generations) in the energy sector proves effective and efficient supply of energy. The over-dependence on fossil fuel in energy generation causes global warming, and also due to its limited availability the global energy demand can be more or less fulfilled by renewable energy sources. This paper basically discusses the stand-alone microgrid in islanded mode of operation, and the MPPT used here ensures the maximum power that is extracted from the P–V source under different condition [1, 2]. The microgrid is a small grid with S. Chakraborty (B) · D. Kumar Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] D. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_38

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several microsources and loads connected to it, and suitable control action should be ensured to maintain the system stability [3]. In this paper, dual-stage integration of P–V has been described, the output of the solar P–V array is fed to the DC–DC boost converter where maximum power is to be tracked with suitable step up of the voltage and fed it to the inverter, and the feedback signal is to be taken from the output so as to generate the PWM pulses for firing purpose to inverter’s gate [4, 5]. The efficient battery energy storage system is also designed which can basically be used to enhance the continuation of the power flow to the system. The contribution of this paper mainly focuses on as follows: (1) Designing of dual-stage solar photovoltaic system operated in an islanded mode. (2) Designing of MPPT tracking system to track the maximum power from the photovoltaic array. (3) Designing of closed loop feedback V-f control strategy to ensure the stability of the microgrid. (4) Implementation of battery energy storage system with bidirectional power flow ensures the charging and discharging of the battery.

2 Solar Photovoltaics and MPPT The P–V array considered in this simulation uses Kyocera 200GT, subsequently boost converter is incorporated whose gate is fired by the MPP controller in which P&O (Perturb and observe) algorithm is used to track the maximum power point [6]. The feedback is taken to give suitable firing pulses to the inverter. The single diode model of solar photovoltaic is shown in Fig. 1. The governing equation for I-V characteristics are shown in Eq. 1.   I = I pv − I0 exp

V + Rs V ther malα



 −1 −

V + I Rs Rsh

(1)

where I pv represents the photocurrent, I 0 represents the diode saturation current, and the ideality factor α ranges from 1 to 1.5. The thermal voltage of the PV array is

Fig. 1 Single diode model equivalent of PV module

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385

represented in Eq. 2. Vthermal =

NsK T q

(2)

where Ns represents the number of cells connected in series, K represents the Boltzman constant, T represents temperature in Kelvin, and q denotes the charge in Coloumb. The photocurrent represents in Eq. 3.   I pv = I pv,n + K T G/G n

(3)

I pv,n represents the photo current in standard condition of irradiance 1000 W/m2 and temperature of 25 °C. The divergence between the temperature is represented by T, G represents the irradiance, and Gn represents the nominal radiation of the surface. In the proposed model of Kyocera, 200GT consists of 124 strings where each strings consists of four series-connected panels.

3 Integrated Frequency Control Strategy The implementation of the voltage–frequency control strategy is depicted in Fig. 2. The controller is solely responsible to manage the frequency under several paracitic events that occur in a system. The closed loop feedback is to be taken, the PI controller used to remove the stead- state error, subsequently with the phase-shifting technique the sine wave then fed to the conditional block to generate the suitable PWM signal which will later fed to the inverter’s gate, for firing purpose [7]. The above figure indicates the PV array with MPP tracking mechanism to track the maximum power, and subsequently, boost converter is employed along with DC–AC inverter, and the feedback control strategy is used as a firing pulse to the inverter in which PI1 is basically used to minimize error in frequency. The PI2 is used to control the power [8]. The control strategy of the controller is depicted in Fig. 3.

Fig. 2 Block Diagram of closed loop photovoltaic system

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Fig. 3 Control strategy of V-f controller

Table 1 Controller gain parameters for the closed loop strategy

Frequency controller Power controller

KP

0.8458 × 104

KI

5.328 × 10−3

KP

0.8 × 10−9

KI

0.8 × 10−9

The K p and K I values of the controller are obtained using fine tuning of PID controller in MATLAB, which is represented in appendix of Table 1. When any abnormalities occur in a system, the frequency will get distorted, and the controller reacts to these abnormal changes and generates a suitable pulse width modulated signal. The governing equation for the above controller is given in (4) and (5), respectively. t β1 = K p1 ( f ref − f measured ) + K I 1

( f ref − f measured )dt

(4)

(PAC − PDC )dt

(5)

0

t β2 = K p2 (PAC − PDC ) + K I 2 0

The DC and AC side mean value is computed to obtain the final phase shift of β*, represented in Eq. 6. β ∗ = β1 + β2 /2

(6)

The output obtained is then compared with the reference voltage of the PCC, with the suitable phase shift, and the signal is then sent to the conditional block where the reference is compared with the carrier signal and a suitable PWM signal is obtained.

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Fig. 4 Block diagram of BESS (battery energy storage system)

4 Battery Energy Storage System The battery energy storage system plays an important role for continuation of power flow into the system [9]. When the irradiance is very high with less load, the excess power is fed to the battery, and when the SOC (state of charge) is less than 20%, the battery will be in charging condition from the excess power by solar photovoltaic. If the battery SOC is above 90%, the battery will be in discharging state, if the excess power is needed by the load. The flowchart for the charging and discharging of the battery by the solar photovoltalitv is shown in Fig. 4. The above figure illustrates that how the battery reacts with the change in load for the continuation of the power flow into the system. When the battery voltage go below the operating voltage, the BESS will be subjected to a voltage lockout to prevent being further discharged below the SOA (safe operating area) [10]. Three condition arises for charging and discharging of battery: (i) When the output of solar photovoltaic is not sufficient to meet the load, then the rest of the power will be fed by battery unit. (ii) When the load is very less during the off-peak hours, the extra power of the solar photovoltaic will be curtailed through battery unit, by charging it. (iii) When the output of the solar P–V array will be just sufficient to meet the load, the battery unit will stay idle at that time.

5 Results and Discussions The results of the dual-stage close loop integrated frequency controller is tested by step load change from 5 to 10 KW. The results are plotted in order to show how the

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Fig. 5 a RMS voltage after fault, b frequency after fault

controller will react to any abnormal content that happens within the system. The voltage and frequency obtained under fault is shown in Fig. 5. The above figure indicates the fault and post-fault condition of the system under study. The voltage and the frequency waveform under the load change is represented in Fig. 6. When any malicious events take place in a system, the controller tries to maintain the voltage and the frequency stable with less number of oscillations. The SOC of the battery management system under charging condition is shown in Fig. 7. When the SOC crossed the upper and lower bound of 20–90%, respectively, the suitable charging and discharging takes place. It is very essential to save the battery from sulfuration, and for that purpose, a dummy load of 700 W is to be connected, so that when the battery is unused for long time, its life will not get hampered. The RMS voltage of around 445.7 V is maintained at the terminal even after the fault and sudden load changes, which shows that the controller is quite effective in responding to sudden activities which occur in a microgrid.

6 Conclusion The complete manuscript introduces the coordinated frequency control, and under any malicious content in a microgrid like fault or load changes, the stability of the system will get hampered due to its low inertia. The strategy retains the stable procedure that function as a feedback loop which compares the frequency by reference,

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Fig. 6 a RMS voltage during load change, b frequency during load change

Fig. 7 Battery during charging condition from solar photovoltaic

and the error will be minimized by using PI controller. The stability of the system will be ensured with fewer number of oscillations under any abnormalities, and at the same time the controller should be fast enough to respond to the shortage so as there is no interruption in power supply. The future prospect of this work will encompass the possible measures so as to utilize the proper battery capacity and improve the trickle stage of charging. The simulated model was executed in MATLAB, and the results clearly show the effective use of controller.

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References 1. Yıldıran N, Tacer E (2016) Identification of photovoltaic cell single diode discrete model parameters based on datasheet values. Solar Energy 127:175–183 2. Salas V, Olias E, Barrado A, Lazaro A (2006) Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Sol Energy Mater Sol Cells 90(11):1555–1578 3. Lopes JP, Moreira CL, Madureira AG (2006) Defining control strategies for microgrids islanded operation. IEEE Trans Power Syst 21(2):916–924 4. Nagarajan R, Chandramohan J, Sathishkumar S, Anantharaj S, Jayakumar G, Visnukumar M, Viswanathan R (2016) Implementation of PI controller for boost converter in PV system. Int J Adv Res Manage Architext Technol Engineering (IJARMATE) 11:6–10 5. Dave MR, Dave KC (2012) Analysis of boost converter using PI control algorithms. Int J Eng Trends Technol 3(2):71–73 6. Femia N, Petrone G, Spagnuolo G, Vitelli M (2009) A technique for improving P&O MPPT performances of double-stage grid-connected photovoltaic systems. IEEE Trans Industr Electron 56(11):4473–4482 7. Adhikari S, Li F (2014) Coordinated Vf and PQ control of solar photovoltaic generators with MPPT and battery storage in microgrids. IEEE Trans Smart Grid 5(3):1270–1281 8. Steinke JK (1999) Use of an LC filter to achieve a motor-friendly performance of the PWM voltage source inverter. IEEE Trans Energy Convers 14(3):649–654 9. Duryea S, Islam S, Lawrance W (1999) A battery management system for stand-alone photovoltaic energy systems. In: Conference record of the 1999 IEEE industry applications conference. Thirty-fourth IAS annual meeting (Cat. No. 99CH36370), vol 4, pp 2649–2654 10. Jha SK, Kumar D (2019) Demand side management for stand-alone microgrid using coordinated control of battery energy storage system and hybrid renewable energy sources. Electric Power Components and Systems pp 1–13

A Review on Intelligent PID Controllers in Autonomous Vehicle Vagisha Vartika, Swati Singh, Subhranil Das, Sudhansu Kumar Mishra, and Sitanshu Sekhar Sahu

Abstract In recent times, autonomous vehicle is gaining popularity in several of applications ranging from conventional transport system to nuclear reactor. The importance of autonomous vehicle increases manifold for those applications where it is not possible or very much risky for human to reach out. Many Artificial Intelligence techniques have been successfully applied in this field. In this paper, an adequate number of recent machine learning-based techniques to design intelligent PID controllers in autonomous vehicles have been reviewed and suitably compared with some other competitive approaches. Particularly, the parameters tuning of Kp, Ki, Kd as well as the rise time characteristics by two methods, namely Ziegler– Nichols and genetic algorithm (GA), have been discussed. Also, we have compared the properties of rise time and percentage overshoot by conventional PID controllers and fuzzy-based PID controllers. Keywords Genetic algorithm · Fuzzy logic · Ziegler–Nichols · Autonomous ground vehicle

V. Vartika (B) · S. Singh · S. Das · S. K. Mishra · S. S. Sahu Control Systems, Electrical and Electronics, BIT Mesra, Ranchi, India e-mail: [email protected] S. Singh e-mail: [email protected] S. Das e-mail: [email protected] S. K. Mishra e-mail: [email protected] S. S. Sahu e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_39

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1 Introduction An autonomous vehicle is an autonomous robot which operates without any human onboard, by applications of Artificial Intelligence techniques [7]. Autonomous vehicles can be used for those applications where humans cannot reach or maybe it is very inconvenient and dangerous for humans to reach out [1]. With the support of governments and cooperation of information technology sector, recent advances have been made in the area of robotics. The application of autonomous vehicles (AV) has been greatly expanded in recent decade in the areas, such as, industrial, mining, natural environment, area mapping, scene manipulation understanding, etc. Combination of mapping and localization methods as well as obstacle detection and avoidance could be translated into some useful platforms which can increase safety of products in smart industries [2]. Sensors have been embedded in the vehicle so as to develop understanding of the environment and used by some control algorithms for the determination of next action. The autonomous robots can carry out tasks, such as assisting friendly troops and bomb defusing in the fields of military operations [7]. However, AV is not mature enough to operate in the complex environment, and significant research is yet to come. The path planner is the vital part of AV, but still has many problems which are not being solved.

2 Tuning of PID Controllers in Autonomous Ground Vehicles (AGVs) The concept of developing proportional integrative derivative controllers had been started in 1911 by Elmer Sperry [1]. After 2 decades, Taylor Instrumental Company (TIC) developed its own controlled mechanism for efficiently tuning proportional controller. Henceforth, control engineers devised first PID pneumatic controller with derivative action so as to get rid of steady-state error. In 1942, the three parameters of PID controllers is tuned by Ziegler and Nichols. Let K p , T d , T i have been considered as proportional gain, derivative of time constant, integral of time constant, respectively, input signal is u(t). Let K d = K p × T d and K i = K p /T i . The standard form of PID controller is:   de(t) 1  u(t) = K p e(t) + T (1) + ∫ e(t)dt dt Ti 0 where e(t) = y (t) − y(t), y (t) is reference input, y(t) is overall output of the system. The sole objective of tuning the three parameters of PID controller is based on different system specifications. Among these, different time domain specifications and several frequency domain specifications have to be considered. Moreover, uncertainties present in system model and internal and external disturbances has to

A Review on Intelligent PID Controllers in Autonomous Vehicle Table 1 ZN first method of tuning

Type of controller

Kp

P

T L

PI

0.9

PID

1.2

T L T L

393 Ti

Td

∞

0

L 0.3

0

2L

0.5L

be addressed [2]. Equation 1 can also be written as u(t) = K p e(t) + K d

 de(t) + K i ∫ e(t)dt dt 0

(2)

Industrial applications of PID controllers began in mid-1950s. For the robust operation of PID controllers, internal model control (IMC) tuning methods have been widely applied in industry. Controller tuning can be effortlessly executed by adjustment of filter parameters in first order plus delay time (FOPDT) model [1]. PID controllers are ideally suitable for first and second order equations. The implementation of PID controllers in industries involve higher order set of equations where tuning becomes necessary condition. The tuning of PID controller is carried out by applying empirical Ziegler-Nichol formula and modified Zeigler- Nichol formula in [3]. After the introduction of Artificial Intelligence (AI) techniques with the combination of PID controllers have emerged as compared to Ziegler- Nicholas method (ZN).

2.1 Ziegler-Nichols First Method of Tuning The TF, C(s)/U(s) by using Ziegler-Nichols as: K e_Ls C(s) = U (s) Ts + 1

(3)

This is approximated by considering a lag function. ZN have put forward the required values of a set of K p , T i ,T d as shown in Table 1.

2.2 Ziegler-Nichols Second Method of Tuning The K p , T i , T d values which are obtained from critical stability and critical gain is depicted as in Table 2. The disadvantage of ZN method is the result does not provide satisfactory performance every time. The concept of fuzzy logic (FL) has been introduced to search out for the desired performance so as to rectify the drawbacks of ZN method. The

394 Table 2 ZN first method of tuning

V. Vartika et al. Type of controller

Kp

Ti

Td

P

0.5Kcr

∞

PI

0.45Kcr

1 1.2

PID

0.6Kcr

0.5 Pcr

0 Pcr

0 0.125 Pcr

control parameters of PID are modified according to plant necessity with the help of new approaches. Among these, FLC, GA-based approach has been explored in detail in subsequent sections.

3 Fuzzy Logic Fuzzy logic (FL) resembles the human thinking process and can be exploited to control different complex plant. Fuzzy logic control (FLC) has the advantage of incorporating human knowledge for making PID as an adaptive controller [5]. Fuzzy PID controller can be self-tuned which can automatically adjust the three parameters in order to improve the results by means of with or without fuzzy tuning of nonlinear systems [5]. The error and derivative of error have been considered to optimize the control parameters [6]. Recently, an improved fuzzy-based PID controller have been designed by adding more control laws to fuzzy PI as well as fuzzy PD which has been shown in Fig. 1 [7]. Positive very big (PVB), positive big (PB), positive medium (PM), positive small (PS), zero (Z), negative small (NS), negative medium (NM), negative big (NB), negative very big (NVB) are some of the commonly used abbreviations in fuzzy inference system.

Fig. 1 PID controller tuned with FLC

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FLC technique guarantees quality performance of set point and superior performance in load disturbance, making this technique promising for PID tuning implementations.

4 Implementation of GA-Based Optimization of PID Controller In contrast to traditional methods of tunable PID controllers, genetic algorithms (GA) have resulted improved performance. Darwin’s principle forms the basis of GA and requires no pre-existing information of initial conditions of given system. The searching process considered as a heuristic approach where set of chromosomes has been considered as a total population. Each chromosome represents one solution. The chromosomes are represented by string of ones and zeroes. The chromosome having highest fitness value is considered as fittest solution. This fittest individual forms a new generation of individuals with more improved fitness values. The measure of fitness of an individual depends upon the performance in the problem domain. In last few decades, GA is emerged as one of the widely applied optimization algorithm. Basically, GA works on three operating conditions namely, selection, mutation, and crossover. Selection provides better chances of reproduction to the fittest individual. In crossover process, the genetic characteristics get exchanged among two chromosomes involved. The probability of mutation is normally 5–10% which have resulted in a random change of a genetic material. The evolution process guides the population through more promising regions in the search space. After performing of these three operations, new set of individual is being created which may be improved solution as compared to the parents. This algorithm finally stops when the optimum solution is reached [8]. The implementation of the tuning procedure through GA starts with the definition of the chromosome representation where K p , K i , and K d are represented by three individual chromosomes which is given in Fig. 2. The three gains are considered to be binary string. Each gain have been entitled by binary numbers. These numbers make every chromosome having a total of thirty genes as shown in Fig. 3. Fig. 2 Chromosome definition

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Fig. 3 PID controller in combination with genetic algorithm

4.1 Steps of Classic Genetic Algorithm (CGA)-Based PID Tuning 1. Binary encoding have been adopted where each binary cluster has one value between 0 and 1024 (210 ). The approximate length of each parameter is being determined and encoded. 2. Starting random population, N has been created. The gene expression k = [k p , k i , k d ] coded in binary number to increase the efficiency and improve the calculation speed and robustness. 3. In starting population represented as N, every population member deciphered. For every individual, the required fitness values have been calculated. The fitness function is defined as ∞ (w1|e(t)| + w2u2(t))dt + w3tu

J=

(4)

0

e(t) is the error between the reference signal and the overall output of the system. u(t) is controller output, t u is defined as the rising time, wi are weights. A penalty function has been counted in the required objective function to avoid overshoot. When the error is less than 0, then function have been changed to: ∞

J = ∫ (w1|e(t)| + w2u2(t) + w4|e(t)|)dt + w3dt

(5)

0

where w4 is the value of weights. The fitness function is being associated with function by the relation Fitness =

1 J

(6)

1. Fitness is evaluated, and fittest one is selected. 2. Create new population N + 1, if number of iterations reached this defined number. Or terminate the algorithm if the output is better than the predefined threshold values.

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Fig. 4 Flowchart of classic genetic algorithm-based PID tuning

3. From above steps which are being repeated till the indexes or parameters are concurrent (Fig. 4) [8].

5 Results and Discussion The properties of rise time and percentage overshoot have been compared with traditional PID and FLC controllers which has been given in Table 3. In addition to that, the properties of K p , K i , K d as well as rise time have been compared with ZN method of tuning of PID controllers and GA-based PID controllers which has been given in Table 4.

398 Table 3 Parameter comparison between conventional and FLC-based PID [9]

Table 4 Parameter comparison between conventional and GA-based PID [10]

V. Vartika et al. Properties

PID

Rise time

30.99

7.85

1.28

13.66

Percentage overshoot

FLC-based PID (FPID)

Properties

ZN method of tuning

GA-based PID

Rise time

601.6

487.2

Kp

28.4

62

Ki

74.1

134

Kd

2.72

16

It is concluded that the adaptive PID controllers involving fuzzy logic controller (FLC), genetic algorithm (GA) give better performance parameters while tuning as compared to conventional ZN method.

6 Conclusion In this paper, extensive review has been carried out to list most of the machine learning-based techniques for designing of intelligent controller in applications to autonomous vehicle. It is concluded that the adaptive intelligent PID controllers involving fuzzy logic controller (FLC), genetic algorithm (GA) ensure improved performance parameters while tuning the parameters of PID as compared to conventional ZN method. There are lot of other optimization methods, such as PSO, AGA which can be effectively applied for tuning three control parameters of proportional integrative derivative. Intelligent fractional PID controller may be designed to achieve better performance.

References 1. Gopi Krishna Rao PV, Subramanyam MV, Satyaprasad K (2013) Model based tuning of PID Controller. J Control Instrum oCI 4(1):16–22 2. Åström KJ, Hägglund T (1995) PID controllers: theory, design, and tuning, vol 2. Instrument society of America, Research Triangle Park 3. Meshram PM, Kanojiya RG (2012) Tuning of PID controller using Ziegler-Nichols method for speed control of DC motor. In: IEEE-international conference on advances in engineering, science and management (ICAESM-2012), IEEE, pp 117–122 4. Lilly JH (2011) Fuzzy control and identification. Wiley, New York, ISBN: 978-1-118-09781-6 5. Kha NB, Ahn KK (2006) Position control of shape memory alloy actuators by using self tuning fuzzy PID controller. In: 1ST IEEE conference on industrial electronics and applications, Singapore, IEEE, pp 1–5

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6. Muhammad Z, Yusoff ZM, Kasuan N, Nordin MNN, Rahiman MHF, Taib MN (2013) Online tuning PID using fuzzy logic controller with self-tuning method. In: 2013 IEEE 3rd international conference on system engineering and technology, IEEE, pp 94–98 7. de Souza LB, Soares PP, Mendonça M, Mourhir A, Papageorgiou EI (2018) Fuzzy Cognitive Maps and Fuzzy Logic applied in industrial processes control. In: 2018 IEEE international conference on Fuzzy systems (FUZZ-IEEE), Rio de Janeiro, 2018, pp 1-8 8. Lin G, Liu G (2010) Tuning PID controller using adaptive genetic algorithms. In: 2010 5th international conference on computer science & education, IEEE, pp 519–523 9. Wijesinghe YM, Narayana M (2019) Genetic algorithm tuned PID controllers for multivariable level control system. In: 2019 Moratuwa engineering research conference (MERCon), IEEE, pp 175–180 10. Butkiewicz BS (2000) System with hybrid fuzzy-conventional PID controller. In: SMC 2000 conference proceedings. 2000 IEEE international conference on systems, man and cybernetics. Cybernetics evolving to systems, humans, organizations, and their complex interactions (cat. no. 0), vol 5. IEEE, pp 3705–3709 11. Sain D, Swain SK, Saha A, Mishra SK, Chakraborty S (2018) Real-time performance analysis of FOI-PD controller for twin rotor MIMO system. IETE Tech Rev 1–22 12. Acharya DS, Mishra SK, Ranjan PK, Misra S, Pallavi S (2018) Design of optimally tuned two degree of freedom fractional order PID controller for magnetic levitation plant. In: 2018 5th IEEE Uttar Pradesh section International conference on electrical, electronics and computer engineering (UPCON), IEEE, pp 3705–3709 13. Kumar T, Mishra SK, Swain SK (2017) Design of fractional order controllers satisfying frequency domain specifications for magnetic levitation system with time delay. In: 2017 IEEE international conference on intelligent techniques in control, optimization and signal processing (INCOS), IEEE

Analysis of Solar Fed Grid-Connected Single-Stage Single-Phase Boost Inverter Prabhat Kumar Ranjan, Piyush Kumar Ojha, P. R. Thakura, and Aditya Kumar Singh

Abstract Growing demand of electricity and need of reducing greenhouse gas emission towards clean and green energy generation system has prompted renewed interest in the renewable energy resources like solar photovoltaic (PV), fuel cells and wind power-based distributed generation systems. The single-stage single-phase differential boost inverter (SSSPDBI) appears as an attractive solution in photovoltaic system where interfacing and regulating of low DC voltage power source with AC loads or utility grid becomes indispensable. The SSSPDBI has a remarkable advantage with respect to a two-stage conversion strategy, consisting of a DC-DC boost converter and a traditional buck voltage source inverter in between the low voltage DC sources and AC output. This paper focuses on the detailed simulative analysis of solar PV fed SSSPDBI towards synchronisation with grid. The pure and smooth sinusoidal output voltage having low total harmonics distortion (THD) is obtained at output terminal of inverter and grid for proper synchronisation. Finally, MATLAB simulations have been performed under different load conditions to validate the effectiveness of overall system. Keywords Single-phase boost inverter · Renewable energy · Differential boost inverter · Grid tied inverter

P. K. Ranjan (B) · P. K. Ojha · P. R. Thakura · A. K. Singh Department of Electrical & Electronics Engineering, Birla Institute of Technology Mesra, Off Campus Deoghar, Deoghar, Jharkhand, India e-mail: [email protected] P. K. Ojha e-mail: [email protected] P. R. Thakura e-mail: [email protected] A. K. Singh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_40

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1 Introduction Alternative energy sources like as wind, solar, fuel cells, etc. and the distributed generation systems based on storage devices attract more attention to cope with the issues like global warming and environmental changes. Non-conventional energy sources like wind, solar, etc. generate inadequate power, which cannot fulfil the requirements of load demand having high power [1]. For two-stage conversion of power (boosting and inversion stage) in case of renewable energy sources having low output voltage, a conventional DC–DC step-up converter and voltage source inverter become indispensible for generation of AC output power from the input DC voltage for various applications like integration with electric grid and various AC loads. Various single-stage inverters like buck and boost configurations are responsible for green energy conversion of renewable energy sources efficiently with the help of [2] by reducing overall inverters size. Traditional two-level voltage source inverters (VSI) are fundamental converter topology, which is responsible for DC to AC buck conversion only. SSSPDBI circuit is useful for those applications where stepping up the DC input voltage into AC output voltage in a single stage is necessary. Unique operating behaviour of SSSPDBI was proposed in which output AC voltage generated in single stage higher than input voltage [3, 4]. The load is connected in differential manner with two DC-DC symmetrical step-up converters and a common input DC voltage. Volume and cost of the system reduces and consequently efficiency improves towards contribution as a major advantage for using SSSPDBI as compared to two-stage inverter topology. The buck and boost inverters contribute as power electronics converters for interfacing of grid with distributed generation, storage devices, and hybrid electric vehicles [5, 6]. Proposed single-stage topology offers additional advantages like minimum power switches, compactness, and low cost. Three-phase and single-phase inverters require closed-loop adaptive control strategy for standalone distributed generation systems. Appearance of harmonics current ripple component minimises in SSSPDBI and can be interfaced with renewable energy and grid [7–9]. Controlling of the boost inverters nature around a particular operating point for generalised model is generally accomplished by small signal linear model [10–12]. The main contribution of this paper to interface the PV system connected to grid through SSSPDBI. This paper reflects not only simultaneous boosting and inversion capability but also efficient and smooth interfacing of solar PV fed SSSPDBI system with the grid.

2 Topology and Modelling of Overall Proposed System The block diagram & circuit diagram is depicted in Figs. 1 and 2, respectively. The output from the photovoltaic energy system provides constant DC voltage for SSSPDBI which converts the input DC voltage into sinusoidal boosted AC voltage in

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403

Fig. 1 Block diagram of overall system

Fig. 2 Proposed circuit diagram of SPV fed SSSPDBI with grid

single stage and finally proper synchronisation occurs efficiently with the grid. The inverter is connected to the grid through the filter. Filters reduce the THD by filtering the harmonics and help in the smooth and pure sinusoidal shape in synchronisation with the grid voltage. SSSPDBI produces a sinusoidal AC output in which the load is connected differentially across the two symmetrical DC–DC boost converters whose output is DC-biased sine wave in nature. The operation of boost inverter (SSSPDBI) can be explained as follows. The operation of above circuit can be explained on the basis of following two modes given below: Mode I: Switch S1 and S2 will be in turn on and switch S3 and S4 will be in turn off condition for mode 1(Fig. 3). Consequently, IL2 rises due to magnetisation and stored energy of inductor L 2 through switch S 2 and input supply. Inductor L 1 demagnetises and delivers energy to the capacitor C 1 . Also the capacitor C 2 d charges through the capacitor C 1 and load resistance R, and finally, output voltage appears across the load. Mode II: switch S3 and S4 will be in turn on and switch S1 and S2 will be in turn off condition for mode II (Fig. 4). Consequently, IL1 rises due to magnetisation and stored energy of inductor L 1 through switch S 4 and input supply. Inductor L 2 demagnetises and delivers energy to the capacitor C 2 . Capacitor C 1 discharges through the capacitor C 2 and load resistance R; consequently, the output voltage appears across the load. Continuous conduction mode of boost inverter has following mathematical equations given below:

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Fig. 3 Mode I

Fig. 4 Mode II

The mathematical equations for mode I are di L 2 dt

(1)

∫ iC1 dt di L 2 + dt C1

(2)

VS = L 2 VS = L 1

∫ iC1 dt ∫ iC2 dt = + io2 R C2 C1

(3)

VS − V L 1 = (1 − d1 )VO

(4)

ic1 + io1 = (1 − d1 )i L 1

(5)

V L 1 ,i L 1 , V L 2 , i L 2 are the voltage and current across the inductor L 1 and L 2 , respectively. The voltage and current across the capacitor C 1 and C 2 are V C1 , ic1 ,

Analysis of Solar Fed Grid-Connected Single-Stage …

405

V C2 , ic2 respectively. V o1 , io1 , V o2 , io2 are the load voltage and load current. The average duty cycle of boost converter 1 is d1 . Now, the mathematical equations for mode II are symmetrically opposite to that of above-mentioned equations. Solving the above equations, the following output voltage Eqs. (6) and (7) will be obtained   2d1 − 1 VS VS − = VS Vo = V o1 − V o2 = 1 − d1 d1 d1 (1 − d1 ) Vo = V o1 − V o2 = (V s + Vm sin wt) − (V s − Vm sin wt) = 2Vm sin wt

(6) (7)

Hence from above Eq. (7) it is clear that SSSPDBI produces a sinusoidal ac output, which is equal to the difference of the two outputs for DC-biased sine wave DC-DC boost converters. This property of single-phase boost inverter is reflected in a single stage.  P=

 sin θ

(8)

di g = Vo − Vg dt

(9)

di g = Vomax. sin(ωo t + θ ) − Vg sin ωo t dt

(10)

Lg Lg

Vo × Vg Zt

di g = Vc1 − Vc2 − Vg sin ωo t (11) dt    Vomax. sin(ωo t + θ ) − Vdc − sin(ωo t + θ) − Vg sin ωo t 2 (12) Lg

Lg

 di g Vomax. = Vdc + dt 2

Vo = out putvoltageo f boostinveter , Vg = gridvoltage, Z t linkinglineimpedance, θ = anglebetweenVo and Vg , Lg = gridinductor .

=

3 SimulationResults and Discussion Table 1 consists of all the circuit design parameters used in this paper and the simulation model of the Photovoltaic (PV) fed SSSPDBI interfacing with grid is shown in Figs. 5 and 6. The output voltage and current waveform of the single-phase boost inverter are shown in Fig. 7. Output AC voltage waveform of inverter (across the R load) is 325 V (peak value) which is more than input voltage (118 V) and the output frequency is 50 Hz.

406 Table 1 The simulated circuit design parameter

P. K. Ranjan et al. Parameters

Values

Photovoltaic cell Output voltage

118 V

Boost inverter inductor, L = L 1 = L 2 8mH Boost inverter capacitor, C 1 = C 2 = 4mF C Boost inverter load

400 , 8mH

Boost ınverter output voltage V o

230.1 V(RMS)

Grid inductance L g Grid voltage(V g ) with specification

0.5mH 1 ∅, 50 Hz, 230 V(RMS)

Filter resistor, R

0.5 

Filter capacitance, C 3

320 µF

Switching frequency, f s

1 kHz

Output frequency

50 Hz

Fig. 5 Boost inverter with solar photovoltaic fedlSSSPDBI interfacig with grid

SSSPDBI with R load has been simulated to verify the outcomes of the inverter. Figure 7a, b depicts the waveform of the output current through the load and output voltage across the resistive load, respectively. It can be observed that the output voltage and current waveform is smooth sinusoidal waveform in nature as desired having low THD with also the voltage step-up action occurs in the operation. These simulation results validate the desired simultaneous boosting and inversion characteristics of SSSPBI. In the operation of the SSSPDBI with RL load, the desired results from the inverter output terminal are obtained. Also, the boosting of solar fed input DC voltage is done in this operation with smooth amplified waveform of AC output voltage with low THD values. The THD analysis of SSSPDBI for RL load is shown Fig. 7h. THD of the single-phase boost inverter is 0.21%. The output voltages and currents waveforms across the load are depicted in Fig. 7c, d, respectively.

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Fig. 6 Grid Subsystem diagram

The photovoltaics produce the DC power supply which then fed to the boost inverter, and finally, output of boost inverter is fed to the grid. The complete circuit consists of solar photovoltaic fed SSSPDBI interfacing with grid has been tested and analysed in the MATLAB Simulink platform. The results are shown in Fig. 7e–h. It can be concluded that the output voltage of the inverter and the grid is in same phase having almost same magnitude at a given frequency(50 Hz). Also, the desired output current is obtained.

4 Conclusion This paper focuses on extensive analysis which reflects simultaneous boosting and inversion capability as well as efficient and smooth interfacing of solar PV fed SSSPDBI with the grid. The pure and smooth sinusoidal output voltage having low total harmonics distortion (THD) is obtained at output terminal of inverter and grid for proper synchronisation of grid tied SSSPDBI. Filters are utilised with the proposed system for the smooth synchronisation of solar PV fed SSSPDBI with the grid. These inverters play a vital role in the field of interfacing issues of distributed generation based on renewable energy sources with AC motor drives, storage devices, and hybrid electric vehicles. The future work will be the different design issues of duty cycle controller, regulated output voltage, and system stability issues of grid-connected SSSPDBI for renewable energy applications.

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a

b Output Voltage(V)

Output current(A)

400 50

0

-50 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

200 0 -200

0

0.4

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.3

0.35

0.4

0.3

0.35

0.4

time(seconds)

time (seconds)

c

d Output Voltage (V)

Output current (A)

400 50 0 -50 0

0.05

0.1

0.2

0.25

0.3

0.35

0 -200 -400 0

0.4

time (seconds)

e

0.05

0.1

0.15

0.2

0.25

time (seconds)

f 50

200

Load current(A)

Synchronised voltage(V)

0.15

200

100 0 -100 -200 0

0.05

g

0.1

0.15

0.2

0.25

time(seconds)

0.3

0.35

0

-50 0

0.4

0.05

0.1

0.15

0.2

0.25

time(seconds)

h

Fig.7 a Output current of boost inverter (R Load),b output voltage of boost inverter(R Load), c output current of boost inverter (RL Load), d output voltage of boost inverter (RL Load), e synchornised voltage output (inverter and grid voltages), f load current of SSSPDBI, g boost inverter output current, h FFT of the output voltage of inverter (SSSPDBI)

References 1. Mazumdar P, Enjeti PN, Balog RS (2014) Analysis and design of smart PV modules. IEEE J Eng Selected Topics Power Electron 2(3):451–459 2. Ibrahim ME, Mansour AS, Abd-Elhady AM (2017) A novel single-stage single-phase buckboost inverter. Electr Eng 99(1):345–356 3. Caceres R, Barbi I (1995) A boost DC–AC converter: operation, analysis, control and experimentation. In: Industrial electronics, control, and instrumentation. Proceedings of the 1995 IEEE IECON 21st International Conference, pp 546–551

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4. Caceres RO, Barbi I (1999) A Boost DC-AC Converter: analysis, design, and experimentation. IEEE Trans Power Electron 14(1):134–141 5. Kavitha A, Senthil Kumar N, Vanaja N (2016) Design and control of grid synchronization of renewable energy sources. In: 2016 international conference on circuit, power and computing technologies (ICCPCT). IEEE, pp 1–8 6. Freddy TKS, Rahim NA (2016) Photovoltaic inverter topologies for grid integration applications. In: Advances in solar photovoltaic power plants. Springer, Berlin, pp 13–42 7. Zhou Y, Huang W, Hong F, Wang C (2016) Modelling analysis and power loss of coupledinductor single-stage boost inverter based grid-connected photovoltaic power system. IET Power Electron 9(8):1664–1674 8. Saroha J, Pandove G, Singh M (2018) Modelling and simulation of grid connected SPV system with active power filtering features. J Institut Eng (India): Ser B 99(1):25–35 9. Myrzik JM (2001) Novel inverter topologies for single-phase stand-alone or grid-connected photovoltaic systems. In: 4th IEEE international conference on power electronics and drive systems. IEEE PEDS 2001-Indonesia. Proceedings (Cat. No. 01TH8594). 1, IEEE, pp 103–108 10. Sanchis P, Ursæa A, Gubia E, Marroyo L (2005) Boost DC-AC inverter: a new control strategy. IEEE Trans Power Electron 20(2):343–353 11. Rashid MH (ed) Power electronics handbook. Butterworth-Heinemann 12. Zhu GR, Xiao CY, Wang HR, Tan SC (2016) Closed-loop waveform control of boost inverter. IET Power Electron 9(9):1808–1818

Two-Port Network-Based Modeling and Analysis of Three-Phase Self-excited Induction Generator Used in Renewable Energy Systems Sambaran Ray, Himadri Sekhar Chatterjee, Dipanjan Samajpati, Sankar Narayan Mahato, and Nirmal Kumar Roy Abstract This paper presents the binary search algorithm (BSA)-based performance analysis of the three-phase self-excited induction generator (SEIG). The generator is modeled as two-port network. By isolating the magnetizing reactance branch for the modeling of three-phase SEIG, BSA is applied to determine the value of generated per unit frequency of model equation which is important part of theoretical performance analysis of SEIG. The waveforms of generated voltages and currents in the generator are also examined, and their corresponding harmonic contents are recorded. To validate the study of SEIG using BSA, an experimental setup is established, and the simulated results are verified with the experimental results, which show a close match between both the results. The analysis of the behavior of three-phase SEIG using BSA is easier, more efficient, and accurate. Keywords Binary search algorithm · Renewable energy · Self-excited induction generator · Two-port network · Waveform

1 Introduction Energy is essential for all parts of society. The environmental degradation and the exhaustion of fossil fuel have compelled the global focus on better solutions for S. Ray (B) · H. S. Chatterjee · D. Samajpati · S. N. Mahato · N. K. Roy Department of Electrical Engineering, National Institute of Technology Durgapur, Mahatma Gandhi Avenue, Durgapur 713209, India e-mail: [email protected] H. S. Chatterjee e-mail: [email protected] D. Samajpati e-mail: [email protected] S. N. Mahato e-mail: [email protected] N. K. Roy e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_41

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energy demand. To fulfill the energy demand of remote and rural areas, off-grid renewable energy, including stand-alone systems, and mini-grids, has become a major, cost-effective option to access electricity. The SEIG has a significant role for stand-alone operation [1–3], and it is widely used in renewable energy sector because of its advantages over synchronous generator such as availability, small size, and low maintenance cost. To understand steady-state behavior of SEIG, the modeling equations are developed from its equivalent circuit and are solved by some classical methods [1–3] and some recently developed algorithms such as GA [4], PSO [5, 6] and binary search algorithm (BSA) [7]. The major contributions of this paper are listed below: • The three-phase SEIG is represented by two-port network model which simplifies in developing performance equation to analyze the steady-state behavior of generator. • Binary search algorithm [7]-based optimization technique is used to analyze the SEIG without load and same with load. • The waveforms of generated voltages and currents in the generator are also investigated, and their corresponding harmonic contents are recorded to test the power quality standard of generated waveforms. • All the simulated results found in this work are validated through experimentation.

2 Two-Port Network Modeling of Three-Phase SEIG To generate voltage across stator terminals of SEIG, proper capacitor bank assembly is required to provide reactive power for its voltage build-up. Figure 1 shows a diagram of three-phase SEIG with excitation capacitor bank. The modeling equations for performance analysis of SEIG are developed from two-port network model of the generator with balanced static resistive load shown in Fig. 2. Here, ‘a’ denotes per unit frequency, i.e., ratio of generated frequency and base frequency, and ‘b’ denotes per unit speed, i.e., ratio of operating rotor speed (nr ) and synchronous speed (ns ). In Fig. 2, V 1 and V 2 are taken as independent variables, and I 1 and I 2 are taken as dependent variables, respectively. The equations for two-port network are written as I1 = y11 V1 + y12 V2

(1)

I2 = y21 V1 + y22 V2

(2)

where y11 , y12 , y21 , and y22 are admittance (or y) parameters of two-port network. The values of the parameters are as y11 = Y1 + Ym ;y22 = Y1 + Yc ; y12 = y21 = −1  −1 −1   R2 −Y1 ; Y1 = Ra1 + j X 1 ; Y2 = a−b + j X 2 ; Ym = [ j X m ]−1 ; Yc = −aj 2X c −1  and Y L = RaL + j X L .

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413

Prime mover

Three-phase load

Three-phase induction generator

Three-phase capacitor bank

Fig. 1 Diagram of three-phase SEIG

Two-port network I1

Y2

R1/a

jX1

I2

Y1 R2/(a - b) V1 jX2

jXm Ym

Yc

-jXc/a2

RL/a YL

V2 jXL

I 2'

I 1' Fig. 2 Two-port network model of three-phase SEIG

From the two-port network, it is seen that I1 = −V1 Y2 and I2 = −V2 Y L . Putting the values of I1 and I2 in Eqs. (1) and (2), an equation is formed as y11 + Y2 −

y12 y21 =0 y22 + Y L

Inserting the values of parameters in Eq. (3), the Eq. (3) becomes as

(3)

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Y = where Y = Y1 + Y2 − it is obtained as

Y12 . Y1 +Yc +Y L

j Xm

(4)

Separating the real and imaginary parts of Eq. (4), real(Y ) = 0 img(Y ) =

1 Xm

(5) (6)

Equation (5) does not contain ‘X m ’ but only unknown quantity ‘a’ for given values of speed, load, load power factor, excitation capacitance, and machine parameters. This Eq. (5) is solved by binary search algorithm (BSA) for ‘a.’ The value of ‘a’ is then put in Eq. (6) to get the value of ‘X m. ’ The values of ‘a’ and ‘X m ’ are used in performance equations of SEIG to analyze its steady-state behavior.

3 Binary Search Algorithm (BSA) The generated per unit frequency for definite values of load, power factor, excitation capacitance, speed, and machine parameters is solved by binary search algorithm [7] without simplifying Eq. (5). The flowchart of BSA for analysis of steady-state behavior of SEIG is shown in Fig. 3.

4 Experimental Set-Up To test the efficiency and accuracy of binary search algorithm and to study the behavior of SEIG, a squirrel cage induction machine with Y-connection, three-phase, four-pole and rating of 415 V, 4.5 A, 50 Hz, 2.2 kW, 1440 rpm, is used in laboratory. The parameters of the induction machine are: stator resistance (R1 ) = 3.71 , rotor resistance (R2 ) = 2.846 , stator leakage reactance (X 1 ) = 4.98 , and rotor leakage reactance (X 2 ) = 4.98 . The magnetization characteristic, i.e., relationship between magnetizing reactance (X m in ohm) and air-gap voltage (E g in volt), given in Eq. (7) is obtained from the test conducted in the laboratory. This characteristic is utilized to develop performance equations of SEIG to analyze its steady-state behavior. ⎡

⎤ 249.1 for X m ≤ 76.5 E g = ⎣ 0.000229X m3 − 0.104X m2 + 11.6X m − 134 for 76.5 < X m ≤ 141.65 ⎦ 0 for 141.65 < X m (7)

Two-Port Network-Based Modeling and Analysis …

415 Stop

Start

Read R 1, R 2, X 1, X 2, C, R (X m ) max

L, N,

Evaluate SEIG performance Yes

Evaluate b

No operating state under chosen operating condition Is X m ≤ (X m ) max

No

? Set maximum iteration (iter_max) and tolerance

Set a

max = (b/1.0001) and a min = (b/1.1)

Select the value of a and compute X m using (6) No Is Iter ≤ iter_max ?

Yes

Set real(Y) = -1

Iter = iter +1

Set iteration: iter = 1

No Is real(Y) > 0.00001 ?

Yes a max = a a min = a Compute a = (a max + a min )/2

Yes

No Is real(Y) < 0 ?

Put the value of a in (5) and compute real(Y)

Fig. 3 Flowchart of BSA for analysis of SEIG

5 Result and Discussion 5.1 Performance Analysis of SEIG Using BSA 5.1.1

Performance Without Load

The BSA technique is used to study the steady-state behavior of a three-phase SEIG with static resistive load. The generator is operated under no-load condition and rotated at 1500 rpm. Figure 4 shows the variations of the stator current and terminal voltage with excitation capacitance under no load. The excitation capacitance greatly influences the stator current and terminal voltage. There is close match between experimental and simulated results. Figure 5 shows the waveform of stator line voltage between phases a and b and its harmonic spectrum with shunt capacitance of 32.5 µF and the rotor speed of 1500 rpm under no-load condition.

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Fig. 4 Experimental and simulated results showing variation of a stator current and b terminal voltage with shunt capacitance for N = 1500 rpm

Fig. 5 Experimental result showing a waveform of stator line voltage between phases a and b (V ab = 200 V/div; Time scale = 10 ms/div), b harmonic order of voltage in stator circuit for C = 32.5 µF and N = 1500 rpm under no-load condition

5.1.2

Performance with Load

The steady-state behavior of SEIG is investigated by running the rotor at 1500 rpm and by connecting three-phase load and a three-phase capacitor bank of 35 µF each across the stator terminals shown in Fig. 6. Figure 6 presents the change of generated frequency and terminal voltage with change in output power. Both the frequency and terminal voltage will reduce if output power is increased. ˙If the load of the generator is very large, the output power, frequency and load voltage will decrease after certain value of load. It is observed from Figs. 7 and 8 that the presence of harmonics in both the voltage and current waveforms are very less which indicate that the SEIG generates and supplies good quality of power. Total harmonic distortion (THD) for voltage and current waveforms are 0.133% and 0.177%, respectively.

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Fig. 6 Experimental and simulated results showing variation of a generated frequency, b terminal voltage with output power at p.f = 1, N = 1500 rpm and C = 35 µF

Fig. 7 Experimental result showing a waveform of stator line voltage between phases a and b (V ab = 200 V/div; Time scale = 10 ms/div), b harmonic order of stator voltage for C = 37.5 µF, N = 1500 rpm and three-phase load = 1200 W

Fig. 8 Laboratory test result showing a load current in phase-b (I b = 2 A/div; Time scale = 10 ms/div), b harmonic order of load current for C = 37.5 µF, N = 1500 rpm and three-phase load = 1200 W

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6 Conclusion In this paper, the three-phase SEIG is represented as two-port network to develop the circuit equations easily, and further binary search algorithm is used for easy solution of the equations to study the steady-state behavior of three-phase SEIG. There is good matching of the simulated and the experimental results observed from this work. The distortion of current and voltage waveforms due to presence of harmonics has also been examined to understand the power quality standard by measuring %THD of individual waveform. The generated voltage and current meet the international power quality standard (IEEE 519—harmonic control in electrical power systems, EN 50,160:1999 and CIGRE WG 36-05).

References 1. Murthy SS, Malik OP, Tandon AK (1982) Analysis of self-excited induction generator. Proc Inst Elect Eng C 129(6):260–265 2. Quazene L, McPherson G Jr (1983) Analysis of an isolated induction generator. Power App Syst 102(8):2793–2798 3. Chan TF (1994) Steady-state analysis of self-excited inductiongenerators. IEEE Trans Energy Convers 9(2):288–296 4. Kumaresan N (2005) Analysis and control of three-phase self-excited induction generators supplying single-phase AC and DC loads. IEE Proc Electr Power Appl 152(3):739–747 5. Lu X, Iyer LV, Mukherjee K, Kar NC (2012) A wavelet/PSO based voltage regulation scheme and suitability analysis of copper- and aluminium-rotor induction machines for distributed wind power generation. IEEE Trans Smart Grid 3(4):1923–1934 6. Ray S, Mahato SN, Roy NK (2014) Performance analysis of isolated 3-phase self-excited induction generator using graph theory and PSO technique. In: TENCON 2014—2014 IEEE region 10 conference, pp 1–6 7. Arthisri K, Anusha K, Kumaresan N, Kumar SS (2017) Simplified methods for the analysis of self-excited induction generators. IET Electr Power Appl 11(9):1–9

Generalized Review on MPPT Methods Under Variant PSC Niteesha Kumari, S. Shiva Kumar, and V. Laxmi

Abstract Maximum power point is the key in extracting solar power. This paper provides a structured review on different maximum power tracking techniques. It mainly emphasizes the evolution of maximum power point tracking (MPPT) technique. Various algorithms have been discussed here and categorized on the basis of performance and implementation. Later on, recent trend of choosing various MPPT techniques based on the performance on the locating maximum power point (MPP) under constantly varying partial shading condition (PSC) is taken into account. Keywords Variant PSC · Metaheuristic optimization · Hybrid MPPT method · Global MPP

1 Introduction In recent years, our dependence on fossil fuels has caused adverse effects on climate change, greenhouse gas emissions and moreover their depletion will obstruct the progress of mankind. Harvesting of green energy to satisfy energy demand could be an environment-friendly alternative approach. The availability in abundance, ecofriendly and noise-free characteristics of solar energy make it best alternative among various renewable energy sources. Commercially existing solar cells has efficacy around 38–43% [1]. Hence, it is essential to maximize the solar energy efficacy to reduce the overall cost [2, 3]. Enhancing the PV module output with MPPT has become a trending research topic. The nonlinear power–voltage (P–V) and current–voltage characteristics indicate that MPP can be obtained only at single point [4]. As shown in Figs. 1 and N. Kumari (B) · S. S. Kumar · V. Laxmi Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] S. S. Kumar e-mail: [email protected] V. Laxmi e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_42

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Fig. 1 P–V characteristics at vivid solar irradiance level

Panel Power (P)

950 Watt/m2 750 Watt/m2

550 Watt/m2 350 Watt/m 2 150 Watt/m2

Panel Voltage (V)

2 [1], this point varies with the variation in temperature and insolation. If there is a mismatch between source and load characteristic, the power delivery to the load will be reduced which will cause the power loss. Hence, the power interface is used between source and load to match the impedance and hence evade the power loss known as MPPT [5]. There are plenty of methods proposed to locate the MPP at ideal condition without considering the PSC. The popular conventional methods among them are incremental conductance method, hill climbing method [6–8] and perturb and observe (P&O) method [9], which have convincing results. However, they all failed during the PSC. They were unable to locate the MPP during the swift change in operating condition. Also, during the non-uniform of irradiation levels in PV modules, they terribly failed to locate the MPP. Therefore, a number of methods were developed to overcome Fig. 2 P–V characteristics at vivid temperature level o

Panel Power (P)

10 C o

20 C o

30 C o

40 C

Panel Voltage (V)

Generalized Review on MPPT Methods … L1

421 D

Cc

PV Panel S L2

Digital MPPT Controller

C0

R0

Duty Cycle

Fig. 3 PV array interfaced with SEPIC converter with load Ro

the complication of MPP tracking. Different soft computing techniques and metaheuristic optimization approach are trending as they assure the accuracy of locating the MPP at non-favorable atmospheric conditions. In recent times, single ended primary inductor converter (SEPIC) is mostly preferred to use as power interface between PV module and sink, as shown in Fig. 3 [10]. As it gives the large operating region in P–V curve, so that the chances of finding MPP is increased [10, 11]. Moreover, its input current is continuous and non-inverted output voltage is present. MPPT methods are classified in this paper on the basis of their performance and complexity of implementation. This paper mainly focuses on merit and demerit of MPPT techniques. In this work, reason of choosing the particular MPPT technique has been classified with explanation. PSC, varying insolation level, rate of convergence, steady-state oscillation and implementation complexity has been taken into account while dividing the MPPT categories.

2 Evolution of Maximum Power Point Technique MPPT plays a very significant role in terms of extracting the MPP from solar module and numerous methods for this purpose have been developed. Pilot cell technique is utilized to calculate the MPP by open circuit voltage (V oc ) extraction, which is used to calculate the maximum voltage (V m ) by multiplying a coefficient K (around 0.8) [12]. There are several power converters proposed for impedance matching between the solar array and the sink [13]. Constant voltage method and constant current method are introduced depicting the fact that MPP occurs between 72 and 78% of V oc and between 78 and 92% of short circuit current (I sc ), respectively [14–16]. These techniques are easy to implement and rate of convergence is also fast but its accuracy is limited.

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The P&O and hill climbing methods are later proposed which gives more precise results compared to above methods. [17, 18]. These methods are almost similar, with hill climbing considering duty ratio as perturbing parameter and P&O as operating voltage a perturbing parameter. P&O algorithms depict the P–V characteristics of solar array for tracking the maxima, hence, it also called true MPPT [10]. These algorithms are easy to implement, simple, require less parameter and hence take lesser time to execute. However, they possess some demerits such as steadystate oscillations at MPP as the perturbation is not constant but to maintain the MPP, changing in both directions constantly. By minimizing the perturbation size, oscillation can be regulated, but time to achieve the MPP will increase and oscillations will not be completely eliminated, as shown in Fig. 4. So, there should be compromise between perturbation size and perturbation rate. Incremental conductance (IC) algorithm has been introduced to reduce the oscillation and to enhance the efficacy of photovoltaic systems, but it is unable to remove oscillation. All these methods are unable to perform during those time interval when atmospheric conditions are changing [19]. Numerous modified versions of the IC algorithm have been proposed to increase the maximum power point tracking capability while the insolation rate is rapidly changing [20, 21]. Trigonometric rule has been proposed to achieve a fast MPP tracking to exhibit relationship between I-V characteristic and the load line.[22]. Soft computing method is incorporated with MPPT to find the MPP during continuously changing insolation level with accuracy compared to existing methods available [23–26]. These methods are hard to implement and accuracy is also compromised sometimes during varying atmospheric condition. During PSC, above-mentioned methods failed at some point to extract the global maxima. To obtain the global peak and to obtain the maximum efficiency, metaheuristic optimization methods such as cuckoo search algorithm [27], evolutionary algorithm (EA) [28], particle swarm optimization [29], genetic algorithm [30] have been introduced. These algorithms have stochastic characteristic that permits to explore the MPP in whole P–V characteristic, thus, chances of finding the maximum power is very much plausible. These methods are complex, involve more parameters, hence, the convergence rate is sluggish and system becomes bulky. Recent

Panel Power (P)

Fig. 4 Steady-state oscillation around MPP

1

2

(dP/dV)>0

Panel Voltage (V)

3 (dP/dV) 0 and M ∈ n×n ≥ 0. OCP is defined as; find the control vector which minimizes the PI subject to system dynamics. The feedback control law is defined by considering the stabilizability and detectability properties of the FOCSS [23] as u(t) = K x(t) + ν(t)

(3)

where ν(t) ∈ m×1 is the new control vector and K ∈ m×n is the feedback gain matrix. We can choose K in order to satisfy the following relation deg (|s E − (A + B K )|) = rank (E)

(4)

We may choose two matrices based on the Lemma given in [5] in order to satisfy the following relations FEG = diag(In 1 , O), F and G must be non-singular

˜ In 2 ) F(A + B K )G = diag(,

(5)

[5].

˜ is a new state matrix of order n 1 × n 1 , O is a nilpotent matrix of order where  n 2 × n 2 , n 1 = rank(E) and n 1 + n 2 = n. Coordinate transformation is considered as [5] 

 x1 (t) x(t) = G , x1 (t) ∈ n 1 , x2 (t) ∈ n−n 1 x2 (t)

(6)

By using Eqs. (3), (5) and (6), we can write Eq. (1) as RL α 0 Dt x 1 (t)

˜ 1 (t) + 1 ν(t) = x

0 = x2 (t) + 2 ν(t)  1 . where F B = 2 By using the Eq. (6) and (8), we can obtain 

(7) (8)

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x(t) = u(t)



I 0 K I





x(t) = ν(t)



⎤ ⎤ ⎡ ⎡     I 0  x1 (t) x1 (t) G 0 ⎣ G 0 ⎣ x2 (t) ⎦ = 0 −2 ⎦ ν(t) KG I KG I 0 I ν(t) (9)

Substitute Eq. (9) in Eq. (2) and over simplifying we get 1 J= 2

1

 x1T (t) M˜ x1 (t) + ϑ T (t) S˜ ϑ (t) dt

(10)

0

⎤T ⎤ ⎡  T    I 0 I 0 G 0 M 0 G 0 ⎣ 0 −2 ⎦ , M˜ = where = ⎣ 0 −2 ⎦ KG I 0 S KG I 0 I 0 I −1 ˜ −1 ˜ ˜ ˜ M − Y S Y and ϑ(t) = ν(t) + S Y x1 (t). Substitute ν(t) = ϑ(t) − S˜ −1 Y˜ x1 (t) in Eq. (7), we obtain 

MY Y˜ S˜



RL α 0 Dt x 1 (t)

⎡

˜ − 1 S˜ −1 Y˜ )x1 (t) + 1 ϑ(t) = x1 (t) + 1 ϑ(t) = (

(11)

Therefore, by using coordinate transformation, we have transformed the FOCSS (1) into SFOCS (11). Then, the formulation of OCP can obtain by using transformed system dynamics (11) and transformed PI (10). By using Lagrange multiplier λ(t), the augmented PI is written as ⎧ ⎫  1 ⎨ 1  T ⎬ x1 (t) M˜ x1 (t) + ϑ T (t)ϑ(t) 2 Ja =   dt ⎩ ¯ 1 (t) + 1 ϑ(t) −0R L Dtα x1 (t) ⎭ +λT (t) x 0 The first variation of an augmented PI becomes ⎧

⎫ T ⎪ ˜ pt x1 (t) +  ¯ T λ(t) −tR L D1α λ(t) δx1 (t)⎪ ⎪ ⎪ M ⎪ ⎪ ⎪ 1 ⎪ ⎨

⎬ T T dt δ Ja = ˜ + Sϑ(t) + 1 λ(t) δϑ(t) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 0 ⎪ ⎪ T ⎩ ¯ ⎭ + x1 (t) + 1 ϑ(t) −0R L Dtα x1 (t) δλ(t)

(12)

For optimum δ Ja = 0 [4]. Therefore, the necessary conditions are RL α 0 Dt x 1 (t)

=  x1 (t) + 1 ϑ (t)

(13)

RL α t D1 λ(t)

T = M˜ x1 (t) +  λ(t)

(14)

Solving an Optimal Control Problem of Fractional-Order Continuous-Time …

ϑ (t) = − S˜ −1 1T λ(t)

447

(15)

For fixed final time-free final state problem, the transversality condition is λ (1) = 0. In this paper, we solve the optimal conditions using GLA-based solution scheme. We can approximate the necessary conditions given by Eqs. (13) and (14) using GLA as j 1  (α) w x1 ( j h − i h) = x1 ( j h) − 1 S˜ −1 1T λ( j h), j = 1, 2, ...N h α i=0 i N− j 1  (α) T w λ( j h + i h) = M˜ x1 ( j h) +  λ( j h), j = N − 1, N − 2, ...0 h α i=0 i

(16)

We can solve the Eq. (16) by using MATLAB and then obtain the optimal states and optimal control.

3 Numerical Simulation In order to check the applicability of the formulation, an example is illustrated in this section by using solution scheme based on GLA. Consider a FOCSS given by Eq. (1) with 

 10 E= , 00



 0 1 A= , −1 0

  1 B= 1

Consider a PI given by Eq. (2) with 

 10 M= ,S=1 01 Boundary conditions are x1 (0) = 1 and λ(1) = 0. We can adopt F, G and K as  F=

     10 1 0 , G= and K = 1 −1 . 01 0 −1

The following results are obtained by using foregoing considerations as Numerical simulation results of both the states and control for distinct α and N = 5 are shown in Figs. 1, 2 and 3. From the results, we conclude that amplitudes of both the states and control decreases as α is decreased like in [7, 8] and it demands less control effort.

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Fig. 1 x1 (t) for distinct α, N = 5.

Fig. 2 x2 (t) for distinct α, N = 5

4 Conclusions In this paper, a formulation and approximated solution scheme for LQOCP of FOCSS is presented. System dynamics is expressed is described by FDEs with RL derivative. PI is considered in general form. Due to complex nature of SSs, the solution is difficult. Therefore, before applying OCP formulation, we have converted FOCSS to SFOCS by using coordinate transformation. Thereafter, necessary conditions have been solved by using GLA-based numerical technique. In order to demonstrate the applicability of formulation and solution scheme, an example is illustrated. Numerical simulation has been carried out at distinct values of α. From the results, we

Solving an Optimal Control Problem of Fractional-Order Continuous-Time …

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Fig. 3 u(t) for distinct α, N = 5

observe that when α increases amplitudes of both states and control are increased and it demands more control effort.

References 1. Podlubny I (1999) Fractional differential equations. Academic Press, San Diego 2. Suribabu G, Chiranjeevi T (2016) Implementation of fractional order PID controller for an AVR system using GA and ACO optimization techniques. In: IFAC-ACODS conference, NIT Trichy, India, vol 49(1), pp 456–461, 1–5 Feb 3. Dzielinski A, Sierociuk D, Sarwas G (2010) Some applications of fractional order calculus. Bull Pol Ac: Tech 58(4):583–592 4. Naidu DS (2003) Optimal control systems. CRC Press, New York 5. Dai L (1989) Singular control systems. In: Thoma M, Wyner A (eds) Lecture notes in control and information sciences. Springer, Berlin, pp 1–340 6. Chiranjeevi T, Biswas RK, Chudamani S (2019) Optimal control of fractional order singular system. Int J Electr Eng Educ. https://doi.org/10.1177/0020720919833031 7. Agrawal OP (2004) A general formulation and solution scheme for fractional optimal control problems. Nonlinear Dyn 38:323–337 8. Biswas RK, Sen S (2011) Fractional optimal control problems with specified final time. ASME J Comput Nonlinear Dyn 6:0210091–0210096 9. Biswas RK, Sen S (2014) Free final time fractional optimal control problems. J. Franklin Inst 351:941–951 10. Dzielinski A, Czyronis PM (2013) Fixed final time and free final state optimal control problem for fractional dynamic systems-linear quadratic discrete-time case. Bull Pol Ac: Tech 61(3):681–690 11. Dzielinski A, Czyronis PM (2012) Fixed final time optimal control problem for fractional dynamic systems-linear quadratic discrete-time case. In: Mikolaj B, Krzysztof (eds), Advances in control theory and automation. Printing House of Bialystok University of Technology, Poland, pp 71–80 12. Chiranjeevi T, Biswas RK (2017) Discrete-time fractional optimal control. Mathematics 5(2):1– 12 13. Chiranjeevi T, Biswas RK (2018) Formulation of optimal control problems of fractional dynamic systems with control constraints. J Adv Res Dyn Control Syst 10:201–212

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14. Biswas RK, Sen S (2009) Numerical method for solving fractional optimal control problems. In: ASME IDETC/CIE conference, San Diego, California, USA, pp 1–4, 30 Aug–3 Sept 2009 15. Behroozifar M, Habibi N (2018) A numerical approach for solving a class of fractional optimal control problems via operational matrix Bernoulli polynomials. J Vib Control 24(12):2494– 2511 16. Alizadeh A, Effati S (2018) Modified Adomian decomposition method for solving fractional optimal control problems. T. I. Meas Control 40(6):2054–2061 17. Chiranjeevi T, Biswas RK (2019) Closed-form solution of optimal control problem of a fractional order system. J King Saud Univ Sci 31:1042–1047 18. Sabouri KJ, Effati S, Pakdaman M (2017) A neural network approach for solving a class of fractional optimal control problems. Neural Process Lett 45:59–74 19. Mashayekhi S, Razzaghi M (2018) An approximate method for solving fractional optimal control problems by hybrid functions. J Vib Control 24(9):1621–1631 20. Nemati A, Yousefi SA (2017) A numerical scheme for solving two-dimensional fractional optimal control problems by the Ritz method combined with fractional operational matrix. IMA J Math Control Inform 34(4):1079–1097 21. Biswas RK, Sen S (2010) Fractional optimal control problems: a pseudo-state-space approach. J Vib Control 17(7):1034–1041 22. Biswas RK, Sen S (2011) Fractional optimal control within Caputo’s derivative. In: ASME IDETC/CIE conference, Washington, DC, USA, pp 1–8, 28–31 Aug 2011 23. Moubarak MRA, Ahmed HF, Khorshi O (2018) Numerical solution of the optimal control for fractional order singular systems. Differ Equ Dyn Syst 26:279–291

Impact of Reconfiguration and Network Topology on Voltage Stability Margin V. V. S. N. Murty Vallem and Ashwani Kumar

Abstract This paper presents the impact of feeder reconfiguration and network topology on voltage stability margin in distribution sytems. This work includes: (i) voltage stability margin analysis in mesh network and reconfigured distribution system, (ii) a new stability index is established for mesh networks, (iii) voltage stability margin is determined for various voltage-dependent load models, (iv) voltage stability margin enhancement is studied with PV penetration and OLTC, (v) probabilistic load model and uncertainty of PV power output are also taken care in the evaluation of voltage stability margin. The analysis has been conducted on IEEE 33-bus distribution network. Keywords Distribution system · Reconfiguration · Mesh distribution system · Load models · Voltage stability margin · OLTC · Distribution generation

1 Introduction Power system operating point should be stable, secure and meeting various operational constraints under normal and contingency scenarios. However, due to economical and environmental constraints, the present power system networks are highly stressed and weak and are being operated at the verge of stability limits. ˙In this scenario, maintaining the voltage stability is a challenging task for the system operator which is essential for power system planning and operational aspect. The popular topology of distribution system is radial structure. However, meshed topology is also used to provide reliable system for areas with heavy load density. Many authors have focussed reconfiguration problem and mesh topolgy for reduction in energy loss and voltage drop. In addition to this, voltage stability margin investigation is another essential aspect must be analysed. Voltage instability means fall in bus voltage below V. V. S. N. Murty Vallem (B) · A. Kumar Department of Electrical Engineering, NIT Kurukshetra, Kurukshetra, Haryana, India e-mail: [email protected] A. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_45

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its rated value due to outage of generator, transformer, line, overloading and reactive power deficiency [1, 2]. Driving factors for voltage instability are outage of lines, generators and transformers. In addition to this, inadequate reactive power support, radial configuration of the system, affects voltage stability. Sudden disturbances of reactive power demand drive the system into instability region. Voltage stability of the system can be enhanced with adequate reactive power compensation, load shedding during contingencies, parallel lines. Distribution network operator at control centre needs to monitor change in load demand, bus voltage and reactive power reserve closely. Various conventional approaches such as PV curve, QV curve, continuous power flow [3, 4] and singularity of jacobian matrix [5– 7] exist to analyse voltage stability phenomenon. Branch equivalent was adopted for network reduction [8]. Static stability index was formulated for radial system based on network load admittance ratio [9] and loop system [10]. The effect of network topology and reconfiguration in standalone microgrids has been studied for small signal stability [11]. In reconfiguration, network topology is modified by opening/closing tie lines to minimise power loss, improve voltage profile [12, 13], maximisation of loadability [14]. Reconfiguration of islanded microgrids presented in [15, 16] for loss minimisation and loadability maximisation. Maximum allowable loading values were determined in radial distribution system subject to voltage stability [17]. Various static voltage stability indices were presented in [18, 19]. Voltage stability margin improvement was addressed with distribution generation [20]. The rest of this paper is organised as follows. The proposed stability index is presented in Sect. 2. In Sect. 3, simulation results are provided. Finally, conclusions of this work are given in Sect. 4.

2 Proposed Voltage Stability Index A simple mesh distribution system with three loops is shown in Fig. 1. Load flow equations for mesh distribution system are described below and new voltage stability index is established for mesh system using power flow equations (Fig. 2). The effective power at each node is calculated as follow: Apply Kirchhoff’s voltage law in each loop: For loop1:     ∗ Pe(4) + j Qe(4) /V (4) 5∗ + Z L 45 ∗ Pe(5) + j Qe(5) /V (5)  ∗  − Z L 36 ∗ Pe(6) + j Qe(6) /V (6) + Z L 56 ∗ Iloop (1) = 0 (1)

Z L 34 ∗



  Z L 34 ∗ {[Pe(4) + j Qe(4)]/V (4)}∗ + Iloop (1) − Iloop (2)   + Z L 45 ∗ {[Pe(5) + j Qe(5)]/V (5)}∗ + Iloop (1) − Iloop (2)

Impact of Reconfiguration and Network Topology …

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Fig. 1 Typical loop distribution system

Fig. 2 Single mesh distribution system

  − Z L 36 ∗ {[Pe(6) + j Qe(6)]/V (6)}∗ − Iloop (1) − Iloop (3) + Z L 56 ∗ Iloop (1) = 0 (2)

    (Z L 34 + Z L 45 + Z L 36 + Z L 56 ) ∗ Iloop (1) − (Z L 34 + Z L 45 ) ∗ Iloop (2) + Z L 36 ∗ Iloop (3)  = − Z L 34 ∗ {[Pe(4) + j Qe(4)]/V (4)}∗ + Z L 45 ∗ {[Pe(5) + j Qe(5)]/V (5)}∗  −Z L 36 ∗ {[Pe(6) + j Qe(6)]/V (6)}∗ (3)

For loop 2:     − (Z L 34 + Z L 45 )∗Iloop (1) − (Z L 34 + Z L 45 + Z L 35 )∗Iloop (2)  = − −Z L 34 ∗{[Pe(4) + j Qe(4)]/V (4)}∗  −Z L 45 ∗{[Pe(5) + j Qe(5)]/V (5)}∗

(4)

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For loop 3: 

   Z L 36 ∗Iloop (1) + (Z L 23 + Z L 36 + Z L 26 )∗Iloop (3)  = − −Z L 23 ∗{[Pe(3) + j Qe(3)]/V (3)}∗  −Z L 36 ∗{[Pe(6) + j Qe(6)]/V (6)}∗

(5)

[Zloop]∗Iloop = −[V Dloop]

(6)

Iloop = [Zloop]−1 ∗[−V Dloop]

(7)

P(5) − j ∗ Q(5) = Iloop ∗ V5∗

(8)

 Pe(2) + j ∗ Qe(2) ∗ (Z L 12 + Z L 23 + Z L 15 + Z L 34 + Z L 45 )Iloop = −Z L 12 V (2)  Pe(3) + j ∗ Qe(3) ∗ − Z L 23 V (3)  Pe(4) + j ∗ Qe(4) ∗ − Z L 34 V (4)  Pe(5) + j ∗ Qe(5) ∗ − Z L 15 V (5) (9) (Z L 12 + Z L 23 + Z L 15 + Z L 34 + Z L 45 )Iloop = −(V (1) − V (5))

(10)

(V (1) − V (5)) ∗ V4∗ (Z 12 + Z 23 + Z 14 + Z 34 + Z 45 )

(11)

Z L = (Z L 12 + Z L 23 + Z L 14 + Z L 34 + Z L 45 ) = R + j X

(12)

(P − j Q)(R + j X ) = V52 − V1 V5 cos δ − j V1 V5 sin δ

(13)

V52 − V1 V5 cos δ = P R + Q X

(14)

V1 V5 sin δ = Q R − P X

(15)

P(5) − j ∗ Q(5) = −

P=

Q R − V1 V5 sin δ X

V52 − V1 V5 cos δ =

Q R − V1 V5 sin δ R + QX X

(16) (17)

Impact of Reconfiguration and Network Topology …

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V52



 R sin δ R + V1 V5 − cos δ + −Q X+ =0 X X 

R 2 V12 (sin(δ − θ))2 ≥ −4Q sin θ 2 X + X  2 4Q sin θ 2 X + XR 1≥ V12 (sin(δ − θ ))2  2 4Q sin θ 2 X + XR new VSI = V12 (sin(δ − θ ))2

(18)

(19)

(20)

(21)

Under normal operating condition, value of VSI should be |Id7_set | (or) |Id5 | > |Id5_set |

Is fault detected (Y/N)

Id7

Id5

No fault

0

0

N

N

AG BG

0.04

−0.013

Y

Y

0.018

−0.006

Y

Y

CG

−0.01

0.0034

Y

Y

AB

0.09

−0.030

Y

Y

BC

−0.0141

0.0045

Y

Y

AC

0.025

−0.0081

Y

Y

AB-G

0.064

−0.021

Y

Y

BC-G

0.006

−0.0019

Y

Y

AC-G

0.03

−0.010

Y

Y

ABC

0.082

−0.027

Y

Y

condition as fault. The estimated d-axis components are shown in Fig. 3a, b. For this fault condition, the arrival time of first traveling wave at both terminals of the line 7–5 are t1 = 0.40326211 s and t2 = 0.40292022 s, respectively, and the proposed algorithm has estimated distance for this ABC fault using Eq. (1) as 301.2835 km as shown in Table 2. Similarly, when an AG fault with the fault conditions considered above occurs at 300 km from bus-7, the peak magnitude of d-axis components of traveling waves at bus-7 and bus-5 are 0.04 and −0.013, respectively, as shown in Table 1. Since these d-axis components are greater than the set values, fault has been detected. Under this condition, the times of arrival of the first raveling waves are t1 = 0.40326017 s and t2 = 0.40291991 s, respectively. Therefore, the calculated distance of the fault AG is 301.039 km as shown in Table 2. The error in estimated fault distance is about Id2

Id1 0.100

Id1

0.020

Id2

0.080

0.010

0.060 0.040

0.000

0.020 0.000

-0.010

-0.020 -0.040

-0.020

-0.060 -0.080 x

-0.030

0.40318 0.40320 0.40323 0.40325 0.40327 0.40330 0.40332 0.40335 0.40337 0.40340

(a)

x

0.40285

0.40290

0.40295

0.40300

0.40305

0.40310

(b)

Fig. 3 Direct axis component (I d7 and I d5 ) of traveling waves estimated at two terminals of the line 7–5 for ABC fault FR = 10  and FIA = 20°) at 300 km from bus-7

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Table 2 Validation of fault location logic for different faults with FR = 10  and FIA = 20° at 300 km from bus-7 Fault type

Time of arrival of first incident wave

Fault location

t1

t2

AG

0.40326017

0.40291991

301.039

BG

0.40326017

0.4029199

301.0405

CG

0.40325918

0.4029197

300.922

AB

0.40326001

0.40291924

301.1155

BC

0.40325539

0.40291415

301.186

CA

0.40326017

0.40291993

301.036

AB-G

0.40325115

0.40291106

301.0135

BC-G

0.40325285

0.40291194

301.1365

CA-G

0.40326016

0.4029185

301.249

ABC

0.40326211

0.40292022

301.2835

±0.3%. A similar explanation holds good for all other faults as shown in Tables 1 and 2. The discussion carried above shows that the proposed algorithm detects and locates the faults accurately using the d-axis components of the traveling waves under different fault conditions. From the discussion, it can be derived that the proposed traveling wave-based algorithm could support EHV/UHV transmission line protection effectively under all fault conditions.

5 Conclusions This paper has presented a traveling wave-based protection methodology for detection and location of fault. A fault is detected using the magnitude of the direct axis component of traveling waves obtained after passing through d-q transformation of the first level detail coefficients. These coefficients are estimated from the three-phase current signals using DWT-MRA technique. The fault distance is estimated using the arrival time of the first incident traveling waves received at transmission line’s both ends. The results show that the proposed methodology reinforces the EHV/UHV transmission lines protection.

References 1. Ram B, Vishwakarma DN (2005) Power system protection and switchgear. Tata Mc-Graw Hill Publication Company Limited, New Delhi, 6–9 and 119–123

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2. Liang J, Elangovan S, Devotta JBX (1998) A wavelet multiresolution analysis approach to fault detection and classification in transmission lines. Int J Electric Power Energy Syst 20(5):327– 332 3. Jaya Bharata Reddy M, Venkata Rajesh D, Mohanta DK (2013) Robust transmission line fault classification using wavelet multiresolution analysis. Comput Electric Eng 39(4):1219–1247 4. Jaya Bharata Reddy M, Mohanta DK (2008) Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location incorporating effects of power swings. IET Gener Transm Distrib 2(2):235–244 5. Costa FB, Souza BA, Brito NSD (2012) Real-time classification of transmission line faults based on maximal overlap discrete wavelet transform. PES T&D 2012, Orlando, FL, pp 1–8 6. Costa FB (2014) Fault-induced transient detection based on real-time analysis of the wavelet coefficient energy. IEEE Trans Power Delivery 29(1):140–153 7. Osman H, Malik OP (2004) Protection of parallel transmission lines using wavelet transform. IEEE Trans Power Delivery 19(1):49–55 8. Chanda D, Kishore NK, Sinha AK (2003) Application of wavelet multiresolution analysis for classification of faults on transmission lines. In: IEEE conference on convergent technologies for Asia-Pacific Region. TENCON, vol 4, pp 1464–1469 9. Chanda D, Kishore NK, Sinha AK (2003) A wavelet multiresolution analysis for location of faults on transmission lines. Int J Electric Power Energy Syst 25(1):59–69 10. Liang J, Elangovan S, Devotta JBX (2000) Application of wavelet transform in traveling wave protection. Int J Electric Power Energy Syst 22(8):537–542 11. Evrenosoglu CY, Ali A (2005) Travelling wave-based fault location for teed circuits. IEEE Trans Power Delivery 20(2):1115–1121 12. https://web.iitd.ac.in/~sumeet/WaveletTutorial.pdf 13. Pal D, Mallikarjuna B, Reddy RJ, Reddy MJB, Mohanta DK (2017) Synchrophasor assisted adaptive relaying methodology to prevent zone-3 mal-operation during load encroachment. IEEE Sens J 17(23):7713–7722 14. Schweitzer EO, Guzman A (2014) Locating faults by the traveling waves they launch. In: Texas A&M conference for protective relay engineers

Microgrid Situational Awareness Using Micro-PMU Kunjabihari Swain, S. S. Mahato, S. K. Mandal, and Murthy Cherukuri

Abstract Situational awareness of microgrid is essential for timely decision making and protection by power system engineers. Real-time monitoring is required for adequate situational awareness of microgrid. Phasor measurement units (PMUs) are instrumental in tracking the real-time behavior of microgrid. PMU interfaced with a virtual instrumentation tool (LabVIEW) provides a faster computation leading to enhanced situational awareness. In this paper, the perception of fault is achieved by recognizing the deviations in voltage and frequency. The comprehension of the fault is accomplished by using the K-NN algorithm. Based on the comprehension of the fault, which phase of the transmission line to be disconnected is projected. Keywords Micro-PMU · Microgrid · Situational awareness · K-NN algorithm

1 Introduction The integration of distributed energy sources (DES) into the distribution grid of today’s power system improves the generation capacity of the existing system to fulfill modern application requirements. Renewable energy sources are the main components of such a distribution grid, which enables the system to operate parallel along with the grid or in an islanding mode in case of a grid fault. The penetration of DES raises the power and voltage level to fulfill the peak demand of the distribution grid and improves the voltage profile of the grid. However, DES penetration K. Swain · M. Cherukuri (B) Electrical and Electronics Engineering, National Institute of Science and Technology, Berhampur 761008, Odisha, India e-mail: [email protected] S. S. Mahato Electronics and Communication Engineering, National Institute of Science and Technology, Berhampur 761008, Odisha, India S. K. Mandal Electronics and Communication Engineering, Centurion University of Technology and Management, Paralakhemundi 761211, Odisha, India © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_57

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causes the steady-state voltage and current disturbances and power quality problems because of the harmonic disturbances, which affect the reliability of the load equipment’s [1]. Hence, real-time monitoring is unavoidable in today’s microgrid and distribution systems for stable operation. Traditionally, supervisory control and data acquisition (SCADA) systems are used to monitor power system events. In the SCADA system, the measurements are slow and not synchronized. Hence, it could not give the dynamic picture of the power system events. With the advent of PMUs, it is possible to track the fast transient by the representation of dynamic behavior of power system event with its higher sampling rate and time-stamped measurements. PMUs are equipped to realize power system events, which are capable of wide-area power system stability monitoring, post-mortem analysis, adaptive protection and control for enhancing the situational awareness of the transmission system. Many researchers were focused on wide-area monitoring, control and protection of transmission lines for improving situational awareness of the transmission system. However, the microgrid is still lacking in that respect. Due to its diverse environment and different level of measurement, microgrid needs more precise and accurate measurement than that of the transmission line. It needs higher reporting, negligible instrumental error and reduced effect of white noise. Hence, the special type of PMU called micro-PMU (μ PMU), which can provide real-time monitoring in a microgrid is developed [2]. Situational awareness in a microgrid is achieved by gathering the information, analyzing it to facilitate efficient and effective decision making in the critical emergency situation. It is mainly categorized into three parts. 1. Perception: Gathering valuable information such as current, voltage, frequency, and thereby detect if any fault occurs in the microgrid. 2. Comprehension: Analyzing the information and understand the type of fault from the three-phase voltage and the data computation algorithm, i.e., K-NN algorithm. 3. Projection: Anticipate what could happen and make the decision based on the comprehension and protect the microgrid by isolating the faulty line by sending the trip signal to the respective circuit breaker [3]. The rest of the paper is organized as follows. Section 2 gives a brief idea about micro-PMU. Section 3 presents the microgrid situational awareness using microPMU. Conclusions are presented finally in Sect. 4.

2 Micro-PMU(µ PMU) A PMU is a measuring device that provides the real-time voltage and current phasor along with some derived parameters like frequency, rate of change of frequency (ROCOF). μ PMU has the same fundamental components as that of PMUs used in the transmission system with reduced signal level and higher accuracy as shown in Fig. 1. Three-phase analog voltage and currents are given through the potential

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Fig. 1 Block diagram of μ PMU

transformer (PT) and current transformer (CT). Low pass and antialiasing filters eliminate the high-frequency noise to satisfy Nyquist criteria. Analog to digital converter converts the analog signal to digital signal using sampling clock pulses obtained from the phase-locked loop. Phase-locked loop (PLL) receives 1 pulse per second (PPS) from the global positioning system (GPS) through GPS receiver. GPS also provides the referenced time synchronization by providing the universal coordinated time (UTC) and the geographical location (latitude and longitude) from the satellite. The central processing unit (CPU) is basically a microprocessor or DSP processor having the capability to perform discrete Fourier transform (DFT) calculations. After processing, the time-synchronized data along with the location will be sent to the phasor data concentrator (PDC) through a communication channel [4, 5].

3 Micro Grid Situational Awareness Using Micro-PMU The microgrid situational awareness using micro-PMU is achieved by perception, comprehension and projection of its situation. All these three aspects are detailed in this section. Figure 2 below shows the block diagram of a microgrid. Microgrid A contains x number of micro-PMUs and microgrid B comprises of y number of micro-PMUs. All the micro-PMUs are connected to PDC for further processing to discover the situational awareness of the complete system.

576

Fig. 2 Block diagram of microgrid

Fig. 3 Flowchart of a comprehension algorithm b K-NN algorithm

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3.1 Perception of the Fault The fault perception is based on the measurements obtained from μ PMU. In order to avoid false tripping, in addition to the voltage data, ROCOF data is also used for effective fault perception. The most frequently used phasor estimation technique in PMU is full cycle discrete Fourier transform (FCDFT) as shown in Eq. (1) [6]. √ N −1     N −1 2 2 2π n 2π n −j x(n) · cos x(n) · sin N n=0 N N n=0 N

√ Xˆ (k) =

(1)

where x(n) is the input AC signal; n is the nth sample; N is the total number of samples in a full cycle AC input signal. The sampling rate depends on the type of application, generally higher than that of PMU used in the transmission line. The voltage phasor angle variation and the ROCOF are monitored continuously. When these values are varied from the tolerable threshold, it indicates the occurrence of the fault.

3.2 Comprehension of the Fault The comprehension of fault is accomplished by using the data computation algorithm, K-NN based on past history [7]. The data sample of the three-phase voltage is considered for fault comprehension by taking its FFT coefficients at DC, fundamental and second harmonics for the known fault type. K-NN is employed phase-wise individually, parallel for each phase (i.e., phase A, phase B and phase C) to reduce the computational complexity. The working of the algorithm is shown in Fig. 3a, b. Three-phase voltage is acquired from μ PMU, then the FFT is performed to find DC, fundamental and second harmonics using Eqs. 2a–c, respectively [8].  ψ(0) =  ψ(1) =  ψ(2) =

1 N 1 N

1 N

 N −1

 N −1

x(n)

(2a)

n=0

x(n) · e−(

2πn N

)

(2b)

)

(2c)

n=0

 N −1 n=0

x(n) · e−(

4πn N

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where N is the total no of samples in a full cycle; n is nth sample; ψ(0),ψ(1) and ψ(2) are the DC, fundamental and second harmonic of the input signal x(n). Then, the training data set is formed using the FFT coefficients of the three-phase voltage and the fault class. Next, the K-NN is applied to the new test data using the training data set by calculating the Euclidian distance from each data of the data set. The K-NN algorithm is explained in the following section.

3.3 K-NN Algorithm The K-NN algorithm uses past information before performing the prediction. It is a nonparametric method. It does not learn the model, but only stores the data set. It is efficient to predict the complex data structure by the lookup table and matching the new pattern. It makes the prediction directly based on the Euclidean distance of the new data and the training data set using the following Eq. (3). Euclidean Distance (ψnew (k), ψold (k)) =

 k i=1

 2 ψnew,i (k) − ψold,i (k)

(3)

where ψnew (k) is test data and ψold (k) is a data from the training data set; k = 0, 1 and 2 represent the DC, fundamental and second harmonic. Table 1 presents the K-NN-based data set obtained from past history for the purpose of comprehension. Due to the space constraint, only a part of the result has been shown by taking 5 elements for each type of fault class for a single phase. The faults considered are normal operation (no-fault), line to ground (LG), line to line (LL), line to line to ground (LLG) and line to line to line fault (LLL). For simplicity, the fault classes are denoted numerically as class 0 (normal or no-fault condition), class 1 corresponding to LG fault, class 2 corresponding to LL fault, class 3 corresponding to LLG fault and class 4 corresponding to LLL fault. The fault prediction is achieved for the new data based on the Euclidian distance which is evaluated using Eq. (3). Then, the five (considering k neighbor up to 5 values) minimum Euclidean distances are selected and the classes corresponding to these five distances are considered. Finally, the most repeated class has been considered as the predicted class. Table 1 Selected Euclidian distance for prediction of class

Selected Euclidean distance for the new data Respective class 0.0872474

2

0.13757

2

0.145594

2

0.145594

2

0.148656

2

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Fig. 4 LabVIEW program for K-NN algorithm

A new data are taken for prediction, whose ψ(0) = 0.730418, ψ(1) = 0.119204, ψ(2) = 0.06133 and its Euclidian distances are calculated from each data. Five minimum Euclidean distances are considered for the prediction with their respective classes as shown in Table 1. Prediction is done based on the class, which is repeated the most. It can be observed from Table 1 that the class repeated most is class 2. Hence, the predicted class for the new data is class 2 which corresponds to LL fault. The result is cross-verified using the SEL-311 transmission protection relay for the same reading connected with the same system. The simulation is carried out using the LabVIEW virtual instrumentation platform. The LabVIEW program for the K-NN algorithm is shown in Fig. 4.

3.4 Projection Fault-resilient microgrids at unbalanced short-circuits are often not expected to isolate the healthy phases [9]. Once the comprehension is done based on the K-NN algorithm, in order to protect the microgrid, the trip signal is sent to the particular faulty line to open that particular line. In the case of a complete breakdown of the microgrid or utility, the respective circuit breaker will operate to isolate the faulty area.

4 Conclusion This paper presents the situational awareness of microgrid using micro-PMU. The fault perception is accomplished using the deviation of the voltage phasor angle and the ROCOF. The comprehension is accomplished using the K-NN algorithm

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on the three-phase voltage based on its frequency components at DC, fundamental and second harmonic to make the process immune to the fault resistance and fault inception angle. Additionally, a protection scheme is presented to isolate only the faulty phases, without disturbing the healthy phases. The future scope of the proposed work is that with the help of micro-PMUs, the microgrids can share the information of their situational information and the power flow among them keeping the function of microgrid in a stable state. Acknowledgements The authors would like to thank Science and Engineering Research Board (SERB), India for providing the research funding under the Early Career Research Award category to carry out the research work. [Grant No.—ECR/2017/000812]

References 1. Wagner VE et al (1993) Effects of harmonics on equipment. IEEE Trans Power Delivery 8(2):672–680 2. Jamei M et al (2018) Anomaly detection using optimally-placed µ PMU sensors in distribution grids. IEEE Trans Power Syst 33(4):3611–3623 3. Hooshyar A, El-saadany EF, Sanaye-pasand M (2016) Fault type classification in microgrids including photovoltaic DGs. IEEE Trans Smart Grid 7(5):2218–2229 4. Dutta S, Sadhu PK, Reddy MJB, Mohanta DK (2018) Smart inadvertent islanding detection employing p-type µPMU for an active distribution network. IET Gener Transm Distrib 12(20):4615–4625 5. Mohanta DK, Murthy C, Sinha Roy D (2016) A brief review of phasor measurement units as sensors for smart grid. Electric Power Comp Syst 44(4):411–425 6. Phadke AG, Thorp JS (2008) Synchronized phasor measurements and their applications, vol 1. Springer, New York 7. Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. In: OTM confederated international conferences “on the move to meaningful internet systems”. Springer, pp. 986–996 8. Gopakumar P, Reddy DK, Maddikara Jaya Bharata M (2015) Transmission line fault detection and localization methodology using PMU measurements. IET Gen Transm Distrib 9(11):1033– 1042 9. Hooshyar A, El-Saadany EF, Sanaye-Pasand M (2016) Fault type classification in microgrids including photovoltaic DGs. IEEE Trans Smart Grid 7(5):2218–2229

Fault Classification Comparison in Power System Using Nearest Neighbor and Decision Tree Dhruba Kumar

Abstract The applications of artificial intelligence in power system are becoming a major concern to the power communities. In this chapter, fault case studies are comparatively detected by two renowned classification algorithms namely nearest neighbor and classification tree. The time complexity as well as execution time of the algorithms have been established. A dimension reduction algorithm is introduced to reduce the number of dimensions of the measured data using the principal component analysis. The measurements are obtained from the IEEE 9 bus system. The reduced dimensions are two. The classifiers are trained using these two dimensional data. Tree structures have been determined by running the tree classifier several times. A surface plot has been presented to determine the span of a particular fault in the two-dimensional surface under consideration. Finally, the classification accuracy is compared. Keywords Phasor measurement unit · Nearest neighbor · Decision tree · Classification

1 Introduction Model-free, as well as the model-based scheme, has been proposed in [1] for classification of faults using modified Kohonen-type neural network. A non-intrusive fault detection using power-spectrum-based hyperbolic S-transform monitoring as discussed in [2] can operate for different breaker conditions using electromagnetic transients program software. Faults have been detected in [3] using long short-term memory (LSTM) networks and support vector machine (SVM). The results of hidden Markov model-based classifier in [4] for fault detection is having 0.03% error rate and less computational cost. New fuzzy-ARTMAP neural network indices have been

D. Kumar (B) Department of Electrical Engineering, National Institute of Technology Durgapur, Durgapur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_58

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experimented in real-time distribution system in [5] for fault location and powerquality analysis. As in [6], group sparse representation is helpful for fault classification in power system with noise containing signals. Hilbert–Huang transform can be incorporated with deep neural network for fault classification in distribution system as mentioned in [7]. Summation-wavelet extreme learning machine has been proposed in [8] for fault location identification and classification. Based on current phase angle, an artificial neural network can classify faults in distribution system as experimented in [9]. DC offset and fundamental current component have been estimated in [10] for fault studies in 9 bus transmission system. Faults in underground distribution can be identified as in [11] using wavelets and neural network. Ordinary power system faults, as well as high frequency faults, can be identified using mathematical morphology as discussed in [12] in IEEE 13 bus system. Eight types of voltage sag sources have been studied in [13] from several recorded events. Decaying DC component is helpful as in [14] for fault classification in 22kv distribution system. Multi-task learning algorithm has been implemented in [15] for fault detection in distribution system. Fault locations as well as fault types have been detected in [16] using 3D polarization ellipse parameter. Islanding is a special type of fault which can be detected using neural network [17] and hidden Markov model [18]. There are more applications of neural network-based fault detection in power system in [19–21]. The major contributions of the paper are listed below: • Application of principal component analysis for dimension reduction, followed by training of classifiers, namely decision tree and nearest neighbor • Theoretical and simulated analysis of the above-mentioned algorithms to find out the superior classifier. The description of the test system, procedure of fault classification along with complexity analysis has been discussed in Sect. 2. The results are discussed in Sect. 3. The article has been concluded in Sect. 4.

2 Test System and Methodology of Fault Classification The IEEE 9 bus transmission system which is depicted in Fig. 1 is chosen for fault classification using nearest neighbor and classification tree. The faults are simulated near bus 5. The information on phasors, frequencies, rate of change of frequencies are obtained from phasor measurement units. Any maloperation in the power system is reflected in these measurements which are further processed by principal component analysis to convert the higher dimensional measurements into two-dimensional data which are put forward for the training of the classification algorithms. 7 fault cases and 23 subcases for each fault are analyzed in this research. The fault cases are listed below: • Case 1: Phase A of line 5–7 is open

Fault Classification Comparison in Power System …

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Fig. 1 Prototype of IEEE 9 bus test system

• • • • • •

Case 2: Phase A and B of line 5–4 are open Case 3: All phases of line 5–7 and line 4–6 are open Case 4: Triple line to ground fault at 5 Case 5: Phase A-B to ground fault at 5 Case 6: Phase A to ground fault at 5 Case 7: Phase A to ground fault at 4.

The 23 subcases can be performed under each of the above-mentioned cases by varying the 125 MW load and 300 MW generation. The procedure of dimension reduction and classification algorithms is discussed hereafter.

2.1 Principal Component Analysis The principal component analysis is a mathematical procedure to convert higher dimensional measurements into two-dimensional data. This procedure is performed

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in such a way that the important measurements come to the former dimension. The measurements are given importance by the corresponding variance. The greater the variance the higher is the priority or importance of the measurement. The first principal component can be visualized by obtaining a projection of the higher dimensional measurements in a manner that the projected data is having highest variance. First of all the eigenvalues are calculated from the given higher dimensional data. Only two major components are required in this proposed methodology, and the other dimensions are neglected. So, the two highest eigenvalues are selected for creating two-dimensional data.

2.2 Nearest Neighbor Another name of the nearest neighbor algorithm is proximity search. So, it is involving an optimization algorithm which tries to find the nearest known data point starting from a given unknown data point. A distance function between two known and unknown data point can be defined. The distance represents similarity of the known and unknown data points. A 1-nearest neighbor can be developed from the simplest intuition. For this 1nearest neighbor classifier 1 particular known data point which is nearest to the unknown data point is searched and the unknown data point belongs to that particular class only. Similarly, for k-nearest neighbor classifier, k number of known data points which are closer to the unknown data point are searched. The unknown data point is finally classified under that known class which is adjacent to it. In this chapter, all-nearest neighbor classifier has been implemented. For this, distances from all known data points are calculated from the unknown data point.

2.3 Decision Tree A tree is having one root, several nodes and leaves. All the data points which are to be classified are accumulated in the root. The data points reach the leaf when these are properly classified. There is a classification rule termed as a decision tree to reach the leaf starting from the root. For a 7 class problem, all the data points are classified into two broad-classes in the first place. All the subclasses belong to these two broad-classes. Secondly, the binary separation rule is applied again to get another two broad-classes. This procedure is followed over and over again until all the classes are isolated by rule of the tree flowchart.

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2.4 Comparison of Time Complexity of Nearest Neighbor and Decision Tree The training times of the classifiers in real-time processor can be figured out by calculating time complexity which is a big O notation defined mathematically. The time complexity of k-nearest neighbor classifier is O(n ×d +n ×k) for n training samples of dimension d, whereas time complexity of decision tree of depth l is estimated as O(n × d × l) for n training samples and d dimensions. The time complexities are further estimated in Sect. 3 for the two classifiers.

3 Results and Discussion The data points for all 7 case studies can be anticipated using surface plot which is shown in Fig. 2. The fault cases are represented by separate colors. If an unknown data point lies within a specific color it is to be assimilated under the corresponding class represented by the color code. As the tree classifier imposes greater than or less than rule, the color frontiers are parallel to X or Y-axis. In case of nearest neighbor the color surfaces are irregular and intensified in the neighborhood of known data points. The training data points are used to build a tree. Two different trees, as shown in Figs. 3 and 4, are obtained by running the algorithm several times. For both of the trees, the first two subclasses contain {case 1, case 2, case 3} and {case 4, case 5, case 6, case 7}, respectively. The depth of the tree can be defined as the longest path Fig. 2 Surface plot for: case 1 (blue), case 2 (green), case 3 (red), case 4 (cyan), case 5 (magenta), case 6 (yellow), case 7 (black), using (a) decision tree, (b) nearest neighbor

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Fig. 3 Decision tree of type 1

Fig. 4 Decision tree of type 2

from root to leaf. The depth of trees of Fig. 3 and Fig. 4 are 4 and 5, respectively. The size which represents a number of nodes in a tree which is given by 5 and 6, respectively, for the two trees. Misclassification occurs in the boundary region of the surfaces plot. The average classification/misclassification rate (%) is depicted in Fig. 5 and Fig. 6 for nearest neighbor and tree classifier, respectively. The diagonal numbers in the matrix represent correct classification accuracies in %. The off-diagonal numbers represent false classification rate in %. As the diagonal numbers are much higher than the off-diagonal numbers, the faults can be classified reliably. When the values of n d k and l are 119, 2, 119 and {4, 5}, respectively, the time complexities of the nearest neighbor and decision tree become O(14399) and {O(952)O(1190)}, respectively. The average training times are 0.008 s and 0.006 s

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Fig. 5 Average classification and misclassification rate (multiple run) of 7 cases using nearest neighbor

Fig. 6 Average classification and misclassification rate (multiple run) of 7 cases using decision tree

for nearest neighbor and decision tree, respectively, when executing in an Intel Core i7-7500U processor in Linux environment. The comparison of nearest neighbor and decision tree for fault classification has been briefed in Table 1. Table 1 Comparison of decision tree and nearest neighbor S. No.

Decision tree

Nearest neighbor

1

Average classification accuracy (%) for multiple run

92

92.71

2

Average misclassification rate (%) for multiple run

17.533

14.643

3

Time complexity

O(n × d × l)

O(n × d + n × k)

4

Time complexity for given parameters

O(952) and O(1190)

O(14399)

5

Average training time (s)

0.006

0.008

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4 Conclusion Application of dimension reduction algorithm does not corrupt or diminish the measurement quality from the PMU, and the lower-dimensional data is really necessary for the training of any type of classifiers. The nearest neighbor classifier is more efficient than the tree classifier, but the training time is more for nearest neighbor. The estimation of time complexity has also validated that nearest neighbor is more complicated than a tree. A more complicated algorithm should have more time consumption which has been justified in simulation. The performance of a tree can be improved by increasing its size as well as depth. Hereof, forest classifier which is a legitimate combination of trees can be introduced for enhanced accuracies.

References 1. Chowdhury FN, Aravena JL (1998) A modular methodology for fast fault detection and classification in power systems. IEEE Trans Control Syst Technol 6(5):623–634 2. Chang H, Linh NV, Lee W (2018) A novel nonintrusive fault identification for power transmission networks using power-spectrum-based hyperbolic S-transform—part I: fault classification. IEEE Trans Ind Appl 54(6):5700–5710 3. Zhang S, Wang Y, Liu M, Bao Z (2018) Data-based line trip fault prediction in power systems using LSTM networks and SVM. IEEE Access 6:7675–7686 4. Arouche Freire JC, Garcez Castro AR, Homci MS, Meiguins BS, De Morais JM (2019) Transmission line fault classification using hidden Markov models. In: IEEE Access, vol 7, pp 113499–113510 5. Bíscaro AAP, Pereira RAF, Kezunovic M, Mantovani JRS (2016) Integrated fault location and power-quality analysis in electric power distribution systems. IEEE Trans Power Delivery 31(2):428–436 6. Shi S, Zhu B, Mirsaeidi S, Dong X (2019) Fault classification for transmission lines based on group sparse representation. IEEE Trans Smart Grid 10(4):4673–4682 7. Guo M, Yang N, Chen W (2019) Deep-learning-based fault classification using HilbertHuang transform and convolutional neural network in power distribution systems. IEEE Sens J 19(16):6905–6913 8. Chen YQ, Fink O, Sansavini G (2018) Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Trans Industr Electron 65(1):561–569 9. Rocha Oliveira A, Augusto Nepomuceno Garcia P, Willer Oliveira L, Jose Oliveira E, Antonio Silva H (2016) Fault Classification in distribution systems bsaed on fault current angles. IEEE Latin Am Trans 14(1):199–205 10. Shukla SK, Koley E, Ghosh S (2019) DC offset estimation-based fault detection in transmission line during power swing using ensemble of decision tree. IET Sci Meas Technol 13(2):212–222 11. Karmacharya IM, Gokaraju R (2018) Fault location in ungrounded photovoltaic system using wavelets and ANN. IEEE Trans Power Delivery 33(2):549–559 12. Kavi M, Mishra Y, Vilathgamuwa MD (2018) High-impedance fault detection and classification in power system distribution networks using morphological fault detector algorithm. IET Gener Transm Distrib 12(15):3699–3710 13. Wei P, Xu Y, Wu Y, Li C (2017) Research on classification of voltage sag sources based on recorded events. CIRED Open Access Proc J 2017(1):846–850 14. Zhu K, Pong PWT (2019) Fault classification of power distribution cables by detecting decaying DC components with magnetic sensing. IEEE Trans Instrum Measure (Early access)

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15. Gilanifar M et al (2019) Multi-task logistic low-ranked dirty model for fault detection in power distribution system. IEEE Trans Smart Grid (Early access) 16. Alam MR, Muttaqi KM, Saha TK (2019) Classification and localization of fault-initiated voltage sags using 3-D polarization ellipse parameters. IEEE Trans Power Delivery (Early access) 17. Kumar D, Bhowmik PS (2018) Artificial neural network and phasor data-based islanding detection in smart grid. IET Gener Transm Distrib 12(21):5843–5850 18. Kumar D, Bhowmik PS (2019) Hidden Markov model based ıslanding prediction in smart grids. IEEE Syst J (Early access) 19. Malla P, Coburn W, Keegan K, Yu XH (2019) Power system fault detection and classification using wavelet transform and artificial neural networks. In: International symposium on neural networks. Springer, Cham, pp 266–272 20. Kari´c A, Konji´c T, Jahi´c A (2017) Power system fault detection, classification and location using artificial neural networks. In: International symposium on ınnovative and ınterdisciplinary applications of advanced technologies. Springer, Cham, pp 89–101 21. Jamil M, Sharma SK, Singh R (2015) Fault detection and classification in electrical power transmission system using artificial neural network. In: SpringerPlus 4(1):334

Closed-Loop V/f Control of Symmetrical 6-Phase Induction Motor Using Cascaded H-Bridge Multilevel Inverter Spandan Dhamudia, Vishal Rathore, and K. B. Yadav

Abstract This work presents the closed-loop volt/hertz control of H-bridge multilevel inverter (MLI) fed induction motor drive. A five-level MLI generates a stepped sinusoidal waveform with lower level harmonics by raising the levels by which harmonics can further be reduced but results in high switching losses. H-bridge MLI of five-level is feminized by using SPWM. The waveform of output of the MLI is so developed for assuring with minimum THD, and the output is then filtered to get desired sinusoidal waveform. Then, the output is fed into the symmetrical sixphase induction Motor (6PIM), mathematical modeling is done by using a procedure sequence of equations. Induction motor speed control is utilized by controlling the actual speed by making V/f constant. After that PI controller is designed to get the motor speed closer to the reference speed. The method is also called feedback control and is prejudged for several reference speeds and variation in its dynamic speed and load. Comparative estimation of steady-state and transient characteristics based on their performance and efficiency of the system is analyzed for Hbridge fed induction motor drive. The results of the simulation are validated by using MATLAB/SIMULINK-2018 setup. Keywords 6PIM · MLI · SPWM · Closed-loop v/f control

1 Introduction Normally, electrical drives are electromechanical devices connected in which synthesis of energy is done to control the machine [1]. These machines play a critical role in the field of industries and provide a path for a wide range of exploration. Nowadays, researchers are putting more attention toward multiphase drives. For this, 6PIM is an innovative headway in the field of drive applications. The speed of 6PIM S. Dhamudia (B) · V. Rathore · K. B. Yadav National Institute of Technology, Jamshedpur, Jharkhand, India e-mail: [email protected] K. B. Yadav e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_59

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predominate with responses to the load variation [2, 3]. The motor is designed to operate with extreme ranges as well as wide derived speed in the latency of a machine. Regulation of speed in 6PIM is guaranteed with its application in drives, reducing the uttermost range of the operation and metalizes a way for the requirement of drives. 6PIM gets several control schemes categorized by the first scalar control scheme and the second vector control scheme [4–6]. A scalar control scheme-based drives are easy to build and are widely ennoble in industry sectors. The scalar control scheme is based only on magnitude is different from the position and magnitude control scheme in its monitoring variables. As the scalar control name expresses that the magnitude is change with the variables and it rejects the effect of the coupling machine. In the vector control scheme, the magnitude and alignment of the phase must control [8, 9]. There is some scalar control those are stator voltage, frequency, voltage and frequency, current, rotor-resistance, and slip. Out of these methods, voltage and frequency control is widely used in high power industries [9]. There are two modes of control operation first is closed-loop mode and the other is openloop mode. Configuration which is used for the supply of the 6PIM is nothing but a DC to AC converter [10–13]. Multilevel inverter (MLI) and voltage source inverter (VSI) are the major power circuits for the 6PIM. The proposed topology using MLI is highly preferable due to its excellent THD mitigation nature and can generate output waveform close to sinusoidal. The MLI having an “m” number of bridges and “h” order harmonics shows that they are inversely proportional to each other. By increasing the “m”, the waveform move toward the sinusoidal. There are three types of MLIs which are generally used flying capacitor, cascaded H-bridge, and diode clamped [14].

2 V/f Close Loop Control Scheme In the proposed V /f control of 6PIM, to place  the linkage flux of stator winding close to rated value and per phase, voltage Vph which is a function of the frequency of motor (f ) [15, 16] denoted by Vph = V0 + C0 f, f < f rated Vph = Vph,rated , f ≥ f rated C0 =

Vph,rated − V0 f rated

where V o is the enhancement of the voltage to neutralize the stator resistance drop at low the frequencies. Figure 1 implies the characteristic voltage versus frequency

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Voltage

Vphase,rated

Vo Frated

Frequency

Fig. 1 Voltage-frequency graph by V /f control

I s3 V

I s2

Stator winding

s3

V

Rotor winding

I s4

V

s2

α

s4

V V

s1

I s1

s5

V

I s5

s6

I s6

5 LEVEL H-Bridge

A

n

5 LEVEL H-Bridge

B n

5 LEVEL H-Bridge

C

5 LEVEL H-Bridge

n

D

5 LEVEL H-Bridge

n

E

n

5 LEVEL H-Bridge

F n

Fig. 2 Connection of H-bridge MLI to Symmetrical 6PIM

of the motor [16]. From the voltage command, frequency command can be obtained and those commands are used to drive a PWM converter.

3 6-Phase Induction Motor Fed by Cascaded MLI A 6PIM requires six-phase supply in an attempt to limit the starter current and power with the help of cascaded MLI. The inverter requires DC source as an input and transforms it into an alternating wave which is used to trigger the pulses for the switches. Most of the time IGBTs play a dominant role as compared to other switches [8] (Fig. 2). In short, the level of the MLI given by L = (2 * d) + 1, where d = number of DC sources. To provide the gating signals to the MLI consisting of switches

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like (IGBTs, MOSFET), PWM play a very vital role. Several techniques are widely used in reducing harmonics uses complex methodology like space vector, PWMsbased carrier signal, etc. [17, 18]. Out of which the PWMs-based carrier scheme is enlightened. Such a scheme involved, comparison between reference sinusoidal signal and repeating signals wave to get the desired pulse. The required number of the carrier signal is (L − 1) for an L-level inverter.

4 Symmetrical 6PIM Modeling There are two ways to modeled 6PIM one is vector space decomposition (VSD) in which machine variables [Fk ] are transformed into a stationary frame of variables [Fαβx y ] via transformation matrix [D6 ] [10–13]. And the other method is by individual equations which are used in this paper and winding phasor as shown in Fig. 3. The equations of voltage which represent symmetrical 6PIM in the stationary reference frame are as follow [8], vq1 = r1 i q1 + ωk λd1 + pλq1

(1)

vd1 = r1 i d1 − ωk λq1 + pλd1

(2)

vq2 = r2 i q2 + ωk λd2 + pλq2

(3)

vd2 = r2 i d2 − ωk λq2 + pλd2

(4)

vqr = rr i qr + ωk λdr − ωr λdr + pλqr = 0

(5)

vdr = rr i dr − ωk λqr + ωr λqr + pλdr = 0

(6)

The equation of flux linkage of six-phase induction motor is given as: Stator winding

Fig. 3 Symmetrical six-phase induction motor

I s3

I s2 V

s3

V

Rotor winding

I s4

s2 α

V

s4

V

V I s5

s5

V

s6

I s6

I s1 s1

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λq1 = L l1 i q1 + L mq i q1 + L mq i q2 + L mq i qr + L lm i q1 + L lm i q2 + L dq i d2

(7)

λd1 = L l1 i d1 + L mq i d1 + L mq i d2 + L mq i dr + L lm i d1 + L lm i d2 + L dq i q2

(8)

λq2 = L l2 i q2 + L mq i q1 + L mq i q2 + L mq i qr + L lm i q1 + L lm i q2 + L dq i d1

(9)

λd2 = L l2 i d2 + L md i d1 + L md i d2 + L md i dr + L lm i d1 + L lm i d2 + L dq i q1

(10)

λqr = L lr i qr + L mq i q1 + L mq i q2 + L mq i qr

(11)

λdr = L lr i dr + L md i q1 + L md i q2 + L md i qr

(12)

Assuming, L dq = 0 and L m = L mq = L md

(13)

L 1 = L l1 + L lm + L m

(14)

L 2 = L l2 + L lm + L m

(15)

L 3 = L lm + L m

(16)

L r = L lr + L m

(17)

Also

On substituting the above value in Eqs. (7)–(13), the resultant flux linkage is represented as λq1 = L 1 i q1 + L 3 i q2 + L m i qr

(18)

λd1 = L 1 i d1 + L 3 i d2 + L m i dr

(19)

λq2 = L 2 i q1 + L 3 i q2 + L m i qr

(20)

λd2 = L 2 i d1 + L 3 i d2 + L m i dr

(21)

λqr = L r i qr + L m i q1 + L md i q2

(22)

λdr = L r i dr + L m i d1 + L m i d2

(23)

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PI CONTROLLER

REFERENCE SPEED

PWM CONTROLLER

Gating Signal

H-BRIDGE INVERTER

ACTUAL SPEED

6PH IM

Fig. 4 Closed-loop V /f scheme

And the resultant electromagnetic torque (Te ) is given by the expression,     P  3 λmd i q1 + λmd i q2 − λmq i d1 + λmq i d2 Te = 2 2

(24)

5 Closed-Loop Control Fed MLI Based 6PIM Figure 4 shows the proposed speed control scheme [19]. The constant speed is given by N syn = (120 * f )/p, where, f = frequency and p = poles. By increasing the frequency or by decreasing the no of pole results in speed increment. By decreasing the speed flux increase, which causes the saturation of poles and with the increase in magnetizing current by reducing the power factor.

6 Simulation and Results Simulation model of closed-loop V /f speed control of 6PIM is shown in Fig. 5 and the output voltage of the MLI is presented in Fig. 6 Similarly, Fig. 7 shows that the content of harmonics in the output voltage of the MLI is 26.70%.

Fig. 5 Model for closed-loop V /f scheme fed 6PIM

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Fig. 6 Output voltage waveform of five-level H-bridge MLI and THD profile

Fig. 7 a Electromagnetic torque, b rotor speed, c stator current, d harmonic analysis of stator current

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Figure 7a shows that electromagnetic torque corresponding to its speed is shows in Fig. 7b ,the speed it shows that is close to the reference speed and having an error of 0.82%. Figure 7c shows that stator current in this figure from 0 to 0.3 shows the subtransient current, after that from 0.3 to 0.6 shows the transient current and from 0.65 onward shows the steady-state current in the 6PIM and the content of harmonics present in stator current is 19.89%.

7 Conclusion The closed-loop-based volt/hertz control scheme of a 6PIM is developed in this paper using H-bridge multilevel inverter in MATLAB/SIMULINK-2018. For the commercial application, a conventional VSI fed scheme is used in which problems like high ripple in current and torque occur which reduces the performances of the system. By using cascaded H-bridge MLI scheme such problems are rectified, and as a result, cascaded H-bridge MLI-based motor drive has better performance than that of the conventional inverter. It has been observed in this paper that the error in speed is 0.82% which is acceptable and the harmonic content in the stator current is 19.89%.

References 1. Datta A, Poddar G (2019) Improved low frequency operation of hybrid inverter for medium voltage induction motor drive under V/f and vector control mode of operation. IEEE J Emer Sel Topics Power Electron 2. Mahato BKC, Jana, Thakura PR (2019) Constant V/f control and frequency control of isolated winding induction motor using nine-level three-phase inverter. Iran J Sci Technol Trans Electric Eng 43(1):123–135 3. Annuar KA (2019) Metal. Squirrel cage induction motor scalar control constant V/F analysis. Telkomnika 17(1):417–424. 4. Bose BK (2002) Modern power electronics and AC drives. In: Chapter 8-control and estimation of induction motor drives. Pearson Education, Inc. 5. Kumar S, Agarwal P (2019) A novel FLC based closed-loop V/f control of five-level inverter fed open-end winding induction motor drive. J Inst Eng (India) Ser B 100(3):193–200 6. Kumwong P, Narongrit, Wang, M-S (2018) Full-order observer for direct torque control of induction motor based on constant V/F control technique. ISA Trans 73:189–200 7. Kumar KS, Durga Rao R (2019) Analysis and comparison of space vector and sinusoidal PWM controlled two level inverter fed 3-ϕ induction motor. Trends Electric Eng 3(1):1–11 8. Chinmaya KA, Singh GK (2019) Experimental analysis of various space vector pulse width modulation (SVPWM) techniques for dual three-phase induction motor drive. Int Trans Electric Energy Syst 29(1):e2678 9. Rathore V, Dubey M (2012) Speed control of asynchronous motor using space vector PWM technique. Int J Electric Eng Technol (IJEET) 3(3):222–233 10. Baba M, Lascu C, Boldea I (2012) Z converter control of a V/f induction motor drive, IEEE, 529–534

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11. Khomfoi S, Praisuwana N (2010) A hybrid cascaded multilevel inverter for interfacing with renewable energy resources. Int Power Electron Conf 2912–2917 12. Tunser S, Dandil B (2008) Four quadrant control of multi-level inverter induction motor drives. J Sci Industr Res 67: 688–696 13. Grouni S, Ibtiouen R, Kidouche M, Touhami O (2008) Improvement approach on rotor time constant adaptation with optimum flux in IFOC for induction machines drives. World Acad Sci Eng Technol 356-360 14. Behera PK, Behera MK, Sahoo AK (2015) Speed control of induction motor using scalar control technique. Int J Comput Appl ETCC-15 0975–8887 15. Akin B, Garg N (2009) Scalar (V/f) control of 3-phase induction motors. In: C200 systems and applications. Modelling practice and theory 17 science direct. Texas instruments, Inc., pp 1071–1080 16. Kusagur A, Kodad SF, Shankar Ram BV (2005–2009) Modelling of induction machine and control of speed using hybrid controller technology. J Theoret Appl Inf Technol 17. Lee K, Ahmed S, Lukic SM (2017) Universal restart strategy for scalar (V/f) controlled induction machines. IEEE Trans Ind Appl 53(6):5489–5495 18. Chitra A et al (2017) Performance comparison of multilevel inverter topologies for closed loop v/f controlled induction motor drive. Energy Proc 117:958–965 19. Duranay ZB, Guldemir H (2017) Selective harmonic eliminated V/f speed control of singlephase induction motor. IET Power Electro 11(3):477–483

Real Time Simulation of 3-ϕ Grid-Connected Converter with Real and Reactive Power Control Under Different Grid Fault Conditions Gaurav Gautam and Sudipto Poddar

Abstract In this article, a flexible pq reference current generation technique is used to control active and reactive power for a 3-ϕ grid connected converter fed by a 800 V DC source. Grid connected operation is controlled by a converter output voltage, and output voltage of converter is controlled by a controller that generate pulses for converter. Controller uses phase locked loop (PLL) for calculating phase and frequency of grid voltage, P and Q reference is set according to load then compared with V d and V q quantities and then feed to PI-based current controller to generate pulses. Various grid fault conditions low voltage ride through (LVRT), high voltage ride through (HVRT), low frequency ride through (LFRT) and high frequency ride through (HFRT) are injected for 0.5 s and then analysis had been drawn between healthy and faulty conditions. Keywords PLL · LVRT · HVRT · LFRT · HFRT · Grid connected converter

1 Introduction Renewable energy resources are future sources of energy, different distributed sources of energy like solar, wind, fuel cell, etc. are used to generate power for less dependency on non-renewable energy resources. So, with generation, there is also a need of integration of clean energy with main grid with the help of power electronics equipment that leads to challenges like injection of harmonics in grid, poor powerfactor, stability issue, etc. and make system more complex. Recent development in power electronics with sophisticated controller allow us to integrate distributed energy sources with main grid and allow us to control such complex system. Most of distributed generating units use DC-AC power converter to interface with main power grid [1, 1]. According to need of operation, we can operate converter as gridconnected mode or as landing mode of operation where converter is disabled from main grid [3]. During grid connected operation, converter usually injects power in G. Gautam (B) · S. Poddar Quarbz Info. Systems, Kanpur 208001, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_60

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the grid; this injected power is in the form of real and reactive power. P and Q reference are used to control real and reactive power pumping into grid; this real and reactive power should be in synchronisation with grid voltage. For synchronisation, we need to measure angle between the three-phase grid voltage and frequency of grid voltage these can be measured with the help of three-phase PLL. P and Q (real and reactive) power reference and Vd and Vq are the voltage components of threephase grid voltage which is used to generate current reference and compared with actual current measured from grid, and then error is passed through PI controller that generate pulses to control converter, converter pumps power into grid based on switching pulses. This chapter describes a grid connected 3-ϕ converter using a current control method based on pq theory. Different conditions have been analysed in this work starting with healthy grid operation, grid voltages and current is captured, and analysis had been done [4]. Then, faults are injected in grid side starting with low voltage ride through (LVRT) or under voltage ride through (UVRT), high voltage ride through (HVRT), low frequency ride through (LFRT) and high frequency ride through (HFRT) [5]. The organisation of this paper is as follows; Sect. 2 discuses about structure description and control strategy of 3-ϕ grid connected converter. Section 3 discuses about the real-time results using Typhoon HIL emulator, current and voltage waveforms had been analysed with balanced condition and with injecting LVRT, HVRT, LFRT and HFRT faults. Section 4 end with conclusions of chapter.

2 Structure Description and Control Strategy of 3-ϕ Grid-Connected Converter 2.1 Plant, Controller and PLL Description Three phase grid connected converter is shown in Fig. 1 which consists of a basic description of plant part. Plant part consists of a DC source of 800 V followed by a DC link capacitor which helps to maintain the DC voltage constant and provides a smooth input to three-phase converter; DC link voltage is also being monitored on regular basis, and a regular feedback is taken by current controller in order to generate switching signals. LCL filter with damping resistor is used for smoothing

Fig. 1 ϕ grid connected converter

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Fig. 2 ϕ grid connected converter controller

the electric power before pumping power into the grid. Plant is control by a system consists of grid current controller for grid connected operations. A PLL controller is used for synchronisation between the output voltage of converter and grid voltage (Fig. 2). Three phase grid connected converter is control by current controller as shown in Fig. 3; current controller is feed with reference Id and Iq with actual Id and Iq current. Reference and actual currents compare and pass through PI controller in order to generate switching signals for converter. Table 1 shows parameters used in plant and controller [6, 7]. PLL Description: With grid connected converter algorithm, it is necessary to determine precisely phase angle between grid voltage for independent control of flow of active and reactive power between grid side and converter input side. So, due to this, it is very important to consider synchronisation unit design and test with different voltage conditions relevant to real utility grid. So, PLL is used to track signal which causes them and also make sure that output signal must be in synchronisation with reference signal in terms of phase as well as frequency; a typical structure of PLL is shown in Fig. 3 [8]. Now, consider ui and uo be input and output signals, which represented as follow: u i (t) = A cos(ωi t + θi )

(1)

u 0 (t) = B cos(ω0 t + ψ0 )

(2)

here ωi and ω0 are angular frequencies of input and output signal, and phase constants of these signals are represented by θ i and ψ0. Phase detector is a signal multiplier, and Ve(t) is output signal of phase detector which is given by Ve (t) = K d cos[(ωi − ω0 )t + θi − ψ0 ] + cos[(ωi − ω0 )t + θi − ψ0 ]

(3)

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Fig. 3 dq-PLL synchronisation system block diagram

Table 1 Plant and controller parameters value

S. No.

Parameter

Value

1

DC link voltage

800 V

2

DC link capacitor

2e− 3F

3

LCL filter

L = 3e− 3H C = 20e− 6F L = 5e− 6H

4

Switching frequency

4 kHz

5

Kp

0.01

6

Ki

1

Here, Kd is gain of phase detector and ωi + ω0 are high frequency component which is eliminated by loop filter or low pass filter, so output of loop filter is Vc (t) = K d cos[(ωi − ω0 )t + θi − ψ0 ]

(4)

Real Time Simulation of 3-ϕ Grid-Connected Converter …

605

After the transient period, output of voltage controlled oscillator u0 becomes synchronous with ui input signal, so u0 can be expressed as u 0 (t) = B sin(ωi t + ϕ0 )

(5)

On comparing (2) and (5), we can see that ψ0 phase in (2) is a linear function and can be express as ψ0 = (ωi − ω0 )t + ϕ0

(6)

hence, loop filter output after adding ψ0 in (4) becomes a DC signal and Vc(t) can be expressed as Vc (t) = K d cos(θi − θ0 )

(7)

Voltage controlled oscillator is frequency-modulated oscillator; Vc(t) is linear function of instantaneous angular frequency ωins t of voltage controlled oscillator, around central angular frequency ω0, d ωi ns =

d (ω0 t + ψ0 ) = ω0 + K v VC (t) dt

(8)

and dψ0 = K v VC (t) dt

(9)

Kv is voltage controlled oscillator sensitivity this gives us ωi − ω0 = K d K v cos(θi − ϕo ) ϕ0 = θi − cos−1

ωi − ω0 Kd Kv

(10) (11)

By comparing (11) and (7), we get Vc Vc =

ωi − ω0 Kv

(12)

So, Vc is a DC signal clearly indicates by Eq. (12) that changes voltage controlled oscillator frequency through its central value ω0 to input signal frequency ωi ωi ns = ω0 + K v Vc = ωi If KdKv product is small than ωi = ω0, thus for convenience

(13)

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θ0 = ϕ0 +

π 2

(14)

so the (7) gives us Vc = K d sin(θi − θ0 )

(15)

Here in (15), θ i − θ 0 is error in phase between two signal and PLL is minimise this error so that the output signal of PLL is in synchronisation with input signal in terms of phase and frequency.

3 Real-Time Results Using Typhoon HIL Emulator Modelling has been done in software environment provided by Typhoon HIL, and real-time results have been taken using Typhoon HIL 402 emulator, which is compact, powerful and powered by a 4-core FPGA processor, it provides all the necessary tools need to test power electronics converter with a wide range of applications. The ultrahigh fidelity emulator supports simulation of 4–3 phase 3-level converters with fixed time step of 1-μs or less, it supports a pwm switching frequency of 200-kHz. Setup is shown in the Fig. 4.

Fig. 4 Photograph of Typhoon HIL 402 setup

Real Time Simulation of 3-ϕ Grid-Connected Converter …

607

Fig. 5 Grid current during balanced condition

3.1 Balanced Grid Condition A constant voltage source of 800 V DC is feeding to converter and converter feed to grid; under balanced grid condition, grid connected converter is pumping power into grid and there is no fault injected in this condition, grid current, and voltage waveform is shown in Figs. 5 and 6, with a zoom part for a better understanding of current and voltage. Results are taken for a time period of 5 s with a sample rate of 200 samples per second. The simulation step is set to 1e−6 s for plant part and time step of controller is set to 250e−6 s. Grid voltage is set to 120 V at a frequency of 60 Hz.

Fig. 6 Grid voltage during balanced condition

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Fig. 7 Grid current during LVRT condition

3.2 Unbalanced Grid Condition Different faults discussed below has been injected in grid for a time period of 0.5 s and response has been observed [9, 10]. 1. Low Voltage Ride Through: Unbalanced grid condition is created by injecting faults in grid, in first condition, we can inject low voltage fault and observe that how current and voltage behaves during low voltage ride through condition; fault is injected for 0.5 s as shown in Figs. 7 and 8 effect of voltage dip on current and voltage is shown; current is raised during voltage dip for power balancing. 2. High Voltage Ride Through: In high voltage fault condition, fault is injected for 0.5 s and voltage and current has been analysed, and it is clearly visible in Figs. 9

Fig. 8 Grid voltage during LVRT condition

Real Time Simulation of 3-ϕ Grid-Connected Converter …

609

Fig. 9 Grid current during HVRT condition

and 10 that voltage is rise and current is dipping during fault time for balancing the power here also similar like others simulation is run for a period for 5 s. 3. Low Frequency Ride Through: During low frequency faults injected in grid for 0.5 s voltage and current has been captured and shown in Figs. 11 and 12 effect of the fault is clearly visible in results at a nearby time period of 2.5 s; here, only fault is injected for 0.5 s so after that time period grid is again stable so current and voltage again came to settle down. 4. High Frequency Ride Through: In high frequency fault condition, voltage and current have been captured and in Figs. 13 and 14 clearly change in frequency is visible near 2.5 s. In high frequency fault condition, frequency is increased for a period of 0.5 s.

Fig. 10 Grid voltage during HVRT condition

610

Fig. 11 Grid current during LFRT condition

Fig. 12 Grid voltage during LFRT condition

Fig. 13 Grid current during HFRT condition

G. Gautam and S. Poddar

Real Time Simulation of 3-ϕ Grid-Connected Converter …

611

Fig. 14 Grid voltage during HFRT condition

4 Conclusion and Future Scope This chapter presented real-time simulation of a three-phase grid connected converter with real and reactive power control; different faults LVRT, HVRT, LFRT, HFRT have been injected and there effects on voltage and current have been observed. Typhoon HIL 402 emulator has been used to perform real-time simulation. In this simulation, controller is designed only with fixed real and reactive power reference, for future, one can design a dynamic controller that has the ability to generate pulses in such a way that converter can supply required voltage during fault occurrence in order to perform ride through event.

References 1. Golbon N, MoschopoulosG, Khajehoddin SA (2013) A control strategy for a solar gridconnected inverter. In: 2013 26th IEEE Canadian conference on electrical and computer engineering (CCECE). Regina, SK, pp 1–4. 2. Mechouma R,Azoui B, Chaabane M (2012) Three-phase grid connected inverter for photovoltaic systems, a review. In: 2012 first international conference on renewable energies and vehicular technology. Hammamet, pp 37–42 3. Popadi´c B, Dumni´c B, Kati´c V, Sˇ ljivac D, Pelin D (2017) Overview of grid connected converter synchronization techniques under different grid voltage conditions. In: 2017 international conference on smart systems and technologies (SST). Osijek, pp 99–105 4. Jia J, Yang G, Nielsen AH (2018) A review on grid-connected converter control for short-circuit power provision under grid unbalanced faults. IEEE Trans Power Delivery 33(2):649–661 5. Afshari E, Farhangi B, Yang Y, Farhangi S (2017) A low-voltage ride-through control strategy for three-phase grid-connected PV systems. In: 2017 IEEE power and energy conference at Illinois (PECI). Champaign, IL, pp 1–6 6. Hsieh G-C, Hung JC (1996) Phase-locked loop techniques. A survey. IEEE Trans Industr Electron 43(6):609–615. https://doi.org/10.1109/41.544547

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7. Xie D, Zhang D, Gao P (2016)Research on phase-locked loop control and its application. In: 2016 IEEE information technology, networking, electronic and automation control conference. Chongqing, pp 818–821 8. Chiu W, Huang Y, Lin T (2010) A dynamic phase error compensation technique for fast-locking phase-locked loops. IEEE J Solid State Circuits 45(6):1137–1149 9. Lammert G, Heß T, Schmidt M, Schegner P, Braun M (2014) Dynamic grid support in low voltage grids—fault ride-through and reactive power/voltage support during grid disturbances. In: 2014 power systems computation conference. Wroclaw, pp 1–7 10. Foruzan E, Algrain MC, Asgarpoor S (2017) Low-voltage ride-through simulation for microgrid systems. In: 2017 IEEE international conference on electro information technology (EIT). Lincoln, NE, pp 260–264

Operational Resiliency Enhancement Using Synchrophasor Measurement Anant Milan Khalkho and Dusmanta Kumar Mohanta

Abstract The recent occurrences of various high-impact events have caused disruption of power to the consumers. The grid in such cases must spring back from unstable to stable conditions and must be resilient. Conventionally, grid hardening is used for the purpose of enhancement of resiliency, but such approach is expensive. The phasor measurement units (PMUs) have the ability to provide with real-time data both during normal conditions as well as disruptive events. The measured data can be used to take proactive action against the disruption to improve grid operation. Since the volume of synchrophasor data is very high, principal component analysis (PCA)-based data reduction has been used to identify the critical set of generators for a IEEE-39 bus test system. The governor as well as bypass operations of steam turbines are controlled based on inferences derived from the dynamic state of critical generators, so as to enhance the operational resiliency. Keywords Phasor measurement unit (PMU) · Operational resiliency · Turbine governing system · Turbine by pass valve · Smart grid

1 Introduction Maintaining a reliable supply of power to consumers is a key requirement for power system. Major high-impact events like floods, tsunamis, wind storms, etc. cause power system disruption [1]. These low-probability incidents are expected to increase in the current climate change scenario [2]. To counteract these events and maintain a reliable supply of electricity to consumers, effective strategies are required. Resilience enhancement refers to the power system’s ability to respond to unplanned events with low probability of high impact. The most common method of improvement of resiliency is hardening of the grid. Grid hardening refers to A. M. Khalkho (B) · D. K. Mohanta Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] D. K. Mohanta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_61

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the construction of a safer physical infrastructure for power transfer. Methods for improving system resilience by hardening the system are discussed in [3–6]. The damaged distribution system may not be able to meet the load demands in the event of extreme weather conditions, despite the availability of resources. The solution could be to distribute generation resources, and methods have been discussed in [7–9]. The methods mentioned above refer to planning the power system’s resiliency, which involve huge investments. The operational resiliency can be enhanced by suitable control actions based on real-time monitoring. With the technological advents related to information and communication technology (ICT), there have been some attempts in this domain [10–13]. Resilience of the interconnected power system refers to a wider area and must take into account the network of generation and transmission. Resiliency is drastically reduced during disturbances such as generator failure, generation, and load demand imbalance, severe transmission line failures, etc. With the advent of phasor measuring units (PMUs), it is possible to monitor the stability of the power system over a wide geographic area [14]. The time synchronization of PMU with the global positioning system (GPS) makes it possible to monitor the system in real time and convert conventional grids into smart grids [15, 16]. Smart grids can sense abnormal conditions and action on the available network can be taken in the event of disturbances. Fault identification is not adequate to determine resilience. Identifying critical generators, especially during post-transients, is important for proactive measures to alleviate operational resiliency. The unique contribution of this work is to control real power output of turbine and also to provide damping through bypass operation of turbine so as to enhance the operational resiliency. The case studies with results validate the efficacy of the proposed methodology.

2 PMU-Based Monitoring of Operational Resiliency Dynamics of Synchronous Generators 2.1 Synchronous Generator Dynamics Power system consists of large number of synchronous generators, and for stable operation, the generators must operate in synchronism with each other. Disturbance in the power system’s operating conditions will cause the generators to go out of synchronism with the rest of the system. High-impact events cause major power system disruptions, such as faults, line breakdowns, removal of a generator in service. The operation of synchronous generators reflects these major disturbances. The power system must be resilient in various hazardous events, i.e., it must recover quickly from the impact of such events. Different dynamic parameters obtained from synchrophasor measurements will determine the stability of the power system under

Operational Resiliency Enhancement …

615

severe disturbance. Oscillations of each individual synchronous generator can be observed in a disrupted state in rotor angle (δ) estimated from PMU data [17]. The oscillations of the ‘ith’ generator are described by swing equation in a ‘N’ generator system and are given by (1): Mi δ¨i = Bi − Pi − Di δ˙i

(1)

where δi = angular displacement of the ith generator field from the reference axis also referred as rotor angle,Mi = inertia constant of ith generator, Bi = mechanical input to the ith generator minus all the prime mover and generator losses,Pi = electrical power output of ith generator, Di = damping coefficient of ith generator. Electrical power output of ith generator depends on its excitation voltage E i and rotor angle δ and is given by (2) Pi =

E i Vi sinδ X si

(2)

where E i =excitation voltage of ith generator, Vi =terminal voltage of ith generator, X si =synchronous rectance of ith generator. The resilience of power system can be improved by controlling P and D described in (1). The electrical power output of ith generator can be controlled by controlling the excitation voltage E i estimated from synchrophasor measurements [17].

2.2 PCA-Based Identification of Critical Generators and Resiliency The power grid components operate in synchronism with each other. Disturbances affect the operation of synchronous generators and tend to pull the generators out of step. Generator coherency determination after clearance of fault can be effective for operational resiliency enhancement. If generators can be classified on the basis of their criticality, appropriate measures can be taken to save generators with less criticality. As the real power is directly proportional to rotor angle δ, it can be efficiently used for coherency determination. The PMU can provide data with high resolution of 240 samples/s. Effective analysis of large amount of data can be used before the critical generators go out of step. The principal component analysis (PCA) can be used for generator buses to minimize information obtained from PMU. The rotor angles obtained from the connected generator buses can be reduced to determine the coherency of the generators. PCA is used to reduce a large volume of data to a smaller number of dimensions.

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Let S be i × j data matrix obtained from PMU, where i= rotor angles obtained at various instance for a particular generator and j=number of connected generators. The matrix S is normalized to obtain S N , which is used to calculate covariance matrix V (3). The eigen vector E and eigen value λ are calculated from (4). The eigen vectors are arranged in descending order according to their eigen values to form matrix Q. The original data matrix S is reduced to F using (5). V =

 1   SN × SN i −1

(3)

V × E =λ×E

(4)



F =E ×S

(5)

2.3 PCA-Based Identification of Critical Generators and Resiliency Any system’s performance can be quantified in time and shown in Fig. 1. The synchronous generator post transient encounter power fluctuations and can be considered as a parameter for functionality of generator P T F T (t). Let to is the time instant after clearance of fault, te is the time instant 5 cycles after clearance of fault, tc is the time instant the protective action is initiated, and t f is the time instant after counter action against instability has taken place. The resilience of a generator denoted by R E S(t) is calculated according to (6). Figure 1 shows resiliency curve with normal PTFT(t)

Final Operating State Re st Pr orat oc ion ess

re ilu rs Fa ccu O

Original Operating State

to

te

tc2 tc1

tf2

Resiliency curve after PMU based action of turbine governor and FSSS

Resiliency curve under normal conditions

tf1

Time (seconds)

Fig. 1 Resilience curve showing the performance of the system

Operational Resiliency Enhancement …

617

operation and resiliency with PMU-based operation. R E S(t) =

P T F T (t f ) − P T F T (t c ) P T F T (t o ) − P T F T (t c )

(6)

3 Synchrophasor-Based Action on Turnine-Governor System for Enhancing Operational Resiliency: A Case Study With identification of critical generators, effective actions are required so that less critical generators can be saved from going out of step. Once PCA has identified less critical generators, the PMU data is used to estimate turbine mechanical input Pm [21]. The turbine combustion cycle with operating valves is shown in Fig. 2 [22] which needs to be operated according to estimated Pm . After clearance of fault, the electrical power output is not balanced with mechanical power input which can cause large generator power swings, causing instability. However, if the bypass valve is opened and the steam is adjusted to estimated value of Pm , the generator can be brought back to safe operating limits. The opening of bypass valve for steam also necessitates for control of generation of steam in the boiler which is done by furnace Reheated steam line Main steam line

HPBV

IPCV

HPCV HPT

IPT

LPT Generator

Boiler LPBV

HPCV- High pressure control valve HPBV- High pressure bypass valve IPCV- Intermediate pressure control valve LPBV- Low pressure bypass valve

Fig. 2 Combustion cycle of steam turbine

Condenser

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A. M. Khalkho and D. K. Mohanta

Main Control Centre (MCC)

Fig. 3 Block diagram representing synchrophaor-based resiliency enhancement

ata

ient d

nt da ta

trans

Post trans ie

Post

Co turb mmand ine to estim and FS generat ated SS ba or se valu e of d on Pm

Identification of critical generators based on estimated value of (δ)

PMU

Generators

PMU

Transmission system

supervisory safeguard system (FSSS). The block diagram showing the proposed method to enhance the resiliency of power grid is shown in Fig. 3. The IEEE-39 bus system, consisting of 10 generators labeled according to the bus to which they are connected, is shown in Fig. 4. A fault was created between lines 7–8 at 0.1 s and was cleared after 6 cycles. The PCA is used for identification of critical generators, taking δ values for 12 cycles post transient and is shown in Fig. 5. The PCA plot represents the maximum number of nearby generators as strong generators. Generators connected to bus 32 and 31 are very far from them so they are most likely to go out of step. Generators connected to bus 39, 30, and 38 are less critical and can be saved from becoming unstable as they are nearer to the strong group of generators. The rotor angle plot of generators represented by (d30–d39) is shown in Fig. 6. Here, to is the time instant disturbance has ended, i.e., 0.2 s, te . If the time for restoration with the help of PMU data is 0.4 s, then tc1 = 0.65 s, tc2 = 0.60 s, t f 1 = 0.85 s, and t f 2 = 0.75 s. With obtained results for generator G30, P T F T (t 0 ) = 846.05 MW, P T F T (t c1 ) = 801 MW, P T F T (t c2 ) = 811 MW, P T F T (t f 1 )= 835 MW, and P T F T (t f 2 ) =840 MW. The resiliency with action of PMU is R E S 1 and without PMU action is R E S 2 calculated from (6), and the calculations for G30 is shown. The resiliency calculation for all the generators which are less critical are shown in Table 1. R E S1 =

835 − 801 = 0.75 846.05 − 801

R E S1 =

840 − 811 = 0.83 846.05 − 811

Operational Resiliency Enhancement … G30

619

G37

37

30

26

24

2

18

38

17

3

G38

16

G39 G39

39

29

27

25

1

28

G35

4

35

15 5

12

14

6

21

19

22

7 8

13

11

9

23 20

10 31 G31

32

34 G32

36

33 G34

G33

Fig. 4 IEEE-39 bus test system

Fig. 5 PCA plot for fault case between line 7–8 for 12 cycles post transient

G36

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Fig. 6 Rotor angle plot of generators for fault at lie 7–8

Table 1 Comparison of generator resiliency with and without synchrophasor-based action Generator

Resiliency without synchrophaor-based control RES 2

Resiliency with synchrophasor-based control RES 1

G30

0.75

0.83

G38

0.74

0.82

G39

0.74

0.83

4 Conclusion The PMU in recent years have become very effective in monitoring any critical event in the grid. The PMU data can be used to control various generator parameters in order to change the operating conditions. This can be used for stable operation of generators under high-impact events. PCA is very effective in reducing a voluminous data. The turbine valve opening and the action of governor pertaining to the critical generators are very effective for enhancing the resiliency of the smart grid. The results validate enhancement of operational resiliency.

References 1. SouthWell P (2014) Disaster recovery within a cigre strategic framework: network resilience, trends and areas of future work. SC C1 CIGRE Tech. Committee 2. IPCC: Climate change 2013: The physical science basis (2013) Contrib. Work. Group I 5th Assess. Rep. Intergovernmental Panel Climate Change 3. Yuan W, Wang J, Qiu F, Chen C, Kang C, Zeng B (2016) Robust optimization-based resilient distribution network planning against natural disasters. IEEE Trans Smart Grid 7(6):2817–2826 4. Romero N, Xu N, Nozick LK, Dobson I, Jones D (2011) Investment planning for electric power systems under terrorist threat. IEEE Trans Power Syst 27(1):108–116 5. Alguacil N, Delgadillo A, Arroyo JM (2014) A trilevel programming approach for electric grid defense planning. Comput Oper Res 41:282–290

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6. U.S. Government Accountability Office (2014) Climate change: energy infrastructure risks and adaptation efforts U.S. Dept. Energy, Washington, DC, USA. Technical Report, GAO-14-74 7. Li J, Ma XY, Liu CC, Schneider KP (2014) Distribution system restoration with microgrids using spanning tree search. IEEE Trans Power Syst 29(6):3021–3029 8. Pham TTH, Bésanger Y, Hadjsaid N (2009) New challenges in power system restoration with large scale of dispersed generation insertion. IEEE Trans Power Syst 24(1):398–406 9. Li B, Roche R, Miraoui A (2016) System resilience improvement using multiple energy supply systems under natural disasters. In: IECON 2016—42nd annual conference of the IEEE ındustrial electronics society. IEEE, Florence, pp 3912–3917 10. Li Y, Xie K, Wang L, Xiang Y (2019) Exploiting network topology optimization and demand side management to improve bulk power system resilience under windstorms. Electric Power Syst Res 171:127–140 11. Erol-Kantarci M, Mouftah HT (2011) Management of PHEV batteries in the smart grid: towards a cyber-physical power infrastructure. In: 2011 7th ınternational wireless communications and mobile computing conference. Istanbul, pp 795–800 12. Panteli M, Trakas DN, Mancarella P, Hatziargyriou ND (2016) Boosting the power grid resilience to extreme weather events using defensive islanding. IEEE Trans Smart Grid 7(6):2913–2922 13. Al-Rubaye S, Kadhum E, Ni Q, Anpalagan A (2017) Industrial internet of things driven by SDN platform for smart grid resiliency. IEEE Internet Things J 6(1):267–277 14. Ghosh S, Ghosh D, Mohanta DK (2017) Situational awareness enhancement of smart grids using intelligent maintenance scheduling of phasor measurement sensors. IEEE Sens J 17(23):7685–7693 15. Thorp JS, Phadke AG, Karimi KJ (1985) Real time voltage-phasor measurement for static state estimation. IEEE Trans Power Apparat Syst 11:3098–3106 16. De La Ree J, Centeno V, Thorp JS, Phadke AG (2010) Synchronized phasor measurement applications in power systems. IEEE Trans Smart Grid 1(1):20–27 17. Ghahremani E, Kamwa I (2011) Dynamic state estimation in power system by applying the extended Kalman filter with unknown inputs to phasor measurements. IEEE Trans Power Syst 26(4):2556–2566 18. Grainger JJ, Stevenson WD (1994) Power system analysis, vol 67. McGraw-Hill, New York 19. Mahto DK, Kibriya F, Khalkho AM, Mohanta DK (2019) Data driven approach for wide area generator coherency detection using synchrophasor measurements. In: TENSYMP-2019. IEEE, Kolkata 20. Canizares C, Fernandes T, Geraldi E, Gerin-Lajoie L, Gibbard M, Hiskens I, Kersulis J, Kuiava R, Lima L, DeMarco F, Martins N (2016) Benchmark models for the analysis and control of small-signal oscillatory dynamics in power systems. IEEE Trans Power Syst 32(1):715–722 21. Ariff MAM, Pal BC, Singh AK (2014) Estimating dynamic model parameters for adaptive protection and control in power system. IEEE Trans Power Syst 30(2):829–839 22. Hoizumi S, Abe N, Ueno T, Arakawa T, Hodozuka K (1987) Steam turbine plant having a turbine bypass system. U.S. Patent 4,693,086, Hitachi Ltd

A Multi HIL-Based Approach for Real-Time Phasor Data Monitoring Using Phasor Measurement Unit Rohit Kumar, Sourav Kumar Sahu, Debomita Ghosh, and Sarbani Chakraborty

Abstract Rapid growth in renewable energy is continuously changing the dynamics of the power system. To visualise the ever-changing dynamics, a very precise measuring instrument with advanced measuring capability is inevitable. In this context, phasor measurement unit (PMU) is one of the essential components in the electrical power system. In this chapter, a laboratory prototype model is developed to monitor the behaviour of solar power plants using PMU through hardware in loop using Typhoon HIL and data acquisition (DAQ) using non-recursive method. Keywords Phasor measurement unit · Hardware in loop · Non-recursive DFT algorithm

1 Introduction Online estimation of voltage and current phasors, frequency and rate of change of frequency have become essential because of drastic increase in distributed generation in the form of microgrid. Further, wide-area monitoring (WAMS) of these data are important to analyse the system behaviour and taking corrective measures. Phasor measurement units (PMUs) are the key components to WAMS. Various works on the application of PMU are done in [1]. The basics of PMUs are analysed with recursive and non-recursive algorithms based on DFT for the estimation of phasor and upadation of phasor in [2]. In [3], the author discusses about the global positioning system (GPS), phasor data concentrator (PDC), hierarchy of phasor measurement system. The protection scheme of the transmission line is explained in case of the occurrence of a fault in [4]. The advantage of synchrophasor over SCADA is well explained in [5]. In [6], the author analysed the concept of SCADA system and also about the wide-area monitoring at the power grid. In this paper, the authors discussed about the communication infrastructure to monitor the time-synchronised data, the uses of WAMS and also the tools used to monitor data. The authors found R. Kumar (B) · S. K. Sahu · D. Ghosh · S. Chakraborty EEE Department, BIT Mesra, Ranchi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_62

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that the synchrophasor provide high speed coherent data that are not possible with the SCADA system as in [6]. The wide-area protection and control are done using synchronised phasor angle difference as in [7]. The viability analysis of communication for a smart grid with a phasor measurement unit is done [8]. In [9], the author presents the placement of PMUs techniques for state estimation and also the reliability, accuracy of the system network is considered. The modelling of PMUs is done using the DFT algorithm by using non-recursive and recursive for updating phasor in MATLAB and NI LabView platform in [10, 11]. Mostly the modelling is done in software in this proposed literature. Single-phase PMU prototype has been designed without consideration of harmonics in [12]. The design of low voltage profile µPMUs is discussed in [13]. A hardware development of µPMU for the application of distribution system is seen in [14]. The main drawback of the above hardware modelling shown in the literatures are that they are limited to a single-phase measurement. Further, flexibility of testing of the hardware model in different operating conditions are not possible unlike in hardware in loop (HIL) scenario. In this paper, the operation of PMU to monitor the voltage, current phasor and frequency have been tested in multi HIL. The HIL technology provides accuracy, repetitiveness, safety for testing the developed models in variable conditions and can be customised as per requirement. For analysis and real-time monitoring, a plant is modelled in Typhoon HIL 402 series which has been interfaced with LabView using HIL breakout board and NI DAQ 6001. The PMU module developed in LabView monitors the entire plant behaviour in real time to prove its efficacy.

2 Methodology 2.1 Development of PV Plant Using Typhoon HIL Typhoon HIL offers integrated solutions for the testing of PV converters and the rapid development of software control. With a HIL400 system, all components of traditional PV hardware systems can be emulated in real-time, enabling controller testing in a safe and comfortable environment [14]. The rate of updation is 1 MHz. The model of PV plant has been developed in HIL for analysing the dynamic behaviour of the plant measurable in HIL as in Fig. 1. To test the developed PV plant in the real-time platform and obtain the phasor data, a PMU model is developed in LabView which is interfaced with HIL to monitor its dynamic behaviour.

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Fig. 1 Shows the PV plant model developed in HIL

2.2 Development of PMU in LabView Using Non-recursive DFT The key innovation of national instruments (NI) is that it uses graphical representation rather than code which makes it more intuitional for engineers. LabView also holds the application of specific libraries of code for data presentation, general-purpose interface bus (GPIB), data presentation, data acquisition (DAQ), serial instrument control, data analysis, for the storage of data and communication through the internet. In LabView through DAQ card, a real-time analogue or digital signal can be given to its input and extracted from its output. The estimation of phasor can be done by recursive and non-recursive DFT. Recursive and non-recursive methods are used for updating phasor data. In this chapter, non-recursive DFT method is used for the modelling of PMU for the estimation of magnitude, phase angle, frequency. In nonrecursive DFT, a fresh calculation is performed for every new iteration whereas in recursive, the estimations are done based on the previous calculation, so there is a high probability of error in recursive DFT [2]. The flow chart is proposed as in Fig. 2. A sinusoidal signal is taken which can be expressed by phasor with a fixed amplitude, phase angle, frequency and also has a rotating vector as in Eq. (1). In this chapter, input signal is taken from the PV plant developed in Typhoon HIL. x(t) = X i cos(ωt + ∅)

(1)

where X i is a magnitude, ø is a angular position, ω is a angular frequency. So, for the above input, i.e. Eq. (1) [2] the L data samples xl : (l = 0 … L − 1) are: xl = X i cos(ωt + ∅)

(2)

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Fig. 2 Flowchart for development of PMU in LabView using non-recursive DFT algorithm

Thus, the equation for updating the phasor is done by using non-recursive technique is as in Eq. (3) [2]: √

X kL−1

2 = xl (cosklθ − jsinklθ ) N l=0 √

X kL =

L−1

2 xl+1 (cosklθ − jsinklθ ) L l=0

(3)

L−1

(4)

where ‘θ ’ is the sampling angle measured in terms of angle. Therefore, a nonrecursive method has been adopted for updating the phasor. For the update of a new window which starts from l = 1 and ends at l = N.

2.3 Interfacing Typhoon HIL with LabView The following flow chart is proposed for interfacing the PMU operation as in Fig. 3.

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Fig. 3 Flowchart for model development and interfacing for real-time simulation

3 Results A PV plant was developed in Typhoon HIL and implemented with a two-level threephase inverter with a current control loop. The PV plant used in the HIL400 system is an array of PV cells made up of sequence-connected components. The detail specification of component parameters for the PV plant are as in Table 1. The developed PV plant in Typhoon HIL is used for real-time signal generation which is interfaced with NI USB 6001 for data acquisition through PMU. A PMU model is developed in NI LabView using a non-recursive DFT method for the estimation of phasor with a reporting rate of 12 frames per second for system frequency of 60 Hz. The hardware setup for obtaining the phasor data of the plant is as shown in Fig. 4. From the hardware model, the voltage and current phasors and frequency at different irradiance condition like at 500 (W·m−2 ) and 1000 (W·m−2 ) at different time stamping is obtained.

3.1 Case-1: For Irradiance of 500 (W m−2 ) To prove the efficacy of the claimed laboratory prototype, phasor plots for voltage at an irradiance of 500 (W m−2 ) at a specific time in a day is plotted as in Fig. 5. Some Table 1 Specification for PV plant component parameter

S. No.

Parameters (PV plant)

Specificatıon

1

Nominal voltage

480 V

2

Nominal power

5.0 MVA

3

Nominal frequency

60.0 Hz

4

PV plant efficiency

20%

5

Power transformer ratio

480 V/13.8 kV

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Fig. 4 Hardware prototype model for the development of PMU

Fig. 5 Phasor plot of voltage at irradiance 500 (W·m−2 ) and 1000 (W·m−2 )

values of the PMU are given in Table 2 with time stamping corresponding to each value.

3.2 Case-2: For Irradiance of 1000 (W m−2 ) To prove the efficacy of the claimed laboratory prototype, phasor plots for voltage at an irradiance of 1000 (W m−2 ) at a specific time in a day is plotted as in Fig. 5. Some values of the PMU are given in Table 3 with time stamping corresponding to each value.

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Table 2 RMS voltage and current values at 500 (W·m−2 ) from laboratory prototype Time

V R (V)

V Y (V)

V B (V)

I R (A)

I Y (A)

I B (A)

11-23-2019 11:22:43.61

13,757.9

12,826.3

13,714.8

103.50

110.29

102.70

11-23-2019 11:22:44.51

13,076.0

13,271.7

13,721.5

108.63

106.55

103.46

11-23-2019 11:29:55.74

12,880.2

13,834.2

13,039.6

108.537

102.24

109.96

11-23-2019 11:29:56.63

13,597.8

12,861.2

13,824.2

105.351

109.52

102.11

Table 3 RMS voltage and current values 1000 (W·m−2 ) from laboratory prototype Time

V R (V)

V Y (V)

V B (V)

I R (A)

I Y (A)

I B (A)

11-23-2019 10:55:26.46

13,010.9

13,259.7

13,657.0

109.63

105.01

104.79

11-23-2019 10:55:27.36

12,784.7

13,668.3

13,249.0

109.66

102.15

108.96

11-23-2019 11:05:18.51

13,370.4

13,577.1

12,804.4

104.64

105.17

109.55

4 Conclusion A laboratory prototype model of PMU was developed and was tested in a real-time environment. To show the effectiveness, a real-signal was taken from the HIL emulator to test the PMU in real-world scenario. A precise time stamping with the dynamic change in the values can be observed which is significant for enhancing the situational awareness of the network.

References 1. Rep EF (1997) Assessment of applications and benefits of phasor measurement technology in power systems. GE Power Syst Eng 2. Phadke AG, Thorp JS (2008) Synchronized phasor measurements and their applications, vol 1, p 81. Springer, New York 3. Phadke AG (2002) Phasor measurement unit and phasor data concentrator. In: Transmission and distribution conference and exhibition 2002, vol 1. IEEE/PES, Asia Pacific, pp. 476–479 4. Neyestanaki MK, Ranjbar AM (2015) An adaptive PMU-based wide area backup protection scheme for power transmission lines. IEEE Trans Smart Grid 6(3):1550–1559 5. Donolo M (2006) Advantages of synchrophasor measurements over SCADA measurements for power system state estimation. In: SEL application note, pp 2006–2010 6. Mynam MV, Harikrishna A, Singh V (2011) Synchrophasors redefining SCADA systems. Schweitzer Engineering Laboratories, Inc. 7. Martínez E, Juárez N, Guzmán A, Zweigle G, León J (2006) Using synchronized phasor angle difference for wide-area protection and control. In: Proceedings of the 33rd annual western protective relay conference. Spokane, WA 8. Ghosh D, Ghose T, Mohanta DK (2013) Communication feasibility analysis for smart grid with phasor measurement units. IEEE Trans Industr Inf 9(3):1486–1496 9. Abood HG, Sreeram V (2014) A review on phasor measurement units placement for state estimation studies. In: 2014 Australasian universities power engineering conference (AUPEC). IEEE, pp 1–6

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10. Jain A, Verma MK (2016) Development of DFT based MATLAB and LABVIEW models for phasor measurements. Int J Inf Electron Eng 6(6):331–336 11. Hareesh SV, Raja P, Selvan MP (2015) An effective implementation of Phasor measurement unit (PMU) by using non-recursive DFT algorithm. In: 2015 ınternational conference on condition assessment techniques in electrical systems (CATCON). IEEE, pp 195–199 12. Sharma RB, Dhole GM, Tasare MB (2016) Design of single phase phasor measurement unit prototype. In: 2016 international conference on computing communication control and automation (ICCUBEA). IEEE, pp 1–5 13. Pinte B, Quinlan M, Reinhard K (2015) Low voltage micro-phasor measurement unit (µPMU). In: 2015 IEEE power and energy conference at Illinois (PECI). IEEE, pp 1–4 14. Das HP, Pradhan AK (2016) Development of a micro-phasor measurement unit for distribution system applications. In: 2016 national power systems conference (NPSC). IEEE, pp 1–5

Fault Detection Using Harmonic Analysis of Single Terminal DC Current Signal of HVDC Line Shobha Agarwal, Rakesh Kumar Singh, and Vimlesh Verma

Abstract In this paper, the LCC-based HVDC converter using harmonic analysis of single terminal DC current signal of HVDC transmission line has been proposed. The model is simulated and analysed using FFT, after that THD is determined. The HVDC lines are very much economical for long distance, so at the far end distance, fault identification is important for safety of transmission lines. The LCC-based HVDC converter scheme is designed in MATLAB. The results show that proposed method can discriminate the faults. None of the papers has done the THD analysis under fault conditions. In this paper, the factors considering, here, the effect of fault location and fault resistances are taken in account for the reliability, accuracy, and selectivity. Keywords Fault · Harmonics · HVDC · FFT · THD

1 Introduction HVDC transmission system is less expensive as well as least electrical losses; due to this, it is very much beneficial from technical as well as economical point of view and it is best suited for long distance transfer of bulk power. It may be suitable option to achieve maximum power transfer. HVDC transmission line links with VSC-HVDC. It has very much remarkable advantage over traditional LCC-HVDC links. It is gaining attention to be considered as the main technology advancement for further HVDC installations [1]. It independently controls reactive power as well as active power. It does not require external voltage for its self-commutating device. It has important advantages of bidirectional flowing of power. It enables VSC-HVDC/VSCMTDC grids [2]. It is highly flexible for transmission of power between multiple AC S. Agarwal Department of Higher Technical Education and Skill Development, Government of Jharkhand, Ranchi, Jharkhand, India R. K. Singh (B) · V. Verma Department of Electrical Engineering, NIT Patna, Patna, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_63

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regions [3], but technical challenge is also exist, which is present in the protection as well as control of VSC-MTDC lines [4]. Fast fault isolation is only possible using DC circuit breaker (DCCB) [5] or converters with fault blocking capability [6]. Selection and fast detection of faults are more difficult for the protection for proposed algorithm, because it requires less than 4–5 ms in order to get minimum damage in HVDC converter switches [7] and correctly detect the faulty section of the transmission grid. According to the mentioned challenges in the protection of VSCMTDC grids, designing fast, accurate, and selective fault detection algorithm is one of the important topics in the field of HVDC protection. The latest literature review in designing protection algorithms for VSC-MTDC grid is based on fault voltage or current traveling wave (CTW) [8, 9]. Fuzzy-based method [10], VSC-based HVDC transmission lines [11], harmonics-based algorithms [12], VSC-HVDC for wind farm [13] voltage or current transients [14], the rate of change of voltage (ROCOV) and the rate of change of current (ROCOC) [15] as well as artificial intelligence (AI)-based methods [16]. TW method gives accurate fault detection results [17], but it needs high sampling, it highly depends the peak value of fault waves. The transient-based methods have best selectivity to determine the faulty section, but it is highly sensitive to noise. In [12], an overcurrent method to detect a certain range of frequencies in the fault current waves using the short-time fourier transform (STFT). It gives reasonable fault detection results, but the selectivity of this method is not verified for faults close to one of the measurement locations in the VSC-MTDC grid. Actually, overcurrentbased methods simply consider a certain range of frequencies may not have the required selectivity. The harmonic-based techniques extracts the existing harmonics in fault voltage as well as current waves for designing protection algorithms. It is using various signal for processing methods as a wavelet transform (WT) [18, 19]. In [20], it is proposed for harmonic-based protection technique for LCC-HVDC links. Another harmonic-based protection is used first carrier frequency harmonic (FCFH) current signals for making algorithms of fault detection for VSC-HVDC transmission systems [21].

2 Model for Simulation The disadvantages of mercury arc-based LCC, nowaday’s thyristor is used. It has benefits over conventional but conducts unidirectional. With the help of time margin or extinction angle, forward blocking capability is achieved. In this paper, two series connected Graetz Bridge model is used. Here, the three-phase sinusoidal ac source is given to the Graetz Bridge as an input. AC as well as DC filters compensate reactive power. It acts at fundamental frequency just as a capacitor. In this paper, with the help of MATLAB/Simulink, we transmit the power from a 500 kV, 5000 MVA, 50 Hz system to a 345 kV, 10,000 MVA, 50 Hz system. Here, we connect different two frequencies of AC source to HVDC lines. Due to high series reactor on HVDC lines, it is very much necessary to provide protection as well as smoothing of dc current. Here, the line length is taken 900 km for the simulation. The different fault conditions

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are enforced on the DC transmission lines. If a fault persist permanently in the pole, power transfer fails to continue but it has the advantage and the advantage is this, less power loss because of ground return path.

3 Fast Fourier Transform (FFT) Fast Fourier transform (FFT) is used for computing the total harmonic distortion of signals after fault. But for study , two-point DFT and four-point DFT are generalized for higher eight-point, sixteen-point, ……,(two)r -point, finally we get the FFT algorithm. FFT calculates the DFT using O(m log m) multiplies and adds.

4 Total Harmonic Distortion (THD) THDs are used to measure a distortion of waveform because of harmonics. Fundamental frequency of signal is F a with power Pa , and the harmonics h are at F h with power Ph , the THD is THD =



Ph /Pa = (Ptotal −Pa )/Pa

h >1

Here, Ptotal is nothing but the total power of a waveform. It is the sum of signal and harmonics. It may be defined as the ratio between powers of all harmonics to the fundamental frequency of power. It measures the system nonlinearity. Here, squared rms voltages are equivalent to power and can be replaced it in the THD equation is THDs =

 (Vh )2 /(Va )2 = (Vtotal )2 −(Va )2 /(Va )2 h>1

However, sometimes, THD is defined as an amplitude ratio; it is instead of a power ratio    √  √ √ THDamp = (Vtotal )2 −(Va )2 /(Va )2 = THD (Vh )2 /(Va )2 = h>1

It leads to a value, which is the square root of the THD.

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5 Algorithm for Detection of Faults The following step by step methods are using here to find out DC line faults (1) The DC rectifier end fault current signal is drawn out and it is given to input data for FFT. (2) The input signal THD is noted before fault. (3) Threshold selection level is completed as fault DC current signal THD is almost zero under no fault condition. (4) With help of the comparator, DC fault current THD is determined and if it is higher than threshold value, output signal is high otherwise it is low. In Fig. 1. Flowchart on FFT algorithm is shown using DC fault current signal and same analysis is done fault voltage.

Start

Obtain the DC Fault Current Signals

FFT Analysis

Yes

THD Noted

Fault Detected

Fig. 1 Fault detection flowchart

If FFT Displayed

No

Output Zero

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DC = 2.649e+05 , THD= 16.32%

Mag (% of DC)

3.5 3 2.5 2 1.5 1 0.5 0

0

50

100

150

200

250

300

Frequency (Hz)

Fig. 2 THD of DC voltage during no fault

FFT window of dc fault current

In Fig. 2. DC voltage analysis is studied at different frequencies under no fault but the magnitude of total harmonic distortion was 16.32%, but the DC fault current signal has zero harmonics and proves to be better than DC voltage signal. In Fig. 3. DC fault current signal was extracted for location of 897 km and was zero for the period there was no fault. It was observed that when fault occurs at 1.4 s, high frequency fault signals were obtained. In Fig. 4. High total harmonic distortion was obtained when fault occurs at 1.4 s with magnitude of 816.54%. The high THD magnitude has an importance over the DC voltage analysis. In Fig. 5. DC voltage analysis is studied under fault conditions and total three cycles were selected, each cycle is of 0.5 s period in which fault occurs at 1.4 s. After the fault, DC voltage signal is decreased to zero and oscillates. In Fig. 6. DC voltage harmonics id studied and it was observed that the distortion in voltage was 56.10%, which is high in comparison to no fault condition but is not as high as current for same location. In Table 1. Magnitude of DC fault current was extracted for FFT analysis to determine total harmonics distortion under different fault resistances. Since the nature and type of fault are unknown, so variation of resistance from 1 to 100  is important 7000 6000 5000 4000 3000 2000 1000 0 -1000 -2000 0

1

0.5

Time (s)

Fig. 3 DC fault current, FFT of DC fault current is observed, output detection at 897 km

1.5

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Mag (% of DC)

160 140 120 100 80 60 40 20 0

0

50

100

150

200

250

300

Frequency (Hz)

Fig. 4 THD of DC current during fault Selected signal: 3 cycles. FFT window (in red): 2 cycles

105 3 2 1 0 -1 -2 0

1

0.5

1.5

Time (s)

Fig. 5 DC voltage signal during fault DC = 2.376e+05 , THD= 56.10% 25

Mag (% of DC)

20 15 10 5 0

0

50

100

150

Frequency (Hz)

Fig. 6 THD of DC voltage under fault condition

200

250

300

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Table 1 Variation of resistance under different conditions Fault resistance ()

Signal

Magnitude of (THD) of DC fault current

Fault detected

1

I

High

Yes

5

I

High

Yes

100

I

High

Yes

Table 2 Varying far end fault locations near inverter end of transmission line Fault distance (km)

Signal

Magnitude of (THD) of DC fault current

Fault detected

895

I

High

Yes

897

I

High

Yes

899

I

High

Yes

to check the accuracy of algorithm. Under all condition, the THD was very high from no fault condition wherein THD is zero so fault was detected. In Table 2. Some of the test results for magnitude of THD of DC fault current of transmission lines from 895 to 899 km were studied. Since the far end, fault detection is an important parameter for reliable operation of HVDC lines. FFT analysis reliability is checked by simulating under varying conditions.

6 Results and Discussions The model is simulated, analysis using FFT is done, and THD is found out. THD under no fault condition in voltage is very low; it is as low as 16.32%. Fault analysis is done for DC fault current since the fault is of DC nature. DC fault current is obtained in FFT for time 1.5 s because the fault is done at the time 1.4 s. There is transient current and THD is very high, and it is as high as 816.5%. Since under no fault, the THD is very small, it is almost zero and other fault if condition THD is very high. This is very effective method to discriminate the fault condition from no for condition. This also describes the THD during the fault condition, which goes very high.

7 Conclusion The technique using THD analysis of current signal is an important parameter to discriminate the no fault condition from fault condition. Since the difference in THD of DC voltage is not appreciable from no fault conditions, so analysis of current signal is better in comparison to other signals. None of the papers has done the THD analysis under fault conditions.

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References 1. Jovcic D, Ahmed K (2015) High voltage direct current transmission: converters, systems and dc grids. Wiley, Hoboken 2. Shakerighadi B, Ebrahimzadeh E, Blaabjerg F, Bak CL (2018) Large-signal stability modeling for the grid-connected VSC based on the Lyapunov method. Energies 11:2533 3. Blond SL, Bertho R, Coury D, Vieira J (2016) Design of protection schemes for multi-terminal HVDC systems. Renew Sustain Energy Rev 56:965–974 4. Zarei SF, Mokhtari H, Ghasemi MA, Blaabjerg F (2018) Reinforcing fault ride through capability of grid forming voltage source converters using an enhanced voltage control scheme. IEEE Trans Power Deliv 5. Nguyen VV, Son HI, Nguyen TT, Kim HM, Kim CK (2017) A novel topology of hybrid HVDC circuit breaker for VSC-HVDC application. Energies 10:1675 6. Wang Y, Yang B, Zuo H, Liu H, Yan H (2018) A DC short circuit fault ride through strategy of MMC-HVDC based on the cascaded star converter. Energies 11:2079 7. Cwikowski O, Wood A, Miller A, Barnes M, Shuttleworth R (2018) Operating DC circuit breakers with MMC. IEEE Trans Power Deliv 33:260–270 8. Li Y, Gong Y, Jiang B (2018) A novel traveling-wave-based directional protection scheme for MTDC grid with inductive DC terminal. Electr Power Syst Res 157:83–92 9. Zhang S, Zou G, Huang Q, Gao H (2018) A traveling-wave-based fault location scheme for MMC-based multi-terminal DC grids. Energies 11:401 10. Agarwal S, Swetapadma A, Panigarhi C, Dasgupta A (2019) A method for fault section identification in High voltage direct current transmission lines using one end measurements. Electr Power Syst 172:140–151 11. Agarwal S, Swetapadma A, Panigrahi C (2018) An improved method using artificial neural network for fault detection and fault pole identification in voltage source converter-based high-voltage direct current transmission lines. Arab J Sci Eng 43:4005–4018 12. Wang L, Liu H, Dai L, Liu Y (2018) Novel method for identifying fault location of mixed lines. Energies 11:1529 13. Agarwal S, Aleena, Panigrahi C, Dasgupta A (2019) Fault analysis method of integrated high voltage direct current transmission lines for onshore wind farm. J Mod Power Syst Clean Energy 7(3):621–632 14. Ashouri M, Khazraj H, da Silva FF, Bak CL (2018) Protection of multi-terminal VSC-HVDC grids based on the response of the first carrier frequency harmonic current. In: Proceedings of the 2018 53rd international universities power engineering conference (UPEC), Glasgow, UK, 4–7 September 2018 15. Ashouri M, Silva FF, Bak CL (2018) Application of short-time Fourier transform for harmonicbased protection of meshed VSC-MTDC grids. J Eng 16. Li S, Chen W, Yin X, Chen D (2017) Protection scheme for VSC-HVDC transmission lines based on transverse differential current. IET Gener Transm Distrib 11:2805–2813 17. Li R, Xu L, Yao L (2017) DC fault detection and location in meshed multiterminal HVDC systems based on DC reactor voltage change rate. IEEE Trans Power Deliv 32:1516–1526 18. Yang Q, Blond SL, Aggarwal R, Wang Y, Li J (2017) New ANN method for multi-terminal HVDC protection relaying. Electr Power Syst Res 148:192–201 19. Azizi S, Sanaye-Pasand M, Abedini M, Hassani A (2014) A traveling-wave based methodology for wide-area fault location in multiterminal DC systems. IEEE Trans Power Deliv 29:2552– 2560 20. Ashouri M, Khazraj H, Bak CL, da Silva FF (2018) Application of shunt bus-bar capacitor installations for protection of VSC-MTDC grids. In: Proceedings of the 2018 IEEE international energy conference (ENERGYCON), Limassol, Cyprus, 3–7 June 2018 21. Jiang L, Chen Q, Huang W, Wang L, Zeng Y, Zhao P (2018) Pilot protection based on amplitude of directional travelling-wave for voltage source converter-high voltage direct current (VSCHVDC) transmission lines. Energies 11:2021

Resilience Trapezoid-Based Operational Reliability of Distribution System Sonal, Debomita Ghosh, and Dusmanta Kumar Mohanta

Abstract The operational reliability analysis to events with low probability and high impact is crucial for power system. The cyber-physical threats to a power system possess several challenges to a reliable and efficient power supply to its customers. In this paper, operational reliability of a microgrid is quantified using the “resilience trapezoid.” This paper aims to study the time-dependent metrics that capture distribution system degradation phases due to any external threat and its recovery process based on the trapezoid method. It illustrates the sensitivity of the distribution system due to the effect of abnormal weather conditions or cyber threats and how it can recover back to its original form in a fast and efficient way. The operational reliability analysis of a distribution network consisting of lines, loads, and wind turbine generator is done to identify its performance during such high impact conditions. Loss of load probability and expected energy not served are calculated to have a proper assessment of the system and its measures to keep the network intact for a comparatively more time duration thus preventing further damage. Keywords High impact · Distribution system · Resilience trapezoid · Cyber-physical · Sensitivity analysis

1 Introduction The power system is exposed to various uncertainties of nature like the natural disasters, for example, windstorm, hurricane, heavy precipitation, etc. Also the man-made disasters have increased manifold like the threats to power system, for example, cyber attack, human-made errors, etc. Resilience analysis of power system and Sonal (B) · D. Ghosh · D. K. Mohanta EEE Department, BIT Mesra, Ranchi, India e-mail: [email protected] D. Ghosh e-mail: [email protected] D. K. Mohanta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_64

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its enhancement is necessary to prevent the complete disruption of power-related services. Resilience definition is mentioned in a considerable amount of literature works cited. C. S. Holling in 1973 made a pioneering attempt to define resilience as “the ability of a system to absorb changes of state variables, driving variables and parameters.” The definition of resilience in terms of critical infrastructure is well explained by Brunean et al., 2003 as the “ability of social units to mitigate hazards, contain the effects of disasters when they occur, and carry out recovery activities in ways that minimize social disruption and mitigate the effects of future disasters.” More than about 70 such attempts to define resilience is available in the literature works. Though the resilience definition might vary from one literatue to another but two of its features are common among all the definitions which are adaptation and recovery [1–5]. In [6–9] the author has explained the concept of resilience triangle and its use to analyze the power system performance. It is a performance parameter based on pattern of damage caused to the system over time. Petit et al. have illustrated a resilience measurement index (RMI) which is used to compare the resilience of the power system infrastructure. Ji et al. explained in detail the challenges for mathematically representing resilience metrics and suggested important aspects of the metrics. In Hung et al. [10, 11], classified the resilience of power system into three sub-parts, namely planning, response followed by restoration. It helps to better understand the enhancement techniques of resilience of a power system. The author has explained the measures to prepare electrical infrastructure to better self-sustain itself with distributed generation. Many a time, failure of a distribution system results in loads not being supplied to all the desired locations. As a result, the expected load not being supplied can be calculated for the system which forms the basis for analysis during failures. The reliability evaluation methods and comparison of reliability of components and systems in order to improve the qualitative and quantitative aspects of the system have been discussed. The application of statistical and probabilistic techniques for alternate modeling of power system problems can be analyzed to evaluate the system reliability [12–14]. In this paper, operational reliability analysis of a distribution network constituting distributed generation is analyzed using resilience trapezoid in extreme weather condition of windstorm. Operational reliability is calculated to check if the distribution system can self-sustain itself to mitigate its load requirement.

2 Brief Review on Resilience Trapezoid The quantification of resilience of a microgrids can be depicted using a resilience trapezoid. The indicators used to quantify the multi-phases of this framework are as mentioned below:

Resilience Trapezoid-Based Operational Reliability …

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Fig. 1 Resilience trapezoid with its multi-phases [3]

a. b. c. d.

Minimum value of drop of resilience due to extreme cyber-physical conditions. Rate of drop of resilience due to the extreme cyber-physical conditions. Duration of disturbance state after the extreme event has occurred. Rate of recovery of microgrid back to its original pre-disturbance condition.

These different phases of resilience determine the variation of resilience indicator with respect to time. The phases considered are pre-disturbance, degraded state after disturbance progress, and restorative states (Fig. 1). These parameters of resilience measurement are expressed on a time-dependent perspective. The trapezoid thus obtained can also be assessed quantitatively by using its area under different phases to gain further information on the performance of the microgrid. However, at times, multiple extreme events may hit the distribution system within a short interval of time. This may lead to further degradation of the system’s resilience.

3 Formulation of Resilience Trapezoid-Based Reliability Metrics The distribution system’s reliability can be expressed in terms of the availability of its units or components. The overall availability of the distribution network A can be

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expressed as a function of its n components as given by [8]: A = lim A(t) = t→∞

MTBF MTBF + MTTR

(1)

The system availability corresponds to the availability of some or all of its n components. The system availability can be more accurately expressed as a function of its respective failure rate and repair rate λi and μi of any component i which is given by [8]: A = f (A1 , A2 , A3 , . . . , An ) = f (λ1 , λ2 , λ3 , . . . , λi ; μ1 , μ1 ,μ1 , . . . , μi )

(2)

The resilience trapezoid at any time is given by [4]:

TWTG

⎧ ⎫ ⎨ Max. RI, Pre - disturbance State ⎬ = R(w), DegradedState ⎩ ⎭ Max. RI,Restorative State

(3)

The survivability of the network depends on the available capacity of distributed generation and connectivity of the power lines of the system considered. Three levels of wind speed (Vwind ), have been considered which are low (10–15 m/s), medium (15–20 m/s), and high (20–25 m/s) which is given by [13]:

Vwind

⎧ ⎫ ⎨ Low, (10 − 15)m/s ⎬ = Medium, (15 − 20)m/s ⎩ ⎭ High, (20 − 25) m/s

(4)

The output power of wind turbine (PWTG ) and wind velocity (v) is related by [13]: ⎧ ⎨

⎫ 0, ∀0 < v < vcut in ⎬ = (av k + b Prated , ∀vcut in < v < vrated ⎩ ⎭ Prated , ∀vrated < v < vcut in

(5)

Prated Prated and b =  3 a= 3 3 3 vrated − vcut in vcut out − vrated

(6)

PWTG

where,

The rated power output from wind turbine is given by Prated . The values of vcut in , vcut out and vrated are 3 m/s, 12 m/s, and 25 m/s, respectively. The total energy associated with the load curve F(x) for integrating in time duration T is given by [5]:

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xmax

E t = ∫ F(x)dx

(7)

0

Let us assume that the total capacity of all the generating units in operation during the interval is ct . The duration of time for which the distribution system’s load demand is greater than the generation is t L which is given by [5]: t L = F(ct )

(8)

The expression for loss of load probability (LOLP) for the network is given by: F(ct ) = f (ct ) T

LOLP =

(9)

Also, the value of expected energy not served (EENS) can be expressed as [6]: xmax

xmax

ct

ct

EENS = ∫ F(x)dx = T

∫ f (x)dx

(10)

The fragility function for different windspeeds is given by [6]: F(x) =

ln x − μ σ

(11)

The cumulant method is used to calculate the LOLP and EENS parameters of system. The central moments Mγ and cumulants are expressed as mentioned below [7]: Mγ =



pi (xi − μ)γ

i

K γ = K Lγ +



K jγ

(12) (13)

j

where K γ , K Lγ and K jγ are the cumulants of equivalent load duration curve, load, and generating unit of each order of unit j. The respective moments and cumulants are obtained for γ = 1, 2, …, 8. The probability of different levels of load in the load curve is denoted by pi . The expectation of random variable x under such conditions can be described as [7]: μ = αi =



pi xi

(14)

i

The percentage of generation connected at multi-phases of windstorm is taken from the resilience trapezoid. The respective moments and cumilants of load and

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generation are calculated using the cumulant method to get the cumulants of equivalent load duration curve. The cumulative probability is obtained using the Gram– Charlier series expansion in order to facilitate the cumulant calculation for the equivalent load duration curve. LOLP and EENS are obtained by calculating energy output from the generating unit and its difference from the total generation available at that time for different wind speeds. The failure of components of a system in extreme conditions is attributed to overstressing of components and sometimes the improper design leading to a weak power system infrastructure. In such abnormal conditions, the system is unable to meet the load demand. TheLOLP and EENS indices measure the operational reliability of the distribution network necessary to keep the system intact.

4 Case Study and Results In this section, the operational reliability based on resilience trapezoid is applied for the distribution system. It consists of two wind turbine generators with its maximum capacity 1 kw, 13 kv, distribution lines and its respective loads. A comprehensive study is done and the results for the loss of load probability and expected energy not served of the distribution system in different windspeed condition are analyzed. For the distribution system, operational reliability has been analyzed taking into consideration the resilience trapezoid for the time-dependent generation and load characteristics for the network. The resilience trapezoid will help to measure the respective generation connected. Also it enables us to understand the strategies to enhance performance of the network and how to mitigate it from any further degradation due to the abnormal weather condition like windstorm, hurricanes, etc. During the disturbance progress phase, the operational reliability of the distribution system drops from the pre-disturbance reliability. It should be noted that based on the severity of the windstorm hitting the network, the operational reliability may vary (Table 1). The slope of resilience trapezoid curve varies according to the distribution system and severity of windstorm hitting it. During the post-disturbance degraded operational reliability phase, the time duration for this degraded state can be minimized depending on the desirable solution. In the restorative phase, the operational reliability should be enhanced back to its pre-disturbance value within a short time span. Table 1 Failure rate (1/h) input table for L (low), M (medium) and H (high) windspeed [15] S. No.

Phases

L

M

H

1

Pre-disturbance

0.0001

0.0001

0.0004

2

Degraded state

0.8630

0.6910

0.4611

3

Restorative state

0.0001

0.0002

0.0003

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The resilience trapezoid thus obtained for the network in high wind speed condition as shown in Fig. 2 gives in depth assessment of operational reliability framework. From Table 2, it can be observed that the LOLP values in low speed, medium speed, and high speed wind condition are 0.03676, 0.10333, and 0.31881, respectively. The EENS values in low speed, medium speed, and high speed wind condition are 0.02559 MWh, 0.06748 MWh, and 0.98162 MWh, respectively. The operational reliability of the distribution network shows better results in case of low wind speed while in case of high wind speed, the resilience trapezoid of the system degrades more resulting

Fig. 2 Operational reliability based on resilience trapezoid distribution system generation

Table 2 Operational reliability of distribution system network in different windspeed Weather condition Phases of based on wind resilience Speed trapezoid

Operational reliability based on resilience trapezoid Loss of load probability

Expected energy not served (MWh)

Sensitivity ranking

Low Wind Speed (10–15 m/s)

0.00135

0.01899

3

Degraded state

0.03676

0.02559

Restorative state

0.00116

0.00989

0.00135

0.01899

Pre-disturbance

Medium wind Pre-disturbance speed (15–20 m/s) Degraded state High wind speed (20–25 m/s)

0.10333

0.06748

Restorative state

0.0099

0.00899

Pre-disturbance

0.00133

0.01899

Degraded State

0.31881

0.98162

Restorative state

0.00986

0.00899

2

1

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in more loss of load for the system. The expected energy not supplied also increases as the abnormal weather condition possess restriction on its operation. From Fig. 2, it can be observed that the different degradation and recovery slopes correspond to the different operational reliabilities of the distribution network. The operational reliability of the system in extreme weather condition is found to be lower than in normal weather condition due to decrease in microgrid’s components failures. It can be improved by allocating targeted reliabilities of components to get the desired value. The results obtained illustrate the flexibility of the system’s load and generation to quantify the operational reliability when exposed to abnormal weather condition.

5 Conclusion The trapezoidal method has been used to methodically quantify the operational reliability of a distribution network. The operational reliability along with the sensitivity of the network enables to observe the time-dependent performance of the network under abnormal weather condition like windstorm. The mitigation strategies can accordingly be used for the network to remain intact during abnormal weather condition. The different phases of distribution network prior to, during the onset and restorative stages can be analyzed using the proposed method to prevent any further cascading effects. It acts as an indicator to reach to the targeted value of resilience for the microgrid if the resilience value during any phase of the system is inadequate.

References 1. Holling C (1973) Resilience and stability of ecological systems. Annu Rev Ecol Syst 4(1):1–23 2. Ouyang M, Dueñas-Osorio L, Min X (2012) A three-stage resilience analysis framework for urban infrastructure systems & Structural Safety 3:23–31 3. Ouyang M, Wang Z (2015) Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis. Reliab Eng Syst Safety 141:74–82 4. Atwa YM, El-Saadany (2009) Reliability evaluation for distribution system with renewable distributed generation during islanded mode of operation. IEEE Trans Power Syst 24(2):572– 581 5. Zamani A, Sidhu T, Yazdani A (2010) A strategy for protection coordination in radial distribution networks with distributed generators. In: IEEE power and energy society general meeting; Minneapolis, New York, USA, pp 1–8, 25–29th July 2010 6. Nedic DP, Dobson I, Kirschen DS, Carreras BA, Lynch VE (2012) Criticality in a cascading failure blackout model. Elsevier, Science direct 7. Xu X, Mitra J, Wang T, Mu L (2014) Evaluation of operational reliability of a microgrid using a short-term outage model. IEEE Trans Power Syst 29(5):2238–2247 8. Acharya JR (2005) Weather effect consideration in reliability evaluation of electrical transmission and distribution systems. Ph.D Thesis, University of Saskatchewan, Saskatoon 9. Panteli M, Mancarella P (2015) Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies. IEEE Transaction Power Electr Power Syst Res Del 127:259–270

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10. Liu X, Shahidehpour M, Liu X, Cao Y, Bie Z (2016) Microgrids for enchancing the power grid resilience in extreme conditions. IEEE Trans Smart Grid 93(5):1253–1261 11. Wang Y, Chen C, Wang J, Baldick R (2016) Research on resilience of power systems under natural disasters—a review. IEEE Trans Smart Grid 31(2):1604–1613 12. Trakas DN, Hatziargyriou ND, Optimal distribution system operation for enhancing resilience against wildfires. IEEE Trans Power Syst 13. Farzin H, Fotuhi-Firuzabad M, Moeini M (2016) Enhancing power system resilience through hierarchical outage management in multi-microgrids. IEEE Trans Smart Grid 7(6):2869–2879 14. Khodaei A (2016) A larger role for microgrids. IEEE Trans Smart Grid 28(3):6431–6445 15. Zhou Y, Pahwa A, Yang SS (2006) Modeling weather-related failures of overhead distribution lines. Power Syst. IEEE Trans 21:1683–1690. https://www.org/10.1109/TPWRS.2006.881131

Spectral Kurtosis-Based Fault Detection for a Highly Penetrated Distributed Generation: A Real-Time Analysis Sourav Kumar Sahu and Debomita Ghosh

Abstract Increment in the difference between demand and generation for the power compelled the researcher and policymakers to opt for renewable energy sources. Along with the constraints such as low running cost, high government subsidy, and free fuel availability playing a significant role for the exponential boom in the solar photovoltaic integration to the existing power system network. Among the various distributed energy resources, sources with voltage-soured converter are gaining popularity due to the flexibility in control as well as high-quality power to the end-user. Due to some of the aforementioned causes, ample amount of solar photovoltaic integration is making the distribution side further closer to self-sufficiency but due to the integration some of major issues such as uneven distribution of voltages, relay setting problems are very common. As the relay settings get disturbed, so it is very important to detect the various types of faults with maximum reliability. Spectral kurtosis-based fault analysis on a microgrid is proposed to mitigate this protection problem. To demonstrate the proposed fault detection technique, a four-bus microgrid is considered in Typhoon HIL real-time environment. Keywords Spectral kurtosis · Fault detection · Hosting capacity · Microgrid · Solar photovoltaic

1 Introduction The development in latest technology in this century is the major fuel for an enhanced lifestyle. The ever increasing use of machines/equipment in day-to-day life such as a home air conditioning unit to daily commute like electric vehicles is responsible for increment in per capita energy consumption. Previously, the total loads were on traditional fossil powered plants, but due to rapid enhancement in distributed energy S. K. Sahu (B) · D. Ghosh Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] D. Ghosh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_65

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resources (DERs) technology, it is now possible to shift a considerable amount of load to the renewable DERs. The DERs that are the source of localized power in the distribution system can not be connected to the utility without proper planning. If proper planning is not done, then due to the integration of these DERs in the utility may cause various power systems parameter violations such as voltage magnitude, ampacity, and harmonic content in the distribution system [1]. Proper calculation of these DERs should be made at all nodes, so as to avoid all these power system problems. This estimation of DER quantity is known as hosting capacity (HC)calculation. HC can be defined as the maximum amount of power that can be put into or taken away from a microgrid without violating the power system constraints such as voltage, ampacity, THD, so on. On the addition of power DERs at utility side, the traditional power flow from the source to the utility now no longer true. This bidirectional power flow may cause various problems like maloperation of relay, equipment overheating, insulation break down, etc. A spectral kurtosis is an important tool for non-stationary signals [2] and it is an emerging tool used in fault analysis. The application of SK is very common in mechanical engineering on bearing fault detection analysis [3, 4]. In the microgrids where solar photovoltaics are highly hosted, protection is one the major issue and is a priority as these DERs will also contribute to the fault current [5, 6]. Due to the presence of uncertainty in the DER sources, the fault detection may get a challenging task for the utility engineer. In this work, a proper detection of the faults in a highly SPV penetrated microgrid is performed using spectral kurtosis analysis. Various possible fault conditions are created in real-time environment, and the signals are being analyzed using SK to demonstrate its ability to detect the faults. Later, in this paper, Sect. II is focused on hosting capacity analysis for a typical microgrid, Sect. III enlighted about spectral kurtosis, which is followed by results and discussion.

2 Hosting Capacity Analysis for a Typical Microgrid The four-bus microgrid with SPV injection at every node is as shown in Fig. 1. Pv Plant

Z1 Bus 1

Pv Plant

Z Bus 2

2

Bus 3 Load

Fig. 1 Microgrid network

Pv Plant

Pv Plant

Z3 Bus 4 Load

Load

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The change in the voltage profile down the figure can be calculated by the equation as in equation VS − V R =

Rline (PL − PDG ) + X line (Q L ± Q DG ) V

(1)

where each term in Eq. (1) is as follows. V S : Voltage at the starting node of the line. V R : Voltage at the ending node of the line. Rline : Line resistance. X line : Line reactance. PL: Active power load at the node. QL :Reactive power at the node. PDG : Active power generated at the node by the SPV. ±QDG : Reactive power absorbed/generated by at the node. If there is no load connected at the node, then Eq. (1) can be rewritten as in Eq. (2).The dependency of the active power injected at a specific node on voltage can be well established from Eq. (2). V R = VS +

Rline (PDG ) + X line (±Q DG ) V

(2)

From Eq. (2), it is evident that the SPV injection at the node can improve the voltage profile. But excessive addition of the SPV may result in the shooting the voltage to an unacceptably high value. The impact in the voltage value will further cause decrement in the power injection at the nodes. Exposing the conductor to a high temperature with light load conditions may also increase the value of voltage to an unacceptable range. The bidirectional power flow due to the SPV connected at the nodes will also cause the reverse power flow and in some cases, it may be seen as the fault current by the protection equipment.

3 Spectral Kurtosis Spectral kurtosis(SK) is introduced to estimate non-Gaussian component in signal along with their particular location in the frequency domain. SK has a wide application in mechanical engineering for bearing fault detection [4]. Moreover, it is specifically used for nonstationary signals. SK can be represented by Eqs. (3) and (4). SK N ( f ) =

< Z 4 (t, f ) > −2 < Z 2 (t, f ) >2

(3)

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Z (t, f ) =

t+N w−1 

     w n  − t N n  e−2π f n

(4)

n  =t

SK based on short-time   Fourier transform can be expressed as in (3) where Z (t, f ) is given as per (4), N n  is the discrete samples of N (t),  <  > is the time averaging operator, and N w is the length of analysis window w n  [7, 8]. Therefore, SK at a specific expresses the peakiness of the squared envelope |Z (t, f )d M( f )|2 and thus it is a good indicator of signal impulsiveness, i.e., transients and the corresponding frequencies at which those patterns occur. In SK graph, positive peaks designate non-stationary (transients) constituents of a signal and a value of −1 corresponds to a stationary constituent [9]. SK is noise resistant as it is a fourth-order spectrum. This shows the impulsiveness more clearly which further helps in identifying electrical irregularities more precisely. SK is fast and computationally less costly [10]. In this paper, a Hanning window of length 256 having 75% overlap is used for computation SK.

4 Results and Discussion For real-time analysis, a four-bus system with the specification mentioned in Table 1 is developed in Typhoon HIL environment as in Fig. 2. From Fig. 2, it can be noticed that all the nodes have solar photovoltaic power plant along with a load. Fault blocks are placed near the load to create the required faults. Two core couplings are connected at bus 3 and bus 1 for the parallel processing of the microgrid in real-time environment. Figures 3 and 5 show line-to-ground and line-to-line fault for the further processing in the SK. Figures 4, 6 and 7 are the analysis for LG, LL, and LLL fault, respectively, which show the visual difference using the proposed method for fault detection in a highly penetrated PV system. Due to the reason that SK analysis is free from noise, it can prove to be an efficient method for fault analysis where the use of converter is frequent. Another important aspect of using SK analysis is the time taken by it to detect the fault in highly penetrated photovoltaic system. Table 1 Microgrid specification Rated voltage = 230 V S. No.

From

To

R (ohm)

X (ohm)

PL (kW)

QL (kVAR)

1

1

2

0.0922

0.0477

100

60

2

2

3

0.439

0.2511

90

40

3

3

4

0.366

0.1864

120

80

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Fig. 2 Hardware in loop-based microgrid modeling

Fig. 3 Line-to-ground fault

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Spectral Kurtosis Value

1

0

-1

-2

0

0.2

0.4

0.6

0.8

Normalized frequency Fig. 4 LG fault

Fig. 5 Line-to-line fault

Spectral Kurtosis Value

0.5 0 -0.5 -1 -1.5 -2

0

0.2

0.4

0.6

Normalized frequency Fig. 6 LL fault

0.8

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Spectral Kurtosis Value

1

0

-1

-2

0

0.2

0.4

0.6

0.8

Normalized frequency Fig. 7 LLL fault

5 Conclusion The above analysis shows an effective way to estimate the fault in a highly penetrated solar photovoltaic system. The visual differences can be clearly seen for the different faults conditions, which seldom proving the efficacy of the proposed fault detection technique in the highly distributed generation penetrated microgrid.

References 1. Westacott P, Candelise C (2016) Assessing the impacts of photovoltaic penetration across an entire low-voltage distribution network containing 1.5 million customers. IET Renew Power Gener 10(4):460–466. https://doi.org/10.1049/iet-rpg.2015.0535 2. Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20(2):282–307 3. Hu Y, Bao W, Tu X, Li F, Li K, An adaptive spectral kurtosis method and ıts application to fault detection of rolling element bearings. IEEE Trans Instrum Measure. https://doi.org/10. 1109/TIM.2019.2905022 4. Immovilli F, Cocconcelli M, Bellini A, Rubini R (2009) Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals. IEEE Trans Industr Electron 56(11):4710–4717. https://doi.org/10.1109/TIE.2009.2025288 5. Sadeghkhani I, Golshan MEH, Guerrero JM, Mehrizi-Sani A, A current limiting strategy to improve fault ride-through of inverter interfaced autonomous microgrids. IEEE Trans Smart Grid (to be published). https://doi.org/10.1109/TSG.2016.2517201 6. Haj-Ahmed MA, Illindala MS (2014) The influence of inverter-based DGs and their controllers on distribution network protection. IEEE Trans Ind Appl 50(4):2928–2937 7. Wang Y, Liang M (2011) An adaptive SK technique and its application for fault detection of rolling element bearings. Mech Syst Signal Process 25(5):1750–1764 8. Tian J, Morillo C, Azarian MH et al (2016) Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbour distance analysis. IEEE Trans Industr Electr 63(3):1793–1803 9. Vrabie V, Granjon P, Serviere C (2003) Spectral kurtosis: from definition to application. In: IEEE international workshop on nonlinear signal and ımage processing

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10. de la Rosa J, Sierra-Fernández J, Palomares-Salas et al (2015) An application of spectral kurtosis to separate hybrid power quality events. Energies 8(9):9777–9793

Recent Trends in Microgrid Operation and Planning

Energy Simulation and Modelling of Corporate Building: A Case Study Om Prakash, Ankish Aman, Saurabh Ranjan, and Namrata Dhamankar

Abstract Many corporate offices and industries in India are endeavouring to make their campuses progressively sustainable. Their maintainability attempt usually incorporates expanding energy efficiency of new and existing structures. There are a few buildings and equipment incorporating energy efficiency, using energy modelling technique to make them energy efficient. While energy models are broadly utilized during the design of the building system and its equipment. Further energy conservation method (ECM) is proposed to enhance building energy efficiency. The paper deals with a contextual investigation, including the utilization of a universal energy model framework to assess using energy simulation, which has undergone several remodels for a long duration. The baseline case energy simulation has been done concerning the ASHRAE 90.1-2010 modelling protocol. The paper describes the collected data, the modelling process which is determined via simulation to obtain the result that the building has potential to reach overall energy savings of 40.5% in energy costs over ASHRAE 90.1-2010 baseline design. Keywords Energy modelling · Design alternatives · Sustainable life cycle cost · Existing building · Energy consumption · Energy modelling retrofits

O. Prakash (B) · A. Aman Mechanical Department, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] A. Aman e-mail: [email protected] S. Ranjan · N. Dhamankar Sprout - Greening Ideas, Pune, India e-mail: [email protected] N. Dhamankar e-mail: [email protected] N. Dhamankar Dr. BN College of Architecture, Karvenagar, Pune, India © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_66

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1 Introduction There is huge responsibility for the university and corporate building to make campus more sustainable. The university campus is good examples for their networks in teaching the individuals who build up the social, monetary and mechanical for turnaround global warming and help make a flourishing, traditional and economic society. American College & University Presidents’ Climate Commitment (ACUPCC) is a climate change mitigation program in USA and same initiative needs to be taken place in India as well [1]. The dedication requires a large corporate office to find a way to diminish greenhouse gas emissions. By and by, buildings represent 73% of the total power consumption in India and 38% of CO2 emissions [2]. Hence, any fruitful climate change alleviation plan ought to incorporate exercises that fundamentally chopped down energy consumption in corporate office building and industry. The energy efficiency improvement in the buildings is one of the best and moderate approaches to alleviate green house gas (GHG) outflow emissions on a vast scale. Evaluation report of the four Intergovernmental Panel on Climate Change (IPCC) predicts that by 2030, savvy energy efficiency measures in buildings would alone spare 5.3–6.7 GtCO2 eq/year globally [3]. A thought that is increasingly mainstream backing is ‘net-zero’ yearly energy consumption by joining progressively effective building energy use with on-location sustainable power source age along these lines, developing an interest in energy modelling devices to help the analysis of building’s energy use [4]. The paper examines a case study, including the utilization of a energy model framework to assess the presence of an 80,000 m2 (square feet) corporate building. The goal of the research is to utilize energy modelling software to anticipate energy consumption in the building and contrast it with accurate utility information. The paper depicts the information gathered, the modelling procedure and the outcomes.

2 Energy Modelling Data Collection As building energy modelling tools ended up being increasingly advanced, the measure of client information and information required to characterize the models developed. Getting every single required datum with satisfactory accuracy is especially testing in existing buildings, as the data accessible in drawings could be obsolete because of redesigns/updates that have been performed. The data required for energy simulation are as follows :

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2.1 Architectural Data The buildings architectural design impacts its energy demand. At the point when thoroughly educated about the unpredictable synergies between building design and energy investment funds included in their buildings principally. The architectural information required for modelling is:• General building data—The orientation of building direction and the city where it is arranged in for weather subtleties, the overall situation of any tall structure that tosses a shadow on the structure. • Floor height and area—These consist of the overall floors, ventilated area all conditioned and unconditioned areas on every one of those floors, plenum space, floor-to-ceiling height, floor-to-floor height. • Materials of Envelope Construction—Materials of building floor and roof slabs, exterior walls, doors, windows, type in walls, insulation thickness, flooring material and wall-slab construction joint type • Fenestration areas—Size number and position of windows and all exterior doors, any overhangs if available, blades, blinds or curtains on windows. • Shading coefficient (SC), U-value and fenestration—SC of an exterior window, U-value (conductance) and door materials.

2.2 Mechanical Data In numerous business buildings, HVAC frameworks are among the most noteworthy energy-consuming frameworks. In this way, definite information concerning their size and zoning is crucial. • HVAC zoning or space—The dispersion of adapted floor region into different spaces zones as demonstrated by the serving air-handling units (AHUs) • Zones activity—Each particular HVAC zone is allocated to a particular activity or space for example office, library and classroom, etc. and has sf/person (square feet per person) and minimum cfm/person assigned to it ( cubic feet per minute per person). • Design flow rates—Each air-handling unit has a minimum design flow (cfm/sf) and minimum flow percent for core and perimeter areas defined. • Equipment description—These include system type, cooling source, heating source, hot water source and returns air path. • Control sequences—Particularly each zone has maximum and minimum set-point temperature characterized for both involved and abandoned condition.

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2.3 Electrical Data • Electrical hardware is another significant purchaser of power in structures. Electrical equipment—various types of lighting present in the building (interior and exterior end use), office equipment, interior lighting, self-contained refrigeration, and exterior lighting profiles and loads (W/sf).

2.4 Internal Loads Data • Peak inhabitancy (by zone)—Maximum structure inhabitancy (sf/individual) • Peak lighting load (by zone)—Watt per square foot (W/sf) of every single distinctive lighting introduced in the structure • Peak hardware load (by zone)—Watt per square foot (W/sf) of all extraordinary electrical structure gear

3 eQUEST eQUEST is a prevalent building energy modelling software. eQUEST has two design wizards, the supposed schematic design (SDW) and design development wizards (DDW). The design stages during simulation varies substantially depending upon degree of detail the building framework contain. Request wizards contain a wizard screens which lead the client to enter and additionally change information. These screens incorporate predefined default esteems (recognized by green textual style) to which the client can roll outfitting improvements. eQuest has a energy design resources used for evaluating the performance of the building. eQuest can even be used for evaluation of the building constructed before the 1960s after it has undergone several redesigns for a life long duration. The building primarily contains operation room and managerial workplaces. The building’s architect gave the research group the information required, including CAD drawings everything being equal, floor plans demonstrating subtleties of building construction, HVAC plans, test and balancing reports. The building’s architect also provided drawings of functional usages on every floor; this was essential to help decide sufficient calendars (occupancy, lighting, equipment). Figure 1 demonstrates an image of the building utilized for the situation study with the dimension, specification and properties. Plot area–41,066.72 m2 , builtup area–108,004 m2 , basement area–68,842.5 m2 . Number of floors–B1 + B2 + G + 3 and conditioned area-86,320 m2 . The eQuest model, as appeared in Fig. 2, was made utilizing the design development (DD) wizard. was embedded into the model with the help of constructed building design framework. The analysed results are mode used to predict a project’s annual energy consumption based on architectural and MEP design. The design case energy

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Fig. 1 Building layout for case study

Fig. 2 eQUEST building rendering

consumption is then compared with the ASHRAE 90.1-2010 baseline. Subsequently, energy conservation measures (ECMs) are also identified and proposed with an aim to explore further energy savings potential and meet the set energy targets for this project which is represented in Figs. 2 and 3 showing IGBC green nb points which are awarded.

4 Simulation Input Parameters The detail input parameters for baseline and the proposed case are done for energy conservation, and the result is analysed according to the proposed in Table 1.

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Fig. 3 eQUEST 2D mode

5 Results As simulated, the design case energy consumption is 650.38 MWh/year achieving 9.5% energy savings over the baseline case. The energy end-uses comparison between the baseline versus central plant system is shown in the graph represented in Fig. 4. • Space cooling savings: Saving of space cooling up to 34.04% this is achieved due to efficient glass or energy-efficient glazing, to portray the twofold or triple coating use in current windows in a structure. The first single twofold coating and vitality proficient coating joint covered (low-emissivity) glass avert heat getting away through the windows [5]. Thus, making the windows high thermally insulated improves the energy efficiency of the building and saving income on heating bills. Higher COP leads to a reduction of energy consumption for cooling purpose • Space heating: Heat pump instead of electric resistance lead to more energy efficiency in space heating. Up to 46.47% savings is achieved in the buildings from improved HVAC equipment and controls. • Ventilation fan savings: Efficient fan motor is exceptionally viewed as the main decision for vitality productive engine and fan arrangement known for their advancement and high-quality product. Electronically commutated (EC) often considered as a brushless DC electric (BLDC motor). Here, in this consumption motor case, 44.35% of energy is saved using the EC

5.1 Energy Conservation Method An energy conservation measure (ECM) is the development strategies and like an assortment of works done to apparatuses, fittings and focal plant and gear, including: replacements and upgrades refurbishments systems, optimization and re-commissioning works. Simulation software quantifies ECMs to deliver accurate predictions about individual components and assemblies. Energy conservation is achieved by the use of various energy-efficient parameters represented in Fig. 5 shows the graphical representation of the energy-saving percentage for different

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Table 1 Detail input parameter for baseline and proposed case Building elements

Unit

Climate zone city

Base case

Design case

Composite Ranchi

Composite Ranchi

Exterior wall assembly

U-Value: Btu/hrft2 °F

0.124

0.15 (200 mm thick AAC blocks)

Roof assembly

U-Value: Btu/hrft2 °F

0.063 Btu/hr.ft2 °F

0.07 (150 mm thick RCC slab + 75 mm thick XPS over deck insulation)

Glazing U-Value 1.2 (Btu/hrft2 °F)

0.26

Glazing shading coefficient

0.29

0.27

Glazing VLT (%)

N/A

50%

Lighting power W/ft2 density (building area method)

Office–0.9 W/m2 Parking–0.25 W/m2 Office – 0.9 W/m2 Parking– 0.30 W/m2

Sensors

N/A

None

Daylight sensors N/A

None

Occupancy sensors

Same as Design Case

Office – 1.5 W/m2

System type

System 6 Packaged VAV

Central plant system

Fan power (kW/cfm)

0.0011 kW/cfm

0.0005 kW/cfm

Winter heating

Source

Electric resistance

Heat pump

Chiller parameters

Chiller type

N/A

2 Nos. X 75 TR water cooled screw chiller

COP

3.2

4.8

Chilled water T

N/A

10 °F

Equipment power density

Office

HVAC system

(W/ft2 )

CHW reset

N/A

No

Cooling tower

Fan control

N/A

Variable

Pumps efficiency

Primary pumps

N/A

65%

Pumps motor class

Primary pumps

N/A

VFD

Secondary pumps

N/A

One speed (continued)

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Table 1 (continued) Building elements

Unit

Base case

Condenser water N/A pumps

% Energy Conservaon

Fig. 4 Design case energy 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00%

Energy Conservation measures

Fig. 5 ECM graph

Design case Variable

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energy conservation or energy-efficient parameters incorporated in the building. ECMs typically require mechanical and electrical engineering expertise to design and instal energy-saving solutions to achieve desired energy effectiveness outcomes, i.e. cost savings. ECMs can also provide the following benefits such as: ECM leads to a decrease in expenditure on utility bills, reduction in maintenance costs reduction of CO2 emissions, improves occupancy comfort and increases asset value.

6 Conclusions As simulated, the proposed office building can achieve up to 40.5% saving of energy over a baseline case by incorporating several ECMs. With about 40% of energy usage is accounted for by buildings in India, any sustainability effort aiming to reduce greenhouse gases must incorporate huge exercises to improve the energy proficiency of structures. The paper depicts a case study that included developing an energy model of an existing corporate building. eQuest is used for evaluation of the building performance, heating and cooling loads and minimization of energy consumption as the main objectives. To summarize retrofit procedures, for example, solar integration combination can decrease energy up to 40% by and large, pursued is coating methodology that diminished energy in the investigation test by 9% overall [6]. It has been exhibited how houses in an extraordinary atmosphere can be structured proficiently and how air-conditioning and cooling can be improved in case of hot temperatures and high humidity [7]. The joined solace and monitoring energy demand is of worth, to increase a significant comprehension of the response of a building to the particular atmosphere just as of clients’ conduct or client acknowledgment. Since energy labels are gradually receiving positive responses and recognition from the market, this outcome is also significant.

References 1. Jin W et al (2019) Occupant comfort management based on energy optimization using an environment prediction model in smart homes 11(4):997 2. Hekmatipour F et al (2019) Economic feasibility and technical possibility of net-zero energy building in Tehran 12:25 3. Fayaz M, Kim DJE (2018) Energy consumption optimization and user comfort management in residential buildings using a bat algorithm and fuzzy logic 11(1):161 4. Afroz Z et al (2018) Modeling techniques used in building HVAC control systems: a review 83:64–84 5. Taner T et al (2018) A model of energy management analysis, case study of a sugar factory in Turkey 43(3):42 6. Heo Y et al (2012) Calibration of building energy models for retrofit analysis under uncertainty 47:550–560 7. Yu Z et al (2011) A systematic procedure to study the influence of occupant behavior on building energy consumption 43(6):1409–1417

MGWOSCACSA: A Novel Hybrid Algorithm for Energy Management of Microgrid Systems Bishwajit Dey, Sourav Basak, and Biplab Bhattacharyya

Abstract Optimal scheduling of distributed energy resources (DER) in a microgrid system is a crucial step to accord an economic check in the planning and operation of the system. Among the many DERs, involvement of renewable energy sources (RES) also plays an important role in diminishing the release of harmful pollutants to the atmosphere from fossil-fuelled generators. This paper involves a novel hybrid method of recently developed three strong optimization methods viz. grey wolf optimizer (GWO), sine cosine algorithm (SCA) and crow search algorithm (CSA) to minimize the overall cost of a grid-connected microgrid system. The results were then compared to that of GWO, MGWO and those mentioned in literature. Numerical and pictorial results assert that proposed MGWOSCACSA outperformed all the optimization techniques in yielding consistent and superior quality results. Keywords Microgrid · Energy management · Grey wolf optimizer · Sine cosine algorithm · Crow search algorithm

1 Introduction At a power generating station, the load demand is not sufficed by a single generating entity. Rather a conglomerate of such entities fulfil the total demand. Moreover to produce the same amount of power, each unit is incurred with its own cost function (price bid). Economic load dispatch (ELD) works on the fact that not all generating units incur the same amount of cost to suffice same amount of load, rather same are relatively more costly than others for equal amount of production. So, aptly allocating a certain share of the entire demand could actually lower the fuel cost. The total load demand is distributed among various generators which in turn affects the estimation, invoicing, unit commitment and numerous related functions [1]. The total generation of power has to comply with the total current demand. To B. Dey (B) · S. Basak · B. Bhattacharyya Indian Institute of Technology (Indian School of Mines), Dhanbad, India B. Bhattacharyya e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_67

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address this, the ELD could be further categorized into two variations depending upon the nature of load demand. The constant load, classical static economic load dispatch (SELD) ignores practical constraints because every load consuming area does not have a constant all day load demand characteristics but its nature depends upon the prevalent climatic factors, location and attributes of job undertaken by the inhabitants [2, 3]. In opposition to this, a dynamic economic load dispatch (DELD) efficiently handles the practical constraint [4]. In DELD, we forecast the demand for the upcoming hours and accordingly distribute the load among different generations to optimize the production. Energy management strategy (EMS) of microgrids falls in DELD category of cost minimization but is more complicated than SELD. To begin with, microgrid can be imagined as a collection of distributed energy resources (DERs) and loads within a confined geographical area. DERs include fossil-fuelled generators, various renewable energy sources (RES) depending upon the availability of the microgrid location, micro-turbines, fuel cells, energy storage systems (ESS) such as battery and flywheel, etc. [5] It is because of the individual modelling and constraints associated with these DERs that economic dispatch of microgrid becomes a complex and cumbersome process for power engineers. Microgrid basically operates in two modes; either islanded or utility connected [6]. It is quite obvious that the utility-connected mode is more reliable and efficient as the microgrid can sell/buy power from the utility depending upon the surplus/deficit production of power from its DERs. Also utility-connected microgrid can rely on the grid in case one of its DER fails thus preventing from an unwanted and major shutdown of the system. The last decade has witnessed a lot of research in the microgrid energy management area. Matrix real coded GA (MRCGA) and imperialist competitive algorithm (ICA) were used by authors in [7, 8] to minimize the generation cost of a gridconnected microgrid, wherein various cases were studied to analyse the capability of algorithms in handling tight operating ranges of DERs, variable loads and fluctuating electricity price. Cuckoo search algorithm (CuSA) yielded better results than PSO and DE when both SELD and DELD were performed by author in [9]. An islanded microgrid system was considered for DELD which consisted of two wind turbines (WT) to be separately modelled based on wind speed. Authors performed paretooptimal front-based economic-emission dispatch on a utility-connected microgrid system using adaptive modified PSO (AMPSO) in [10] and GAMS in [11].

2 Objective Function Formulation The objective function consists of generation cost function and emission cost function for a microgrid mathematically stated as follows [12]: Min F(Pit ) =

ng  i=1

Fg (Pit ) +

n  g=1

Fe (Pit )

(1)

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where F(Pi ) is operating cost of microgrid with ng number of DG units. Generation cost is Fg (Pi ), and Fe (Pi ) is emission cost of the DGs, respectively. Pi is the output power from the ith DG. t is the hour which varies from 1 to 24. Again generation cost is summation of fuel costs, operation and maintenance cost and depreciation cost [12], i.e. t t ∗ PGRID F(Pit ) = F f c,i (Pit ) + Fo&m,i (Pit ) + Fdc,i (Pit ) + cGRID

(2)

where fuel cost is F f c,i (Pit ), operation and maintenance cost is Fo&m,i (Pit ), and Fdc,i (Pit ) is the depreciation cost of ith DG source represented as a function of their t is market price of electricity bought/sold by the respective power outputs Pi . cGRID th grid at t hour. The objective function mentioned in (1) are bound to some constraints such as: n 

t t Pit + PGRID = Pload

(3)

i=1 t t Pi,min ≤ Pit ≤ Pi,max

(4)

t t t −Pi,GRID,min ≤ PGRID ≤ Pi,GRID,max

(5)

3 Hybrid Grey Wolf Optimizers This paper implements GWO and hybrid MGWO-SCA-CSA for performing EMS on microgrid systems. The mathematical modelling of these algorithms is detailed below.

3.1 Grey Wolf Optimizer (GWO) GWO [13] mimics the hunting behaviour of the wolves while devouring its prey. A pack of 10–12 wolves maintains a hierarchy among themselves. The leader wolf is said to be alpha (α). It guides the pack but might not be the strongest in the pack. Next in rank is beta (β) whose prime duty is maintaining discipline in the pack and assisting alpha to reach the prey. Delta (δ) comes third in rank and may be considered as a scapegoat. Rest all the wolves fall in the omega (Ω) category and comes last in the pack. In the GWO algorithm, the best three solutions are α, β and δ. Rest of the solutions are Ω. The hunting procedure of the wolves can be mathematically represented as:

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 ⎫  α = C1 . X α − X  ⎪ D ⎪ ⎪ ⎬  ⎪    β = C2 . X β − X  D ⎪  ⎪ ⎪   ⎪   Dδ = C3 . X δ − X  ⎭

(6)

And the position updating procedure of the wolves is given as: ⎫  α )⎪ X 1 = X α − A1 .( D ⎪ ⎬ β) X 2 = X β − A2 .( D ⎪ ⎪ δ) ⎭ X 3 = X δ − A3 .( D

(7)

X 1 + X 2 + X 3 X (iter+1) = 3

(8)

The value of vectors A and C can be calculated as: A = 2. a . r1 − a C = 2. r2

(9)

Wolves move away from the current prey if absolute value of vector A is more than 1 and is forcefully pulled towards the prey when absolute value of vector A is more than 1. ‘a’ decreases linearly from 2 to 0 iteration-wise using the formula  a =2∗ 1−

iter Max_iter

(10)

3.2 Modified GWO To eliminate the possibility of the solution getting trapped within the position of the Ω wolves, authors in [14] proposed that a few number of Ω wolves also take part in the hunting procedure along with the δ wolves. The hunting equation will therefore differ from earlier GWO algorithm by:  ⎫  α = C1 . X α − X  ⎪ D ⎪ ⎪ ⎪  ⎪ ⎪   ⎪  β = C2 . X β − X  ⎪ ⎬ D    δ = C3 . X δ − X  ⎪ ⎪ D ⎪ ⎪ ⎪  ⎪ ⎪   ⎭   = C4 . X  − X ⎪ D

(11)

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The position updating procedure will be performed including the δ in the family of wolves as: ⎫ α) ⎪ X 1 = X α − A1 .( D ⎪ ⎪ ⎬ β) ⎪ X 2 = X β − A2 .( D (12) ⎪ δ) ⎪ X 3 = X δ − A3 .( D ⎪ ⎪ ⎭  ) X 4 = X  − A4 .( D ⎫ X 3 + X 4 ⎪ ⎪ X 3 = ⎬ 2 (13)  1 + X 2 + X 3 ⎪ X ⎪ ⎭ X (iter+1) = 3 Hereafter, the hybridization will be done with GWO and not GWO as the results of MGWO were obviously found better and promising than GWO.

3.3 Modified GWO-SCA-CSA Hybrid MGWO-SCA-CSA is the amalgamation of MGWO, SCA and CSA in which the mathematical implications of SCA [15] is done in the hunting method of grey wolves as follows: ⎫    α = rand ∗ sin(rand) ∗ Cα . X α − X  if rand > 0.5⎪ ⎬ D   (14)  α = rand ∗ cos(rand) ∗ Cα . X α − X  otherwise ⎪ ⎭ D Similarly, we calculate the hunting vectors Dβ , Dδ and DΩ . Crow search algorithm (CSA) [16] is a recently developed optimization technique which imitates the memory-based sly nature of the crows to hide their food from other crows and also steal food from others. The flight length (fl) of the crow broadens or concise the search space, while the awareness probability (AP) helps in the transition from exploration to exploitation stage. The iteration updating step of MGWO, i.e. (13) is changed to: ⎫

 X (iter+1) = X + f l ∗ rand ∗ ( X 1 − X ) + ( X 2 − X ) + ( X 3 − X ) /3 if A P > rand ⎬ X (iter+1) = X + f l ∗ rand ∗ ( X 1 − X ) otherwise

⎭

(15)

AP decides whether to consider all the alpha, beta, delta and omega wolves for updation process or to rely on the alpha (leader) wolf only. To reduce the cumbersome task of tuning a parameter, AP which is a probabilistic value changes in every using the formula:

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 AP = 1 −

1.01 ∗ iter3 Max_iter3

(16)

The pseudo code of MGWOSCACSA is mentioned below: Hybrid MGWO-SCA-CSA Initialize the grey wolves population Xi(i=1,2,3,….N) Initialize a, A and C Define Maxiter = maximum number of iterations Calculate hunting positions Dα, Dβ, Dδ, DΩ using Eq. (14) Evaluate objective function for each search agent Xα = best search agent Xβ = second best search agent Xδ = third best search agent Xω = remaining search agent while t < Maxiter do for each search agent do Perform position updation of the existing search agent by Eq. (15) end for Update a, A and C Evaluate objective function for all search agents Update Xα, Xβ, Xδ, and Xω t = t+1 end while return Xα

4 Results and Analysis System Description. GWO, MGWO and proposed MGWOSCACSA, coded in MATLAB2013a environment installed in a desktop with core i3 processor and 4 GB RAM, were used as optimization tools to minimize the overall generation cost of a grid-connected microgrid system. The system parameters and constants of the subject microgrid system, which consisted of three fossil-fuelled generators and one each of microturbine (MT), fuel cell (FC), photo voltaic system (PV) and wind turbine (WT), were gathered from [12]. The codes were executed with 50 population size and 1000 iterations to maintain an unbiased comparative analysis with the algorithms mentioned in literature. Analysis of Results. Four cases were evaluated viz. grid-connected mode, without RES, islanded mode and fixed base load mode of microgrid operation, and the generation costs obtained are listed in Table 1. Among the four cases evaluated, it was obvious that the grid-connected mode (Case 1) which is the most efficient mode of microgrid operation, turned out to be the cheapest one with $882.5. The second case did not consider the RES, and hence, the generation cost rose up to $1701 due to the increase in contribution of emission cost of the fossil-fuelled DERs. The generation

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Table 1 Comparative analysis (in $) Optimization tool

Grid connected

Without RES

Islanded

Fixed base load

Case 1

Case 2

Case 3

Case 4

PSO [12]

889

1709

1158

978

DE [12]

886

1704

1113

964

DEGL [12]

883

1703

1112

963

GWO [S]

1086

1803

1217

1123

MGWO [S]

1049

1746.5

1167.6

1063.5

MGWOSCACSA [P]

882.5

1701.5

1111.34

962.28

S studied; P proposed

cost of the system in islanded mode of operation turned out to be $1111 as yielded by the proposed MGWOSCACSA. Case 4 is fixed base load scenario where the most efficient and least pollutant emission DER (in this case FC) is fixed to generate the base load (25 kW), and the generation cost is evaluated. This turned out to be the second cheapest case with generation cost $962. Figures 1 and 2 show the hourly load sharing of Case 1 and Case 2, respectively. The increase in the participation of grid and the fossil-fuelled generators in Case 2 increased the emission cost, and hence, the generation cost of the system is maximum in Case 2. Comparative analysis of Optimization tools. All of GWO, MGWO and MGWOSCACSA were executed for 30 individual trials, and their best results are reported in Table 1. Figures 3 and 4 depict the cost convergence curve of all the three optimization techniques used to minimize the generation cost of the microgrid

Fig. 1 Hourly output of DERs for Case 1 using MGWOSCACSA

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Fig. 2 Hourly output of DERs for Case 2 using MGWOSCACSA Fig. 3 Cost convergence graph of case 1

Fig. 4 Cost convergence graph of case 2

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Table 2 Statistical analysis of MGWOSCACSA Cases

Min. cost ($)

1

882.5

2 3 4

Max. cost ($)

Avg. cost ($)

Hits

STD

p-value (e-07)

28

0.38

1.01

884

882.60

1701.5

1709

1701.75

29

1.37

0.68

1111.34

1112

1111.36

29

0.12

0.68

962.28

964

962.39

28

0.44

1.01

system for Cases 1 and 2, respectively. It can be seen from Figs. 3 and 4 that for all the scenarios proposed MGWOSCACSA yielded the minimum cost among all the optimization tools reported in literature and studied. The maximum time taken by MGWOCSASCA taken to attain the minimum generation cost was 48 s which is much less than the results reported in [12]. It can be seen that MGWOSCACSA converged pretty early yielding best solution for both the cases. Statistical analysis viz. Wilcoxon’s signed rank test was also performed for the proposed algorithm. Let H 0 be the hypothesis that there is no significance difference between the methods used to evaluate the generation cost, and all the results discussed so far are obtained using one technique. And let H 1 be the reverse hypothesis that contradicts H 0 . As per Wilcoxon’s signed rank test if p-value of the superior algorithm is less than 0.05, the hypothesis H 0 stands obsolete. It can be seen from Table 2 that the p-value for all the cases and scenarios studied is much less than 0.05. This means that there are at least two methods involved to minimize the generation costs out of which proposed MGWOSCACSA is the superior one. Minimum value of standard deviation and maximum hits (93–96%) to the best solution also claim the consistency and robustness of the proposed approach.

5 Conclusion Four different modes of operation were evaluated to minimize the overall generation cost of a grid-connected microgrid system. It was seen that grid-connected mode was the cheapest and also can be considered to be the most reliable mode of operation, whereas excluding RES increases both the generation cost and pollutants emission in atmosphere. Proposed MGWOSCACSA proved to be the superior algorithm in terms of efficiency, consistency, robustness and less computational time when compared to the algorithms reported in the literature as wells as GWO and MGWO. Owing to these capabilities, MGWOSCACSA may be implemented to solve much complex constrained engineering problems.

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References 1. Sihna N, Some studies on application of intelligent techniques to economic operation of power systems. Jadavpur University, Calcutta 2. Yalcinoz T, Short MJ (1997) Neural networks approach for solving economic dispatch problem with transmission capacity constraints. IEEE Power Eng Rev 17(10):40 3. Dhillon JS, Parti SC, Kothari DP (1993) Stochastic economic emission load dispatch. Electr Power Syst Res 26(3):179–186 4. Wang C, Shahidehpour SM (1994) Ramp-rate limits in unit commitment and economic dispatch incorporating rotor fatigue effect. IEEE Trans Power Syst 9(3):1539–1545 5. Hatziargyriou N (2013) Microgrids: architectures and control 6. NA Luu (2014) Control and management strategies for a microgrid acknowledgments 7. Chen C, Duan S, Cai T, Liu B, Hu G (2011) Smart energy management system for optimal microgrid economic operation. IET Renew Power Gener 5(3):258 8. Kasaei MJ (2018) Energy and operational management of virtual power plant using imperialist competitive algorithm. Int Trans Electr Energy Syst 28(11):1–13 9. Basu M, Chowdhury A (2013) Cuckoo search algorithm for economic dispatch. Energy 60:99– 108 10. Moghaddam AA, Seifi A, Niknam T, Alizadeh Pahlavani MR (2011) Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy 36(11):6490–6507 11. Fan QY, Cheng H (2018) Multi-objective stochastic optimal operation of a grid-connected microgrid considering an energy storage system. Appl Sci 8(12) 12. Dey B, Bhattacharyya B (2019) Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique. Int Trans Electr Energy Syst 29(1):1–14 13. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61 14. Khandelwal A, Bhargava A, Sharma A, Sharma H (2018) Modified grey wolf optimization algorithm for transmission network expansion planning problem. Arab J Sci Eng 43(6):2899– 2908 15. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. KnowlBased Syst 96:120–133 16. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

Design and Analysis of a 1.1 kW BIPV-Based Residential Grid-Connected System Debayan Sarkar, Anand Kumar, and Pradip Kumar Sadhu

Abstract Building integrated photovoltaic (BIPV)-based residential system is emerging in the recent scenario where buildings or residential places generate clean energy in the distribution side. In this paper, detailed design and analysis of a 1.1 kW BIPV-based residential grid-connected system in MATLAB/Simulink software is presented. The main objective of this paper is to utilize and simulate residential bifacial BIPV modules as photovoltaic (PV) generators instead of conventional PV modules. Two-stage DC–DC boost converter along with single-phase fullbridge (1-FB) grid-connected inverter (GCI) and the design of L–C–L filter is effectively described. The overall proposed 1.1 kW BIPV-based residential gridconnected system is mathematically modeled and simulated. This infers the importance of BIPV-based grid-connected system in the upcoming scenario for integrating renewable energy into the existing grid. Keywords BIPV · Boost converter · L–C–L filter · Grid-connected inverter (GCI) · Single-phase full bridge (1-FB)

1 Introduction Building integrated photovoltaic (BIPV) that consists of integrated photovoltaic (PV) modules into a building architecture is one of the recent applications of gridconnected PV systems [1, 2]. BIPV is becoming more popular in the modern scenario due to increased encouragement in addition of renewable energy resources onto the

D. Sarkar (B) · A. Kumar · P. K. Sadhu Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India e-mail: [email protected] A. Kumar e-mail: [email protected] P. K. Sadhu e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_68

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Fig. 1 Block diagram of the proposed 1.1 kW grid-connected BIPV system

BIPV Array

Two-Stage DC-DC Boost Converter

Grid Single-phase full-bridge (1Φ-FB) gridconnected inverter (GCI)

L-C-L Filter

conventional grid as well as cost reduction of PV modules and associated components [3, 4]. A grid-connected BIPV (GC-BIPV) system with partial shading effect is investigated by [5] in the atmospheric condition of Bangladesh. The result of performance monitoring a grid-connected BIPV system of Yiwu international trade city for ten successive days in Gejiang is provided by [6]. A basic technique by [7] estimates the loss of output power resulted from partial shading in GC-BIPV system. A 3- 3-level neutral point clamped (NPC) grid-connected inverter for BIPV system is designed by [8]. The result of operating an installed grid-connected BIPV system of Colombia after monitoring for three consecutive years is summarized by [9]. A detailed design of the BIPV system in Hong Kong is made by [10] that encounter the configurations of the system, interfacing of the grid along with protection and power quality. The performance of 334 grid-connected PV installations of 14 different countries is presented by [11] along with comparative analysis and discussions. A 1 kWp BIPV array is used by [12] for determining the performance of the BIPV system at low irradiance conditions. Evaluation of performance for a case study on BIPV-based battery storage system in India (TERI, Gurgaon, Haryana) is made by [13]. Modeling and management of energy for a grid-connected BIPV-based battery energy storage (BES) is performed by [14]. Real-time applications related to grid-connected residential BIPV systems with battery storage along with charging points for electric vehicles are in use by Tesla Powerwall [15]. The main objective of this paper is to design and analyze a 1.1 kW BIPV-based residential grid-connected system in MATLAB/Simulink. The rating of the individual BIPV module is 195 Wp. The BIPV array consists of 6 BIPV modules, i.e., (195 × 6) = 1.17 kWp. The block diagram of the BIPV grid-tied system is shown in Fig. 1.

2 Design of BIPV Array SANYO HIT Double (HIP-195DA3) [17] bifacial BIPV module having power rating of 195 Wp for an individual module is used for making the BIPV array. The BIPV array consists of total 6 BIPV modules (three parallel strings where two BIPV modules are connected in series in each string). The capacity of the BIPV array is 1.17 kWp. The arrangement of placing the BIPV modules is given in Fig. 2. The I–V and P–V characteristics of the 195 Wp bifacial (HIP-195DA3) BIPV module

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Fig. 2 Placement of Sanyo HIP (HIP-195DA3) 195 Wp Bifacial BIPV modules [16]

are presented in Fig. 3. The BIPV array on simulation at STC gives a DC output voltage (V BIPVarray ) of 80 V and is shown in Fig. 4. The advantages of using bifacial BIPV modules are as follows: (1) Electricity can be generated from both incoming and reflected lights. (2) 30% increase in power generation as compared to other modules.

Fig. 3 I–V and P–V characteristics of (HIP-195DA3) 195 Wp bifacial BIPV modules

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Fig. 4 Simulated output voltage (V BIPVarray ) of the BIPV array

3 Design of Two-Stage DC–DC Boost Converter Two-stage (N = 2) DC–DC boost converter as shown in Fig. 5 is employed for increasing the output voltage of the BIPV array corresponding to the grid voltage (325 V peak or 230 V RMS). In the first stage, BIPV array output voltage (V BIPVarray = V S1(in) ) of 80 V DC is boosted to 160 V DC (V S1(out) ), and in the second stage, 160 V DC (V S2(in) ) is again re-boosted to 350 V DC (V S2(out) ). To compensate for the voltage drop in 1-FB GCI and associated components, the output voltage (V S2(out) ) of stage 2 is fixed to 350 V DC instead of 325 V DC. The switching frequency (f sw1 ) of stage 1 is taken as 25 kHz while the switching frequency of stage 2 is taken as 26 kHz. PWM gate pulse is given to drive the MOSFET switches of the two individual stages of the boost converter. Towards single phase grid-connected inverter LS1boost

LS2boost

LS1boost 25kHz

CS1boost

25kHz

Boost converter stage 1

26kHz

Boost converter stage 2

Fig. 5 MATLAB/Simulink circuit of the two-stage DC–DC boost converter

RS2

CS2boost

C

26kHz

RS1

C

CS1boost

BIPV Array

LS2boost

CS2boost

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3.1 Duty Cycle, Inductor and Capacitor Selection of Boost Converter Stage 1 In order to determine the values of duty cycle (DS1boost ), inductor (L S1boost ) and capacitor (C S1boost ), of boost converter stage 1, the following information is required. 1. 2. 3. 4. 5. 6. 7. 8.

Input DC voltage (V S1(in) ) of stage 1 = 80 V. Output DC voltage (V S1(out) ) of stage 1 = 160 V. Switching frequency (f sw1 ) of MOSFET switch for stage 1 = 25 kHz. Maximum current (I lmax1 ) flowing through the inductor = 90 A. Estimated ripple current (ΔI l1 ) of the inductor = 1.75% of I lmax1 = 1.575 A. Estimated output voltage ripple (ΔV S1(out) ) = 0.05% of V S1(out) = 80 mV. Load resistance (RS1 ) connected at the output of boost converter stage 1 = 40 . Maximum output current of stage 1 (I S1(out) ) = V S1(out) /RS1 = 160 V/40  = 4 A. Duty cycle (DS1boost ) for stage 1, DS1boost = 1 −

80 VS1 (in) = 0.5 =1− VS1 (out) 160

(1)

Inductor value (L S1boost ) estimation for stage 1, L S1boost

  VS1(in) × VS1(out) − VS1(in) 80 × (160 − 80) ≈ 1 mH = = Il1 × f sw1 × VS1(out) 1.575 × 25,000 × 160

(2)

Capacitor value (C S1boost ) estimation for stage 1, CS1boost =

IS1(out) × DS1boost 4 × 0.5 ≈ 1 mF = f sw1 × VS1(out) 25,000 × 0.8

(3)

The design parameters of boost converter stage 1 are: DS1boost = 50%, L S1boost = 1 mH, C S1boost = 1 mF, f sw1 = 25 kHz, and RS1 = 40 . The output DC voltage (V S1(out) ) of boost converter stage 1 is shown in Fig. 6. The output DC voltage (V S1(out) ) of boost converter stage 1 becomes the input DC voltage (V S2(in) ) of boost converter stage 2 (i.e., V S2(in) = V S1(out) ) which is 160 V DC in this case.

3.2 Duty Cycle, Inductor and Capacitor Selection of Boost Converter Stage 2 In order to determine the values of duty cycle (DS2boost ), inductor (L S2boost ) and capacitor (C S2boost ), of boost converter stage 2, the following information is required. 1. Input DC voltage (V S2(in) ) of stage 2 = 160 V.

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Fig. 6 Simulated output DC voltage (V S1(out) ) of DC–DC boost converter stage 1

2. 3. 4. 5. 6. 7. 8.

Output DC voltage (V S2(out) ) of stage 2 = 350 V. Switching frequency (f sw2 ) of MOSFET switch for stage 2 = 26 kHz. Maximum current (I lmax2 ) flowing through the inductor = 100 A. Estimated ripple current (ΔI l2 ) of the inductor = 26% of I lmax2 = 26 A. Estimated output voltage ripple (ΔV S2(out) ) = 0.1% of V S2(out) = 0.35 V. Load resistance (RS2 ) connected at the output of boost converter stage 2 = 50 . Maximum output current of stage 2 (I S2(out) ) = V S2(out) /RS2 = 350 V/50  = 7 A. Duty cycle (DS2boost ) for stage 2, DS2boost = 1 −

VS2(in) 160 = 0.54 =1− VS2(out) 350

(4)

Inductor value (L S2boost ) estimation for stage 2, L S2boost =

  VS2(in) × VS2(out) − VS2(in) 160 × (350 − 160) = = 130 μH Il2 × f sw2 × VS2out 26 × 26,000 × 350

(5)

Capacitor value (C S2boost ) estimation for stage 2, CS2boost =

IS2(out) × DS2boost 7 × 0.54 = 415 μF = f sw2 × VS2(out) 26, 000 × 0.35

(6)

The design parameters of boost converter stage 2 are: DS2boost = 54%, L S2boost = 130 μH, C S2boost = 415 μF, f sw2 = 26 kHz, and RS2 = 50 . The output DC voltage (V S2(out) ) of boost converter stage 2 is shown in Fig. 7.

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Fig. 7 Simulated output DC voltage (V S2(out) ) of DC–DC boost converter stage 2

4 Design of Single-Phase Grid-Connected Inverter The single-phase full-bridge (1-FB) grid-connected inverter (GCI) consists of four MOSFET switches for switching purposes. The output of the boost converter stage 2, i.e., 350 V DC (V S2(out) ) is fed to the inverter for DC to AC conversion as shown in Fig. 8. The inverter converts 350 V DC to around 230 V RMS AC voltage (grid voltage). PWM signals are used to drive the MOSFET switches of the inverter. Four MOSFET switches of the single-phase full-bridge inverter require four sets of gate signals. PWM pulse generator generates a square wave (having 50% duty cycle with the frequency of 50 Hz) for giving gate pulses to MOSFET switches S1 and S2. A NOT gate is used to make the gate pulse 180º out of phase for giving gate pulses to MOSFET switches S3 and S4. In, switching process of the inverter, four PWM Pulse generator

Inverter

Switching pulse for S1,S2

Switching pulse for S3,S4

LCL Filter

From Boost Converter Stage 2

Lfilter1 S1

S3

Single phase fullbridge gridconnected Inverter

S4

Lfilter2

Cfilter

R L

S2

Fig. 8 Schematic diagram of single-phase full-bridge (1-FB) grid-connected inverter (GCI) in MATLAB/Simulink

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MOSFET switches are grouped into two groups. Switches S1 and S2 in group one while switches S3 and S4 in group two. When switches S1 and S2 are switched ON and S3 and S4 are forced to OFF, the output voltage of the inverter will be positive. Again, when switches S3 and S4 are switched ON and S1 and S2 are forced to OFF, output voltage of the inverter will be negative [18, 19]. Hence, the single-phase fullbridge (1-FB) GCI produces square wave output voltage (V out-unfil ) consisting of harmonics.

5 Design of L–C–L Filter In order to filter out higher-order harmonics (that gets generated from the inverter output) on the grid side, L–C–L (T-shaped) filter is employed here. The output current (I out ) equation of the L–C–L filter is calculated using the equation [20] as defined by Eq. (7) later.   Vinp 1 Z load 1 − Iout ∼ = Z ch Q Z ch

(7)

where V inp is input voltage, Z load is load impedance, Z ch is characteristics impedance which is determined by L–C–L filter components, L filter (L filter1 = L filter2 = L filter ) and C filter :  L filter Cfilter

Z ch =

(8)

Q is quality factor which is expressed as, Q=

ωL filter rin

(9)

where ω = 2 × π × f ; r in signifies the inductor internal resistance. When r in of the inductor in neglected and taken zero value, the Eq. (7) becomes, Vinp Iout ∼ = Z ch

(10)

L–C–L filter employed here reduces the harmonic content of the inverter output as well as maintains constant current supply at the load terminals. The values of the inductor (L filter ) and capacitor (C filter ) for L–C–L filter is determined using the cut-off frequency (f cut-off ) condition of the low-pass filter (LPF) and is specified by, Z ch = X cut-off =

1 2π f cut-off Cfilter

(11)

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Fig. 9 Simulated output AC voltage (V out-unfil ) of the inverter without using L–C–L filter

In the proposed L–C–L filter design, Cut-off frequency (f cut-off ) = 50 Hz and characteristics impedance (Z ch ) = 50 . Hence, the values of L filter and C filter for L–C–L filter design are calculated using, 1 ≈ 64 μF 2 × 50 × 50

(12)

2 L filter = Cfilter Z ch = 64 × 10−6 × 502 = 0.16 H

(13)

Cfilter =

1 2π f cut-off Z ch

=

Thus, for designing the T-shaped L–C–L filter, the design parameters are: L filter1 = L filter2 = L filter = 0.16 H and C filter = 64 μF.

6 Simulation Results and Discussions The simulated output AC voltage (V out-unfil ) waveform of the inverter without using L–C–L filter is presented in Fig. 9. Simulated output AC voltage (V out-fil ) and current (I out-fil ) waveform of the inverter after using L–C–L filter are given in Fig. 10 and Fig. 11, respectively. Figure 12 shows the output AC voltage (V out-fil ) and current (I out-fil ) waveform of the 1-FB GCI after using the L–C–L filter.

7 Conclusion This paper presents a 1.1 kW BIPV-based residential grid-connected system to increase renewable energy penetration to the grid as well as distributed generation. BIPV array is used due to some additional advantages of aesthetics and more efficient modules that can be incorporated into buildings or residential systems. The overall 1.1 kW BIPV-based grid-connected system is designed and analyzed in

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Fig. 10 Simulated output AC voltage (V out-fil ) of the inverter after using L–C–L filter

Fig. 11 Simulated output AC current (I out-fil ) of the inverter after using L–C–L filter

Fig. 12 Simulated output AC voltage (V out-fil ) and current (I out-fil ) of the inverter after using L–C–L filter

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MATLAB/Simulink. The BIPV array generates about 1.17 kWp power and output DC voltage of 80 V. A two-stage DC–DC boost converter is utilized to boost the voltage from 80 to 160 V DC and 160 V DC to 350 V DC, respectively. The 350 V DC voltage is inverted to 220 V AC RMS through the application of single-phase full-bridge (1-FB) grid-connected inverter (GCI) and finally connected to the grid. L–C–L filter is used for filtering out the harmonic content of 1-FB GCI.

References 1. Yatim Y, Yahya MW, Tajuddin MFN, Ismail B, Sulaiman SI (2017) Tecno-economic analysis of PV module selection for residential BIPV with net metering implementation in Malaysia. In: 15th IEEE student conference on research and development (SCOReD), IEEE, Putrajaya, Malaysia, pp 361–365 2. Sarkar D, Shukla A, Kumar A, Sadhu PK (2018) Role of ınternet of things (IoT) in maximum power extraction from BIPV modules: a review for developing smart zero energy buildings. Int J Eng Technol 7(3.12):1033–1037 3. Haque A, Rahman MA, Ahsan Q (2012) Building integrated photovoltaic system: cost effectiveness. In: 7th international conference on electrical and computer engineering, IEEE, Dhaka, pp 904–907 4. Sarkar D, Kumar A, Sadhu PK (2019) A survey on development and recent trends of renewable energy generation from BIPV systems. IETE Tech Rev 5. Chowdhury S, Al-Amin M, Sanjari S, Tasnim S, Ahmad M (2012) Performance parameter analysis of grid connected building integrated photovoltaic application in Bangladesh. International conference on informatics, electronics & vision (ICIEV). (ICIEV). IEEE, Dhaka, pp 870–875 6. Zou X, Li B, Zhai Y, Zhou L (2012) Performance of a grid-connected BIPV system at Jiangsu province, China. In: Asia-Pacific power and energy engineering conference, IEEE, Shanghai, pp 1–4 7. Drif M, Pérez PJ, Aguilera J, Mellit A (2012) A method for evaluating shading losses involved in a GC-BIPV using real data. International multi-conference on systems, signals & devices. IEEE, Chemnitz, pp 1–3 8. Kim Y, Cha H, Song B, Lee KY (2012) Design and control of a grid-connected three-phase 3-level NPC inverter for building integrated photovoltaic systems. In: IEEE PES innovative smart grid technologies (ISGT), IEEE, Washington DC, pp 1–7 9. Aristizabal J, Gordillo G (2008) Performance of the first grid-connected, BIPVS installation in Colombia over three years of continuous operation. In: 33rd IEEE photovoltaic specialists conference, IEEE, San Diego CA, pp 1–5 10. Lo EWC (2006) Recent projects of building integrated photovoltaic in Hong Kong. In: 2nd international conference on power electronics systems and applications, IEEE, Hong Kong, pp 62–67 11. Jahn U, Nasse W (2003) Performance analysis and reliability of grid-connected PV systems in IEA countries. In: Proceedings of 3rd world conference on photovoltaic energy conversion, IEEE, Osaka, pp 2148–2151 12. Smiley EW, Stamenic L (2002) Optimization of building integrated photovoltaic systems. In: Conference record of the twenty-ninth IEEE photovoltaic specialists conference, IEEE, New Orleans, LA, USA, pp 1501–1503 13. Sharma A, et al. (2019) Performance assessment of building integrated photovoltaic and battery energy system: a case study of TERI-retreat facilty in India. In: Proceedings of 4th international conference on smart and sustainable technologies (SpliTech), IEEE, Croatia, pp 1–5

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14. Argyrou MC, et al. (2019) Energy management and modeling of a grid-connected BIPV system with battery energy storage. In: Proceedings of 54th international universities power engineering conference (UPEC), IEEE, Romania, pp 1–6 15. Tesla. Powerwall: the Tesla home battery. https://www.tesla.com/powerwall 16. Aesthetics—prism solar. https://www.prismsolar.com/aesthetics 17. SANYO HIT Double 195 Wp bifacial photovoltaic module datasheet. https://s3.amazonaws. com/ecodirect_docs/SANYO/195DA3.pdf 18. Rashid MH (1993) Power electronics, circuits, devices, and applications. Prentice-Hall, Englewood Cliffs 19. Mohan N, Undeland T, Robbins W (1995) Power electronic: converters, applications, and designs, 2nd edn. Wiley, New York 20. Liserre M, Blaabjerg F, Hansen S (2005) Design and control of an LCL-filter-based three-phase active rectifier. IEEE Trans Ind Appl 41(5):1281–1291

Support Vector Machine-Based Fault Detection, Classification and Section Identification Scheme for DC Microgrid Shankarshan Prasad Tiwari, Ebha Koley, and Subhojit Ghosh

Abstract In recent years, due to the significant development in the industrial and commercial infrastructure, the power demand is growing day by day. The considerable reduction in non-renewable sources of energy and their impact on the environmental pollution due to the emission of carbon has caused rising awareness toward the utilization of green energy resources, i.e., wind, biomass and solar energy sources. However, the integration of the renewable sources has posed serious protection challenges in DC microgrid system. The variation in short circuit level of current in grid-connected and islanded mode makes the protection task more challenging. In this regard, this paper presents a support vector machine-based scheme to perform the task of fault detection and classification, as well as section identification for both modes of microgrid operation, i.e., islanded and grid-connected mode of DC microgrid. Keywords DC microgrid protection · Support vector machine · Pole to ground fault · Pole to pole fault

1 Introduction In recent years, the considerable reduction in the availability of non-renewable sources of energy (coal, petroleum products) and the rise in carbon emission has motivated toward the accelerated utilization of renewable resources in power generation. Renewable sources are non-exhaustible, neat and clean sources of energy and hence play a leading role in the reduction of air pollution. The penetration of renewable energy sources in the recent power distribution structure to ensure reliable power S. P. Tiwari (B) · E. Koley · S. Ghosh Department of Electrical Engineering, N.I.T Raipur, Raipur, Chhattisgarh, India e-mail: [email protected] E. Koley e-mail: [email protected] S. Ghosh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_69

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with enhanced power quality has led to the concept of the microgrid. A microgrid is a small unit of distributed energy resources, converters and various types of load, i.e., linear and nonlinear load [1]. Based on the type of electrical output power, the microgrid can be classified as AC, DC or hybrid. The development of electronics devices such as mobile phones, LEDs, grinders, savers and other DC power fed equipment has led to the requirement of DC microgrid. In DC system, the power conversion stages are quite less as compared to the AC system, due to which the heat losses are always within the desirable limit, thus increasing the efficiency of the system. The DC system has got numerous advantages over the AC system in terms of efficiency, stability reliability and power transfer capability [2]. With regard to the mode of operation, the microgrid is operated in both modes. The switch at the PCC facilitates the bidirectional power flow. During the islanded mode, the utility grid is disconnected from the rest of microgrid network. Load variation and occurrence of fault with high resistance in DC microgrid quite often show similar variation in the rate of change of current [3]. On the other hand, during the grid-connected mode, fault current is much higher around 10 to 50 times to that of the islanded mode being 2–5 times. Also, the DER-dependent (inverter and synchronous-based) variation in fault current from the inverter and synchronous-based DERs further makes the protection more challenging [2]. In this context, the present works aim at the development of a microgrid protection with necessary immunity against the diverse operating scenarios and fault levels. With regard to the protection of DC microgrid, a number of protection methods have been proposed in the literature including, a signal handshaking methods for locating and isolating the faults [4], differential current-based fault detection [5], probe power unit-based protection scheme [6], artificial neural network-based DC microgrid protection [7], SVM-based protection scheme [8, 9], superimposed current-based unit protection scheme [10], wavelet transform-based efficient protection scheme for low-voltage DC micro-grid [11], centralize unit base protection scheme [12], voltage source converter-based over current protection [13]. A common limitation with all these techniques pertains to high-computational complexity arising because of the inclusion of calculations for extracting features from raw post-fault time domain data. In order to develope a reliable and accurate protection algorithm for microgrids with reduced computational complexity, the present work proposes a SVM-based scheme using raw voltage–current data.

2 Test Model of DC Mıcrogrıd The test model of a 350 V DC ring microgrid is shown in Fig. 1 [14]. The DC microgrid system consists of three sources, i.e., PV array, synchronous diesel generator and wind turbine. The test microgrid is simulated in MATLAB/Simulink environment for the implementation of the proposed scheme. There are six sections, namely A1, A2, A3, A4, A5 and A6 with each section being extended over the length of 1 km. The DERs are equipped with the power conditioning

Support Vector Machine-Based Fault …

AC Grid

AC to DC converter

AC Load

DC to AC converter

PV Array

DC Load

DC to DC converter

693 PCC

A1

A6

A2

A5

A3

DC to DC converter

A4

AC to DC Converter

Wind Generator

DC to DC Converter

Battery

AC to DC converter

SDG

Fig. 1 Test microgrid model of DC microgrid

units with efficient control systems to facilitate the appropriate interface within the microgrid. To demonstrate the voltage–current profile post-fault in the DC microgrid, a pole to ground fault in section A1 is simulated at t = 0.3 s as depicted in Fig. 2. At the

Fig. 2 Voltage and current during a pole to ground fault in section A1

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instant of fault inception, an abrupt variation in voltage and current profile can be observed.

3 Overvıew of Support Vector Machıne (SVM)-Based Scheme Support vector machine is the widely used method of machine learning which is mostly used to perform the classification and regression task. It is based on supervised learning and works on the principle of pattern reorganization for the classification of input data. In SVM, a set of hyperplanes in infinite space is used for the classification of input data. Separation of any class is done on the basis of estimating the largest distance data point to the nearest data point. If the margin between the separation points will be large, then classifier will produce less error, thus improving the accuracy of the system. The equation of hyperplane for the separation of class is given by the following equation T = (ai , bi ), < i < n

(1)

where ai shows the real vector and bi represent the class of data. Both classes have been represented as w.x − c = 0

(2)

where w is the normal vector at the origin point, c is the bias and x is the setting point in a hyperplane. It is desirable that the distance between the planes should be maximum. Equations (3) and (4) show the class of data, i.e., first and second class. These equations also help in ignoring the data lying in the hyperplane. The classification probability is estimated to be good if the margin between the planes will be maximum. The equation for classification is represented as w.x − c ≥ +1

(3)

w.x − c ≤ −1

(4)

Here +1 and −1 represent the values of bi for the first and second class, respectively.

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4 Development of Protectıon Scheme In the proposed protection scheme, the sampled signals of voltage and current are utilized for fault detection and classification. In Fig. 3, the outline of the proposed SVM-based protection scheme is illustrated with the help of the flow chart. The postfault voltage and current signals is recorded at the PCC by simulating the diverse scenarios involving dissimilarity in resistance, location of fault and load variation which is utilized to train the SVM classifier. The task of mode detection is developed by SVM-1 with two output class, i.e., GC and IM representing grid-connected and islanding mode of operation of DC microgrid, respectively. The values of parameters used to train the SVM modules are detailed in Table 1. The detailed description of the development of SVMs dedicated to perform the intended protection tasks of fault detection, classification and section identification is given below. Samples of Voltage and Current signals Mode detection SVM 1

Grid connected mode Section identification SVM 4

Islanded mode Section identification SVM 3

Fault detection/ classification SVM 2

Fault detection/ classification SVM 5

No fault Relaying decision

No fault Relaying decision

Type of fault

Type of fault

Relay trip signal

Relay trip signal

Fig. 3 Flow chart of support vector machine-based algorithm

Table 1 Values of parameters for SVM modules Parameters

SVM-1

SVM-2

SVM-3

SVM-4

SVM-5

No. of parameters

2

2

2

2

2

Kernel function

Linear

Linear

Linear

Quadratic

Linear

Box constrained level

1

1

1

1

1

Kernel scale

1

3

2

2

3

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4.1 Fault Detection/Classification (FD/C) The task of FD/C is performed by SVM-2 and SVM-5 in grid-connected and islanded mode, respectively. The sampled voltage–current signals constitute the input data to be fed to the SVM. The respective SVM modules provide the output regarding the state of the system (faulty or healthy) and the type of fault among the probable fault types, i.e., pole to ground and pole to pole fault.

4.2 Section Identification For section identification, the modules SVM-4 and SVM-3 are developed to perform the faulty section identification in both modes, respectively. In the proposed protection model, six sections have been considered. In case a fault is detected, then depending on the output of section identification module regarding the faulty section, a trip signal is issued by the relay to disconnect the healthy section from the faulty section in the microgrid. A dataset of 726 PG (pole to ground), 66 PP (pole to pole) fault scenarios in each mode has been considered during the training of SVM.

5 Performance Evaluatıon The effectiveness of the proposed scheme (FD/C schemes) has been analyzed for different operating conditions involving variation in fault parameters, i.e., faults resistance, location of the fault in each mode of operation. The total number of test scenarios in each mode of operation includes 132 PG, 66 PP and 100 no-fault cases. The description of results for the tested cases is detailed in next subsections.

5.1 Mode Detection Performance evaluation of proposed SVM-based mode detector has been evaluated and results are depicted in Table 1. An accuracy of 100% has been achieved by mode detector during the gridconnected mode while in islanded mode, it is 98.66%. The higher level of accuracy in both modes of operation indicates the appropriateness of mode detection module in identifying the appropriate mode of operation.

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Table 2 Classification from detection/classification Type of fault

Types of mode

Overall accuracy (%)

Grid-connected mode (%)

Islanded mode (%)

Pole to ground fault

100

92.42

96.21

Pole to pole fault

98.66

91

94.83

Table 3 Testing accuracy (%) Section

Mode of operation (accuracy in percentage) Grid-connected mode (%)

Islanded mode (%)

Overall accuracy percentage (%)

A1

98.48

97.48

97.98

A2

97.96

96.48

97.22

A3

98.48

96.96

97.92

A4

96.96

95.48

96.22

A5

96.96

98.48

97.72

A6

98.47

95.48

96.97

5.2 Fault Detection/Classification The performance of SVM-based fault detector/classifier in grid-connected as well as in the islanded mode is summarized in Table 2. As observed, the testing accuracy is higher for both the fault types which reflect the efficiency of the scheme in detecting and classifying the fault scenarios accurately and reliably.

6 Section Identification of Faulty Section Performance of SVM-based scheme for section identification in grid-connected and islanded mode has been analyzed and the obtained result is summarized in Table 3. As observed, the higher degree of testing accuracy in identifying the faulty section in both the modes of operation of DC microgrid has been achieved with the proposed SVM-based section identifier, which indicates its effectiveness in identifying the faulty section accurately.

7 Conclusion The integration of DERs in the power distribution network poses a serious protection challenge due to extensive dissimilarity in the voltage–current profile during both modes of operation. In this regard, a SVM-based algorithm is developed for the

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protection of the DC ring microgrid system. The proposed scheme involves postfault sampled signals (voltage and current) utilized by SVM for performing the tasks of fault classification/detection as well as section identification under both modes of operation. The above scheme has been validated for various scenarios involving extensive disparity in fault and no-fault conditions. The performance evaluation indicates the ability of the scheme in providing accurate and reliable protection to the DC microgrid.

References 1. Fotuhi-Firuzabad M, Iravani R, Aminifar F, Hatziargyriou N, Lehtonen M (2012) Guest editorial special section on microgrids. IEEE Trans Smart Grid 3(4):1857–1859 2. Bayati N, Hajizadeh A, Soltani M (2018) Protection in DC microgrids: a comparative review. IET Smart Grid 1(3):66–75 3. Meghwani A, Srivastava SC, Chakrabarti S (2016) A non-unit protection scheme for DC microgrid based on local measurements. IEEE Trans Power Delivery 32(1):172–181 4. Tang L, Ooi BT (2007) Locating and isolating DC faults in multi-terminal DC systems. IEEE Trans Power Delivery 22(3):1877–1884 5. Dhar S, Dash PK (2017) Differential current-based fault protection with adaptive threshold for multiple PV-based DC Microgrid. IET Renew Power Gener 11(6):778–790 6. Park JD, Candelaria J, Ma L, Dunn K (2013) DC Ring-bus microgrid fault protection and identification of fault location. IEEE Trans Power Delivery 28(4):2574–2584 7. Yang Q, Li J, Le Blond S, Wang C (2016) Artificial neural network based fault detection and fault location in the DC microgrid. Energy Proc 103:129–134 8. Koley E, Shukla SK, Ghosh S, Mohanta DK (2017) Protection scheme for power transmission lines based on SVM and ANN considering the presence of non-linear loads.: IET Gener Trans Distrib 11(9):2333–2341 9. Manohar M, Koley E (2017) SVM based protection scheme for microgrid. In: 2017 international conference on intelligent computing, instrumentation and control technologies (ICICICT). IEEE, New York, pp 429–432 10. Mohanty R, Pradhan AK (2018) A superimposed current based unit protection scheme for DC microgrid. IEEE Trans Smart Grid 9(4):3917–3919 11. Som S, Samantaray SR (2018) Efficient protection scheme for low-voltage DC micro-grid. IET Gener Transm Distrib 12(13):3322–3329 12. Monadi M, Gavriluta C, Luna A, Candela JI, Rodriguez P (2016) Centralized protection strategy for medium voltage DC microgrids. IEEE Trans Power Delivery 32(1):430–440 13. Cairoli P, Rodrigues R, Zheng H (March, 2017) Fault current limiting power converters for protection of DC microgrids. In: SoutheastCon 2017. IEEE, New York, pp 1–7 14. Mohanty R, Pradhan AK (2017) Protection of smart DC microgrid with ring configuration using parameter estimation approach. IEEE Trans Smart Grid 9(6):6328–6337

Spectral Kurtosis-Based Island Detection Technique Sikander Singh, Soham Dutta, Sourav Kumar Sahu, and Pradip Kumar Sadhu

Abstract The increasing rate of energy demand in the recent times is unprecedented and it is driving the increasing demand of renewable distributed generations in distribution system on a large scale. This significantly brings uncertainty to grid operation. Inadvertent islanding of microgrid is a major issue in today’s smart distribution grid. An efficient islanding detection technique is introduced in this paper for a renewable distributed generation-based microgrid. A micro-phasor measurement unit (µPMU) is employed as an islanding detection device. Spectral kurtosis (SK) of negative sequence angle of voltage signals is employed for detecting islanding instants. The implementation is demonstrated in an IEEE 34 node test feeder simulated in MATLAB/Simulink software. Keywords Islanding detection · Distributed generation · Negative sequence phase angle · Micro-phasor measurement unit · Spectral kurtosis

1 Introduction In order to reach the ever-expanding load demand, several new strategies are emerging, such as addition of new distributed generation resources. Previously, it was the utility’s responsibility to generate, transmit and distribute the electricity to customers. The one-directional power flow is now replaced with bidirectional power flow due to the advancement in the field of renewable power generation. Thus, the concept of microgrid, entirely supported by distributed generator, was born. However, there are many issues to be addressed with the microgrid such as unintentional S. Singh · S. Dutta (B) · P. K. Sadhu EE Department, IIT (ISM), Dhanbad, Jharkhand 826004, India e-mail: [email protected] S. Singh e-mail: [email protected] S. K. Sahu EEE Department, BIT, Mesra, Ranchi 835215, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_70

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islanding [1, 2]. Several negative effects are observed during unplanned islanding, such as poor power quality in microgrid and safety hazards to utility workers who works on the event of islanding, unknown to the lines still powered by the distributed generators. In actual fact, islanding must be properly and effectively detected [3]. Several methods of islanding detection are found in the literature. These methods can be classified into six groups: passive, active, hybrid, signal processing, communication and classifier techniques [4]. Generally, passive methods continuously monitor certain system parameters with a suitable threshold for islanding detection [6] and active methods are based on injection of a small disturbance (harmonics) into the system. After that, the system is analyzed for islanding detection with the change in output parameters [5]. Active methods may inject unwanted harmonic content to the system where the passive methods suffer from shortcomings like large non-detection zone (NDZ) and appropriate selection of threshold value [7]. Hybrid method gives combine effect of both active and passive for islanding detection. In hybrid technique, first passive methods are applied, then active methods are applied for detection of islanding [8]. Communication methods use communication signal between distributed generators and utility for obtaining islanding detection. In signal processing methods, several transform methods are used for island detection such as short time Fourier transform (STFT), fast Fourier transform (FFT), Stockwell transform (ST), discrete cosine transform (DCT), time–time transform (TT), wavelet transform (WT), Hilbert Huyang transform (HHT), etc. The classifier methods utilize pattern recognition and data training techniques in classification of islanding instants [4]. In the proposed method, the voltage signals are directly obtained through micro-phasor measurement unit (µPMU) from the solar generator (SG) bus. After obtaining negative phase sequence angle, it is processed with spectral kurtosis (SK) within the µPMU, to detect island scenarios. The rest of the paper constitutes of four sections. Section 2 explains the SK analysis technique, Section 3 describes a µPMU, Section 4 includes the methodology used in simulation and results. Section 5 concludes the research.

1.1 Spectral Kurtosıs SK is a statistical tool which is designed for the measurement of the peak value of a real-valued random variable which is follow the probability distribution function. Higher kurtosis value denotes more of the variance in a random variable due to infrequent deviation. The kurtosis value is calculated over a P-data point record and it is denoted byg2 as per (1), where m4 is the fourth sample moment about the mean value, m2 is the second sample moment about the mean value, and x m is the sample mean value. 1 P (xi − xm )4 m4 P (1) g2 = 2 −3 =  i=1 2 −3 m2 1 P 2 (x − x ) m i=1 i P2

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Ideally, each frequency component of a random variable is represented by kurtosis and this process is called SK. For calculation of kurtosis, we will consider Q realization of the process and each realization having P points, i.e., we consider Q measurement weeps and each sweep has P-data points. A suitable range of sampling period of the data acquisition unit, which is the time space between successive points, is to P,Q is the kurtosis be defined. The unbiased estimator SK is given as in (2), whereG 2,X value for this Fourier frequency, and m is the frequency index [9]. ⎡

P,Q G 2,X

⎤ 4 Q  i   Q ⎢ (Q + 1) i=1 X N (m) ⎥ = ⎣  

2 −2⎦  2 Q−1 Q  i  i=1 X N (m)

(2)

Thus, SK can be used for finding non-Gaussian components in a AC signal and their location in frequency domain. It can easily and accurately detect disturbances in the signal even in the presence of noises, transients and harmonics.

2 Micro-phasor Measurement Unit PMU is an electronic instrument which is installed into the transmission grid to measure various electrical parameters particularly current and voltage phasor, rate of change of frequency and frequency with the help of global positioning system (GPS). µPMUs are similar instruments used in distribution system. The block diagram of a general µPMU is shown in Fig. 1. Analog signals consist of phase currents and voltages available from current transformers (CTs) and potential transformers (PTs). In order to filter out the high frequencies, from the input signals, low-pass filter is used which is also called anti-aliasing filter. After this, the filtered signals are sent

GPS Antenna

One Pulse per Second GPS Module

AC Supply

Switch Mode Power Supply Module

Analog Input

CT/PT Module

Communication Interface

Phase-Locked Oscillator

Anti Aliasing Filter (LPF)

Fig. 1 Block diagram of a µPMU

A/D Converter

PDC

Phasors

CPU Module

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to analog to digital (A/D) converter to obtain digital samples. The A/D converter works in phase-locked oscillator with GPS pulses. The GPS signal is converted by phase-locked oscillator at 1 pulse per second into required timing pulses used in the waveform. The microprocessor unit computes phasors with discrete Fourier transform. Finally, the phasors are time-tagged and uploaded to phasor data concentrator (PDC) through communication interface [10]. The power needed by all the module is supplied with a switch mode power supply module.

3 Methodology of the Proposed Technıque 3.1 Test System Considered An IEEE 34 bus distribution test feeder connected with SG is designed in MATLAB/Simulink software for simulation of different islanding conditions as well as non-islanding conditions like capacitor switching, fault and so on. The solar plant is rated at 100 kW at an irradiance of 1000 W/m2 . Figure 2 shows the layout of the system. In this paper, the circuit breaker (CB) is opened to simulated islanding condition. The details about the SG can be found in [11].

3.2 Proposed Methodology The negative sequence angle of the voltage signal at SG bus obtained from the µPMU is further evaluated by SK. In this paper, the test system is simulated for 1 s in Simulink and various conditions (except normal condition) are initiated at 0.3 s. Just after the initiation of islanding, a total of 340 voltage samples obtained from the µPMU are considered for SK. A Hanning window is used for SKwith window size of 256 with 75% overlap. The SK for normal, island and fault conditions are represented in Figs. 3, 4 and 5. In Fig. 3, for normal condition, the SK pattern is almost a straight line (constant) since in a distribution system, the voltage signal is slightly unbalanced. SK value of −1 indicates stationary component. However, as seen in Fig. 4, during islanding, the obtain waveform contains lots of peakedness which indicates transients, i.e., the angle changes very rapidly. During fault condition, as shown in Fig. 5, there is also change in the peakedness of the graph as the system voltages and currents are highly unbalanced, due to unequal phase shift. However, these changes are relatively less as compared to that of island. Thus, the SK graph of the negative sequence angle is significantly different for normal, islanding and fault condition. The proposed method is represented with the help of flow chart in Fig. 6. If islanding like waveform is observed, suitable actions are taken or else the process is repeated.

Spectral Kurtosis-Based Island Detection Technique Fig. 2 Test system considered for the proposed technique

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Fig. 3 SK for normal condition

Fig. 4 SK for islanding condition

Fig. 5 SK for fault condition

4 Conclusion The main advantage of the proposed methodology over other methods for islanding detection is the non-requirement of the any other hardware unit or software unit because the method uses µPMU which is the primary component of a smart distribution system. Due to this advantage, implementation time and cost reduces. Hence, the method is simple and economical. The proposed technique was also tested for

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Fig. 6 Flowchart of the proposed methodology

other renewable sources such as wind power plant. It was found to give accurate results. Hence, the proposed technique is not limited to solar micro-grids. Hardware implementation of the proposed method is considered in the future work.

References 1. Kong X, Xu X, Yan Z, Chen S, Yang H, Han D (2018) Deep learning hybrid method for islanding detection in distributed generation. Appl Energy 210:776–785 2. Singam B, Hui LY (2006) Assessing SMS and PJD schemes of anti-islanding with varying quality factor. In: 2006 IEEE ınternational power and energy conference. IEEE, New York, pp 196–201 3. Redfern MA, Usta O, Fielding G (1993) Protection against loss of utility grid supply for a dispersed storage and generation unit. IEEE Trans Power Delivery 8(3):948–954 4. Dutta S, Sadhu PK, Reddy MJB, Mohanta DK (2018) Shifting of research trends in islanding detection method: a comprehensive survey. Protect Control Modern Power Syst 3(1):1 5. Trujillo CL, Velasco D, Figueres E, Garcerá G (2010) Analysis of active islanding detection methods for grid-connected microinverters for renewable energy processing. Appl Energy 87(11):3591–3605 6. Reigosa DD, Briz F, Charro CB, Guerrero JM (2017) Passive islanding detection using inverter nonlinear effects. IEEE Trans Power Electron 32(11):8434–8445 7. Raza S, Mokhlis H, Arof H, Laghari JA, Wang L (2015) Application of signal processing techniques for islanding detection of distributed generation in distribution network: a review. Energy Convers Manage 96:613–624

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8. Zamani R, Golshan MEH, Alhelou HH, Hatziargyriou N (2019) A novel hybrid islanding detection method using dynamic characteristics of synchronous generator and signal processing technique. Electric Power Syst Res 175:105911 9. de la Rosa JJG, Sierra-Fernández JM, Agüera-Pérez A, Palomares-Salas JC, Moreno-Muñoz A (2013) An application of the spectral kurtosis to characterize power quality events. Int J Electr Power Energy Syst 49:386–398 10. Dutta S, Sadhu PK, Reddy MJB, Mohanta DK (2020) Role of microphasor measurement unit for decision making based on enhanced situational awareness of a modern distribution system. In: Decision making applications in modern power systems. Academic Press, Cambridge, pp 181–199 11. Dutta S, Sadhu PK, Reddy MJB, Mohanta DK (2018) Smart inadvertent islanding detection employing p-type µPMU for an active distribution network. IET Gener Transm Distrib 12(20):4615–4625

Frequency Control of Wind Power Plant Assisted Hybrid Power System Akshay Anand, Akshay Kumar, and Gauri Shankar

Abstract The addition of wind power plants (WPPs) into the mainland system is rising continuously since its large availability and eco-friendly nature. However, WPP works at maximum power point, and hence, WPP may not help in frequency regulation. Therefore, in this paper, frequency control techniques are introduced on WPP for the improvement of primary frequency regulation. This may be realized by creating a reserve power margin using pitch angle control and overspeed operations. The reserve power margin depends on wind velocity and blade pitch angle. The frequency support from deloaded WPP (WPP operates at reduced power point instead of maximum power point) may control based on the droop value, which depends on the available reserve and frequency deviation. From the MATLAB simulation results analysis, the effect of droop action is investigated in terms of minimizing the frequency deviation and smoothening power fluctuations of WPP. Keywords Blade pitch angle control · Deloading · Droop control · Frequency control · Wind power plant

1 Introduction Due to the continuous increase in pollution levels, it has become a matter of discussion among the international bodies to find the alternative of non-renewable energy sources. Since energy productions by renewable sources have a very little environmental impact, so most of the country are paying more concern to increase electric power generations by renewable sources [1]. Due to the easy accessibility of A. Anand · A. Kumar (B) · G. Shankar Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India e-mail: [email protected] A. Anand e-mail: [email protected] G. Shankar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_71

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winds, it is dominated to be used over other renewable energy sources for generating electrical power. If wind energy is effectively captured, then the environmental problems caused by fossil fuels-based conventional plants may be minimized to a large extent. The above facts give the motivation for generating wind power which is better in terms of performance and efficiency [2]. The wind energy conversion system (WECS) extracts the mechanical power of wind and sends it to the permanent magnet synchronous generator (PMSG), which converts this mechanical power to electrical power. The popularity of PMSG is gaining more since the speed of PMSG may be varied according to wind speed and load demands. Also, PMSG has the highest efficiency among all machines since all movable magnetic sources are inside itself [3, 4]. Most of the wind power plants (WPPs) operate at the maximum power point (MPP) to generate maximum power from a particular wind speed which is effective in case of running cost. However, this type of WPP may not help in primary frequency support during disturbance between generations and load demands. Also, WPP offers low inertia to the power system when compared to similar sized conventional power plants. The flow of wind is a natural phenomenon, and it may not smooth throughout the day. Hence, the developed electrical power from WPP varies according to speed of wind. To make the participation in primary frequency regulation and smoothening power output fluctuations of WPPs, WPPs may have sufficient reserve margin, and this depends on deloading operation [5]. Deloding operation may be achieved by either speed control strategy or pitch angle control strategy [6]. Speed control strategy may be achieved by either underspeed control or overspeed control. In general, overspeed control strategy is implemented on WPP due to the stability point of view [7]. In the pitch angle control strategy, the value of blade pitch angle increases to enhance reserve power margin. The value of blade pitch angle may vary between 0◦ and 90◦ . Pitch angle control strategy may also use for controlling the speed of the wind turbine [8, 9]. The emphasis of the present work is to develop a mechanism to control the frequency support from deloaded WPPs (WPP operates at reduced power point instead of maximum power point) on the basis of the available power margin. This may be achieved using overspeed operation, and speed variation is controlled through pitch angle. The amount of power supply from WPP to the power system during system disturbances may be achieved by droop value [10]. The aim of the present paper is to develop a model of WPP with PMSG to control the frequency deviation and smoothening power output of WPP. Also, variable droop control strategy is implemented on WPP to provide frequency support according to frequency deviation. The theoretical results are fully verified by the MATLAB/SIMULINKR . The rest of the paper is summarized in the following way: The modeling of WPP components is discussed in Sect. 2; different control strategies on WPP are obtainable in Sect. 3; simulation outcomes and discussions are presented in Sect. 4. Lastly, conclusion is discussed in Sect. 5.

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2 WPP Modeling In WECS, the kinetic energy of wind is first transformed into mechanical energy by wind turbine. Then, the wind turbine fed this energy to the generator from where the electrical power is generated. The rotation of wind turbine is a subject of speed of wind speed and certain mechanical parameters like the angle at which the wind strikes the blade of wind turbine and direction of flow of wind [11]. Wind turbine is an important part of WECS, so wind turbine becomes important to study the different characteristics. From modeling point of view, WPP consists of the following components:

2.1 Wind Turbine Modeling The mechanical power output (Pm ) extracted from wind speed (Vw ) by a wind turbine which is a function of power coefficient (C p ). Pm may be calculated as Pm = 0.5ρ AVw3 C p

(1)

where ρ is the wind density, A is the area covered by the blade of wind turbine, and C p is the function of blade pitch angle (β), and tip speed ratio (λ). C p may be defined as   C p = 0.73 151

1 0.003 − 3 λ − 0.02β β +1



   − 0.58β − 0.002β 2.14 − 13.2 exp −18.4

1 0.003 − 3 λ − 0.02β β +1



(2)

λ may be calculated as λ = ω R/Vw

(3)

where ω is the rotor speed of turbine, and R is the radius of the blade. In most of the cases, λ remains constant under all operating conditions; however, its value may be varied according to the what percentage of power is extracted from the wind turbine. Figure 1 shows actual mechanical power extraction versus rotor speed at rated wind speed (i.e., Vw = 14 m/s). Figure 2 shows mechanical power output versus rotor speed at different wind speeds. According to Fig. 2, initially, the generated power increases for increasing the value of rotor speed. As the generated power goes to the MPP, it starts decaying from the optimum value for a fixed value of wind speed. Also, it is clear from the figure that as the wind speed increases the value of generated power gets increase. In other words, power observed by the wind turbine for any wind speed is maximum at a certain value of rotor speed.

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Fig. 1 Mechanical power output versus turbine speed at rated wind speed

Fig. 2 Mechanical power output versus rotor speed at different wind speeds

2.2 Pitch Angle Control Modeling Pitch angle control may be used for deloading operation and controlling the rotor speed of turbine. Pitch angle may be controlled through proportional integral derivative (PID) controller. The input of pitch controller is the error between actual rotor speed and reference speed (ωref ). The value of ωref may be calculated as ωref = Pref /Tm

(4)

where Pref is the power reference, and Tm is the measured mechanical torque. In Fig. 3, the power coefficient versus tip speed ratio curve at rated wind speed is presented. It may be shown that as the value of beta increases, the value of power coefficient decreases. The wind turbine has the maximum value of power at zero pitch angle (i.e., Beta = 0). The wind turbine produces power at each and every point of the curve and that point is known as the operating point. There is also a point on the curve for each value of pitch angle for which power is maximum. This operating point is known as MPP. At this point, turbine gives the maximum amount of power with maximum efficiency. It is understood from Fig. 3 that wind turbine gives maximum power at a point for a specified tip speed ratio and pitch angle values. Hence, power

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Fig. 3 Power coefficient versus tip speed ratio at different values of pitch angle

coefficient will have a single maximum value for wind speed. To create a reserve power margin, in the present paper, value of beta is fixed at 4◦ .

3 Different Control Strategies on WPP 3.1 Overspeed Operation WPP may be operated at a deloaded power point with a decreased power level despite MPP. Reserve power available is only due to deloading operation that may be further utilized to maintain the power system stability for frequency regulation during active power mismatch between generations and load demands. Deloading operation may be achieved by overspeed operation. Deloded power point (Pdel ) may be calculated as Pdel = Pmax (1 − x)

(5)

where Pmax is the MPP, and x is the deloading value. In some scenarios, Pdel is taken as Pref . In deloading operation, speed of wind turbine reaches beyond the acceptable range. To avoid this, pitch control scheme activates and the value of pitch angle increases.

3.2 Droop Control Droop control may be implemented on WPP as a primary control to improve the short-term frequency dynamics. In this work, deloading parameter is fixed, and the droop parameter of WPP is dynamically adjusted. The droop control gives better power system performances in terms of frequencies excursion and smoothing the power output from WPP. Mathematically, droop (R) may be written as

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Fig. 4 Dynamic model of WPP

R=

f × 100 P

(6)

where  f is the frequency dip, and P is the change in power output. Figure 4 shows the dynamic model of WPP.

4 Simulation Results and Discussion To study the effect of WPPs into the power system, a single-area system model comprising of four WPPs and one diesel power plant (DPP) is considered (see Fig. 5). The different parameters of WPP, DPP, and power system are displayed in Table 1. The simulation outcomes are conducted in MATLAB/SIMULINKR . The different cases are taken as follows: Different penetration levels of WPP into the power system without primary frequency control. Effects of WPP under with and without frequency control.

Fig. 5 Dynamic model of the studied system

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Table 1 The different parameters of WPP, DPP, and system are as follows Model

Parameters

WPP

Capacity = 1.5 MW, rated rotor speed (ω) = 13 rpm, rated wind speed (Vw ) = 14 m/s, λ = 4.94, swept area (A) = 5346 m2 , rotor blades = 3, blade length = 38 m, gearbox ratio = 1:1, Mw = 5 s, T p = 0.1 s, pitch angle limits: 0◦ to 90◦ , k p1 = 0.1, ki1 = 0.2, kd1 = 0.3

DPP

Capacity = 5 MVA, TD1 = 0.1 s, TD2 = 0.25 s, R1 = 0.06, k p2 = 2, ki2 = 7, kd2 = 5

System M = 10 s, D = 0.6, f = 50 Hz

Frequency support by WPP under different droop values (i.e., R = 4% and R = 6%). Case (a): Initially, the value of M is taken as 10, and after the penetration of WPP, the values of M changes and these are: 8 (20% penetration level), 6 (40% penetration level), and 4 (60% penetration level). A load of 1% disturbance is applied at t = 2 s. When penetration level of WPP into the system (WPP without frequency support) increases, then frequency deviation increases (see Fig. 6). Hence, it may activate under frequency relay, and the system may become unstable. Therefore, penetration of WPP without frequency support into the system is not good for the stable system. Case (b): To study the effect of frequency regulation, the simulation results are conducted with two strategies and these are: (a) without primary control from WPP and (b) with primary control from WPP. In these strategies, 1% of step load disturbance is applied at t = 2 s, and WPP is operating at a wind speed of 14 m/s having deloading value of 10% with blade pitch angle of 4°. The value of M, D, and R are taken as 10, 0.6, and 0.04, respectively. From Fig. 7, it is clearly found that without primary control, the frequency deviation is high, as compared to with primary control. Figures 8, 9, and 10 show that without primary control, power output from WPP is zero, and there are no variations in rotor speed and blade pitch angle. However, in case of with primary frequency control, power output from WPP (see Fig. 7) increases when load increases to support frequency regulation by changing rotor speed and pitch angle (see Figs. 8, 9, and Fig. 6 Frequency deviation versus time

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Fig. 7 Frequency deviation versus time

Fig. 8 Wind power output versus time

10). Pitch angle supports rotor speed so that it does not go beyond the permissible range. Case (c): In this case, WPP is operating at a wind speed of 14 m/s having deloading value of 10% with blade pitch angle of 4°. Under a step increase in load demand, the dynamic performance analysis of load frequency control is carried out for the following different conditions: (a) system simulation takes place with droop control value of 4%, and (b) system simulation takes place with droop control value of 6%. In these cases, 1% of step load disturbance is applied at t = 2 s. The response profiles of frequency deviation, power output deviation from WPP, rotor speed deviation from WPP, and power output deviation from DPP using different cases are shown in Figs. 11, 12, 13, and 14. From the simulation results, it is found that if the value of droop control is low, then the frequency deviation is minimum

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Fig. 9 Rotor speed versus time

Fig. 10 Pitch angle versus time

(see Fig. 11). At R = 4%, the maximum frequency deviation is -2.83 × 10–4 pu at t = 5.63 s. As further increasing the R value to 6%, the maximum change of frequency profile is −3.436 × 10–4 pu at t = 5.65 s, and its settling time is large. In Fig. 12, power output variation of WPP is shown. WPP supports active power during transient state, and it becomes zero during steady state. The extreme value of power injection is 5.15 × 10–3 pu when R is 6%, and the extreme value of power injection is 6.21 × 10–3 pu when R is 4%. In Fig. 13, when power increases then speed of wind turbine decreases. The minimum value of speed is -6.04 × 10–5 pu at t = 7.14 s when R is 6%. The minimum value of speed is -4.8 × 10–5 pu at t = 7.24 s when R is 4%. DPP increases power output to compensate load demand rapidly, and it achieves peak deviation output to 0.01141 pu in case of 4% regulation and 0.01106 pu in case of

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Fig. 11 Frequency deviation versus time

Fig. 12 Wind power output versus time

6% regulation (see Fig. 14). It is found that with primary control of WPP, WPP may help to decrease the burden on DPP and improves the system stability.

5 Conclusion In this present work, PMSG-based WPP is chosen as a renewable energy source to generate active power during system disturbances. Deloading operation is performed

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Fig. 13 Rotor speed versus time

Fig. 14 Diesel power output versus time

on WPP and speed of rotor are controlled by pitch control. In this work, WPP is deloaded by 10% to generate reserve margin for supporting frequency regulation, and pitch angle is set at 4°. To analysis, the primary frequency contribution from WPP, droop control technique is introduced. For optimum power output, the droop parameter is regulated, and it depends on the availability of the reserve margin. In case of low value of droop, WPP supplies large power to the system; however, speed of rotor decreases drastically. Therefore, pitch angle control limits the speed of rotor. From simulation results, it is found that if load demand increases, then power output from WPP increases quickly to compensate generation and load demand. It is also seen that frequency does not deviate very much.

References 1. Goldemberg J (2012) The case for renewable energies. Renew Energy 10:31–42

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2. Xu M, Zhuan X (2013) Optimal planning for wind power capacity in an electric power system. Renew Energy 53:280–286 3. Vidyanandan KV, Nilanjan S (2013) Primary frequency regulation by deloaded wind turbines using variable droop. IEEE Trans Power Syst 28:837–846 4. Kumar A, Shankar G (2018) Optimal load frequency control in deloaded tidal power generation plant based interconnected hybrid power system. IET Renew Power Gener 12:1864–1875 5. Francisco DG, Hau M, Sumper A, Bellmunt OG (2014) Participation of wind power plants in system frequency control: Review of grid code requirements and control methods. Renew Sustain Energy Rev 34:551–564 6. Abouzeid SI, Guo Y, Zhang HC, Ma X (2018) Improvements in primary frequency regulation of the grid-connected variable speed wind turbine. IET Renew Power Gener 13:491–499 7. Rakhshani E, Gusain D, Sewdien V, Torres JL, Vander MA (2019) A key performance indicator assess the frequency stability of wind generation dominated power system. IEEE Access 7:130957–130969 8. Zeng X, Liu T, Wang S, Dong Y, Chen Z (2019) Comprehensive coordinated control strategy of PMSG-based wind turbine for providing frequency regulation services. IEEE Access 7:63944– 63953 9. Tang X, Yin M, Shen C, Xu Y, Dong ZY, Zou Y (2018) Active power control of wind turbine generators via coordinated rotor speed and pitch angle regulation. IEEE Trans Sustain Energy 10:822–832 10. Vorobev P, Greenwood D, Bell JH, Bialek JW, Taylor PC, Turitsyn K (2019) Deadbands, Droop and Inertia Impact on Power System Frequency Distribution. IEEE Trans Power Syst 1:1–11 11. Kumar A, Shankar G (2017) Quasi-oppositional harmony search algorithm based optimal dynamic load frequency control of a hybrid tidal-diesel power generation system. IET Gener Transm Distrib 12:1099–1108

Adapting the Control Strategies of Microgrid to Propel the Future Research Towards Demand Side Management: A Review Sumit Kumar Jha and Deepak Kumar

Abstract The electrical demand of the world is shifting from conventional gridbased electricity to non-conventional-based renewable resources technologies. Distributed generation (DG) plays the vital role in accommodating various kinds of renewable sources of energy with the state of the art technology termed as microgrid. The consolidation of different types of energy sources becomes possible with the technology of voltage source converters. An efficacious interfacing of renewable resources is established by VSI with sophisticated control mechanism. This paper is motivated to review different types of control strategies to effectively improve the stability, reliability, power quality and power sharing mechanism of microgrid network. Moreover, the advantages and disadvantages of inner loop and primary control are discussed and compared to lead the future research towards robust development of control topologies for the microgrid. This review paper discusses the demand side management by adopting the method of voltage reduction, and finally, it is intended to direct the research towards power saving phenomena to accommodate greater number of loads in near future. Keywords Microgrid · Demand side management · Conservative voltage reduction · Distributed generation · Voltage source converters

1 Introduction The conventional grid network requires paradigm shift from old burdensome structure to smart technologies. The new infrastructure needs huge costs; hence, microgrid becomes the potential technology to integrate and simultaneously control various renewable resources. Microgrid comprises certain advantages such as it does not require long-distance transmission lines as compare to grid network. The power is S. K. Jha (B) · D. Kumar Department of EEE, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected]; [email protected] D. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_72

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generated onsite with the help of renewable sources of energy that includes photovoltaic, fuel cells, wind power, and it is sent to distribution lines. The current rate of degradation of fossil fuels tends to bring the power system to incorporate alternative renewable resources of energy such as photovoltaic, wind energy sources, micro turbines [1–3]. Distributed generation consider integration of small-scale technologies to generate electricity in close proximity to the consumer. The DG technologies employ renewable-based sources, and it becomes the potential technology to provide higher power reliability, security and lower cost electrical energy with lesser carbon emission as compared to traditional grid-based utility. In earlier years, the distribution system is designed to supply power in radial fashion, and current is subjected to flow from substation source to the load. The Public Utility Regulatory Policies Act of 1978 introduces distributed generation in the distribution system as it has begun to appear frequently. The distributed photovoltaic (PV) system became more common, and it is seen as an alternative to conventional circuit upgrade as it has improved the economic viability, incentives and public utility commissioning. The high incorporation of DG into the microgrid network causes unpredictable and highly unreliable output. The impact of increasing the penetration of PV requires to be investigated using time varying method that includes behaviour of fast acting voltage source converters, changing loads and interlinking voltage controlled devices on the feeder. The high penetration of PV causes baseline impact in which network issues are common due to lesser integration of renewable resources [4]. The effect of high penetration of PV causes problems in subtransmission as well as transmission networks. The key challenges faced by the increase penetration of the DG in the distribution network are eradicated by the emerging technology termed as microgrid [5–7]. The concept of microgrid is a potential solution for coordinating different kinds of renewable resources with sophisticated control mechanism [8, 9]. The recognition of the microgrid is established by the consortium for electric reliability technology solutions (CERTS) to maintain the growing demand of electrical power. The carbon emission is greatly slashed by introducing the microgrid in the distribution network. The characteristics of microgrid allow the user to supervise the voltage, power and frequency by using monitors, and it also carry out the grid connected as well as islanded mode of operation in different situation. The low voltage (LV) network that incorporates number of DG units with specific hierarchical control approach, it supports reliable and uninterrupted power supply at the time of grid failure, it has the capability to establish desired voltage and frequency using precise control mechanism, and it efficiently handles the fault situation as it carries standard protection schemes [10–12]. The inclusion of these features of microgrid is defined this state of the art technology as congregation of interconnected load demands and distributed energy resources (DERs) that carry out control mechanism with respect to grid network [13].

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2 Microgrid in Grid Connected Mode A power system is depicted in Fig. 1. The grid connected mode of microgrid characterized by control of voltage and frequency with the support of utility grid [14, 15]. The incorporation of VSI in grid connected operation of microgrid is to supply controlled power with regulated voltage. The power generation is regulated by inphase current component, and it directly affects the power demand of the network. A current reference is generated by power setpoints for the current controller. The controller operates in closed loop guarantees that reference value with zero steady state error is tracked by output current. Figure 2 depicts the control diagram of microgrid in grid forming mode in conjunction with voltage source inverter. The current controller is in closed loop fashion, and it facilitates power delivery at the output side by estimating the error signal measured and setpoint signals. The determination of desired current is computed by

Fig. 1 Representation of power system

Fig. 2 Depicting the strategy of current control in grid forming mode of microgrid

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the controller parameters. The supervisory controller sets the power demand and each voltage source inverter control its output current. The power quality of the inverter is greatly influenced by harmonics of the network. However, the proper choosing of filter parameters and controller improves the quality of the power to a great extent [16]. The inner loop mechanism carrying the features of precise current regulation, desired bandwidth, about 5–6 times faster dynamic response with respect to outer voltage control mechanism, minimum distortion in current and damping in filter resonance. These characteristics are reported in many literatures with the adoption of different control strategies with various types of microgrid models [17, 18].

3 Stand-Alone Mode of Microgrid The lack of grid utility connection with the microgrid network demands the estimation of reference voltage and frequency which is provided by the inverters [19, 20]. Figure 3 illustrates the function of VSI without including the current control loop; however, some literatures reported the shortcomings of the following control model. The lack of current control causes huge transient current that can burn power semiconductor switches during fault [21]. The voltage measurement at the capacitor side does not provide actual information about the parameters of the network. The control mechanism of voltage source inverter has several drawbacks; hence, cascaded control strategy comes into picture that includes both current and voltage loop mechanism shown in Fig. 3. The inner loop control strategy shows faster response time forming round the inductor as compared to outer voltage control which form round the capacitor, and it enables faster current control response. The merit of this control strategy is that excessive current is curbed by the outer voltage control loop.

Fig. 3 A cascaded control mechanism for regulating grid voltage

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4 Control Hierarchical of Distributed Generation Unit Reliable control strategies are required to operate the microgrid in a stable manner [22]. The economic aspect also plays the vital role in the sustainability of microgrid. The key control parameters need to be regulated by the microgrid are voltage and frequency regulation, effective power sharing, optimization in cost parameter and prediction of generation and demand of power. To fulfil the following control criteria, a hierarchical control mechanism is required for the microgrid [23].

5 Control Strategy Adopted for Inner Loop Control Numerous literatures have been reported about the control strategies to enhance the quality of power, to reject the disturbance of the network and to efficiently track the parameters of inverter output such as voltage an current. The control mechanism for the microgrid is developed by considering the characteristics of microgrid. The control methods are depicted in Fig. 4. The use of PI controller is greatly enhanced by using cross-coupling and/or feed-forward voltage term. The incorporation of feed-forward voltage enhances the controller dynamics at the time of voltage fluctuation. The merits of proportionalintegrator controller lies in two axis dq reference frame as it carries the features of minimal steady state error. Hence, it is very efficacious in establishing correct behaviour of real and reactive power by directly regulating the current component of power [24–26]. The PR controller easily eliminates steady state error of electrical

Fig. 4 Inner loop control strategies

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parameters as it considers high value of gain near resonant frequency. The merit of PR controller is that it can be applied both in abc and ab reference frame [27, 28]. The controller such as deadbeat (DB) controller efficiently controls the inverter current. The dynamic performance of predictive controller efficaciously tracks the instantaneous current, and it also includes the feature of high bandwidth. The error compensation of DB controller is well known, and only difficulty lies is the high sensitivity of the network parameters. The deadbeat control focuses on to minimize the forecast error for correct tracking of current. The control action of the present state is being utilized to anticipate the future action of the controller parameters. The controller adopts optimum number of switching states according to the criteria of cost function. The mathematical-based model predictive control is very sensitive to parameter variation. The control strategy incorporating hysteresis control structure being very lucid and produces quick response to generate the pulses for the voltage source inverter. The error signal generated after performing the subtraction operation on reference and measured signal is compared, and if it exceeds a certain limit, then this controller yields signal. The hysteresis controller is characterized by easy implementation, fast and high dynamic response and simple in architecture. The feature of H ∞ controller includes robust performance in change in the value of parameters as well as worst case disturbances. The H ∞ controller primarily reduces the disturbances at the output side, and in this method, optimization process is utilized to formulate the problem, and then, controller is employed to solve the issue. The repetitive controller (RC) is a simple learning algorithm that eliminates error in the dynamic state by adopting the principle of internal model. The attenuation resonant peaks carrying high frequency gains of the controller are carried out by low-pass filter by using repetitive control mechanism. Therefore, RC exhibits the performance of low harmonic distortion in the parameters of voltage and current even though the model contains large non-linearity in the loads. The imitation of the operation of biological nerve is carried out by neural network (NN) with the incorporation of time delay, and it also facilitates the processing of information in a systematic way. The input layer, hidden layers and output layers are the general architecture of NN, and all the layers are interconnected with each other and carry out the operation in parallel fashion to transmit the signal from one layer to another for accomplishing a particular processing task. The NN algorithm incorporates the characteristics of self learning, and it also facilitates easy implementation for various types of operating condition and disturbances in grid network. Fuzzy logic controller deals with linguistic as well as logic values, and 1 is considered to be ’true’, and 0 exhibits ’false’ scenario. The fuzzy architecture incorporates the concept of membership that includes fuzzy set which is utilized in the theory of fuzzy network. Fuzzy logic controller focuses on reduction of overshoot and improvement of tracking performance. The robust performance is exhibited by the sliding mode controller (SMC) in case of deviation in the parametric value of the concerned system. The controller response is strong enough to tackle the deviation of the plant from normal operating condition. SMC experiences the problems of chattering. The advantage of SMC is easy implementation, rejection of disturbances and lesser sensitivity to the value of the parameters. The transient and steady behaviour condition are effectively carried

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out by the algorithm of linear quadratic regulator (LQR). The LQR method shown inherent stability and order of the system does not affect the method. The tracking performance at the time of variation of load is one of the demerit of the algorithm. The cost function of the system gets reduced with the adoption of linear quadratic integrator (LQI) to achieve dynamic response faster. The LQI method negates the effect of error in the steady state which arises from the difference between voltage of the grid and parameters of the grid taken as reference during changes in load condition [19]. The gain of the integral is generated from outside disturbances, and it is used to minimize the error in the process of tracking the reference voltage. The method shows zero error in the steady state with the proper choice of optimal gain. The control strategy constituting inner loop is dependent on the characteristics of microgrid network. The design of robust controllers is essential in case of regulating sensitive parameters as well as having higher uncertainty in the model. The analysis of different kind of controllers has been carried out, and it is observed that individual controller does not solve the problem, and hence, further investigation is required to develop effective controller for microgrid application.

6 Adopting Inner Loop Mechanism to Execute the Process of Demand Side Management (DSM) The inner and outer loop controller are used recently in the literature to save appreciable amount of power [29–32]. The utilization of cascaded control mechanism that incorporates inner loop control is employed to perform the algorithm of demand side management shown in Fig. 5. The droop mechanism constituting V-I droop efficiently maintains the voltage, and it provides the potential to achieve reduction in power consumption of the autonomous microgrid. However, different kinds of droop strategy in the literature are interlinked with other electrical variables which show sudden change by varying coupling parameters. The droop strategies such as P–V droop mechanism show coupling of active power and voltage. The change in

Fig. 5 Adopting inner loop control strategies to perform demand side management

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the value of voltage shows sudden change in consumption of active power in low voltage (LV) network. The line parameters are emulated by taking droop coefficient in the microgrid. The current from the VSI is multiplied with the droop coefficient, and again it is added with nominal voltage to give escalated voltage. The concept is used to customize the voltage by 10%, and it is within the limit of the IEEE 1547.2 standards which permit the safe operation of DG in the microgrid within the value of 88% - 110% of the nominal voltage.

7 Conclusion and Future Scope This paper exhibits the technical analysis of inner loop control strategies for distributed generated units for a stand-alone microgrid. The paper is motivated to provide the thorough and detailed review of various types of control strategies which are very crucial for designing the smart microgrid. The classical controllers are mainly used by the industry, and it is depicted in detail in the literature survey. The important aspect derived from this paper is the agglomeration of different types of controllers with the application of these controller to create the power reserve in the future research of the stand-alone microgrid. It is realized by analyzing different literatures that future microgrid has the potential to contribute to the area of power saving algorithm. The voltage–current droop mechanism shows the superior performance towards demand side management utilizing the concept of voltage reduction method. The power quality, power sharing, flexibility and reliability are efficaciously regulated by droop-based control. Moreover, this paper is motivated to propel the future research towards adopting the droop-based control to establish the power reserve in smart microgrid.

References 1. Jin M, Feng W, Liu P, Marnay C, Spanos C (2017) MOD-DR: microgrid optimal dispatch with demand response. Appl Energy 187:758–776 2. Jin M, Feng W, Marnay C, Spanos C (2017) Microgrid to enable optimal distributed energy retail and end-user demand response. Appl Energy 3. Hossain MA, Ahmed MR (2013) Present energy scenario and potentiality of wind energy in Bangladesh. World Acad Sci Eng Technol 7:1001–1005 4. Bilakanti N, Lambert F, Divan D,Integration of distributed energy resources and microgrids utility challenges 5. Lasseter RH (2002) Microgrids. In: Proceeding 2002; IEEE: Piscataway, NJ, USA, 2002; vol 1, pp 305–308 6. Hossain MA, Pota HR, Haruni AMO, Hossain MJ (2017) DC-link voltage regulation of inverters to enhance microgrid stability during network contingencies. Electr Power Syst Res 147:233–244 7. Bhaskara SN, Chowdhury BH (2012) Microgrids—a review of modeling, control, protection, simulation and future potential. In: Proceedings of the 2012 IEEE power and energy society

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general meeting, San Diego, CA, USA, 22–26 July 2012; IEEE: Piscataway, NJ, USA, 2012; pp 1–7 Saleh M, Esa Y, Mhandi Y, Brandauer W, Mohamed A (October 2016) Design and implementation of CCNY DC microgrid testbed. In: 2016 IEEE industry applications society annual meeting, pp 1–7 Saleh MS, Althaibani A, Esa Y, Mhandi Y, Mohamed AA (October 2015). Impact of clustering microgrids on their stability and resilience during blackouts. In: 2015 international conference on smart grid and clean energy technologies (ICSGCE), pp 195–200 Katiraei F, Iravani R, Hatziargyriou N, Dimeas A (2008) Microgrids management. IEEE Power Energy Magn 6:54–65 Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids. IEEE Power Energy Magn 5:78–94 Nejabatkhah F, Li YW (2015) Overview of power management strategies of hybrid AC/DC microgrid. IEEE Trans Power Electron 30:7072–7089 DOE Microgrid Workshop Report (PDF) Green T, Prodanovi M (2007) Control of inverter-based micro-grids. Electr Power Syst Res 77:1204–1213 Prodanovi M, Green T (2002) Power quality improvement in grid connection of three-phase inverters. In: Proceedings of the international conference on power electronics, machines and drives, 2002 (Conf. Publ. No. 487), Sante Fe, NM, USA, 4–7 June 2002; IET: London, UK, 2002; pp 24–29 Abeyasekera T, Johnson CM, Atkinson DJ, Armstrong M (2005) Suppression of line voltage related distortion in current controlled grid connected inverters. IEEE Trans Power Electron 20:1393–1401 Sofla MA, Wang L, King R (2012) Modeling and control of DC-AC power converters of distributed energy resources in microgrids. In: Modeling and control of sustainable power systems. Springer, Berlin/Heidelberg, pp 341–366 Issa W, Abusara A, Sharkh S (2014) Impedance interaction between islanded parallel voltage source inverters and the distribution network. In: Proceedings of the 7th IET international conference on power electronics, machines and drives (PEMD 2014), Manchester, UK, 8–10 April (2014) Hossain MA, Azim MI, Mahmud MA, Pota HR (2015) Primary voltage control of a singlephase inverter using linear quadratic regulator with integrator. In: Proceedings of the 2015 Australasian universities power engineering conference (AUPEC), Wollongong, Australia, 27– 30 September (2015) Hossain MA, Pota HR (2015) Voltage tracking of a single-phase inverter in an islanded microgrid. Int J Renew Energy Res 5:806–814 Mohan N, Undeland TM (2007) Power electronics: converters, applications, and design. Wiley, Hoboken, NJ Piagi P, Lasseter RH (2001) Industrial application of microgrids. Power System Engineering Research Center, University of Wisconsin-Madison, Madison, WI Guerrero JM, Vasquez JC, Matas J, Vicuna D, García L (2011) Castilla, M: Hierarchical control of droop-controlled AC and DC microgrids—A general approach toward standardization. IEEE Trans Ind Electron 58:158–172 Mehrizi-Sani A, Iravani R (2010) Potential-function based control of a microgrid in islanded and grid-connected modes. IEEE Trans Power Syst 25:1883–1891 Karimi H, Nikkhajoei H, Iravani R (2008) Control of an electronically coupled distributed resource unit subsequent to an islanding event. IEEE Trans Power Deliv 23:493–501 Issa WR, Abusara MA, Sharkh SM (2015) Control of transient power during unintentional islanding of microgrids. IEEE Trans Power Electron 30:4573–4584 Lu X, Guerrero JM, Sun K, Vasquez JC, Teodorescu R, Huang L (2014) Hierarchical control of parallel AC-DC converter interfaces for hybrid microgrids. IEEE Trans Smart Grid 5:683–692 Guerrero JM, Chandorkar M, Lee TL, Loh PC (2013) Advanced control architectures for intelligent microgrids. Part I: decentralized and hierarchical control. IEEE Trans Ind Electron 60:1254–1262

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29. Jha SK, Kumar D (2020) Assessment of battery energy storage system with hybrid renewable energy sources to voltage control of islanded microgrid considering demand-side management capability. Iran J Sci Technol Trans Electr Eng 44:861–877. https://doi.org/10.1007/s40998019-00273-9 30. Jha SK, Kumar D (2018) A power saving approach to self-sustained microgrid. In: 2018 international conference on power energy, environment and intelligent control (PEEIC), Greater Noida, India, 2018, pp 430–436 31. Jha S, Kumar D, Kamwa I (2018) Smart demand response management of islanded microgrid using voltage-current droop mechanism. Int J Emerg Electr Power Syst 19(1):20170238. https:// doi.org/10.1515/ijeeps-2017-0238 32. Jha SK, Kumar D (2019) Demand side management for stand-alone microgrid using coordinated control of battery energy storage system and hybrid renewable energy sources. Electr Power Compon Syst 47:14–15, 1261–1273. https://doi.org/10.1080/15325008.2019.1661544

A New Approach Based on Indicators for Demand Side Management in Multiobjective Framework Mayank Singh and Rakesh Chandra Jha

Abstract This paper proposes a new approach based on indicators for demand side management (DSM) in a multiobjective environment. The proposed approach determines the best possible solution based on the achievement of multiobjective DSM (MODSM) in a power network. The use of this framework is showed through simulation performed in a grid tide microgrid context. The acquired microgrid made up of three units of diesel generators and two renewable energy sources. Due to its simplicity and effectiveness, non-dominated sorting genetic algorithm II (NSGA-II) has been used as optimization tool. The simulation has been performed for mean wind and mean solar power obtainability. The proposed framework helps in decision making to the operator to choose a best solution among multiple available solutions. Keywords Optimization · DSM · Microgrid · Solar · Wind · NSGA-II

1 Introduction Nowadays, the power industry is experiencing various challenges due to its rapidly changing composition. The unification of compact size sustainable energy sources with the typical grid has become a crucial task. In inclusion to this, the wavering of power obtainability from viable energy sources is also a major point. The quest for a worthy scheme which yield a solution for scientific and mercenary challenges related to the new power structure is the primary region of research for system operators. DSM has emerged as a potential approach for several of these issues. The roots of DSMconcept lie in the increased cost and capacity shortages. The oil crisis in the year 1973 after the price hike by the organization of the petroleum exporting countries (OPEC) have marked two distinct eras in the global power sector [1]. M. Singh (B) · R. C. Jha Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India e-mail: [email protected] R. C. Jha e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_73

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Many different approaches are used in DSM [2–9]. Like load shifting is also an possible procedure of executing DSM [10]. In this approach, the remedial load shifting initiative is found to be a preferable choice to typical preventive load shifting methods. In some other work, loads are grouped in rigid and flexible types and DSM is carry out considering the adaptability of the flexible loads [7–9]. Application of DSM in grid tied microgrid environment involves solving multiple objectives. Involvement of multiple objectives of different nature together with variety of system constraints and financial constraints make the DSM problem in grid integrated microgrid more complex. Thus, the nature of a DSM complications can be categorizing as a multiobjective optimization problem (MOOP). To solve a MOOP, there is a need of suitable multiobjective optimization technique. Non-dominated sorting genetic algorithm II (NSGA-II) is a multiobjective genetic algorithm, proposed by Deb et al., in 2002 [11]. It is an augmentation of NSGA, which is offered earlier by Srinivas and Deb, in 1995. NSGA-II uses elitism to create a diverse Pareto-optimal front. The main features of NSGA-II are less numeric complexity, parameter less diversity preservation, elitism, and real-valued representation.

2 Mathematical Model of MODSM Problem The MODSM issue in the current work involves two principle aims. The first one is fuel cost minimization and the second one is utility benefit maximization together with minimization of transferable energy with the grid. These two objective functions are modeled as Eqs. (1) and (2). The numeric solution tool used in this paper, NSGAII. So, to implement NSGA-II, both functions should be handled independently despite having different nature. Detailed descriptions related to these two equations are can be found in [12]. In the present work, constraint of the objective function is presented by Eqs. (3)– (12).  Min G(X ) → Min

T  L  t

Min H (X ) → Min





Cl Pl,t +

 Cr (Prt )

(1)

1

l T (=24)  n t=1

T 



yi,t − λi,t x j,t



(2)

i=1

where xi,t , yi,t , Pw,t , Ps,t , Pr,t , Pl,t are decision variables. In a optimization problem, there are various techniques to deal with the constraint of a function. In this work, we outline the constraints of main function by implementing an exterior penalty approach. In this approach, variables which are infeasible are penalized while feasible one remains untouched [13].

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2.1 Constraints Related to Objective Function Modeled for NSGA-II Optimization The constraint of function, Eq. (1), subjected to optimize using NSGA-II is given below.  n    L   (Pl,t ) + {Pst + Pwt + Prt } + (x j,t ) − Dt = 0 (3) l=1

T  

i=1

yi,t −

Pl,min ≤ Pl,t ≤ Pl,max

(4)

0 ≤ Pst ≤ St

(5)

0 ≤ Pwt ≤ Wt

(6)

−Prmax ≤ Prt ≤ Prmax

(7)

−D Rl ≤ Pl,t+1 − Pl,t ≤ U Rl

(8)

  

2 + K 2,i xi,t − (K 2,i xi,t θi ) ≥ 0 K 1,i xi,t

(9)



t=1

here i is ranging as: 1 ≤ i ≤ n T  

yi,t −



  

2 + K 2,i xi,t − (K 2,t xi,t θi ) K 1,i xi,t

t=1

≤

T  

y(i−1),t −



  

2 + K 2,(i−1) xi,t − (K 2,(i−1) x(i−1),t θ(i−1) ) K 1,(i−1) xi,t (10)

t=1

≤

T  

y(i−1),t −





   2 K 1,(i−1) xi,t + K 2,(i−1) xi,t − (K 2,(i−1) x(i−1),t θ(i−1) )

t=1

i is ranging as: 2 ≤ i ≤ n T  n  t=1 i=1

yi,t ≤ T B

(11)

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M. Singh and R. C. Jha 24 

xi,t ≤ C Pi

(12)

t=1

3 MODSM Solution Using the NSGA-II Algorithm The below mention steps are used for the application of NSGA-II. Step 1: Input of the design variables for the DSM MOOP.

 X = Pl,t Pr,t Pw,t Ps,t xi,t yi,t Step 2: Selection of the parameters like number of populations, the maximum number of iterations, crossover, and mutation probabilities. Step 3: Generation of the starting population. Step 4: Assessment of objective 1 and objective 2 for the starting population. Step 5: Set the iteration count. Step 6: Perform the polynomial mutation for the set of individuals. Step 7: Perform non-dominated sorting. (i.e., sorting the population according to each of the objective function value in ascending order of magnitude). Step 8: Calculation of the crowding distance between the solutions. Step 9: Carry out the selection based on tournament selection. This allotted a superior fitness to individuals located on a sparsely populated part of the front. Step 10: Increase in the iteration count and rerun the steps from 6 to 9 until the count come to the specified highest number of iterations. After solving the microgrid MODSM formulation ‘Z’ using NSGA-II, a Pareto front is obtained, and the next problem is to choose the suitable solution among all the available non-dominated Pareto front solution from Pareto front. In the next section, a framework to solve this problem is proposed.

4 Proposed Indicator-Based Framework to Determine MODSM Solution in Multiobjective Framework Figure 1 represents the developed framework work based on OOUI indices adopted from the paper [14]. Firstly, a microgrid MODSM formulation ‘Z’ is prepared. Secondly, NSGA-II is used to work out ‘Z ’, and the optimized outcomes are acquired in the form of a Pareto front. Out of, the feasible solutions present in Pareto front, solution is selected using reference point approach. The selection of suitable OOUI depends on operator interest.

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733 Indices based framework For MODSM solution

Adopted MO algorithm (NSGA -II Algorithm ) Objective 1: Minimization of fuel cost function : G(X)

multiobjective formulation of Objective 1 & Objective 2 Z=[ G (X) H (X)]

Objective 2: Maximization of utility benefit function : H (X)

Proposed framework

Solutions (Pareto front )

Opertor

Determination of reference solution

Calculation of distance of each solution from reference solution

Comparison of preferred OOUI Indicator

Determination of solution with minimum distance from reference solution

Determination of final solution

Providing support in DSM policy related issues

Fig. 1 Indicator-based multiobjective framework for MODSM

4.1 Compromised Solution Using Reference Point Approach This is a two-step process; in first, reference solution is to be determined. The reference solution is a point on XY plane, which is comprised of the personal best of two objective function values [15]. In second step, the compromised solution with the help of reference solution is determined using the previous step solution. A) Determination of reference solution: X and Y-coordinate of reference solution is determined with the help of a feasible solution present in Pareto front. Out of all the feasible solution present in Pareto front, the solution which is best in objective represented on Y-axis determines the X-coordinate of reference solution. Similarly, out of the feasible solution present in Pareto front, the solution which is best in objective represented on X-axis determines the Y-coordinate of reference solution. Table 1 and Fig. 2 show the determination of reference solution and in a hypothetical set of Pareto-optimal front for two-objective minimization problem. (B) Determination of compromised solution: After determination of the reference solution, distance of each feasible solution of Pareto front from reference solution point is calculated, as shown in Fig. 2. The Pareto front solution, which holds Table 1 Reference solution determination Description

X-axis

Y-axis

Solution best in objective one P(s)

min_ob1(n)

max_ob2(n)

Solution best in objective two P(e)

max_ob1(n)

min_ob2(n)

Reference solution coordinate P(r )

min_ob1(n)

min_ob2(n)

Random solution point on Pareto front P(n)

ob1(n)

ob2(n)

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ob2 : Objective 2

P (k1)

Pareto Front Solutions R(n)

P(e)

P(r)

ob1 : Objective 1

Reference Solution

Fig. 2 Determination of compromised solution using reference point

minimum distance among all non-dominated solution, will be considered as the compromised solution. If coordinate of the reference solution,P(r ) = (ob1(r ), ob2(r )), is as according to Table 1 and coordinate of a random point on Pareto front is P(n) = (ob1(n), ob2(n)), then distance between them can be given as 1  R(n) = (ob1(n) − ob1(r ))2 + (ob2(n) − ob2(r ))2 /2 (0.1) The point which has minimum distance { min(R(n))} from reference point is chosen as feasible compromised solution.

5 Simulation Results and Discussion All the simulations have been done using MATLAB software. Microgrid model described in Ref. [12] is adopted to demonstrate the proposed framework. It is considered that: (i) Microgrid operator has the details regarding interruptible energy limit of the user on daily bases, (ii) the aforesaid statistics is utilize to mark the endusers in order  of growing interest to reduce their energy need, (iii) the coefficients  K 1,i &K 2,i related to outage cost functions of involved participants are assumed known to operator, and (iv) the overall daily budget of the utility is considered 500 dollars. All loads are equally distributed and it is also considered that different types of loads have equal right to make decision regarding participation. The solution of MODSM is obtained using the NSGA-II algorithm for a given availability of renewable energy. The solar and wind energy data in the microgrid are shown in Tables 2 and 3. This scenario is considered as case M. Total scope of

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735

Table 2 Wind energy data Time

(hours)

1

2

3

4

5

6

7

8

Wind

(kW)

7.56

7.5

8.25

8.48

8.48

9.42

9.82

10.35

Time

(hours)

9

10

11

12

13

14

15

16

Wind

kW

10.88

11.01

10.94

10.68

10.42

10.15

9.67

8.98

Time

(hours)

17

18

19

20

21

22

23

24

Wind

kW

8.37

7.61

6.7

5.72

7.21

7.75

7.88

7.69

2

3

4

Table 3 Solar energy data Time

(hours)

1

5

6

7

8

Solar

kW

0

0

0

0

0

0

0

7.99

Time

(hours)

9

10

11

12

13

14

15

16

Solar

kW

10.56

13.61

14.97

15

14.78

14.59

13.56

11.83

Time

(hours)

17

18

19

20

21

22

23

24

Solar

kW

10.17

7.66

0

0

0

0

0

0

sustainable power is 346.24 kW. Under these conditions, MODSM program is run using NSGA-II optimization algorithm, and the set of compromised solutions are obtained, as Pareto-optimal front shown in Fig. 3. Information listed in Table 4 are obtained from the Pareto front. From Pareto front data, it is determined that the among all the feasible solutions present in Pareto front, the solution P(s), coordinate on XY plane is (397.6018, 478.1021), which is the best for objective one (represented on X-axis). While the second P(e), coordinate on XY plane are (783.1686,464.7694), which is best for second objective (represented on Y-axis). All other solutions present in Pareto front are trading between these two extreme solutions. With the use of P(s) and P(e) reference solution coordinate on

Fig. 3 Determination of compromised solution using reference point approach

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Table 4 Coordinates of different key solutions Parameter

X-axis

Y-axis

Upper edge point coordinates of Pareto solutions P(s)

397.6018

478.1021

Lower edge point coordinate of Pareto solutions P(e)

783.1686

464.7694

Reference or ideal point P(r)

397.6018

464.7694

The solution obtained using the reference point approach P(k1)

401.6207

476.5688

XY plane is determined that is P(r), coordinate onXY plane (397.6018,464.7694). After determination of reference point, the compromised solution, which is best trade fit among all feasible solution is determined by the approach discussed in the previous section. Value of this compromised solution is obtained using reference point approach is P(k1), XY coordinates are (401.6207,476.5688). The load curves corresponding to the best feasible solutions P(k1), obtained from reference point approach. The obtained load curves are showed in Fig. 4 to gather with NO-DSM load curve. Values of parameters obtained from these load curves are listed in Table 5. Attributes obtained from DSM curve is tabulated in Table 5. The total demand and power factor when DSM is not applied is 864.9 kW and 0.84. Whereas average and peak demand in NO-DSM case is 36.0375 kW and 42.09 kW. These are the required values of parameters for OOUI computation [14].

Fig. 4 Load curves obtained for reference point solution of MODSM

Table 5 Load curve data for solution P(k1) of case M Solution

Total demand

Avg. demand

Peak demand

Load factor

Renewable energy availability

P(k1)

687.7405 kW

28.65586 kW

36.1368 kW

0.79298

346.24 kW (50.34%)

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Table 6 OOUI for P(k1) (wpps = 0.5, wrei (wpps = 0.1, wrei (wpps = 0.9, wrei = 0.5) = 0.9) = 0.1) m s1 dsmuf

m s2 dsmuf

m s3 dsmuf

0.944027 0.503446 0.7237

0.5475

0.9000

Solutions(Case m pps M) P(k1)

m rei

6 Calculation of Indices and Determination of Compromised Solution In the present framework, overall the compromised solution is determined by comparing the OOUI indices calculated for solutions P(k1). Thus, for this proposes, first OOUI indices needed to be calculated. The computation of the OOUI are done on the approach presented in [14]. Table 6 depicts the calculated numeric data of these indices for the solutions obtained for case M(P(k1)). Table 6 presentsvalues of OOUI in three varying weight scenarios. In scenario 1, equal weightage wpps = wrei = 0.5 is given to both peak shaving and renewable energy integration indices. The weightage (wrei = 0.9) to the sustainable energy unification has been given more in the second scenario. The peak power shaving is preferred and has more weightage (wpps = 0.9) in the third scenario. Observation of Table 6 indicates the gain of peak power shaving index and discloses two information (1) concerning peak shaving, (2) load factor. Obtained for solutions P(k1), this show that the percentage donation of renewable in satisfying total load demand in case of solution P(k1).

7 Conclusion In the present work, a solution of MODSM is determined using an indicator-based framework. NSGA-II optimization algorithm is used in this work as a solution technique to solve the MODSM problem. From the Pareto front solutions obtained after optimization method implementation, feasible solutions are accomplished by applying reference point approach. In the proposed framework, the operator can determine the suitability of solution and keeping consideration of his policy. Thus, the proposed framework not only helps to determine the optimal solution from the Pareto front, but it also helps to determine the suitability of the solution with the help of indicators. In the future work, the obtained solution from reference point approach can be compared to solutions obtained with different approaches incorporating different optimization methods.

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References 1. Gellings CW (1985) The concept of demand-side management for electric utilities. Proc IEEE 73(10):1468–1470 2. Nilsson H (1994) The many faces of demand-side management. Power Eng J 8(5):207–210 3. Stanway J (1994) The Holyhead experience (demand side management). Power Eng J 8(5):221– 224 4. Chattopadhyay D, Banerjee R, Parikh J (1995) Integrating demand side options in electric utility planning: a multiobjective approach. IEEE Trans Power Syst 10(2):657–663 5. Ghatpande AM(1995) Frequency dependent load chipping in as a demand side management (DSM) tool-concept and issues. In: Proceedings of IEEE/IAS international conference on industrial automation and control, pp 383–388 6. Klaassen EAM, Van Gerwen RJF, Frunt J, Slootweg JG (2017) A methodology to assess demand response benefits from a system perspective: a Dutch case study. Utilities Policy 44:25–37 7. Jabir H, Teh J, Ishak D, Abunima H (2018) Impact of demand-side management on the reliability of generation systems. Energies 11(8):2155 8. Nguyen A-D, Bui V-H, Hussain A, Nguyen D-H, Kim H-M (2018) Impact of demand response programs on optimal operation of multi-microgrid system. Energies 11(6):1452 9. Liu Z, Zheng W, Qi F, Wang L, Zou B, Wen F, Xue Y (2018) Optimal dispatch of a virtual power plant considering demand response and carbon trading. Energies 11(6):1488 10. Gellings CW (2017) Evolving practice of demand-side management. J Mod Power Syst Clean Energy 5(1):1–9 11. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197 12. Nwulu NI, Xia X (2017) Optimal dispatch for a microgrid incorporating renewables and demand response. Renew Energy 101:16–28 13. Deb K (2012) Optimization for engineering design: algorithms and examples. PHI Learning Pvt. Ltd. 14. Singh M, Jha RC (2019) Object-oriented usability indices for multi-objective demand side management using teaching-learning based optimization. Energies 12(370) 15. Deb K, Sundar J (2006) Reference point based multi-objective optimization using evolutionary algorithms. In: Proceedings of the 8th Annual conference on genetic and evolutionary computation, pp 635–642

Techno-economic Benefits of Grid Penetrated 1 MW PV System in India G. Swaminathan, Shriram S. Rangarajan, Jayant Sharma, D. P. Kothari, and Tomonobu Senjyu

Abstract As the penetration of PV system with the grid has become the crux of a smart grid, the energy savings and monetary benefits associated with its operation have resulted in demand reduction and the reduction of utility bills. This paper presents the techno-economic analysis and benefits of 1 MW grid-connected PV system commissioned at SASTRA Deemed University in Thanjavur, Tamil Nadu, India. The analysis is done by comparing the actual power and energy with and without the PV system. Keywords Distribution system · PV system · Techno-economic analysis · Smart grid

1 Introduction The global energy demand that is unable to be met by the conventional power plants could be met by the penetration of renewable energy resources like PV, as the grid is becoming smarter [1]. The cost-effective solution with less maintenance is provided G. Swaminathan · S. S. Rangarajan (B) · J. Sharma SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India e-mail: [email protected] G. Swaminathan e-mail: [email protected] J. Sharma e-mail: [email protected] S. S. Rangarajan Clemson University, Clemson, SC, USA D. P. Kothari Indian Institute of Technology, Delhi, New Delhi, India e-mail: [email protected] T. Senjyu University of the Rykyus, Okinawa, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_74

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by PV technology as it has reached a widespread acceptance commercially, requiring minimal manpower of operation. Since the emission of carbon dioxide must be minimized, the solution of PV integration in power system network provides a nonpolluting and flexible solution compared to a conventional power plant. A typical megawatt PV plant system consists of solar PV modules, tracking systems, inverters, transformers and grid interfacing components [2]. The PV system supplied is troublefree, long-lasting and cost effective. The capacity of rooftop PV system is carefully designed to match the on-grid loads that are connected. The solar modules generate DC power based on the insolation from the sun. Some of the environmental factors that influence the PV generation are the ambient temperature, solar irradiation and the dust rate on the surface. The three-phase grid-tied inverter exports the available solar power to the utility grid through an isolation transformer interface. The research in the form of techno-economic analysis presented in this paper was aimed at witnessing the significant energy saving and demand reduction in the utility power (Tamil Nadu Electricity Board—TNEB) from the rooftop installed capacity of 1 MW PV at SASTRA Deemed University, Thanjavur, India. With the revision of IEEE Std 1547 (interconnection and interoperability of distributed energy resources), the IEEE std 1547a-2014 and IEEE Std 1547–2018 mandated the reactive power injection from the PV inverters [3]. The R&D facility at SASTRA Deemed University will further consider the techno-economic analysis. The analysis presented in this paper will serve as a preliminary benchmark for the implementation of PV inverters as smart inverters in near future.

2 System Components and Working Principle Although the equipment associated with PV systems is different from that employed in conventional electromechanical power generating systems, the principles of operation and interfacing with other electrical systems remain the same. Although a PV array produces power when exposed to sunlight, other components are required to properly conduct, control, convert and distribute, the energy produced by the array. The solar modules are the most important component of the solar power system. The solar radiation falling on the modules is converted to DC energy by photovoltaic principle. The DC energy generated by solar modules is converted into AC energy and fed to the grid. Solar module mounting structure is used to mount the solar modules with specified tilt angle. Inverter converts DC energy produced by solar modules to AC energy and performs supporting operations required for proper functioning of solar power system & export power to grid [4, 5]. DC isolation box is used for string isolation. AC distribution board (ACDB) is used to connect the output of the inverters at one common point and output of ACDB to be connected to the existing LT panel at site. ACDB is also used to monitor the output parameters at one common point.

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3 Architecture of 1 MW PV System Multi-crystalline-type PV modules (4166 in all) have been installed on the rooftops of ten buildings considering irradiation and shadow at the rooftop of buildings. The nominal output of one solar panel is 240 W which is connected in series and parallel. The DC array junction boxes are used between the string of solar modules and the input of inverter [6, 7]. The inverter output is connected to the input AC distribution box and finally the AC output from the inverter is synchronized with the grid power of electricity board [8–18]. The fixed tilt angle range of 10° is provided for module mounting structure to get full irradiation at the rooftop. The inverter 30/20 kVA is designed considering wide DC voltage range, maximum DC power input and to get maximum AC output voltage. This inverter is an outdoor type with good operating temperature range for conventional mounting. AC distribution board is used to connect the output of inverter at one common point and output of ACDB to be connected to the existing LT panel at site. ACDB is also used to monitor the output parameters at one common point. Figure 1 presents the installation of PV system at SASTRA Deemed University, Thanjavur, Tamil Nadu, India. It could be seen from Fig. 2 that the DC output from the PV system is given to the combiner. This is further fed to the central inverter for the conversion of DC to AC. The PV inverter is then synchronized with the grid, and at the same time, the power from PV also satisfies the load demand of the university loads. Figure 3 presents the output from PV during the 27th day of January 2019 in the School of Electrical and Electronics Engineering (Vidyut Vihar building, SASTRA University, Thanjavur, India) considered from the real-time tracker of installed PV. The corresponding yield overview at different buildings of SASTRA Deemed University for the month of January 2019 is shown in Fig. 4. The irradiation, module and ambient temperature of PV modules are in Fig. 5. With all the information that is available, the techno-economic benefits of 1 MW PV installation at SASTRA University are carried out based on the data that was extracted for the year 2018 in Sect. 4.

Fig. 1 PV system installed at SASTRA Deemed University, Thanjavur, India. An image captured at the Vidyut Vihar building (School of Electrical and Electronics Engineering)

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Fig. 2 Architecture of rooftop PV system installed at SASTRA Deemed University, Thanjavur, India

Fig. 3 PV generation at the department of Electrical and Electronics Engineering at a particular day in January 2019 at SASTRA Deemed University, Thanjavur, India

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Fig. 4 Yield overview of PV from the real-time tracker for January 27, 2019

4 Techno-economic benefits of 1 MW PV Installation at SASTRA Deemed University, Thanjavur, Tamil Nadu, India—Cost and Savings Benefit Analysis with PV Plant and Without the PV Plant (TNEB) The techno-economic benefit analysis has been carried for the year 2018 based on the available data. It could be well witnessed from Table 1 that the amount paid with PV is calculated by multiplying the consumed units from PV and cost/unit and further adding the taxes associated with it. The total amount (including taxes) with PV adds to Rs. 11,267,521 (in Indian rupees). This corresponds to USD of $1,59,641.84 calculated at the rate of USD $1 = Rs. 70.58 approx. during the month of February 2019. It could be seen from Table 1 that the cost/unit associated with PV is Rs. 6.8. Table 2 presents the cost and units consumed when associated with Tamil Nadu Electricity Board (TNEB) without the PV plant in place. The cost/unit is Rs. 6.35. The cost/unit times the number of the units (remaining the same from Table 1) gives the payable amount without PV. Apart from this cost, there is also fixed cost for 500 kVA demand at the rate of Rs. 350/kVA, and this comes around Rs. 175,000. Further, the 5% on fixed and running cost is calculated. The last column in Table 2

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Fig.5 Irradiation, module and ambient temperature from PV module Table 1 Cost and units associated with PV for the year 2018 Month/Year 2018 consumed units from PV Cost/unit (INR) Amount paid (including taxes) with PV December

85,618

5.80

609,790

January

125,549

5.80

883,865

February

139,237

6.80

980,229

March

127,755

6.80

899,395

April

141,118

7.07

1,033,932

May

122,227

7.07

895,129

June

135,484

7.07

992,217

July

120,295

7.07

880,966

August

149,713

7.07

1,096,424

September

140,762

7.07

1,030,870

October

125,738

7.07

920,842

November

142,536

7.07

1,043,862

Total (INR)

1,557,032

11,267,521

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Table 2 Cost and units associated with TNEB (without PV) for the year 2018 Month/year Cost/unit Payable amount Fixed cost for 5% tax on fixed Total without PV 500 KvA cost & running (INR) demand (Rs. cost 350/KvA) December

6.35

550,024.3

17,500

36,251.215

January

6.35

797,236.15

17,500

48,611.8075

1,020,847.958

February

6.35

884,154.95

17,500

52,957.7475

1,112,112.698

March

6.35

811,244.25

17,500

49,312.2125

1,035,556.463

April

6.35

896,099.3

17,500

53,554.965

1,124,654.265

May

6.35

776,141.45

17,500

47,557.0725

June

6.35

860,323.4

17,500

51,766.17

July

6.35

763,873.25

17,500

46,943.6625

August

6.35

950,677.55

17,500

56,283.8775

1,181,961.428

September

6.35

893,838.7

17,500

53,441.935

1,122,280.635

October

6.35

798,436.3

17,500

48,671.815

1,022,108.115

November

6.35

905,103.6

17,500

54,005.18

1,134,108.78

Total (INR)

761,275.515

998,698.5225 1,087,089.57 985,816.9125

12,586,510.86

presents the total cost which is the sum of payable amount without PV, fixed cost for 500 kVA demand and the 5% tax on fixed cost and running cost. The net total is calculated to be around Rs. 1,25,86,510.86 which is approximately equivalent to USD $1,78,329.71 (USD $1 = Rs. 70.58 approx.) during the month of February 2019. Tables 1 and 2 give a comparison of cost associated with the unit consumption when operated with PV plant alone and with the TNEB alone, respectively. It could be seen that the net total cost associated with PV comes around USD. $1,59, 641.84 whereas the cost associated with TNEB alone comes around USD. $1,78, 329.71. The net savings is obtained by subtracting the cost associated when PV plant is in place from the cost associated with TNEB alone. This amounts to be USD. $18,687.87 which corresponds to Rs. 13,18,989.86. The savings that could be witnessed from this research analysis is a huge amount if renewable energy like PV is in place compared to the consumption of units from the utility (TNEB).

4.1 Cost and Savings Benefit Analysis Without the PV Plant (TNEB Alone) and TNEB + PV Plant in Place Table 3 presents the cost and savings benefit analysis with and without the PV plant. The last two columns of Table 3 presents the cost associated during the operation of TNEB + PV plant and TNEB alone. It could be well witnessed that the net

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Table 3 PV plant (TNEB alone) and TNEB + PV plant in place Month/year 2018

TNEB units and bill amount excluding PV units amount (INR)

TNEB + consumption amount paid (INR)

TNEB only (INR)

December

310,020

2,656,629

3,266,419

3,417,905

January

428,360

3,450,631

4,334,496

4,471,479

February

484,480

3,828,211

4,808,440

4,940,324

March

502,280

3,990,253

4,889,648

5,025,809

April

558,760

4,329,000

5,362,932

5,453,654

May

419,660

3,394,183

4,289,312

4,392,882

June

257,560

2,308,944

3,301,161

3,396,034

July

492,592

3,937,407

4,818,373

4,923,224

August

509,184

3,949,336

5,045,760

5,131,297

September

520,784

4,012,513

5,043,383

5,134,794

October

513,472

3,973,006

4,893,848

4,995,114

November

449,104

3,495,124

4,538,986

4,629,233

43,325,237

54,592,758

55,911,748

Total(INR)

cost incurred during the operation of PV + TNEB accounts to Rs. 5,45,92,758 and that corresponds to USD $7,73,487.65. The net cost incurred during the time when unit consumption is considered with TNEB alone amounts to Rs. 5,59,11,748. This corresponds to USD $7,92,175.517. The net savings obtained due to the installation of 1 MW PV plant at SASTRA Deemed University, Thanjavur, Tamil Nadu, India, accounts to Rs. 13,18,990, and this corresponds to USD of $18,687.87.

5 Additional Savings Due to the Operation of Diesel Generator During Outage Diesel generator sets play a vital role during the time of power outage by satisfying the load demand. Table 4 presents the contribution from diesel generator sets when there is power shut down. It could be seen from Table 4 that during power outage at SASTRA Deemed University, 2 DG sets rated at 500 kVA each are operational. The DG sets supply the load demand along PV plant during the time of outage, thereby replacing the TNEB utility. As a result, this brings in additional savings in terms of cost–benefit analysis. The additional savings obtained due to DG sets amounts to Rs. 15,08,800, and this corresponds to USD $21,377.16.

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Table 4 Contribution from diesel generator sets during power outage Month/year 2018

Contribution of diesel generators (DG sts-2* 500 kVA) during outage consumed units during outage

Amount (INR)

December

4400

70,400

January

4900

78,400

February

13,000

208,000

March

5300

84,800

April

6900

110,400

May

8300

132,800

June

7000

112,000

July

12,700

203,200

August

5700

91,200

September

14,500

232,000

October

6500

104,000

November

5100

81,600 1,508,800

6 Discussion It has been well witnessed from the earlier sections that the installation of PV plant has been effective and has provided a huge amount of savings. In addition to that, the savings obtained from DG sets during outage has also provided savings. The total savings obtained for the year 2018 at SASTRA Deemed University is the sum of savings due to PV and savings due to DG sets is around Rs. 28,27,790 (Rs. 13,18,990 + Rs. 15,08,800) which is approximately equivalent to USD $40,065.032.

7 Conclusions As the grid is becoming smarter with increased integration of renewable energy resources like PV, more benefits could be obtained in terms of cost reduction associated with it. Further, the research presented in this paper will serve as a benchmark for researchers and power engineers to perform more such studies to witness the techno-economic benefits of PV integration. Acknowledgements Support from SASTRA Deemed University, Thanjavur, Tamil Nadu, India are gratefully acknowledged.

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References 1. Rangarajan SS, Randolph Collins E, Curtiss Fox J, Kothari DP (2017) A survey on global PV interconnection standards. In: IEEE power and energy conference at Illinois (PECI), 23–24 Feb 2017 2. Rangarajan SS et al (2018) Consolidated compendium of PV interconnection standards and guidelines across the globe in a smart grid. J Energy Technol Res 2(1):1–29 3. Rangarajan SS, Collins ER, Fox JC (2018) Smart PV and SmartPark inverters as suppressors of TOV phenomenon in distribution systems. IET Generation Trans Distrib 12(22):5909–5917 4. Rangarajan SS, Collins ER, Fox JC (2017) Harmonic resonance repercussions of PV and associated distributed generators on distribution systems. In: 2017 North American Power Symposium (NAPS), Morgantown, WV, pp 1–6 5. Darie S (2012) Guidelines for large photovoltaic system integration. In: Transmission and distribution conference and exposition (T&D). IEEE PES, pp 1–6, 7–10 May 2012 6. Mohan N, Undeland TM, Robbins WP (2003) Power electronics: converters, applications, and design, 3rd edn. Wiley, Hoboken, NJ 7. Rashid MH (2007) Power electronics handbook: devices, circuits, and applications. Elsevier, Amsterdam 8. Varma RK, Rangarajan SS, Axente I, Sharma V (2011) Novel application of a PV solar plant as STATCOM during night and day in a distribution utility network. In: 2011 IEEE/PES power systems conference and exposition, Phoenix, AZ, pp 1–8 9. Rangarajan SS, Collins ER, Fox JC (2017) Interactive impacts of elements of distribution systems on network harmonic resonances. In: 6th IEEE international conference on renewable energy research and applications (ICRERA), San Diego, CA, USA 10. Rangarajan SS, Collins ER, Fox JC (2017) Detuning of harmonic resonant modes in accordance with IEEE 519 Standard in an exemplary North American Distribution System with PV and wind. In: 6th IEEE international conference on renewable energy research and applications, San Diego, CA, USA 11. Rangarajan SS, Collins ER, Fox JC (2017) Comparative impact assessment of filter elements associated with PWM and hysteresis controlled PV on network harmonic resonance in distribution systems. In: 6th IEEE International conference on renewable energy research and applications, San Diego, CA, USA 12. S. S. Rangarajan, Sreejith S. and S. Nigam, “Effect of distributed generation on line losses and Network Resonances,” 2014 International Conference on Advances in Electrical Engineering (ICAEE), Vellore, 2014, pp. 1–6. 13. Rangarajan SS, Sreejith S (2013) Novel 24 hour usage of a PV solar farm for reducing line loss. In: 2013 international conference on energy efficient technologies for sustainability, Nagercoil, pp 381–386 14. Rangarajan SS, Sreejith S, Sabberwal SP (2013) Cost estimation and recovery analysis of a PV Solar farm utilized round the clock. In: 2013 IEEE global humanitarian technology conference: South Asia Satellite (GHTC-SAS), Trivandrum, pp 286–291 15. Rangarajan SS, Collins ER, Fox JC (2019) Efficacy of smart PV inverter as a virtual detuner in mitigating network harmonic resonances. Elsevier Electric Power Syst Research J 16. Rangarajan SS (2018) Efficacy of smart PV inverter as a strategic mitigator of network harmonic resonance and a suppressor of temporary overvoltage phenomenon in distribution systems. All Dissertations. 2235. https://tigerprints.clemson.edu/all_dissertations/2235 17. Poyyamani Sunddararaj S, Rangarajan SS, Gopalan S (2019) Neoteric fuzzy control stratagem and design of chopper fed multilevel inverter for enhanced voltage output involving plug-in electric vehicle (PEV) applications. Electronics 8:1092 18. Mozumder S, Dhar A, Rangarajan SS, Karthikeyan SP (2014) Coordinated operation of multiple inverter based renewable distributed generators as an active power injector and reactive power compensator. In: 2014 international conference on computation of power, energy, information and communication (ICCPEIC), Chennai, pp 298–303

State-of-the-Art Review on Microgrid Control Strategies and Power Management with Distributed Energy Resources Nada Ali and Deepak Kumar

Abstract The conceptualization of microgrid has led to various technological advances in power generation and consumption as it facilitates the integration of environment-friendly renewable energy sources. Microgrid generally comprises of several distributed energy resources interfaced with the utility grid through power converters. The system’s frequency and voltage are sustained in order that the microgrid’s operation is secure and reliable. This paper encompasses futuristic control strategies defining the functioning of the microgrid when connected to the grid and likewise in stand-alone mode. Primary level of control, secondary level of control and tertiary level of control form the three hierarchical control structures for the microgrid. A comparative analysis of various controls has also been shown in this paper. Keywords Microgrid · Frequency control · Voltage control · Centralized control · Decentralized control · Distributed generations (DGs) · Renewable energy sources (RESs) · Primary control · Secondary control · Tertiary control

1 Introduction Latterly, microgrids have been a great topic of research due to which RES integration rather than dependency on conventional energy sources is being promoted to cover the increasing power demand as well as reducing environmental impact. Renewable energies like wind, photovoltaic and hydroelectric power are clean and limitless; hence, they are used mostly for the generation of electricity [1–4]. Microgrids can be defined as integrated energy systems comprising of DERs and a cluster of interconnected loads working either in islanded mode or grid-connected N. Ali (B) · D. Kumar Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] D. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_75

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mode [5–9]. In the islanded mode, microgrid separates from the utility grid when the latter fails or when its power quality is unsatisfactory. This paper discusses various control techniques applied for frequency and voltage control to strengthen the stability of grid-tied along with stand-alone microgrids. In [10], Lyapunov theorybased control technique has been given for the stable operation of the microgrid. Jha et al. [11] have presented a method for controlling the battery energy storage system (BESS) output power which can prevent imbalance in supply of power and its demand in a microgrid at the time of sudden load change. [12] and [13] have proposed a virtual impedance scheme in which power can be shared more effectively between the DGs of the stand-alone microgrid in addition to those connected to the grid. The other sections of the paper have been arranged in the following way—Sect. 2 explores the conception of the microgrid along with its components, Sect. 3 reviews various classification of hierarchical control levels in the microgrid and in the end, and Sect. 4 draws the inference.

2 Microgrid Control The security, sustainability and reliability of any microgrid ultimately depend on its control structure. Figure 1 shows the hierarchical levels of control, that is, the primary, secondary and tertiary levels, which play an integral role for the microgrid control [14].

Fig. 1 Hierarchical construction of a microgrid control

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2.1 Primary Control The primary control is expected to respond quickest among all the control levels as it is the foremost level in the hierarchical structure of control. This level is involved in islanding detection, output control and power sharing [15]. In this level of control, local data are collected without the need for any communication infrastructure. VSIs are employed as an interface for DC sources while providing proper frequency regulation. The VSI controllers consist of two stages, namely the inverter output controller and DG power sharing controller. DG power sharing controller regulates the sharing of microgrid reactive and active power mismatches. Based on the usage of droop concept, it can be categorized into various droop control methods. The representation of the microgrid primary control is given in Fig. 2. Inverter Output Control: In the islanded mode, usually, a VSI is involved in a voltage-controlled process for frequency as well as voltage regulation of the microgrid. Better the design of the voltage controller, better is the inverter output voltage quality. The control strategies are classified on the basis of their frame of reference— natural (abc) frame of reference, synchronous (dq) frame of reference and stationary (αβ) frame of reference [16]. Inverter output control regulates output currents and voltages which is attained by two loops, which are inner current and outer voltage control loop. Power Sharing Control: Since power sharing control utilizes centralized control, it can be considered as a part of secondary level of control. This control can be classed according to the usage of droop control. Some of the controls are discussed in Table 1 where δr , Vr , Pr and Q r are the reference values of frequency, voltage magnitude, active power and reactive power, respectively. l, m, K P and K Q are the droop coefficients. Vout ,ωout , Pout and Q out are the values of output voltage, frequency, active power and reactive power of distributed generation system, respectively. P  and Q  are the virtual powers, Z v is the virtual impedance and I0 is the output current. Non- Droop based control: There are various non-droop-based control methods in a multi-DER microgrid. One of them is the master–slave strategy where the master

Fig. 2 Diagrammatic representation of primary control

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Table 1 Comparison of various droop controls Droop control

Equation

Advantages

Disadvantages

Angle droop Control [17]

δout = δr − l(Pout − Pr ); Vout = Vr − m(Q out − Q r )

• Frequency regulation is constant

• GPS signals are required • Poor performance of power sharing

Conventional p/f droop control [18]

ωout = • Simple ωr − K P (Pout − Pr ); • Communication not required Vout = Vr − K Q (Q out − Q r ) • High reliability, flexibility and expandability

P/f droop control with Vout = Vr − Z v Iout virtual output impedance [19]

• Decoupled P/Q • Difficult to design a control high-efficiency • Increase in system algorithm stability • Increase in dynamic response of the system

P/f droop control with ωout = • Decoupled P/Q frame transformation ωr − K P  P  − P  ; control r out • Improved system [20] Vout = reliability   Vr − K Q Q out − Q r VPD/FBQ droop control [21]

• Poor voltage regulation • Poor harmonic sharing • Slow dynamic response • Poor performance with RES

Vout = • Simple Vr − K P (Pout − Pr ); • Communication not ωout = required ωr − K Q (Q out − Q r )

• Beforehand knowledge of physical parameters required • Poor frequency and voltage regulation

is a foremost DG unit whose responsibility is to preserve the system voltage within its allowable limits [22].

2.2 Secondary Control Even in steady state, primary control may lead to frequency and voltage deviations. ESS can supply power for deviation compensation, but because of its short energy capacity, it cannot be used for long duration. The frequency and voltage deviations resulted from the primary control are compensated by the secondary control as depicted in Fig. 3. The secondary control operation time is lesser than that of primary control. This level of control hierarchy can also be mentioned as the energy management system (EMS) [23, 24]. Centralized and decentralized controls are the two major approaches that can be identified in this field.

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Fig. 3 Structure of the secondary control

Centralized Secondary Control: It is preferred for fixed infrastructure and critical demand–supply balances, usually occurring in case of isolated microgrids. It relies extensively on high-speed and reliable communication network. All the relevant information like cost functions, operating limits, etc., within the microgrid is received by the central controller to determine suitable dispatch of the resources as well as appropriate unit commitment following the preferred objectives. For EMS, an optimization-based control strategy called Model Predictive Control (MPC) can be used to predict future events like variation in output power, instantaneous demand and energy prices [25]. It is involved in unit commitment and optimal dispatch of DGs by using ANN or various heuristic optimization procedures like ACO, PSO, GA, etc. (Table 2) [26, 27]. Decentralized Secondary Control: In decentralized control, the control operation is applied at the local converters within each microgrid where each converter is Table 2 Advantages and disadvantages of centralized and decentralized control Advantages

Disadvantages

Centralized control

It allows the implementation of online optimization

It does not allow the preferable feature of plug-and-play; complexity is high

Decentralized control

It allows the effortless incorporation of new DER units without doing any continual adjustment to the controller settings, complexity is low

It faces complications handling the working of microgrids which require high levels of coordination

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connected via communication lines [6]. This method implements hierarchical structure for exchange of data contrary to the fact that control variable decisions are made locally. In this control, communication lines of high bandwidth are not needed, and also, single point of failure can be averted. A three-level hierarchical structure can be used for this approach which are local controllers (LCs), microgrid central controller (MGCC) and distribution network operator (DNO) [7]. In a decentralized architecture, information important to the operation of the microgrid can be exchanged among the LCs and the MGCC. Going by the characteristics of the decentralized secondary control systems, they are generally executed through the MAS framework [28]. MAS is a system comprising of various intelligent agents which get the localized data for communication with each other to achieve numerous local and global objectives [29]. Contrary to MAS, in master/slave control, the master has no current control but only has voltage control [30]. Hence, transient current is delivered, and also, the compensation for wrong weighting factors is done by this unit. The responsibility of the master module is to regulate the output voltage and identify the reference current of each inverter. It provides the current command which is tracked by the slave units to attain an equal current distribution. In the presence of multiple owners and the number of DER units changing at a fast pace in microgrids connected to the host grid, decentralized control is a more suitable approach for it.

2.3 Tertiary Control Microgrid control hierarchy has tertiary control as the last level of control, thus exhibiting the slowest response among all the control levels. It coordinates multiple microgrids present in the system for the flow of power among the microgrid and the host grid as well as involves economic affairs for the optimal operation of the microgrid. The flow of active and reactive power is administered by managing the frequency and voltage of the grid-connected microgrid. The diagrammatic representation of the tertiary control is given in Fig. 4 where firstly the measurement of the active and reactive power is done which are then collated with their reference values. As a result, the reference values of frequency and voltage are achieved which are used in the secondary control [31]. As mentioned earlier, tertiary control is concerned about economic affairs by using different algorithms like gossiping algorithm [32].

3 Conclusion Extensive researches are being conducted on microgrids to elevate the inclusion of RES into the electrical network due to their innumerable advantages. Due to different microgrid operating modes and the sporadic nature of the RES, appropriate controls are required for its smooth and reliable operation. This paper discusses

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Fig. 4 Structure of the tertiary control

various components of the microgrid and recent developments in its control. Different control techniques of the hierarchical structure have been reviewed in the literature. It can hence be deduced that the droop control is suitable for small capacity microgrids at the primary level of control, and also, at the secondary level of control, the control structure can be centralized or decentralized. It has been observed that the centralized control is more suitable for small-scale microgrids and the decentralized control for larger microgrids. Grid-connected microgrid can be operated by employing tertiary control. Future research can be done on microgrid energy management system (EMS) through coordinated frequency control.

References 1. Karimi M et al (2016) Photovoltaic penetration issues and impacts in distribution network—a review. Renew Sustain Energy Rev 53:594–605 2. Kumar Y et al (2016) Wind energy: trends and enabling technologies. Renew Sustain Energy Rev 53:209–224 3. Rehman S et al (2015) Pumped hydro energy storage system: a technological review. Renew Sustain Energy Rev 44:586–598 4. Hossain MS et al (2016) Role of smart grid in renewable energy: an overview. Renew Sustain Energy Rev 60:1168–1184 5. Bouzid AM, Guerrero JM, Cheriti A et al (2015) A survey on control of electric power distributed generation systems for microgrid applications. Renew Sustain Energy Rev 44:751–766 6. Zhao J, Wang C, Zhao B, Lin F, Zhou Q, Wang Y (2014) A review of active management for distribution networks: current status and future development trends. Electric Power Components Syst 42:280–293 7. Olivares DE, Mehrizi-Sani A, Etemadi AH et al (2014) Trends in microgrid control. IEEE Trans Smart Grid 5:1905–1919

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8. Hirsch A et al (2018) Microgrids: a review of technologies, key drivers, and outstanding issues. Renew Sustain Energy Rev 90:402–411 9. Planas E et al (2015) AC and DC technology in microgrids: a review. Renew Sustain Energy Rev 43:726–749 10. Mehrasa M et al (2015) A control plan for the stable operation of microgrids during gridconnected and islanded modes. Electric Power Syst Res 129:10–22 11. Jha SK, Kumar D (2019) Assessment of battery energy storage system with hybrid renewable energy sources to voltage control of islanded microgrid considering demand-side management capability. Iran J Sci Technol Trans Electr Eng 12. Kim J, Guerrero JM, Rodriguez P, Teodorescu R, Nam K (2011) Mode adaptive droop control with virtual output impedances for an inverter-based flexible AC microgrid. IEEE Trans Power Electron 26:689–701 13. Savaghebi M, Jalilian A, Vasquez JC, Guerrero JM (2012) Secondary control for voltage quality enhancement in microgrids. IEEE Trans Smart Grid 3:1893–1902 14. Kabalci E (2019) Hierarchical control in microgrid. Microgrid Architect Control Protect Methods 381–401 15. Zhang H et al (2017) Distributed adaptive virtual ımpedance control for accurate reactive power sharing based on consensus control in microgrids. IEEE Trans Smart Grid 8:1749–1761 16. Hossain MA, Pota HR, Issa W, Hossain MJ (2017) Overview of AC Microgrid controls with inverter interfaced generations. Energies 10:1300 17. Kolluri RR et al (2017) Power sharing in angle droop controlled microgrids. IEEE Trans Power Syst 32:4743–4751 18. Palizban O, Kauhaniemi K (2015) Hierarchical control structure in microgrids with distributed generation. Renew Sustain Energy Rev 44:797–813 19. Sun Y, Hou K, Yang J, Han H, Su M, Guerrero JM (2017) New perspectives on droop control in AC microgrid. IEEE Trans Ind Electron 64:5741–5745 20. Brabandere KD et al (2007) A voltage and frequency droop control method for parallel inverters. IEEE Trans Power Electron 22:1107–1115 21. Rocabert J et al (2012) Control of power converters in AC microgrids. IEEE Trans Power Electron 27:4734–4749 22. Han H, Hou X, Yang J, Wu J, Su M, Guerrero JM (2016) Review of power sharing control strategies for islanding operation of AC microgrids. IEEE Trans Smart Grid 7:200–215 23. Jha SK, Kumar D (2019) Demand side management for stand-alone microgrid using coordinated control of battery energy storage system and hybrid renewable energy sources. Electric Power Components Syst 24. Jha SK, Kumar D, Kamwa I (2018) Smart demand response management of ıslanded microgrid using voltage-current droop mechanism. Int J Emerg Electric Power Syst 19 25. Gu W, Wang Z, Wu Z, Luo Z, Tang Y, Wang J (2017) An online optimal dispatch schedule for CCHP microgrids based on model predictive control. IEEE Trans Smart Grid 8:2332–2342 26. Khan MW, Wang J, Ma M, Xiong L, Li P, Wu F (2019) Optimal energy management and control aspects of distributed microgrid using multi-agent systems. Sustainable Cities Soc 44:855–870 27. Bevrani H, Habibi F, Babahajyani P, Watanabe M, Mitani Y (2012) Intelligent frequency control in an AC microgrid: Online PSO-based fuzzy tuning approach. IEEE Trans Smart Grid 3:1935–1944 28. Bidram A, Davoudi A, Lewis FL, Guerrero JM (2013) Distributed cooperative secondary control of microgrids using feedback linearization. IEEE Trans Power Syst 28:3462–3470 29. Li Q, Chen F, Chen M, Guerrero JM, Abbott D (2016) Agent-based decentralized control method for islanded microgrids. IEEE Trans Smart Grid 7:637–649 30. Federico I, Jose E, Luis F (2017) Master–slave DC droop control for paralleling auxiliary DC/DC converters in electric bus applications. IET Power Electron 10:1156–1164 31. Moayedi S, Davoudi A (2016) Distributed tertiary control of DC microgrid clusters. IEEE Trans Power Electron 31:1717–1733 32. Mateska AK, Borozan V, Krstevski P, Taleski R (2018) Controllable load operation in microgrids using control scheme based on gossip algorithm. Appl Energy 210:1336–1346

An Energy Storage Planning and Analysis of Microgrid: A College Campus Case Study Jeet Dhoriyani, Renison Macwan, and Chetan Upadhyay

Abstract Recently, there has been a push by various governments and environment organizations for the utilization of renewable energy resources like wind and solar energies. However, the unreliable availability of such sources is a huge concern and a major roadblock to their wide-scale implementation. One proposed solution of this problem is the use of batteries for storing the energy from such fluctuating energy resources. In this paper, a feasibility study has been carried out for battery storage system (BSS) with in the college campus. Campus consists of five different solar generating nodes of 5 kW each. Analysis has been done for the various generation and load data of these plants and calculated the optimal amount of energy backup required for microgrid in terms of battery bank. Keywords Solar · Battery · Renewable · Feasibility

1 Introduction For the betterment of world climate, most countries of the world have given a muchdeserved push to generate electricity by using renewable energy sources. Government of India and Government of Gujarat state are also encouraging usage of more and more renewable resources like solar energy and wind energy. Gov. of India is targeting 175 GW of solar energy generation till 2022 [1]. Government to achieve this set target is providing subsidies on buying solar rooftop plant. Almost all Gov. institutions are getting solar rooftop plants to achieve goal of government and to reduce electricity bill. L.D. College of Engineering is a

J. Dhoriyani (B) · R. Macwan · C. Upadhyay Electrical Enginnering, L.D. College of Engineering, Ahmedabad, India e-mail: [email protected] R. Macwan e-mail: [email protected] C. Upadhyay e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_76

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government institution located in Ahmedabad, Gujarat, which is also beneficiary of this government scheme and had a various installed solar rooftop plants [2]. Battery storage has become a major research area because of the utility of storage systems in making the all-day use of renewable energy sources worthwhile. Storage is being proposed to solve many issues on the electric power grid, especially those issues related to renewable generation such as wind and solar generations. Batteries are the current and most efficient solution of variable generation of the renewable sources. A heavy input is required from the grid while using hybrid energy supply systems currently. However, if a proper and efficient storage system is utilized, the input taken from grid is highly affected. Also with upcoming techniques for grid modernization and dynamic pricing initiatives, the fluctuations generated in the energy demands can be easily compensated by the use of BSS [3, 4]. As more and more computational technology growing day by day, there are many modifications coming in the traditional power grids. There are many real-time microgrids are coming in existence due distributed generation (DG). With use energy backup and smart meter bidirectional power flow is happening and new concept of smart meters present in most of the cases, whose analysis capabilities go further beyond the basic automated meter readings for billing purposes, integrating home or building area networks (HAN/BAN), alarms and power quality indicators in some cases [5]. For making use of solar energy available in a region optimally, various technological advances have been achieved ranging from computational algorithms to sophisticated solar trackers. Many different sizing methods such as mathematical method and artificial intelligence method have been reported to design a techno-economically optimum hybrid renewable energy system. In this paper, a numerical analysis method has been used to calculate the parameters [6]. Via proper load scheduling in college, the load pattern has been found and various generation data of the solar plants for a long period of time gathered as per actual generation. As per [7–9], vital data acquisition is done and concluded with whole case study in this paper.

2 Case Study 2.1 General Overview College campus—L.D. College of Engineering has been considered for this case study. Data from five distinct solar roof top generation plants have been acquired which are installed on the campus. Solar generation capacity and other vital details are shown in Table 1. In this paper, information of the total solar power generated, and the total power consumed in the five buildings is recorded through various meter readings for a definite period of time.

An Energy Storage Planning and Analysis of Microgrid … Table 1 Generatıon capacity of solar rooftop plants for the chosen buildings

759

Sr. No.

Location

Capacity (kW)

1

Electrical engineering dept

5

2

Transport center

5

3

ACPC building

5

4

EC department

5

5

Textile technology dept

5

The grid considered here consists of three main subcomponents: main grid power line component, solar photo-voltaic generation component, and a battery energy storage system. The local power plant generates electrical power, in accordance with the solar energy resources, to supply a part of the total load; the battery bank forms the energy storage system that can supply the load when there is lack of electrical supply from the PV plant and/or the main grid, and acts as a storage system for the surplus power when the power generated by the PV plant exceeds the load. After collecting and analyzing the data, there were clear proof that these five solar rooftop plants have less consumption then generation of energy. In such given scenario, a battery energy storage system not only allows to maximize the utility of available solar power but it also helps in providing a stable power source during its discharge time period, in turn helping to overcome the constantly fluctuating solar power availability [7] (Fig. 1).

2.2 Case Study—Buildings 1, 2, and 3 The total power generated in kWh and total power consumed in kWh in each of buildings 1, 2, and 3 are displayed in Figs. 2, 3 and 4. The difference in power generated and power consumed for each day is also shown in separate figures. The data of over 20 days has been chosen for the case study [10]. Consider the case of building-1. In this case, the net sum of difference between the total power generated and total power consumed over the selected time period is calculated by measuring the area under the curve. It is found that net summation is positive. A large amount of research has been done in order to develop the very best batteries by considering various combinations of elements and their electrochemical properties. There have been major studies regarding Lead–Acid, Sodium–Sulfur (Na–S), Lithium ion (Li-ion), and Redox Flow battery types, with particular emphasis on their applicability to electricity storage. The studies have aimed for minimizing many of the issues surrounding current large-scale battery storage such as energy density, cost, and safety. From these studies, it has been concluded that Li-ion batteries. Similarly, for buildings 2 and 3 also the total area under curve has been found positive. However, the net area under curve for buildings 4 and 5 is negative. Hence an energy storage system connected to buildings 4 and 5 will not be of great utility.

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Fig. 1 Campus map and location of five plants

In order to improve the microgrid reliability, reduce its operation cost, and address operational challenges, when the difference between power generated and power consumed at a location is sufficiently positive, the excess power produced can be stored in batteries or if possible used to power a different entity [11] (Table 2).

2.3 Calculation of Batteries Required Here, assumption has been done that all the excess energy produced in a welldesigned and stored in compatible BSS (Table 3). battery charging hours = For building-1 = For building-2 =

kWh2 value of area under curve Voltage * Amp−hours 469.68 ∗ 1000 = 81.54 48 ∗ 120

4103.13 ∗ 1000 = 712.34 48 ∗ 120

An Energy Storage Planning and Analysis of Microgrid … Fig. 2 Monitoring of power-generation and load (Building-1)

Fig. 3 Monitoring of power-generation and load (Building-2)

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Fig. 4 Monitoring of power-generation and load (Building-3)

Table 2 Total area under power difference curve for buildings 1, 2, and 3

Table 3 Number of battery requıred for buıldings 1, 2, and 3

Sr. No.

Location

1

Building-1

2

Building-2

3

Building-3

Capacity (kW) 469.68 4103.136338 562.67

Sr. No.

Location

Number of batteries required

1

Building-1

64

2

Building-2

556

3

Building-3

75

For building-3 =

562.67 ∗ 1000 = 97.68 48 ∗ 120

(1) For a required value of Ampere-hours, considering Ahrequired , while taking Ahfixed as 32 Ah. N S = number of batteries connected in series. N P = number of battery strings in parallel. N = Total number of batteries required.

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Where N = N S * N P . Taking various appropriate value of number of batteries connected in series and approximate while Np = Ahrequired /Ahfixed. (2) For a required value of voltage, considering V required , while taking V fixed as 6 V. N S = number of batteries connected in series. N P = number of battery strings in parallel. N = Total number of batteries required. Where N = N S *N P . Taking various appropriate value of number of batteries connected in parallel and approximate while Ns = V required /V fixed. Taking lesser value of N from the above two values [10].

2.4 Case Study—Buildings 4 and 5 A. Preliminary calculation. By replicating the process done for buildings 1, 2, and 3 for the remaining buildings, the time period when the power-generation exceeds the power consumption is not sufficient for the BSS to work. B. After increasing the generation capacity. Hence, with supposing to increase the generation capacity of the buildings from 5 kW to 10 and 15 kW for buildings 4 and 5, respectively (Figs. 5 and 6). After increasing the generation capacity, a theoretically r enough power consumption and generation difference has been achieved (Figs. 7 and 8). Following the rest of the steps and calculating the theoretical number of batteries required (Table 4). For building-4 =

146.78 ∗ 1000 = 25.65 48 ∗ 120

For building-5 =

615.71 ∗ 1000 = 106.89 48 ∗ 120

3 Citations and Bibliography From the result, it is concluded here that battery storage system is the best option to optimize the distributed generation using load and generation prediction. The cost of the batteries and distributed generation resources eventually can be completely recovered with better life of battery.

764 Fig. 5 Monitoring of power-generation and load (Building-4)

Fig. 6 Monitoring of power-generation and load (Building-5)

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An Energy Storage Planning and Analysis of Microgrid … Fig. 7 After doubling power scenario of building-4

Fig. 8 After doubling power scenario of building-5

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J. Dhoriyani et al. Sr. No.

Location

Number of batteries required

1

Building-4

20

2

Building-5

84

4 Future Work Data has been collected during a concise period of time, it is recommended to collect more data of for longer term to make more robust dataset. Also in depth analysis of batteries where SOC is considered as parametre should be needed.

References 1. Ram R, Gambhir G (2015) Report of expert group on 175GW RE by 2022. NITI Ayog, p iii 2. Ministry of New & Renewable Energy (2014) Off-grid and decentralized solar application scheme: operational guidelines for grid connected rooftop and small solar power plants programme 3. Ministry of New & Renewable Energy (2018) To smoothen the wind solar hybrid power further, appropriatecapacity of storage may also be added to the project, p 1 4. Dugan, RC, Taylor JA, Montenegro D (2016) Energy storage modeling for distribution planning. In: 2016 IEEE rural electric power conference (REPC). IEEE, pp 12–20 5. Palacios-Garcia EJ, Rodriguez-Diaz E, Anvari-Moghaddam A, Savaghebi M, Vasquez JC, Guerrero JM, Moreno-Munoz A (2017) Using smart meters data for energy management operations and power quality monitoring in a microgrid. In: 2017 IEEE 26th international symposium on industrial electronics (ISIE). IEEE, pp 1725–1731 6. Soni, SR, Upadhyay CD, Chandwani H (2015) Analysis of battery-super capacitor based storage for electrical vehicle. In: 2015 international conference on energy economics and environment (ICEEE). IEEE, pp 1–7 7. Daggett, A, Qadrdan M, Jenkins N (2017) Feasibility of a battery storage system for a renewable energy park operating with price arbitrage. In: 2017 IEEE PES innovative smart grid technologies conference Europe (ISGT-Europe). IEEE, pp 1–6 8. Xiao J, Bai L, Li F, Liang H, Wang C (2014) Sizing of energy storage and diesel generators in an isolated microgrid using discrete Fourier transform (DFT). IEEE Trans Sustain Energy 5(3):907–916 9. Saboori H, Hemmati R, Ghiasi SMS, Dehghan S (2017) Energy storage planning in electric power distribution networks–a state-of-the-art review. Renew Sustain Energy Rev 79:1108– 1121 10. Upadhyay C, Chandwani H (2014) Parameter calculation of super capacitor for hybrid storage system. In: International conference ICEI PDPU, Gandhinagar 11. Alsaidan I, Khodaei A, Gao W (2016) Determination of battery energy storage technology and size for standalone microgrids. In: 2016 IEEE power and energy society general meeting (PESGM). IEEE, pp 1–5

Effect of Modulation Index of Nonlinearly Decreasing Inertia Weight on the Performance of PSO Algorithm for Solving ELD Problems Nimish Kumar, Promit Kumar Saha, Nitai Pal, and Neha Kumari

Abstract The adoption of an efficient approach for tuning inertia weight is one of the most effective methods of improving the performance of particle swarm optimization (PSO). Recently, the introduction of the modulation index in the nonlinearly decreasing inertia weight enhances the convergence characteristics of PSO. Economic load dispatch (ELD) is a famous optimization problem, and it is effectively solved by PSO with certain advantages. In this paper, influences of modulation index on PSO have been presented for ELD problem of IEEE 5, 14, and 30 bus systems. The minimum, maximum, average, and standard deviation of generation cost, average power loss, average iteration, and average computational time of 20 trial runs for ten different values of modulation index are computed using MATLAB program. The simulation results conclude that the value of modulation index 0.8, 0.4, and 1.8 provides high-quality solutions for IEEE 5, 14, and 30 bus systems, respectively. Keywords Particle swarm optimization · Nonlinear decreasing inertia weight · Modulation index · Economic load dispatch · Operational constraints

N. Kumar (B) · N. Kumari Department of Electrical and Electronics Engineering, Bakhtiyarpur College of Engineering, Patna, India e-mail: [email protected] N. Kumari e-mail: [email protected] P. K. Saha · N. Pal Department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, India e-mail: [email protected] N. Pal e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_77

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1 Introduction Economic load dispatch (ELD) is the prominent optimization problem of the power market. Basically, ELD is the determination of optimal combination of the power output of numerous generating units to meet the consumer’s load demand, at the minimum possible generation cost, satisfying all the operational constraints. Many (traditional as well as smart) techniques are present to solve ELD problems. The traditional techniques include Lagrangian multiplier method, lambda iteration method [1], linear programming method [2], etc. Most of these methods are unable to search optimal solution due to the drawback of trapping at the local minimum solution. On the other hand, many smart techniques such as ant colony optimization [3], genetic algorithm [4], particle swarm optimization (PSO) [5] have proven that they are intelligent than traditional techniques in solving ELD problems due to their skill to handle global optimization. Among all these techniques, PSO became most popular due to its advantages like easy algorithm concept, few control parameter involvement, fast convergence characteristics, ability to provide a high-quality solution, etc. The PSO comes into the category of swarm intelligence method that is preferably adapted to find the solution of an extremely complicated optimization problem. It is basically a generation-based metaheuristic optimization algorithm, introduced in 1995 by Eberhart and Kennedy [6], which is influenced by the group behavior of the natural process such as animal herding, bird flocking, fish schooling. The PSO method is globally used to handle ELD problems [7]. In this paper, nonlinearly decreasing inertia weight (NLDIW) is used in the PSO algorithm. Ten different values of modulation index (MI) are implemented in the NLDIW of the PSO algorithm for the solution of the ELD problems of IEEE 5, 14, and 30 bus systems. Detailed comparative investigations for 20 trial runs have been carried out for different MI. The minimum, maximum, average, and standard deviation of generation cost have been calculated for different MI of each bus system. The average power loss, average iteration, and average computational time have also been computed. Finally, recommendations for suitable MI have been presented for each bus systems.

2 Economic Load Dispatch Problem Formulation The ELD is an aspect in which the generating units of the power plant are scheduled such that the generation cost should be as minimum as possible for the consumer’s demand without violating operational constraints. The ELD problems are defined as follows:

Effect of Modulation Index of Nonlinearly Decreasing …

769

Minimize F =

n 

 (ai Pi2

+ bi Pi + ci ) + K ∗

PD + PL −

i=1

n 

 Pi

(1)

i=1

subjected to operational constraints. The objective function of ELD problems, i.e., ‘F’ in Eq. (1) is composed of two terms. The first term is the generation cost of the power plant (F G ) having ‘n’ number of thermal generators and is given by: FG =

n 

Fi (Pi ) =

i=1

n  (ai Pi2 + bi Pi + ci )

(2)

i=1

The generation cost of ith thermal generator, i.e., ‘F i (Pi )’ is expressed by the quadratic/parabolic expression of its power generation (Pi ) with the help of cost coefficients (ai , bi and ci ). The second term is the penalized demand constraint to impose the penalty on the power plant in case of violating the power commitment. ‘K’ is the penalty coefficient and is picked up correctly for the faithful solution. The demand constraint is basically the equality operational constraint which reveals that the total power generation should be equal to the sum of consumer’s demand (PD ) and system losses (PL ) and is represented as: n 

Pi = PD + PL

(3)

i=1

The loss in the system is formulated with the help of power generation and loss coefficients (Bij , Bi0 and B00 ) and is given by: ⎛ ⎞ n n n    ⎝ PL = (Pi Bi j Pj )⎠ + (Bi0 Pi ) + B00 i=1

j=1

(4)

i=1

Further, for the system security point of view, the inequality operational constraints are introduced which reveals that power generation of the generating units must be between the lower and upper limits of its power generation for stable operation and is expressed as: Pimin < Pi < Pimax

(5)

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3 Particle Swarm Optimization Algorithm The PSO is a population-oriented stochastic optimization technique, which is effectively applicable to both continuous and discrete optimization problem of the globe. The population is basically the potential/candidate solutions which are individually called as a particle and massively called swarm. The particles are initially introduced with random position and random velocity. Each particle is then appraised by objective function to trace whether they are the best solution or not. Further, they fly in the search space from an old position to a new position with the help of new velocity. The new velocity and the new position are formulated using the following equations: V new = V old + c1r1 (P best − X old ) + c2 r2 (G best − X old )

(6)

X new = X old + V new

(7)

where X old and V old are the position and velocity at the old stage. Pbest and Gbest are the best position of the particle and best position of the swarm. c1 and c2 are the acceleration coefficients. r 1 and r 2 are the uniformly distributed random numbers between 0 to 1. X new and V new are the position and velocity at the new stage. The movement of the particles can be seen in Fig. 1. Two phenomenons occur in the PSO search mechanism, so-called exploration and exploitation. To balance between them, the concept of inertia weight ‘w’ has been introduced by Shi and Eberhart [8] in PSO and also suggested that ‘w’ should be linearly decreasing from 0.9 to 0.4. The introduction of ‘w’ is given by: V new = wV old + c1r1 (P best − X old ) + c2 r2 (G best − X old )

(8)

Chatterjee and Siarry [9] proposed a NLDIW for the improvement in the performance of PSO by the following equation: Fig. 1 Search mechanism of PSO

Pbest

Gbest Xnew

Vnew

c2r2(Gbest - Xold)

c1r1(Pbest – Xold)

old

X

Vold

Effect of Modulation Index of Nonlinearly Decreasing …

w = wfinal + (winitial − wfinal )[(I tmax − I t)/I tmax ]m

771

(9)

where winitial (0.9) and wfinal (0.4) are the value of inertia weight at the start and at the end of the run, respectively. It max and It are the maximum numbers of iteration set by the user end and the current value of ongoing iteration during run respectively. m is the nonlinear MI. If m = 1, then Eq. (9) becomes linearly decreasing inertia weight proposed by Shi and Eberhart. Generally, the process for PSO algorithm composed of the following steps: Step 1: Initialization. 1.1 Control parameters 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5

Swarm size ‘P.’ Maximum number of iteration ‘It max .’ Tolerance limit for termination ‘T.’ Inertia weight ‘w’ and modulation index ‘m.’ Constant parameters c1 , c2 , r 1 , r 2 .

1.2 Positions of the particles. 1.3 Velocities of the particles. Step 2: Optimization. 2.1 Evaluate objective function. 2.2 Assign Pbest and Gbest . 2.3 Modify all particle velocities using Eq. (8). 2.4 Modify all particle positions using Eq. (7). 2.5 Calculate objective function using a new position. 2.6 Modify Pbest and Gbest . Step 3: Termination. 3.1 Investigate whether reached stopping condition. 3.2 If yes, then Gbest is the optimal solution. If no, then go to 2.3.

4 Computational Results and Discussions In this paper, the solution of ELD problems of IEEE 5, 14, and 30 bus systems with the help of PSO has been presented. The NLDIW has been used in PSO, and variations of MI have been implemented. Ten different values of MI have been chosen in a regular pattern. The values of the control parameters that are considered for PSO have been shown in Table 1. The value of the penalty coefficient of objective function has been taken as 50 for all bus systems. The standard load demand, loss coefficients, maximum and minimum limits of power generation, and cost coefficients data for bus systems have been taken from [10].

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Table 1 Control parameters of PSO and value taken

Control parameters

Value

Swarm size ‘P’

25

Maximum number of iteration ‘It max ’

500

Tolerance limit for termination 10–6 ‘T ’ Acceleration coefficients (c1 and c2 )

2

Random numbers (r 1 and r 2 )

0.5

winitial and wfinal

0.9 and 0.4

Modulation Index ‘m’

0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0

The minimum, maximum, average, and standard deviation of generation cost, average power loss, average iteration, and average computational time have been formulated for 20 trial runs for each bus systems. These values for ten different MI have been presented in Table 2, Table 3, and Table 4 for IEEE 5, 14, and 30 bus systems, respectively. In Table 5, at least two MI have been selected that gives better performances on the basis of optimum cost, consistent result, minimum loss, and fast convergence for each bus system. The optimum cost is the measure of minimum generation cost, and the consistent result is the measure of the standard deviation of generation cost. Similarly, the minimum loss is based on average power loss, and the fast convergence is based on average iteration or average computational time. The Table 2 Summarized results of IEEE 5-bus system Modulation Generation cost ($/h) Average Average Average index (m) iteration computational Minimum Maximum Average Standard power loss time (s) deviation (MW) 0.2

761.135

762.462

761.516 0.551

5.189

257

0.824

0.4

761.142

764.775

761.783 1.153

5.172

227

0.600

0.6

761.141

763.461

762.344 0.779

5.137

204

0.527

0.8

761.135

762.253

761.479 0.446

5.176

188

0.510

1

761.365

763.358

762.223 0.671

5.129

171

0.489

1.2

761.135

762.616

761.666 0.558

5.168

164

0.544

1.4

761.135

764.775

762.159 1.246

5.160

155

0.483

1.6

761.135

762.756

761.701 0.565

5.158

144

0.438

1.8

761.135

763.499

761.697 0.851

5.174

135

0.431

2

761.135

764.553

762.356 1.347

5.165

127

0.386

The bold value indicates the optimum value. It has been bolded to select the corresponding value of modulation index (MI) for the recommendation

Effect of Modulation Index of Nonlinearly Decreasing …

773

Table 3 Summarized results of IEEE 14-bus system Modulation Generation cost ($/h) index (m) Minimum Maximum Average

Average Average Average iteration computational Standard power time (s) deviation loss (MW)

0.2

1144.494

1179.062

1166.614 16.442

7.645

280

1.115

0.4

1144.132

1182.576

1153.549 11.566

8.660

242

0.914

0.6

1145.750

1184.927

1158.515 12.384

8.738

242

0.852

0.8

1147.902

1194.409

1165.406 15.736

8.008

199

0.797

1

1144.775

1179.684

1155.258 10.720

8.569

186

0.710

1.2

1145.365

1199.287

1160.688 16.921

8.226

173

0.631

1.4

1147.760

1182.551

1158.502 13.455

8.149

163

0.603

1.6

1145.029

1183.750

1157.575 13.539

8.210

149

0.675

1.8

1144.426

1181.790

1162.121 16.293

8.091

152

0.596

2

1146.904

1176.795

1160.070 12.385

7.914

149

0.519

The bold value indicates the optimum value. It has been bolded to select the corresponding value of modulation index (MI) for the recommendation

Table 4 Summarized results of IEEE 30-bus system Modulation Generation cost ($/h) index (m) Minimum Maximum Average

Average Average Average iteration computational Standard power time (s) deviation loss (MW)

0.2

1256.211

1273.791

1259.293 5.238

11.811

277

0.883

0.4

1256.290

1266.082

1259.666 3.332

12.262

244

0.736

0.6

1256.354

1284.145

1261.864 8.170

11.542

215

0.707

0.8

1256.488

1273.002

1259.833 5.223

11.396

200

0.656

1

1256.273

1269.604

1259.658 4.275

11.395

185

0.635

1.2

1256.218

1267.539

1259.438 3.791

11.565

174

0.587

1.4

1256.195

1275.694

1260.603 6.082

11.434

158

0.584

1.6

1256.400

1285.036

1263.598 9.110

10.962

165

0.608

1.8

1256.194

1264.528

1258.936 3.433

11.579

150

0.565

2

1256.476

1270.762

1260.492 5.614

11.398

144

0.533

The bold value indicates the optimum value. It has been bolded to select the corresponding value of modulation index (MI) for the recommendation

best value of MI has also been recommended on the basis of overall performances in Table 5 for each bus system. The minimum generation cost of 761.135 $/h has been seen when MI is taken as 0.2, 0.8, 1.2, 1.4, 1.6, 1.8, and 2, while the maximum cost of 764.775 $/h has been accounted in case of MI value 1.4 is chosen for IEEE 5 bus system. The best average

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Table 5 Suitable modulation index on different basis Bus system

Optimum cost

Consistent result

Minimum loss

Fast convergance

Recommendations

IEEE 5

0.2, 0.8, 1.6, 1.8

0.2, 0.8

0.6, 1.0

1.8, 2.0

0.8

IEEE 14

0.4, 1.8

0.4, 1.0

0.2, 2.0

1.8, 2.0

0.4

IEEE 30

1.4, 1.8

0.4, 1.8

1, 1.6

1.8, 2.0

1.8

The bold value indicates the optimum value. It has been bolded to select the corresponding value of modulation index (MI) for the recommendation

generation cost of 761.479 $/h and the minimum average power loss of 5.129 MW have been obtained in case of MI value 0.8 and 1 are taken respectively. Further, the standard deviation of 0.446 $/h in case of MI value 0.8 is taken and an average iteration of 127 and average computational time of 0.386 s in case of MI value 2 is taken have been observed. Further, the minimum generation cost of 1144.132 $/h has been seen when value of MI is taken as 0.4, while the maximum cost of 1199.287 $/h has been accounted in case of MI value 1.2 is taken for IEEE 14 bus system. The best average generation cost of 1153.549 $/h and the minimum average power loss of 7.645 MW have been obtained if the value of MI 0.4 and 0.2 is chosen respectively. Further, the standard deviation of 10.720 $/h in case the MI value taken is 1 and an average iteration of 149 and average computational time of 0.519 s in case the taken value of MI is 2 have been observed. Similarly, the minimum generation cost of 1256.194 $/h has been seen when MI is taken as 1.8, while the maximum cost of 1285.036 $/h has been accounted in case of the value of MI taken is 1.6 for IEEE 30 bus system. The best average generation cost of 1258.936 $/h and the minimum average power loss of 10.962 MW have been obtained in case the taken value of MI is 1.8 and 1.6 respectively. Further, a standard deviation of 3.332 $/h in case of the value of MI is 0.4 and an average iteration of 144 and the average computational time of 0.533 s in case the value of MI taken is 2 have been observed. The value of MI ‘0.8’ has been recommended for IEEE 5 bus system because it gives optimum generation cost of 761.135 $/h and best average generation cost of 761.479 $/h with consistency, i.e., minimum standard deviation of 0.446 $/h. For IEEE 14 bus system, the value of MI ‘0.4’ has been suggested because it provides optimum generation cost of 1144.132 $/h and best average generation cost of 1153.549 $/h with a standard deviation of 11.556 $/h. Similarly, the value of MI ‘1.8’ in case of IEEE 30 bus system grants optimum generation cost of 1256.194 $/h and best average generation cost of 1258.936 $/h with a standard deviation of 3.433 $/h.

Effect of Modulation Index of Nonlinearly Decreasing …

775

5 Conclusions and Future Scopes The MI in inertia weight significantly improves the performance of PSO. Proper selection of MI plays a vital role in solving the ELD problems with the help of PSO. Different MI has some advantages over others. In general, when MI increases from 0.2 to 2, the convergence rate of PSO also increases for all bus systems. More clearly, the higher value of MI takes less iteration and less computational time to give the result and lower value of MI takes more iteration and more computational time to give the result. Further, the value of MI 0.8, 0.4, and 1.8 is much more suitable for IEEE 5, 14, and 30 bus systems, respectively. In future scopes of work, other different values of MI such as 0.1, 0.3, 0.5, and their influence can be investigated in NLDIW of PSO for solving ELD problems. The other characteristics of generating unit like valve point effect can be included in the formation of the objective function. The multi-objective function of ELD problems can be solved using PSO. In the multi-objective such as minimization of emission cost, minimization of system losses, etc. can be included.

References 1. Wadhwa CL (2009) Electrical power systems. New Age International, New Delhi 2. Jabr R, Coonick A, Cory B (2000) A homogeneous linear programming algorithm for the security constrained economic dispatch problem. IEEE Trans Power Syst 15(3):930–936 3. Sum IT (2004) Economic dispatch by ant colony search algorithm. In: Cybernet intelligent systems, vol 1, IEEE, Singapore, pp 416–421 4. Chen PH, Chang HC (1995) Large-scale economic dispatch by genetic algorithm. IEEE Trans Power Syst 10:1919–1926 5. Meng K, Wang HG, Dong ZY, Wong KP (2010) Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Trans Power Syst 25(1):215–222 6. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: International conference on neural networks, IEEE, Perth, pp 1942–1948 7. Kumar N, Pal N, Kumar P, Kumari A (2018) Impact of different inertia weight functions on particle swarm optimization algorithm to resolve economic load dispatch problems. In: International conference on recent advances in information technology. Dhanbad, pp 1–5 8. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: International conference on evolution computing. IEEE, Nagoya, pp 69–73 9. Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res 33:859–871 10. Kumar N, Nangia U, Sahay KB (2011) Economic load dispatch using improved particle swarm optimization algorithms. In: International conference on power India. IEEE, New Delhi, pp 1–6

Sensitivity Factor Analysis of Hybrid AC–DC Distribution System and Overload Alleviation Using Modified Generator Shift Distribution Factor Abhinandan Kumar and T. Ghose

Abstract A hybrid AC–DC distribution system is an integration of AC and DC sub-grids with the help of interlinking converters (ILC) and its controller. This type of microgrid architecture may consist of several AC and DC loads, sources and lines. The lines are subjected to cases of overload as the loads present in a system are unpredictable by nature. The generation shift distribution factor (GSDF) or also known as K-factor is the measure of sensitivity for power flowing in a particular line with respect to any change in the current injected at the generator buses of the system. This paper presents a detailed analysis of calculating modified GSDF (Kfactors) for a hybrid AC–DC distribution system consisting of multiple AC and DC lines. The sensitivity of every line of the system with respect to the generators is used to determine the changes in the generation to be made to overcome the line overloading problem. A 13-bus hybrid microgrid (HMG) is used for the analysis and investigation of generation re-dispatch for the corrective action taken to remove line overload contingency. Keywords AC–DC hybrid distribution system · Microgrid · Line overload contingency · Generation re-dispatch · Generation shift distribution factor

1 Introduction In recent times with the introduction of various DC-based sources like photovoltaic (PV) cells and loads like electric vehicle (EV) charging, the co-existence of AC and DC system is taking over conventional AC grid [1–3]. The power system being highly unpredictable and complex always encounters the challenges of contingencies like line overload, line outage, protective equipment failure, transformer failure, unexpected load changes, etc. The post-contingency actions are very much important A. Kumar (B) · T. Ghose Birla Institute of Technology, Mesra, Ranchi 835215, India e-mail: [email protected] T. Ghose e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_78

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to decide the fate of the system further in time. During the case of overload of line due to load changes or subsequently due to outage of other lines, generation re-dispatch is sought as a solution to remove the contingency. In [4], the single and multiple line contingencies have been analysed by formulating GSDF or K-factors for AC networks with the help of its Z-bus. The K-factors are calculated using line reactance values only and by neglecting the line resistances. In [5], the authors have proposed a linear programming-based technique calculating GSDFs and line outage distribution factor (LODF) of every lines to remove line overload by rescheduling of generation avoiding load shedding. It is to be noted that the formulation of GSDFs and LODFs is based on Z-bus which itself is calculated neglecting line resistances. In [6], although the authors have formulated GSDFs considering complete line impedances for IEEE 9 bus system, the inference is very complex and is limited to purely AC network. In [7], the authors have exhibited the benefits of using HVDC lines for reducing cascaded outage to avoid blackouts of the system. The work shows the analysis of how HVDC link helps reduce stress on parallel AC lines to avoid cascaded outages. The authors in [8] have developed a virtual instrument based on DC power flow for contingency analysis for estimation of the impact due to generator and/or line outage. Most of the works discussed above have neglected the contribution of line resitance while calculating the GSDFs. Thus getting the K-factors as real values. The study presented in the paper has proposed calculation of modified GSDFs developed for AC–DC hybrid distribution system considering line impedances in terms of line resistance and reactances together. The impedance matrix or the Z-bus of the system is used to determine the sensitivity of each line with each generator presented in the system, i.e. the corresponding change in power flow through a particular line when the injected power at any generator bus is changed. The modified GSDFs or K-factors will show how much generation we should change in order to reduce overloading of a particular line. A 13-bus hybrid AC–DC distribution system [9] has been considered for the purpose of analysis. Thus, the modified GSDFs or the K-factors provide us with an idea regarding which generation or what combination of generation is to be changed by how much amount to alleviate overloading of a particular line.

2 Generation Shift Distribution Factor 2.1 Formulation of Z-bus The hybrid AC–DC distribution system accomodates utilities of power systems which are in the domain of AC as well as DC. The lines present in the system are likewise AC or DC as well. It is well established that an AC line impedance is complex in nature, whereas DC lines are purely resistive. The Z-bus of the AC–DC network can be formulated using the conventional four types of modification techniques available which are type-I: addition of new bus to reference (ground potential) bus, type-II: addition of a new bus to an existing old bus, type-III: connecting old bus to reference

Sensitivity Factor Analysis of Hybrid AC–DC Distribution …

779

bus and type-IV: adding a line between two existing old buses. ⎡

Z bus

⎢ ⎢ ⎢ =⎢ ⎢ ⎢ ⎣

Z 11 Z 21 Z 31 .. . Z n1

Z 12 Z 13 Z 22 Z 23 Z 32 Z 33 .. .. . . Z n2 Z n3

· · · Z 1n · · · Z 2n · · · Z 3n . .. . .. · · · Z nn

⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(1)

where Z ii are the thevenin impedances of representative ith buses and the thevenin impedance between the buses j and k is given as follows: Z th, jk = Z j j + Z kk − 2Z jk

(2)

2.2 Calculation of Modified GSDFs for AC–DC Network Let us consider a system of N bus and it is assumed that the system is in steady state of operation with the voltages at N buses as V 1 , V 2 , V 3 , ….V N . Now when an additional current I m is injected at mth bus, the steady-state voltages are bound to change. The changes in bus voltages are given by: ⎤ ⎡ ⎤ ⎡ ⎤ V1 − V1 V1 0 ⎢ . ⎥ ⎢ ⎥ .. ⎢ .. ⎥ ⎢ ⎥ . ⎥ ⎡ ⎢ ⎤ . ⎢ ⎥ ⎢ ⎥ ⎢ .. ⎥ column ⎢ ⎥ ⎢  ⎥ ⎢ ⎥ ⎢ Vi ⎥ ⎢ Vi − Vi ⎥ ⎥ ⎣ o f ⎦ Im ⎢ ⎥=⎢ ⎥ = Z bus ⎢ ⎢ Im ⎥ = ⎢ V j ⎥ ⎢ V j − V j ⎥ ⎢ . ⎥ ⎢ . ⎥ ⎢ ⎥ Z bus ⎣ .. ⎦ .. ⎢ . ⎥ ⎢ ⎥ ⎣ . ⎦ ⎣ ⎦ . 0 VN VN − VN ⎡

(3)

Thus, the respective changes in voltage at ith and jth buses due to injection of I m at mth bus are given as: Vi = Z im Im V j = Z jm Im

(4)

The current in the line between the buses i and j with impedance Z c is given by: Ii j =

Vi − V j Zc

(5)

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Also, the corresponding change in current of line i–j is given as: Ii j =

Vi − V j Zc

(6)

Now, the complex power flowing in the line i–j can be stated as: Si∗j = Vi∗ Ii j

(7)

And the change in complex power flowing in the line i–j is given as: Si∗j = Vi∗ Ii j

(8)

Using Eqs. (4) and (6) in Eq. (8), we get: ∗ Im∗ Si∗j = Z im

Vi − V j Zc

(9)

On solving further and simplifying:  Z im − Z jm Im∗ ∗ Sm∗ Z Vm∗ im Zc  ∗  Z im Z im − Z jm Sm∗ Si∗j = ∗ Z mm Zc

Si∗j =



(10) (11)

From the definition, the K-factor obtained for line i–j and generator bus m: 

K i j,m

Si j = = Sm



Z im Z mm



∗ Z im − Z ∗jm



Z c∗

(12)

The superposition principle is valid to study the effect of power injection changes at multiple generator buses. 



Si j = K i j,m Sm + K i j,n Sn . . .

(13)

It is significantly deduced from [6] that individual impacts on real power flow of line and its reactive power flow can be stated as: 



 Pi j = Real Si j = Real K i j,m Sm + K i j,n Sn . . .



  Q i j = I mag Si j = I mag K i j,m Sm + K i j,n Sn . . .

(14)

Sensitivity Factor Analysis of Hybrid AC–DC Distribution …

781

2.3 Significance of K -factor In [5], the authors have stated that the sign associated with the K-factor symbolizes the increase or decrease of power flow in the line for associated change in power injected at the generator bus. Negative K-factors symbolize that with current injection in the generator bus, the power flowing in the consecutive line decreases. Whereas the positive K-factors symbolize that with power injection in the particular bus, the power flowing in the corresponding line will increase. The power change in a particular line is an algebraic sum of the proportional power change in every generator. So, the sign of the proportionality factor, i.e. the K-factor, gives the balancing effect on the equation. However, while dealing with any AC–DC hybrid distribution system, the GSDF obtained is complex in nature in majority of the lines. The GSDFs for AC–DC distribution system are given by Eq. (12) and are denoted by K ij,m . From experimental and mathematical analysis, it is found that no conclusive statement regarding significance of the sign of the K-factor and the product rules of complex numbers are applicable suitably for the justification of changes in power flow of the corresponding line. Equation (14) gives a valid and substantial evidence of how real and reactive power flows of a line are collectively affected by real and reactive power changes at generator buses. The K-factors in conjunction with the Newton Raphson (NR) load flow helps alleviation of overload while estimating new steadystate operation points of the distribution system. The knowledge of the amount of line overload with pre-determined K-factors helps to calculate proportional changes in generation. A 13-bus AC–DC hybrid distribution system is shown in Fig. 1 and the line data for the network is given in Table 1.

3 Results and Discussion The K-factors obtained for 13-bus hybrid AC–DC distribution system are found from line data (Table 2) using Eq. (12). The line power flow data for the considered 13-bus AC–DC test system from unified load flow analysis based on NR technique is presented in Table 3.

3.1 Overload Removal of Line 7–8 (AC Line) From Table 3, we see that real power flowing through the line 8–7 is 1.5766 MW and let us suppose line 8–7 gets overloaded beyond power flow of 1.50 MW and rest other line have sufficient margin for power flow. Thus, the line experiences an overload of 0.0766 MW. The K-factors of the line associated with the three generators present can be seen in Table 2. Now by using (13), the operator can select all possible combinatios of changes in injected generation so that the line 8–7

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DC AC

Slack bus

V1 = 1.05 p.u. d1 = 0

9

1

P2 = 2.0 MW Q2 = 0.4 MVar

V8 = 1.0 p.u. P8gen = 2.5 MW Q8(min) = 0.10 MVar Q8(max) = 0.75 MVar

V3 = 1.0 p.u. 2 P3gen = 2.5 MW Q3(min) = 0.10 MVar Q3(max) = 0.75 MVar 3 AC DG

10 Zc

AC DG

V10dc

M = 0.99

P3 = 2.5 MW Q3 = 0.5 MVar

P9 = 2.5 MW Q9 = 0.5 MVar

P8 = 1.0 MW

8 Q8 = 0.1 MVar

Zc 11

M = 0.99

V11dc

4 P4 = 1.0 MW

7

12 V12dc

Zc P7 = 2.5 MW Q7 = 0.5 MVar

M = 0.97

Zc 13

M = 0.96

V13dc V5 = 1.0 p.u. 5 P5(min) = 0.5 MW P5(max) = 2.0 MW

6 P6 = 1.0 MW

DC DG

Fig. 1 13-bus AC–DC hybrid distribution test system

is relieved from the overload and its power flow is restricted to its rated capacity. Here, a case was taken such that generator at bus 3 was rescheduled by a change of (−0.2–0.2j) MVA and generator at bus 8 by a change of (0.2 + 0.2j) MVA. It is considered that the generator at bus 5 which is DC DG is non-despatchable for the case. This rescheduling is suggested in accordance with the K-factors obtained fort he system.This rescheduling of generation in the system brings down the power flow in the line 7–8 by 0.07782 MW and the new power flowing in the line is 1.4987 MW which within the capacity of the line considered. This is verified against the power flow solution by NR method for new set points of generation and the line 8–7 experiences a power flow of 1.4913 MW which is approximately same as the estimated value. It is to be duely taken care that while changing the injected generation at the generator buses, the constriant of net generation as the sum of net load and net losses

Sensitivity Factor Analysis of Hybrid AC–DC Distribution … Table 1 13-bus AC–DC hybrid distribution system line data

From bus

To bus

783 R ()

X ()

1

2

0.2218

0.3630

1

9

0.2218

0.3630

2

3

0.8870

1.4520

3

10

0.0500

0.7540

3

11

0.0500

0.7540

4

5

0.2208*

0.0000

4

11

0.4415*

0.0000

5

6

0.2208*

0.0000

6

13

0.4415*

0.0000

7

8

0.4435*

0.7260

7

12

0.0500

0.7540

7

13

0.0500

0.7540

8

9

0.4435

0.7260

10

12

0.8830*

0.0000

* represents DC lines

is honoured. Thus, these constraints in addition to framed objective function help the operator select most appropriate and feasible combinations of generator rescheduling without shedding any load. This can be implemented using a suitable optimization technique.

4 Conclusion The generator rescheduling is one of the major remedial techniques for overload removal of a line without shedding of loads. The calculation of modified GSDFs is not specific to AC–DC distribution system but for varied systems where Z-bus is computed considering not only reactances but resistances as well. These sensitivity factors provided the operator with a knowledge of possible combinations to remove overload of line at stress. It can be confidently concluded that the power flow of a line, i.e. real and reactive, are sensitive to both real and reactive powers injected at any generator bus. The judicious application of modified GSDFs or K-factors thus helps the operator to remove line overload by selecting proper combinations of generators.

784 Table 2 K-factors or GSDFs for 13-bus AC–DC distribution network

A. Kumar and T. Ghose Lines

Genr. bus 3 (AC)

Genr. bus 5 (DC)

Genr. bus 8 (AC)

1–2

0.0000 + j0.0000

0.0000 + j0.0000

0.0000 + j0.0000

1–9

0.0000 + j0.0000

0.0000 + j0.0000

0.0000 + j0.0000

2–3

−0.1274 − j0.0034

−0.3339 − j0.0759

−0.0202 − j0.0002

3–10

−0.8819 + j0.5162

−0.3166 − j0.3226

0.2538 − j0.1286

3–11

−0.8819 + j0.5162

1.0184 − j0.0844

0.2538 − j0.1286

11–4

4.7433 + j8.7592

4.2662 + j8.0759

0.0000 + j0.0000

4–5

0.2886 + j0.0011

−0.1034 − j0.0157

−0.1371 + j0.0081

5–6

0.2886 + j0.0058

0.8956 − j0.0185

−0.1391 + j0.0047

6–13

−2.7286 − j4.2488

−10.8927 − j10.8734

1.2936 + j2.1817

2.5605 − j1.1686

40.4324 − j10.4300

0.0000 + j0.0000

7–12

−0.9487 + j0.3797

2.6574 − j0.9658

0.7825 − j0.3965

7–8

0.2067 + j0.0055

1.6414 + j0.2720

−0.1771 + j0.0108

8–9

0.1240 + j0.0033

1.0017 + j0.2276

0.7823 + j0.0103

10–12

0.0000 + j0.0000

−1.4165 − j0.1403

0.0000 + j0.0000

13–7

Sensitivity Factor Analysis of Hybrid AC–DC Distribution … Table 3 Line power flow for 13-bus AC–DC distribution network

From bus

To bus

785

Real power (MW)

Reactive power (MVar)

1

2

2.2650

0.5314

1

9

2.6651

0.7032

2

3

0.2022

0.0284

3

10

0.6203

0.2407

3

11

0.5797

0.1243

11

4

0.4847

0.0000

5

4

0.4485

0.0000

5

6

1.4338

0.0000

6

13

0.4531

0.0000

13

7

0.4053

0.0583

8

7

1.5766

0.3679

9

8

0.0768

0.0587

10

12

1.5325

0.0000

12

7

0.5869

0.2102

References 1. Hatziargyriou N (2014) In: Microgrids architectures and control. Wiley. ISBN: 978-1-11872068-4 2. Farhangi H (2017) Smart microgrids: lessons from campus microgrid design and ımplementation. Taylor and Francis. ISBN:13: 978-1-4822-4876-0 3. Sechilariu M, Locment F (2017) Urban DC microgrid: ıntelligent control and power flow optimization. Elsevier. ISBN: 978-0-12-803736-2 4. Grainger JJ, Stevenson WD (1994) Power system analysis. McGraw-Hill 5. Malkhandi A, Ghose T (2016) Line overload contingency filtering and fast corrective generation re-dispatch action using linear programming. In: World Congress on Engineering Application, Bangkok 6. Dongping A, Hai B, Yihan Y. A new method of calculating generation linear distribution factors by least square fitting. In: IEEE 2009 international conference on sustainable power generation and supply 7. Benasla M, Allaoui T, Brahami M, Boudali A. Benefits of HVDC for reducing the risk of cascading outages and large blackouts in AC/DC hybrid grid. In: IEEE 2015 3rd international conference on control, engineering & information technology (CEIT) 8. Slimane S, Benhamida F, Salhi Y, Bendaoud A, Gherbi FZ (2013) Sensitivity factor for power system security analysis using LabView. In: International renewable and sustainable energy conference (IRSEC) 9. Ahmed HMA, Eltantawy AB, Salama MMA (2018) A generalized approach to the load flow analysis of AC–DC hybrid distribution systems. IEEE Trans Power Syst 33(2)

Study of Different Passive Islanding Detection Techniques and Verification by MATLAB Simulation Jayshree, Jai Prakash Sharma, and Om Hari Gupta

Abstract This paper presents a delineation of passive islanding detection techniques. APV-connected microgrid system is studied and simulated in MATLAB/Simulink and a trip signal is generated in case of detection of islanding. Broadly classified, the two methods of islanding detection are remote and local methods. Passive methods being one of local methods that utilize local parameters are used for the analysis. Local parameters at PCC are taken into account and compared with threshold to generate trip signal for breaker so as to disconnect the DG after islanding. Passive islanding detection technique being of the most sophisticated technique in implementation has a great disadvantage of non-detectable zone, still can be used in small system where remote technique becomes cumbersome in implementation. Keywords Islanding · Non-detectable zone · Microgrid · Distributed generator · Islanding detection

1 Introduction Exhaustion of conventional energy resources and proliferate demand of electrical energy has led to search of alternate energy sources. The non-conventional energy sources lead to lesser pollution and taint on environment is greatly reduced. Solar being one of the most profuse source of energy can be harvested to meet this demand to a great extent. Most of the solar energy generation systems are installed at the distribution site owing to reduction in transmission losses. These distributed generators Jayshree (B) · J. P. Sharma · O. H. Gupta Department of Electrical Engineering, National Institute of Technology Jamshedpur, Jamshedpur, India e-mail: [email protected] J. P. Sharma e-mail: [email protected] O. H. Gupta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_79

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are connected to utility grid so as to maintain continuity of power to the consumers, also in case of surplus energy generation, the generated energy can be fed to the utility grid. When the solar energized system is disconnected from utility grid, in case of power mismatch, large fluctuation in voltage and frequency at the consumer end can be observed [1]. In such cases, it is required to shut down the system or provide alternate sources of energy. Islanding may be done intentionally for maintenance purpose or it may happen unintentionally [2]. Unscheduled transition is an unpremeditated event as cribable to equipment failure or faults in the system. So it is essential to find out islanding condition and remove the system in operation. There is various islanding detection techniques predominantly categorized as local and remote methods. Remote detection methods are costlier so we generally prefer local methods which are based on change in parameters at the DG set [3]. Passive, active, and hybrid are the local detection methods [2]. Passive is landing detection methods dependent on the basis of comparison of system data before and after islanding [4]. The paper presents an analysis of different passive methods.

2 Test System Model The test system consists of a photovoltaic-connected system linked to a DC–DC boost converter and a inverter with MPPT tracking system implemented with Perturb and observe algorithm [5]. Parallel RLC load is connected as per IEEE Std. 1547.1. Utility grid is connected at PCC through a transformer [6]. Here, in the given system, the rating of DG, PDG , and QDG are 100 kW and 0 kVA respectively and different loads (PL and QL ) are connected for three different cases. Three cases that has been taken for % mismatch in power. The % mismatch for these cases with respect to load is: Case (a) PL = 33.33%, QL = 38.3%; Case (b) PL = 20%, QL = 18.57%; and Case (c) PL = 0%, QL = 0%. A representative case of solar-based DG connected to grid and load is shown in Fig. 1 which also illustrates the islanding.

3 Passive Techniques Passive techniques are based on change in system parameters before and after islanding. When islanding occurs in the system, large fluctuation in system parameters is observed [7]. These data are compared with set threshold and if it exceeds the threshold, a trip signal initiates the operation of circuit breaker. The corresponding flow chart is included in Fig. 2. Different passive techniques are discussed next.

Study of Different Passive Islanding Detection …

789

Island

PG+QG

PDG+QDG DC-DC Conv.

DC-AC Conv.

PCC GRID

PL+QL

Irradiation

Xmer LOAD

Fig. 1 Solar-based DG and islanding phenomenon in a microgrid Fig. 2 General flowchart of passive islanding detection

START

Read the value of COF,COV,ROCOAP,ROCORP, ROCOF/RP etc.(A)

No A>=Threshold

Yes Trip signal (Islanding detected)

To Breaker

790

Jayshree et al.

3.1 Change of Voltage (COV) If there is an active power mismatch between DG power and load demand, the change in voltage can be observed at PCC which is compared with the threshold voltage and a trip signal is generated [8]. However, COV method is unable to detect the islanding in case of insignificant power mismatch. Islanding detection by this for Case (a) simulation result is shown in Fig. 3a.

3.2 Change in Frequency (COF) A large fluctuations in frequency can be observed if there is reactive power mismatch at between supply and demand [7]. The change in frequency is compared with threshold frequency and a trip signal is generated as shown in Fig. 3b. It may be noted that COF method is unable to detect islanding in case of less reactive power fluctuation.

3.3 Rate of Change of Frequency (ROCOF) The ROCOF is observed for five continuous cycles to eliminate the false detection due to the presence of transients and then compared with the threshold to generate trip signal [9].

3.4 Rate of Change of Active Power/Reactive Power (ROCOAP/ROCORP) The ROCOAP and ROCORP were found crossing the threshold due to power mismatch and utilized to detect islanding. Moreover, it may be noted that this method become futile in the case of lesser power mismatch, i.e., when supply and demand are equal [10].

3.5 Rate of Change of Negative Sequence Voltage/Current (ROCONSV/ROCONSC) The sequence component is found out by sequence analyzer. The positive sequence component remains present in both islanded and grid connected mode while negative sequence component might present only in islanded mode of operation [11]. By

Study of Different Passive Islanding Detection …

Trip

Rms voltage (V)

Voltage (V)

10

791

4

2 0 -2 0

10

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

4

1.5 1 0 1 0.5 0

0

Time (seconds)

Frequency Hz

(a) Islanding detection by change in voltage 68 66 64 62 60 0

0.2

0.4

0.6

0.8

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1

1.2

1.4

1.6

1.8

2

Trip

1 0.5 0

Time (sec)

(b) Islanding detection by change in frequency

THD

0.2 0.1

Trip signal

0

0

0.2

0.4

0.6

0.8

0

0.2

0.4

0.6

0.8

1 1.2 Time (sec.)

1.4

1.6

1.8

2

1.4

1.6

1.8

2

2 1 0

1

1.2

Time (sec.)

(c) Islanding detection by total harmonic distortion Fig. 3 Simulation results for islanding for Case a Islanding detection by change in voltage, b Islanding detection by change in frequency, c Islanding detection by total harmonic distortion, d Islanding detection by rate of change in negative sequence voltage, e Islanding detection by impedance calculation at PCC

Jayshree et al.

V2

792 1000 500 0 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

dV2 /dt

10

5

2 0 -2

Trip

1 0.5 0

Time (seconds)

(d) Islanding detection by rate of change in negative sequence voltage. 10

6

Z (Ohms)

5

0 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Trip

1.5 1 0.5 0

Time (seconds)

(e) Islanding detection by impedance calculation at PCC. Fig. 3 (continued)

observation of ROCONSV and ROCONSC, islanding is being detected easily [12]. A trip signal can be generated after comparison with threshold shown in Fig. 3d. The advantage of this method is that it has negligible non-detectable zone.

3.6 Rate of Change of Positive Sequence Voltage/Current (ROCOPSV/ROCOPSC) Observation of change in positive sequence voltage and current is useful in islanding detection. Sequence components are being calculated by sequence analyzer to find

Study of Different Passive Islanding Detection …

793

out ROCOPSV and ROCOPSC, then compared with set threshold to detect islanding [13].

3.7 THD of Voltage/Current The THD is a measure of harmonics in AC signal. A perfect sign wave will have zero THD. When system is islanded, a large disfigurement in sinusoidal signal is observed [14]. When compared with appropriate threshold value, a trip signal can be generated, which is shown in Fig. 3c.

3.8 Change in Impedance Impedance at PCC can be easily obtained by the measurement of voltage and current at PCC. Generally, magnitude of impedance (Z mg ) of microgrid is large as compared to that of utility grid (Z ug ), i.e.,|Z mg | >>> |Z ug |, which can be easily identified as being seen in Fig. 3e. Though it being a simple method, it gives better result for islanding detection with negligible NDZ [13].

3.9 Rate of Change of Frequency Over Reactive Power/Active Power The islanding leads to large change in frequency over active/reactive power as in contrast to normal operation of microgrid [7]. These changes are helpful in detection of islanding by the selection of appropriate threshold value [15]. Simulation graphs for 5 cases have been shown above in Fig. 3. It is observed that for Case (a) taken the generated trip signal for islanding was as shown above, islanding was easily detected for Case (a), for rest of systems, similar graphs can be obtained. The denouement for different cases is as shown in Table 1. It can be inferred that, islanding is positively detected.

4 Conclusion Various islanding detection techniques were implemented and evaluated using the microgrid test model under different loading conditions. Passive islanding technique, being easier to implement, has some disadvantage of non-detectable zone. It was observed that most of passive detection techniques like change in voltage,

794 Table 1 Simulation results for various cases of load variations

Jayshree et al. Detection method

Time (ms) Case (a)

Case (b)

Case (c)

COV

15.4

218

ND

COF

64.9

66.9

ND

Impedance

18.9

18.7

13.0

THD (Voltage)

7.45

13

ND

THD (Current)

18.6

ND

ND

ROCOF

32

32.1

ND

ROCOAP

6.3

ND

ND

ROCORP

19.8

ND

ND

ROCOF/AP

158.8

235.8

ND

ROCOF/RP

105

151

ND

ROCONSV

3.1

3.4

ND

ROCOPSV

13.0

ND

ND

ROCONSC

5.8

12.4

ND

ROCOPSC

7.6

ND

ND

#ND-Not detected

change in frequency, change in active power/reactive power, were having large nondetectable zone as compared to other techniques like impedance calculation at PCC, total harmonic distortion, negative sequence voltage calculation during islanding. Out of above all methods, impedance method and negative sequence voltage method give optimum results.

References 1. Rukonuzzaman M (2019) Islanding detection and control of grid connected inverter. In: International conference on energy power engineering and power progress (ICEPE 2019) 2. Shrestha A et al (2019) Comparative study of different approaches for islanding detection of distributed generation systems. Appl Syst Innov 2(3):25 3. Yao X, Tan B, Hu C (2017) Islanding detection for PV plant using instantaneous power theory. In: Proceedings of 2016 IEEE advanced. Information management communication electronics. Automation control conference IMCEC 2016, no. 111, pp 1495–1498 4. Suresh K, Anusha P, Najma S, Rajkumar BI, Reddy CR, Lakshmi BP (2019) A passive islanding detection method for hybrid distributed generation system under balanced islanding. Indones J Electr Eng Comput Sci 14(1):9–19 5. Park S, Kwon M, Choi S (2018) Reactive power P&O anti-islanding method for a gridconnected inverter with critical load. IEEE Trans Power Electron 34(1):204–212 6. C. 21 (2011) IEEE standards coordinating. In: IEEE guide for design, operation, and integration of distributed resource island systems with electric power systems 7. Lakshmanan SA (2019) Islanding detection for grid connected solar PV system. In: India international conference on power electronics, IICPE

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8. Chandak S, Dhar S, Barik SK (2016) Islanding disclosure for grid interactive PV-VSC system using negative sequence voltage. In: 2015 IEEE power, communication, information, technology, conference (PCITC 2015)–Proceedings, pp 497–504 9. Gupta P, Bhatia RS, Jain DK (2017) Active ROCOF relay for islanding detection. IEEE Trans Power Deliv 32(1):420–429 10. A. Mathematics (2017) Islanding detection method for inverter based distributed generation based. 117(19):433–440 11. Liu N, Aljankawey A, Diduch C, Chang L, Su J (2015) Passive islanding detection approach based on tracking the frequency-dependent impedance change. IEEE Trans Power Deliv 30(6):2570–2580 12. Liu N, Diduch C, Chang L, Su J (2015) A reference impedance-based passive islanding detection method for inverter-based distributed generation system. IEEE J Emerg Sel Top Power Electron 3(4):1205–1217 13. Gupta OH, Tripathy M, Sood VK (2019) Islanding detection scheme for converter-based DGs with nearly zero non-detectable zone. IET Gener Transm Distrib 13(23):5365–5374 14. Jose BK, Vincent G (2018) Harmonic current based islanding detection for grid connected PV systems. In: IEEE international conference circuits systems ICCS 2017, vol 2018 (ICCS), pp 191–195 15. Nikolovski S, Baghaee HR, Mlakic D (2019) Islanding detection of synchronous generatorbased dgs using rate of change of reactive power. IEEE Syst J 1–11

Operation of Virtual Droop Resistance-Based Parallel Connected Boost Converters for Power Sharing and Minimization of Circulating Current Shalini Sinha and T. Ghose

Abstract More and more installation of DC sources like solar photovoltaic sources encourages to bring back the DC loads that uses DC generated power directly to meet the DC loads. In such DC microgrid system, voltage stability poses a major challenge because of low inertia in absence of rotating mass-based generating system. This paper proposes a controller for boost converter associated with DC source that ensures the parallel operation in multi-source-based system. Other than this objective, the controller performs well to restores its voltage under different contigent conditions like occurence of fault, large variation in load demand, etc. In response to voltage oscillations due to such unforseen events, the proposed controller controls the converter’s voltage by properly generating PWM pulses to the boost converters. To ensure proper load sharing between the sources and minimization of circulating currents, a virtual droop control method is incorporated by providing virtual Rdroop in series with cable resistance of DC microgrid. Also robustness of the proposed controller is tested under occurence of fault. The results clearly attest the effectiveness of the controller in proposed stand-alone microgrid. Keywords Boost converter · Circulating current · Droop resistance · DC microgrid · Photovoltaics · Voltage controller

1 Introduction Due to the marketprice of solar photovolatic (SPV) trending down day by day and favorable government policy, increased number of DC loads along with the advancement of power electronics-based converters have introduced DC microgrid system. Since traditional power system is dominated by AC microgrid and it is not possible to replace AC system completely but co-existence of AC–DC-based hybrid system S. Sinha (B) · T. Ghose Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] T. Ghose e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_80

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becomes a reality [1]. There are several advantages of DC microgrid over AC microgrid as reported in literature [2]. In DC microgrid, DC bus voltage is local phenomena that needs to be controlled for proper operation. There are different hierarchical levels of control of DC microgrid such as primary, secondary and tertiary control levels [3]. In primary control level, droop control method is conventional method for DC voltage stablization [4]. However, it has disadvantages of having poor load sharing due to the methodology adopted for droop action. Depending upon the nature of load, i.e., constant current load, constant power load, constant impedence load, simple resistive load, different stablization technique has adopted in the literature [5, 6]. To improve the primary control of DC voltage, an energy efficient method has proposed in [7]. This paper evaluates the performance of DC microgrid in islanded mode of operation having feedback voltage controller [8]. The feedback control strategy used to stabilize the system when the fault occurs. It will minimize the circulation current issues in DC microgrid [9]. The work addresses the following issues of DC microgrid system: (a) Implementation of virual resistance based droop control method by changing Rdroop values, (b) analysis of proper power sharing and minimization of circulating current between the converters, (c) voltage stability analysis on occurence of fault.

2 Closed-Loop Control Strategy of Two Parallel Connected Boost Converters The design of DC microgrid having parallel connected boost converter whose PWM pulse is gived by closed-loop voltage controller is given in Fig. 1. There are two PV arrays whose i–v characteristics are given in Eq. (1) connected with boost converter.

Fig. 1 Block diagram of parallel connected boost converter in DC microgrid

Operation of Virtual Droop Resistance-Based Parallel …

799

    v + rs v + ir se −1 − i = i pv − i 0 exp vT η r sh

(1)

There are two cable resistance r1 and r2 connected in series with virtual droop resistance (Rdroop) feeding the common DC resistive load. Mainly, it is closed-loop strategy to make the system stable when faults occurs.

3 Feedback Voltage Controller and Analysis of Circulation Current Upon alteration of load, the voltage deviates from its nominal value and leads to error signal. To minimize this error signal, parameter tuning of PI controller has taken place for stablize the system to its nominal voltage. Therefore, the measured PCC voltage at the DC bus is compared with the reference voltage; the error so produced is sent to the PI controller which give the PWM pulse to the boost converter gate as shown in Fig. 2. The control equation is given as: t

v(resultant) = v1(t)[(1 + kp(v1∗(t) − v1(t)) + ki ∫(v1∗(t) − v1(t))]

(2)

0

The value one has been added in Fig. 2 to ensure output voltage become equal to the PCC voltage when there will be no PV generation. For the proposed method, the output voltage of converter 1 and converter 2 is represented as V (conv1) and V (conv2) . Due to the mismatch of these converter’s output voltage, circulating current (I c ) flows. To minimize the circulation current that arises due to a mismatch in the converter’s output voltage, a Virtual droop [9] resistance method has been used which according to the deviation produced in the output voltage, will update the droop resistance value so that there will be proportional load sharing and circulation current minimization. In the proposed method, we connect a

Fig. 2 Block diagram of feedback voltage controller

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virtual resistance (Rdroop) in series with cable resistance (r1 and r2) and fine tuned the Rdroop values of converters 1 and 2 so that voltage difference between converters 1 and 2 become zero and circulation current reduces to zero. Applying KVL in Fig. 1. Suppose i1 and i2 be the branch current flows through the conv1 and conv2 , r 1 and r 2 is the cable resistances and V (conv1) and V (conv2) is the output voltage of converters 1 and 2, respectively. Applying KVL in Fig. 1 V(conv1) − i 1r1 − i LrL = 0

(3)

V(conv2) − i 2 r2 − i LrL = 0

(4)

Solving Eqs. (3) and (4), we get i1 =

(r1 + rL )V (conv1 ) − rL V(conv2) r1 r2 + r1 rL + r2 rL

(5)

i2 =

(r1 + rL )V (conv2 ) − rL V(conv1) r1 r2 + r1 rL + r2 rL

(6)

As we know when there will be mismatch in converter’s output voltage, then circulating current (I c ) flows which is given by: Ic =

V(conv1) − V(conv2) r1 + r2

i1 − i2 (i f r1 = r2 ) 2 i 1r 1 − i 2r 2 (i f r1 = r2 ) Ic = r1 + r2

(7)

Ic =

(8)

In the proposed method, the feedback voltage controller adapts itself at every droop resistance value in such a way, so that in all cases, it will makeV (conv1) = V (conv2) . Thus from Eq. (7), as r 1 = r 2 Ic = 0

(9)

4 Results and Discussion The proposed method has been applied considering three bus systems where two buses are connected with photovoltaic sources and the other bus is a load bus. Kyocera 200GT is used in the simulation and the input data are given in Table 1. The network

Operation of Virtual Droop Resistance-Based Parallel … Table 1 Input data

801

PV–Source 1 Voc of each string = 36.3 V kWp of each string = 200.143 W Number of parallel strings = 125 Number of series strings = 4 PV–Source 2 Voc of each string = 36.3 V kWp of each string = 213.15 W Number of parallel strings = 115 Number of series strings = 3 Cable resistance between source 1 and load = 100 m Cable resistance between source 2 and load = 100 m

is simulated at different droop values. The input data considered for PV sources and line resistances are given in Table 1. As seen in the first row of Table 2, the results are taken when there is no droop resistance are connected with the cable resistance. The value of circulation current is high having value 2A as calculated by Eq. (11). This high value of circulating current causes unnecessary heating and damage the insulation. Therefore, it needs to be minimized. The controller action helps to maintain the DC bus voltage at 243 V. The value of load resistance is 13 . The power sharing for case 1 in both the converter is given by Pconv1 = 3 kW, Pconv2 = 2.1 kW, whereas load power Pload = 5 kW, as shown in Fig. 3. The difference is lost in line resistances. When droop resistance is inserted in the system with values Rdroop1 = 0.05 , Rdroop2 = 0.24  with the given cable resistance, then the difference between converter’s voltage is negative. As a result, the direction of the circulation current in comparison to case 1 is negative. The power sharing between the converters is given as Pconv1 = 2.4 kW and Pconv2 = 2.7 kW and Pload = 5 kW shown in Fig. 4. Both the results show that a proper value of virtual resistance is to be chosen to make the circulation current small. As the difference in converter output voltage is found to be negative, then after tuning the R droop value to some extent, we get new Rdroop value as, Rdoop1 = −0.004  and Rdroop2 = 3.2 , then circulating current reduces to value 1.75A as shown in Eq. (11) and difference in converter output voltage get reduced and load voltage improves. To reduce the value of ripple in the output waveform, the value of inductor and capacitor is, L = 2 mH, C = 970 µF. The power sharing between the converter’s is given as, P(conv1) = 2.3 kW, P(conv2) = 2.8 kW and P(load) = 5 kW and the bus voltage improves to the value of 243 V as shown in Fig. 5.

V (conv1)

243.5

243.8

243.2

Cases

1

2

3

244

245

242

V (conv2)

242.9

242.6

243

V (load)

9.5

9.7

12

i1 (Amp)

Table 2 Case study for power sharing and circulation current

11.65

11.5

8

i2 (Amp)

-0.004

0.05

0

R(droop1)

0.32

0.24

0

R(droop2)

1.75

2.2

2

Circulation current (A)

19.45

19

18

Load current (A)

802 S. Sinha and T. Ghose

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Fig. 3 Converter’s and load output power having no droop resistance value

Fig. 4 Converter’s and load output power having two positive droop resistance values

Fig. 5 Converter’s and load output power having positive and negative droop resistance values

4.1 Result on Stability Analysis The result of closed-loop control of boost converter in DC microgrid at fault condition is shown below. The time of operation of circuit breaker has been set between 0.08– 0.1 s. On the occurence of fault at 0.08 s, the nominal voltage drops, and after 0.1 s, the proposed controller restores the output voltage of converter to 243 V with minimal

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Fig.6 Post-fault DC voltage

number of oscillations. The value of inductor and capacitor is tuned in such a way that it will reduce the ripple in the output waveforms as shown in Fig. 6.

5 Conclusion This paper proposes a closed-loop voltage controller which employs feedback topology in DC microgrid having virtual droop resistance connected with cable resistance connected with two parallel boost converters. In the proposed method, to enhance the load sharing and to minimize the circulating current issues in parallel connected DC microgrid, the value of virtual droop resistance is fine-tuned so that voltage controller makes DC bus voltage to restore its value nearly equal to 243 V at every droop value. The analysis shows that the circulation current reduces to maximum level by tuning the virtual droop values. The value of circulating current is high when the droop resistance is absent and it minimizes significantly on proper addition of droop resistance. The circulation current also plays a vital role in power sharing between the converters. The controller also shows its effectiveness in restoring the voltage during faults condition occurred in a microgrid in the nominal voltage of 243 V. The stability of the microgrid is ensured by analyzing the robustness of the controller which makes the system stable with minimal number of oscillations that occur in a system.

References 1. Justo JJ, Mwasilu F, Lee J, Jung JW (2013) AC-microgrids versus DC-microgrids with distributed energy resources: a review. Renew Sustain Energy Rev 24:387–405 2. Zhao J, Dörfler F (2015) Distributed control and optimization in DC micro-grids. Automatica 61:18–26 3. Dahale S, Das A, Pindoriya NM, Rajendran S (2017) An overview of DC-DC converter topologies and controls in DC microgrid. In: 7th International conference on power systems (ICPS), pp 410–415 4. Tahim APN, Pagano DJ, Lenz E, Stramosk V (2014) Modeling and stability analysis of islanded DC microgrids under droop control. IEEE Trans Power Electron 30(8):4597–4607

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Evaluation of the Performance of Droop Control Scheme for Interlinking Converter in Stand-Alone Hybrid Microgrid Shivaditya Chatterjee, Aftab Alam, and T Ghose

Abstract In an AC-DC hybrid microgrid, the AC and DC sub-grids accommodate both the sources and the loads which can be either AC or DC by nature. Interfacing of these AC and DC sub-grids are done by interlinking power converters. The converter can operate in rectifier and inverter or stop mode, according to the situation condition demands. Interlinking power converter which links the AC and DC sub-grids in the hybrid microgrid plays a vital role for achieving the stability of whole microgrid. A bidirectional droop control method for the converter is proposed, which measures the power demand of AC sub-grid and DC sub-grid in accordance with terminal AC bus frequency and terminal DC bus voltage, respectively. It also determines the direction and the magnitude of the power transfer through converter. The control of power being transferred between AC and DC sub-grids can be achieved easily using the proposed strategy. The effectiveness of control strategy is verified by time-domain simulation in MATLAB/Simulink

1 Introduction The idea of DER has encouraged the mix of sustainable power source assets to the AC main grid [1]. AC microgrid is formed due to rapid integration of AC source to AC grid with different loads and can be associated with the principle utility grid by interfacing converter [2]. In the event that by one way or another, the AC microgrid was over-burden and requires more power, and utility can be a reinforcement for help. In comparative manner, on the off chance that it was underloaded, the additional power from AC microgrid can be infused into the utility [3]. As of late, S. Chatterjee (B) · A. Alam · T. Ghose Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] A. Alam e-mail: [email protected] T. Ghose e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. J. B. Reddy et al. (eds.), Advances in Smart Grid Automation and Industry 4.0, Lecture Notes in Electrical Engineering 693, https://doi.org/10.1007/978-981-15-7675-1_81

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Load Load

=

-

+ =

Interlinking Converter

Load

DC Source

Load

DC Microgrid

=

Load

=

-

STS

+

Main Grid

Pgrid

=

Pactive AC microgrid

DC Source Load

Fig. 1 A hybrid microgrid distribution system

there has been an increase in DC loads, expanded performance and advancement of power electronic converters prompted the DC microgrids establishment. These DC microgrids mainly comprises DC sources, for example, energy components batteries and solar photovoltaic. DC grid consists of AC sources which were connected by converters. The presence of both AC and DC microgrid prompted another idea of hybrid microgrid [4]. AC and DC sub-grids are the part of hybrid microgrid connected by ILC. In future, smart grid frameworks ILC will play an important role. A hybrid AC/DC microgrid can operate in two modes in steady-state condition, i.e., in either grid-connected mode or isolated mode. In the grid-connected mode of operation, the microgrid is connected to the utility by a controlled switch [5]. The utility can be utilized to give and retain extra power whenever required. On the off chance that by one way or another, the microgrid builds up any shortcoming, and the switch can be utilized to detach the microgrid from the utility. For this situation, the microgrid works in isolated mode. There ought to be a legitimate balance among demand and supply in this mode since the utility is never again accessible to retain extra power. An AC/DC microgrid system is shown in Fig. 1. In an AC/DC hybrid microgrid, power sharing and control strategies are more complicated than DC or AC grid. In [6], an ILC operating in single-phase is shown, which interfaces one of the phases in the AC sub-grid with DC sub-grid. In [7], the thought process of multiple ILCs connected to a central controller is given. This controller architecture helps in improving the performance and precision of the system; however, because of multiple points of failure, the reliability of the system gets reduced. To overcome the problems that are faced in the centralized scheme, independent control strategy is proposed which is mostly founded on droop control. In [8], an AC-DC droop control strategy, V 2 dc + (Pac − Pdc ) and ω-Pac characteristics are considered, and a hybrid microgrid bifurcates into two sub-grids. Considering different working states of the AC/DC microgrid, a dead band is presented and to forestall the flow of power under different light load conditions. A hierarchical control architecture has been implemented in [9] by the authors to attain equal power sharing among parallel ILCs. In generators’ parallel operation, the change in voltage and frequency are very important. In [10], virtual synchronous generators’ concept has been introduced to limit the deviations, yet the system is not reasonable for hybrid AC/DC microgrid. Normalization of both AC frequency and DC voltage droop

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control scheme for ILC is given in [11]. There is a limitation on peak power flow through tie line connecting AC/DC grid; however, it is not taken into consideration in the bidirectional droop control scheme.

2 Challenges for Control of Bidirectional Converter: In many of the previous work, droop scheme was limited to interfacing of the AC frequency and DC voltage. Here, the different loading condition determines the active power flow. AC-side terminal active power–frequency droop is applied, and at DCside terminal of converter, DC power–voltage droop is executed, and the active power flow can be managed. AC-side frequency, DC voltage and active power are taking into consideration while generating droop scheme for ILC, which was lacking in normal droop strategy. In the greater part of literary works, maximum power flowing in between the lines connecting AC grid to utility mains is disregarded, and the limit of every network ought to be considered while considering the droop scheme. To address these drawbacks, the proposed work implements droop scheme for controlled operation of ILC to showcase power sharing among the AC/DC microgrid. At AC sub-grid, the ILC works on frequency droop, and at DC side, it works in voltage droop, so any deviation in DC sub-grid side voltage or AC sub-grid frequency during underloaded or overloaded condition changes the direction of active power flow. Droop control of the bidirectional converter is achieved by taking AC-side frequency, DC-side voltage and active power flow through the converter [12].

3 Droop Control in AC/DC Sub-grids A hybrid microgrid control first requires the control of the individual sub-grids and then proper coordination between the AC and DC sub-grids by an interlinking control approach applied to the ICs.

3.1 AC Microgrid In AC microgrid, active power P and reactive power Q values are measured, and it regulates AC voltage V and frequency ω by droop regulation as follows [13]. ωi = ωnom + m i Pi

(1)

Vi = Vnom + ti Q i

(2)

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ω

ω

ωnom

ωnom m2

ωac

m1

ωac

ωmin

m1

ωmin

Pac Pac2,max Pac2

Pac1 Pac1max

Pac

Pac1+Pac2 Pac,max

Fig. 2 Droop characteristics of individual DERs and the merged droop characteristics of DERs in an AC sub-grid

For ith unit, ωnom and Vnom are the desired frequency and voltage, m i and ti are the droop coefficient values. The values of droop coefficient should be tuned properly so that source which meets load demand proportionately to their ratings S is given as: m 1 S1 = m 2 S2 = · · · = m i Si

(3)

Distributed energy resource (DER) droop characteristics in an AC sub-grid are shown in Fig. 2. By adding the separate droop characteristics of these DERs, the entire AC grid droop can be obtained. Pacmax is th maximum power generation capacity of the grid. At a different frequency ω, the total power is controlled by the frequency droop regulation of the individual DER units.

3.2 DC Microgrid In DC microgrid, there is no need for reactive power, so DC voltage droop control is much easier to implement compared to AC droop control. The active power flow through converter controls the voltage magnitude as shown in [14]: Vy = V0 + n y Py

(4)

where V0 and n y are desired voltage and droop coefficient value for yth unit. Assuming that transmission line is lossless, DER voltage droop characteristics in DC-side subgrid are shown in Fig. 3. For any voltage VDC , the total DC sub-grid power produced is given by the summation of the power produced by DER1 (Pdc1 ) and DER2 (Pdc2 ). To get the droop characteristics for enitre DC sub-grid, we can combine the voltage droop characteristics of DER units present in DC sub-grid.

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V

V V0 VDC

n2

Vmin

Pdc2,max Pdc2

n

V0

n1

VDC

Pdc Pdc1+Pdc2 Pdc,max

Pdc

Pdc1

Pdc1,max

Fig. 3 Droop characteristics of individual DERs and the combined droop characteristics of DC sub-grid

4 Droop Control of Interlinking Converter In this paper, we have taken stand-alone operating mode of microgrid where the main utility grid is not supporting the microgrid and steady-state power transfer is preferred between AC and DC sub-grids. The droop control structure of the hybrid microgrid with ILC is shown in Fig. 4. In AC grid, if there is any load variation taking place, then power flow through the ILC will also deviate in accordance with frequency droop regulation. In same manner, DC voltage droop comes into action when there is load change in the DC side which results in variation of voltage and power flow through the ILC. The equation thus formed is, k1 VDC + k2 Pactive − k3  f = 0

(5)

V dc

I abc -

dq0

+

PLL

f +

abc

θ

Id

ωL ωL

Iq

abc

K3

V abc

dq0

I qref = 0

+

Vd

+ +

K2

PI

+

f ref

P active +

PI

+

PI

Fig. 4 Control strategy for interlinking converter in AC–DC microgrid

-

P active,ref

+

-

K1

+

+

-

V dc,ref

-

dq0 abc

Vq

ILC PWM

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Further modified,   k1 VDC + k2 Pactive − k3 f − k1 VDC,ref + k2 Pactive,ref − k3 f ref = 0

(6)

Pactive is the power flowing from AC sub-grid to DC sub-grid. AC sub-grid power generation will be varying for any load change in the AC side, and in this following relation, we can calculate the power flowing through the converter: PL ,ac = PG,ac − Pactive

(7)

In an AC sub-grid,change in frequency will take place for any load change. In accordance with the AC frequency droop regulation, the change in power generation at the AC side is due to deviation in frequency which is given by PG,ac =

2π f m

(8)

Pactive is the total active power change from AC sub-grid to DC sub-grid which is given as, Pactive =

k3  f − k1 VDC k2

(9)

Putting the values of equation of (8) and (9) in Eq. (7) gives us: PL ,ac =

k3  f − k1 VDC 2π f − m k2

(10)

Equation (10) illustrates the change in AC frequency and DC voltage due to change in AC load in hybrid microgrid. For any change in DC load, it directly affects the DC droop regulation that will change DC source power generation which is given by, PG,dc =

VDC n

(11)

Then, the DC load power consumption can be expressed as: PL ,dc =

k3  f − k1 VDC VDC + n k2

(12)

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5 Simulations Results A stand-alone AC/DC microgrid model is developed under MATLAB/Simulink environment. The parameters that have been taken into consideration while modeling the AC/DC hybrid microgrid are given in Table 1. In Fig. 5, the AC source power is 40 kW. The voltage source converter works as a rectifier when the power is flowing from AC to DC busbar if there is increase in DC load connected to DC busbar as shown in Fig. 7. In Fig. 6, load is increased in 7.5 kW to 10 kW at time 0.01 s. After dying out oscillations in ILC, the controller stabilizes the power little bit more than 2 kW from AC busbar to DC busbar through ILC. Figure 5 shows the AC grid voltage and current at AC busbar at the time of power sending AC to DC side. The output voltage and current waveform reveal that the controller for voltage source converter perfectly performs to get the nominal voltage of 600 maximum to get three-phase 600 voltage rms. Another case is considered to demonstrate the power flow from DC-AC side when AC load is increased at AC busbar as shown in Fig. 8. The power flow direction is changed to DC to AC from AC to DC is shown in Fig. 8. The positive power flowing through interlink converter is considered AC to DC. Figure 9 depicts the performance of frequency droop control associated with inverter at AC Table 1 Droop coefficient values are predefined and assumed constant [15]

Parameter

Values

AC-side grid frequency (f )

50 Hz

DC-side grid voltage (V dc )

700 V

AC-side grid frequency droop coefficient (m)

0.0003

DC-side droop coefficient (n)

0.0005

VSC reference power (PILC )

10 Kw

K1

0.5

K2

0.005

K3

10

Fig. 5 Active power flow from AC sub-grid

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Fig. 6 Voltage and current waveform across PCC point

Fig. 7 Power flow from AC-DC when increase in DC load

bus which perfectly makes the frequency error to zero within 0.2 secs. Figure 6 depicts the change in DC busbar voltage due to increase in DC load at 0.01 s.

6 Conclusion The paper attempts to implement the droop control strategy of an ILC to verify the power sharing between AC and DC sub-grids controlled by droop controllers. According to different load conditions along the AC and DC sub-grids, power flow through the converter also changes. AC/DC energy conversion gets reduced in an AC/DC hybrid microgrid because of the increase in efficiency. For stable operation

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Fig. 8 Power flow from DC-AC when increase in AC load

Fig. 9 Frequency deviation due to load change in AC side

in the distribution system, the frequency deviation should be minimized, and it can only be achieved by integrating more renewable energy to the distribution system. The AC busbar frequency deviation occurs due to change in AC load demand and the result shows when load is increased either in AC bus or DC bus the interlink converter helps to provide the required power by controlling the droop components of AC and DC bus.

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