Power Electronics for Next-Generation Drives and Energy Systems: Volume 2: Clean Generation and Power Grids 1839534699, 9781839534690

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IET ENERGY ENGINEERING SERIES 207

Power Electronics for Next-Generation Drives and Energy Systems

Other volumes in this series: Volume 1 Volume 4 Volume 7 Volume 8 Volume 10 Volume 11 Volume 13 Volume 14 Volume 15 Volume 16 Volume 18 Volume 19 Volume 21 Volume 22 Volume 24 Volume 25 Volume 26 Volume 27 Volume 29 Volume 30 Volume 31 Volume 32 Volume 33 Volume 36 Volume 37 Volume 38 Volume 39 Volume 40 Volume 41 Volume 43 Volume 44 Volume 45 Volume 46 Volume 47 Volume 48 Volume 49 Volume 50 Volume 51 Volume 52 Volume 53 Volume 55 Volume 56 Volume 57 Volume 58 Volume 59 Volume 62 Volume 63 Volume 65 Volume 66 Volume 67 Volume 68 Volume 69 Volume 70 Volume 72 Volume 73 Volume 74 Volume 75 Volume 76 Volume 78 Volume 79 Volume 81 Volume 82 Volume 86 Volume 87 Volume 88 Volume 89 Volume 90

Power Circuit Breaker Theory and Design C.H. Flurscheim (Editor) Industrial Microwave Heating A.C. Metaxas and R.J. Meredith Insulators for High Voltages J.S.T. Looms Variable Frequency AC Motor Drive Systems D. Finney SF6 Switchgear H.M. Ryan and G.R. Jones Conduction and Induction Heating E.J. Davies Statistical Techniques for High Voltage Engineering W. Hauschild and W. Mosch Uninterruptible Power Supplies J. Platts and J.D. St Aubyn (Editors) Digital Protection for Power Systems A.T. Johns and S.K. Salman Electricity Economics and Planning T.W. Berrie Vacuum Switchgear A. Greenwood Electrical Safety: a guide to causes and prevention of hazards J. Maxwell Adams Electricity Distribution Network Design, 2nd Edition E. Lakervi and E.J. Holmes Artificial Intelligence Techniques in Power Systems K. Warwick, A.O. Ekwue and R. Aggarwal (Editors) Power System Commissioning and Maintenance Practice K. Harker Engineers’ Handbook of Industrial Microwave Heating R.J. Meredith Small Electric Motors H. Moczala et al. AC–DC Power System Analysis J. Arrillaga and B.C. Smith High Voltage Direct Current Transmission, 2nd Edition J. Arrillaga Flexible AC Transmission Systems (FACTS) Y-H. Song (Editor) Embedded generation N. Jenkins et al. High Voltage Engineering and Testing, 2nd Edition H.M. Ryan (Editor) Overvoltage Protection of Low-Voltage Systems, Revised Edition P. Hasse Voltage Quality in Electrical Power Systems J. Schlabbach et al. Electrical Steels for Rotating Machines P. Beckley The Electric Car: Development and future of battery, hybrid and fuel-cell cars M. Westbrook Power Systems Electromagnetic Transients Simulation J. Arrillaga and N. Watson Advances in High Voltage Engineering M. Haddad and D. Warne Electrical Operation of Electrostatic Precipitators K. Parker Thermal Power Plant Simulation and Control D. Flynn Economic Evaluation of Projects in the Electricity Supply Industry H. Khatib Propulsion Systems for Hybrid Vehicles J. Miller Distribution Switchgear S. Stewart Protection of Electricity Distribution Networks, 2nd Edition J. Gers and E. Holmes Wood Pole Overhead Lines B. Wareing Electric Fuses, 3rd Edition A. Wright and G. Newbery Wind Power Integration: Connection and system operational aspects B. Fox et al. Short Circuit Currents J. Schlabbach Nuclear Power J. Wood Condition Assessment of High Voltage Insulation in Power System Equipment R.E. James and Q. Su Local Energy: Distributed generation of heat and power J. Wood Condition Monitoring of Rotating Electrical Machines P. Tavner, L. Ran, J. Penman and H. Sedding The Control Techniques Drives and Controls Handbook, 2nd Edition B. Drury Lightning Protection V. Cooray (Editor) Ultracapacitor Applications J.M. Miller Lightning Electromagnetics V. Cooray Energy Storage for Power Systems, 2nd Edition A. Ter-Gazarian Protection of Electricity Distribution Networks, 3rd Edition J. Gers High Voltage Engineering Testing, 3rd Edition H. Ryan (Editor) Multicore Simulation of Power System Transients F.M. Uriate Distribution System Analysis and Automation J. Gers The Lightening Flash, 2nd Edition V. Cooray (Editor) Economic Evaluation of Projects in the Electricity Supply Industry, 3rd Edition H. Khatib Control Circuits in Power Electronics: Practical issues in design and implementation M. Castilla (Editor) Wide Area Monitoring, Protection and Control Systems: The enabler for Smarter Grids A. Vaccaro and A. Zobaa (Editors) Power Electronic Converters and Systems: Frontiers and applications A.M. Trzynadlowski (Editor) Power Distribution Automation B. Das (Editor) Power System Stability: Modelling, analysis and control A.A. Sallam and B. Om P. Malik Numerical Analysis of Power System Transients and Dynamics A. Ametani (Editor) Vehicle-to-Grid: Linking electric vehicles to the smart grid J. Lu and J. Hossain (Editors) Cyber-Physical-Social Systems and Constructs in Electric Power Engineering S. Suryanarayanan, R. Roche and T.M. Hansen (Editors) Periodic Control of Power Electronic Converters F. Blaabjerg, K. Zhou, D. Wang and Y. Yang Advances in Power System Modelling, Control and Stability Analysis F. Milano (Editor) Cogeneration: Technologies, Optimisation and Implentation C.A. Frangopoulos (Editor) Smarter Energy: from Smart Metering to the Smart Grid H. Sun, N. Hatziargyriou, H.V. Poor, L. Carpanini and M. A. Sa´nchez Fornie´ (Editors) Hydrogen Production, Separation and Purification for Energy A. Basile, F. Dalena, J. Tong and T.N. Vezirog˘lu (Editors) Clean Energy Microgrids S. Obara and J. Morel (Editors)

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Fuzzy Logic Control in Energy Systems with Design Applications in Matlab/Simulink‡ ˙I.H. Altas¸ Power Quality in Future Electrical Power Systems A.F. Zobaa and S.H.E.A. Aleem (Editors) Cogeneration and District Energy Systems: Modelling, Analysis and Optimization M.A. Rosen and S. Koohi-Fayegh Introduction to the Smart Grid: Concepts, technologies and evolution S.K. Salman Communication, Control and Security Challenges for the Smart Grid S.M. Muyeen and S. Rahman (Editors) Industrial Power Systems with Distributed and Embedded Generation R. Belu Synchronized Phasor Measurements for Smart Grids M.J.B. Reddy and D.K. Mohanta (Editors) Large Scale Grid Integration of Renewable Energy Sources A. Moreno-Munoz (Editor) Modeling and Dynamic Behaviour of Hydropower Plants N. Kishor and J. Fraile-Ardanuy (Editors) Methane and Hydrogen for Energy Storage R. Carriveau and D.S-K. Ting Power Transformer Condition Monitoring and Diagnosis A. Abu-Siada (Editor) Surface Passivation of Industrial Crystalline Silicon Solar Cells J. John (Editor) Bifacial Photovoltaics: Technology, applications and economics J. Libal and R. Kopecek (Editors) Fault Diagnosis of Induction Motors J. Faiz, V. Ghorbanian and G. Joksimovic´ Cooling of Rotating Electrical Machines: Fundamentals, modelling, testing and design D. Staton, E. Chong, S. Pickering and A. Boglietti High Voltage Power Network Construction K. Harker Energy Storage at Different Voltage Levels: Technology, integration, and market aspects A.F. Zobaa, P.F. Ribeiro, S.H.A. Aleem and S.N. Afifi (Editors) Wireless Power Transfer: Theory, Technology and Application N. Shinohara Lightning-Induced Effects in Electrical and Telecommunication Systems Y. Baba and V.A. Rakov DC Distribution Systems and Microgrids T. Dragicˇ evic´ , F. Blaabjerg and P. Wheeler Modelling and Simulation of HVDC Transmission M. Han (Editor) Structural Control and Fault Detection of Wind Turbine Systems H.R. Karimi Modelling and Simulation of Complex Power Systems A. Monti and A. Benigni Thermal Power Plant Control and Instrumentation: The control of boilers and HRSGs, 2nd Edition D. Lindsley, J. Grist and D. Parker Fault Diagnosis for Robust Inverter Power Drives A. Ginart (Editor) Monitoring and Control using Synchrophasors in Power Systems with Renewables I. Kamwa and C. Lu (Editors) Power Systems Electromagnetic Transients Simulation, 2nd Edition N. Watson and J. Arrillaga Power Market Transformation B. Murray Wind Energy Modeling and Simulation Volume 1: Atmosphere and plant P. Veers (Editor) Diagnosis and Fault Tolerance of Electrical Machines, Power Electronics and Drives A.J.M. Cardoso Characterization of Wide Bandgap Power Semiconductor Devices F. Wang, Z. Zhang and E.A. Jones Renewable Energy from the Oceans: From wave, tidal and gradient systems to offshore wind and solar D. Coiro and T. Sant (Editors) Wind and Solar Based Energy Systems for Communities R. Carriveau and D. S-K. Ting (Editors) Metaheuristic Optimization in Power Engineering J. Radosavljevic´ Power Line Communication Systems for Smart Grids I.R.S. Casella and A. Anpalagan Hydrogen Passivation and Laser Doping for Silicon Solar Cells B. Hallam and C. Chan (Editors) Variability, Scalability and Stability of Microgrids S.M. Muyeen, S.M. Islam and F. Blaabjerg (Editors) Wind Turbine System Design: Volume 1: Nacelles, drive trains and verification J. Wenske (Editor) Medium Voltage DC System Architectures B. Grainger and R.D. Doncker (Editors) Condition Monitoring of Rotating Electrical Machines P. Tavner, L. Ran, C. Crabtree Energy Storage for Power Systems, 3rd Edition A.G. Ter-Gazarian Distribution Systems Analysis and Automation, 2nd Edition J. Gers SiC Power Module Design: Performance, robustness and reliability A. Castellazzi and A. Irace (Editors) Power Electronic Devices: Applications, failure mechanisms and reliability F. Iannuzzo (Editor) Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems M. Benbouzid (Editor) Energy Generation and Efficiency Technologies for Green Residential Buildings D. Ting and R. Carriveau (Editors) Lithium-ion Batteries Enabled by Silicon Anodes C. Ban and K. Xu (Editors) Electrical Steels, 2 Volumes A. Moses, K. Jenkins, Philip Anderson and H. Stanbury Advanced Dielectric Materials for Electrostatic Capacitors Q Li (Editor) Transforming the Grid Towards Fully Renewable Energy O. Probst, S. Castellanos and R. Palacios (Editors) Microgrids for Rural Areas: Research and case studies R.K. Chauhan, K. Chauhan and S.N. Singh (Editors) Artificial Intelligence for Smarter Power Systems: Fuzzy Logic and Neural Networks M.G. Simoes Digital Protection for Power Systems, 2nd Edition Salman K Salman Advanced Characterization of Thin Film Solar Cells N. Haegel and M. Al-Jassim (Editors) Power Grids with Renewable Energy Storage, integration and digitalization A.A. Sallam and B. OM P. Malik Small Wind and Hydrokinetic Turbines P. Clausen, J. Whale and D. Wood (Editors) Reliability of Power Electronics Converters for Solar Photovoltaic Applications F. Blaabjerg, A.l Haque, H. Wang, Z. Abdin Jaffery and Y. Yang (Editors) Utility-scale Wind Turbines and Wind Farms A. Vasel-Be-Hagh and D.S.-K. Ting Lighting interaction with Power Systems, 2 volumes A. Piantini (Editor) Silicon Solar Cell Metallization and Module Technology T. Dullweber (Editor)

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n-Type Crystalline Silicon Photovoltaics: Technology, applications and economics D. Munoz and R. Kopecek (Editors) Protection of Electricity Distribution Networks, 4th Edition J. Gers and E. Holmes Surge Protection for Low Voltage Systems A. Rousseau (Editor) Compressed Air Energy Storage: Types, systems and applications D. Ting and J. Stagner Synchronous Reluctance Machines: Analysis, optimization and applications N. Bianchi, C. Babetto and G. Bacco Electric Fuses: Fundamentals and new applications, 4th Edition N. Nurse, A. Wright and P.G. Newbery Overhead Electric Power Lines: Theory and practice S. Chattopadhyay and A. Das Offshore Wind Power Reliability, availability and maintenance, 2nd Edition P. Tavner Cyber Security for Microgrids S. Sahoo, F. Blaajberg and T. Dragicevic Battery Management Systems and Inductive Balancing A. Van den Bossche and A. Farzan Moghaddam Model Predictive Control for Microgrids: From power electronic converters to energy management J. Hu, J.M. Guerrero and S. Islam Electromagnetic Transients in Large HV Cable Networks: Modeling and calculations Ametani, Xue, Ohno and Khalilnezhad Nanogrids and Picogrids and their Integration with Electric Vehicles S. Chattopadhyay Superconducting Magnetic Energy Storage in Power Grids M.H. Ali Blockchain Technology for Smart Grids: Implementation, management and security H.L. Gururaj, K.V. Ravi, F. Flammini, H. Lin, B. Goutham, K.B.R. Sunil and C. Sivapragash Battery State Estimation: Methods and Models S. Wang Industrial Demand Response: Methods, best practices, case studies, and applications H.H. Alhelou, A. Moreno-Mun˜oz and P. Siano (Editors) Wide Area Monitoring of Interconnected Power Systems, 2nd Edition A.R. Messina Advances in Power System Modelling, Control and Stability Analysis, 2nd Edition F. Milano (Editor) Fusion-Fission Hybrid Nuclear Reactors: For enhanced nuclear fuel utilization and radioactive waste reduction W.M. Stacey AI for Status Monitoring of Utility Scale Batteries Shunli Wang, Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez and Josep M. Guerrero Power System Protection, 4 volumes

Power Electronics for Next-Generation Drives and Energy Systems Volume 2: Clean Generation and Power Grids Edited by Nayan Kumar, Josep M. Guerrero, Debaprasad Kastha and Tapas Kumar Saha

The Institution of Engineering and Technology

Published by The Institution of Engineering and Technology, London, United Kingdom The Institution of Engineering and Technology is registered as a Charity in England & Wales (no. 211014) and Scotland (no. SC038698). † The Institution of Engineering and Technology 2022 First published 2022 This publication is copyright under the Berne Convention and the Universal Copyright Convention. All rights reserved. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may be reproduced, stored or transmitted, in any form or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publisher at the undermentioned address: The Institution of Engineering and Technology Futures Place Kings Way, Stevenage, Hertfordshire SG1 2UA, United Kingdom www.theiet.org While the authors and publisher believe that the information and guidance given in this work are correct, all parties must rely upon their own skill and judgement when making use of them. Neither the author nor publisher assumes any liability to anyone for any loss or damage caused by any error or omission in the work, whether such an error or omission is the result of negligence or any other cause. Any and all such liability is disclaimed. The moral rights of the author to be identified as author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

British Library Cataloguing in Publication Data A catalogue record for this product is available from the British Library

ISBN 978-1-83953-468-3 (Volume 1 ISBN 978-1-83953-470-6 (Volume 1 ISBN 978-1-83953-469-0 (Volume 2 ISBN 978-1-83953-471-3 (Volume 2 ISBN 978-1-83953-496-6 (2 Volume

hardback) pdf) hardback) pdf) set hardback)

Typeset in India by MPS Limited Printed in the UK by CPI Group (UK) Ltd, Croydon Cover image: Anton Petrus/Moment via Getty Images

Contents

About the editors

1 Performance of modern industrial plants with renewable power generation: a comprehensive system analysis Maneesh Kumar, Gagandeep Singh, Pravin Kumar and Harshit Mohan 1.1 Introduction 1.1.1 Contributions 1.2 IEEE Standards 1.3 State of the art 1.4 Challenges and opportunities 1.5 Test system simulation and result discussion 1.5.1 Test system description 1.5.2 Analysis under different cases 1.6 Conclusion References 2 Maximum power extraction from partially shaded photovoltaic power conversion systems Hassan M. Hussein Farh and Abdullrahman A. Al-Shamma’a 2.1 Introduction 2.2 PV partial shading problem 2.2.1 Causes and effects of partial shading problem 2.2.2 Partial shadowing remediation 2.3 Standalone and grid-interconnected PV power conversion systems 2.3.1 General description of standalone and grid-interconnected PV power systems 2.3.2 Configuration of the PV power conversion system under study 2.4 PV nature-inspired MPPT algorithms 2.4.1 Classification of the nature-inspired MPPT algorithms 2.4.2 Proposed MPPT algorithms brief overview 2.5 Results and discussions 2.6 Conclusions References

xi

1 2 3 4 4 5 6 6 7 17 17

21 22 23 23 24 25 25 27 28 28 29 34 38 38

viii 3

4

5

Power electronics for next-generation drives and energy systems: Volume 2 Transformerless grid-connected inverter for PV integration M.K.P. Muhammed Ramees, Md Waseem Ahmad and Aasim 3.1 Need of transformerless inverter 3.2 Classification of transformerless PV inverter 3.2.1 Conventional full-bridge TLI 3.2.2 Transformerless inverters with decoupling 3.2.3 Transformerless inverters with clamping 3.3 Summary References PMSG and DFIG-based wind energy conversion systems Mojtaba Eldoromi, Davood Fateh, Mahsa Rostamzade and Ali Akbar Moti Birjandi 4.1 Introduction 4.2 Power converter 4.2.1 Convertor topology 4.3 Modeling of WECS 4.3.1 Different types of generators 4.3.2 Modeling and control of DFIG-based WECS 4.3.3 Modeling and control of PMSG-based WECS 4.4 Control strategies and MPPT 4.5 Power quality 4.6 Case study 4.7 Conclusion References Novel AI, machine, deep learning, and optimization-based computing for energy systems Samira Sadeghi, Abed Bagheri, Ali Hesami Naghshbandy and Parham Moradi 5.1 An introduction to modern energy systems 5.1.1 Challenges in modern power systems 5.2 Definition of energy systems problems 5.2.1 Planning 5.2.2 Operation 5.2.3 Control 5.3 Technology of intelligent systems 5.3.1 Neural network 5.3.2 Decision tree 5.3.3 Support vector machine 5.4 Applications of computational intelligence methods in energy systems studies 5.4.1 Power systems and big data 5.4.2 Operation and control 5.4.3 Optimization

43 44 45 45 51 59 65 65 67

68 70 71 73 74 76 77 77 83 83 86 86

91

93 94 96 96 97 98 98 101 102 103 103 104 105 108

Contents 5.4.4 Decision making 5.4.5 Fault detection 5.4.6 Stability analysis 5.4.7 An example of power flow by neural network 5.5 Future perspectives of dynamic security assessment by ML 5.6 Conclusions References 6 Converter topologies for grid-integration of renewable power sources Ashok Kumar 6.1 Introduction 6.1.1 Renewable energy applications of buck-boost inverter: solar PV micro-inverters 6.2 State of the art 6.2.1 Review based on operating modes of single-stage BBI 6.2.2 Review of single-stage inverters: salient points 6.3 Case study: bi-modal fourth-order inverters – derivation and working principle 6.3.1 Why fourth-order converters? 6.3.2 Basic requirements 6.3.3 Merging two converters for bipolar output 6.3.4 Second-order buck–boost inverter 6.3.5 Possible combinations with fourth-order converters 6.3.6 Schematics of two new inverter circuits 6.3.7 Topology-1 (C´uk+SEPIC) 6.3.8 Topology-2 (CSC-IL+SEPIC) 6.3.9 Comparison among SOBBI, topologies-1 and 2 inverters 6.3.10 Additional details on topology-2 operation 6.3.11 New switching strategy-single mode inverter 6.4 Conclusion 6.5 Future trends/future possibilities References 7 PV powered DC microgrid with plug-in energy harvesting and EV incorporated functions Chang-Ming Liaw and Min-Ze Lu 7.1 Introduction 7.2 The established PV powered microgrid 7.2.1 Governing equation for a PV cell 7.2.2 Parameter determination 7.2.3 I–V curves and effects on temperature and irradiance 7.3 PV array with followed interleaved boost converter 7.3.1 Simulated PV array 7.3.2 The established interleaved boost converter

ix 109 109 110 114 115 116 116

127 128 129 137 137 145 145 145 146 147 147 150 152 152 154 155 158 159 163 164 164

167 169 170 172 172 173 174 174 174

x

Power electronics for next-generation drives and energy systems: Volume 2 7.3.3 Evaluation on MPPT and interleaving operations 7.3.4 Whole system operation 7.4 Plug-in energy harvesting mechanism with AC source 7.4.1 Power circuit 7.4.2 Controller design 7.4.3 Evaluation of the proposed control scheme 7.4.4 PV array with plug-in single-phase AC source 7.5 Interconnected M2V/V2M operations between PV powered DC microgrid and EV SRM drive 7.5.1 V2M discharging operation 7.5.2 M2V charging operation 7.6 Conclusion References

8

Power electronics technology and applications in clean generation and power grids Abdullrahman A. Al-Shamma’a and Hassan M. Hussein Farh 8.1 Introduction 8.2 Renewable sources-based shunt active filter 8.3 Renewable sources-based dynamic voltage restorer 8.4 Renewable sources-based UPQC 8.5 Dual UPQC-based OEW transformers (case study) 8.5.1 PV modeling and MPPT method 8.5.2 VSCs control 8.5.3 Simulation results 8.6 Summary References

Index

176 178 179 179 179 188 188 191 193 194 194 195

199 199 201 205 212 218 218 220 221 225 225 233

About the editors

Nayan Kumar is a post-doctoral fellow at the School of Interdisciplinary Research, Indian Institute of Technology Delhi, India. His research interests include power electronics and its applications such as in photovoltaic systems, wind turbines, electric vehicles, reliability, harmonics, and adjustable speed drives. He received his Ph.D. in electrical engineering from the National Institute of Technology Durgapur, Durgapur, West Bengal, India, in 2018. Josep M. Guerrero is a professor with the Department of Energy Technology, Aalborg University, Denmark. His research interests include power electronics, distributed energy-storage systems, energy management systems, smart metering and the Internet of Things for AC/DC microgrids. He serves as an associate editor for the IEEE Transactions on Power Electronics, IEEE Transactions on Industrial Electronics, and IEEE Industrial Electronics Magazine, and as an editor for the IEEE Transactions on Smart Grid. Debaprasad Kastha is a professor at the Indian Institute of Technology Kharagpur, India. Prior assignments include the Research and Development Division of Crompton Greaves, Ltd., Mumbai. He has been performing research on power electronics and drives for more than two decades and has authored or coauthored about 50 technical papers, books, and electronic teaching aids. His research interests include the areas of wind power generation, machine drives, dc power supply, and distribution systems. Tapas Kumar Saha is a professor at the National Institute of Technology Durgapur, India. He is well known in the field of Power Electronics and Machine Drives and their applications in Renewable Energy Generation. He also serves as a reviewer of numerous IEEE transactions and conferences. His current research interests include control and implementation of renewable energy generation systems through power electronics and machine drives.

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Chapter 1

Performance of modern industrial plants with renewable power generation: a comprehensive system analysis Maneesh Kumar1, Gagandeep Singh1, Pravin Kumar1 and Harshit Mohan1

Nowadays, industrial plants are integrating more and more renewables-based power generation (RPG) in the existing grid-connected system for economic and environmental benefits. The RPG has the advantage of providing green and clean energy, thereby reducing the carbon footprints and annual energy production cost. For encouraging renewable penetration into the system, various subsidies are provided to the utilities and the consumers. The RPG, along with various industrial loads (linear and non-linear), constitutes the small-scale microgrid, which operates in grid-connected or islanded mode. Therefore, these grids can work in conjunction with the existing main grid as well as in a self-reliance mode to supply the connected loads. Usually, the integration of RPG is done using power electronics converters. On the other hand, the majority of the loads in the industrial units are the electric motors, which are also being controlled using power electronics converters. The converters (supply-side as well as load-side), along with other nonlinear loads, create issues in the operation and control of industrial units. Furthermore, the events such as switching, sudden load change, the occurrence of temporary faults, intermittency of RPG, and islanding are major concerns to the industrial unit (small-scale microgrid). Therefore, in this chapter, a thorough study and analysis of the aforementioned events in the small-scale microgrid are discussed under various cases such as the effect on the power-electronic controlled industrial motor drive during microgrid operating modes, namely grid-connected/ islanded and the transition between these two modes, also the effect of non-linear loads such as power electronic controlled industrial motors on the photovoltaic (PV)-based generation, and vice versa is observed at different operating conditions such as steady-state mode, acceleration mode, deceleration mode, etc. In addition, the effect of transients such as capacitor switching, load switching, temporary fault, and islanding has also been discussed. In the prescribed study, the above key issues 1

Department of Electrical Engineering, IIT Roorkee, India

2

Power electronics for next-generation drives and energy systems: Volume 2

are presented with the help of analysis over a small-scale industrial microgrid system (IMS) with the renewable energy integration. An extensive simulation is performed under the MATLAB“/SIMULINK“ environment.

Nomenclature CFP DG GCM IEDs IM IMS PCC PEIMD PMU RESs RPG SG SPV

carbon footprint distributed generation grid-connected mode intelligent electronic devises isolated mode industrial microgrid system point of common coupling power electronic controlled industrial motors phasor measurement unit renewable energy resources renewable power generation synchronous generator solar photovoltaic

1.1 Introduction With a rapid industrialization across the globe, a multi-dimensional development has been observed. The overall development, as well as the economic resilience, are also a subset of industrial growth for any developing country. There are numerous small as well as large-scale industries, and to operate them in manual as well as automation mode, several electrical drives are being used. Although industrialization increases the per capita of a country and boosts the gross domestic production (GDP), at the same time, it exerts pressure on the ecosystem and affects the goal of sustainable developments. To reduce these issues and to encourage the harness of cleaner and greener energy, the penetration of renewable energy sources (RESs) has been increased. Many industries and utilities are increasing their capacity through the addition of RESs. The government in many countries is also encouraging the use of RESs for energy production and utilization in terms of monetary subsidies. The inclusion of energy-generating sources at the load site, such as industrial plants, developed the concept of Industrial Microgrids that can operate in two modes, either grid-connected mode (GCM) or islanded mode (IM). The microgrid requires advanced monitoring devices such as intelligent electronics devices (IEDs) and phasor measurement devices (PMU) so as to detect and differentiate events such as intentional or unintentional islanding. Nevertheless, to say that more and more penetration of RESs into the existing grid reduces the carbon

Performance of modern industrial plants with renewable power generation

3

Industrial microgrid system (IMS)

Operational modes Grid-connected mode (GCM)

Isolated mode (IM)

Test system analysis

Effect of above operational modes on IMS power-electronics controlled drives Effect of IMS nonlinear loads on RPG under various operating conditions Effect of RPG on IMS motor drives under various system conditions

Effects of transients on IMS drives

Figure 1.1 The overall system analysis layout under two operational modes footprints (CFP), it also increases the issue of their proper integration into the system. This issue mainly arises because of the existence of many non-linear loads such as power electronic fed induction motor drives, voltage-dependent loads, and other electronics and electrical components being used in the system. Therefore, there is a requirement for a comprehensive analysis to check the performance of the industrial drives under various RESs penetration levels. This analysis should also provide information about the transient behavior of the system under various conditions such as RESs intermittency, temporary faults, and capacitor switching. Hence, in this chapter, a study has been carried out thoroughly to analyze the aforementioned issues. The layout diagram for the overall system analysis under the two operational modes is shown in Figure 1.1.

1.1.1 Contributions The key contributions in terms of analysis are given below: 1. 2.

3.

Effect on the power-electronic controlled industrial motor drive during gridconnected/islanded and during the transition mode of operation. Effect of non-linear loads such as power electronic controlled industrial motors on the photovoltaic (PV)-based generation plant at different operating conditions such as steady-state mode, acceleration mode, and deceleration mode. Effect of PV generations on the industrial motor drives at different operating conditions, such as PV penetration level, and intermittency of PV.

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Power electronics for next-generation drives and energy systems: Volume 2

4.

Effect of transients such as capacitor switching, load switching, PV shading, temporary fault, and islanding on modern industrial units with PV-based generation plant.

1.2 IEEE Standards Various standards pertaining to industrial drive and the RESs integration with the grid network are available. Some of them are discussed here. IEEE Standards related industrial drives, ●

●

●

●

●

IEEE Std. 3004.8-2016 [1] This standard covers the theme of motors protection which is used in industrial as well as commercial power systems. IEEE Std. 3001.11-2017 [2] This standard mainly covers the selection and automation controllers for industrial as well as commercial power systems themes. IEEE Std. 3001.2-2017 [3] It covers the design of electrical services for industrial, institutional, and commercial sectors for the interconnection with the transmission or a distribution utility. For this purpose, the information about utility characteristics required by the design engineer and the electrical load required by the utility for a proper interconnection between the serving utility and the distribution system is considered. IEEE Standards related grid integration of RESs [4] IEEE Std. 1547 and IEEE Std. 2030 These two standards provide the practices for interconnection and interoperability of distributed energy resources (DERs) with the electric grid for the control methods of industrial motors drives. Since 2009, the IEEE 2030 and the IEEE 1547 by the American national standards have made comprehensive progress for smart grid interoperability and the DERs integration, respectively. These standards also focus on the technical issues while integrating the smart grid and the DERs. IEEE standard related to power quality:

IEEE 519-1992 [5] IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems and IEEE 1159-1995 [6] Practices for monitoring Electric Power Quality. These two standards provide the detail of power quality problems and their limits in the power system.

1.3 State of the art The integration of the RESs with the grid requires an appropriately designed power electronic converter system. These converters have the potential to harness maximum power from the resources and also provide power conditioning as per the load requirements [7]. The impact of the integration of RESs and the grid has been

Performance of modern industrial plants with renewable power generation

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well investigated in [8]. The detailed study pertains to the fast-growing semiconductor market for industries, and other sectors are discussed here with the RESs integration. Ref. [9] discussed the global energy scenarios and their impact in the 21st century. Here, the impact of rapidly growing power electronics-based technologies such as semiconductor devices, converters, PWM techniques, and industrial drives is discussed. A very large number of industrial drives are based on induction machines. Industrial motor drives cover a significant portion of the industrial load. Fast primary frequency response is required for improved performance of the drive. In [10], a novel motor drive controller is proposed for the fast primary frequency response. A high gain dc–dc converter feed industrial induction motor drive through RESs (more specifically PV system) is proposed in [11] with closed-loop voltage control. The key power quality issues, such as voltage sagswell, frequency fluctuations, and production of harmonics because of power electronic devices used for interconnection, between RESs and the grid, have been investigated in [12–15]. Several existing approaches for the mitigation of voltage sag and swell are presented in [16]. With the integration of distributed generation (DG), the need for advanced monitoring devices (namely, IEDs and PMUs) arises. The aspect of deploying the PMUs at the distribution level is discussed in [17]. Many industries require medium voltage and high power for their production. The applications of multilevel converters are increased for such industries with the integration of RESs and motor drives [18]. A comprehensive survey of power electronic devices and their impact on smart grids has been given in [19,20]. The role of RESs in the smart grid system has also been studied for their integration through semiconductor devices. With the use of RESs-based technologies in the grid system, undoubtedly, the system becomes more sustainable in terms of production and utilization of cleaner and greener energy. Also, the optimal utilization of RESs in the small-scale microgrids is always encouraged to increase the reliability of the supply as well as to reduce the carbon footprints [21]. A sustainable industrial plant has been proposed in [22] with an energy management approach that encourages industrial waste management through operational costs as well as emission by controlling multiple industrial processes. The industrial processes use various components, such as stand-alone converters and drives, that need to be controlled properly. In [23], a review of such control techniques, namely fractional control, is discussed. Some industries, such as chemical plants, utilize adaptive process control techniques to control the inflow and outflow of the product. One such adaptive control of a power electronic converter is proposed in [24] for voltage control.

1.4 Challenges and opportunities The utilization of RESs for grid network support is a promising solution in a significant way. In addition, several highly efficient power electronics converter is readily available for modern industries to work in a renewable energy-based environment. However, the more and more integration of RESs to the grid for

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Power electronics for next-generation drives and energy systems: Volume 2

improving the reliability of supply for household, commercial, and industrial scenarios creates several unwanted problems in the system. Some of them are listed here. 1.

2.

3. 4.

5.

6.

The power quality issues such as voltage fluctuations and frequency regulation are two of the major issues with the integration of renewable energy with the grid. The use of semiconductor-based converters for power conversion through RESs also injects harmonics into the system, affecting the smooth working of industrial drives. The multiple switching operations also affect the transient behavior of the system in an adverse way. The intermittency of the RESs makes these sources variable in nature; therefore, there is always a need to add a dispatchable unit such as storage, rotating generation employing conventional fossil fuel or bio-diesel-based generation, or a grid that is considered as an infinite source. With the external grid only, the power flow is unidirectional, i.e., from the external grid to the industrial plant, so traditional protection such as overcurrent relays are adequate to provide protection against short-circuits faults. However, with the presence of DGs at the consumer premises, the power flow is bidirectional, and hence, it requires alternative protection schemes. With this, the industrial microgrid requires advanced monitoring and protection devices. The non-rotating-based generation, such as solar PV (SPV), generates a limited amount of reactive power. Therefore, networks with non-rotating based DGs require reactive power compensating devices to maintain bus voltage and prevent the network from voltage instability problems. An opportunity in this regard is to find the optimal location of reactive power compensation devices.

There are certainly many opportunities to integrate renewable energy into the existing grid system to supply the industrial loads. Few of them are described here. 1. 2. 3. 4.

The cost of RESs such as solar and wind are continuously decreasing, which provides the system engineers with more ways to add them into the system. The availability of highly efficient semiconductor devices encourages the integration of RES into the system. With an increase in RESs penetration into the grid system, the burden on the generation utilities would decrease. The utilization of the renewable energy available for running the industrial motor drives near them would be relatively cheaper than grid-purchasing electricity.

1.5 Test system simulation and result discussion 1.5.1

Test system description

For the analysis, an industrial microgrid test system is considered, the layout of which is presented in Figure 1.2.

Performance of modern industrial plants with renewable power generation

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Figure 1.2 A layout of RES-grid supported industrial microgrid

The test system includes a solar PV (SPV) and a small synchronous generator (SG) present at industry premises. The SPV plant is connected with the AC bus through a converter, and the SG is also connected with the same AC bus. A linear load is considered, which is connected to the common bus of SPV and SG sources. As SPV and SG are connected to low-voltage industry premises, they are collectively referred to as DGs. These DGs are connected to the industrial load through a transformer. The main grid system is connected with the industrial load through a point of common coupling (PCC), which can be connected or disconnected for analysis of the system under different modes of operations. The parameters of the devices in the test system are depicted in Table 1.1.

1.5.2 Analysis under different cases In this section, the analysis through simulation is presented with multiple scenarios.

1.5.2.1 Case 1: Effect on the power-electronic controlled industrial motor drive (PEIMD) during gridconnected/islanded and during transition mode of operation For this, the test network is started with PCC in ON-state, i.e., grid-connected mode (GCM), and transition to islanded mode (IM) is performed by operating PCC at

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Power electronics for next-generation drives and energy systems: Volume 2

Table 1.1 Parameters of network S. no. Equipment

Ratings

1. 2. 3. 4. 5. 6.

13.8 kV, 60 Hz 500 kVA, 13.8/0.46 kV, 60 Hz 500 kVA, 5/0.46 kV, 60 Hz 500 kW, 5 kV, 60 Hz 250 kW, 5 kV, 60 Hz 150 kVA, 0.46 kV, 60 Hz, 4-pole

External grid Grid transformer DG interface transformer Synchronous generator (diesel-based) Inverter based DG (PV) Load—power electronic controlled industrial drive Load—PQ specified (voltage-dependent load) Switch-S1

7. 8.

200 kW, 0.82 pf, 0.46 kV, 60 Hz For islanding operation

Voltage (pu)

1 0.9 0.8 0.7 0.6 3

3.5

4

4.5 5 Time (sec)

5.5

6

6.5

Figure 1.3 Average load voltage in per-unit (pu) 4 seconds (sec). Here, a 150 kVA inverter fed induction motor drive is considered for the analysis. The parameters of the motor are shown in Table 1.1. During the transition, the change in the voltage (pu) at the load bus (N3), i.e., industrial plant, is shown in Figure 1.3. The load voltage drops from its initial value of 0.973 pu in GCM to as low as 0.637 pu during the transition, then settles back to 0.951 pu in IM. The active and reactive power exchange during the transition from GCM to IM is shown in Figure 1.4(a) and (b). Initially, in GCM, the industrial microgrid generates surplus active power injecting into the external grid network after satisfying its load demand. During GCM, reactive power is injected by the external grid and DGs to maintain load voltage. After the transition to IM, the DGs in the industrial microgrid serve the load and also fulfill reactive power demand. The effect on PEIMD during the transition from GCM to IM is shown in Figure 1.5. The effect is shown in terms of speed, torque, and DC bus voltage of the drive. It can be observed that the control strategy of the AC drive maintains the speed, torque, and DC bus voltage after the transition to IM.

Power (kW)

600 400 DG Active Power Grid Active Power

200 0 –200

(a)

3

3.5

4

4.5

5

5.5

6

6.5

Power (kVAr)

300 200 100 0 DG Reactive Power Grid Reactive Power

–100 –200

3

3.5

4

(b)

4.5 5 Time (sec)

5.5

6

6.5

Speed (rpm)

Figure 1.4 Power exchange: (a) active power and (b) reactive power

1,800 1,600 1,400

Ref Speed

3

(a)

Torque (N-m)

Actual Speed

1,200 1,000 800 3.5

4

4.5

5

5.5

1,200 1,000 800

6

6.5

Actual Torque Ref Torque

600 400 200

(b)

3

3.5

4

4.5

5

5.5

6

6.5

3

3.5

4

4.5

5

5.5

6

6.5

Voltage (Volt)

700 600 500 400 300 200 (c)

Time (sec)

Figure 1.5 PE-controlled induction motor drive: (a) speed, (b) torque, and (c) DC bus voltage

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Power electronics for next-generation drives and energy systems: Volume 2

1.5.2.2

Case 2: Effect of non-linear loads such as power electronic controlled industrial motors on the PVbased generation plant at different operating conditions such as steady-state mode, acceleration mode, and deceleration mode

(A) Starting of power electronic controlled industrial motors The load torque of the motor is varied to simulate three modes: acceleration, deceleration, and steady-state mode. The DG parameters such as PV irradiance and diesel generator reference are kept constant. The variations in rotor speed, electromagnetic torque, and DC bus voltage are observed, which are shown in Figures 1.6 and 1.7 during GCM and IM, respectively. In GCM, before t=0.5 sec, only speed reference (ref) of 500 rpm is provided, and the internal speed controller loop of the drive generates reference electromagnetic torque (around 300 N m) to achieve the specified speed reference. At t=0.5 sec, the reference load torque of 792 N m and a speed reference of 1,780 rpm is provided, resulting in an increase in reference electromagnetic torque

Speed (rpm)

2,000

(a)

515 510

1,000

0

Actual Speed Ref Speed

505 0.6 0.4

0.605 0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

Torque (N-m)

1,200 1,000 800 400 200

Voltage (volts)

(b)

0.4

0.6

0.8

1

1.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

1.6 1.4 Time (sec)

1.8

2

2.2

2.4

700 600 500 400

(c)

Actual Torque Ref Torque

600

Figure 1.6 Drive starting in GCM: (a) speed, (b) torque, and (c) DC bus voltage

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Speed (rpm)

2,000 Actual Speed Ref Speed

1785

1,000

1780 2

0 (a)

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2.5

2

2.2

2.4

Torque (N-m)

1,200 1,000 800 Actual Torque Ref Torque

600 400 200

(b)

0.4

0.6

0.8

1

1.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

1.4

1.6

1.8

2

2.2

2.4

Voltage (volts)

700 600 500 400 (c)

Time (sec)

Figure 1.7 Drive starting in IM: (a) speed, (b) torque, and (c) DC bus voltage

to around 1,104 N m. This increase in torque is associated to achieve the target speed. During this operation, the DC bus voltage of the inverter is maintained constant, which represent the precision of the drive controller. A similar analysis can be done for the IM operation using Figure 1.7(a)–(c).

1.5.2.3 Case 3: Effect of PV generations on the Industrial motor drives at different operating conditions, such as PV penetration level and PV shading (A) Operation at different penetration levels and simulating temporary fault The calculation for obtaining the DG penetration level in the network is done using Eq. (1.1): DPL ¼

PDG  100 PL

ð%Þ

(1.1)

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Power electronics for next-generation drives and energy systems: Volume 2 800

1

600 Power (kW)

500 400 300

0.98

0.95

0.975 At 400 PV I rr

Voltage (pu)

DG Active Power-400 PV Irr Grid Active Power-400 PV Irr DG Active Power-700 PV Irr Grid Active Power-700 PV Irr DG Active Power-1000 PV Irr Grid Active Power-1000 PV Irr

700

0.97 3.8

0.9

At 700 PV I rr

3.85

At 1000 PV I rr

3.89

0.85

200 100

0.8

0 –100 3.8

4

4.2

4.4

4.6

4.8

5

5.2

3.8

5.4

Time (sec)

(a)

4

4.2

4.4

4.6

4.8

5

5.2

5.4

Time (sec)

(b)

Figure 1.8 Effect of PV penetration during LG fault in GCM on (a) power and (b) voltage 400

1 0.95

350

0.9

0.94

0.8

250 200

DG Active Power-400 Irr

150

DG Active Power-700 Irr DG Active Power-1000 Irr

Voltage (pu)

Power (kw)

300

Grid Power

0.93 3.8

0.7

3.85

3.9 At 400 PV Irr At 700 PV Irr

0.6

100

At 1000 PV Irr

0.5

50 0

0.4 4

(a)

4.5

5 Time (sec)

5.5

3.8

(b)

4

4.2

4.4

4.6

4.8

5

5.2

5.4

Time (sec)

Figure 1.9 Effect of PV penetration during LG fault in IM on (a) power and (b) voltage

(B)

where DPL is the penetration level of DGs, PDG is the cumulative active power from DG, and PL is the cumulative active power load in the microgrid network. The variation in the power between grid and an industrial microgrid at different penetration levels of PV with LG fault is shown in Figures 1.8(a) and 1.9(a). In this case, LG fault with fault resistance of 0.1 ohms is applied from 4 to 4.8 sec. The change in load voltage (pu) is also presented in Figures 1.8(b) and 1.9(b). Here, three penetration levels are achieved by altering the irradiance of the PV plant, i.e., 400, 700, and 1,000 W/m2. In GCM, at the irradiance of 1,000 W/m2, the industrial microgrid generates enough power to inject back into the grid, while at 400 and 700 W/m2, the grid also provides some active power to meet the load demand. Due to the presence of dispatchable generation (voltage source) that is grid support in GCM and SG in the IM, the network is able to sustain temporary fault. It can be seen from Figures 1.8(b) and 9(b) that voltage is restored after the fault is cleared at 4.08 sec. Effect of PV intermittency and partial shading during GC and IM operation Here, step changes in PV irradiance are considered to analyze the response of industrial microgrid in the GC operation mode. The change in irradiance is

Performance of modern industrial plants with renewable power generation

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Irradiance (W/m2)

1,000 800 600 400 200 4

5

6

7 Time (sec)

8

9

10

Grid and DGP (KW)

Figure 1.10 Variation in PV irradiance for GCM

100

200

80 60

0 6

8

40 4

10

(b)

400

–164 Load Q (kVAr)

(a)

300 200 100 0 4 (c)

DG Active Power Grid Active Power

120

400

4

Load P (kW)

140

DG Active Power Grid Active Power

600

6

8 Time (sec)

10

6

8

10

6 8 Time (sec)

10

–166 –168 –170 4 (d)

Figure 1.11 Changes in power with variation of PV irradiance in GCM: (a) generated active power, (b) generated reactive power, (c) load active power, and (d) load reactive power shown in Figure 1.10, and the corresponding variation of generated active and reactive power in grid and microgrid is shown in Figure 1.11(a) and (b). Before t = 5 sec, the industrial microgrid acts as the net supplier of active power to the grid. At t = 5 sec, PV irradiance is reduced to 650 W/m2, which decreases total active power generation at the industrial microgrid. Similarly, the PV irradiance is changed to other values of 150, 700, and 950 W/m2 at

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Power electronics for next-generation drives and energy systems: Volume 2

Irradiance (W/m2)

1,000 800

X=7 Y=950

600 X=5 Y=650

400

X=6 Y=150

200 0

1

2

3

4

5 6 Time (sec)

7

8

9

10

Figure 1.12 Variation of PV irradiance for IM operation

250 DG Active Power Grid Active Power

400

Power (kVAr)

Power (kw)

600

200 0

6 Time (sec)

8

50 4

6 Time (sec)

8

10

4

6 Time (sec)

8

10

(b) –50 Load Q (kVAr)

Load P (kw)

100

10

400

(c)

150

0 4

(a)

300 200 100 0

DG Reactive Power Grid Reactive Power

200

4

6 Time (sec)

8

10

(d)

–100

–150

Figure 1.13 Changes in power with variation of PV Irradiance in IM: (a) generated active power, (b) generated reactive power, (c) load active power, and (d) load reactive power

t = 6, 8, and 9 secs, respectively. The corresponding active and reactive generation variation can be visualized in Figure 1.11(a) and (b). For IM, the linear variation of PV Irradiance is considered, as shown in Figure 1.12. The changes in generated active and reactive power and load active and reactive power are shown in Figure 1.13(a)–(d), respectively.

Performance of modern industrial plants with renewable power generation

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1.5.2.4 Case 4: Effect of transients such as capacitor switching, load switching, temporary fault, and islanding on modern industrial units with PV-based generation plant (A) Capacitor switching in grid-connected and islanded mode operation In the industrial units, generally, the switching of the capacitor bank is done to improve the power factor of the plant or to compensate for reactive power. For three-phase industrial consumers, billing for electricity consumed is done based on kVA rating; therefore, maintaining an appropriate power factor is important to avoid unnecessary charges. Here, the switching of the capacitor bank of 100 kVAr to increase the power factor up to 0.999 in gridconnected and Islanded Mode. The effect of capacitor switching is considered on the DC bus voltage of PEIMD, and reactive power generation (grid and DGs) in GCM and IM. Figure 1.14(a) and (b) shows the effect on the DC bus voltage of PEIMD in GCM and IM, respectively. It can be observed that there is a sudden increase in voltage in IM operation. Similarly, Figure 1.15(a) and (b) presents the exchange of reactive power during capacitor switching in the GCM and IM, respectively. In both modes, the reactive power demand is reduced with the switching of the capacitor. This results in monetary saving for the industrial plant.

DC Bus Voltage (Motor)

DC Bus Voltage (V)

800 600 400 200 0 5.5

6

6.5

7

(a)

800 600 400 200 0 5.5

6

(b)

6.5

7

Time (sec)

Figure 1.14 DC bus voltage of PEIMD during capacitor switching in (a) GCM and (b) IM

60 40 20 0 5.5

(a)

150

Q-DG Q-Grid

80

Reactive Power (kVAr)

Reactive Power (kVAr)

100

6

6.5 Time (sec)

7

7.5

50 0 5.5

(b)

Q-DGs Q-Grid

100

6

6.5

7

Time (sec)

Figure 1.15 Reactive power during capacitor switching in (a) GCM and (b) IM

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Power electronics for next-generation drives and energy systems: Volume 2

(B)

Temporary fault in grid-connected and islanded mode operation A most occurring and least severe fault line to ground (LG) type is simulated at the line between industrial plant and grid. The duration of fault is between 4 and 4.04 secs with phase-A to ground is faulty.

The speed and torque of PEIMD during LG fault in GCM and IM are shown in Figure 1.16. The load voltage of phase-A at node N3 during LG fault is depicted in Figure 1.17(a) and (b) for GCM and IM, respectively. During the IM operation, the reference electromagnetic torque of the motor changes due to the sudden change in node voltage, as shown in Figure 17(b). As the fault is temporary in nature, the speed and the reference torque are retained by the effect of the controller of PEIMD for a short duration. Therefore, the network returns back to its initial operation state after the fault clearing. Therefore, it can be 1,780 Speed (rpm)

Speed (rpm)

1,780 1,779.9 Actual Speed

1,779.8

Speed Ref

1,779.7

Ref Speed

1,760 1,750

1,779.6 3.9 3.92 3.94 3.96 3.98

4

1,740 3.9 3.92 3.94 3.96 3.98

4.02 4.04 4.06 4.08 4.1

(a)

4

4.02 4.04 4.06 4.08 4.1

(c) 1,200

1,400 Actual Torque Torque Ref

1,000

Torque (N-m)

Torque (N-m)

Actual Speed

1,770

800 600

Actual Torque Ref Torque

1,000 800 600

400 3.9 3.92 3.94 3.96 3.98 (b)

1,200

4

400 3.9 3.92 3.94 3.96 3.98

4.02 4.04 4.06 4.08 4.1 (d)

Time (sec)

4

4.02 4.04 4.06 4.08 4.1

Time (sec)

1

1

0.9

0.8

Load Voltage (pu)

Load Voltage (pu)

Figure 1.16 Effect of temporary LG fault on PEIMD: (a) speed in GCM, (b) torque in GCM, (c) speed in IM, and (d) torque in IM

0.8 0.7 0.6 0.5 3.9 3.92 3.94 3.96 3.98

(a)

4

4.02 4.04 4.06 4.08 4.1

Time (sec)

(b)

0.6 0.4 0.2 0 3.9 3.92 3.94 3.96 3.98 4 4.02 4.04 4.06 4.08 4.1 Time (sec)

Figure 1.17 Per-unit load voltage of phase-A during LG fault in (a) GCM and (b) IM

Performance of modern industrial plants with renewable power generation

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seen that a close-loop PEIMD is capable of working efficiently during temporary short-circuit faults in the network.

1.6 Conclusion A comprehensive analysis is performed under the MATLAB/SIMULINK environment with various system scenarios. A test microgrid system is considered for the analysis, which consists of an industrial plant having linear load, non-linear load as AC drives, a solar PV generation unit, a synchronous generator-based unit, and the main utility grid. Broadly four system scenarios have been considered pertaining to the various system operating conditions such as GCM, IM, and the transition mode. Similarly, the effect of intermittency of RES has been discussed on the industrial drives and vice versa. Other than that, various switching operations and system responses during fault conditions have also been incorporated. Numerous system parameters like voltage, currents, industrial motor load torque, motor speed, and active/reactive powers under various cases have been obtained. With this analysis, it is observed that in grid-connected mode, the reactive-power requirement of the consumer load is fulfilled mainly by the grid, which exerts an extra burden over the utility grid. To overcome this issue up to a certain level, it is recommended that the DGs connected to the industrial load shall be made capable of supplying reactive VARs during steady-state and transients. Specifically, the new technologies which are being integrated with RESs, such as injection of negative sequence current into the system along with positive sequence current, shall be used. Certainly, the requirements for robust controller increases that can preciously control various system parameters under very high system dynamics.

References [1] [2] [3] [4] [5]

IEEE stds-18-0003-00-PUBS-3004-8-2016. IEEE stds-18-0002-00-PUBS-3001-11-2017. IEEE stds-18-0001-00-PUBS-3001-2-2017. IEEE 1547-2030. IEEE 519 Working Group, “IEEE recommended practices and requirements for harmonic control in electrical power systems.” IEEE STD (1992): 5191992. [6] IEEE 1159-1995, “IEEE Recommended Practice for Monitoring Electric Power Quality” [7] V. Anand, V. Singh, and S. Mekhlief, ‘Power electronics for renewable energy systems’, in N.K. Prabhansu (ed.), Renewable Energy for Sustainable Growth Assessment, New York, NY: IET Wiley Publication; 2022. pp. 81– 117, doi:10.1002/9781119785460. [8] J.M. Carrasco, L.G. Franquelo, J.T. Bialasiewicz, et al., ‘Power-electronic systems for the grid integration of renewable energy sources: a survey’, IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1016, 2006.

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[9] B.K. Bose, ‘Global energy scenario and impact of power electronics in 21st century’, IEEE Trans. Ind. Electron., vol. 60, no. 7, pp. 2638–2651, 2013. [10] D.J. Ryan, H.D. Torresan, R. Razzaghi, and B. Bahrani, ‘Frequency response of motor drive loads in microgrids’, IEEE Trans. Energy Convers., vol. 36, no. 2, pp. 1197–1206, 2021. [11] L.K. Uttarala, K. Kumar, Vanukuru, et al., ‘RES fed high gain DC–DC converter with closed loop control for induction motor drive’, in: IEEE International Conference (SCOPES), pp. 1404–1409, 2016. [12] S.M. Gazafrudi, A.T. Langerudy, E.F. Fuchs, and K. Al-Haddad, ‘Power quality issues in railway electrification: a comprehensive perspective’, IEEE Trans. Ind. Electron., vol. 62, no. 5, pp. 3081–3090, 2014. [13] X. Liang, ‘Emerging power quality challenges due to integration of renewable energy sources’, IEEE Trans. Ind. Appl., vol. 53, no. 2, pp. 855–866, 2017. [14] Md. Shafiul Alam, F. Al-Ismail, A. Salem, and M.A. Abido, ‘High-level penetration of renewable energy sources into grid utility: challenges and solutions’, IEEE Access, vol. 8, pp. 190277–190299, 2020. [15] A. Iqbal, A. Waqar, R. Madurai Elavarasan, et al., ‘Stability assessment and performance analysis of new controller for power quality conditioning in microgrids’, Int. Trans. Int. Trans. Electrical Energy Syst., vol. 31, no. 6, pp. 1–27, 2021, https://doi.org/10.1002/2050-7038.12891. [16] G.S. Dua and R. Kaur, ‘Enhancement of power quality in distribution network using DVR’, in: 2015 Annual IEEE India Conference (INDICON), pp. 1–6, December 2015. [17] G.S. Dua, B. Tyagi, and V. Kumar, ‘Deploying micro-PMUs with channel limit in reconfigurable distribution systems’, IEEE Syst. J., vol. 99, pp. 1–12, 2021. [18] J.I. Leon, S. Vazquez, and G. Leopoldo, ‘Multilevel converters: control and modulation techniques for their operation and industrial applications’, Proc. IEEE, vol. 105, no. 11, pp. 2066–2081, 2017. [19] Q. Alex, ‘Power semiconductor devices for smart grid and renewable energy systems’, Proc. IEEE, vol. 105, no. 11, pp. 2019–2047, 2017. [20] S. Thamballa, ‘Power electronic converters and operational analysis in microgrid environment’, in: P.F. Prabhakaran, U. Subramaniam, J.L.F. Daya, et al. (eds.), Smart Grids and Microgrids: Technology Evolution, New York, NY: IET Wiley Publication; 2022, pp. 241–272, DOI: https://doi. org/10.1002/9781119760597. [21] M. Kumar and B. Tyagi, ‘An optimal multivariable constrained nonlinear (MVCNL) stochastic microgrid planning and operation problem with renewable penetration’, IEEE Syst. J., vol. 14, no. 3, pp. 4143–4154, 2020. [22] M. Choobineh and S. Mohagheghi, ‘Sustainable industrial plants: energyefficient, asset-aware, and waste-averse’, IEEE Trans. Ind. Appl., vol. 54, no. 3, pp. 1966–1974, 2018.

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[23] P. Warrier and P. Shah, ‘Fractional order control of power electronic converters in industrial drives and renewable energy systems: a review’, IEEE Access, vol. 9, pp. 58982–59009, 2021. [24] M. Kumar and B. Tyagi, ‘A robust adaptive decentralized inverter voltage control approach for solar PV and storage based islanded microgrid’, IEEE Trans. Ind. Appl., 2021, vol. 57, no. 5, pp. 5356–5371, 2021.

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Chapter 2

Maximum power extraction from partially shaded photovoltaic power conversion systems Hassan M. Hussein Farh1 and Abdullrahman A. Al-Shamma’a1,2

Partial shadowing conditions (PSCs) take place when certain PV cells/panels fall in partial or total shadowing by surrounding buildings, towers, trees, dust, deterioration, clouds, and other factors. The partially shaded photovoltaic (PSPV) decreases power output and causes hot spots. They also diminish the PV power conversion system’s generated power output and efficiency. The energy losses of the photovoltaic (PV) system due to partial shadowing or an incorrect peak power tracker are significant and can exceed 70% of total power supplied. As a result, following the peak power is required, particularly in PSCs, to attain high energy efficiency. In case of PSCs, the power–voltage characteristic curve become more sophisticated where it has many power peaks; one global power peak (GPP) and some other local power peaks (LPPs). Numerous maximum power point tracker (MPPT) approaches were executed to discern the GPP instead of falling into one of the LPPs. This chapter will focus on the partial shading causes, effects in addition to the remediation methodologies of this problem. Moreover, it will focus on the nature inspired MPPT algorithms that can deal with this problem efficiently. They can track the GPP instead of trapping into one of the LPPs with a less convergence time and higher efficiency. Additionally, they have no fluctuations around the GPP. These nature-inspired MPPT algorithms are classified into evolutionary/artificial intelligence (E/AI) algorithms, physics/chemistry (P/C)based algorithms, and bio-inspired (BI) algorithms. On the other hand, tracking the maximum power using three distinct MPPT algorithms (PSO, CSO, and P&O) from partially shaded PV (PSPV) energy conversion system were provided, discussed, and analyzed. The simulation findings revealed that CSO surpassed PSO in tracking the GPP without fluctuations and having shorter tracking time for both uniform irradiance and PSCs. Whereas P&O approach was trapped to the first

1 Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia 2 Department of Mechatronics Engineering, College of Engineering, Taiz University, Yemen

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Power electronics for next-generation drives and energy systems: Volume 2

peak irrespective of whether it is local peak or GPP and it fails to discern the GPP in case of PSCs.

2.1 Introduction Renewable photovoltaic (PV) power conversion systems represent the greatest potential sustainable energy technology due to, when opposed to ordinary sources of energy, they are affordable, noise free, and environmentally beneficial. The tracking of the PV global peak power (GPP) may improve the amount power supplied by over 30%, making it an appealing research topic. By adjusting the duty cycle, many DC–DC converters are utilized to adjust the PV output voltage and therefore, the supplied electricity [1,2]. Buck, boost, buck–boost, flyback, and SEPIC converters are some of these DC/DC converter types that can be utilized to find the GPP. Because the output voltage needs to be boosted to be adequate for the loads, Among the most common DC–DC power converters found in many PV power systems is the DC–DC boost converter [3–5]. The maximum power tracker provided by these PV energy conversion systems under uniform and partial shadowing conditions (PSCs) is crucial since it may increase the PV system’s power generated, reliability, efficiency, and power quality. However, partial shading diminishes the power captured and leads to a hot spot problem causing damage of shaded PV modules. Once a portion of the photovoltaic (PV) cells and/or panels are shadowed by nearby buildings, trees, dust, moving clouds, or other factors, partial shading occurs. When partial shading is used, the P– V curve differs from that of a uniform situation with only one peak. The characteristic curve between the PV system’s output power and voltage reveals only one GPP under uniform solar radiation. Classical maximum power point tracker (MPPT) approaches, such as perturb and observe (P&O), hill climbing, and incremental conductance are sufficient to discover this unique GPP in the power–voltage characteristic graph in this scenario. PSCs, on the contrary, produce a single global peak power (GPP) and multi local power peaks (LPPs), which could lead classic MPPT algorithms to append to one of the LPPs under these PSCs [6–8]. As a result, after failing the classical MPPT approaches to follow the GPP in certain PSCs situations, extremely effective and current MPPT approaches based on nature inspired MPPT approaches are used to discern the GPP instead of LPPs. Several simulation investigations were conducted to determine the optimal metaheuristic MPPT approaches in terms of convergence time and GPP tracking [7,9–12]. In [10], an enhanced ant bee colony (ABC) optimizer was presented to follow the GPP in PSCs, revealing that the modified ABC outperforms the particle swarm optimizer (PSO), genetic algorithm (GA), and ABC in term of GPP tracking. Furthermore, the authors in [11] shown that the flower pollination (FP) optimizer outperforms the PSO and P&O in terms of tracking time and GPP tracking. The cuckoo search optimizer (CSO) and PSO have the ability to discern the GPP in case of PSCs, according to Jubaer and Salam [13], however CSO may take shorter time than PSO to track the GPP [13]. PSO’s performance has improved to the point

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where it is comparable to the firefly algorithm (FA) and CSO [14,15]. Finally, the authors in [9] discovered that FP, CSO, FA, grey wolf (GW) optimizer, PSO, ABC, and ant colony (AC) optimizer are the seven most effective metaheuristic MPPT approaches under PSCs [9]. They came to the realization by evaluating key factors and confirming it with earlier study simulation findings [9,11]. On the contrary, there have been less experimental works [16–18] dealing with PSCs for detecting the GPP using metaheuristic MPPT approaches. Fuzzy logic control mixed with artificial intelligence approaches, for example, surpasses traditional PSO based on tracking time and GPP efficiency, additionally, no fluctuations around steady state, according to both simulation and experimental data [16]. Furthermore, the simulation and experimental results in [17,18] highlighted the effectiveness of the ABC over PSO in case of detecting the GPP quickly and having less oscillations [17,18]. According to the above literature, classical MPPT algorithms are incapable of dealing with this issue (PSCs) and fail to follow the global peak. They also exhibit noticeable fluctuation around the steady state, which may cause a disruption in the PV energy conversion system. Nature-inspired MPPT algorithms, on the other hand, cope with this issue effectively. When compared to classical MPPT algorithms, they can follow the global peak instead of trapping inside the local peak with reduced tracking time and improved efficiency. Furthermore, there are no fluctuations around the steady-state condition. Therefore, this chapter aims to alleviate the effects of this serious PSCs problem through tracking the GPP instead of falling into any one of the LPPs based on the nature inspired MPPT algorithms. These nature-inspired MPPT algorithms are classified into evolutionary/artificial intelligence (E/AI) algorithms, physics/chemistry (P/C)-based algorithms, and bioinspired (BI) algorithms. On the other hand, tracking the maximum power using three distinct MPPT algorithms (PSO, CSO, and P&O) from partially shaded PV (PSPV) energy conversion system will be provided, discussed, and analyzed.

2.2 PV partial shading problem 2.2.1 Causes and effects of partial shading problem In the case of partial shadowing, some PV cells/panels are partially or totally shaded by neighboring buildings, trees, towers, dust, and degeneration because of aging, clouds and so on. A few of these factors and causes are connected to the location directly, that can be addressed at the start of the PV systems implementation. For instance, selecting the proper and best locations for PV systems implementation in related to location physical features (temperature and solar radiation) and no large neighborhoods and skyscrapers might very well lead to a substantial improved performance in the output electricity supplied and PV system efficiency of the PV system. Numerous different contributing factors may indeed be unavoidable, like aging, destroy, dust, clouds, or surrounding buildings and skyscrapers. Furthermore, certain factors might arise and create partial shadowing just after construction of the PV power system. The shadowed PV cells/modules are required to confront the high current of some other non-shaded cells/panels, using

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Power electronics for next-generation drives and energy systems: Volume 2

electricity rather than creating power. Even as proportion of the shadowing surface expands, so do the energy loss and the power output. The partially shaded PV (PSPV) state decreases produced power and causes hot spot issues, that may contribute to the collapse of shaded PV modules that are not kept safe by bypass diodes. As a result, the shadowed PV cells/panels decrease the power output and PV system efficiency. The energy losses owing to partial shadowing or erroneous maximum power tracker are quite substantial and can surpass 70% of overall power supplied. As a result, following the GPP, particularly under PSCs, is necessary to attain high energy efficiency, low power-losses, and high power supplied [19–22]. As demonstrated in Figure 2.1, the power–voltage characteristic curve in case of PSCs becomes more sophisticated where many peaks are produced. The highest one is GPP that represents the global peak power of the PV power system while the other peaks are LPPs. Whereas the characteristic curve has a single peak under uniform radiation as demonstrated in Figure 2.1.

2.2.2

Partial shadowing remediation

The remediation of partial shadowing problem has paramount value. Several mitigation techniques are used to remedy the partial shadowing impacts to optimize and improve the output power generated and overall efficiency. Numerous studies have been conducted to investigate current and effective MPPT approaches for tracking the GPP under PSCs to enforce the PV system efficiency. While fewer studies discuss the PV system topologies that also mitigate and alleviate partial shading effects [23–27]. For this purpose, partial shadowing remediation approaches may be divided into two groups: PV system design circuits and the nature 500 450

Uniform [900 900] SP#1 [900 600]

400

SP#2 [900 300]

Power (W)

350 300

Global peak

250

Local peak

200 150 100 50 0 0

10

20

30

40

50

Voltage (V)

Figure 2.1 Uniform and partial shadowing cases

60

70

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inspired MPPT methodologies. Bypass and blocking diodes, PV energy system structures, PV array layout, and PV array restructuring are all types of the PV system design circuits. These nature-inspired MPPT algorithms can be categorized into three types: E/AI algorithms, P/C-based algorithms, and BI algorithms.

2.3 Standalone and grid-interconnected PV power conversion systems 2.3.1 General description of standalone and grid-interconnected PV power systems The PV energy systems are classified into standalone and grid connected. The gridconnected PV power conversion system is interconnected to the electricity company via DC–DC power converter and DC–AC inverter as demonstrated in Figure 2.2. Whereas standalone PV power conversion system is interconnected to the load directly via the DC–DC power converter. The maximum power possible from the PV system is followed by the DC–DC power converter via providing the optimal duty ratio by the MPPT controller. Among the most prominent DC–DC converters found in several PV system is the boost converter. Since the output voltage has to be raised to be appropriate for the loads. The DC–AC inverter, on the contrary, has been used to transfer DC voltage to AC voltage so that it can be interconnected to an electricity company. The controlling scheme contains three controllers: one is for PV MPPT and another for real and reactive power generation from the PV energy system. The MPPT controller is used to adjust the duty ratio of the DC–DC converter to follow the maximum power. Real and reactive power adjustment in the grid-connected inverter is accomplished by adjusting the d-axis and the q-axis current. For the first MPPT controller, the DC–DC boost converter (Figure 2.2) was implemented to discern the GPP by adjusting the PV output voltage, Vd,in through modifying the duty ratio, D as a consequence of variations in Vd,in. The formula that

dc/dc boost converter

m_PV

L Irradiance (W/m2)

Ir

m

Irradiance Temperature

Temperature (deg. C)

T

Utility system

Db C

+ Temp

Inverter

–

PV array

VPV IPV

MPPT

Discrete PWM generator

Figure 2.2 The PV power conversion system interconnected to the electricity company/grid

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Power electronics for next-generation drives and energy systems: Volume 2

determine the input voltage and output voltage relationship of the DC–DC boost converter is expressed in the following [3,8]: Vd;out 1 ¼ Vd;in ð1  DÞ

(2.1)

where Vd,out is the DC output voltage; D is the duty ratio. As illustrated in Figure 2.2, the three-phase PWM inverter circuit is made up of three legs, one per phase. The three-phase PWM inverter’s goal is to shape and regulate the amplitude and frequency of the output voltages (constant voltage constant frequency) with a constant input voltage for electric grid-interconnected. Furthermore, the inverter is employed to aid the MPPT approach by guaranteeing that the DC-link voltage becomes unchanged under different operating situations. The modulation index, ma, of the 3-ph PWM converter can be calculated as follows [3,8]: ^

ma ¼ ^

V control ^

V tri

^

V LLC ¼ Vd;out

(2.2) ^

where V control is the control signal peak value; V tri is the triangular signal peak value. The grid controller has two different control paths for managing the real and reactive power. The inner and outer control paths are two control paths. By using the outer loop, the dc-link voltage can be adjusted to be fixed. By using the inner loop, the reactive power can be regulated via adjusting the q-axis current reference to become zero for attaining power factor of unity. Figure 2.3 depicts two current regulators for both the direct, Id, and quadrature, Iq, parameters of output current of inverter. In addition, as a grid synchronization approach, phase locked loop (PLL) was employed to match the control circuit with the electricity grid phase angle. The real and reactive power, Ps and Qs, respectively can be defined [3,8]:  3
vd id þ vq iq 2  3
Qs ¼ vd iq  vq id 2

Ps ¼

(2.3) (2.4)

By aligning the reference frame d-axis with the utility voltage vq=0; the real and reactive powers are collected as follows: 3 P s ¼ vd i d 2 3 Qs ¼ vd iq 2

(2.5) (2.6)

From Eq. (6), it can be observed that the real and reactive power are therefore adjusted through changing the q-axis and d-axis grid current.

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Filter

PWM converter

vdc

ia

ib

ic

PWM generator

Utility grid

Inverse of Park transformation dq/abc_ref vd* CCd

ωt

vq*

vd vq

CCq

id

iq id*

PLL & d–q measurements id iq

Va Vb Vc

ωt

CCq: q-axis current controller CCd: d-axis current controller VC: voltage controller

VC iq*=0

vdc vdc*

Figure 2.3 The control circuit of the grid inverter

2.3.2 Configuration of the PV power conversion system under study Figure 2.4 depicts the schematic diagram of the PV power conversion system under study. Two series-interconnected PV panels, a DC–DC boost converter, and a resistive load comprises the solar PV energy conversion system. Though multiplying the PV output voltage and current, the PV output power can be computed. The output power captured is fed into the MPPT controller that generates the optimal duty ratio, D. The boost converter is forced to drive in line with this optimal D, that leads to the desired voltage (VMPP). Depending on the site characteristics (Riyadh area, Kingdom of Saudi Arabia), three distinct shadowing patterns are selected to be applied for the PV panels to replicate uniform and PSCs, as illustrated in Figure 2.1. The irradiances supplied to the PV panels are the same under uniform conditions [900; 900], and the power– voltage characteristic curve indicates a single GPP having a power output of 400 W. To replicate the PSCs, two distinct shading patterns are used. The P–V curve is more complicated in PSCs, with several peak power points. The global maximum power supplied by the PV power conversion system is the GPP, whereas the rest are

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Power electronics for next-generation drives and energy systems: Volume 2

Boost converter

Ir1 L Ir2

Cout Load

PV current sensor

PV modules D

PV voltage sensor

IPV VPV CSO PSO P&O

MPPT

Figure 2.4 Schematic diagram of the PV power conversion system

LPPs. The GPP of the first shadowing pattern, pattern#1 [900; 600], is located at the power–voltage curve’s extreme right with 286 W as an output power. The GPP of pattern#2 [900; 300], on the other hand, is toward the beginning of the power curve and produces 196 W. This work proposes a CSO technique-based MPPT to swiftly track the GPP while avoiding entrapment at any LPs. The PV current and the voltage serve as the CSO’s inputs, while the duty cycle serves as its output as shown in Figure 2.4. Through following of the boost converter’s optimal duty cycle (D), the CSO follows and tracks the GPP. The CSO is contrasted to a PSO method and classical P&O to demonstrate its advantages.

2.4 PV nature-inspired MPPT algorithms 2.4.1

Classification of the nature-inspired MPPT algorithms

It is crucial to discern the global peak power output from these PV systems in case of both uniform and PSCs. This can enhance the output power generated efficiency, reliability, and PV system quality. The power–voltage curve of PSCs became increasingly complicated, with multiple power peaks: one GPP and several additional LPPs. Classical MPPT techniques, such as Incremental Conductance, Perturb and Observe (P&O), and Hill Climbing [28–32], are incapable of dealing with this issue and fail to detect the GPP. They also exhibit noticeable fluctuations, which

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Table 2.1 The nature inspired MPPT algorithms classification Nature-inspired MPPT algorithms Category#1 E/AI algorithms

Category#2 P/C-based algorithms

Category#3 BI algorithms

Differential Evolution (DE)

Harmony Search (HS)

Genetic Algorithm (GA)

Simulated Annealing (SA)

Flower Pollination (FP) Particle Swarm Optimizer (PSO) Ant Colony (AC) Ant Bee Colony (ABC) Bat Algorithm (BA) Grey Wolf (GW)

Fuzzy Logic Controller (FLC) Big Bang-Big Crunch Artificial Neural Network Water Cycle Algorithm (ANN) Adaptive Neuro Fuzzy Gravitational Search Algorithm Inference System (ANFIS) (GSA)

may cause a disruption in the PV power conversion system. Numerous MPPT approaches were used to deal with the PSCs determine the GPP rather than one of the LPPs [26,33,34]. This section seeks to mitigate the consequences of this major PSCs problem by following the GPP rather than falling into any of the LPP based on nature inspired MPPT approaches. These nature-inspired MPPT algorithms can be categorized into three types: E/AI algorithms, P/C-based algorithms, and BI algorithms as introduced in Table 2.1.

2.4.2 Proposed MPPT algorithms brief overview 2.4.2.1 Cuckoo search optimizer algorithm The cuckoo search optimizer (CSO) is a nature inspired MPPT optimization method inspired by these cuckoo species’ sponging reproductive strategies. Only some varieties of cuckoos are recognized to adopt brood parasitism tactics, in which they lay their eggs in the nest toward another bird. The CSO method is reflected in the following principles [35,36]: – – – –

The cuckoo lays an egg in an arbitrary chosen nest. The nicest nests having excellent quality eggs could be passed down to succeeding generations. To increase the likelihood of the hatchlings acquiring more food, the cuckoo kills numerous eggs of the host. The host detects a strange egg. Throughout this case, the host usually kills the cuckoo’s eggs or destroys the nest in order to build a new one in a distinct location.

On the bases of the previous four principles, the CSO is used to detect the GPP as follows: First: The CSO specifications factors for the GPP searching are first established. The duty ratios of the boost converter, that is, Di (i = 1, 2 . . . n), and Le´vy multiplication coefficient (Lm) chosen as initial factors in this investigation.

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Power electronics for next-generation drives and energy systems: Volume 2

Second: The initial duty ratios Di are transmitted to the DC–DC boost converter in sequence, and the corresponding power is determined. As the current best sample, the maximum power and duty cycle are retained. The Le´vy flight and the following formula are used to generate new duty cycle sample data: !  u
k kþ1 k Dbest  Dki (2.7) Di ¼ Di þ Lm 1 jvjb where b = 1.5, Lm is the Le´vy multiplication coefficient, and v and u are drawn at random chosen from the normally distributed form as follows:

 u  N 0; s2u and v  N 0; s2v (2.8) The gamma function is used to compute the su and sv as follows: 0

1b1

BGð1 þ bÞ  sinðpb=2ÞC su ¼ @   b1 A and sv ¼ 1 2  b  ð 2 Þ G 1þb 2

(2.9)

Third: The updated duty ratios are transmitted to the DC–DC boost converter, and the output power is monitored. As the new best sample, the greatest energy obtained by the duty cycle is picked. Furthermore, with a likelihood of Pa, only those data are randomly ignored in order to replicate the acts of the host detecting and discarding the cuckoo’s eggs. As a result, randomly new samples are created instead of the damaged eggs, and the power supplied values of new samples are calculated. Through comparing the output power values obtained, the optimal current is determined. Fourth: Once the stopping prerequisites are met, the CSO is turned off, and the best duty ratio is produced, which matches to the GPP. Figure 2.5 depicts a flowchart that includes these four processes.

2.4.2.2

PSO algorithm

PSO is a nature-inspired approach that is distinguished by its simplification, effectiveness, and durability. The GPP is detected based on particle velocity and location that are amended to determine the correct path of the GPP rather than capturing to one of LPs. The particle’s corrected location may be determined by the equation [7,15]: Dikþ1 ¼ Dki þ vikþ1

(2.10)

The particle velocity vik+1 may be determined utilizing the present location xik, particle velocity vik, and global best location (Gbest) by the following formula [7,37]:

 (2.11) vikþ1 ¼ wvki þ c1 r1 Pbest; i  Dki þ c2 r2 Gbest  Dki where w is the exploration region’s inertia weight while c1 and c2 are the acceleration factors [38,39]. The GPP exploration phase in the PSO approach begins by

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Start Input data Initialize n, β, Pa, k, itermax , Dmin , Dmax

CSO initialization

i = i+1

i=1:n Send Di to boost converter Sense Ppv(i), Vpv(i) and Ipv(i) No

All sample evaluated? Yes Iter = 1:itermax Update Dbest (i)

Updating and sending Di to the converter

Update the Di using Lévy flight equation Sense Ppv(i), Vpv(i) and Ipv(i) No

iter = iter+1

i = i+1

I = 1:n

All sample evaluated? Yes Select the worst nest

rand>Pa

No

Yes Destroy the worst nest Update the Di using Lévy flight equation Sense Ppv(i), Vpv(i) and Ipv(i) All iteration evaluated? Yes

No

Output the best duty cycle Dbest

end

Figure 2.5 Flowchart of the MPPT based on cuckoo search optimizer delivering the duty ratios Di to the boost converter. As illustrated in Figure 2.6, the PV output voltage and output PV current have been multiplied to calculate Pbest and Gbest. Using the above indicated location and velocity formulas (2.10) and (2.11), the corrected duty cycles are computed and reissued.

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Power electronics for next-generation drives and energy systems: Volume 2 Start Input data Initialize population n, ω, itermax , Dmin , Dmax i=1:n

PSO initialization

i = i+1

Send Di to boost converter Sense Ppv(i), Vpv(i) and Ipv(i) Pbest (i)=Di No

All sample evaluated? Yes iter=1:itermax Update Gbest (i)

Updating and sending Di to the converter

i=1:n

Update particles velocity and Di using (1) and (2)

Best individual fitness value?

Yes

Update Pbest (i)

No Best Global fitness value?

Yes

Update Gbest (i)

iter = iter+1

i = i+1

Sense Ppv(i), Vpv(i) and Ipv(i)

No No

All sample evaluated? Yes All iteration evaluated?

No

Yes Output the best duty cycle Gbest

end

Figure 2.6 The particle swarm optimizer flowchart

2.4.2.3

Perturb and observe (P&O) approach

Because of its minimal cost and straightforwardness, the P&O is often utilized to capture the GPP [8]. The generated current and voltage of a PV system are monitored using this approach. The system output voltage is then altered, and the power

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estimated between the two voltages is evaluated. For every perturbation, the P&O evaluates the PV system’s produced power before and after every perturbation. The trend of every perturbation is determined by the power captured: if the PV system supplies more power in case of the system voltage varies, the perturbation should be in the same trend; alternatively, the perturbation should be in the opposite trend. These attempts are repeated indefinitely, and the reference voltage is generated and provided to the controller of DC/DC converter as illustrated in Figure 2.7 [4,8]. The formula of the P&O can be described as in the following: dPpv ðk Þ Ppv ðk Þ  Ppv ðk  1Þ ¼ dVpv ðk Þ Vpv ðk Þ  Vpv ðk  1Þ

(2.12)

where Ppv ðk Þ and Ppv ðk  1Þ represent the current and previous output power; Vpv ðk Þ and Vpv ðk  1Þ denote the present and previous PV output voltage.

Start

Measure V(t), I(t)

Calculate P(t)

Yes

P(t)-P(t-1)=0 No

No

V(t)-V(t-1) ˂ 0 No

Yes

P(t)-P(t-1) ˃ 0

Yes

V(ref) = V + ∆V

Yes

V(t)-V(t-1) ˃ 0

No

V(ref) = V – ∆V

Return

Figure 2.7 The P&O MPPT algorithm flowchart

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Power electronics for next-generation drives and energy systems: Volume 2

2.5 Results and discussions To evaluate the capabilities of three different MPPT approaches (PSO, CSO, and P&O) under both uniform and PSCs. As illustrated previously in Figure 2.1, three distinct uniform and partial shading scenarios are chosen depending on the location features. The following are the features of the main results obtained depending on three various MPPT approaches (CSO, PSO, and P&O) under uniform and PSCs: Case study # 1: The subjected irradiances for the two PV panels are same under uniform circumstances [900; 900]. In the power–voltage characteristic curve demonstrated in Figure 2.1, a single GPP with PV power and voltage magnitudes of 400 W and 53 V is formed. Some findings from Figure 2.8 could be best summed up: –

– –

Even though the PSO, CSO, and P&O can discern the single maximum output power; 400 W, the P&O approach detects the maximum output power in less time than the CSO and PSO. The P&O approach produces noticeable oscillations around the produced peak power and voltage that could be reduced by lowering the sampling rate. Owing to their setup and sophisticated computational complexity, the PSO and CSO need longer convergence time to catch the GPP than the P&O approach. As a result, in uniform conditions, the P&O approach effectively follows the unique peak.

Case study # 2: Pattern #1 [900 600] has a GPP value of 286 W that is situated at the end of the power–voltage characteristic curve. Brief conclusions from Figure 2.9 could be summed up: –

–

The MPPT-based P&O approach has not been successful to discern the GPP and had to rely on the initial local peak power: 196 W. Furthermore, as illustrated in Figure 2.8, the fluctuation is noticeable. Even though both the CSO and the PSO catch the GPP, the CSO needs shorter convergence time (0.73 s) than the PSO time (1.03 s). As a result, in comparison to the PSO and P&O approaches, the CSO performs better based on convergence time and GPP detection.

Case study # 3: SP #2 [900 300] has a GPP value of 196 W and its GPP is placed at the beginning of the power–voltage characteristic. Brief conclusions from Figure 2.10 could be summed up: –

the P&O approach can identify the GPP with a value of 196 W; considerable fluctuation happens around the GPP. This stresses that the P&O trap at the initial peak, irrespective of whether it is an local peal or GPP. Consequently, the P&O approach is inapplicable in PSCs.

(400 W)

Voltage (V)

80 70 60 50 40 30 20 10 0

Theoretical CSO PSO P&O

t-PSO=1.03 sec .

450

t-CSO=0.73 sec.

400 350 300 250

0

0.5

1

Power (W)

Power (W)

450 400 350 300 250 200 150 100 50 0

500 450 400 350 300 250 200 150 100 50 0 0

0.6

0.8

2

1.5

1

1.2

2.5 Time (sec.)

3.5

3

Uniform [900 900] SP#1 [900 600] SP#2 [900 300]

4

4.5

(400 W, 53 V)

Global peak

10

20

30 Voltage (V) 40

50

60

70

CSO PSO P&O

(53 V)

0

0.5

1

5

1.5

2

2.5 Time (sec.)

3

3.5

4

4.5

Figure 2.8 The PV power and voltage under uniform irradiances (Case study #1)

5

300 (286 W)

200 (196 W)

150 100

Power (W)

Power (W)

250

50 0

0.5

Theoretical CSO PSO 0.6

0.7

0.8

0.9 Time (sec.)

1

Power (W)

0

199 198.5 198 197.5 197 196.5 196 195.5 195 194.5 194 0.5

1

1.1

P&O

1.2

1.5 500 450 400 350 300 250 200 150 100 50 0 0

4

3.5

4.5

5

Uniform [900 900] SP#1 [900 600] SP#2 [900 300]

(286 W, 57.5 V)

Global peak (196 W, 26 V)

10

20

30 Voltage (V)

70

40

50

60

70

(57.5 V)

60 Voltage (V)

3

2.5 Time (sec.)

2

50 40

CSO PSO P&O

(26 V)

30 20 10 0

0

0.5

1

1.5

2

2.5 Time (sec.)

3

3.5

4

Figure 2.9 The PV power and voltage under PSCs (Case study #2)

4.5

5

(196 W) Theoretical CSO PSO P&O

200 180 Power (W)

160 140 120 100 80 60 40 20 0

0

0.5

0.2

0.4

0.6

1.5

1

Power (W)

Power (W)

220 200 180 160 140 120 100 80 60 40 20 0

500 450 400 350 300 250 200 150 100 50 0

0.8 Time (sec.)

1

2

1.2

1.4

3

2.5 Time (sec.)

3.5

4.5

4

5

Uniform [900 900] SP#1 [900 600] SP#2 [900 300]

Global peak (196 W, 26 V)

0

10

20

30

Voltage (V)

40

50

60

70

60 CSO PSO P&O

Voltage (V)

50 40 (26 V)

30 20 10 0

0

0.5

1

1.5

2

2.5 Time (sec.)

3

3.5

4

Figure 2.10 The PV power and voltage under PSCs (Case study #3)

4.5

5

38 –

Power electronics for next-generation drives and energy systems: Volume 2 The CSO and the PSO successfully detect the GPP, however the CSO needs less convergence time (0.73 s) than the PSO time (1.52 s). As a result, in comparison to the PSO and P&O approaches, the CSO performs better.

2.6 Conclusions The uniform solar radiation of the PV panels produces a single peak in the power– voltage characteristic, whereas PSCs produce numerous peaks; one GPP and several LPPs. Numerous MPPT approaches were used to deal with the PSCs determine the GPP rather than one of the LPPs. The nature inspired MPPT approaches mitigate the consequences of this major PSCs problem by following the GPP rather than falling into any of the LPP based on. These nature-inspired MPPT algorithms are categorized in this study into three types: intelligence/AI algorithms, P/C-based algorithms, and BI algorithms. Regarding the site features in this study, three distinct GPP case studies are used to the PV system in case of uniform irradiance and PSCs. The obtained results from the PSO, CSO, and P&O approaches were analyzed and compared to assess the performance of these three different MPPT approaches based on GPP tracking and convergence time. Considering on all these analyses, it is significant to mention that CSO outperforms PSO in tracking the GPP without fluctuations and having shorter tracking time under uniform irradiance and PSCs. When compared to the PSO approach, the CSO consumes shorter time for global power tracking. As a result, the PV system’s efficiency and stability keep improving. Based on GPP tracking and fluctuations around the GPP, the PSO-based MPPT approach outperforms the P&O. Finally, rather than tracking the global maximum power, P&O approach was trapped to the first peak irrespective of whether it is local peak or GPP. Furthermore, there is noticeable fluctuation around the generated power, that may cause a disruption in the PV power conversion system. Consequently, the P&O-based MPPT approach is inapplicable in PSCs.

References [1] F. A. Alturki, A. A. Al-Shamma’a, and H. M. H. Farh, “Simulations and dSPACE real-time implementation of photovoltaic global maximum power extraction under partial shading,” Sustainability, vol. 12, no. 9, p. 3652, 2020. [2] M. Z. Malik, H. M. Farh, A. M. Al-Shaalan, A. A. Al-Shamma’a, and H. H. Alhelou, “A novel single-input-multi-output converter for flexible-order power-distributive with MPPT capability,” IEEE Access, vol. 9, pp. 131020– 131032, 2021. [3] A. M. Eltamaly and H. M. Farh, “Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWOFLC,” Solar Energy, vol. 177, pp. 306–316, 2019.

Maximum power extraction

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[4] H. Farh, M. Othman, A. Eltamaly, and M. Al-Saud, “Maximum power extraction from a partially shaded PV system using an interleaved boost converter,” Energies, vol. 11, no. 10, p. 2543, 2018. [5] H. M. Farh, A. M. Eltamaly, and M. S. Al-Saud, “Interleaved boost converter for global maximum power extraction from the photovoltaic system under partial shading,” IET Renewable Power Generation, vol. 13, no. 8, pp. 1232–1238, 2019. [6] A. A. Al-Shamma’a, A. M. Abdurraqeeb, A. M. Noman, A. Alkuhayli, and H. M. H. Farh, “Hardware-in-the-loop validation of direct MPPT based cuckoo search optimization for partially shaded photovoltaic system,” Electronics, vol. 11, no. 10, p. 1655, 2022. [7] H. M. Farh and A. M. Eltamaly, “Maximum power extraction from the photovoltaic system under partial shading conditions,” in Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, New York, NY: Springer, 2020, pp. 107–129. [8] H. M. Farh, M. F. Othman, and A. M. Eltamaly, “Maximum power extraction from grid-connected PV system,” in 2017 Saudi Arabia Smart Grid (SASG), New York, NY: IEEE, 2017, pp. 1–6. [9] A. M. Eltamaly, H. M. Farh, and M. F. Othman, “A novel evaluation index for the photovoltaic maximum power point tracker techniques,” Solar Energy, vol. 174, pp. 940–956, 2018. [10] A. Fathy, “Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm,” Renewable Energy, vol. 81, pp. 78–88, 2015. [11] J. P. Ram and N. Rajasekar, “A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC),” Energy, vol. 118, pp. 512–525, 2017. [12] A. M. Eltamaly, H. M. Farh, and M. S. Al-Saud, “Grade point average assessment for metaheuristic GMPP techniques of partial shaded PV systems,” IET Renewable Power Generation, vol. 13, no. 8, pp. 1215–1231, 2019. [13] J. Ahmed and Z. Salam, “A maximum power point tracking (MPPT) for PV system using Cuckoo Search with partial shading capability,” Applied Energy, vol. 119, pp. 118–130, 2014. [14] J. P. Ram and N. Rajasekar, “A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions,” Applied Energy, vol. 201, pp. 45–59, 2017. [15] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation,” IEEE transactions on Power Electronics, vol. 27, no. 8, pp. 3627–3638, 2012. [16] R. Boukenoui, H. Salhi, R. Bradai, and A. Mellit, “A new intelligent MPPT method for stand-alone photovoltaic systems operating under fast transient variations of shading patterns,” Solar Energy, vol. 124, pp. 124–142, 2016.

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Power electronics for next-generation drives and energy systems: Volume 2

[17]

K. Sundareswaran, P. Sankar, P. S. R. Nayak, S. P. Simon, and S. Palani, “Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony,” IEEE Transactions on Sustainable Energy, vol. 6, no. 1, pp. 198–209, 2014. A. Soufyane Benyoucef, A. Chouder, K. Kara, and S. Silvestre, “Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions,” Applied Soft Computing, vol. 32, pp. 38–48, 2015. S. Silvestre and A. Chouder, “Effects of shadowing on photovoltaic module performance,” Progress in Photovoltaics: Research and Applications, vol. 16, no. 2, pp. 141–149, 2008. J. Hemandez, O. Garcia, and F. Jurado, “Photovoltaic devices under partial shading conditions,” International Review on Modelling and Simulations, vol. 5, no. 1, pp. 414–425, 2012. M. Q. Duong, G. N. Sava, G. Ionescu, H. Necula, S. Leva, and M. Mussetta, “Optimal bypass diode configuration for PV arrays under shading influence,” in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), New York, NY: IEEE, 2017, pp. 1–5. Y.-H. Ji, D.-Y. Jung, J.-G. Kim, J.-H. Kim, T.-W. Lee, and C.-Y. Won, “A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions,” IEEE Transactions on Power Electronics, vol. 26, no. 4, pp. 1001–1009, 2011. E. Kandemir, N. S. Cetin, and S. Borekci, “A comprehensive overview of maximum power extraction methods for PV systems,” Renewable and Sustainable Energy Reviews, vol. 78, pp. 93–112, 2017. A. Mohapatra, B. Nayak, P. Das, and K. B. Mohanty, “A review on MPPT techniques of PV system under partial shading condition,” Renewable and Sustainable Energy Reviews, vol. 80, pp. 854–867, 2017. S. K. Das, D. Verma, S. Nema, and R. Nema, “Shading mitigation techniques: state-of-the-art in photovoltaic applications,” Renewable and Sustainable Energy Reviews, vol. 78, pp. 369–390, 2017. A. Bidram, A. Davoudi, and R. S. Balog, “Control and circuit techniques to mitigate partial shading effects in photovoltaic arrays,” IEEE Journal of Photovoltaics, vol. 2, no. 4, pp. 532–546, 2012. S. Sajadian and R. Ahmadi, “Distributed maximum power point tracking using model predictive control for photovoltaic energy harvesting architectures based on cascaded power optimizers,” IEEE Journal of Photovoltaics, vol. 7, no. 3, pp. 849–857, 2017. H. M. H. Farh, A. M. Eltamaly, and M. F. Othman, “Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system,” PLoS One, vol. 13, no. 11, p. e0206171, 2018.

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

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[29] H. M. Farh, M. F. Othman, and A. M. Eltamaly, “Maximum power extraction from grid-connected PV system,” in 2017 Saudi Arabia Smart Grid (SASG), New York, NY: IEEE, December 2017, pp. 1–6. [30] M. A. G. De Brito, L. Galotto, L. P. Sampaio, G. d. A. e. Melo, and C. A. Canesin, “Evaluation of the main MPPT techniques for photovoltaic applications,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, pp. 1156–1167, 2013. [31] K. Ishaque, Z. Salam, and G. Lauss, “The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions,” Applied Energy, vol. 119, pp. 228–236, 2014. [32] I. Al-Wesabi, Z. Fang, H. M. H. Farh, et al., “Cuckoo search combined with PID controller for maximum power extraction of partially shaded photovoltaic system,” Energies, vol. 15, no. 7, p. 2513, 2022. [33] J. Teo, R. H. Tan, V. Mok, V. K. Ramachandaramurthy, and C. Tan, “Impact of partial shading on the PV characteristics and the maximum power of a photovoltaic string,” Energies, vol. 11, no. 7, pp. 1–22, 2018. [34] I. R. Balasubramanian, S. I. Ganesan, and N. Chilakapati, “Impact of partial shading on the output power of PV systems under partial shading conditions,” IET Power Electronics, vol. 7, no. 3, pp. 657–666, 2013. [35] X.-S. Yang and S. Deb, “Cuckoo search via Le´vy flights,” in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), New York, NY: IEEE, 2009, pp. 210–214. [36] H. Rezk, A. Fathy, and A. Y. Abdelaziz, “A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions,” Renewable and Sustainable Energy Reviews, vol. 74, pp. 377–386, 2017. [37] A. M. Eltamaly, H. M. H. Farh, and M. S. Al Saud, “Impact of PSO reinitialization on the accuracy of dynamic global maximum power detection of variant partially shaded PV systems,” Sustainability, vol. 11, no. 7, p. 2091, 2019. [38] K. Ishaque and Z. Salam, “A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition,” Renewable and Sustainable Energy Reviews, vol. 19, pp. 475–488, 2013. [39] A. M. Eltamaly, H. M. Farh, and A. G. Abokhalil, “A novel PSO strategy for improving dynamic change partial shading photovoltaic maximum power point tracker,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1–15, 2020.

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Chapter 3

Transformerless grid-connected inverter for PV integration M.K.P. Muhammed Ramees1, Md Waseem Ahmad2 and Aasim3

As of 2018, the cumulative installed capacity of the solar PV system has surpassed 505 GW which represents around 2% of the global energy output. Gridconnected system account for 60%, whereas the rest are being from distributed applications. By 2022, it is expected to increase to 1000 GW [1]. By 2030, it is expected that 80% of the electrical power would flow through power electronics. Depending on the rating of solar PV plant, several inverter architectures are available to integrate PV plants/panels to loads/utility. To effectively utilize the PV module, solution such as micro inverter, dc power optimizer has been investigated and commercialized but their market share is about 4%. Large PV forms (such as floating PV and roof top PV systems) are integrated to the grid via power converters and conventional line-frequency (LF)/high-frequency transformers or with inverter structure known as transformerless grid-connected inverter. The chapter intends to explore the need of transformerless inverter for grid integration of PV system. Different approaches of eliminating the leakage current such as clamping the common mode voltage (CMV), or decoupling from ac side or dc side, are available in the literature. An overview of the classification based on the above approaches and operation of various transformerless inverters are discussed. Detailed analysis of generation mechanism of leakage current in the various transformerless inverter topologies would be discussed. Discussion on simulation results of the developed model of various topologies in MATLAB
– Simulink
is provided to show their comparative performance in mitigating the leakage current issues in PV system.

1

Government College of Engineering Kannur, India National Institute of Technology Karnataka, India 3 National Institute of Technology Patna, India 2

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Power electronics for next-generation drives and energy systems: Volume 2

3.1 Need of transformerless inverter Figure 3.1 shows a general layout of PV system. The PV is integrated through the power converter through filter to the grid. The converter extracts power from PV and inject it into the grid by means of a sinusoidal current. If the inverter used does not have transformer to connect to the grid such configuration is known as Transformerless inverter (TLI). Since there is no transformer, these inverters are lighter, compact, and less costly. Heat generated due to mechanical switches is reduced because electronic switches are used in TLI. One of the main disadvantages of transformerless inverter is that there is no galvanic isolation between PV array and grid. This will lead to leakage ground current created through the potential fluctuations between the grid and PV array. The parasitic capacitor created between the frame and the surface of the PV are charged and discharged by these voltage fluctuations. The leakage current depends mainly on the topology and the modulation strategy used [2]. The main problems are—an increase in losses, current harmonics, safety issues, and interference problems of electromagnetic effect. Since leakage current directly related to the individual safety and equipment quality, some standards have been established to fix a maximum allowable leakage current such as the German DIN VDE 0126-11 standard which states that the grid must be disconnected within 0.3 s if the rootmean-square (RMS) value of leakage current is more than 30 mA. Based on the VDE0126-1-1 standard, a residual current monitoring unit should be used to monitor secure operation of a grid-connected PV system. Leakage current also called as common mode current consists of lowfrequency component at the grid frequency, medium-frequency component near to the switching frequency and high-frequency component. Low-frequency components do not cause much problem as the impedance in the common mode loop is higher and amplitude of the grid voltage is low. High-frequency component causes electromagnetic interference noise which can be eliminated using the EMI filter. Medium-frequency components are trouble causing component and may be much higher than the limitations if the switching modulation is not suitable [3].

PV array

Inverter

Filter

Grid

Cpv ileakage

Figure 3.1 General layout of PV system without transformer

Transformerless grid-connected inverter for PV integration

45

3.2 Classification of transformerless PV inverter Transformerless inverters are mainly classified based on approaches of eliminating the leakage current as TLI with clamping, TLI with decoupling and common ground TLIs.

3.2.1 Conventional full-bridge TLI Conventional full-bridge TLI is the most popular topology, and the circuit is shown in Figure 3.2. It has four switches (S1–S4), Cdc is the input side filter capacitance, L1, L2, R1, and R2 are the output side filter inductors and resistance, respectively. For the control two types of modulation, techniques are commonly used namely unipolar SPWM and bipolar SPWM.

3.2.1.1 Bipolar SPWM modulation In this modulation, technique pulses are generated by comparing the reference sine wave with triangular carrier wave as shown in Figure 3.3. Diagonal switches S1 and S4 or S2 and S3 are synchronously switched on at high frequency based on which there are four modes of operation can be obtained as shown in Figures 3.4–3.7. In mode 1 and mode 3, energy is transferred from DC side to grid. In mode 2 and mode 4, energy is fed back to DC side from AC side. Taking terminal N as the reference, the commonmode voltage (CMV) vCM and differential-mode voltage (DMV) vDM are defined as V1N þ V2N 2 ¼ V1N  V2N

vcm ¼

(3.1)

vDM

(3.2)

Simulation result of bipolar SPWM modulated conventional full-bridge TLI is shown in Figures 3.8 and 3.9. Based on the mode of operation, the DMV and CMV vary as given in Table 3.1 from Figure 3.8. DMV is a two-level voltage with frequency equal to switching frequency and DMV characteristic is poor. CMV is constant in all modes of operation. Hence, leakage current is low and has better leakage current performance. However, poor DMV characteristic results in too Ipv

+

L1 D1

S1

S3

R1

D3

A Vpv

PV

Vg

Cdc S2

–

Ig

B D2

S4

D4 L2

N

Figure 3.2 Conventional full-bridge TLI

R2

46

Power electronics for next-generation drives and energy systems: Volume 2 VM

Vtri ωt

S1,S4 ωt S2,S3 ωt VAB ωt

Figure 3.3 Modulation strategy – bipolar SPWM Ipv +

L1 D1

S1

Vpv PV

Vg

B D2

Ig

D3

A Cdc S2

–

S3

R1

S4

D4 L2

N

R2

Figure 3.4 Equivalent circuits of operating mode 1 under bipolar SPWM modulation L1 +

D1

S1

Vpv PV

N

Vg

B D2

S4

Ig

D3

A Cdc S2

–

S3

R1

D4 L2

R2

Figure 3.5 Equivalent circuits of operating mode 2 under bipolar SPWM modulation

Transformerless grid-connected inverter for PV integration L1 +

D1

S1

S3

R1

D3

A

Vpv PV

Cdc

Vg

B

S2

–

D2

Ig

S4

D4 L2

N

R2

Figure 3.6 Equivalent circuits of operating mode 3 under bipolar SPWM modulation L1 +

D1

S1

Vpv PV

S3

R1

D3

A Cdc

Vg

B

S2

–

D2

S4

Ig

D4 L2

N

R2

Figure 3.7 Equivalent circuits of operating mode 4 under bipolar SPWM modulation Grid voltage (Vg) 200 0 –200 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds)

0.13

0.135

0.13

0.135

0.13

0.135

0.14

Grid current (Ig)

40 20 0 –20 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds) DMV waveform

200 100 0 –100 –200 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds)

0.14

Figure 3.8 DMV waveform of bipolar SPWM modulation

47

48

Power electronics for next-generation drives and energy systems: Volume 2 Grid voltage (Vg) 200 0 –200 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds)

0.13

0.135

0.14

0.13

0.135

0.14

0.13

0.135

0.14

Grid current (Ig)

40 20 0 –20 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds) CMV voltage

250 200 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds)

Figure 3.9 CMV waveform of bipolar SPWM modulation Table 3.1 DMV and CMV for different modes of operation with bipolar SPWM Mode Mode Mode Mode Mode

1 2 3 4

VAN

VBN

VDM

VCM

Vpv 0 0 Vpv

Vpv/2 Vpv/2 Vpv/2 Vpv/2

Vpv/2 Vpv/2 Vpv/2 Vpv/2

Vpv/2 Vpv/2 Vpv/2 Vpv/2

many harmonic components in the output voltage. Efficiency is low because in each active mode two switches are conducting and due to bipolar variation core losses are high. Another disadvantage is too much reactive power exchange during the freewheeling period.

3.2.1.2

Unipolar SPWM modulation

In this modulation technique, there are four modes of operation as shown in Figures 3.10 and 3.11 and pulses for the switches are generate by comparing the sine wave and triangular wave as shown in Figure 3.12. Based on the mode of operation the DMV and CMV vary as given in Table 3.2. DMV is a three-level voltage with an RMS voltage less than bipolar SPWM modulation as shown in Figure 3.13. Hence, DMV characteristics are better in unipolar SPWM modulation.

Transformerless grid-connected inverter for PV integration L1 +

D1

S1

Vpv PV

S3

R1

Iε

D3

A Cdc

Vg

B

S2

–

D2

49

D4

S4

L2

N

R2

(a) L1 +

D1

S1

Vpv PV

S3

R1

Iε

D3

A Cdc S2

–

Vg

B D2

D4

S4

L2

N

R2

(b)

Figure 3.10 Equivalent circuits of operating modes of unipolar SPWM in case of positive cycle (a) mode 1 and (b) mode 2 L1 +

D1

S1

Vpv PV

Vg

B D2

Ig

D3

A Cdc S2

–

S3

R1

D4

S4

N

L2

R2

L1

R1

(a)

+

D1

S1

Vpv PV

D3

A Cdc S2

–

S3

Vg

B D2

S4

D4 L2

N

Ig

R2

(b)

Figure 3.11 Equivalent circuits of operating modes of unipolar SPWM in case of negative cycle (a) mode 3 and (b) mode 4

VM

Vtri ωt

S1

ωt

S2

ωt

S3

ωt

S4 VAB

ωt ωt

Figure 3.12 Modulation strategy – unipolar SPWM Table 3.2 DMV and CMV for different modes of operation with unipolar SPWM Mode Mode Mode Mode Mode

1 2 3 4

VAN

VBN

VDM

VCM

Vpv Vpv 0 0

0 Vpv Vpv 0

Vpv 0 Vpv 0

Vpv/2 Vpv Vpv/2 0

Grid voltage (Vg) 200 0 –200 0.1

0.105

0.11

0.13

0.135

0.14

0.13

0.135

0.14

0.13

0.135

0.14

Grid current (Ig)

40 20 0 –20 0.1

0.115 0.12 0.125 Time (seconds)

0.105

0.11

0.115 0.12 0.125 Time (seconds) DMV waveform

200 100 0 –100 –200 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds)

Figure 3.13 DMV waveform of unipolar SPWM modulation

Transformerless grid-connected inverter for PV integration

51

Grid voltage (Vg) 200 0 –200 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds) Grid current (Ig)

0.13

0.135

0.14

0.105

0.11

0.115 0.12 0.125 Time (seconds)

0.13

0.135

0.14

0.13

0.135

0.14

40 20 0 –20 0.1

CMV waveform 400 200 0 0.1

0.105

0.11

0.115 0.12 0.125 Time (seconds)

Figure 3.14 CMV waveform of unipolar SPWM modulation S5 L1 D5

+

D1

S1

S3

R1

D3

A

Vpv

PV

Cdc

– N

Vg

B S2

D2

S4

Ig

D4 L2

R2

Figure 3.15 H5 inverter

But CMV contains switching frequency component as shown in Figure 3.14 and CMV performance poor compared to bipolar SPWM modulation a.

3.2.2 Transformerless inverters with decoupling 3.2.2.1 H5 inverter It is a modified version of H-bridge with one additional switch at the DC side as shown in Figure 3.15. Additional switch will help to decouple the DC side during zero state which will improve the efficiency and eliminate the high-frequency component in the CMV during zero state. Modulation strategy for the inverter is

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Power electronics for next-generation drives and energy systems: Volume 2

shown in Figure 3.16. Switch S1 and S3 are operated at grid frequency and switch S2, S4, and S5 are operated at high frequency. According to the grid voltage, there are four modes of operation [4] as shown in Figures 3.17 and 3.18. VM

Vtri ωt

S1

ωt

S2

ωt

S3

ωt S4

ωt

S5

ωt

VAB

ωt

Figure 3.16 Modulation strategy of H5 inverter S5 L1 + Vpv PV

D5

S3

Vg

B S2

D2

D4

S4

L2

Vpv PV – N

R2

S5 L1

+

Ig

D3

A Cdc

– N (a)

D1

S1

R1

D5

D1

S1

S3

R1

D3

A

Cdc

Vg

B S2

D2

S4

Ig

D4 L2

R2

(b)

Figure 3.17 Equivalent circuits of operating modes of H5 inverter in case of positive cycle: (a) mode 1 and (b) mode 2

Transformerless grid-connected inverter for PV integration

53

S5 L1 + Vpv PV

D5

D1

S1

S3

R1

Ig

D3

A Cdc S2

– N

Vg

B D2

S4

D4 L2

R2

(a) S5 L1 + Vpv PV – N

D5

D1

S1

S3

R1

D3

A

Cdc

Vg

B S2

D2

S4

Ig

D4 L2

R2

(b)

Figure 3.18 Equivalent circuits of operating modes of H5 inverter in case of negative cycle: (a) mode 3 and (b) mode 4

During the positive half cycle, switch S5 and S4 are pulse modulated and S1 is continuously turned on with all other switches (S2 and S3) turned off. For active state, S1 and S4 are turned on to obtain terminal voltage “+Vpv” as shown in Figure 3.17a. Zero state is obtained when switch S5 and S4 are turned off and switch S1 in turned on. During this mode, the inverter pole voltage is vAB=0 and current flows through switch S1 and D3 as shown in Figure 3.17b. Similarly, during the negative half cycle of grid current, switch S2 and S5 are operated and S3 is continuously turned on while all other switches (S1 and S4) turned off. During active state, the current will flow through the switch S5, S2, and S3 to the grid, resulting in “Vpv” terminal voltage. For zero state in negative half cycle switch S5 and S2 are turned off keeping switch S3 turned on. The inverter pole voltage will be vAB=0 and current will flow through switch S3 and D1 as shown in Figure 3.18. During the active state energy get transferred from the PV to grid and during the zero-state filter will feed the grid. The switching state of the converter with CMV and DMV is given in Table 3.3. Leakage current will decrease due to weak CMV. EMI filtering requirement and the leakage current peaks is lower as leakage voltage has only grid frequency component. Core losses are lower because of unipolar voltage variation and efficiency is high about 98%.

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Power electronics for next-generation drives and energy systems: Volume 2

Table 3.3 DMV and CMV for different modes of operation of H5 inverter Mode Mode Mode Mode Mode

1 2 3 4

VAN

VBN

VDM

VCM

Vpv Vpv/2 0 Vpv/2

0 Vpv/2 Vpv Vpv/2

Vpv 0 Vpv 0

Vpv/2 Indeterminate Vpv/2 Indeterminate

Table 3.4 Specifications of the system used in simulation Parameters

Value

Parameters

Value

Input voltage Grid voltage Grid frequency Rated power

400 V 220 V 50 Hz 1,000 W

Switching frequency DC side capacitor Filter inductance Equivalent series resistance

5 kHz 470 mF 2.5 mH 0.1 W

(A)

(V)

Grid voltage (Vg) 200 0 –200 0.1

0.11

0.12

0.13

0.14 0.15 Grid current (Ig)

0.16

0.17

0.18

20 0 –20 0.1

0.11

0.12

0.13

0.14 0.15 DMV waveform

0.16

0.17

0.18

0.11

0.12

0.13

0.15

0.16

0.17

0.18

0.14 0.15 Time (seconds)

0.16

0.17

0.18

(V)

500 0 –500 0.1

0.14

(V)

CMV waveform 400 200 0 0.1

0.11

0.12

0.13

Figure 3.19 DMV and CMV waveform of H5 inverter Simulation of the H5 inverter done in MATLAB/Simulink to verify the performance. System parameter used in simulation is given in Table 3.4. Simulation waveform of common mode voltage and leakage current of H5 inverter is shown in Figures 3.19 and 3.20. RMS value of leakage current is about 12 mA at switching

Transformerless grid-connected inverter for PV integration

55

Grid voltage (Vg) (V)

200 0

–200 0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

Grid current (Ig) (A)

20 0

–20 0.1

0.11

0.12

0.13 0.14 0.15 Leakage current

0.16

0.17

0.18

0.11

0.12

0.13 0.14 0.15 Time (seconds)

0.16

0.17

0.18

(A)

0.5 0

–0.5 0.1

Figure 3.20 Leakage current waveform of H5 inverter

Ipv

Ig

+ Vpv PV –

D1

S1

S3

D3

S5

A

C

D5 Vg

B D6

S2

D2

S4

D4

S6

Figure 3.21 HERIC inverter

frequency which is below the limit 30 mA. DMV is a three-level voltage and CMV is close to the typical bipolar SPWM performance.

3.2.2.2 HERIC inverter The circuit diagram of the HERIC converter is shown in Figure 3.21. It has a conventional H-bridge inverter with four active switches (S1–S4) that are switched depending on the current direction to obtain the inverter terminal voltage as Vpv. There are two additional switches (S5 and S6) for obtaining zero state (ac coupling) [4]. Modulation strategy for the inverter is shown in Figure 3.22.

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Power electronics for next-generation drives and energy systems: Volume 2 VM

Vtri ωt

S1,S4

ωt ωt

S2,S3

ωt

S5 S6

ωt ωt

VAB

Figure 3.22 Modulation strategy of HERIC inverter L1 +

D1

S1

Vpv PV

Cdc

D3

S5

D2

S4

Ig

D5 Vg

B

S2

–

S3

A

R1

D4

D6

S6 L2

N

R2

(a) L1 +

D1

S1

Vpv PV

Cdc S2

– N

S3

D3

A

S5

S4

Ig

D5 Vg

B D2

R1

D4

D6

S6 L2

R2

(b)

Figure 3.23 Equivalent circuits of operating modes of HERIC inverter in case of positive cycle: (a) mode 1 and (b) mode 2 During the positive half cycle, switch S1 and S4 are pulse modulated and S6 is continuously turned on with all other switches (S2, S3, and S5) turned off. For the active state, S1 and S4 are turned on to obtain terminal voltage “+Vpv” as shown in Figure 3.23a. The zero state is obtained when switch S1 and S4 are turned off and switch S6 in turned on. During this mode, the inverter pole voltage is vAB=0 and current flows through switch S6 and D5 as shown in Figure 3.23b. Similarly, during

Transformerless grid-connected inverter for PV integration

57

the negative half cycle of grid current, switch S2, S3, and S5 are operated while all other switches (S1, S4, and S6) turned off. During the active state, the current will flow through the switch S2 and S3 to the grid, resulting in “Vpv” terminal voltage as shown in Figure 3.24a. For the zero state in negative half cycle, switch S2 and S3 are turned off keeping switch S5 turned on. The inverter pole voltage will be vAB=0 and current flows through switch S6 and D6 as shown in Figure 3.24b. Core losses are lower because of unipolar voltage variation and efficiency is high about 97%. The switching state of the converter with CMV and DMV is given in Table 3.5. As during the zero-voltage switching, the decoupling of the grid from the PV generator on the DC side and AC side occurs, therefore, both H5 and HERIC are quite similar in behavior. HERIC has only two switches conducting at the same time, while H5 has three. Additionally, of both switches, one switches at the grid frequency and two switches at high frequency. Simulation of the HERIC inverter done in MATLAB/Simulink to verify the performance. System parameter used in simulation is given in Table 3.6. Simulation L1 +

D1

S1

Vpv PV

Cdc

D3

A

D2

S4

S5

D5

D6

S6

Ig

Vg

B

S2

–

S3

R1

D4

L2

N

R2

(a) L1 +

D1

S1

S3

D3

A

Vpv PV

Cdc S2

–

S5

D5

D6

S6

Vg

B D2

S4

D4

L2

N

Ig

R1

R2

(b)

Figure 3.24 Equivalent circuits of operating modes of HERIC inverter in case of negative cycle: (a) mode 3 and (b) mode 4

Table 3.5 DMV and CMV for different modes of operation of HERIC inverter Mode Mode Mode Mode Mode

1 2 3 4

VAN

VBN

VDM

VCM

Vpv Vpv/2 0 Vpv/2

0 Vpv/2 Vpv Vpv/2

Vpv 0 Vpv 0

Vpv/2 Indeterminate Vpv/2 Indeterminate

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Power electronics for next-generation drives and energy systems: Volume 2

Table 3.6 Specifications of the system used in simulation Parameters

Value

Parameters

Value

Input voltage Grid voltage Grid frequency Rated power

400 V 220 V 50 Hz 1,000 W

Switching frequency DC side capacitor Filter inductance Equivalent series resistance

5 kHz 470 mF 2.5 mH 0.1 W

Grid voltage (Vg) (V)

200 0 –200 0.1

0.11

0.12

0.13 0.14 0.15 Grid current (Ig)

0.16

0.17

0.18

0.1

0.11

0.12

0.13

0.16

0.17

0.18

0.16

0.17

0.18

0.16

0.17

0.18

(A)

20 0 –20 0.14

0.15

CMV waveform

(V)

400 200 0 0.1

0.11

0.12

0.13

0.14

0.15

DMV waveform (V)

500 0

–500 0.1

0.11

0.12

0.13 0.14 0.15 Time (seconds)

Figure 3.25 CMV and DMV waveform of HERIC inverter

waveform of common mode voltage and leakage current of HERIC inverter is shown in Figures 3.25–3.26 EMI filtering requirement and the leakage current peaks are lower in this topology. RMS value of leakage current is about 14mA at switching frequency which is below the limit 30 mA. DMV is a three-level voltage and CMV is close to the typical bipolar SPWM performance.

Transformerless grid-connected inverter for PV integration

59

Grid voltage (Vg)

(V)

200 0 –200 0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.16

0.17

0.18

0.16

0.17

0.18

Grid current (Ig)

(A)

20 0 –20 0.1

0.11

0.12

0.13

0.14

0.15

Leakage current

0.4

(A)

0.2 0 –0.2 –0.4 0.1

0.11

0.12

0.13 0.14 0.15 Time (seconds)

Figure 3.26 Leakage current waveform of HERIC inverter

3.2.3 Transformerless inverters with clamping 3.2.3.1 Full-bridge DC bypassed inverter The circuit diagram of full-bridge DC bypassed inverter (FB-DCBP) is shown in Figure 3.27. This topology consists of conventional H bridge with two additional switches for DC side decoupling and two diodes connected at the middle point of the DC bus for clamping [5]. Here the zero-voltage grounding is ensured by the clamping diodes and PV is decoupled during the zero state with the help of additional switches. Modulation strategy for the inverter is shown in Figure 3.28. Switch S1, S2, S3, and S4 are operated at grid frequency and switch S5 and S6 are operated high frequency. There are four operating modes based on the directions of grid voltage and grid-in current. During the positive half cycle, switches S1, S4 are continuously turned on and S5 and S6 are pulse modulated with all other switches (S2 and S3) turned off. For the active state, S5 and S6 are turned on to obtain terminal voltage “+Vpv” as

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Power electronics for next-generation drives and energy systems: Volume 2 Ds L1 Cdc1

+ Vpv PV –

S5

Cdc2

S6

S3

Ig

D3

A

D7 D6

D1

S1

R1

Vg

B S2

D2

S4

D4 L2

N

R2

D6

Figure 3.27 Circuit diagram of FB-DCBP

VM

Vtri ωt

S1,S4 S2,S3

ωt ωt

S5,S6 VAB

ωt ωt

Figure 3.28 Modulation strategy – FB-DCBP shown in Figure 3.29a. The zero state is obtained when switch S5 and S6 are turned off and switch S1 and S4 is turned on. During this mode, the inverter pole voltage is vAB=0 and the current splits into two paths: S1 and the freewheeling diode D3, and S4 and the freewheeling diode D2 as shown in Figure 3.29b. Freewheeling circuit is disconnected with PV side and the leakage current loop is broken. Similarly, during the negative half cycle of grid current, switch S2, S3, S5, and S6 are operated while all other switches (S1 and S4) turned off. During the active state, the current will flow through the switch S2, S3, S5, and S6 to the grid, resulting in “Vpv” terminal voltage. For the zero state in negative half cycle, switch S5 and S6 are turned off keeping switch S2 and S3 turned on. The inverter pole voltage will be vAB=0 and the current splits into two paths: S3 and the freewheeling diode D1, and S2 and the freewheeling diode D4 as shown in Figure 3.30. Freewheeling circuit is disconnected with PV side and the leakage current loop is broken.

Transformerless grid-connected inverter for PV integration D5 L1 +

Cdc1

Cdc2

D6 S6

D1

S1

S3

D3 Vg

B S2

D2

S4

D4 L2

N (a)

Ig

R1

A

D7

Vpv PV –

S5

R2

D6 D5 L1 +

Cdc1

Cdc2

D6 S6

D1

S1

S3

Ig

D3 Vg

B S2

D2

S4

D4 L2

N (b)

R1

A

D7

Vpv PV –

S5

R2

D6

Figure 3.29 Equivalent circuit of operating mode of FB-DCBP inverter in the case of positive half cycle: (a) mode 1 and (b) mode 2

D5 L1 +

Cdc1

Cdc2

D6 S6

D1

S1

S3

Ig

R1

D3

A

D7

Vpv PV –

S5

Vg

B S2

D2

S4

D4 L2

N

R2

D6

(a)

D5 L1 +

Cdc1

Cdc2

D6 S6

N (b)

D1

S1

S3

Ig

D3

A

D7

Vpv PV –

S5

R1

Vg

B S2

D2

S4

D4 L2

R2

D6

Figure 3.30 Equivalent circuit of operating mode of FB-DCBP inverter in the case of negative half cycle: (a) mode 3 and (b) mode 4

61

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Power electronics for next-generation drives and energy systems: Volume 2

The working principles of clamping circuit during the positive grid cycle are shown in Figure 3.31. The clamping mode when freewheeling circuit potential rises is shown in Figure 3.31a. The clamping mode when freewheeling circuit potential decreases is shown in Figure 3.31b. In freewheeling stages, leakage current will flow into DC side through D8 if its potential rises and it will flow from DC side through D7 if its potential decreases. Thus, CMV is clamped at half of DC input voltage. Based on these modes, the potential of points 1 and 2 against N as well as DMV and CMV is given in Table 3.7. From this table, DC side and AC side are disconnected in freewheeling stages. DMV maintains unipolar SPWM

D5 L1 +

Cdc1

D7

Vpv PV –

S5

Cdc2

D6 S6

D1

S1 ICM

S3

A

S2

B D2

S4

Ig

R1

D3 IDM

Vg

D4 L2

N

R2

D6

(a)

D5 L1 +

Cdc1

S1

D1

S3

A D7 I CM

Vpv PV –

S5

Cdc2

D6 S6

S2

B D2

S4

D3 IDM

Vg

D4 L2

N

Ig

R1

R2

D6

(b)

Figure 3.31 Equivalent circuit when clamping circuit conducting in FB-DCBP: (a) potential increases and (b) potential decreases

Table 3.7 DMV and CMV for different modes of operation of FB-DCBP inverter Mode Mode Mode Mode Mode

1 2 3 4

VAN

VBN

VDM

VCM

Vpv Vpv/2 0 Vpv/2

0 Vpv/2 Vpv Vpv/2

Vpv 0 Vpv 0

Vpv/2 Vpv/2 Vpv/2 Vpv/2

Transformerless grid-connected inverter for PV integration

63

performance. CMV of FB-DCBP is constant. Its common-mode performance is well. Core losses are lower because of unipolar voltage variation and efficiency is about 96%. Simulation of the FB-DCBP inverter done in MATLAB/Simulink to verify the performance. System parameter used in simulation is given in Table 3.8. Simulation waveform of common mode voltage and leakage current of FB-DCBP inverter is shown in Figures 3.32–3.34. DMV is a three-level voltage and CMV is

Table 3.8 Specifications of the system used in simulation Parameters

Value

Parameters

Value

Input voltage Grid voltage Rated power Switching frequency

400 V 220 V 1,000 W 5 kHz

DC side capacitor Filter inductance Equivalent series resistance PV parasitic capacitor

470 mF 2.5 mH 0.1 W 0.1 mF

Grid voltage (Vg)

(V)

200 0

–200 0.42

0.43

0.44

0.45

0.46

0.47

0.48

0.49

0.5

0.48

0.49

0.5

0.48

0.49

0.5

Grid current (Ig) 20 (A)

10 0 –10 –20 0.42

0.43

0.44

0.45

0.46

0.47

CMV waveform

(V)

200

0 0.42

0.43

0.44

0.45 0.46 0.47 Time (seconds)

Figure 3.32 CMV waveform of FB-DCBP

Power electronics for next-generation drives and energy systems: Volume 2 Grid voltage (Vg)

(V)

200 0

–200 0.42

0.43

0.44

0.45

0.46

0.47

0.48

0.49

0.5

Grid current (Ig) 20 (A)

10 0 –10 –20 0.42

0.43

0.44

0.45 0.46 0.47 DMV waveform

0.48

0.49

0.5

0.43

0.44

0.45 0.46 0.47 Time (seconds)

0.48

0.49

0.5

(V)

500 0

–500 0.42

Figure 3.33 DMV waveform of FB-DCBP Grid voltage (Vg) (V)

200 0 –200 0.42

0.43

0.44

0.45

0.46

0.47

0.48

0.49

0.5

(A)

Grid current (Ig) 20 10 0 –10 –20 0.42

0.43

0.44

0.45 0.46 0.47 Leakage current

0.48

0.49

0.5

0.43

0.44

0.45 0.46 0.47 Time (seconds)

0.48

0.49

0.5

0.1 (A)

64

0

–0.1 0.42

Figure 3.34 Leakage current waveform of FB-DCBP

Transformerless grid-connected inverter for PV integration

65

almost a constant without any high frequency component. The EMI filtering requirement and the leakage current peaks is lower in this topology. RMS leakage current is about 6 mA at switching frequency and largely reduced compared to decoupling topologies.

3.3 Summary Various full bridge transformerless PV inverters are analyzed in this chapter. It can be classified as TLI with decoupling and TLI with clamping. CMV performance of clamping type TLI is better than with decoupling. DMV and CMV performance along with leakage current analysis is done for the above-mentioned topologies.

References [1] IEA PVPS, Trends 2022 in Photovoltaic Applications [Online]. Available: https://iea-pvps.org/wp-content/uploads/2022/09/PVPS_Trend_Report_2022. pdf, IEA Photovoltaic Power Systems Programme, REPORT IEA PVPS T1-43, 2022. [2] W. Li, Y. Gu, H. Luo, W. Cui, X. He, and C. Xia, ‘Topology review and derivation methodology of single-phase transformerless photovoltaic inverters for leakage current suppression’, IEEE Transactions on Industrial Electronics, 2015, 62 (7), pp. 4537–4551. [3] M. N. H. Khan, M. Forouzesh, Y. P. Siwakoti, L. Li, T. Kerekes, and F. Blaabjerg, ‘Transformerless inverter topologies for single-phase photovoltaic systems: a comparative review’, IEEE Journal of Emerging and Selected Topics in Power Electronics, 2020, 8(1), pp. 805–835. [4] L. Zhang, K. Sun, Y. Xing, and M. Xing, ‘H6 Transformerless full-bridge PV grid-tied inverters’, IEEE Transactions on Power Electronics, 2014, 29 (3), pp. 1229–1238. [5] L. Zhang, K. Sun, L. Feng, H. Wu, and Y. Xing, ‘A family of neutral point clamped full-bridge topologies for transformerless photovoltaic grid-tied inverters’, IEEE Transactions on Power Electronics, 2013, 28(2), pp. 730– 739.

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Chapter 4

PMSG and DFIG-based wind energy conversion systems Mojtaba Eldoromi1, Davood Fateh1, Mahsa Rostamzade1 and Ali Akbar Moti Birjandi1

Wind energy generation systems are confronted with growing demands for power quality and active power control. Wind energy conversion systems (WECS) have become a key technology to harvest wind energy worldwide. With the advances in power electronics technology, the quick growth of variable speed WECS is now witnessed. This chapter explains the control of a grid tied WECS using permanent magnet synchronous generator (PMSG) and doubly-fed induction generator (DFIG). A comparative study on grid connected WECS (PMSG and DFIG) in terms of voltages, currents, and output powers and dynamic responses is presented. Both systems are connected to power grid through power converters. The PMSG is dominantly used in the present wind energy systems with full scale power converter. Due to the full-scale power converter, this type has smooth grid connection over the entire range of speed. The DFIG is modeled using the direct-quadrature rotating reference frame circuit along with the aligned stator flux, and the fieldoriented control approach is applied for independent control of the active and reactive power and the DC-link voltage at the grid side. Improvement of the DFIG controllers from the rotor side and the grid side converters is done to enhance the dynamic performance. This chapter reviews the modeling of WECS, control strategies of controllers, and various maximum power point tracking (MPPT) technologies that are being proposed for efficient production of wind energy from the available resource. The MPPT of the wind turbines along with unity power factor operation of the system is also presented. The comparison also aims to present in a thorough and coherent way the aspects of power quality and stability during the fault period. Modeling of control strategies for both systems are explained clearly. In addition, control topologies applicable to power electronics converter/inverter used in wind electric generators are discussed. In this case, various control strategies prevalent to both the PMSG and DFIG have been analyzed. The dynamic performance comparison of both systems with the control strategies are presented using MATLAB
/Simulink
. 1

Electrical Faculty, Shahid Rajaee Teacher Training University, Iran

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Power electronics for next-generation drives and energy systems: Volume 2

4.1 Introduction Wind energy is one of the most promising renewable energy sources (RESs), and the wind power generations have been developed rapidly over the past decades due to fossil energy crisis and environment pollution [4,5]. With the advances in power electronics technology, the rapid growth of variable speed WECS is now witnessed. As an alternative energy source, wind turbines have been pulled in wide consideration due to their a few focal points. The generators that are utilized to change over the mechanical control got from the wind turbine into electric energy are by and large either doubly-fed acceptance generators (DFIG) or Squirrel Cage Induction Generators (SCIG) or PMSGs. For grid-connected wind energy systems, the three-phase line-frequency highpower transformer (LFT) is usually used between the generator and the power converter to achieve isolation between the wind energy system and the grid [6]. However, the line-frequency transformer suffers from the large physical volume and weight which challenge the construction of WECS with reduced footprints such as the offshore wind farms. With the development of semiconductors and magnetic materials, the high-frequency transformer (HFT)-based WECS is proposed to solve this problem [7]. The bulky and heavy LFT could be replaced by HFT, which dramatically reduces the size and weight of the WECS [8]. The WECS is a highly nonlinear and complex coupled system to convert kinetic energy into electricity [9]. Its powertrain includes a wind turbine, an electrical generator, the transmission system, several inverters and converters [10]. Figure 4.1 shows a diagram of a WECS, and Figure 4.2 presents an interconnection of a WECS. The option of generator for the WECS can be a DFIG, a PMSG or a caged induction generator. DFIG-based wind turbines and permanent magnet generators (PMSGs) that connected to the power grid through voltage source converters (VSCs) are widely used in WECSs [12,13]. DFIG is connected to the power grid via rotor-side converter (RSC) and gridside converter (GSC). Conventional vector control (VC) scheme [14–16] is broadly applied for RSC and GSC to realize the control of maximum power point tracking (MPPT) and DC-link voltage, respectively [17]. PMSGs are increasingly being used with back-to-back machine side converters (MSCs) and grid side converters (GSCs). The control systems of grid connected PMSGs commonly provide good performance to guarantee the demanded operation of the grid [18,19]. Power electronic converters have been used in commercial WTs since the beginning of grid-connected operation; this technology has significantly evolved over the years [20,21]. Various combinations of wind generators and power converters have also been developed in commercial WTs to achieve fixed-speed, semivariable-speed, and full-variable-speed operations. Fixed-speed WT (FSWT) technology, which uses a power converter for the startup function (soft-start), is considered obsolete. Variable-speed WTs (VSWTs) process the electric output power of a generator through a power converter and offer enhanced wind energy

PMSG and DFIG-based WECSs

69

Pitch

Low-speed shaft Rotor

Gear box Generator Anemometer

Wind direction

Controller

Brake

Yaw drive Wind vane High-speed shaft

Yaw motor Blades

nacelle

Tower

Figure 4.1 Components in a WECS [11]

Wind

AC/DC

DC/AC Grid

CDC

G

Battery PW

PT

PG DC/DC

Figure 4.2 Illustration of interconnection of the WECS conversion efficiency, power quality, and compatibility with grid codes. To fulfill various technical, operational, and grid code requirements, several generator–converter configurations have been developed for commercial WTs. In addition to the power converter equipment, control system development is important in the safe, successful, and efficient operation of VSWTs. The electrical control system is used to control wind generators and power converters such that maximum energy is extracted from the wind and feeds the energy to the utility grid with high-power quality. Electrical control systems are commonly implemented by digital control platforms such as microcontroller (mC), digital signal processor (DSP), or field

70

Power electronics for next-generation drives and energy systems: Volume 2

programmable gate array (FPGA). With the evolution of digital control expertise, the realization of advanced and high-performance control algorithms is now possible. The finite control-set model predictive control (FCS-MPC) is a new breed of digital control technique for power converters and electric generators [1,22,23]. FCS-MPC opens the doors for controlling WT electric power conversion systems in an easier and more intuitive manner than traditional hysteresis and linear control techniques while offering optimal control performance. This book deals with the FCS-MPC (simply called model predictive control or MPC from here onwards) of power electronic converters in variable-speed WECS. This chapter presents a comparative study on grid connected WECS having two different wind turbine generator systems (WTGS) using DFIG and PMSG. Both WTGS systems are connected to power grid through conventional back-to-back converters and unconventional power electronic interface (UPEI) in different cases.

4.2 Power converter Wind generator output voltage and frequency changes with respect to the rotational speed (wind speed). The generator output terminals can be directly coupled to the grid or can be interfaced through a power electronic converter. The power converter changes the generator AC output voltage to DC voltage by a rectifier (AC/ DC converter) and then back to AC with a fixed voltage magnitude and frequency by an inverter (DC/AC converter). In most WTs, the configuration of both AC/DC and DC/AC converters is the same and is known as a back-to-back (BTB) connected converter. By arranging the power switching devices in different ways, possibly with DC-link elements such as capacitors or inductors, numerous power converter topologies can be derived. The power electronics technology contributes to low cost of energy, extraction of maximum possible energy from wind, enhancement of reliability and power density, fault-tolerant operation, reduction of weight and footprint, superior grid power quality, compliance with the strict grid codes, etc. These power electronic converters can be combined with electric generators to form a wide variety of WECS configurations. Switching harmonics are inevitable when using power converters. To solve this issue, harmonic filters are used in wind machine (generator)-side converters (MSCs) and grid-side converters (GSCs). The harmonic filter in the MSC helps reduce the harmonic distortion of the generator currents and voltages. This process leads to a reduction in harmonic losses incurred in the magnetic core and winding of the generator. The harmonic filter in the GSC helps meet the strict harmonic requirements specified by the grid codes [24]. To enable the grid connection of these WTs, the variable voltage/ frequency of the wind generator should be converted into a fixed voltage/ frequency. Thus, a wide variety of power conversion stages can be employed as highlighted in Figure 4.3 [25].

PMSG and DFIG-based WECSs

71

3f Gearbox

Power Converters

3f

3f

SCIG, DFIG, PMSG, WRSG, HTS-SG

Multi-Modular MC

Other MCs

Buck/ Boost

Multi-Channel Buck/Boost

AC/AC Converter

Uncontrolled Converter

Half-Controlled Converter

Multilevel Buck/Boost

DC/DC Converter

Fully-Controlled Converter

Flying Capacitor Converter (DCC)

Cascaded H-Bridge (CHB) Converter

Voltage Source Converter

Current Source Converter (CSC)

Load Commutated Pulse Width Inverter (LCI) Modulated CSC

Diode-Clamped Converter (DCC)

3f

DC/DC Converter

AC/AC Converter

Matrix Converter (MC)

Step-Up Transformer

Switch-Clamped (ANPC) Converter

Two-Level (2L) VSC

Modular Multilevel Converter (MMC)

Multilevel VSC

Other Hybrid Converters

Figure 4.3 Taxonomy of power converters for type 3 and 4 variable-speed WECS techniques [26]

4.2.1 Convertor topology The main purpose of convertor topology in power inverters is to control the output voltage and to supply variable voltage and frequency to ac drives. Modification in pulse-widths will improve the generation of harmonics. In PWM technique, the input is a fixed dc voltage is given to the inverters and an output is controlled ac voltage. The output is obtained by adjusting inverter components. Various PWM techniques are triangular wave sampling-natural sampling, uniform sampling, selective harmonics elimination, space vector modulation SVM or SVPWM, sinusoidal pulse width modulation, random PWM, calculation based on equal area criterion, and sinusoidal PWM (SPWM), current controlled PWM (CCPWM). The contribution of power electronics in WECS is based on motive of increasing its reliability, energy yield, improving performance of the WECS by

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Power electronics for next-generation drives and energy systems: Volume 2 Wind turbine

Gear-box

Variable speed double fed Induction generator Load/ Grid

AC DC

DC AC

(a) Wind turbine

Transformer

PMSG DC Link AC DC

DC AC

Load /Grid

(b)

Figure 4.4 Variable speed (a) DFIG and (b) PMSG with power converter

bringing down the mechanical stress. It enables whole wind energy system to act like controllable generation unit making it able for better integration of wind power to grid [27]. The power electronics plays an important role in variable speed wind turbine system. In Figure 4.4, it clearly shows the role of power electronics converter for variable speed operation. Even in the fixed speed wind turbine system, where wind power generators are directly connected to the grid, the role soft starter can be played by thyristor. Though power electronic devices may increase the cost of the system, this expense may be tolerated as power electronic converters help in reducing mechanical complexity, absorb mechanical stress, and minimize the effect of wind gust. In many cases, it eliminates the need of gearbox which is main reason for losses and failure in wind turbine system. To meet the huge growing demands of the wind power conversion system, advanced power electronic technologies are expected to develop with the purpose of designing power electronics converters to achieve power conversion at higher voltage level. Research work is going on designing of a full-scale power converter based on a structure of back to back (BTB) cascaded H-bridge converter, with galvanic insulated dc/dc converters as interface [1]. This may be one of the promising solutions for future WECS.

PMSG and DFIG-based WECSs

73

Power Converter Control (Level I)

Advanced Control

Classical Control

Hysteresis Control

Linear Control

Deadbeat Predictive Control (DBPC)

Hysteresis Based MPC

Continuous ControlSet MPC (CCS-MPC)

Trajectory Tracking MPC

Other (FLC, ANN, SMC, H∞)

Predictive Control

With Modulator

Without Modulator

Other Predictive Control (GPC, DCC)

Direct Model Predictive Control

Finite Control-Set MPC (FCS-MPC)

Other (P-DPC, M2PC, VSF-MPC, S-MPC)

Figure 4.5 Taxonomy of power converter control techniques [29,30] (GPC, generalized predictive control; DCC, direct current control; P-DPC, predictive direct power control; M2PC, modulated MPC; VSF-MPC, variable sampling frequency MPC; S-MPC, switched MPC) A transformer operating at medium-frequency range of several kilohertz is used in DC–DC converter. It reduces the transformer size significantly [28]. The development of digital control techniques for power converters is an ongoing research topic. A brief summary of classical and advanced control techniques for power converters is shown in Figure 4.5 [29,30].

4.3 Modeling of WECS The wind power which is developed by the turbine is given by (4.1): 1 P ¼ Cp rAV 3 2

(4.1)

where Cp is the wind power coefficient, r is the air density (equal to 1.225 kg/ at sea level at temperature T = 288 K), A is the area of the turbine blades in and V is the wind velocity in m/sec. The fraction of kinetic energy which is converted into mechanical energy by the wind turbine is given by the power coefficient. It is a

74

Power electronics for next-generation drives and energy systems: Volume 2 Mechanical aspect

Aerodynamic aspect

Wind

Wind Turbine Rotor

Gearbox

Electrical aspect

Wind Turbine Generator

Power Electronic Converter

Utility Grid

Figure 4.6 Wind conversion system layout

function of blade pitch angle (b) and tip speed ratio (TSR) (l). The tip speed ratio is the ratio of turbine blade linear speed and wind speed given by: l¼

Rw V

Substituting (4.2) in (4.1), we have:  3 1 R w3 P ¼ Cp ðlÞrA 2 l

(4.2)

(4.3)

The corresponding output torque of the wind turbine is given by: 1 Tturbine ¼ rCP V =l 2

(4.4)

where R is the radius of the wind turbine rotor (m). Figure 4.6 shows common wind conversion system layout with gearbox, generator, and power electronics converters connected to the grid.

4.3.1

Different types of generators

• DFIG DFIG is the most commonly used wound-rotor induction generator used in WECS as the stator of the DFIG can be directly connected to the grid and rotor across a partially rated converter [31]. Due to difference of speed between stator and rotor, a gearbox is needed to couple the rotor to the generator. Most commonly used configuration includes variable frequency and BTB AC/ DC/AC and AC/AC voltage source type. Rotor side and grid side convertors are used to make power electronics devices such as IGBT and MOSFETs [32]. The rotor voltage is applied from the power converters. Active and reactive powers as well as harmonics can be controlled from the rotor side convertor (RSC), while power factor is controlled by grid side converter (GSC) and ensures that it is kept in optimum value. A schematic of a DFIG is presented in Figure 4.7 [33]. Advantages of DFIG: ü Limited speed range 30% to 30% around synchronous speed. ü Small capacity PWM inverter.

PMSG and DFIG-based WECSs

75

Gearbox DFIG Turbine

Transformer

RSC

GSC

Figure 4.7 Schematic of a DFIG

Measured variables

Reference values

Wind turbine PMSG/ WRSG/ SCIG

Utility grid

Control system

Coupling transformer

Wind N S Gearbox/ gearless

DC AC

DC AC

LPF Filter

Full-scale power converter

Figure 4.8 Equivalent circuits of operating mode 4 under bipolar SPWM modulation

ü ü ü ü

Rugged and brushless. Active and reactive powers can be controlled completely. Full speed range. High efficiency and energy yield. Limitations of DFIG:

ü There is need for gearbox. ü There is need of slip rings. ü Medium reliability and reduced longevity due to bearings and gear faults. • PMSG By the use of PMSG, it allows direct drives systems by which the use of gearbox can be avoided and hence helps in low maintenance of the system. By using PMSG, there is use of voltage source full power converter (AC/DC/AC) which helps in decoupling of generator from the grid. A schematic of a PMSG is presented in Figure 4.8.

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Power electronics for next-generation drives and energy systems: Volume 2 Advantages of PMSG:

ü ü ü ü ü

Full speed range. There is no need of gear systems. Active and reactive powers can be controlled completely. Brushless and have low maintenance. No power converter for field is required. Limitations of PMSG:

ü Permanent magnet needed. ü Multipole generator is required in case of direct driven topology. ü Full scale power converter.

4.3.2

Modeling and control of DFIG-based WECS

The stator and rotor voltage equations of DFIG expressed in the synchronous rotating d–q reference frame are given by [14,15,34]: vsd ¼ Rs isd þ l_ sd  ws lsq ; vsq ¼ Rs isq þ l_ sq þ ws lsd

(4.5)

vrd ¼ Rr ird þ l_ rd  wslip lrq ; vrq ¼ Rr irq þ l_ rq þ wslip lrd

(4.6)

where vsd and vsq are the stator d-axis and q-axis voltages, respectively; isd and isq are the stator d-axis and q-axis currents, respectively; sd and sq are the stator d-axis and q-axis fluxes, respectively; vrd and vrq are the rotor d-axis and q-axis voltages, respectively; ird and irq are the rotor d-axis and q-axis currents, respectively; rd and rq are the rotor d-axis and q-axis fluxes, respectively; Rs and Rr are the stator and rotor resistances, respectively; wslip is the slip speed, s and r are the synchronous and rotor speeds, respectively. The corresponding flux linkages are expressed by [14,15,34]: lsd ¼ Ls isd þ Lm ird ; lsq ¼ Ls isq þ Lm irq

(4.7)

lrd ¼ Lr ird þ Lm isd ; lrq ¼ Lr irq þ Lm isq

(4.8)

where Ls and Lr are the stator and rotor self-inductances, respectively; Lls and Llr are the stator and rotor leakage inductances, respectively; Lm is the mutual inductance. The stator active power Ps and reactive power Qs are represented as: Ps ¼ 1:5ðvsd isd þ vsq isq Þ; Qs ¼ 1:5ðvsq isd  vsd isq Þ In the d–q reference frame, the GSC is modeled as [14,15,34]:  Lg i_ gd ¼ vgd  Rg igd þ Dgd ; Dgd ¼ ws Lg igq þ vsd Lg i_ gq ¼ vgq  Rg igq þ Dgq ; Dgq ¼ ws Lg igd þ vsq

(4.9)

(4.10)

where igd and igq are the grid d-axis and q-axis currents, respectively; vgd and vgq are the grid d-axis and q-axis voltages, respectively; Rg and Lg are the resistance and inductance of GSC, respectively.

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77

4.3.3 Modeling and control of PMSG-based WECS The PMSG includes a permanent magnet rather than an excitation winding, which results in a smaller pole pitch and compatibility with low-speed applications. The d- and q-axis stator voltages, denoted by vdm and vqm , respectively, can be expressed as [21,35,36]: vdm ¼ Ra idm þ wr Lq iqm  Ld pidm

(4.11)

vqm ¼ Ra iqm  wr Ld idm þ wr lr  Lq piqm

(4.12)

where Ra is the per-phase stator winding resistance; idm and iqm are the d- and q-axis stator currents, respectively; wr is the speed of dq reference frame; Ld and Lq are the d- and q-axis stator inductances, respectively; lr is the rotor flux linkage; and p ¼ d=dt is the derivative operator. Ld and Lq are expressed as: Ld ¼ Lls þ Ldm

(4.13)

Lq ¼ Lls þ Lqm

(4.14)

where Ldm and Lqm are the d- and q-axis magnetizing inductances, respectively; and Lls is the stator winding leakage inductance. The electromagnetic torque generated from the PMSG (Te ) can be expressed as: Te ¼ 1:5P½iqm lr  idm iqm ðLd  Lq Þ

(4.15)

where P is the number of pole pairs.

4.4 Control strategies and MPPT Various MPPT technologies are being proposed for efficient production of wind energy from the available resource. The MPPT of the wind turbines along with unity power factor operation of the system is also presented. Control theory has evolved as an important discipline in modern WTs and WFs. Control schemes enforce WECS to achieve the desired operation, increase wind energy conversion efficiency, reduce energy cost, increase the lifespan of WT components, decrease structural loading, decrease turbine down times, and provide a superior dynamic and steady-state performance. The block diagram of the overall control scheme for a modern variable-speed WECS is shown in Figure 4.9 [1–3]. The overall control structure of the WECS includes a generator-side converter controller with the maximum power extraction to regulate the generator speed and capture the maximum power under varying wind speeds, and a grid-side inverter controller to control the active and reactive power flows into the power grid by controlling the d- and q-axis currents in the synchronously rotating reference frame. The details of these controllers are presented in the following subsections.

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Power electronics for next-generation drives and energy systems: Volume 2 3f WT

MSC/RSC

GSC

DC Chopper

+

+

Gearbox L Filter

+

3f

Step-UP Transformer

Wind Generator

–

vs β

ωm θm υw

–

is

sch

sr is

θm

Generator Current Control

ωm

is

Generator Speed/Torque/ Power Control

vg υdc

Grid VoltageOriented Control

sf υ*dc

Q*g

Control Level III: Grid Integration and MPPT Control MPPT Control

P*s

vg

Grid Integration & Synchronization

ωm

nth WT

sf

Control Level II: Generator and Grid Control

is

β

νdc

i*g

υ*dc

vs

υw

ig

Grid Current Control

DC Chopper Control

ω*m /T*e /P*m /Q*s υw

si

Control Level I: Power Converter Control

i*s

vs

vg

ig

υdc

P*g

Q*s

sf Q*g

Control Level IV: Wind Turbine Centralized Control Electrical Control Mechanical Control Pitch Damping RPG Control Control Yaw Control

Ancillary Services

P*WT,1

FRT

vg υdc sf

Q*WT,1

P*WT,n, Q*WT,n Control Level V: Wind Farm Centralized Control

nth WF

Notation of Variables

xy = [xay xby xcy]T

P*WF,1

Q*WF,1

x Π{v, i, s} y Π{s, g, r, i}

Control Level VI: TSO/DSO Supervisory Control

P*WF,n Q*WF,n

Figure 4.9 Block diagram of the overall control scheme for variable-speed WECS [1–3]

PMSG and DFIG-based WECSs

79

• DFIG control model The DFIG control comprises both the rotor side converter (RSC) and the grid side converter (GSC) controllers so that the RSC controls active and reactive power and the GSC regulates the DC-link voltage as well as generates an independent reactive power that is injected into the grid. Rotor side converter consists of three phase IGBT-diode rectifier connected in Graetz bridge configuration with snubber resistance and capacitance. The grid side converter is used to regulate the voltage of the DC bus capacitor [37]. The pitch angle control is used to limit the power extracted at high wind speeds conditions. The control system uses a torque controller in order to maintain the speed. The reactive power produced by the wind turbine is also regulated at zero MVAR. • The rotor side controller From the rotor side, the control equations may be written as shown in (4.16) and (4.17): dird  wr yrq dt dirq þ wr yrd vrq ¼ Rr irq þ sLr dt vrd ¼ Rr ird þ sLr

(4.16) (4.17)

L2

where s ¼ 1  Lr mLs ðthe leakage factorÞ These allow the determination of the control algorithms for ird and iqd . The determination of the reference quantities for electromagnetic torque control (Tem ): Tem ¼ P

M y irq Ls sd

(4.18)

Then the reference current irqREG is calculated as shown in (4.19): irqREG ¼ 

TemREG PM Ls ysd

(4.19)

It can be observed that the electromagnetic torque depends on the irq current and can be controlled by the vrq . Also, the rotor magnetizing current ird can be controlled by the vrd voltage component. Magnetization of the machine: ysd ¼

vsq ws

ysd ¼ Ls isd þ Mird

(4.20) (4.21)

These expressions show that the grid voltage imposes the magnetization of the machine but this magnetization could also be obtained by the stator (classic) or the rotor by controlling the reference current ird . • Grid side controller The main objective of the grid side controller is to keep a constant DC-link voltage independent of the value and direction of the rotor power flow [38].

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Power electronics for next-generation drives and energy systems: Volume 2 DC Bus

Vdc_measured Vdc_ref

+–

PI iq_ref

id_ref

dq abc

Hysteresis Band

I G B T

PWM

iq_ref = 0 iabc_ g θg Atan2 (Eβ*/Eα*) –90°

Eα*

Eβ*

αβ

iabc_ g

abc

Grid

Figure 4.10 The block diagram of proposed method for GSC To fulfill the objective, a hysteresis current control strategy with a reference frame aligned with the stator voltage position is used as shown in Figure 4.10. This allows independent control of the DC-link voltage and the reactive power between the converter and the grid. The DC-link voltage vdc is controlled by the current id in the voltage vectororiented reference frame. Thus, a reference current idref is derived from the DC-link voltage error Dela(e) and the variation of the error by tuning a PI controller, as shown in Figure 4.10. To guarantee a unity factor power at the grid side converter, the reactive power must be zero, so iqref ¼ 0. After a dq-abc transformation of these reference currents, hysteresis modulation may then be implemented. • PMSG control In order to have a high-performance drive using a PMSG in wind turbine, the control strategy chosen has to highlight all the advantages that this kind of PM motor has. Field-oriented control (FOC) is one of the best solutions for a highperformance drive when having a PMSG [39]. One of the most used linear control strategies, for FOC, is to keep the d axis current isd ¼ 0, so that the produced torque is proportional to the q current component isq : 3 Te ¼ pb Ym isq 2 3 3 Te ¼ pb ðYm isq Þ þ Te ¼ pb ðLd  Lq Þisq isd 2 2

(4.22) (4.23)

PMSG and DFIG-based WECSs

81

A nonlinear method can be used to take advantage of the reluctance torque. Depending on the objective, unity power factor control, constant flux linkage control, maximum torque per ampere control (MTPA) or maximum efficiency control can be implemented [40]. MPPT controllers are used for extracting maximum power from the WECS using different generators such as PMSGs, squirrel cage induction generators (SCIG) and DFIG. The MPPT controllers can be classified into three main control methods, namely tip speed ratio (TSR) control, power signal feedback (PSF) control, and hill climb search (HCS) control. The energy produced from wind source depends upon the accuracy with which the peak power points are tracked by the MPPT controller of the WECS control system irrespective of the type of generator used. Based on the control schemes, the maximum power extraction algorithms are tip speed ratio (TSR) control, power signal feedback (PSF) control, and hill-climb search (HCS) control. In the TSR control method, the rotational speed of the generator is regulated in order to maintain the tip speed ratio to an optimum value during which the power extracted attains the maximum. In this method in order to obtain the maximum output power, the speed from both wind and the turbine is required along with the optimal value of the TSR of the turbine. The block diagram of a WECS with TSR control is shown in Figure 4.11. In PSF control, it is required to have the knowledge of the wind turbine’s maximum power curve, and track this curve through its control mechanisms. The maximum power curves need to be obtained via simulations or off-line experiment on individual wind turbines. In this method, reference power is generated either using a recorded maximum power curve or using the mechanical power equation of the wind turbine where wind speed or the rotor speed is used as the input. Figure 4.12 shows the block diagram of a WECS with PSF controller for maximum power extraction. The HCS control algorithm continuously searches for the peak power of the wind turbine. It can overcome some of the common problems normally associated with the other two methods. The tracking algorithm, depending upon the location of

GENERATOR POWER CONVERTER

νw ω νw

λopt

ω*

+

R

–

MPPT CONTROLLER

ω

CONTROLLER

Figure 4.11 Tip speed ratio control of WECS

TO LOAD

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Power electronics for next-generation drives and energy systems: Volume 2

POWER CONVERTER

νw

TO LOAD

ω Popt

ω

+ CONTROLLER – P

MPPT CONTROLLER

Figure 4.12 Power signal feedback control

PERTURE d=d+Δd

Power (W)

Y

N ΔP>0

CHANGE SIGN

Generator speed (red/s)

Figure 4.13 HCS control principle

GENERATOR POWER CONVERTER

νw

TO LOAD

ω P

x*

+ CONTROLLER –

MPPT CONTROLLER

x

Figure 4.14 WECS with hill climb search control the operating point and relation between the changes in power and speed, computes the desired optimum signal in order to drive the system to the point of maximum power. Figure 4.13 shows the principle of HCS control and Figure 4.14 shows a WECS with HCS controller for tracking maximum power points.

PMSG and DFIG-based WECSs

83

4.5 Power quality Power quality determines the fitness of the electric power to the utility grid. Power quality involves various items such as transient variations, flickers, and harmonics. Thus, it is concluded that PMSG is more effective as compared to DFIG as wind turbine generator and also unconventional power electronic interface is more effective as an interface than conventional power electronic interface. The comparison can be summarized by the statement that, during fault, DFIG draws more reactive power even after improvement than PMSG under the same condition. The DFIG control improvement helps in oscillation reduction aspect of the dynamic behavior of the machine. Time required to recover stability is much for DFIG than PMSG, which means that PMSG is more stable than DFIG [40].

4.6 Case study In this section, a case study in the field of energy conversion systems is presented. Accordingly, a wind system with DFIG has been studied. The results of PI controllers adjusted to different wind speeds have been investigated. First, the wind speed is 7 m/sec. At 100 sec, the wind speed is 8 m/sec. Then in 200 sec the wind speed reaches 9 m/sec. The torque reaches the MPPT values as shown in Figure 4.15. Torque is equal to –0.43 for wind speed of 7 m/sec, for a wind speed of 8 m/sec is equal to 0.58 and for a wind speed of 9 m/sec is equal to 0.73 p.u. Also, according to the diagrams in Figure 4.15, the three signals of reactive power, DC link voltage and stator voltage fluctuate after each speed change and reach their previous value. This means that all signals follow their reference value. Therefore, the configuration of the eight PI controllers is well done. The results related to the fluctuations of different system signals with respect to different wind speeds have been investigated. First, the sub-synchronous mode is

–0.4

1,200.1 7 m/s

1,200.05 VDC(v)

Te(pu)

–0.2

8 m/s

–0.6

9 m/s

1,200

1,199.95

–0.8 100

150

200 Time(s)

250

300

1,199.9

150

200 Time(s)

250

300

100

150

200 Time(s)

250

300

1.1 Vs(pu)

Qs(pu)

0.05

100

0

1.05 1 0.95

–0.05

100

150

200 Time(s)

250

300

0.9

Figure 4.15 Case study conditions

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Power electronics for next-generation drives and energy systems: Volume 2 Subsynchronous mode for different compensation levels (K%=25% : 5%: 85%)

Image Part (rad/s)

250 200 150 Vw=7m/s 100 Vw=9m/s 50 –10

Vw=8m/s

–5

0 5 Real Part (1/s)

10

15

Figure 4.16 Status chart of system eigenvalues at different wind speeds Vwind=7 m/s

Vwind=7 m/s 0.8

0.4 0.2 0 70

0 –1

80

90

100

110 120 Time(s)

130

140

150

Vwind=7 m/s

1.5 1

K%: 20%,60% K%: 20%,55%

0.5

–2 70

80

90

100

0 –0.5

110 120 Time(s)

130

140

150

130

140

150

Vwind=7 m/s

1.5

Vs(pu)

Qs(pu)

K%: 20%,60% K%: 20%,55%

1

K%: 20%,60% K%: 20%,55%

Te(pu)

ωr(pu)

0.6

2

1 K%: 20%,60% K%: 20%,55%

0.5

–1 –1.5 70

80

90

100

110 120 Time(s)

130

140

150

0 70

80

90

100

110 120 Time(s)

Figure 4.17 Values in new conditions for 7 m/sec speed investigated for different wind speeds and different levels of compensation. According to Figure 4.16, for a wind speed of 7 m/sec, from the compensation level of 60% or more, this mode becomes unstable and the system becomes unstable. Also, for a wind speed of 8 m/sec, from the compensation level of 75% and more, this mode becomes unstable and the system becomes unstable. Sub-synchronous mode is stable at different wind compensation levels for wind speeds of 9 m/sec. Figure 4.17 shows the fluctuations of rotor speed, electrical torque, reactive power and stator voltage to investigate the critical wind speed. The initial compensation level was 20%. First, the wind speed of 7 m/sec is considered. According to this figure, at a wind speed of 7 m/sec, the system is stable up to 55% compensation and becomes unstable at 60% compensation.

PMSG and DFIG-based WECSs Vwind=8 m/s

Vwind=8 m/s 0

1

–0.5 K%: 20%, 75%

Te (pu)

ωr (pu)

0.8 0.6

K%: 20%, 70%

0.4

–1 K%: 20%, 75%

–1.5

0.2 0 70

80

90

100

110 120 Time (s)

130

140

150

–2 70

K%: 20%, 70%

80

90

Vwind=8 m/s

120

130

140

150

110 120 Time (s)

130

140

150

1.5 K%: 20%, 75%

K%: 20%, 75%

K%: 20%, 70%

K%: 20%, 70%

Vs (pu)

Qs (pu)

100 110 Time (s) Vwind=8 m/s

1 0.5

85

0

1

–0.5 –1 70

80

90

100

110 120 Time (s)

130

140

150

0.5 70

80

90

100

Figure 4.18 Values in new conditions for 8 m/sec speed First Compensation = 20%, Second Compensation = 70% Vwind = 8 m/s –0.4

Te (pu)

–0.5 –0.6 –0.7 –0.8 70

With SSRIPC Without SSRIPC 80

90

100

110 120 Time (s)

130

140

150

Figure 4.19 Compensation conditions for 70% compensation Now the wind speed is 8 m/sec. According to this figure, at a wind speed of 8 m/sec, the system is stable up to 70% compensation and 75% unstable in compensation. To further investigate the performance of the SSRIPC system and controller at wind speeds of 8 m/sec, torque fluctuations in 70% compensation are shown in Figure 4.19. The system without SSRIPC is stable at 8 m/sec and 70% compensation. The use of SSRIPC has also helped to improve fluctuation damping and stability. But the system is unstable at a speed of 8 m/sec and a compensation of 75% and above. For this purpose, we increased the level of compensation for a speed of 8 m/sec. SSRIPC was found to be able to stabilize the system for 80% compensation at wind speeds of 8 m/sec. This means that if the minimum wind speed in an

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Power electronics for next-generation drives and energy systems: Volume 2 First compensation = 20%, Second compensation = 80% Vwind = 8 m/s 0

Without SSRIPC With SSRIPC

Te (pu)

–0.5

–1

–1.5 70

80

90

100

110 120 Time (s)

130

140

150

Figure 4.20 Compensation conditions for 80% compensation area of 8 m/sec is 80% compensation for wind farms with DFIG is provided using SSRIPC. The results in Figure 4.20 illustrate this well. Due to the fact that low wind speed is effective in the occurrence of sub-synchronous resonance, therefore, in weakening SSR in wind farms with DFIG, a critical speed of 7 m/sec is considered and the aim is to increase the level of compensation at this speed. Therefore, this study was able to provide a 70% compensation level for wind farms with DFIG.

4.7 Conclusion This chapter reviews the modeling of WECS, control strategies of controllers and various MPPT technologies that are being proposed for efficient production of wind energy from the available resource. The MPPT of the wind turbines along with unity power factor operation of the system is also presented. The comparison also aims to present in a thorough and coherent way the aspects of power quality and stability during the fault period. Modeling of control strategies for both systems are explained clearly. In addition, control topologies applicable to power electronics converter/inverter used in wind electric generators are discussed. In this case various control strategies prevalent to both the PMSG and DFIG have been analyzed. Finally, the dynamic performance comparison of DFIG with the control strategies presented using MATLAB/Simulink.

References [1] F. Blaabjerg and K. Ma, “Future on power electronics for wind turbine systems,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 1, no. 3, pp. 139–152, 2013.

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[2] R. Teodorescu, M. Liserre, and P. Rodriguez, Grid Converters for Photovoltaic and Wind Power Systems, New York, NY: John Wiley & Sons, 2011. [3] G. Abad, J. Lopez, M. Rodriguez, L. Marroyo, and G. Iwanski, Doubly Fed Induction Machine: Modeling and Control for Wind Energy Generation, New York, NY: John Wiley & Sons, 2011. [4] H. Mahvash, S. A. Taher, M. Rahimi, and M. Shahidehpour, “DFIG performance improvement in grid connected mode by using fractional order [PI] controller,” International Journal of Electrical Power & Energy Systems, vol. 96, pp. 398–411, 2018. [5] Y. Liu, Z. Wang, L. Xiong, et al., “DFIG wind turbine sliding mode control with exponential reaching law under variable wind speed,” International Journal of Electrical Power & Energy Systems, vol. 96, pp. 253–260, 2018. [6] B. Wu, Y. Lang, N. Zargari, and S. Kouro, Power Conversion and Control of Wind Energy Systems, New York, NY: John Wiley & Sons, 2011. [7] M. Guan, “A series-connected offshore wind farm based on modular dualactive-bridge (DAB) isolated DC–DC converter,” IEEE Transactions on Energy Conversion, vol. 34, no. 3, pp. 1422–1431, 2019. [8] Y. Xu, Z. Wang, Z.-X. Zou, G. Buticchi, and M. Liserre, “Voltage-fed isolated matrix-type AC/DC converter for wind energy conversion system,” IEEE Transactions on Industrial Electronics, vol. 69, pp. 13056–13068, 2022. [9] T. Funabashi, Integration of Distributed Energy Resources in Power Systems: Implementation, Operation and Control, London: Academic Press, 2016. [10] V. Yaramasu and B. Wu, “Predictive control of a three-level boost converter and an NPC inverter for high-power PMSG-based medium voltage wind energy conversion systems,” IEEE Transactions on Power Electronics, vol. 29, no. 10, pp. 5308–5322, 2013. [11] W. Cao, N. Xing, Y. Wen, X. Chen, and D. Wang, “New adaptive control strategy for a wind turbine permanent magnet synchronous generator (PMSG),” Inventions, vol. 6, no. 1, p. 3, 2020. [12] K. Xiahou, Y. Liu, M. Li, and Q. Wu, “Sensor fault-tolerant control of DFIG based wind energy conversion systems,” International Journal of Electrical Power & Energy Systems, vol. 117, p. 105563, 2020. [13] M. S. Eslahi, S. Vaez-Zadeh, and A. Jabbarnejad, “A comparative study of control methods for grid side converters in PMSG-based wind energy conversion systems,” in 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), 2020, New York, NY: IEEE, pp. 979–984. [14] M. Zhao, X. Yuan, and J. Hu, “Modeling of DFIG wind turbine based on internal voltage motion equation in power systems phase-amplitude dynamics analysis,” IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 1484–1495, 2017. [15] J. Ma, Y. Qiu, Y. Li, W. Zhang, Z. Song, and J. S. Thorp, “Research on the impact of DFIG virtual inertia control on power system small-signal stability

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[29] P. Corte´s, M. P. Kazmierkowski, R. M. Kennel, D. E. Quevedo, and J. Rodrı´guez, “Predictive control in power electronics and drives,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4312–4324, 2008. [30] A. Linder, R. Kanchan, P. Stolze, and R. Kennel, Model-Based Predictive Control of Electric Drives, Go¨ttingen: Cuvillier Verlag, 2010. [31] M. Yin, G. Li, M. Zhou, and C. Zhao, “Modeling of the wind turbine with a permanent magnet synchronous generator for integration,” in 2007 IEEE Power Engineering Society General Meeting, 2007, New York, NY: IEEE, pp. 1–6. [32] H. Ahuja, G. Bhuvaneswari, and R. Balasubramanian, “Performance comparison of DFIG and PMSG based WECS,” In IET Conference Proceedings. The Institution of Engineering & Technology, 2011. [33] V. Dahiya and G. Leena. “Comparative study of doubly fed induction generator and permanent magnet synchronous generator in wind energy conversion system,” International Journal of Electrical Engineering and Technology, vol. 10, no. 3, pp. 73–79, 2019. [34] A. Mitra and D. Chatterjee, “Active power control of DFIG-based wind farm for improvement of transient stability of power systems,” IEEE Transactions on Power Systems, vol. 31, no. 1, pp. 82–93, 2015. [35] M. K. K. Prince, M. T. Arif, A. Gargoom, A. M. Oo, and M. E. Haque, “Modeling, parameter measurement, and control of PMSG-based gridconnected wind energy conversion system,” Journal of Modern Power Systems and Clean Energy, vol. 9, no. 5, pp. 1054–1065, 2021. [36] F. Bakhtiari and J. Nazarzadeh, “Optimal estimation and tracking control for variable-speed wind turbine with PMSG,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 1, pp. 159–167, 2019. [37] L. Xu and W. Cheng, “Torque and reactive power control of a doubly fed induction machine by position sensorless scheme,” IEEE Transactions on Industry Applications, vol. 31, no. 3, pp. 636–642, 1995. [38] R. Pena, J. Clare, and G. Asher, “Doubly fed induction generator using backto-back PWM converters and its application to variable-speed wind-energy generation,” IEE Proceedings – Electric Power Applications, vol. 143, no. 3, pp. 231–241, 1996. [39] P. Ramana, B. S. Kumar, K. A. Mary, and M. S. Kalavathi, “Comparison of various pwm techniques for field oriented control vsi fed pmsm drive,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, no. 7, pp. 2928–2936, 2013. [40] G. U. Nnachi, C. T. Mosetlhe, and Y. Hamam, “A comparative power quality study of DFIG and PMSG renewable energy conversion systems,” in 2020 5th International Conference on Renewable Energies for Developing Countries (REDEC), 2020, New York, NY: IEEE, pp. 1–6.

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Chapter 5

Novel AI, machine, deep learning, and optimization-based computing for energy systems Samira Sadeghi1, Abed Bagheri2, Ali Hesami Naghshbandy1 and Parham Moradi3

Dramatically increasing the integration of renewable distributed energy resources and new infrastructures of control in power systems, advanced communication devices, and novel monitoring system makes it essential to build an efficient and resilient power system. Also, due to the large volume of data, model complexity, and various uncertainties in modern power systems, traditional methods have some difficulties to reach the acceptable efficacy. However, these situations require reasonable solution/response time as well as acceptable accuracy to allow the controllers of system and operators of network to take actions that prevent and/or correct the disturbances. So, there is a basic need to choose optimal, fast, and accurate techniques for detection and identification in power systems. Recently, mathematical programming, artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques have been applied to achieve a reliable energy system. In this chapter, first, a quick overview of the modern energy systems and their main components is performed. Next, a brief overview of AI, ML, and DL techniques and their application in modern power systems are provided. Moreover, advanced techniques in the field the energy systems have been reviewed. Furthermore, some real-world applications of intelligent systems on the modern power systems are investigated. This section provides an overview of various study horizons from short-term to long-term and different control modes. Also, dynamic security assessment and stability control issues have been addressed with a focus on computational intelligence applications. Finally, future perspectives of dynamic security assessment by ML methods are given.

1

Department of Electrical Engineering, Faculty of Engineering, University of Kurdistan, Iran Center of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology (IUST), Iran 3 Department of Computer Engineering, Faculty of Engineering, University of Kurdistan, Iran 2

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Abbreviations ACO AI ANN CIG CNN CSAE DDSSO DT DL DQL DQN DRNN DRL DSA ELM FA FACTS FIDVR GMM GA HAC HVDC HBPNN k-NN LSSVM LSTM ML MDP MFNN NN NR PMU PSS PCA PHM

ant colony optimization artificial intelligence artificial neural network converter-based generation convolutional neural network convolutional sparse autoencoder device-dependent sub-synchronous oscillations decision tree deep learning deep Q-learning deep Q-network deep recurrent neural network deep reinforcement learning dynamic security assessment extreme learning machine factor analysis flexible alternating current transmission systems fault-induced delayed voltage recovery Gaussian mixed models genetic algorithm hierarchical agglomerative clustering high-voltage direct current high-performance back propagation neural network k-nearest neighbor least square support vector machine long short-term memory machine learning Markov decision process multilayer feedforward NN neural network Newton–Raphson phasor measurement unit power system stabilizer principal component analysis prognostics and health management

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random forest recurrent neural network reinforcement learning supervisory control and data acquisition sub-synchronous resonance support vector machine static var compensators under frequency load shedding under voltage load shedding wavelet transform weighted kernel extreme learning machine

5.1 An introduction to modern energy systems The first goal in power systems is to generate electrical energy along with the balance between generation and consumption. Then, it must be ensured that the current limits of all lines and the voltage limits of all buses are in place. In fact, in the normal state of operation, at the fault time, and after the fault, the dynamics of the power system (angle, voltage, and frequency) should be in the desired range [1]. Also, in the case of faults and all kinds of disturbances in the network, occurrence of cascading failures and blackouts should be avoided as much as possible, because the economic and social damages caused by blackouts are very costly for society [2,3]. For this reason, it is very important to study and evaluate the power system dynamic security in different situations of operating the network online and in the real-time. On the other hand, various environmental issues, greenhouse gas emissions, and energy management requirements have increased the application of renewable energy sources and integrated distributed generation, which have imposed new provisions on the power systems and challenged the assessment of dynamic security and stability. Because of the wide load changes and uncertainties, load forecasting in the energy management bring up some challenges in the energy exchange between lines, perturbations, and unpredictable faults of the power system now and in the future [4]. In addition, increasing the volume of data, predicting state of the system, and online energy management have made many of the current methods in solving various problems of the power system ineffective. Likewise, newer issues and refinements require smarter and faster solutions, because old methods and new concepts do not match. In this regard, different issues are evaluated with newer and more advantageous methods of machine learning (ML). These magnificent methods, due to their high speed, accuracy, and compatibility, have eliminated the need for complex and inaccurate calculations and models and have overcome the drawbacks of old and traditional methods [5].

Power electronics for next-generation drives and energy systems: Volume 2 Big Data

Resiliency

Uncertainties

Smart Contracts

Local Energy Transaction

Modern Power Systems Challenges

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Storages

Power Electronics

Electrical Vehicles

Protection Equipment Distributed Generation Penetration

Figure 5.1 Important challenges of modern power systems

5.1.1

Challenges in modern power systems

Managing the operation and planning of the power grid will make challenges. Examining challenges and identifying better solutions will also reduce the negative consequences of various issues. In the traditional power systems, communication between local networks and increasing the efficiency of equipment in all parts of the network have been important issues. Today, momentous subjects such as increasing use of renewable energy, distributed generations, presence of electric vehicles, and increasing demand for energy have created some uncertainties on the demand side. Meanwhile, electric vehicles have led to more problems as they add more uncertainty to the network due to their mobility. The local energy exchange between consumers and producers/prosumers has been paid impressive attention in the smart grid paradigm. Also, transfer of energy by electric vehicles brings some issues related to the vehicle charging and its charging position along [6]. Figure 5.1 summarizes the important challenges discussed. As the infiltration of renewable energies and distributed generations increases, the rotational inertia of the network decreases. Diminishing inertia also causes power system instability and consequently some changes in the management and operation of the power grid. Compensations for rotational inertia and improved stability are achieved through power electronic capabilities. At the same time, the need to store energy generated by renewable energies is becoming a drastic challenge. So that this energy is delivered to the grid at the right time [7]. Solving energy system issues by human operators and traditional support systems face many problems. These problems are epitomize as follows: ●

● ●

Being up-to-date and sufficient information on the whole power system cannot be checked only by the human operator. The complexity issue of large power systems preclude timely calculations. The solution method used by the human operator is not able to express the algorithm or mathematical form.

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Operator’s decision makings are not sufficiently fast. Analysis of security, stability, and control issues is based on human judgment and experience.

The modern and smart power system has largely overcome these problems. Such that, these problems in the field of energy systems have led to the search for alternative solutions through the use of mathematical methods, computational and artificial intelligence (AI), and a variety of their techniques. Also, with the rapid and widespread installation of smart communication devices and computers, the electricity industry is also experiencing extremely complex changes. Therefore, the interest in using AI and ML methods in analyzing energy systems issues is constantly increasing. Using these methods, the benefits of ability to identify system states, selectivity of controls, ability to learn to update knowledge, coordination of tasks, flexibility, and ability to control uncertainty can be achieved. Also, the ability to analyze online and offline power system control and stability issues are addressed by utilizing these methods. In this chapter, AI and ML applications will be propounded with a focus on the power system performance, demand, control, optimization, error detection problems, stability analysis in monitoring power systems, and some common management issues. The whole process of the power system is illustrated in Figure 5.2. It seems necessary to get an insight into the general status of the power system using different types of computational methods to improve the conditions of generation, transmission, and distribution of energy. The modern power grid has several important features: including; high penetration of new and renewable energy sources, integration of different types of energy (electricity, gas, heat and cold), and the existence of distributed resources in the interconnected power systems [8]. In these power system, huge amounts of measurement data related to generation, operation, control, transmission,

Electricity pricing Economic dispatch Generation optimization Generation planning Renewable energy planning

n atio ner Ge on

issi

nsm

Tra n

utio

trib

Dis

Power grid planning Grid loss identification Transmission line fault detection Outage detection and restoration Islanding detection and isolation Islanding detection and isolation

nd ma De

Load classification Demand response Customer load response Customer load forecasting Demand information system Real -time interaction and energy saving

Power grid planning Grid loss identification Transmission line fault detection Outage detection and restoration Islanding detection and isolation Islanding detection and isolation

Figure 5.2 Whole process of the power system

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distribution, and consumption are continuously collected and processed at high speeds and any given time.

5.2 Definition of energy systems problems The complexity of power systems has increased with increasing population and electricity demand in the field of home consumption, the development of industry and systems, electrical technology and equipment, the development of smart networks, the high penetration of wind and solar energy, and the combination of the various types of energy sources. As a result, complexity and uncertainty in power systems have increased. The main goal of the power system is to supply energy to consumers by maintaining a balance between generation and consumption and, in fact, maintaining the stability of the power grids. Therefore, in order to maintain the stability of the power grids and energy supply, issues related to the planning, operation, and control stages of the power system should be considered. The set of activities, decisions, and predictions that are made to meet the needs and develop plans for the design of power plants and transmission lines, their construction, and the restructuring and development of transmission lines, and other power system equipment is called planning. Planning is done according to the current state of the power grids in the future [9]. The planning stage is about 1–20 years and is mainly related to reducing investment costs, failures and interruptions, and pollution, increasing reliability, and improving security and stability [10]. In the operation phase, the elements and equipment of the power system are used to meet the energy demand of consumers in compliance with economic laws and technical constraints and to meet environmental goals in the control phase, in order to maintain a balance between energy generation and consumption, also, to maintain the security and stability of the power system despite all kinds of disturbances and errors, all kinds of control measures are taken. In the following sections, planning, operation, and control issues are described.

5.2.1

Planning

In the case of planning studies, if some equipment or parts of the power system do not work properly, the energy needed by consumers will not be provided. Therefore, by determining the installation location of the equipment, their type and number, and the installation time of this equipment with the lowest cost, the load can be provided with better quality [11]. Planning studies are conducted in three parts: generation, network, and substations expansion planning [12]. In the generation development section, the objectives include determining the installation location of energy sources, their optimal installation capacity and time to determine the maximum consumer load; and the limitations include considering the number and capacity range of generation units leaving the grid at the time of failure [13]. Line development planning includes transmission, sub-transmission, and distribution lines. Here, too, the type of line, the capacity of the line, and the

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time of its development and installation must also be considered. Under normal operating conditions or in the event of a disturbance, the allowable capacity of the equipment in the network must be calculated. If the load increases, the distribution network equipment may not be responsive to the increase in load, so over a planned period of time, either higher capacity equipment must be installed or the number of substations must be increased [12, 14].

5.2.2 Operation There are challenges to operation at all times, such as the occurrence of unexpected natural disasters, human error, and cyber-attacks. Climate change is another challenge whose effects and consequences may become apparent decades later. Some types of disturbances are seen as power outage, transient (surge), sag/swell, noise, harmonic distortion, and undervoltage/overvoltage in the power system. To fully investigate the types of disturbances and the amount of damage caused by them, the operating mode of the control must first be carefully examined. In this regard, the operating conditions of the power system are classified into 5 operating modes: normal, alert, emergency, in-extremis, and restoration. It should also be specified what kind of control action should be taken in each situation. Under normal state where is called safe mode, all variables of power system are within acceptable limits, and no equipment is overloaded. In this case, the system can withstand any possible event without violating any restrictions. The system is operated in a safe state and is able to withstand any disturbance without causing severe deviations from the constraints [15]. In the alarm mode, no equipment is overloaded yet. System variables are still within the allowable range and all restrictions have been met, and preventive control is a practice performed to maneuver the system from alert to normal. A sudden disturbance may overload the equipment and result in an emergency. If the disturbance is very severe, the system may go directly from the alarm state to the in-extremis state [16]. In an emergency, the voltage of many buses is low or the loading of the equipment exceeds the nominal limit, and by applying the necessary controls such as fault elimination, control of the excitation system, opening and closing of steam valves and water valves, reducing generation, using storage capacity, getting help high voltage direct current, and finally load shedding can be entered into the alarm mode. If the above actions are not performed or their implementation does not lead to the solution of the problem, the system enters an in-extremis state, which results in successive interruptions, expansion of cascading failures and possibly blackout of a large part of the system [17]. Restoration mode can be performed after each of the emergency and in-extremis situations. Restoration or repair means electrifying the system or its equipment, reconnecting, and synchronizing the equipment [15]. Restorative control is a slow action performed to restore the system from restorative mode to normal mode [18]. Common repair control measures contain generator output power adjustment, connection and disconnection of generator, load shedding (under frequency or under voltage load shedding), and system islanding [19].

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Control

One of the most original and significant benefits of electrical energy is that it can be controlled with high reliability and efficiency. In this regard, it must first be determined what the conditions of the controlled power system are [17]. The first condition is that the active power and reactive power is in the desired range and according to the defined standard. Since active electrical power cannot be stored at any capacity and there is limited energy storage for it, power plants must always consider spinning storage, so that they can enter it into the circuit at the required time. Also, to prevent voltage drop on the load side, the required reactive power must be provided. The second condition is that standards and technical requirements must be maintained on a permanent basis. This condition is presented as power quality. In the field of power quality, voltage stability and frequency stability along with ensuring high reliability are discussed and evaluated. In order to ensure high stability and reliability, the primary and secondary controls in the control section of the power plant and generator must work properly. The generator control system includes a governor and an excitation system. The prime mover controls the input variables such as boiler temperature, pressure, and flow. Excitation control also regulates the voltage by injecting reactive power and prevents voltage drop. By calculating the network load, the losses in the system, balancing and adjusting the output power of the generators, the generation control is performed. By considering these measures, power exchange takes place between multi-area systems, and the currents of tie lines are obtained. To control power in transmission lines, switched capacitors and reactors, Static Var Compensators (SVC), synchronous condensers, phase-shifter and tap-changing transformers, and high-voltage direct current (HVDC) transmission lines are installed [17, 20].

5.3 Technology of intelligent systems In recent years, AI technology has become popular because of its amazing and broad performance in many issues. AI is one of the branches of computer science that simulates and implements human intelligence in computers, robots, or any other machines. AI is also the ability of a computer to mimic the capabilities of the human mind. The main objective of AI is to create systems that can operate intelligently and independently. The development of AI has increased competition in the application of AI in all scientific fields. The development of AI includes two aspects of theory and application. The theoretical aspects of AI include data swarming, intelligent systems, big data, advanced intelligence, cross-processing, and combining types of intelligence. The application aspect also includes basic software, advanced equipment required, and AI modeling methods. AI is developing and flourishing in various fields. Because large volumes of data are interconnected, the computing power of computers has greatly improved. Also, the advancement of data processing technologies is associated with the rapid advancement of computer hardware and software. Intelligent algorithms have been widely used in various fields and have achieved wide results.

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Using these algorithms, many advances in control and optimization theories have been made [21]. AI, ML, and DL are three commonly used topics in many real-world applications. Metaheuristic algorithms are frequently used and fastest methods which are used to solve complex optimization tasks [22]. In recent years, new techniques of AI such as ML and DL have been increasingly used in various fields [23]. ML techniques are generally applied to model complex tasks from the available data. The main purpose of the ML methods is to extension the learning procedure through some training samples that as a successful explanation of information and data. ML methods first receive the data. Next, they start learning. So that in a certain range, they perform statistical analysis on these data and finally produce output. These methods then allow the obtained algorithm to perform the decision-making approach automatically on the newly arrived samples [24]. Deep learning (DL) is a proper subset of ML methods. This sophisticated method can predict outputs by imitating the brain operation. Using this method and based on different data sources, human experience and thinking ability are learned. DL refers to a subset of neural networks consisting of multiple layers of learning. This approach lets machines and learning algorithms to solve complicated problems when the data are continuous, unstructured, and highly varied [25]. Figure 5.3 summarizes the position of each of these approaches in the optimization-based computing universe. It is necessary to mention that AI is any approach, method, algorithm, or code (pseudocode) that creates the ability to behave like humans in machines. The code of the corresponding algorithm usually contains expressions written by an AI programmer, so that this code is composed of mathematical equations and generates a special value that is considered as a constant value for the input [5].

Mathematical Programming and Computational Intelligence

Artificial Intelligence

Machine Learning

Deep Learning

Figure 5.3 Universe of AI, ML, and DL

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ML methods include different sections. In the first step, the data is considered as input and the pre-processing operation is performed on them, and if this input data contains noise or any disturbance, the noise is first removed by special methods, which will extract a set of data with specific and usable attributes. Finally, pure and noise-free data is given to a learning algorithm (classification or clustering), which performs the classification and prediction operation with the desired decision. The specifications of the ML technique depend on the following [18]: 1.

2.

3.

4.

5.

Data selection: The dataset must clearly represent the problem. Poor quality dataset cannot provide a good/proper model of the problem. Poor quality data is data that is out of range, with little data, noisy, and lacks appropriate features. Here, the best solution to increase data quality before learning is to remove obscure data, obsolete data, and noise. Feature selection: The goal of feature selection in ML is to reduce redundant and unrelated features (including similar and unnecessary features) that reduce the size of the input data space. Model selection: At this stage, as much as possible, models with the best fit to the actual model of the problem are selected. To select the most appropriate model, the parameters and structure of the selected model must be selected very intelligently. Optimization methods (global and local searches) are used to select the problem model parameters. Interpretation and validation: The validation step is to determine the validity of the learning model. In this section, part of the data that is not used to identify the model (test data) is given to the model to determine the accuracy of the model and its percentage of validity. Model use: The obtained model is used for forecasting. In addition to predicting the output of new input data, the model is used upside down to check the result of changing input parameters on the output.

The most common classifications for ML and DL methods are as follows: (1) supervised learning, (2) learning without supervision, and (3) reinforcement learning [26]: Supervised learning: In supervised methods, inputs and outputs are known, and the model is trained according to the result of the outputs from the input data. These methods are divided into two main categories, which are obtained from two categories of countable outputs and uncountable outputs [26]. For discrete (countable) outputs, classification algorithms are used and the model is divided into separate categories. Regression algorithms are also used for continuous (uncountable) outputs, in which the model is obtained as a linear or curved equation. The most important supervised methods include the following: Continuously supervised: Linear regression, logistic regression Discrete supervised: Support vector machine (SVM) [27, 28], k-nearest neighbor (k-NN) [29, 30], decision tree (DT) [31,32], random forest (RF) [33,34], simple Bayesian, concept learning, and neural network (NN). Unsupervised learning: In unsupervised methods, the inputs are known and the outputs are unknown. In this method, the model is obtained using input features. In

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the obtained model, categories with similar characteristics are detected. Clustering and cumulative rules are two general categories of unsupervised methods. In clustering, similarities between features are used to classify and separate data, but in dimensional algorithms (cumulative rule method), model recognition is performed by recognizing the relationships between the properties of the input data. The most important clustering techniques are as follows: k-means [35], C-means [26], Hierarchical Agglomerative Clustering (HAC), and Gaussian Mixed Models (GMM). Reinforcement learning: The reinforcement method has used human behavior during the period of real learning and strengthening the performance of behavior. The algorithm of this method is such that rewards and penalties are considered for it. In return for the feedback received from the environment and based on the right or wrong result of the behavior, a reward or penalty is considered for it. In the reinforcement learning method, due to the lack of input and output data, the need for feedback from the environment is essential. But in the supervised method, there is enough input and output data [36]. This method is used in online situations. Factors must be considered to determine and select the ML method in solving a problem and determining its model. Some of the most important factors include: it must be determined that the purpose of learning is merely a categorization or prediction, or both. Supervised or unsupervised learning methods are then determined for it. Finally, according to the type and nature of discrete or continuous data, the best method and algorithm is selected. In this way, the limitations of the problem should also be considered. In this regard, feature extraction, noise reduction and problem dimensions, and feature optimization should be considered [37, 38]. The most important methods in extracting and selecting features and reducing the size of the problem space are: principal component analysis (PCA), forward feature selection (FFS), backward feature selection (BFS), wavelet transform (WT), and factor analysis (FA). In addition, metaheuristic methods such as genetic Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are used as methods for preprocessing and optimizing data. Some of the most important and widely used ML algorithms such as NN, SVM, and DT are briefly described.

5.3.1 Neural network A neural network is an intelligent machine that mimics the behavior of neurons in the human brain to perform its calculations. Thus, in the neural network method and algorithm, a number of neurons are connected to each other. The connection between each neuron is established by a weighting factor, the correct adjustment of which gives the NN algorithm knowledge. Therefore, the process of adjusting the weighting coefficients can be done by defining an optimization problem and solving it by the Lagrange method. There are different algorithms of the neural network; but in most cases, the Multilayer Feedforward NN (MFNN) is used, which consists of input, hidden, and output layers. The NN algorithm has a great

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capability to build a relationship between input and output (nonlinear mapping). Therefore, it performs the prediction and classification operation well. The capability of this method has led to the use of this algorithm in evaluating various types of power system problems [39].

5.3.2

Decision tree

The decision tree is another ML method algorithm that is extracted from the structure of branches and leaves of tree and is widely used in various power systems analysis problems [40–42]. The function of the decision tree is based on answering yes or no questions. This algorithm determines whether the inputs meet the desired conditions by making inquiries about the conditions of the input data. Depending on the type of answer to each question, for each entry, either a new question is asked or the final answer is specified. So, in this algorithm, there is an important node that divides the inputs into two parts. This important node is introduced as the root node. The root node is the first node that must be selected to create the best in the input set. The nodes that follow the root node are called branch nodes. Similarly, the algorithm searches between branch nodes to rediscover the best segmentation, and performs this iterative process until the final node is found. Such a process causes the tree to overgrow, which is called tree growth, and reduces the accuracy of the prediction. To increase accuracy, some branch nodes become the final node and some final nodes are removed. Because removing nodes is like pruning trees, the process of pruning a tree is called a decision. As a result, the training set needs to be divided into two parts to perform the growth and pruning processes of the tree. One part contains the data that is used to perform the tree growth process and the next part can be used to perform the tree pruning process. Depending on the type of input, there are two types of DT. If the input is in the form of continuous signals, the DT is a regression type that can act as a component selector. If the input is discrete quantities, the classifier decision tree is used to perform the classification. Therefore, Ref. [43] uses the regression decision tree (to obtain the appropriate component of the continuous signals) and the classifier (to classify the inputs into stable and unstable categories) simultaneously. The DT algorithm performs the assessment process faster than other ML methods, and it also has a high ability to make a decision against a wide range of information for training. Another advantage of this method is that by using the tree structure for decision making, how the decision process is determined in each node and branch, and it is much more accurate than the NN method and SVM. For this reason, there are different methods to increase the accuracy and stability of search tree performance that have been suggested in [26, 44]. Ref. [26] uses the component selection method utilizing a binary genetic method to increment the accuracy of dynamic security assessment, because the relationship between the components of training and the indicators of sustainability is non-linear and complex and they cannot be selected through location awareness. Rather, they must be chosen by solving mathematical equations. The process of selecting a component with a larger index

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is a better option for training the trees. Ref. [45] uses a sample selection method to speed up the DT algorithm, which consists of two levels. It first defines an entropy for the training set and selects the samples with the highest entropy based on it. In the first level, an attempt is made to define the boundary line between safe samples and insecure samples, and, in the second level, an attempt is made to select a set of samples that are closer to the boundary line. To increase the speed and stability of the decision tree algorithm, Ref. [44] uses the DT retraining method and updates the training set periodically.

5.3.3 Support vector machine SVM is another ML algorithm that is used for classification. The working basis of this algorithm is linear classification of data. In the linear division of data, a line is selected that has a margin of confidence. To find the optimal linear equation, it is necessary to define and solve the optimization problem with quadratic linear optimization methods. The characteristics of this method are such that they can be used in various problems. Its features include good categorization, relatively simple training, and lack of convergence to the local optimal point (unlike neural networks). The advantage of efficiency over a wide range of high-dimensional information has led to the use of this algorithm in dynamic security assessment [27, 46]. The increasing complexity, uncertainty, size, and dimension of data in the power system have made traditional methods of solving problems difficult. For this purpose, data-based methods such as deep reinforcement learning (DRL) are used to solve complex problems. DRL is a combination of reinforcement learning (RL) and DL that is considered real AI. DRL is also used for complex power system issues.

5.4 Applications of computational intelligence methods in energy systems studies Traditional methods of power system operation and monitoring have made great strides in system stability, efficiency, and security. However, there are problems in the power system that are still solved manually. So, these methods are not efficient and reliable. Therefore, AI techniques may be effective in solving these problems [47]. Nowadays, researchers use the superiority of ML methods over other methods presented in energy system analysis problems, and, in this way, they try to evaluate each problem in the desired operating condition [40, 48]. Operating conditions are considered as one of the inputs for the ML method, and the output will be different for each problem. ML methods relate input to output. Therefore, they can be a good alternative to the equations of different problems. These algorithms can communicate between input and output if trained. Therefore, they need sets of inputs and outputs, which are called training data. ML algorithms in control and stability issues perform the assessment based on data collected from phasor measurement devices such as PMUs. In this case, the ML algorithm is used as an algorithm to compare the information obtained from the phasor measurement units with the

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source of stored information [49]. When using ML methods to solve and evaluate various power system problems, it is necessary that the algorithms are first trained offline. At the end of the training process, the algorithm is able to perform evaluation operations online. The more correctly the training process is performed, the more accurate the algorithm is in performing a timely evaluation. Some research on power systems, such as fault detection and diagnosis using Bayesian Networks [50], Recurrent Neural Network (RNN), model and a long short-term memory (LSTM) [51], and hybrid DL [52], predicting energy consumption using convolutional neural network (CNN) [53] and deep RNN (DRNN) [54], and finding the number and location of distributed network generators using GA method in power system [55] has used AI techniques. Also, some research has used expert systems to analyze and design power systems. In this part, some usages of AI in power networks, including issues related to power system control, optimization and data classification and predicting system status during planning, operation and control, also, fault detection and dynamic security assessment and stability diagnosis are examined. The NN technique has been used extensively in power system control issues such as frequency control and frequency load control. Because this method is very simple and can be used for many problems. In the end, a case study using the NN for analyzing power flow as a simple example demonstrates the ability to use AI techniques in solving power system problems.

5.4.1

Power systems and big data

Modern power systems and future power grids are interconnected and complex networks that contain a variety of renewable energy sources and distributed generations. The modern power grid also includes high-variation loads, various and uncertainty loads and generations, bidirectional smart meters, online management system, and real-time controllers and processors that generate a wide variety of data and information at high speeds. Online monitoring of this large amount of information and data requires intelligent infrastructure. High-volume data should be evaluated online or offline. In this regard, AI and ML methods are being developed day by day. In addition, the exchange of information also has a high cyber security [56]. In the traditional power system, supervisory control and data acquisition (SCADA) devices are used in order to collect data and secure network operations. SCADA provides data on line currents, bus voltages, and injected active and reactive powers [57]. Unlike SCADA, where its sampling rate is slow, phasor measurement units (PMUs) with a sampling rate of 100 samples per second and higher collect the measurement data of voltage phase of all the basses and the current phases of all the lines connected to that bus. Therefore, large volumes of measurement data must be collected and intelligently managed [58]. Most power systems are not ready for managing, storing, and analyzing large amounts of data. Efficient development of ML techniques is essential for computations or for large-scale data. On the other hand, some analytics methods of big

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data management in the power system are not efficient enough. Therefore, highperformance computational methods seem essential.

5.4.2 Operation and control Control actions are performed in all states and modes of power systems. Primary and secondary controls in the normal and alert condition of the power system, using traditional control methods such as PID control, adaptive control, and optimal control provide acceptable solutions. But as power grids become more complex, common techniques often lose their effectiveness. Because common control techniques are usually model-based approaches, and system model is used to design the controller and determine its parameters. In this case, for large and complex power systems, it is problematic to extend the system state-space model accurately. Also, a traditionally designed controller may not achieve the desired performance because the model and actual system are different. The most famous modelless controller is the PID controller, which has been applied extensively in power networks. Nonetheless, the PID controller does not work for some power systems issues. In the PID controller, the parameters are adjusted by trial and error and human experience, which the lack of a good method of PID adjustment makes this controller unreliable. Sometimes, in order to achieve the appropriate parameters in PID controllers, in addition to developing the system model, the parameters are also optimized along with the model. In addition, more advanced and newer methods of adaptive control and optimal control alone do not achieve proper performance using the PID method. Therefore, by using AI methods, the problems in controlling power systems can be overcome, because most AI methods are not model-based. In fact, most of these methods are measurement-based. The power system is a wide, continuous, and uninterrupted system whose characteristics change day by day due to fast developments and interaction with nature and humans. Therefore, due to the existence of new data, the control system should be able to comprehend the new features of the power grids and carry out the control design adaptively. In the following references, some control measures have been performed using control methods based on AI (such as neural networks). By implementing a neural network-based estimator, in a photovoltaic system without energy storage, the power control is performed [59]. Adaptive nonlinear neural network technique has been used to control excitation in the wide-area power networks [60]. The neural network has also been adaptively applied for optimal load-frequency control of a four-area interconnected power system with a hybrid energy storage system (HESS) [61]. The use of neural network methods has become common in many power system control issues. Advanced methods such as CNN and RNN, which are subdivisions of NN, are used to solve more complex power system problems. These more advanced NN methods can be used to predict system performance and determine and select the best control strategy. Because of its suitability for classification, CNN can classify appropriate control strategies according to the current state of the power system, which ultimately selects the appropriate optimal control strategy according to the performance of the power system. RNN is a technique that

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has a memory and is used to characterize the performance condition of the power grid according to the antecedent condition. Table 5.1 examines several papers in terms of operation modes (normal, emergency, and restoration). In all these studies, ML methods have been applied to analyze the problems. Preventive control includes a variety of control measures: programming of reproduction and compensation of switching reactive power; but emergency control includes control measures of direct or indirect load reduction and cessation of generation. Security control is divided into two main categories of preventive control and emergency control. In the past, many large companies relied on preventive control to maintain system security at an acceptable level. In other words, the aim was to

Table 5.1 A summary of the application of ML techniques in the study of various issues related to different states of power system operation States

Problem

ML Algorithm

Normal

Improving Performance of Relays in Power System Protection Maximum Power Point Tracking Automatic Generation Control Demand Response Economic Dispatch Damping Low-Frequency Oscillations by PSS Voltage Stability Converter Interfaced Stability Resonance Stability

ELM

[70]

MDP LSTM, RNN DNN ELM ANN

[71] [72] [73] [74] [75]

SVM, RF, ANN k-NN, Multiple SVM RL, DT, SVM, ANN, RF, k-NN Multiclass SVM DQL Adaptive Ensemble DT SVM RL, DQN RF DRNN HBPNN ANN

[18] [76] [34]

Emergency and in-extremis

Voltage and Angle Instability Angle Instability Frequency Instability Voltage Instability

Restoration

Transient Stability Damping Low-Frequency Oscillation Damping the Inter-Area Oscillations RL, NN (Wide Area) Emergency Load-Shedding ANN and Analytic Hierarchy Process Algorithm Restoration of Power Systems Q-Learning Fault-Induced Delayed Voltage WKELM Recovery (FIDVR) Generation Rescheduling DT

Reference

[27] [36] [68] [46] [67] [77] [19] [78] [79] [69] [80] [81] [82] [65]

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prevent the implementation of emergency control plans as much as possible by implementing preventive control [62]. Preventive control measures have been widely used to ensure an adequate safety margin against some sudden disturbances (e.g. N
1). However, there have been several major blackouts in the United States and Europe over the past two [63]. Therefore, emergency control in real-time operations is essential to minimize the occurrence and impact of power outages or widespread blackouts. Emergency control of the power system is considered as the last step in maintaining the security and resiliency of the power network, before the collapse and to prevent it [64]. In preventive control, the goal is to prepare the system in a normal way in order to be able to deal with uncertain future events in a satisfactory manner. In emergency control, disturbing events have already occurred, and therefore the goal is to control the dynamics of the system in such a way as to minimize the consequences [65]. Emergency and corrective measures include under frequency load shedding [36], load recovery [17], under voltage load shedding [64], and islanding [66]. Preventive measures also include active and reactive power support [67], primary and secondary frequency control, [68], generator output power regulation [69], and programming and limited optimal load distribution [46]. In preventive control, the status of the system is clear but the failures are unclear. In emergency control, failure is inevitable, but the state of the system is often only partially clear. In both cases, the dynamic behavior is unclear. Table 5.2 uses ML methods to control the power networks in preventive and emergency control modes after the occurrence of various faults. It should also be noted that a variety of natural and man-made events can cause power outages in

Table 5.2 Emergency and preventive control by ML methods Control mode

Problem/control method

ML method

Preventive

Angle Instability

DT SVM RL, NN Bayesian NN DT Bayesian NN ELM DRL ELM NN DT Multi Q-Learning k-NN DRL k-means DT SVM ANN

Voltage Instability Emergency

UVLS UFLS

Generation break Rapid Power Change in Buses Islanding Detection Adaptive Regulation of Generator Power via Frequency Control

Reference [83] [46] [69] [29] [83] [29] [82] [36] [84] [85] [65] [67] [86] [36] [87] [88] [89] [90]

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limited and extensive areas. The occurrence of equipment failure is consistently called cascaded failure, in which the occurrence of each event depends on previous events.

5.4.3

Optimization

Optimizing and improving efficiency and increasing savings on various power system issues along with stability are generally the most important features. Therefore, different optimization techniques have been proposed to achieve high efficiency and better savings. One of the most widely used power system problems, in which various optimization techniques are used, is the economic dispatch problem, in which the gradient method and Lagrange coefficient are used to decrease the operating cost. In the case of unit commitment, the dynamic programming method is also used to obtain long-term optimizations [91]. But with the expansion and enlargement of the power system, the problems of the power system are faced with a large number of variables and components, so that in the problem of optimization, the number of variables of the objective function also increases. As a result, the search space for variables also increases exponentially. So, in some complex and large issues where all the factors are considered, optimization and the method used will become a challenge. As in the case of unit commitment, this challenge is more obvious, because unit commitment is done over a long period. Therefore, using traditional optimization methods for problems with many variables is almost impossible. Also, with the development of the use of renewable energy sources around the world, the optimization of the economic dispatch and the commitment of units are facing problems. Because renewable sources such as solar cells and wind turbines have an intermittent and variable nature due to unpredictable changes in environmental conditions and weather, which adds to the uncertainty. In the traditional method of stochastic programming [92, 93] due to uncertainty in renewable energy sources, different scenarios have to be calculated. By considering the types of scenarios, the search space becomes very large and the problem is faced with different objective functions, types of scenarios, and increasing variables. Therefore, in the stochastic programming method, to decrease the dimensions of data and increase the speed of the optimization process, the technique of reducing the number of scenarios has been used [94, 95]. In scenario reduction techniques, by integrating similar scenarios, the problem of balancing the size of the scenarios arises. Scenario reduction and balancing is also a timeconsuming process that keeps optimization speeds slow. Another problem with reducing the scenario is that the actual optimal scenario may be removed during the reduction of the scenario. Therefore, the useful and possible solution to overcome such problems is the use of AI, ML, and DL techniques. AI techniques are used to optimize the objective functions of power system problems, to learn the relationship between the current state and the future state of the power system, and to predict the state. ML and DL methods are not reversible techniques, so all possible scenarios should be considered for them, but optimization techniques can ignore some scenarios and have a higher speed. Therefore, AI methods are very useful in optimizing power system problems.

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5.4.4 Decision making Power system operators must be able to make decisions very quickly and adopt the best solutions when setting up power plants and substations and dealing with sudden disturbances and incidents. When an error or a disturbance happens and causes a physical accident in the power system, the sensors or maintenance personnel detect the exact location of the accident, and the extent of the damage. In this case, the power system control center must first allow the repair staff to repair. Also, the actions related to disconnecting and connecting the switches and circuit breakers must be done with the permission of the control center because the stability of the whole power system must be considered. Because all of these operations are performed manually, human errors can occur if the accident and disturbance are not accurately identified and predicted, and the time of troubleshooting by the control center may not be determined correctly. Also, because disturbance can occur at any time of the day or night, the control center must be operated around the clock. Therefore, it is very difficult for operators to be online and energetic around the clock. Especially during the night, the possibility of operators making mistakes is very high due to fatigue. Therefore, at least, two operators are required to reduce errors in each operation. As explained, in most cases, obtaining permission to perform any operation is not effective. Also, obtaining permission is an extremely time consuming, because operators must first obtain and collect old power system information and data. Sometimes they even need a lot of trial and error to be able to get a permission from the control center for any operation. Therefore, obtaining permission traditionally by the operator is not a good choice. In this case, developing a power system using traditional methods is also a very difficult and time-consuming task. Thus, AI methods, such as ML and consequently DL, may be a better selection for power system development and planning to obtain permission from the control center. ML techniques are also used as decision-making methods based on AI for power systems development. AI-based decision systems have better decision-making capabilities and high efficiency than human operators with manual actions.

5.4.5 Fault detection In power systems, due to high complexity and vastness, the occurrence of all kinds of faults in them is inevitable. Also, since the power system is built in a natural environment and is directly related to it, so natural factors such as very hot or cold weather conditions, wind, storm, rain, snow, and thunderstorms cause faults in it. In this regard, the fault must be correctly identified and diagnosed. Therefore, fault detection techniques are of special importance in all industries, including the electricity industry, and are widely applied in all fields. Fault detection techniques include databased measurement techniques and modeling-based techniques. Another important method used in many areas to diagnose and predict defects is called the prognostics and health management (PHM) technique [96]. In Ref. [97], the convolutional sparse autoencoder (CSAE) technique is used to detect faults in transmission lines. CNN has also been used to detect faults in distribution systems [98].

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Table 5.3 Fault detection by ML algorithms Fault detection problem

ML algorithm

Ref.

Determine the Direction of Fault Current Line Trip Fault Prediction Fault Diagnosis PWM of Open Switch in Voltage Source Rectifier Detecting Fault by Relays Fault Detection Mode Detection, Fault Detection

DT, SVM SVM, LSTM LSSVM

[99] [100] [101]

SVM k-NN, DT, SVM ANN, SVM, DT

[102] [103] [104]

Fault detection techniques are often only able to detect already known and repetitive faults. These techniques can solve some problems that arise only from one factor. As an example, when a ground fault occurs due to a phase connection to a tree, traditional methods can detect this type of fault. So that after identifying the exact location of the fault, the fault will be solvable. But as the power networks get bigger and more complex day by day, many faults are always occurring, either for unknown reasons or due to several different factors. Solving these more complex faults require human experience. Therefore, a completely unknown fault may occur by the environment that humans and sensors using pre-collected data cannot easily identify the cause of the fault and its type. However, operators can use their knowledge in the control center to improve learning from past experiences and to determine the cause of the defect to some extent. In this regard, AI techniques can improve diagnostic methods. So that by classifying the power system into two types of normal and faulty systems, the system status can be easily identified. Also, the state of the system with its fault is classified into different types, which include alert, emergency, and in extremis status. Relays are used to detect faults in the network to isolate and disconnecting the zone containing the fault. Therefore, to optimize the fault detection time and performance time of the main and backup relays, types of optimization techniques including metaheuristic algorithms are applied and to classify and predict the types of faults and their location, ML techniques are applied. In this regard, Table 5.3 lists some papers that have used ML techniques to detect and locate faults in different levels and modes of control and operation.

5.4.6

Stability analysis

Dynamic security assessment (DSA) is performed to analyze the power system stability in terms of frequency, angle, voltage, resonance, and converter-based stability. However, dynamic security assessment is not time efficient. Because data collection is done at long time distances, for example, once every 15 min. In SCADA, at each 15-min interval, about 3–5 min are spent evaluating and calculating power system states. It also takes about 5 min to analyze the power system states by entering the dynamic security assessment system. With these descriptions, it is concluded that the traditional security assessment system for dealing with

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emergencies in the event of unwanted disturbances in the power system has a low speed. One of the recursive techniques in traditional power system analysis is Newton–Raphson (NR) method for calculating AC power flow. In this procedure, the solutions are updated as they approach the actual value [91]. It is noteworthy that as the power grids grows larger and more sophisticated, the efficiency of these recursive and repetitive methods decrease. Therefore, as the power system grows, AI methods, especially deep ML techniques, become more efficient and capable. Since ML methods are not recursive, they have a high speed in analyzing and evaluating system dynamics. NNs, which are supervised techniques, are used to connect the input and output, so that the relationship between input and output must be found. This method is very useful for power flow calculation. In power flow using neural networks, load and production data are considered as primary data for entering the neural network. The output of the algorithm is also compared with the actual outcome of the power flow so that the algorithm can learn and find the problem model (in fact, the relationship between input and output). Furthermore, the outcome of the neural network algorithm can be stability indicators such as short circuit current, spinning reserve rate, and N
1 stability in all types of faults, stability margin, voltage and frequency and angle instabilities, transient instability, voltage and frequency compliance rate, power supply margin, and the small perturbation alarm. In 2004, stability definitions were categorized based on voltage, frequency, and angle, because at the time of writing, the dynamic behavior of power systems was largely determined by the dynamic performance of synchronous generators and their controls, and the dynamic performance of loads [105]. Since 2004, power systems around the world have undergone significant changes, mainly due to the growing influence of technologies that have the power electronic converter interface. New power system technologies include products and equipment such as wind turbines and photovoltaic cells, various equipment for storing electrical energy, flexible AC transmission systems, high voltage DC lines and loads with power electronic interfaces. The remarkable and extensive integration of converter-based generation (CIG) technologies, variable and constant loads, and various types of power transmission equipment, has made the dynamic response of power systems gradually more interdependent on power electronics equipment with fast (complex) response. Thus, it changes the dynamic behavior of the power system. The time scale for CIG controls is from microseconds to milliseconds. Therefore, this time period includes phenomena including wave and electromagnetism. Also, the presence of energy sources with electronic power interfaces (such as batteries and photovoltaic cells) has caused the frequency response to be stable and very fast. In addition, it should also be noted that power electronic switches are switched at frequencies in the range of kHz. Accordingly, in 2020, new definitions and classifications for stability have been introduced in Ref. [106], in which two categories of resonance stability (torsional resonance and electrical resonance) and converterbased stability (with fast and slow interaction) have been added to the 2004 stability classification. New stability problems that have become a concern for power system engineers today are due to the presence of converter-based generations.

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Because these devices reduce the inertia of the whole power system. Also, the interaction between CIG controls and their contribution to short-circuit currents caused by various types of faults has created new problems in the stability of the power system. In torsional controls, the effect of HVDC and FACTS is evident, and in electrical resonance stability, DFIG is effective. In converter-based stability issues, the fast and slow dynamic interaction of control systems related to powerbased electronic systems with fast and slow response components of the system is investigated. Sub-synchronous torsional oscillations are divided into two categories: sub-synchronous resonance (SSR) and device-dependent sub-synchronous oscillations (DDSSO). DFIG generators with variable speed are a type of induction generators that are directly connected to the mains. This direct connection to the power supply will cause an electrical resonance between the generator and the series compensator [107]. Instability due to the fast interaction of converters in power systems can occur in different ways. For example, the interaction of the internal fast loops of the CIG current with the passive components of the system may cause high-frequency oscillations in the range of hundreds of Hz to several kHz. In the field of power electronics, this phenomenon is referred to as harmonic instability [108]. These types of instabilities are caused by the fast dynamic interaction of control systems based on power electronics, including CIG, HVDC, and FACTS with fast power response components such as transmission networks, stator dynamics, synchronous generators, or other devices based on power electronics. Several adjacent inverters may also create interactions that lead to multi-resonant peaks. Instability due to slow interaction is also the electromechanical dynamics of synchronous generators and some generator controllers [109]. With these interpretations, with the presence of renewable energy sources, due to the need to use power electronic converters, the power system will face both types of instability. Therefore, in the event of disturbance and the need for emergency control measures, these instabilities must also be assessed and considered. In this regard, Table 5.4 lists some articles that have used ML techniques to examine the types of stability. In the table, the stability classification is based on the 2020 paper [106]. According to the table, it can be seen that security and stability assessments are performed using ML techniques with the aim of prediction or classification. In these papers, component reduction methods including preprocessing and feature extraction, feature selection, and feature optimization with various optimization methods, especially metaheuristic methods, have been used. Today, the desire to generate electricity economically and environmentally friendly has increased. The increase in such trends has led to an increase in the use of renewable energy sources, such as wind turbines installed in power grids. As their participation in the supply of electricity increases, the operating conditions also change constantly. In addition, the dynamic security has changed, and if they are not analyzed, the user will not have a proper understanding of the situation. Ignoring the effects of wind turbines will ultimately reduce the accuracy of dynamic security assessments. In other words, to analyze and evaluate the dynamic security of the power grid that is heavily influenced by wind turbines, a large number of initial conditions are needed to consider the stochastic nature of wind

Table 5.4 Assessing types of stability in various papers using ML Dynamic Security Assessment and Stability Method

ML

Voltage Instability

DT Multiclass SVM k-NN SVM, RF, ANN Bayesian NN DQL Iterated RF DRNN ELM DT

Angel Instability/Transient Instability

Method

Multiclass SVM DT, RF Adaptive Ensemble DT Ensemble DT

Ref. Features Optimization

Pre-Processing and/or Feature Extraction

Classification

*

* *

*

*

*

*

Frequency Instability Converter Interfaced Instability Resonance Stability

* * *

*

* * * *

* *

*

* *

* RF Iterated RF SVM RL, NN RL, DQN Two-Level CNN based Regression Model EL Bayesian NN DQL RL, DQN k-NN, Multiple SVM RL, DT, SVM, ANN, RF, k-NN k-NN, Multiple SVM

Prediction

* * *

*

* * *

* * * * * *

* *

* *

* * * * * * * * * * * * * * *

* * *

*

[83] [110] [45] [27] [111] [18] [29] [36] [112] [19] [113] [26] [110] [30] [27] [58] [68] [114] [115] [116] [112] [46] [69] [67] [117] [118] [29] [36] [67] [76] [34] [76]

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turbine production. Therefore, the method that can perform dynamic security assessment considering a large number of basic conditions is the ML method. In [119], the effect of wind turbines on the issue of dynamic evaluation is investigated. Ref. [120] uses an innovative ML algorithm that has memory and performs realtime dynamic security assessment despite wind turbine resources in the power grid. Also, online dynamic security assessment by DT algorithm based on measuring devices in the power grid, which also included wind turbine resources, has been performed and investigated in [41].

5.4.7

An example of power flow by neural network

An example of NN for evaluating the power flow is as follows. To calculate the power flow using the NN method, the model of its neurons is as shown in Figure 5.4, which is also formulated as: Xn  w u þ b (5.1) y¼f i i i In the above formula, y represents the output of neurons; the activation function f (x) is usually a sigmoid function as f ðxÞ ¼ sig ðxÞ ¼ ð1 þ e
x Þ; ui is the input to the ith neuron; the values of wi are the weights of the inputs i, and b is also a constant value called the constant bias. The neural network has several layers, the number of these layers in the advanced neural network and DL reaches one hundred layers. Each layer itself consists of several neurons. The first layer inputs are the network inputs, but the next layer inputs are the output of the previous layers. In calculating power flow using neural networks, the primary inputs into neurons include bus voltage, bus phase angle, active and reactive powers generated by generators on the bus, and active and reactive powers absorbed by the load on the bus. Hence, six inputs are considered for each bus. Power flow problem analysis with six inputs at each bus using neural network for the IEEE 69 bus power system has 669=414 inputs. In this problem, the Levenberg–Marquardt method (leastsquares method) is used by optimizing the model f ðW ; bÞ parameters for neural network training. The objective function is as follows: Xm ½y
fi ðW ; bÞ2 (5.2) Minimize F ðW ; bÞ ¼ i¼1 i u1 u2 u3

un

w1

b

w2 w3

∑

x

f

y

wn

Figure 5.4 Model of a neuron in neural network method

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where W is the weights vector of the neurons, b is the biases vector, fi ðW ; b is the ith output, and m is the number of outputs; yi is the actual value of the ith output obtained during neural network training. The parameters p ¼ ðW ; bÞ are updated with p þ d ¼ ðW þ d1 ; b þ d2 Þ by the Levenberg–Marquardt algorithm, where d can be ascertained by the following formula:  T   (5.3) J J þ l J T J d ¼ J T ½y
f ðW ; bÞ where J is the Jacobian matrix; the ith row is as follows: Ji ¼ @fi ðW ; bÞ=@ ½W ; b

(5.4)

The data used in the neural network training process is divided into two categories (train data and test data). Training data is applied to train the neural network algorithm, and test (or validation) data is used to review and validate the outcome of the algorithm training. First, NN is over-fitted with the train data, then the training result is evaluated more accurately by validation data (new data), and finally, the obtained model can be tested using test data. NN is trained several times by training data to learn better, but the number of trainings must be determined correctly to avoid over-fitting. Finally, before the over-fitting is done, the relevant results are selected as the best neural network parameters and the training phase is stopped. In this case, the input data is six states and the output data is six states.

5.5 Future perspectives of dynamic security assessment by ML Occurrence of major blackouts in different countries due to cascading outages has highlighted the role of emergency control measures to prevent the power system from entering a state of crisis. Also, the tendency to use clean energy causes changes in the operating conditions and power passing through transmission lines. Therefore, the presence of renewable energy is effective in performing security assessments due to cascading outages of power system equipment. In addition to this, reduction in voltage and frequency instabilities and converter-based instabilities due to the presence of renewable energy and consequently power electronic converters due to their fast dynamic interaction with the components of the electrical power system is known as a challenge. In order to identify catastrophic events and severe disturbances that cause cascading failures, attempts are made to consider fast dynamic equations such as the dynamic equations of generators. Cascading outage of overloaded lines causes voltage instability in the buses and ultimately causes rotor angle instability and loss of synchronism. Also, the amount of disconnected load in the bus with voltage instability is defined as an indicator to assess the vulnerability of the network, based on which catastrophic events and vulnerable parts of the network are identified and the consequences of these events are investigated. In addition, with the occurrence of disturbances with heavy consequences in a wide area, the frequency is out of the nominal range. Therefore, to

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prevent these catastrophic events, emergency control measures are taken in the form of load shedding based on frequency and voltage. In offline conditions, taking into account changes in network topology (due to the presence of renewable energy in the system), power distribution of generators, variable power of renewable energy and load, and with SCADA data and PMU data in different parts of the power system after the occurrence of a Catastrophic event, the ML algorithm will encounter a huge amount of data. Therefore, to reduce the size of the problem, similar and duplicate SCADA and PMU data are deleted and the most appropriate data are selected. Unrelated data will also be deleted. Then the training input data (selected variables of the power system) are entered into a supervised learning algorithm and modeling the training data (part of the total data) is performed with a specific output (stable stability and dynamic security). In order to validate the created model, test data are also tested on the model. According to the conditions predicted from the previous steps, emergency control measures are performed online. Important factors, such as the proliferation of data on PMUs and the computational complexity of current and future systems operations, illustrate the limitations of common control techniques. Accordingly, it should be noted that in the control of modern and smart systems today, and especially when dealing with global and continuous blackouts, cyberattacks, and other threats, there is a need for methods with advanced, fast, dynamic, and reliable response.

5.6 Conclusions In the near future, with the emergence of new concepts, power systems will face various challenges and issues. These issues will require new methods and calculations in simulation, modeling, and implementation. Most of these new concepts are due to the existence of converter-based equipment, power electronic devices, and new and complex communication and telecommunication systems. These new devices and the expansion and sophistication of the power system generate large volumes of data that reveal the need for accurate decisions, rapid calculations and operations, and capable methods for implementing complex and real problems. Also, new and more up-to-date equipment such as various renewable energy generation, storage devices, and electronic power interfaces will influence decisions and understanding of issues. Changes in the power grid structure and the creation of smart grids may initially face obstacles and resistances, because, in addition to changes in equipment, there will also be changes in the concepts and methods of computation and simulation. Intelligent methods and the development of AI approaches such as ML effectively decrease the challenges of the modern power grid.

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Chapter 6

Converter topologies for grid-integration of renewable power sources Ashok Kumar1

Power electronics is playing a critical and decisive role in utilizing eco-friendly energy sources for feeding power to the utility grid or load. Generally, for interfacing a low voltage renewable energy source, like solar photovoltaic (PV) and small wind turbines, to an ac load or the utility grid, a line frequency transformer is used with a conventional voltage source inverter (VSI). Though it provides galvanic isolation, line frequency transformers are bulky, costly and have higher losses due to the switching harmonic currents which flow through them. The use of a high-frequency transformer mitigates the problem of reduced power density due to the decrease in the size of the magnetic core. However, the increase in the number of stages increases the losses and complexity of the inverter. Hence, transformerless inverters with buck–boost capability serve as a smaller and more efficient grid interface for renewable sources. Additionally, non-isolated micro-inverters, for PV applications, must be equipped with some specific features like common mode leakage current (CMLC) minimization and power decoupling. Moreover, in uninterruptible power supplies (UPS), buck–boost inverters are required for interfacing the battery with the load during under-voltage or power loss conditions. In its most typical form, a buck–boost inverter involves a two-stage conversion comprising a dc–dc voltage boost stage followed by voltage inversion (DC to AC) in buck-mode. However, since this has been alleged to impair overall efficiency, single-stage topologies were reported, combining the boost and inversion stages, claiming higher efficiency. Based on the number of sub-circuits involved in the production of bipolar output, single-stage inverters can be classified into three categories: quad-modal, bi-modal and uni-modal. There are four individual circuits in quad-modal, two for each half of the AC voltage, working synchronously to produce bipolar output. Similarly, bimodal inverters use two distinct topologies, while uni-modal configurations have a single circuit for generating bipolar output. In this chapter, an extensive literature survey of the existing single-stage buckboost inverters will be provided. Furthermore, the possibilities of achieving AC 1

IIT (ISM) Dhanbad, India

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output by combining two different typical dc–dc buck–boost converters, which individually produce voltages of different polarity, will be delivered. The derivation strategy for a bimodal inverter will be explained in detail. Conventional second and fourth-order dc–dc converters will be examined for the creation of inverter topology. Out of the several combinations, only a few feasible bimodal circuits are found to be suitable for a buck–boost inverter system. Some of the essential issues related to micro-inverter topologies will be illustrated.

6.1 Introduction Voltage source inverters (VSI) have been widely utilized in renewable energy applications like small wind turbines (SWT) [1] and photovoltaic (PV) systems. Apart from these, VSI plays an important role in uninterruptible power supplies, PV power pumps [2] and variable frequency drives [3]. This is primarily because of their simple control requirement to regulate the output voltage and frequency. However, the problem with conventional VSI, shown in Figure 6.1, is that it can only operate in buck mode where the peak output voltage, Vp , is always less than the input voltage, Vin , as shown in Figure 6.2. The output voltage magnitude of renewable energy sources is prone to meteorological conditions like wind speed and solar insolation in the case of SWT and PV applications, respectively.

io Lf S2

S1

Vin

Cf

+ –

+ υo –

S3

S4

Figure 6.1 Conventional VSI

υo

Vin

Buck

Vp < Vin

t

Figure 6.2 VSI operating in buck mode

LOAD

Converter topologies for grid-integration of renewable power sources υo Boost Buck

129

Vp > Vin Vin t

Figure 6.3 Operating principle of a buck–boost inverter

DC

Vin

Grid or load

+ – AC

Figure 6.4 Inverter with line-frequency transformer

Moreover, the voltages at their output terminals are also of low magnitude. Therefore, an interface which can operate in both buck and boost modes, depending on the instantaneous magnitudes of input and output voltages, is required. The basic operating principle of the buck–boost inverter (BBI) is explained in Figure 6.3. A BBI operates in buck mode when the required instantaneous output voltage vo is less than input voltage Vin and in boost mode when vo is greater than Vin . An obvious way to amplify the output voltage of a VSI is by a step-up transformer at the output end, as shown in Figure 6.4. It also provides galvanic isolation between source and grid which is good from the safety point of view. However, line-frequency transformers are bulky, costly and incur additional losses due to the switching ripple voltages applied across these. Inverter structures with highfrequency transformer are shown in Figure 6.5. These configurations mitigate the problem of reduced power density, due to the reduced size of the magnetic core. However, an increase in the number of stages increases the losses and complexity of the inverter. Hence, transformer-less inverters with an ability to buck–boost the input voltage provide a better solution. The next section describes some of the main applications of buck–boost inverters.

6.1.1 Renewable energy applications of buck–boost inverter: solar PV micro-inverters Micro-inverters or module integrated inverters interface a PV module with low output voltage, directly to the load/grid as shown in Figure 6.6. Each PV module has its own separate micro-inverter which is generally mounted at the bottom of the panel. The following issues must be addressed by BBI in the case of PV applications.

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Vin

Grid or load

+ –

(a)

Vin

Grid or load

+ –

(b)

Figure 6.5 Inverters with high-frequency transformer: (a) two stages and (b) three stages

PV module

Micro-inverter

Load / grid

Figure 6.6 PV micro-inverter

6.1.1.1

Power decoupling

In a grid-connected BBI, the grid current ig ðtÞ and grid voltage vg ðtÞ are given by ig ðtÞ ¼ I sin ðwo t þ fÞ vg ðtÞ ¼ V sin wo t;

(6.1)

where wo is the grid frequency and f is the phase difference between the injected current and the grid voltage, which is generally maintained zero for unity power factor operation. The instantaneous power is obtained as pðtÞ ¼ 0:5VIcos f þ 0:5VIcos ð2wo t þ fÞ; which contains two components: ● ●

Pav ¼ 0:5VI cos f, the constant average output power and Pac ¼ 0:5VI cos ð2wo þ fÞ, pulsating power at twice the grid frequency.

(6.2)

Converter topologies for grid-integration of renewable power sources IPV

isc

131

(uM P P , iM P P)

PM P P Paυ

Ppυ uOC ∆VPV

VPV

Figure 6.7 PV characteristics Maximum power point tracking (MPPT) algorithms maintain the average power Pav equal to the maximum power PMPP that can be drawn from the PV module. However, the oscillating power Pac also causes a double frequency current drawn from the PV module. Being a weak source, this results in fluctuation of the PV voltage VPV which ultimately reduces average power Pav , as shown in Figure 6.7 [4]. Consequently, the utilization factor KPV , which indicates the extent useful power is harvested from a PV module and expressed as KPV ¼

Pav ; PMPP

(6.3)

decrease. To mitigate this problem, a power decoupling capacitor CPV is used which supplies the double frequency content of the input current. Value of decoupling capacitor depends on the amount of energy that must be stored and is given by [5] CPV ¼

Pdc ; 2pfUdc DUdc

(6.4)

where f is the line frequency, Pdc is the rated power from the PV panel, Udc is the mean voltage across the capacitor, and DUdc is the allowed peak-to-peak variations in the capacitor voltage. As expressed in (6.4), for a micro-inverter with a given power rating and line frequency, the size of the decoupling capacitor is determined by the average capacitor voltage and maximum allowable voltage variation. In a single-stage inverter, the only dc port available for placement of CPV is at the low-voltage input end, across the terminals of the PV panel. The first stage of a two-stage BBI boosts the low PV output voltage to an appropriate high magnitude depending on the grid voltage. Here, CPV is placed at the high voltage port after the front-end boost stage. Single and two-stage micro-inverters, along with CPV , are shown in Figure 6.8(a) and (b), respectively. It is reported that the utilization factor of 0.98 can be achieved if the PV terminal voltage variation DUPV is restricted below 8.5% of the mean value [5]. The values of CPV at low and high-voltage ports are evaluated for a 2% voltage ripple. CPV values, along with the considered specifications, are listed in Table 6.1 for both the cases, where, CPV ðlowÞ and CPV ðhighÞ are CPV values at low-voltage UdcðlowÞ and high-voltage UdcðhighÞ ports, respectively.

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Power electronics for next-generation drives and energy systems: Volume 2 iAC

ipv

ig

iCpv

GRID

Single Stage Inverter

PV Cpv Panel Low voltage port

(a) iDC

ipv

PV Panel

DC-DC Boost

iAC

ig GRID

iCpv VSI Cpv High voltage port

(b)

Figure 6.8 Power decoupling in (a) single and (b) two-stage BBIs using decoupling capacitor CPV Table 6.1 CPV values at low- and high-voltage ports P dc

f

U dcðlowÞ

U dcðhighÞ

DU dc

C PV ðlowÞ

C PV ðhighÞ

300 W

50 Hz

48 V

400 V

2%

4.14 mF

60 mF

Clearly, the value of CPV ðlowÞ is much higher than CPV ðhighÞ . Moreover, CPV ðlowÞ requires large electrolytic capacitors which are bulkier and have lesser life as compared to film capacitors which can be used for implementing CPV ðhighÞ . Hence, in terms of power decoupling capacitor requirement, two stage micro-inverters have an edge over single stage ones.

6.1.1.2

Common mode leakage current

For safety reasons, the PV module frame is connected to the ground [6], which leads to the formation of parasitic capacitance, CPV G , between PV cells and the ground, as shown in Figure 6.9. As PV panels have a large surface area and the distance between cells and frame is less, the magnitude of CPV G is considerable. For crystalline silicon cells, capacitance up to 50–150 nF/kW has been reported, while for thin-film modules, values up to 1 F/kW have been measured [7]. Also, at the grid end, neutral is grounded. So, in the case of non-isolated microinverters, a closed path is formed and voltage fluctuation across CPV G causes the flow of common mode leakage current, as shown in Figure 6.10, where ZG represents the total impedance between the grid neutral and earth, in the vicinity of the PV panel. This includes the neutral earthing impedance, which is usually low as per

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capacitance

Figure 6.9 Formation of parasitic capacitor between PV cells and ground Filter Lf 1

P

ig Lf 2

CPV + VPV

S1

N

–

Q S3 CPV–G

υg

S2 L

Cf

S4 CMLC

ZG

Figure 6.10 Conventional VSI in grid-connected mode the standard grid codes [6], and earth resistance between the neutral earthing pit and the PV location. P and Q are the positive and negative terminals of the PV panel, respectively, with Q directly connected to the neutral wire (N) of the grid. Expression for the common mode voltage, appearing across CPV G , is given by [8] vCM ¼

vLQ þ vNQ 2

(6.5)

where vLQ and vNQ are the voltages of terminals L and N with respect to Q, respectively. The presence of a switching frequency component in vCM results in the high common mode leakage current (CMLC) which gives rise to the following problems [9,10]: ● ● ● ●

Reduction in the life of PV panel Additional inverter losses Distortion in grid current Increased electromagnetic interference (EMI)

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VPV

+ –

L

N

VPV

+ –

N

Q

Q (a)

L

(b)

Figure 6.11 Equivalent circuits during bipolar modulation when (a) S1 ¼ S4 ¼ 1, S2 ¼ S3 ¼ 0 and (b) S1 ¼ S4 ¼ 0, S2 ¼ S3 ¼ 1 However, leakage current can be eliminated by keeping the common mode voltage vCM constant during the operation which can be achieved through careful selection of modulation strategy. In bipolar PWM, the diagonal switch pairs (S1;4 , S2;3 ) are triggered alternately and the bridge output voltage varies between þVPV and VPV . The equivalent circuits during the operation are shown in Figure 6.11. The operation can be described as ● ●

Figure 6.11(a): S1 ¼ S4 ¼ ON, S2 ¼ S3 ¼ OFF, vLN ¼ VPV , vCM ¼ 0:5VPV Figure 6.11(b): S1 ¼ S4 ¼ OFF, S2 ¼ S3 ¼ ON , vLN ¼ VPV , vCM ¼ 0:5VPV ,

which clearly indicates that whatever the switching state common mode voltage vCM ¼ 0:5VPV . Hence, bipolar modulation results in zero CMLC. But this brings problems such as large grid current ripple, high harmonic content and poor efficiency due to increased switching losses [11]. In unipolar modulation, switches of one leg are triggered at grid frequency whereas other leg switches are commutating at the switching frequency which results reduction in overall switching losses. Also, the magnitude of voltage acting across filter is reduced to half of that of bipolar modulation which eventually deceases the size of filter. Equivalent circuits during unipolar modulation are shown in Figure 6.12 and the operation can be explained as ● ● ● ●

Figure Figure Figure Figure

6.12(a): S1 ¼ S4 ¼ ON, S2 ¼ S3 ¼ OFF, vLN 6.12(b): S1 ¼ S2 ¼ OFF, S3 ¼ S4 ¼ ON , vLN 6.12(c): S1 ¼ S4 ¼ OFF, S2 ¼ S3 ¼ ON , vLN 6.12(d): S1 ¼ S2 ¼ ON, S3 ¼ S4 ¼ OFF, vLN

¼ VPV , vCM ¼ 0:5VPV ¼ 0, vCM ¼ 0, ¼ VPV , vCM ¼ 0:5VPV ¼ 0, vCM ¼ 0.

Hence, the common-mode voltage oscillates between 0 and VPV, and a large ground leakage current will flow. This problem was proposed to be resolved by disconnecting the positive and negative terminals of PV from the freewheeling path during zero output voltage level generation [8]. Networks that perform this task are classified into two categories based on the method of decoupling, i.e., dc or ac. The dc-based decoupling configurations incorporate the decoupling switches at the dc side to provide the decoupled freewheeling path, examples being H5 [12], H6 [13], shown in Figure 6.13. The extra switches S5;6 commute at grid frequency and during zero state the common mode voltage is vCM ¼ 0:5VPV .

Converter topologies for grid-integration of renewable power sources

VPV

+ –

L

N

VPV

+ –

Q

L

Q (a)

VPV

+ –

N

L

(b)

N

VPV

+ –

L

N

Q

Q (c)

(d)

Figure 6.12 Equivalent circuits during unipolar modulation when (a) S1 ¼ S4 ¼ 1, S2 ¼ S3 ¼ 0. (b) S1 ¼ S2 ¼ 0, S3 ¼ S4 ¼ 1. (c) S1 ¼ S4 ¼ 0, S2 ¼ S3 ¼ 1. (d) S1 ¼ S2 ¼ 1, S3 ¼ S4 ¼ 0

(a)

(b)

Figure 6.13 DC decoupling-based inverters (a) H5 and (b) H6

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Power electronics for next-generation drives and energy systems: Volume 2

Figure 6.14 AC decoupling in HERIC inverter

D4

S4

S1 L S5 + –

D3 S2

D5 C

LOAD

Vin

S3

Figure 6.15 Doubly grounded topology Hence, throughout the operation, the common mode voltage vCM remains unchanged. Solutions such as HERIC [14], shown in Figure 6.14, on the other hand, include the decoupling switches at the ac side. However, due to the presence of switch junction capacitances, the variation common mode voltage is generated which results in finite CMLC. Moreover, all these topologies operate in buck mode. A class of reported topologies share one common terminal between PV and grid which ensures zero CMLC. In literature, these are also known as doubly or dual grounded inverters [15]. One such topology is shown in Figure 15 [15]. Figure 6.16 shows the generalized configuration of a common-node single-phase PV inverter interfaced with the ac grid. For any common-node topology, it is obvious from Figure 6.16 that for all switching states of the inverter, vLQ = vg and vNQ =0. Hence, the common-mode voltage vCM ¼

vg 2

(6.6)

does not exhibit any high-frequency component, which practically eliminates the problem of CMLC. Based on the number of power processing stages, BBIs can be mainly classified in two categories: (a) two stage and (b) single stage. This chapter focusses on single-stage BBIs whose state of the art is described in the next section.

Converter topologies for grid-integration of renewable power sources POWER INVERTER

L

P VPV

137

υg

ig GRID

PV Q

N CPV-G

ZG

Figure 6.16 Grid connected common-node PV inverter: general schematic

DC

Vin

Grid or load

+ – AC

Figure 6.17 Single stage BBI structure

6.2 State of the art In single-stage buck–boost inverters, as shown in Figure 6.17, voltage boosting and grid-current shaping/output voltage regulation in grid-connected/stand-alone mode are performed in a single power processing stage. The main advantages of the single-stage configuration are lower part count and improved efficiency because of the presence only one stage. However, the main disadvantage of the single-stage configuration is the low-voltage gain. Moreover, since a single stage is responsible for boosting and regulating the output variable, the employed control system becomes complicated. All the single stage BBI can be classified based on the operating modes involved to produce sinusoidal gird current/output voltage. Now, the review of single stage inverters based on their operating modes is presented.

6.2.1 Review based on operating modes of single-stage BBI As described in Figure 6.18, there are four modes of operation depending on the magnitude of output voltage which are listed below: 1. 2.

vo > 0 and jvo j < Vin (T1 ; T3 ) vo > 0 and jvo j > Vin (T2 )

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Power electronics for next-generation drives and energy systems: Volume 2 υo

Vp > Vin Vin t

T1

T2

T3 T4

T5

T6

Figure 6.18 Operating modes of a buck–boost inverter

i1

i2

DBoost SBuck

L1 Vin

C SBoost

S3

L2 DBuck

ig

S1 S2

S4

Vg

Figure 6.19 Cascaded connection of buck and boost converters with current unfolding H-bridge 3. 4.

vo < 0 and jvo j < Vin (T4 ; T6 ) vo < 0 and jvo j > Vin (T5 )

Based on these modes and the equivalent circuits involved to produce bipolar output, single-stage BBIs can be categorized in three ways:

6.2.1.1

Quad-modal BBI

These inverters have four different circuits which are systematically combined to generate the appropriate output voltage during all the modes. Simplest quad-mode BBI is reported in [16] which is shown in Figure 6.19. It is a cascaded connection of boost and buck converters. Grid current shaping is done by controlling the buck inductor (L2 ) current i2 . Current unfolding is performed by the H-bridge formed using switches S14 . The switch pairs S1;4 and S2;3 operate at power frequency for attaining bipolar grid current. For buck operation, boost switch SBoost is OFF and buck switch SBuck receives high switching frequency PWM pulses. In boost mode, SBuck is turned-ON and SBoost is triggered at switching frequency PWM pulses. Hence, this inverter has separate buck and boost mode equivalent circuits during positive and negative halves of grid current. A modified cascaded buck–boost structure was proposed as shown in Figure 6.20 [17] which has a common inductor for buck and boost modes. Two such structures are connected in parallel with separate voltage sources which operate synchronously to produce bipolar output. For positive half, switch S4 is

Converter topologies for grid-integration of renewable power sources LP

Lf

D2 S3

S1

Vin1

S2

Vg

Cf

D1

Vin2

139

S5 D3 S6

D4

LN

S4

Figure 6.20 Modified cascaded buck–boost structure with two inductors and two input sources

AC/AC unit

Vin

S1

S2

C S5

S3

S4

S8 LOAD

S7

L

S6

Figure 6.21 Modified cascaded buck–boost structure with one inductor and one input source kept completely OFF. During buck operation, switches S2 and S3 are turned OFF and ON, respectively, and S1 receives the PWM pulses. For positive half, switches S4 , S5 , and S6 are kept completely OFF. For buck operation, switches S2 and S3 are turned OFF and ON, respectively, and S1 receives the PWM pulses. For boosting, S2 is triggered with PWM pulses and switch pair (S1;3 ) is ON. Similarly, in the negative half, the lower limb of the inverter with input voltage Vin2 is operated. However, as only one power source is utilized over a half cycle, the net output power drawn from the source decreases. Another quad-mode inverter is shown in Figure 6.21 [18]. Again, it can be seen as the improved version of the above circuit with only one inductor being utilized for both the halves of output voltage. During positive half, switches S4 , S5 , and S8 are turned-ON, and switch S2 is turned-OFF. To operate in buck mode, switches S6 and S7 are turned-OFF and ON, respectively, and switches S1 and S3 are operated with complementary PWM pulses. During the boost mode, S1 is always turned-ON and, switches S6 and S7 receive high-frequency complementary switching pulses, and switch S3 is OFF. Similarly, for negative half, S3 , S6 , and S7 are ON and S1 is OFF. In the buck mode, switch pair (S2 ; S4 ) are gated by complementary PWM

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pulses, and switches S5 and S8 are turned OFF and ON, respectively. To operate in boost mode, switch pair (S2 ; S3 ) and switch S4 are always ON and OFF, respectively, and the switch pair (S5 ; S8 ) receives complementary PWM pulses.

6.2.1.2

Bi-modal BBI

Inverters which incorporate only two sub-topologies for producing the two bipolar halves of the output variable come under this category. The inverter circuit shown in Figure 6.21 can also operate in buck–boost mode with two equivalent circuits for bipolar output voltage. The modified switching strategy is explained in [19]. The polarity of input voltage is changed by the H-bridge, formed by switches S14 , which operate at power frequency. As a result, the inverter operates with two buck– boost converter fed with positive and negative to obtain the bipolar output. A second-order bi-modal BBI [15] is shown in Figure 6.22. It is created by combining buck–boost and non-inverting buck–boost dc–dc converters. The switches are appropriately positioned so that their individual unipolar outputs can be synchronized to achieve bipolar output voltage. To obtain positive output, switch S4 and switch pair (S3;5 ) are completely turned-OFF and ON, respectively. Switches S1;2 are triggered at high frequency PWM signal. Similarly, for the negative half, switch pairs (S2;4 ) and (S3;5 ) are turned-ON and OFF, respectively, and switch S1 receives high-frequency PWM. Another configuration obtained from the parallel connection of two buck– boost dc–dc converter is reported in [20] which is shown in Figure 6.23. These converters are individually fed by different power sources and operated alternatively to obtain bipolar output. But, when one converter is operating, the other is idle, which leads to the under-utilization of both the sources. On similar lines, a BBI, as shown in Figure 6.24 [21], uses only one power source to produce bipolar output. The individual buck–boost converters operate in discontinuous conduction mode (DCM). In the positive half, Sp1 operates at high frequency and Sp2 is always ON. Remaining active switches are OFF. Similarly, for the negative half, Sn1 operates at high frequency and Sn2 is always ON, and switch pair Sp1;p1 are completely OFF. Above bi-modal BBIs are obtained from second order buck–boost dc–dc converter. The possibility of connecting two fourth-order buck–boost converters are explored in [22] and [23], and their circuit diagrams are shown in Figures 6.25

L

Vin

S5 D3

S2

S4 D5 C

LOAD

D4 S1

S3

Figure 6.22 Bi-modal BBI with non-inverting and inverting buck–boost converters

Converter topologies for grid-integration of renewable power sources Lf

D1 S1

Vin1

141

S2

L1

Vg

Cf

Vin2 L2 S3

D2 S4

Figure 6.23 Bi-modal BBI with two buck–boost converters and two input sources

Sn2

Vin

Lf

Dp

L1

Cf

Sp1

Vg

Sn1 Dn

L2

Sp2

Figure 6.24 Bi-modal BBI with two buck–boost converters and one input source

L1

L2 S2

Vin

C1

S4

S1

C2 S3

Vg

S5 S6

´ uk derived bi-modal BBI with six MOSFETs Figure 6.25 C

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Power electronics for next-generation drives and energy systems: Volume 2 L1

L2 S2

C1

S4

S1

Vin

C2

Vg

S5

S3

Figure 6.26 C´uk derived bi-modal BBI with five MOSFETs υo LOAD

υo LOAD

S1 C1

S2 L1

L2

S1 C2

S2

C1

C2 S3

S4 L2

S3

Vin

Vin

S4

(a)

(b)

Figure 6.27 Single mode BBIs with differential connection of two: (a) boost converters and (b) buck–boost converters and 6.26, respectively. These inverters are combinations of inverting and noninverting C´uk converters [24]. Similar to previous bi-modal BBIs, the switching sequence of these inverters is arranged to obtain bipolar output by alternatively operating individual converters. The only difference between these two inverters is in their switch counts; [22] requires six devices whereas [23] needs five. In Figure 6.25 [22], during positive half, switch pair (S2 ; S5 ) and switch S6 are OFF and ON, respectively, and switches (S1 ; S3 ; S4 ) and S6 are gated for DTs and ð1  DÞTs durations, respectively. For negative output, S3 , S4 , and S6 are always OFF, and S5 is ON whereas switches (S1 ; S2 ) are gated for DTs period. In Figure 26 [23], for positive output, switch pair (S1 ; S2 ) receives complementary PWM pulses, and switch pair (S3 ; S4 ) is ON and S5 is OFF. For negative output, C´uk converter is formed by turning switch pair (S2 ; S5 ) ON and S4 is OFF, and switch pair (S1 ; S3 ) triggered by complementary PWM pulses.

6.2.1.3

Single-mode BBI

These inverters have only one circuit to synthesis the output variable. By varying the control input to the inverter, bipolar output can be directly generated. Most common inverters that fall under this category are differential connection of boost [25] and buck–boost [26] converters which are shown in Figure 6.27(a) and (b), respectively. In both the inverters, switch pair S2;3 is turned-ON for DTs duration

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143

Figure 6.28 Variation of voltage gain Adiff with duty ratio D for differential boost and buck–boost inverters υo LOAD S2

S1

C1

L2

n2

n2

S3

n1

n1

L1

L1

Vin

L2

C2

S4

Figure 6.29 Single mode BBI obtained from differential connection two coupled inductor boost converters

and, for the remaining part of the switching period Ts , switch pair S1;4 is conducted. The voltage gain of these converters is Adiff ¼

2D  1 : Dð1  DÞ

(6.7)

A plot of voltage gain Adiff variation with duty ratio D is shown in Figure 6.28. Clearly, by varying duty ratio around 0.5 bipolar output voltage can be obtained. Differential boost converter with coupled inductors is proposed in [27], and its configuration is shown in Figure 6.29. The improved voltage gain is given by Adiff ðCLÞ ¼

nð2D  1Þ ; Dð1  DÞ

(6.8)

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Power electronics for next-generation drives and energy systems: Volume 2

S1

Vin

Lf

C1 L1

S3

LOAD

C2 Cf

S2

L2

S4

Figure 6.30 Single-mode BBI with the same voltage gain as differentially connected boost converters

D1 n2

S1 L2 D

n1 Vin

L1

S2 Lf

2

Cf

D3

S3

υo

LOAD

Cdc

Vdc

S4

Figure 6.31 Single mode BBI with coupled inductor boost converter and VSI

which allows enhancement in the voltage gain by selecting proper value of turns ratio n. Another inverter [28] with the same voltage gain as (6.7) is shown in Figure 6.30. The switching strategy of the inverter is same as above mentioned single mode inverters. Figure 6.31 shows the circuit diagram of another single mode inverter [29] which operates in a manner similar to a conventional VSI. However, a cascaded winding coupled inductor boost converter with output voltage Vdc is present. Voltage Vdc also acts as the dc-link voltage of the inverter which allows buck–boost operation. The overall voltage gain of the inverter is A1modeðvsiÞ ¼

ð1 þ nDbst ÞDbk ; 1  Dbst

(6.9)

where nðn2 =n1 Þ is turns ratio of coupled inductors, Dbst is duty ratio of the boost circuit and the duty ratio Dbk appears due to the buck gain of VSI.

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6.2.2 Review of single-stage inverters: salient points Based on the literature survey presented previously, the following points are noted. ●

●

●

●

●

Greater the number of operating equivalent circuits, greater is the complexity in dynamic modeling of the inverters. Hence, the controller design becomes difficult. In this regard, quad and bi-modal operation results in complex controller design and PWM strategy. Single-mode inverters have simpler switching strategy and their controller design is comparatively easier. However, all the switches operate at high switching frequency which causes higher switching loss. Single stage inverters [15, 17, 20, 22, 23] are doubly grounded which alleviates CMLC. In case of single-mode inverters [25–27], only grid frequency component exists in the common mode voltage vCM which alleviates CMLC. Single-stage inverters are often compared with typical two-stage inverter comprising conventional boost dc–dc converter and VSI. The later inverter system uses six total semiconductor devices. Some single-stage inverters like [15–17] use even more devices than boost+VSI inverter. Although some devices are operating at power frequency, which ensures reduction in losses, but more component count incurs increased cost of system.

6.3 Case study: bi-modal fourth-order inverters – derivation and working principle This section explores the possibility of new inverter circuits using existing fourthorder dc–dc buck–boost converters. First, the motivation to go for fourth-order converters is stated, and then their synthesis from the conventional buck–boost converter is described. Two dc–dc circuits which individually produce outputs of opposite polarity are intelligently combined to generate an ac output by using the least possible number of semiconductor devices. A necessary criterion for a pair of dc–dc converters to operate as a bimodal inverter is identified. Two circuits are found suitable for inverter operation. Their distinctive features are discussed later which eventually leads to the best possible inverter configuration.

6.3.1 Why fourth-order converters? Standard second-order inverting and non-inverting circuits are shown in Figure 6.32(a) and (b), respectively. The combination of these two is already reported in [15] which has the problem of discontinuous input and output currents. Moreover, it uses more number of semiconductor devices than a typical two-stage boost-VSI structure. Furthermore, only two bimodal inverters are reported in literature [22–23], which use fourth-order C´uk converter for operating in inverting and non-inverting mode. This section explores the formation of bimodal inverters by using other fourth-order buck–boost dc–dc converters.

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Power electronics for next-generation drives and energy systems: Volume 2 1

S1

2 S

S2

2

L

Vin

2

Vin L

I 1

I

V C

(a)

1 C

R

V

R

(b)

Figure 6.32 Second-order dc–dc buck–boost converters: (a) inverting and (b) non-inverting

Vin

Buck–boost Converter-1

Vo+ Vin

Vin

Buck–boost Converter-2

Buck–boost Vo Inverter

Vo–

Figure 6.33 Principle of bi-modal inverter derivation

6.3.2

Basic requirements

The block diagram of the derivation principle is shown in Figure 6.33. Two converters with outputs of opposite polarity are combined to form the inverter circuit. All single-stage buck–boost inverters, where the generation of bi-polar output voltage requires transition between two converters, must satisfy following two conditions: ●

●

Buck–boost voltage gain: Both the individual converters must have suitable voltage gain for performing buck–boost scaling of the input voltage. Zero crossing states continuity: At the zero crossing of the output voltage, when the transition between two converters takes place, the states of individual converters must be continuous. This to avoid high dv=dt or di=dt, in capacitor voltage or inductor current, which inevitably leads to zero crossing distortion (ZCD) in output voltage/current [30] and adversely effects on the quality of output. Moreover, ZCD can cause resonant interaction between the grid impedance and inverter filter capacitor. Additionally, with a large number of parallel-connected inverters interfacing a weak grid, it can cause voltage distortion that may be large enough to disturb other nearby equipment [30].

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147

6.3.3 Merging two converters for bipolar output Any two dc–dc converters producing output voltages of opposite polarity can be combined by using semiconductor devices, without significantly increasing device count. To fix the upper limit of switch count, single-stage inverters are often compared with typical two-stage boost-VSI (B-VSI) inverter systems, shown in Figure 6.34, which has total five active devices and one diode. So, let us fix a conservative number of six as the upper limit of total active switching devices. However, other factors like the number of devices undergoing switching at high frequency/conduction at any time interval also decide the overall voltage gain and efficiency of the inverter. These points will be studied during evaluation of the feasible circuits. Before going to fourth-order converters, let us first see how the simplest reported non-isolated second-order buck–boost inverter is developed.

6.3.4 Second-order buck–boost inverter Schematic of second-order buck–boost inverter (SOBBI) is shown in Figure 6.35(a), which is termed as a bi-modal inverter (BMI) because of two different circuits are L

D1

io

i

Lf S1

Vin

V

C

+

S4

S2

Cf

-

S3

+

υo

-

LOAD

S5

Figure 6.34 Conventional two stage boost + VSI BBI

υref t

D4 S1

i

Vin

L

S5 D3

S2

io

S4 D5 υ

C

LOAD

S

t

S

t

ON

t

OFF

S3 (a)

t

fp = 1/Tp (b)

Figure 6.35 (a) Second-order buck–boost inverter and its and (b) switching pulses

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Power electronics for next-generation drives and energy systems: Volume 2

Table 6.2 Switching sequence for SOBBI

SOBBI

BB NIBB

S

S

ON

OFF

S1 S1 ; S2

– –

S2 ; S4 S3 ; S5

S3 ; S5 S4

io D4

S1 i

L

S2

S1

D5

Vin D3

υ

(a)

io

C

LOAD

Vin i

L

υ

C LOAD

(b)

Figure 6.36 Operational modes of SOBBI: (a) positive mode (NIBB) and (b) negative mode (BB) working synchronously for positive and negative halves (modes) of the output voltage. It contains five MOSFETs and three diodes. The switching sequence to get bipolar output is shown in Table 6.2, where ON and OFF denote complementary signals at power frequency, fp , and, S and S represents complementary PWM pulses at the switching frequency, fs . All these gate pulses are illustrated in Figure 6.35(b). The equivalent circuits as a result of this switching pattern for two halves are shown in Figure 6.36(a) and (b), respectively, which are identical to non-inverting (NIBB) and inverting buck–boost (BB) topologies, respectively. Magnitude of voltage gain is the same for both the halves and is expressed as Av ¼

D : 1D

(6.10)

Hence, the corresponding variation of duty ratio, without considering internal losses to attain an output voltage, v = Vm sin wt, is given by dðtÞ ¼

Vm j sin wtj : Vin þ Vm j sin wtj

(6.11)

Under these ideal conditions, it is observed that a solution of (6.11) always exists in the range, 0  d  1. Maximum duty ratio Dm is defined by the peak output and input dc voltages, which is expressed as Dm ¼

Vm : Vin þ Vm

(6.12)

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Table 6.3 Inverter specification V in

P

fs

fp

110 V

400 W

50 kHz

50 Hz

Output voltage (v)

60 V

V

200

0

–200 0

0.005

0.01

0.015

0.02

0.005

0.01 Time (sec)

0.015

0.02

Duty ratio (d)

1

0.5

0 0

Figure 6.37 Duty ratio variation with output voltage

Table 6.4 DC values of states at zero crossing of SOBBI Expression

NIBB BB

(D = 0)

V

I

V

I

VinD 1D VinD 1D

Io 1D

0

Io

Io 1D

0

Io

The required variation of duty ratio D, for specifications mentioned in Table 6.3, is shown in Figure 6.37. Duty ratio attains zero and maximum values at zero-crossing and positive/negative peaks of the output ac voltage, respectively. To check the continuity of states at zero crossing, the corresponding dc values for D ¼ 0 are calculated and mentioned in Table 6.4, which shows that both the states are continuous, regardless the nature of load. Therefore, both conditions for the feasibility of inverter operation are fulfilled.

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Power electronics for next-generation drives and energy systems: Volume 2

6.3.5

Possible combinations with fourth-order converters

Derivations of five dc–dc fourth-order converter are just discussed which have identical absolute voltage gain. After switch realization, the circuits of these converters are shown in Figure 6.38 which can be grouped according to their output polarity. Observing all these converters, the following two inferences can be made: ● ●

All these converters have same position of intermediate and output capacitors. Apart from the switches, these only defer in the position of inductors.

It should be noted that more the differences in position of inductors, more the switching devices required for their combining. Therefore, converters are further sorted based on position of inductors, shown in Figure 6.39. To produce bi-polar +

_ +

_

_ +

+ _

R

(a)

(b) +

_

+

+ _

R

R

(c)

_

_ +

_

R

(d)

+

R

(e)

´ uk, (b) SEPIC, (c) ZETA, Figure 6.38 Fourth-order buck–boost converters: (a) C (d) CSC-IL, and (e) CSC-OL Input Inductor Ćuk, CSC-IL

SEPIC Positive Output

Negative Output Ćuk, CSC-OL

ZETA

Output Inductor

Figure 6.39 Grouping of converters based on their output voltage polarity and position of inductors

Converter topologies for grid-integration of renewable power sources

151

voltage, two converters, which generate output voltages of opposite signs separately are to be integrated. Therefore, it can be concluded that SEPIC can be combined with C´uk and CSC-IL. Similarly, combinations of ZETA with C´uk and CSC-OL are possible. Hence, in total, there are four feasible combinations. Let us now examine the continuity of states at the output transition instants for each of these pairs. The large signal equations, mentioned in Table 6.5, where d is the instantaneous duty ratio and d 0 ¼ 1  d, are used to derive the steady-state expressions of converters, which are listed in Table 6.6. Following the same lines as in SOBBI, the dc values for D ¼ 0 are listed in Table 6.6. All converters, except ´ uk ZETA, have V1 ¼ Vin for D ¼ 0. So, the possibilities of pairing the ZETA with C and CSC-OL converters are eliminated. Finally, it converges to only two feasible ´ uk) and (SEPIC+CSC-IL). groups namely (SEPIC+C

Table 6.5 Large signal equations for dc–dc fourth order buck–boost converters Converters (a) C´uk

L1

di1 dt

L2

vin  v1 d 0

di2 dt

C1

v1 d  v2 0

C2

dv2 dt

i1 d 0  i2 d

i2  io

i1 d 0  i2 d

ði1 þ i2 Þd 0  io

(b) SEPIC

vin  ðv1 þ v2 Þd

(c) Zeta

vin d þ v1 d 0

ðvin  v1 Þd  v2

i2 d  i1 d 0

i2  io

(d) CSC-IL

vin  v1 þ v2

v1 d  v2

i1  i2 d

i2  i1  io

(e) CSC-OL

vin  v1 d

0

v1 d  v2 d

0

dv1 dt

0

vin  v1  v2

i1 d  i2

i2  io

Table 6.6 Steady-state expressions and corresponding dc values of various states at zero crossing of dc–dc buck–boost converters Converters

´ uk C CSC-IL CSC-OL SEPIC ZETA

Expressions

(D = 0)

I1

I2

V1

V2

I1

I2

V1

V2

Io D 1D Io D 1D Io 1D Io D 1D Io D 1D

Io

Vin 1D Vin 1D Vin 1D Vin

Vin D 1D Vin D 1D Vin D 1D Vin D 1D Vin D 1D

0

Io

Vin

0

0

Io

Vin

0

Io

Io

Vin

0

0

Io

Vin

0

0

Io

0

0

Io 1D Io Io Io



Vin D 1D

152

6.3.6

Power electronics for next-generation drives and energy systems: Volume 2

Schematics of two new inverter circuits

The complete circuit of the two feasible inverter configurations are shown in Figure 6.40, which are described below.

6.3.7

Topology-1 (C´uk+SEPIC)

This circuit is formed by combining SEPIC and C´uk converters with the help of five switches and two diodes. Switch status for realizing positive and negative halves of the output voltage mentioned in Table 6.7. Equivalent circuits of T-1 during positive and negative halves are shown in Figure 6.41(a) and (b),

_

(a)

LOAD

LOAD

+

(b)

Figure 6.40 Inverter schematics: (a) C´uk+SEPIC (T-1) and (b) CSC-IL+SEPIC (T-2) Table 6.7 Switch status for realizing bipolar output in bi-modal inverters

T-1

S

ON

OFF

S1 S1 S1 S2

– – S4 S3

S3 ; S5 S2 ; S4 S3 S4

S2 ; S4 S3 ; S5 S2 S1

(a)

LOAD

LOAD

T-2

SEPIC C´uk SEPIC CSC-IL

S

(b)

Figure 6.41 Topology-1 equivalent circuits during: (a) positive and (b) negative halves

Converter topologies for grid-integration of renewable power sources

153

respectively. During positive half, S1 and D5 , and in other half S1 and D2 operate at high frequency. Apart from S1 , all other switches are triggered at power frequency. Diode current in both the operating circuits is the sum of inductor currents, I1 þ I2 , which is given as ID2;5 ¼

Io : 1D

(6.13)

Therefore, reversal of current in any half of output voltage is not possible. This happens during time slots 1 and 3 of non-upf loads, as shown in Figure 6.42. Hence, this circuit works fine for slots 2 and 4 when the load current and the output voltage are in phase, but is not suitable for non-upf loads due to the presence unidirectional devices D2 and D5 .

6.3.7.1 Dead-time operation In the necessary dead-time interval between S3 and S4 to avoid short circuit of output capacitor C2 , at mode transitions both these switches are OFF. However, during dead-time, no free-wheeling path is available for the current i2 . Therefore, two more diodes are placed in series with S3 and S4 , as shown in Figure 6.43. As a result, even an accidental overlap of the switches does not cause a short-circuit of capacitor C2 . These diodes only block voltages near the zero crossing of output Negative

Positive

Slot-1

Slot-2

Slot-3

Slot-4

Figure 6.42 Different cases for non-upf loads

Figure 6.43 Final version of topology-1 with diodes D3 and D4

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Power electronics for next-generation drives and energy systems: Volume 2

voltage and during rest of the time they are ON and carry series MOSFET current. Hence, two more diodes are added in the final circuit and its schematic is shown in Figure 6.43.

6.3.8

Topology-2 (CSC-IL+SEPIC)

This inverter circuit is a combination of SEPIC and CSC-IL by using only four active switches. Switch status for positive and negative output voltages are listed in Table 6.7 and corresponding equivalent circuits are shown in Figure 6.44. During both the halves, two switches receive high-frequency PWM pulses and the remaining are triggered at power frequency. This results in the equivalent circuits with synchronous MOSFETs which enables operation for all the slots shown in Figure 6.42. Consequently, this inverter has the capability to power both upf and non-upf loads. For only upf loads, supplying high-frequency PWM pulses to only one switch is sufficient which will introduce one diode instead of active switch in the equivalent circuits.

6.3.8.1

Dead time operation

During both halves of output voltage, the operation gives rise to converters with synchronous switches. Hence, there is need of dead-time between their gate pulses. ●

●

Positive half (SEPIC): Equivalent circuit during the dead-time intervals for positive half is shown in Figure 6.45(a), when no switch, except S3 , is in conduction. Body diodes, D1 and D4 , provide the freewheeling path to inductor currents. Their conduction is decided by the direction of (i1 + i2 ) which depends on the sign of io . For io > 0, (i1 þ i2 ) > 0, D4 conducts while for io < 0, D1 provides the freewheel path. Negative half (CSC-IL): For all dead-time intervals during negative half of output voltage, all switches, except S4 , are OFF. The equivalent circuit is shown in Figure 6.45(b). The conduction of freewheeling diodes is decided by the direction of i2 which, in turn, is a function of io . For io < 0, i2 < 0 which brings D2 in conduction while D3 conducts when io > 0.

The complete dead-time operation is illustrated in Figure 6.46. _

+

(a)

_

LOAD

LOAD

+

(b)

Figure 6.44 Topology-2 equivalent circuits during: (a) positive and (b) negative halves

Converter topologies for grid-integration of renewable power sources

(a)

155

(b)

Figure 6.45 Dead-time circuits during: (a) positive and (b) negative halves

Figure 6.46 Operation during dead-time of topology-2

6.3.9 Comparison among SOBBI, topologies-1 and 2 inverters ●

Magnetics size: Table 6.8 shows the expressions of rms and peak inductor currents for all the inverters, where x¼

Vm : Vin

(6.14)

Inductor size is decided by its area product which is given by Ap ¼

LIpk Irms ; JKBmax

(6.15)

where L is the desired inductor value, Ipk is the peak inductor current, Irms is the rms value of the inductor current, J is the current density, K is the fill factor and Bmax is

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Power electronics for next-generation drives and energy systems: Volume 2

Table 6.8 RMS and peak inductor currents of bi-modal inverters Topology-1 Expression

i1

RMS

Peak

i2

RMS

Peak

pffiffiffi xP 3 2V2 pffiffiffi 2PDm ð1  Dm ÞV2 P V2

Num (A) 8.15

13.22

3.63

pffiffiffi 2P V2

5.13

SiRMS iPeak

126.36

Topology-2 Expression pffiffiffi xP 3 2V2 pffiffiffi 2PDm ð1  Dm ÞV2 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 3x2 4x þ2 þ V2 3p 4 pffiffiffi 2P ð1  Dm ÞV2

SOBBI Num (A)

Expression

8.15

2P V2

13.22

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3x2 2x 1 þ þ 3p 2 8 pffiffiffi 2P ð1  Dm ÞV2

Num (A) 15.27

18.36

10.35

–

–

18.36

–

–

297.77

280.35

the flux density. Area product Ap decides the size of inductor which is proportional to the product of peak and rms values of inductor currents. Hence, the sum of product of peak and rms currents is calculated for all the converters to compare their total size of magnetics. All the numerical values, for the inverter specifications mentioned in Table 6.3, are listed in Table 6.8 which clearly shows that T-1 is best in this context. However, there is not much difference in the values of SOBBI and T-2. ●

Voltage gain: Ideally, all the inverters have identical voltage gain magnitudes but with internal losses their gains differ. For a fair comparison, same semiconductor devices and inductor ESRs are used. The number of switches Ns , triggered by high frequency pulses, contributes to switching loss of the converter whereas conduction loss can be checked by number of switches conducting at any instant Nc . These numbers are listed in Table 6.9 which shows that T-2 is best in this respect. Semiconductor device stresses are shown in Table 6.10. The part numbers of selected MOSFET and diode, with a safety factor of around 1.5, are shown in Table 6.11. The parameter a is defined as a¼

r ; R

(6.16)

where r is the inductor ESR and R is the equivalent load resistance. It is selected as 0.1% for all the inductors. Numerical simulation of the inverters with these practical considerations are performed and plots of obtained voltage gains are shown in Figure 6.47 along with ideal gain. It is seen that T-2 has the lowest voltage gain in the negative half. Maximum voltage gain of the inverter can be checked by observing its gain during both the halves of output voltage. Minimum gain, considering bi-polar output, for a particular input voltage gives

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157

Table 6.9 Comparison among bimodal inverters based on switching pattern SOBBI

Ns Nc

DTs ð1  DÞTs

Topology-1

Topology-2

Positive

Negative

Positive

Negative

Positive

Negative

2 2 3

3 2 4

2 3 4

2 3 4

2 2 2

2 2 2

Table 6.10 Device stress of bimodal inverters Peak voltage stress

Topology-1

Topology-2

SOBBI

Vin 1Dm ,

214.3 V

S1 , D1;5

S14

S1 , D4

Vin Dm 1Dm ,

155.5 V

S25

–

S25 , D3;4

Table 6.11 Devices selected for voltage gain comparison of bimodal inverters MOSFET Diode

IRFP4868PbF MUR3040WT

4 +ve ideal +ve ideal

2

Voltage gain (AV)

+ve T-1 +ve T-1 0

+ve T-2 –ve T-2 +ve SOBBI –ve SOBBI

–2

–4 0.2

0.4

0.6 0.8 Duty ratio (D)

Figure 6.47 Voltage gain comparison among topology-1, 2, and SOBBI

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Power electronics for next-generation drives and energy systems: Volume 2 the idea of maximum ac voltage that can be generated by an inverter. Therefore, the maximum voltage gain of the inverter can be mathematically expressed as Avmax ¼ minfðAvþ ; jAv jÞg:

●

●

●

(6.17)

In this respect, T-1 has best voltage gain. Load: Only T-2 can supply power to both upf as well as non-upf load which is not possible in T-1 and SOBBI because presence of diodes in series with the MOSFET switches. These can only be used for upf loads. Device count and stress: Obviously, in terms of device count T-2 is the best solution. Moreover, it is observed that there are two categories of voltage stresses appearing across the devices which are listed in Table 6.10. In T-2, all MOSFETs experience higher voltage stress. However, in T-1 and SOBBI only one MOSFET suffers from high stress. Input current: T-1 and T-2 have continuous input current whereas it is not so in SOBBI.

T-1 and T-2 are the two new useful inverter circuits which are the product of combining two fourth-order dc–dc converters. Both have some edge over reported SOBBI. However, high component count and incapability to supply non-upf loads are some serious drawbacks of T-1 as compared to T-2. Considering these points, T-2 is selected for hardware realization.

6.3.10 Additional details on topology-2 operation 6.3.10.1

Non-ideal voltage gain

Considering the non-ideality (internal resistance) of inductors, the expressions of voltage gains are modified as Apv ¼ Anv ¼

Dð1  DÞ 2

ðð1  DÞ þ a1 DÞD þ ð1  D þ a2 Þð1  DÞ2 Dð1  DÞ ðð1  DÞ2 þ a1 D2 þ a2 Þ

(6.18)

(6.19)

where ak ; k 2 1; 2 is defined as ak ¼

rk ; R

(6.20)

R being the equivalent load resistance. Voltage gain as a function of duty ratio under various ak values is plotted in Figure 6.48 which clearly shows a difference in the maximum voltage gain between both halves. This problem is resolved by the controller in closed loop operation.

Converter topologies for grid-integration of renewable power sources

159

10 a1=a2=0

Voltage gain (Av)

a1=a2=0.01

Positive Half

5

a1=a2=0.03

0

Negative Half

–5

a1=a2=0.01 a1=a2=0

–10

0

0.2

0.4 0.6 Duty ratio (D)

0.8

1

Figure 6.48 Non-ideal voltage gain of topology-2

S2

i1 Vin

S1

C1 D2

D1 L2

D3 S 3 i2

S4

io

v + 1_

L1 S2

i1

D4 v2 C2

(a)

LOAD

v + 1_

L1

Vin

S1

C1 D2

D1 L2

D3 S 3 i2

ig

S4

Lg

D4 v2

vg C2

(b)

Figure 6.49 Schematic of inverter circuit: (a) stand-alone mode and (b) gridconnected mode

6.3.11 New switching strategy-single mode inverter A single-stage, single-phase, buck–boost inverter which was developed by joining two dc–dc converters was presented in the previous section. However, its bi-modal inversion (BMI) operation may result in usual problems associated with zero crossing distortion (ZCD) and increases control complexity. In this section, a new switching strategy applicable for the bi-modal topology is presented which changes its operation to a single-mode inverter (SMI).

6.3.11.1 Operational principle Figure 6.49(a) shows the circuit arrangement of SMI in SA mode. The inverter consists of two inductors ðL1 ; L2 Þ, two capacitors ðC1 ; C2 Þ and four MOSFETs ðS1 -S4 Þ with their body diodes ðD1 -D4 Þ. In GC operation, shown in Figure 6.49(b), the grid side inductor, Lg , is added to suppress switching ripple in the grid current.

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Power electronics for next-generation drives and energy systems: Volume 2

Figure 6.50 New switching strategy for single-mode operation

i1

C1 D2

S2 S1

is1

(a)

D1

is2 L 2

S4 is4

D3 S 3 i2 is3

io D4 v2 ic2 C2

Vin

L1

ic1 + v1 _

i1

C1 D2

S2 S1

is1

D1

is2

S4 is4

D3 S 3 i L2 i2 s3

io D4 v2 ic2 C2

LOAD

ic1 + v1 _

LOAD

Vin

L1

(b)

Figure 6.51 Operating states of single-mode inverter during: (a) interval-I and (b) interval-II

SMI working principle is illustrated by referring SA operation. The switching sequence for the devices are demonstrated in Figure 6.50 and the resultant equivalent circuits as a result of the proposed switching scheme are mentioned in Figure 6.51 where the switch pair (S1 ,S3 ) is triggered simultaneously with a duty ratio D, while, during remaining switching period, Ts , the pair (S2 ,S4 ) is gated. Effective circuit for intervals I (0  t  DTs ) and II (DTs  t  Ts ) are shown in Figure 6.51(a) and (b), respectively. The voltage and current waveforms for passives during intervals DTs and ð1  DÞTs are shown in Figure 6.52 and their equivalent expressions are also listed in Table 6.12, where v1 (v2 ) is the instantaneous voltage across capacitor C1 (C2 ), i1 (i2 ) is the instantaneous current through inductor L1 (L2 ) and io is the load current. Inductors L1 and L2 are energized in interval-I by the input voltage, Vin , and voltage, V1 , respectively. During the interval-II, stored energy in the inductors is transferred to capacitors, C1;2 . This can also be verified by waveforms shown in Figure 6.52 where inductors experience positive voltage in DTS interval and capacitor currents are positive in ð1  DÞTs interval. The presence of bi-directional MOSFETs always ensures continuous conduction mode (CCM) operation of the SMI irrespective of loading conditions.

Converter topologies for grid-integration of renewable power sources

161

Vin VL1 t V1

Vin

V2

(a) i1 i1

Vin

Vin L1

V1 V2 L1

I1 t

V1

VL2

t V1 V2

(b) i2 i2

V1+V2 L2

V1 L2

I2 t

I1 + I2

iC1

t

I2 (c) V1 V1

I2 C1

V1

I1 + I2 C1

t I1 + I2 – Io iC2 t

Io (d) V2 V2

Io C2

V2 I1 + I2 – Io C2

DTs

t

Ts

Figure 6.52 Operation waveforms of single-mode inverter

6.3.11.2 Voltage gain of inverter Under ideal conditions, the steady-state capacitor voltages are expressed as V2 ¼

Vin ð2D  1Þ Vin : ; V1 ¼ D Dð1  DÞ

(6.21)

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Power electronics for next-generation drives and energy systems: Volume 2

Table 6.12 Operating equations for SMI Time duration

v L1

v L2

iC 1

iC 2

DTs ð1  DÞTs

Vin Vin V1 V2

V1 V1 V2

I2 I1 þI2

Io I1 þI2 Io

The voltage gain of the converter, using (6.21), is obtained as Av ¼

V2 2D  1 : ¼ Vin Dð1  DÞ

(6.22)

Similarly, the steady-state expressions for inductor currents are I1 ¼ Io

2D  1 Io ; I2 ¼ : D Dð1  DÞ

(6.23)

Considering the internal resistances of both inductors, the voltage gains are modified to   DðD  1Þ ð2D  1Þa1 ð1  DÞa2 1 þ þ (6.24) Av ¼ 2D  1 Dð1  DÞ Dð2D  1Þ where R is the load resistance, ak ¼ rk =R and rk is the ESR of inductor Lk , k 2 1; 2. The obtained voltage gain has speciality that bipolar output voltage can be generated by varying duty ratio around 0:5. For D< 0:5, the voltage gain is negative whereas for D> 0:5 output voltage is positive. Variation of Av with D is plotted in Figure 6.53(a) which shows that an ac output voltage can be achieved when D is varied around 0:5. To obtain an ac output voltage of vo ¼ Vm sin wt, the expression for duty ratio is dðtÞ ¼

2 pffiffiffiffi2 b sin2 wt þ 4 b v sin wt þ A 2A

(6.25)

v

b v ð¼ Vm =Vin ) is the maximum voltage gain required of the SMI. Thus, the where A minimum and maximum values of duty ratio are Dmin ¼

2 2 ; Dmax ¼ : pffiffiffiffi2 pffiffiffiffi2 b þ4 b þ4 bv þ A bv þ A 2þA 2  A v v

(6.26)

The variation of duty ratio with time required for generating a 50 Hz ac output voltage is shown in Figure 6.53(b). Figure 6.53(c) shows the asymmetry caused in the duty ratio due to non-ideality of the inductors, i.e. ð0:5  Dmin Þ>ðDmax  0:5Þ. During interval-I, switches S1;4 carry current (I1 þ I2 ) which is expressed as IS1 ¼ IS4 ¼ I1 þ I2 ¼ Io

2D  1 Io Io : þ ¼ Dð1  DÞ D 1  D

(6.27)

Converter topologies for grid-integration of renewable power sources 1

20

=

=.01

0

−20

0

= =

=.001

0.2

0.4

(a)

0.6

1

Duty ratio (D)

1

=2

0.6 0.4 0.2 0 0 (b)

0.005

0.01 0.015 Time (in sec)

0.02

Ideal α1=α2=0.01

0.8 Duty Ratio (D)

=.005

0.8

=4

0.8

Duty Ratio (D)

v

Voltage gain (A)

ideal 10

−10

163

0.6 0.4 0.2 0

0.005

0.01 0.015 Time (in sec)

0.02

(c)

Figure 6.53 (a) Voltage gain plot. (b) Duty ratio against time plot. (c) Asymmetry ^v = 2 in duty ratio for A Similarly, current through switches S2;3 are IS2 ¼ IS3 ¼ I2 ¼

Io : D

(6.28)

6.4 Conclusion Low-voltage power sources require buck–boost inverters (BBI) for interfacing with an ac load/ utility grid. PV and SWT-based renewable energy systems and UPS are some of the main applications of BBI. This chapter has presented converter circuits suitable for a buck–boost inverter system. The chapter mainly describes derivation

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Power electronics for next-generation drives and energy systems: Volume 2

of a new single-stage buck–boost inverter. Fourth-order dc–dc buck–boost converters are used to derive new bi-modal inverter configurations. Various combinations are explored, and screening is performed based on the continuity of states at output zero crossing and device count which resulted in an inverter formed by SEPIC and CSC-IL converters. The proposed inverter uses only four active switches for operation which is even lesser than the most common single and twostage buck–boost inverter systems. The inverter is doubly grounded which alleviates the problem of CMLC in PV applications. The presence of only active switches (MOSFETs) in the circuit allows it to exhibit four-quadrant operation. The proposed inverter has an input side inductor which makes the input current continuous. This feature alleviates the conducted EMI problems caused due to the discontinuity in input current and hence does not require any input filter. Bimodal operation brings challenges in the selection of passives and controller design. A new switching strategy was implemented on the same inverter circuit which results in single mode operation to obtain the required ac output voltage. This new switching strategy changes the entire circuit operation and the new voltage gain shows a change in the output polarity (sign) with the usual variation in duty ratio.

6.5 Future trends/future possibilities This chapter uses only five fourth-order dc–dc buck–boost converters, and the rules to check the possibility of combining them to achieve bi-polar output are presented. Combinations with other dc–dc converters can be explored to get some more meaningful inverter circuits following the discussed derivation rules.

References [1] Pea JCU, de Brito MAG, d A e Melo G, et al. A comparative study of MPPT strategies and a novel single-phase integrated buck-boost inverter for small wind energy conversion systems. In: XI Brazilian Power Electronics Conference, 2011, p. 458–465. [2] Malla SG, Bhende CN, Mishra S. Photovoltaic based water pumping system. In: 2011 International Conference on Energy, Automation and Signal, 2011, p. 1–4. [3] Moghadam MS, Darwish M, Marouchos C. A simple drive induction motor based on buck/boost inverter topology. In: 2013 48th International Universities’ Power Engineering Conference (UPEC), 2013, p. 1–4. [4] Kjaer SB, Pedersen JK, Blaabjerg F. A review of single-phase grid-connected inverters for photovoltaic modules. IEEE Transactions on Industry Applications. 2005;41(5):1292–1306. [5] Hu H, Harb S, Kutkut N, et al. Power decoupling techniques for microinverters in PV systems-a review. In: 2010 IEEE Energy Conversion Congress and Exposition, 2010, p. 3235–3240.

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[6] IEEE Recommended Practice for Grounding of Industrial and Commercial Power Systems. IEEE Std 142-2007 (Revision of IEEE Std 142-1991), 2007, p. 1–225. [7] Schmidt H, Burger B, Kiefer K. Gefhrdungspotenzial transformatorloser Wechselrichter – Fakten und Gerchte, 2003. [8] Li W, Gu Y, Luo H, et al. Topology review and derivation methodology of single-phase transformerless photovoltaic inverters for leakage current suppression. IEEE Transactions on Industrial Electronics. 2015;62(7):4537– 4551. [9] Cavalcanti MC, de Oliveira KC, de Farias AM, et al. Modulation techniques to eliminate leakage currents in transformerless three-phase photovoltaic systems. IEEE Transactions on Industrial Electronics. 2010;57(4):1360– 1368. [10] Islam M, Mekhilef S. High efficiency transformerless MOSFET inverter for grid-tied photovoltaic system. In: 2014 IEEE Applied Power Electronics Conference and Exposition – APEC 2014, 2014, p. 3356–3361. [11] Beristain J, Bordonau J, Gilabert A, et al. Synthesis and modulation of a single phase DC/AC converter with high-frequency isolation in photovoltaic energy applications. In: IEEE 34th Annual Conference on Power Electronics Specialist, 2003. PESC ’03, vol. 3, 2003, p. 1191–1196. [12] Kerekes T, Teodorescu R, Rodriguez P, et al. A new high-efficiency singlephase transformerless PV inverter topology. IEEE Transactions on Industrial Electronics. 2011;58(1):184–191. [13] Victor M, Greizer K, Bremicker A. Method of converting a direct current voltage from a source of direct current voltage, more specifically from a photovoltaic source of direct current voltage, into an alternating current voltage. Google Patents; 1998. U.S. Patent 2005 028 6281 A1. [14] Yang B, Li W, Gu Y, et al. Improved transformerless inverter with commonmode leakage current elimination for a photovoltaic grid-connected power system. IEEE Transactions on Power Electronics. 2012;27(2):752–762. [15] Patel H, Agarwal V. A single-stage single-phase transformer-less doubly grounded grid-connected pv interface. IEEE Transactions on Energy Conversion. 2009;24(1):93–101. [16] Zhao Z, Xu M, Chen Q, et al. Derivation, analysis, and implementation of a boostbuck converter-based high-efficiency PV inverter. IEEE Transactions on Power Electronics. 2012;27(3):1304–1313. [17] Wu W, Ji J, Blaabjerg F. Aalborg inverter – a new type of buck in buck, boost in boost grid-tied inverter. IEEE Transactions on Power Electronics. 2015;30(9):4784–4793. [18] Tang Y, Dong X, He Y. Active buckboost inverter. IEEE Transactions on Industrial Electronics. 2014;61(9):4691–4697. [19] Tang Y, Xu F, Bai Y, et al. Comparative analysis of two modulation strategies for an active buckboost inverter. IEEE Transactions on Power Electronics. 2016;31(11):7963–7971.

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[20]

Kasa N, Ogawa H, Iida T, et al. A transformer-less inverter using buck-boost type chopper circuit for photovoltaic power system. In: Proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems. PEDS’99 (Cat. No.99TH8475), vol. 2, 1999, p. 653–658. Jain S, Agarwal V. A single-stage grid connected inverter topology for solar PV systems with maximum power point tracking. IEEE Transactions on Power Electronics. 2007;22(5):1928–1940. Chamarthi P, Rajeev M, Agarwal V. A novel single stage zero leakage current transformer-less inverter for grid connected PV systems. In: 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 2015, p. 1–5. Rajeev M, Agarwal V. Novel transformer-less inverter topology for singlephase grid connected photovoltaic system. In: 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC), 2015,. p. 1–5. Huang W, Yen K, Roig G, et al. Voltage divided noninverting C´uk converter with large conversion ratios. In: IEEE Proceedings of the SOUTHEASTCON ’91, vol. 2,1991, p. 1005–1007. Caceres RO, Barbi I. A boost DC–AC converter: analysis, design, and experimentation. IEEE Transactions on Power Electronics. 1999;14(1): 134–141. Caceres RO, Garcia WM, Camacho OE. A buck–boost DC–AC converter: operation, analysis, and control. In: 6th IEEE Power Electronics Congress. Technical Proceedings. CIEP 98 (Cat. No. 98TH8375), 1998, p. 126–131. Fang Y, Ma X. A novel PV microinverter with coupled inductors and double-boost topology. IEEE Transactions on Power Electronics. 2010;25 (12):3139–3147. Piazza GL, Barbi I. New step-up/step-down DC AC converter. IEEE Transactions on Power Electronics. 2014;29(9):4512–4520. Abramovitz A, Zhao B, Smedley KM. High-gain single-stage boosting inverter for photovoltaic applications. IEEE Transactions on Power Electronics. 2016;31(5):3550–3558. Kotsopoulos A, Heskes PJM, Jansen MJ. Zero-crossing distortion in gridconnected PV inverters. In: IEEE 2002 28th Annual Conference of the Industrial Electronics Society, IECON 02, vol. 2, 2002, p. 1101–1106.

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28] [29]

[30]

Chapter 7

PV powered DC microgrid with plug-in energy harvesting and EV incorporated functions Chang-Ming Liaw1 and Min-Ze Lu1

This chapter presents the establishment of a photovoltaic (PV) powered DC microgrid with plug-in energy support and electric vehicle (EV) incorporated functions. The DC microgrid common DC-bus voltage is established by the PV panel through a 3-cell interleaved boost converter. The grid-connected isolated bidirectional load inverter consists of a single-phase three-wire (1P3W) 220 V/110 V inverter and a CLLC resonant converter. To enhance the microgrid powering quality, a hybrid energy storage system including a battery bank and a flywheel, is equipped. Moreover, a plug-in energy support mechanism is developed to let the possible harvested sources be inputted to the microgrid through the same PV interface interleaved boost converter. As the solar energy is insufficient to support demanded power, the converter cell in the interleaved converter is switched to accept the harvested sources. The single-phase switch-mode rectifier (SMR) is formed as the basic schematic, while the EV switched-reluctance motor (SRM) drive is interconnected with the PV-powered microgrid through the same schematic. Some measured results are presented to conduct the evaluation.

Nomenclature A Cdc Cpv Ct D fc fs Gci ðsÞ Gci ðzÞ 1

ideality factor microgrid common DC-bus capacitor PV output capacitor current-related temperature coefficient duty ratio corner frequency switching frequency current controller transfer function in s-domain current controller transfer function in z-domain

Department of Electrical Engineering, National Tsing Hua University, Taiwan

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Power electronics for next-generation drives and energy systems: Volume 2

Gcv ðsÞ Gcv ðzÞ Gdd ðzÞ Gdr ðzÞ Gpi ðsÞ Gpi ðzÞ Gpv ðsÞ Gpv ðzÞ iD iL ipv iph ish Iag Isc ,Isco Iso kpl K Ki KI KP KPWM Ks Kv LGðzÞ L3 Li1 ,Li2 ,Li3 nr Ns Pag Pdc Pmax q Rdc Rs Rsh S

voltage controller transfer function in s-domain voltage controller transfer function in z-domain voltage response due to a step output power change closed-loop voltage transfer function in z-domain transfer function between duty and current in s-domain transfer function between duty and current in z-domain plant transfer function in s-domain plant transfer function in z-domain diode current current command of interleaved boost converter PV output current per cell current source output current current through shunt resistor single-phase AC input current short-circuit current saturation current power transfer ratio related to output voltage Boltzmann constant (K ¼ 1:3806505  1023 J=K) current sensing factor integral gain proportional gain transfer ratio of the PWM modulator light intensity coefficient voltage sensing factor open loop gain in z-domain energy storage inductor of single-phase boost SMR energy storage inductors of interleaved boost converter sampling count cell number single-phase AC input power DC-bus power maximum power electron charge (q ¼ 1:6  1019 C) DC-bus resistive load series resistor parallel resistor operating light intensity

PV powered DC microgrid So tr T Ta Ti Ti3 ; Ti6 Tref Tv vdf vpv Vag Vdc Vfw Voc Wv ðsÞ Wi ðsÞ z1 ZA ,ZB ,ZAB DdðsÞ 0 DiL3 ðsÞ,DiL3 ðsÞ DPdc 0 Dvdc ðsÞ,Dvdc ðsÞ Dvpi ðsÞ,Dvag ðsÞ l1 ,l2 h

169

standard light intensity rise time operating temperature (in Kelvin scale, K) ambient temperature sampling time for current control loop IGBT of one-leg interface converter standard temperature sampling time for voltage control loop feedforward voltage PV voltage single-phase input voltage DC-bus voltage SRM-driven flywheel input voltage open-circuit voltage voltage robust controller current robust controller delay operator home appliance loads duty variation current command and feedback variations DC-bus power variation voltage command and feedback variations input voltage variation closed-loop poles efficiency

7.1 Introduction Microgrid [1–8] employing various renewable sources have been attracted much attention worldwide. Wind generator and PV are the two most commonly employed renewable sources, especially the PV, which is more suited for domestic use due to the easier installation reasons. Owing to without line inductance effects, DC microgrid possesses many advantages over the AC microgrid. Hence, this chapter develops a PV-based DC microgrid and its incorporated operation studies to the harvested sources and EV. The detailed designs of system configuration, PV source and all interface converter are demonstrated, which is beneficial for the reader to follow the system planning process. Till now, there exist many researches in maximum power point tracking (MPPT) control for PV. Some commonly used methods are introduced in [9,10],

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Power electronics for next-generation drives and energy systems: Volume 2

which compare the pros and cons of each method in detail. Moreover, researches in [11–14] presented some topologies and controls of PV interface converters, while [15,16] further focused on the interaction between PV and microgrids. In a microgrid, the sources, storage devices, and loads must be connected to the common DC bus through appropriate interface DC–DC converters [17–25]. In the established PV-based microgrid, well-regulated DC bus voltage is established by the PV through a three-leg interleaved boost DC–DC converter. In voltage mode, constant DC bus voltage (400 V) is preserved, while the MPPT operation using perturb and observe (P&O) method is also applied with simple control algorithm. The working cell number of the interleaved converter is determined by the proposed energy management strategy. In addition, harvesting available sources is an important issue to the microgrid. In this chapter, a plug-in energy support mechanism using the embedded converter cells of the PV interleaved boost converter is proposed, which can input AC and DC sources to provide energy support for microgrid. Especially for single-phase AC input, a single-phase boost SMR [26–30] can be formed by one converter cell with externally added diode rectifier, while the H-bridge SMR is directly formed by two cells. It can also let the DC sources be inputted via the same schematic. Moreover, interconnected operation between microgrid and EV [microgrid-tovehicle (M2V) and vehicle-to-microgrid (V2M)] can express their complementarities. In the existing researches [31–33], the possible topologies and operations between microgrid, vehicle, and smart home have been presented. The key problems may be the hosting capacity of the microgrid and the consideration of disturbance when performing the EV charging/discharging operations [34]. Till now, many PV based EV chargers have been proposed [35–38] with different topologies and control strategies. Based on this, the embedded power components of PV interleaved converter are used to demonstrate some operations between the EV SRM drive and the microgrid.

7.2 The established PV powered microgrid Figure 7.1 shows the system configuration of the established PV powered DC microgrid with plug-in energy harvesting and EV incorporated capabilities. It mainly consists of: (i) a PV array with configurable 3-cell interleaved boost-buck interface converter to establish well-regulated common DC-bus voltage. The cell number can be flexibly changed according to the irradiance and the accessible external sources; (ii) a hybrid energy storage system consisting of battery, motordriven flywheel, and super-capacitor; (iii) a bidirectional 1P3W inverter to yield 220 V/110 V 60 Hz AC sources for powering the home appliances. The inverter is connected to the utility grid via an isolated bidirectional CLLC DC–DC converter. The bidirectional operations between microgrid and grid are conductible; (iv) a test load; and (v) the proposed energy harvesting scheme (EHS).

Utility grid 110V

Pu iuA

Po

LA

io1

T1

ZA

iuN

vab

ZAB

vnb

Co

ZB

iuB

iLB

LB

Co

+ TLB

io 2

vdi T4

LC io3

T6

T2

D4

110V

+ THB

+ TLA

D5

D3

D6

M2G/ Microgrid DC-bus G2M 400V

Bidirectional Isolated CLLC DC-DC converter

Pdi T5

T3 D1

i LA

van

220V

Single-phase threewire inverter

Load

Ls

+ THA

Sb1

Db1

Lp

1:1

Lb

Cdi Lm

Cs – TLB

iL

Pb

Cp

– TLA

ib

Sb 2

– THA

– THB

+ vb –

Db2

D2

O

Interleaved bidirectional boost-buck converter

Ppv

Pdc DC

Ti 3

Ti 2

Ti1

DC

MPPT

v pv

Ppv

D pv

PV array

i pv

iL

+ v pv –

SW1 iL1

L1

SW2

L2

SW3

iL 2 iL 3

C pv

Di 2

Di1

Di3

DC

+

vdc –

L3

Di 4

vfw

Di 5

Di 6

Emergency AC generator

G2M/ M2G +

vag –

WG

(X2M)

(X2M)

+ vb –

M2V/ V2M

Data center power supply + Battery

156V

iag

vb

–

(B2M/ M2B)

iag

ωr

M2X/ X2M

Plug-in energy harvesting scheme Fuel cell stack

i1 ~ i4

DC

Ti 6

Ti 5

Ti 4

Cdc

+ vag –

~

DC

DC DC

AC DC DC

400V

Loads

48V

Auxiliary power supplies

Points of loads 12V/5V/1.2V

Figure 7.1 System configuration of the established PV powered DC microgrid with plug-in energy harvesting and EV incorporated functions

172

Power electronics for next-generation drives and energy systems: Volume 2 ipv

iph

+ vpv –

Rs ipv ish

iD

Per cell Current source

+ vsh _

Rsh

+ vpv _/ Ns

Figure 7.2 Equivalent circuit of a PV cell

7.2.1

Governing equation for a PV cell

The equivalent circuit of a PV cell is shown in Figure 7.2, which consists of ideal current source, ideal diode, series, and parallel resistors. The governing equations for current shown in Figure 7.2 are listed as follows: ipv ¼ iph  iD  ish S þ Ct ðT  Tref Þ; T ¼ Ta þ Ks S So
qvsh  vpv  ipv Rs iD ¼ Io eAKT  1 ; Io ¼ Iso ðT=Tref Þ3 ; vsh ¼ Ns

iph ¼ Isco

ish ¼ vsh = Rsh

(7.1) (7.2) (7.3) (7.4)

where Isco ¼ short-circuit current, S ¼ operating light intensity, So ¼ standard light intensity, Ct ¼ current-related temperature coefficient, T ¼ operating temperature (in Kelvin scale, K), Tref ¼ standard temperature, Ta ¼ ambient temperature, Ks ¼ light intensity coefficient, q ¼ electron charge (q ¼ 1:6  1019 C), A ¼ ideality factor, K ¼ Boltzmann constant (K ¼ 1:3806505  1023 J=K), Iso ¼ saturation current, Ns ¼ cell number, Rs ¼ series resistor, and Rsh ¼ parallel resistor.

7.2.2

Parameter determination

To simulate the characteristics of a PV panel, the specifications of the PV module manufactured by Gintung Company are selected as an example and listed in Table 7.1. With defined parameters, Ct , Iso , and Rs can be derived as below.

7.2.2.1

Current-related temperature coefficient

The current-related temperature coefficient Ct describes the change of short-circuit current respect to the temperature, which affects the I–V curve under different operating temperatures. According to the parameters listed in Table 7.1, it can be calculated as Ct ¼ 0:000443Isc ¼ 3:73 mA= C

(7.5)

PV powered DC microgrid

173

Table 7.1 Specifications of the PV module manufactured by Gintung Company GTEC-280G6M (Gintung Company) Parameters from data sheet Number of cell (Ns ) Maximum power (Pmax ) Open circuit voltage (Voc ) Short circuit current (Isco ¼ Isc ) Temperature coefficient of Isc

Defined parameters 72 280 W 44.39 V 8.42 A 0.0443%= C

Standard light intensity (So ) Standard temperature (Tref ) dvpv =dipv (slope) at Voc Ideality factor (A) Parallel resistor (Rsh ) Light intensity coefficient (Ks )

1; 000 W=m2 25 C 0.68V/A 1.2 1,000 W 0

7.2.2.2 Saturation current Saturation current mainly affects the open-circuit voltage and the maximum power that the PV can provide. Assumed that T ¼ Ta ¼ 25 C ¼ 298:15 K and S ¼ 1; 000 W=m2 , this can be obtained at open-circuit operation (ipv ¼ 0, vpv ¼ Voc ¼ 44:39 V). Because the net current in (7.1) is zero, Isco ¼ iph ¼ 8:42A and ish ¼ 0:616528 mA can be calculated from (7.2) and (7.4). Based on these currents and according to (7.3), Iso ¼ 1:78864  108 A is eventually derived.

7.2.2.3 Series resistor The effects from series resistor are similar to saturation current. Substituting (7.2) to (7.4) into (7.1) and doing the derivative (dipv =dvpv ), the equation for series resistor can be expressed as: Rs ¼ 

dvpv 1 1  dipv Ns ð1=Rsh Þ þ ðqIsc =AKT Þ

(7.6)

By the given data in Table 7.1, Rs ¼ 5:778 mW is obtained.

7.2.3 I–V curves and effects on temperature and irradiance Based on the parameters of PV panel obtained from the manufacturer and calculation, the I–V curve describing PV characteristics can be plotted. The simulated results of the PV panel under T ¼ 25 C with different irradiance are shown in Figure 7.3. The normal characteristic can be seen at S ¼ 1; 000 W=m2 , while the maximum power, short-circuit current and open-circuit voltage are reduced with lower irradiance. Moreover, Figure 7.4 shows the simulated results under S ¼ 1; 000 W= C with different operating temperatures. Unlike the effect from irradiance, the open-circuit voltage is reduced rapidly respect to increased temperature, and the short-circuit current remains the same. Because the irradiance and temperature are dependent, the MPPT control is necessary to extract maximum power form PV.

174

Power electronics for next-generation drives and energy systems: Volume 2

10

300

: MPP

Irradiance increases

9 1,000 (W/m2)

: MPP 250

8 2 800 (W/m )

7 Ipv (A)

Ppv (W)

200

6

600 (W/m2)

5

1,000 (W/m2)

Irradiance increases

2 800 (W/m )

150

600 (W/m2)

400 (W/m2)

4

100

3

400 (W/mm2)

200 (W/m2)

2

50 200 (W/mm2)

1 0

0 0

5

10

15

(a)

20

25

30

35

40

45

50

0

5

10

15

20

(b)

Vpv (v)

25

30

35

40

45

50

Vpv (v)

Figure 7.3 Simulated results of the PV panel under T ¼ 25 C with different irradiance: (a) I–V curve; (b) P–V curve

10

350

: MPP

9

: MPP 300

8 250

6 5 4 75◦

3

Ppv (W)

Ipv (A)

7

0◦ 25◦ 50◦

200 150 75◦

100

50◦

0◦ 25◦

2 Temperature increases

1 0

0

(a)

5

10

15

20

25 30 Vpv (V)

Temperature increases

50

35

40

45

0

50

0

5

10

15

20

25 30 Vpv (V)

35

40

45

50

(b)

Figure 7.4 Simulated results of the PV panel under S ¼ 1;000 W= C with different operating temperature: (a) I–V curve; (b) P–V curve

7.3 PV array with followed interleaved boost converter 7.3.1

Simulated PV array

The PV array is simulated by a commercialized power supply (62100H-600S, 600 V, 17 A, 10 kW, manufactured by Chroma). Followed the specifications of the PV module in Table 7.1, the adopted structure of PV array and resulted output characteristics are shown in Figure 7.5, where the ratings are enlarged.

7.3.2 7.3.2.1

The established interleaved boost converter Power circuit

As shown in Figure 7.1, the measured inductances of the interleaved boost converter using IM 3536 LCR meter under test frequency of 30 kHz are

PV powered DC microgrid

175

+

Isc = 8.42 A

Isc =16.84 A Voc = 88.78 V

+ Voc = 44 . 39 V

–

–

(a) 20

1,400

18

MPP (72.27,16.28)

1,000 (W/m2)

MPP (72.27,1177)

1,200

16 800 (W/m2)

800 (W/m2)

12 600 (W/m2)

10 8

(69.06,10.02)

400 (W/m2)

1,000

1,000

(71.01,13.32)

Ppv ( W )

I pv (A)

14

(W/m2)

800

600 (W/m2)

(69.06,692)

400 (W/m2)

600

(67.09,6.73)

(67.09,451.5)

200 (W/m2)

6

(71.01,945.9)

400 200 (W/m2)

4

(64.46,3.366)

(64.46,217) 200

2 0

0 0

10

20

30

(b)

40

50 60 Vpv (V)

70

80

90

100

0

10

20

30

40

50 60 V pv (V )

70

80

90

100

Figure 7.5 The adopted PV array: (a) structure; (b) resulted characteristics

Li1 ¼ 862:7 mH, Li2 ¼ 859:6 mH, and Li3 ¼ 854:5 mH, while three IGBT modules are adopted to construct the three-cell interleaved boost converter. For the filtering capacitors, Cpv ¼ 51; 700 mF and Cdc ¼ 2; 200 mF=450 V are used. To possess configurable capability, three relays and magnetic contactors (MCs) are used to change the operating converter cell. With the preset power or current limit, it is changed to perform current sharing to reduce total inductor current ripple. As the solar energy is insufficient, the available DC and AC sources can be inputted to support PV-based DC microgrid. Some scenarios will be provided to evaluate the effectiveness in the later section.

7.3.2.2 Control scheme Figure 7.6 shows the control scheme of the followed interleaved boost converter for the PV array. Under voltage mode, the outer voltage and inner current loops are used to control the microgrid common DC-bus voltage (400 V). As to the MPPT mode with the employed algorithm shown in Figure 7.7, only the current loop is adopted with perturbed current command of iL ¼ 60 mA=sec. With the sensed PV output voltage, the output power can be calculated and maximum power point (MPP) can be found. The designed voltage and current controllers based on the derived dynamic model with Bode plots and quantitative design (not shown here) are listed below: Gci ðsÞ ¼ 0:2 þ

30 2:6792 0:8 ; Gcv ðsÞ ¼ 3:923 þ ; Wv ðsÞ ¼ s s 1 þ 3:18  104 s

(7.7)

176

Power electronics for next-generation drives and energy systems: Volume 2 Interleaved boost DC-DC converter control scheme

Wv (s )

* vdcr * vdc

vdc v pv

Σ

A/D

v'dc

A/D

v'pv

TMS320F28335

* vdc 1

Σ

V

εv

P

i*L

P

Cell current command generator

i*L 2

v'pv

i*L3

i'L 2 i'L3 A/D A/D A/D iL 2

εi

∑

Ti4

vsaw1 vcb

Gci (s )

Ti5

vsaw2

i'L 2

i'L1

iL1

εi

∑

vca

Gci (s )

i'L1 V

MPPT algorithm

εi

∑

G cv (s )

v'dc v'pv

i*L1

: Voltage mode : MPPT mode

vcc

Gci (s )

Ti6

vsaw3

i'L3

iL 3

Figure 7.6 Control scheme of the followed interleaved boost converter START Sampling = 30kHz Instantaneous value v pv (n) = v pv (n) + v pv (n – 1) i L (n) = i L (n) + i L (n – 1)

A

Count=Count+1

Count=5,000?

B D

Yes

C

∆ Ppv = 0

NO

NO Yes

Yes

MPP

Ppv

vpv

NO

∆ Ppv > 0

Average value

v pv (m) = v pv (n)/5,000 iL (m) = iL (n)/5,000

Count=0

Ppv (m) = v pv (m) * i L (m)

Yes

Decrease iL* A

∆vpv > 0

NO

Increase iL*

Yes

∆vpv > 0

Increase iL* C

B

NO

Decrease iL*

D

∆vpv = v pv (m) – vpv (m – 1) ∆ Ppv = Ppv (m) – Ppv (m – 1)

vpv (m – 1) = vpv (m) Ppv (m – 1) = Ppv (m)

Figure 7.7 MPPT algorithm

7.3.3

Evaluation on MPPT and interleaving operations

A resistive load Rdc ¼ 150 W is connected to the DC microgrid for preliminary test and the switch in Figure 7.6 is at position ℗ for conducting MPPT operation. Figure 7.8 shows the measured results of the simulated PV array. The irradiance of 200 W=m2 is set and MPPT is then performed. As the maximum power point is reached, the irradiance is suddenly changed to 400 W=m2 , and the next new point can be found. Further compared to the designed PV curves shown in the right-hand side of Figure 7.5(b), successful MPPT operation is seen. Followed from Figure 7.8, Figure 7.9 shows the measured results under a step irradiance change of 400 W=m2 ! 600 W=m2 . The cell number is changed based on the PV output power being higher than 500 W. As the results, the interleaved boost converter can change working legs and realize interleaved current sharing operation as the sensed power is higher than 500 W.

PV powered DC microgrid MPPT start (200 W/m2)

177

Step irradiation change (200 W/m2 400 W/m2)

vpv 76.2 V 67 V 64.4 V

20 V

6.74 A

ipv

3.35 A 5A

0.55 A 451.58 W

Ppv

215.74 W

41.91 W 168 V

244 V

300 W 200 V

71 V vdc 20s

Figure 7.8 Measured results of the simulated PV array in MPPT operation

(400 W/m2)

Step irradiation change (400 W/m2 600 W/m2) v pv

67 V

68.61 V i pv

20 V

10.1 A

6.74 A

10 A

Ppv 692.96 W 451.58 W

500 W

vdc 296 V 244 V

i L1

100 V

5A

iL1, iL2

i L2 20 s

Figure 7.9 Measured results of the simulated PV array for evaluating interleaving operation

178

7.3.4

Power electronics for next-generation drives and energy systems: Volume 2

Whole system operation

In whole system operation, the PV array is operated at voltage mode, while the home appliance loads with ZA ¼ ZB ¼ 100 W=110 V and ZAB ¼ 100 W=220 V incandescent lamps are connected to the load inverter output shown in Figure 7.1. Moreover, the energy storage devices including battery and SRM-driven flywheel (rated speed = 6,000 rpm) are used to provide energy support. Figure 7.10 shows PV source (one cell)

PV source (two cells)

Case A iL1

Case B

PV source disconnected battery plugs in

Battery disconnected flywheel discharged

Case C

Case D

iL1 , iL2 10 A ipv 10 A vdc 400 V

vdi

200 V

400 V

vab

200 V

311 V 500 V

vfw 64 V 10 V ω'r 5,200 rpm 1,500 rpm

ib

20 A i1

Battery discharged 100 A SRM

SRG 10s

Figure 7.10 Whole system operation of PV powered DC microgrid with different energy storage systems

PV powered DC microgrid

179

the measured results. To describe the operation in detail, four cases are listed as followed. Case A: The irradiance is chosen to be 600 W=m2 . To provide enough line-to-line voltage to the inverter load, the DC bus voltage is established by one cell of interleaved boost converter. Notice that the flywheel is externally powered by a power supply in advance. Case B: According to Figure 7.5(b), the maximum output current of PV array at 600 W=m2 is around 10 A. To emphasize on interleaving operation, the determination of cell number is based on the PV output current being higher than 5 A. It can be seen that the DC-bus voltage is well-established to support the home appliance. Case C: Suddenly the PV array is disconnected and the battery takes over. Although there exists spike, the performance of load inverter is not affected. Case D: The battery is suddenly disconnected and the flywheel starts to discharge. From the results, the flywheel can maintain the DC bus voltage around 22 sec.

7.4 Plug-in energy harvesting mechanism with AC source In this section, the developed plug-in energy harvesting mechanism with singlephase AC source using PV embedded converter cell is established, which is called single-phase SMR. Here, two constructions can be formed. One is to use one converter cell with externally added diode rectifier, the other is to use two converter cells without any addition. According to Figure 7.1, these two topologies can be depicted in Figure 7.11. The design and implementation of the one in Figure 7.11 (a) with PV array is introduced in detail here.

7.4.1 Power circuit In order to analyze the phenomenon, the voltage mode is applied to the SMR in Figure 7.11(a), at which only cell-3 converter is used. In Figure 7.11(a), a singlephase 220 V/60 Hz AC source with diode rectifier is adopted. The specifications are listed as followed. pffiffiffi Input AC voltage: Vag ¼ 220  10% Vrms , vag ¼ 2Vag sin ð120ptÞ Output DC voltage: Vdc ¼ 400 V Rated output power: Pdc ¼ 1:5 kW Inductor and filtering capacitor: L3 ¼ 854:5 mH=30 kHz, Cdc ¼ 2; 200 mF

7.4.2 Controller design The control scheme of the single-phase boost SMR shown in Figure 7.11(a) is given in Figure 7.12, which adopts outer voltage and inner current loops. The AC

180

Power electronics for next-generation drives and energy systems: Volume 2 L3

Ti3 Di3

iag Ti6

+

vag

Di6

–

Rdc

Cdc

+ vdc –

(a)

Ti1 iag

Ti2 Di1

L1

+ vag –

Di2

+ vinv –

L2

Cdc

Rdc

+ vdc –

Ti5

Ti4

Di4

Di5

(b)

Figure 7.11 Two topologies of plug-in energy harvesting mechanism with singlephase AC source: (a) one converter cell with externally added diode rectifier; (b) two converter cells without any addition

Control scheme of single-phase boost SMR

v*dc

Σ

Gcv (s)

v'dc

i'L3

A/D A/D

vdc

* iLr3 * iL3

iˆL*

Σ

v'ag

v'ag

iL3

Robust current controller Wi (s)

s(t)

Unitvector

v'ag

Σ

' iL3

εi

TMS320F28335

Gci (s) vdf

= 1–

vf bc v'ag

Σ

vcc vsaw3

Ti6

v'dc Feedforward controller

A/D

vag

Figure 7.12 Control scheme of single-phase boost SMR in Figure 7.11(a) voltage is sensed to generate proper current command, which should be in-phase to the grid. Moreover, the current robust and feedforward controllers are adopted to enhance the current tracking performance. In the design process, these two controllers are disable for simplicity.

PV powered DC microgrid

181

The voltage and current controllers are designed in discrete time. Because the switching frequency is set as fs ¼ 30 kHz, which is much higher than the line frequency (60 Hz). Hence it is assumed that the input voltage is constant over one switching cycle.

7.4.2.1 Dynamic modeling and current loop design By applying the state-space averaging method to the boost SMR, one can obtain the block diagram of current loop shown in Figure 7.13(a), where Dvpi ðsÞ ¼ Dvag ðsÞ = input voltage variation, D = duty ratio of PWM, Ki = current sensing factor = 0:303 V =A, Rdc = load resistance, KPWM = transfer ratio of the PWM modulator = 1. 0 The transfer function between duty ratio DdðsÞ and current feedback DiL3 ðsÞ in s-domain can be written as followed.  0  ðRdc Cdc s þ 2ÞVdc D DiL3 ðsÞ  Gpi ðsÞ ¼ (7.8) Dvpi ðsÞ¼0 ¼ Ki DdðsÞ  Rdc Cdc L3 s2 þ L3 s þ ð1  DÞ2 Rdc Then, (7.8) is transferred to z-domain by zero-order-hold (ZOH) method, and the resulted discrete block diagram is shown in Figure 7.13(b). Moreover, the current controller is set to be PI-type and transferred to z-domain using bilinear method as: Gci ðzÞ ¼ KPi þ

Ti 1 þ z1 KIi 2 1  z1

(7.9)

∆vpi (s) * (n) ∆iL3

∑

εi (n)

∆d (n)

∆vcont (n)

Gci (z)

KPWM

D/O

∆d (s)

Vdc

∑

∆i'L3 (n)

1 sL3

∆iL3 (s)

(1–D)

∆vdc (s) Rdc sRdcCdc + 1 Vdc Rdc (1 – D)

D/O: Digital output A/D: Analog to digital converter

A/D

∆i'L3 (s)

∑

(1 – D)

Ki = 0.0303

TMS320F28335

(a) ∆i*L3 (n)

εi (n)

∑

Gci (z)

KPWM

∆d (n)

Gpi (z)

' (n) ∆iL3

∆i'L3 (n)

(b)

Figure 7.13 Block diagrams of current loop for single-phase boost SMR: (a) derived in s-domain; (b) simplified in z-domain

182

Power electronics for next-generation drives and energy systems: Volume 2

where Ti = sampling time for current control loop and z1 denotes a delay operator. In generally speaking, the corner frequency fc of the closed-loop gain should be less than half switching frequency (fc < 0:5fs ). Hence, a proper current controller can be obtained through Bode plot. According to Figure 7.13(b), the loop gain can be derived as LGðzÞ ¼ Gci ðzÞKPWM Gpi ðzÞ

(7.10)

In the realized environment, Rdc ¼ 110 W and duty ratio is chosen a minimum value derived as followed: pffiffiffi b ag; min 400  220 2  0:9 Vdc  V ¼ 0:29996 (7.11) ¼ D ¼ Dmin ¼ Vdc 400 Next, the integral gain is set as KIi ¼ 3;000 to simply obtain a proper KPi . Under the preset sampling time Ti ¼ 33:33 ms (1=fs ), the Bode plots for (7.10) in magnitude with different KPi can be obtained as Figure 7.14 using MATLAB software. In this case, the corner frequency is chosen as fc ¼ 9 kHz < 0:5fs ¼ 15 kHz, and KPi ¼ 3 can satisfy the requirement. Hence the current controller can finally be obtained as followed: Gci ðsÞ ¼ 3 þ

7.4.2.2

3;000 1 þ z1 or Gci ðzÞ ¼ 3 þ 0:05 1  z1 s

(7.12)

Model estimation

Under the assumption that DiL3 ðnÞ  Di0L3 ðnÞ, the block diagram for the voltage loop considering the disturbance from output power can be depicted in Figure 7.15(a).

100 KIi = 3,000

Magnitude (dB)

80 60 40 KPi = 1

15 kHz

20 KPi = 3 KPi = 5

0 20 10–1

100

101

102 Frequency (Hz)

103

104

Figure 7.14 Bode plots of loop gain in magnitude with different values of KPi

PV powered DC microgrid

183

∆Pdc TMS320F28335 ∆v*dc (n)

εv (n)

kpl Gcv (z)

SMR model

iˆL3 (n)

Gpv (s) b s+a

Kv

∆v'dc

∆v'dc (n) (a) ∆Pdc (n) Tv = 0.333ms k pl εv (n)

∆v*dc (n)

Gcv (z)

∆v'dc (n)

iˆL3 (n)

b K (1– e–aTv) a v φ ∆ e–aTv θ

∆

Gpv (z) – ∆v'dc (n) θz 1 1– φ z –1

(b)

Figure 7.15 Block diagram of voltage loop: (a) general illustration; (b) derived block diagram in z-domain

Note that Gcv ðzÞ = voltage controller in discrete time, kpl = power transfer ratio related to output voltage, Kv = voltage sensing factor = 0:002 V =V and Gpv ðsÞ represents plant transfer function in s-domain. The voltage controller is designed in discrete time as well. Considering voltage sensing factor, plant model Gpv ðsÞ is transferred to discrete time using ZOH method as: 

Gpv ðsÞ Gpv ðzÞ ¼ Kv ð1  z ÞZ s 1



aTv

b z1 Kv ð1  e Þ D q z1 ¼ ¼ 1  j z1 a 1  z1 eaTv

(7.13)

where the sampling time for voltage loop is Tv ¼ 0:333 ms. Accordingly, the derived voltage loop block diagram can be expressed as Figure 7.15(b). To estimate the SMR model (kpl , q, and j), tracking and regulation responses under a specific load condition should be applied, which are discussed separately below. Notice that the feedback controller is set to be P-type for simplicity, i.e., Gcv ðzÞ ¼ KPv ¼ 2. Property 1 (tracking response): According to Figure 7.15(b), the closed-loop transfer function can be derived as  0 KPv q z1 D Dvdc ðnÞ  (7.14) Gdr ðzÞ ¼ DPdc ðnÞ¼0 ¼   1 þ ðKPv q  jÞz1 Dvdc ðnÞ

184

Power electronics for next-generation drives and energy systems: Volume 2 Further, the steady-state value of (7.14) can be expressed as  0 KPv q D Dvdc ðnÞ  1 Gdr ðz ¼ 1Þ ¼ DPdc ðnÞ¼0 ¼   Dvdc ðnÞ 1 þ KPv q  j

(7.15)

Through careful derivation, the expression of step voltage response of (7.14) can be found as below: 0

Dvdc ðnÞ ¼

n KPv q ð1  ðj  KPv qÞ Þ; n ¼ 0; 1; 2; :::: 1  ðj  KPv qÞ

(7.16)

0

Define the rise time tr ¼ nr Tv be the time at Dvdc ðnr Þ ¼ ð1  e1 ÞGdr ðz1 ¼ 1Þ, one can obtain: nr ¼

1 lnðj  KPv qÞ

(7.17)

Property 2 (regulation response): The steady-state value of the voltage response due to a step output power change can be derived according to Figure 7.15(b) as  0 kpl q D Dvdc ðnÞ  1 (7.18) Gdd ðz ¼ 1Þ ¼ Dvdc ðnÞ¼0;z1 ¼1 ¼  DPdc ðnÞ 1  j þ Kpv q 0

Figure 7.16(a) shows the measured ðvdc ; vdc Þ due to a step voltage command change of Dvdc ¼ 20 V at the operating point ðvdc ; Pdc Þ ¼ ð400 V; 925 WÞ. One can obtain the steady-state value is 415.3 V and a rise time is 31.15 ms. * vdc

420V 415.3V 10V

v'dc

400V

(a)

0.2s

400V

v'dc

10V 382.5V

(b)

0.2s

Figure 7.16 Measured tracking and regulation responses at the operating point ðvdc ; Pdc Þ ¼ ð400 V; 925 WÞ for KPv ¼ 2: (a) step voltage command change of Dvdc ¼ 20 V; (b) step load change of DPdc ¼ 529:69 W

PV powered DC microgrid

185

Accordingly, nr ¼ tr =Tv ffi 94 can be found. Moreover, Figure 7.16(b) shows the 0 measured vdc due to a step load change of DPdc ¼ 529:69 W at the same operating point. The steady-state value of 382.5 V is obtained. Consequently, three equations can be listed from the measured results as follows: 0

Dvdc ¼ Dvdc Kv ¼ 0:0306 ¼ 0

Dvdc ¼ Dvdc Kv ¼ 0:035 ¼ nr ¼ 94 ¼

KPv q 1 þ KPv q  j

(7.19)

kpl q 1  j þ Kpv q

(7.20)

1 lnðj  KPv qÞ

(7.21)

Further using MATLAB software to solve the parameters, one can obtain: 4

q ¼ 0:004047; j ¼ 0:997513; kpl ¼ 1:72748  10

(7.22)

The simulated voltage tracking and regulation responses using PSIM software due to the same step command and load changes are shown in Figure 7.17. As the results, the estimated system parameters under specific operating point are correct, which can emulate the same behavior as the measured ones.

v*dc

420 V 415.3 V v'dc

400 V

(a)

10 V

0.2 s

400 V 10 V

v'dc 382.5 V

(b)

0.2 s

Figure 7.17 Simulated tracking and regulation responses at the operating point ðvdc ; Pdc Þ ¼ ð400 V; 925 WÞ for KPv ¼ 2: (a) step voltage command change of Dvdc ¼ 20 V; (b) step load change of DPdc ¼ 529:69 W

186

Power electronics for next-generation drives and energy systems: Volume 2

7.4.2.3

Voltage controller design

After obtaining the system parameters, the voltage control is changed to be PI-type controller, which is expressed in z-domain using bilinear method as followed: Gcv ðzÞ ¼ KPv þ

Tv 1 þ z1 KIv 2 1  z1

(7.23)

According to Figure 7.15(b), the transfer function between output voltage and output power change can be carefully derived as: Dvdc ðnÞ  Dv ðnÞ¼0 DPdc ðnÞ dc 0

Gdd ðzÞ ¼

qkpl z1 ð1  z1 Þ 
 Tv Tv 1 þ KPv q þ KIv q  j  1 z1 þ j  KPv q þ KIv q z2 2 2  1 1 1 qkpl z ð1  z Þ ð1  l1 Þqkpl z ð1  l2 Þqkpl z1 1 D ¼ ¼  1  l1 z1 1  l2 z1 ð1  l1 z1 Þð1  l2 z1 Þ l1  l2 (7.24) ¼




where l1 þ l2 ¼ j þ 1  KPv q  l1 l2 ¼ j  KPv q þ

Tv KIv q 2

Tv KIv q 2

(7.25) (7.26)

Assumed that the voltage controller is designed to let two closed-loop poles be real and have the following relationship: 0 < l2 < l1 < 1

(7.27)

Through careful derivation from (7.24), the output voltage response due to a step load power change can be expressed as: n

n

qkpl ðl1  l2 Þ Dvdc ðnÞ ¼ ; n 0 l1  l2 0

(7.28)

From (7.24) to (7.28), some properties of the resulted response can be listed: 0

Property 1: Dvdc ð1Þ ¼ 0, which means zero steady-state error. 0 Property 2: Dvdc ðnÞ  0; 8n 0, which means no overshoot exists. Property 3: According to (7.28), total output voltage change can be calculated as: DvTdc

1 X n¼0

0

Dvdc ðnÞ ¼

n n 1 X qkpl ðl1  l2 Þ qkpl ¼  l ð1  l l 1 2 1 Þð1  l2 Þ n¼0

(7.29)

PV powered DC microgrid

187

400 V 5V

vdc

(a)

0.2 s

400 V

5V vdc

(b)

0.2 s

Figure 7.18 Output voltage due to a step load change of Rdc ¼ 173W ! 110W (Pdc ¼ 529:69 W) at the operating point ðvdc ; Pdc Þ ¼ ð400 V; 925 WÞ: (a) simulated result; (b) measured result Design example: First, two closed-loop poles are defined from (7.27) as l1 ¼ l2 þ d; 0 < d < 1

(7.30)

By defining DvTdc and d, pole l2 can be solved from (7.29) and (7.30) as: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 2 2 T T (7.31) ð2Dv  Dv d þ ðDvTdc Þ d  4qkpl DvTdc Þ l2 ¼ dc dc 2DvTdc Then pole l1 can be obtained accordingly. Further substituting l1 and l2 into (7.25) and (7.26), the voltage controller can be obtained. For example, by defining DvTdc ¼ 10 V; d ¼ 0:02, the designed voltage controller can be obtained as: Gcv ðsÞ ¼ 5:1654 þ

27:4785 1 þ z1 or Gcv ðzÞ ¼ 5:1654 þ 0:00458 1  z1 s

(7.32)

Figure 7.18 shows the simulated and measured results of the DC-bus voltage due to a step load change of Rdc ¼ 173W ! 110W (DPdc ¼ 529:69 W) under the operating point of ðvdc ; Pdc Þ ¼ð400 V; 925 WÞ. One can find the first-order voltage regulation response, while the simulated result is very close to the measured one.

7.4.2.4 Current robust and feedforward controllers Because the current distortion is inherently existed around the zero-crossing point, the current robust and feedforward controllers are added to enhance the current

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Power electronics for next-generation drives and energy systems: Volume 2

tracking performance, which are defined as followed: Wi 0:95 1 ¼ ; fc ¼ ¼ 5:026 kHz 1 þ ti s 1 þ 1:9894  104 s ti  0  0   ¼ 1  vag =vdc

Wi ðsÞ ¼

(7.33)

vdf

(7.34)

7.4.3

Evaluation of the proposed control scheme

To evaluate the proposed control scheme, all the controllers are added orderly, which includes PI only, PI+current robust, PI+current feedforward and PI+current robust+ current feedforward. Figure 7.19 shows the key measured waveforms under rated load, and the measured power qualities are listed in Table 7.2. As the results, as all the control methods are adopted, PF and THD can be effectively improved.

7.4.4

PV array with plug-in single-phase AC source

To evaluate the plug-in energy support mechanism, this part considers the maximum power that PV array can provide. Continued from the control scheme in Figure 7.6, the switch is at the position Ⓥ for conducting voltage mode. Once the

400 V

vdc

400 V

vdc

20 V

vag iag

20 V

vag iag

200 V

200 V 20 A

20 A

i'L3

i'L3 20 A

20 A

i*L3

i*L3

(a) 400 V

(c)

5 ms

vdc

400 V

5 ms

vdc

20 V

vag iag

20 V

vag iag

200 V

200 V

20 A

20 A

i'L3

i'L3 20 A

20 A

i*L3

i*L3

(b)

5 ms

(d)

5 ms

Figure 7.19 Measured results under Rdc ¼ 110W ðPdc ¼ 1;454:54 WÞ with four control methods: (a) PI only; (b) PI+robust; (c) PI+feedforward; (d) PI+robust+feedforward

PV powered DC microgrid

189

Table 7.2 Measured power qualities of the established single-phase boost SMR under Rdc ¼ 110W ðPdc ¼ 1;454:54 WÞ with four control methods Data

PI only

PIþrobust

PIþfeedforward

PIþrobustþ feedforward

Vag ðVrms Þ Iag ðArms Þ Iag;thd ð%Þ PF Pag ðWÞ Vdc ðVÞ Idc ðAÞ Pdc ðWÞ h ð%Þ

217.72 7.205 21.61 0.9565 1,500.37 399.46 3.591 1,434.5 95.61

217.52 7.098 12.92 0.9763 1,505.54 400.22 3.5961 1,439.3 95.60

216.69 7.098 11.07 0.9782 1,504.60 399.7 3.5970 1,437.8 95.56

217.6 6.99 9.86 0.9881 1,502.98 399.76 3.5952 1,437.3 95.63

Interleaved boost DC-DC converter control scheme (PV+ single-phase plug-in mechanism)

Σ

Wv (s)

* vdcr * vdc

DSP TMS320F28335 i*L1

Σ

* vdc 1

Σ

εv

Gcv (s)

iL*

v'dc vdc

A/D

' vdc

v pv

A/D

v'pv

i'L1

Σ

iL' 2

i'L3

A/D A/D A/D

iL1

Cell selector / current command generator

iL 2

iL 3

' vag

A/D

iˆL* s(t ) Unitvector

i*L 2

Σ

εi

vca

Gci (s)

i'L1

vsaw1

εi

vcb

Gci (s)

vsaw2

i'L 2 i*Lr 3

i*L3

Σ

Ti4

Ti5

Wi (s)

Σ

εi i'L3

vcc

Gci (s )

vsaw3

Ti6

vag

Figure 7.20 Control scheme of PV interleaved boost converter with single-phase AC source

solar energy is insufficient, the single-phase AC source can be inputted. Here, the resistive load, battery and flywheel are employed to evaluate the parallel operation. For the case of resistive and battery loads, the irradiance is chosen to be 600 W=m2 , while 1;000 W=m2 is set for powering SRM-driven flywheel. Figure 7.20 shows the control scheme of PV interleaved boost converter with single-phase AC source, which has been designed previously. Notice that the converter cell-3 is ready for conducting plug-in energy support.

7.4.4.1 Parallel operation for resistive load and battery Figure 7.21 provides the measured results of the PV array powered DC microgrid with resistive load and battery. To easily observe the configurable ability, two

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Power electronics for next-generation drives and energy systems: Volume 2 PVsource (two cells)

1Ф AC input plugs in

Battery charged

Battery removed

1Ф AC input plugs out

vpv 77.2 V

69.0 V

20 V

i pv 7.25 A

10.05 A 10 A

Ppv 559.7 W

693.45 W 500 W vdc

400 V

50 V

iL1 , iL2 3.625 A

5.025 A

4.03 A

6.53 A

5A

i L3

10 A

vb 48.3 V

(52.27 V)

10 V

(–6.5 A)

10 A

ib

5s

Figure 7.21 Measured results of PV array powered DC microgrid with resistive load and battery converter cells are directly used. First, the interleaved boost converter is operated under vdc ¼ 400 V and Rdc ¼ 300W. Then a step load change of Rdc ¼ 300W ! 150W (DPdc ¼ 533:3 W) is applied. According to the designed P–V curve in Figure 7.5(b), the maximum power point is 692 W. Hence the single-phase AC source (220 V/60 Hz) is plugged-in automatically to support the microgrid. Next, the battery charging current command of 6.5 A is set, and the power is fully provided by the AC source. From the results, the plug-in energy support mechanism can maintain the powering reliability of the PV powered DC microgrid.

7.4.4.2

Parallel operation for SRM-driven flywheel

Figure 7.22 shows the measured results of the PV array powered DC microgrid with SRM-driven flywheel. At first, one cell of interleaved boost converter is conducted to maintain DC bus voltage and input voltage of asymmetric bridge converter (Vfw ¼ 64V). Then a ramp speed command is applied. To perform current sharing, two cells are conducted as the PV current is higher than 8 A. As the speed is around 2,400 rpm, the maximum power is reached. Hence the single-phase AC source is inputted. Accordingly, similar phenomenon can be seen.

PV powered DC microgrid PV source (one cell)

191

Single-phase AC input plugs in

PV source (two cells)

vpv

88.78 V

72.16 V

ipv

20 V

16.2 A 20 A 1,169 W

700 W

Ppv vdc

400 V

20 V

iL 1 , iL 2

iL 1

8.1 A

10 A iL 3 10 A

vfw 10 V

64 V 5,200 rpm

ω'r

3,000 rpm

ω*r

i1 50 A

Flywheel charged 50 s

Figure 7.22 Measured results of PV array powered DC microgrid with SRMdriven flywheel

7.5 Interconnected M2V/V2M operations between PV powered DC microgrid and EV SRM drive System configuration of the developed PV powered DC microgrid with cooperated EV SRM drive is shown in Figure 7.23. In the battery/SC powered SRM drive, the DC-link voltage is established from the battery via an H-bridge DC–DC converter to let its voltage be higher or lower than the battery voltage, while the SC is interfaced to the DC-link via a one-leg bilateral DC–DC converter to assist the

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Power electronics for next-generation drives and energy systems: Volume 2

M2G/G2M

iuA Pu

MC

a

van ~ vab ~ 220V

Po

A

110V

ZA

iuN

n

v AN

ZAB

110V

iuB

b

ZB v NB

Pdc

Interleaved bidirectional boost-buck converter

Sc

ib 2

Da

vb 2

Cb 2

Psc

iLb Lb 2 isc

Sb

Sd Db

Battery

EV SRM drive

vsc

Lsc

Csc

SC

Microgrid

Battery

D11

Emulated SRM mechanical

L3 EC

i3

i2

Load

A, B, Z

Q4

Q6

D2

D4

Dynamic brake leg

Q8 D6

Q10

D8

Q12 D10

D12

Asymmetric bridge converter and SRM

Emulated mechanical load

Pm

EV wheel load 156V

Q11

D9

load

Emulated mechanical load M2V/ V2M

Q9 D7

L2

i1

Rdb

Q7 D5

L1 vdc 2 idb D13 Q2

SC interface converter

Q5 D3

D1

Sf Df

Dd

H-bridge converter

C dc 2

Q3

Q1

De

Dc

M2V/ V2M

PV-based micro-grid

Q13

Se

vb

Cb

Di 6

Di5

Pdc

Sa

Db 2

Ti 6

DC vb source

M2V/ V2M

Pb

ib Lb

Cdc

Sb 2

Ti5

Ti 4

vag

Db1

vdc

Di 4 - 1P mains - AC source - DC source

vdc

Sb1

Di3

Di 2

Di1

i L3 Li3

SW3

Cd

ip

BESS

Ti3

SW2 i L 2 Li 2

iL

v pv

Ti 2

Ti1

SW1 i Li1 L1

i pv

Cp

i LB

B

Ppv

Lm

Cs

vcp

Load

Utility grid

Pb2

Cdi

vdi

400V DC-bus

Lp

Ls 1:1

Singlephase three-wire inverter

v AB

N

vnb ~

Microgrid

CLLC DC-DC converter

iLA

SRM

Flywheel

Flywheel DCG

vb DCG Oil pump

Rm

Gear box Oil pump

Figure 7.23 Power circuit of the developed PV powered DC microgrid with cooperated EV SRM drive

battery in reducing the frequent short-term charging and discharging operations. Under EV idle mode, the DC microgrid can charge the EV on-board battery under M2V operation, while the battery can support the microgrid in V2M operation. For the PV-based microgrid, it is directly connected to the input of converter cell-3. Some measured results are given to show the possibility of interconnection between microgrid and EV.

PV powered DC microgrid

193

vb2

152.2 V

10 V

i L3

2A (a)

20 μs

399.5 V

vdc

403.2 V

vdi

20 V

20 V

ip

5A vcp

200 V

(b)

10 μs vAB iLA

200 V 5A

vAN iLA

100 V 5A

(c)

5 ms

Figure 7.24 Measured results of V2M operation: (a) EV battery (vb2 ; iL3 ); (b) CLLC resonant converter (vdc ; vdi ), (ip ; vcp ); (c) 1P3W inverter (vAB ; iLA ), (vAN ; iLA )

7.5.1 V2M discharging operation Let the DC microgrid local loads in Figure 7.23 be arranged as the incandescent lamps with ZA ¼ ZB ¼ 100 W=110 V and ZAB ¼ 100 W=220 V being powered by the EV battery through the one-leg interface converter (Ti3 ; Ti6 ; Li3 ). Figure 7.24 shows the measured results of all constituted power stages. Through the one-leg converter, the EV battery voltage is boosted to establish the microgrid common

194

Power electronics for next-generation drives and energy systems: Volume 2 iL1 iL2

6A 1A iL = iL1 + iL2

12 A 1A 400.2 V

vdc

20 V (a)

153.75 V

20 μs vb2

10 V

ib2 = – i L3

2A

(b)

20 μs

Figure 7.25 Measured results of M2V operation: (a) PV interleaved converter ((iL1 ; iL2 ), (iL ; vdc )); (b) EV battery and interface converter (vb2 , (ib2 ¼ iL3 )) DC-bus voltage. Then the single-phase 110 V/220 V AC voltages are generated through the CLLC converter and the 1P3W inverter to power the local home loads with good waveform qualities.

7.5.2

M2V charging operation

Now, the EV on-board battery is charged by the PV powered microgrid via the interleaved converter with two cells. Let the PV voltage be fixed at vpv ¼ 70 V, the battery is charged with a constant current of 5 A. The measured results are plotted in Figure 7.25. Successful charging operation can be aware from the results.

7.6 Conclusion This chapter has developed a PV-based DC microgrid with plug-in energy support and EV cooperated functions. Through a commercialized DC power supply, the behavior of PV array can be simulated. To possess interleaving operation and reconfigurable capability, a three-cell interleaved converter is constructed to establish microgrid common DC-bus voltage. Under light load, the PV can support the microgrid and the converter cell can be changed to maintain good energy conversion efficiency. As the solar energy is insufficient, the converter can further

PV powered DC microgrid

195

switch to extract power from available source, i.e. single-phase AC source in this chapter. Furthermore, to expand the diversity of develop PV-based microgrid, an EV SRM drive is interconnected. Under V2M operation, the EV on-board battery is directly plugged-in through the embedded converter cell of PV interface. The measured results show good operation for powering the household appliance. As to the M2V operation, the battery can be charged by the PV. Normal operation of battery can be seen as well.

References [1] D. Boroyevich, I. Cvetkovic, and D. Dong (eds.). ‘Future electronic power distribution systems a contemplative view’. In Proceedings of 12th IEEE OPTIM; Brasov, Romania, May 2010. pp. 1369–1380. [2] H. Valderrama-Blavi, J. M. Bosque, and F. Guinjoan (eds.). ‘Power adaptor device for domestic DC microgrids based on commercial MPPT inverters’. IEEE Trans. Ind. Electron. 2013, vol. 60(3), pp. 1191–1203. [3] T. Dragicevic, J. M. Guerrero, and J. C. Vasquez (eds.). ‘Supervisory control of an adaptive-droop regulated DC microgrid with battery management capability’. IEEE Trans. Power Electron. 2014, vol. 29(2), pp. 695–706. [4] M. Patterson, N. F. Macia, and A. M. Kannan. ‘Hybrid microgrid model based on solar photovoltaic battery fuel cell system for intermittent load applications’. IEEE Trans Energy Convers. 2015, vol. 30(1), pp. 359–366. [5] P. A. Madduri, J. Poon, and J. Rosa (eds.). ‘Scalable DC microgrids for rural electrification in emerging regions’. IEEE Trans. Emerg. Sel. Topics Power Electron. 2016, vol. 4(4), pp. 1195–1205. [6] T. Ma, M. H. Cintuglu, and O. A. Mohammed. ‘Control of a hybrid AC/DC microgrid involving energy storage and pulsed loads’. IEEE Trans Ind. Appl. 2017, vol. 53(1), pp. 567–575. [7] A. Merabet, K. T. Ahmed, and H. Ibrahim (eds.). ‘Energy management and control system for laboratory scale microgrid based wind-PV-battery’. IEEE Trans. Sustain. Energy. 2017, vol. 8(1), pp. 145–154. [8] M. Kamel, R. A. Zane, and D. Maksimovi´c. ‘Voltage sharing with series output connected battery modules in a plug-and-play DC microgrid’. IEEE Trans. Power Electon. 2021, vol. 36(11), pp. 13118–13127. [9] T. Esram and P. L. Chapman. ‘Comparison of photovoltaic array maximum power tracking techniques’. IEEE Trans. Energy Convers. 2007, vol. 22(2), pp. 439–449. [10] T. K. Soon and S. Mek. ‘A fast-converging MPPT technique for photovoltaic system under fast-verying solar irradiation and load resistance’. IEEE Trans. Ind. Informat. 2015, vol. 11(1), pp. 176–186. [11] H. Choi, M. Ciobotaru, and M. Jang (eds.). ‘Performance of medium-voltage DC-bus PV system architecture utilizing high-gain DC–DC converter’. IEEE Trans. Sustain. Energy 2015, vol. 6(2), pp. 464–473.

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A. Sangwongwanich, G. Angenendt, and S. Zurmu¨hlen (eds.). ‘Enhancing PV inverter reliability with battery system control strategy’. IEEE CPSS Trans. Power Electron. 2018, vol. 3(2), pp. 93–101. V. Monteiro, J. G. Pinto, and J. L. Afonso. ‘Experimental validation of a three-port integrated topology to interface electric vehicles and renewables with the electrical grid’. IEEE Trans. Ind. Informat. 2018, vol. 14(6), pp. 2364–2374. P. K. Achanta, B. B. Johnson, and G. S. Seo (eds.). ‘A multilevel DC to three-phase AC architecture for photovoltaic power plants’. IEEE Trans. Energy Convers. 2019, vol. 34(1), pp. 181–190. H. Mahmood and J. Jiang. ‘Autonomous coordination of multiple PV/battery hybrid units in islanded microgrids’. IEEE Trans. Smart Grid. 2018, vol. 9 (6), pp. 6359–6368. M. Adly and K. Strunz. ‘Irradiance-adaptive PV module integrated converter for high efficiency and power quality in standalone and DC microgrid applications’. IEEE Trans. Ind. Electron. 2018, vol. 66(1), pp. 436–446. N. Mohan, T. M. Undeland, and W. P. Robbins. Power Electronics: Converters, Applications, and Design, 3rd edn. New Jersey: John Wiley & Sons, 2002. F. Blaabjerg, F. Iov, and R. Teodorescu (eds.). ‘Power electronics in renewable energy systems’. In Proceedings of IEEE EPE-PEMC; Portoroz, Slovenia, 2006, pp. 1–17. P. Biczel. ‘Power electronic converters in DC microgrid’. In Proceeding of IEEE CPE; Gdansk, Poland, 2007, pp. 1–6. L. Palma and P. N. Enjeti. ‘A modular fuel cell, modular DC–DC converter concept for high performance and enhance reliability’. IEEE Trans. Power Electron. 2009, vol. 24(6), pp. 1437–1443. S. S. Williamson, A. K. Rathore, and F. Musavi. ‘Industrial electronics for electric transportation: current state-of-the-art and future challenges’. IEEE Trans. Ind. Electron. 2015, vol. 62(5), pp. 3021–3032. T. Dragicevic, X. Lu, and J. C. Vasquez (eds.). ‘DC microgrids – Part II: a review of power architectures, applications, and standardization issues’. IEEE Trans. Power Electron. 2016, vol. 31(5), pp. 3528–3549. D. Kumar, F. Zare, and A. Ghosh. ‘DC microgrid technology: system architectures AC grid interfaces, grounding schemes, power quality, communication networks, applications, and standardizations aspects’. IEEE Access. 2017, vol. 5, pp. 12230–12256. N. D. Dao, D. C. Lee, and Q. D. Phan. ‘High-efficiency SiC-based isolated three-port DC/DC converters for hybrid charging stations’. IEEE Trans. Power Electon. 2020, vol. 35(10), pp. 10455–10465. H. Aljarajreh, D. D. Lu, and Y. P. Siwakoti (eds). ‘A method of seamless transitions between different operating modes for three-port DC–DC converters’. IEEE Access. 2021, vol. 9, pp. 59184–59195. O. Garcia, J. A. Cobos, and R. Prieto. ‘Single phase power factor correction: a survey’. IEEE Trans. Power Electron. 2003, vol. 18(3), pp. 749–755.

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[27] A.V. J. S. Praneeth and S. S. Williamson. ‘A review of front end AC–DC topologies in universal battery charger for electric transportation’. In Proceeding of IEEE ITEC; Long Beach, CA, USA, Jun. 2018, pp. 293–298. [28] L. Huber, J. Yungtaek, and M. M. Jovanovic. ‘Performance evaluation of bridgeless PFC boost rectifiers’. IEEE Trans. Power Electron. 2008, vol. 23 (3), pp. 1381–1390. [29] F. Musavi, W. Eberle, and W. G. Dunford. ‘A high-performance singlephase bridgeless interleaved PFC converter for plug-in hybrid electric vehicle battery chargers’. IEEE Trans. Ind. 2011, vol. 47(4), pp. 1833–1843. [30] F. Musavi, M. Edington, and W. Eberle (eds.). ‘Evaluation and efficiency comparison of front end AC–DC plug-in hybrid charger topologies’. IEEE Trans. Smart Grid. 2012, vol. 3(3), pp. 413–421. [31] V. Monteiro, J. G. Pinto, and J. L. Afonso. ‘Operation modes for the electric vehicle in smart grids and smart homes: present and proposed modes’. IEEE Trans. Veh. Technol. 2016, vol. 65(3), pp. 1007–1020. [32] D. Kumar, F. Zare, and A. Ghosh. ‘DC microgrid technology: system architectures, AC grid interfaces, grounding schemes, power quality, communication networks, applications, and standardizations aspects’. IEEE Access. 2017, vol. 5, pp.12230–12256. [33] M. Abul Masrur, A. G. Skowronska, and J. Hancock (eds.). ‘Military-based vehicle-to-grid and vehicle-to-vehicle microgrid—system architecture and implementation’. IEEE Trans. Transp. Electrific. 2018, vol. 4(1), pp. 157–171. [34] R. Lamedica, A. Geri, and F. M. Gatta (eds.). ‘Integrating electric vehicles in microgrids: overview on hosting capacity and new controls’. IEEE Trans. Ind. Appl., 2019, vol. 55(6), pp. 7338–7346. [35] H. Tahara, N. Urasaki, and T. Senjyu (eds.). ‘EV charging station using renewable energy’. In Proceeding of IEEE 1st Int. Conf. Control. Meas. Instrum; Kolkata, India. Jan. 2016, pp. 48–52. [36] C. Gan, N. Jin, and Q. Q. Sun (eds.). ‘Multiport bidirectional SRM drives for solar-assisted hybrid electric bus powertrain with flexible driving and selfcharging functions’. IEEE Trans. Power Electron. 2018, vol. 33(10), pp. 8231–8245. [37] V. Monteiro, J. G. Pinto, and J. L. Afonso. ‘Experimental validation of a three-port integrated topology to interface electric vehicles and renewables with the electrical grid’. IEEE Trans. Ind. Infomat. 2018, vol. 14(6), pp. 2364–2374. [38] G. R. Chandra Mouli, J. Schijffelen, and M. van den Heuvel (eds). ‘A 10 kW solar-powered bidirectional EV charger compatible with chademo and COMBO’. IEEE Trans. Power Electon. 2019, vol. 34(2), pp. 1082–1098.

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Chapter 8

Power electronics technology and applications in clean generation and power grids Abdullrahman A. Al-Shamma’a1,2 and Hassan M. Hussein Farh1

The deployment of renewable energy sources (RES) results in a degradation of power quality, necessitating the use of power conditioners such as active filters to alleviate these difficulties, while passive filters are inadequate of entirely mitigating power quality concerns in the presence of nonlinear loads. Numerous research have been undertaken to combine RES with active power filters in order to maximize the benefits of both RES and power conditioners in order to offer high-quality energy to customers. This chapter addresses the operation of RES in combination with active power filters. It also includes an interesting section on dual unified power quality conditioner (UPQC) operation and control, which helps clarify the active filter technologies that are viable for inclusion in the DG framework. The suggested UPQC is composed of an open-end winding transformers and series connected voltage source converters (VSCs). The proposed UPQC utilizes two photovoltaic arrays, with each array feeding a different dc-link of a two threephase VSC. The feasibility of the suggested UPQC has been proven by simulated tests using Simulink“/MATLAB“.

8.1 Introduction The fast depletion of conventional energy supplies and the increase in global energy consumption have necessitated an urgent search for alternative energy sources to offset the increase in global consumption during the previous decade. Another motivation to decrease our reliance on conventional energy sources is the phenomenon of global warming, which has resulted in significant environmental change. Therefore, it is critical to develop alternate energy sources to meet the continually rising energy demand over the previous decade while minimizing 1 Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia 2 Department of Mechatronics Engineering, College of Engineering, Taiz University, Yemen

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Power electronics for next-generation drives and energy systems: Volume 2

negative environmental impacts. Distributed generation (DG) technologies such as solar and wind energy have got the interest of energy sectors as viable alternatives for producing electrical energy in grid-connected or islanding modes [1]. DGs can provide economic benefits by lowering transmission line losses and the cost of high-voltage apparatus [2]. Numerous studies have proposed that the DG should be connected to the grid via a VSC, which might be an intermediary DC/DC converter, a transformer, or possibly both. The DC/DC converter is used to extract the maximum power (MPPT) of the system [3, 4]. The VSC’s primary objective is to connect DGs to the grid in a manner that complies with power quality (PQ) regulations. However, if not performed correctly, high-frequency switching of VSC generates extra harmonic components into the grid, resulting in PQ difficulties [5]. Various techniques for addressing PQ difficulties have been offered in various studies to far. Ref. [6] introduced the concept of shunt and series active power filters (APFs) for mitigating voltage and current distortion. A series APF can be used to correct voltage issues such as voltage sag/swell, voltage harmonics, flickering, and imbalances. Shunt APFs are used to handle current issues such harmonics and unbalances, as well as the reactive power adjustment [7]. Recent research on PQ enhancement techniques and devices has demonstrated that the unified power quality conditioner (UPQC) is a perfect solution for all voltage and current concerns; it was initially published in [8], and the experimental results of its setup were given in 1998 [9]. Combining PQ enhancement with RES integration leads in more efficient use of UPQC and cost savings. A shunt APF mitigates current harmonics, imbalance, and reactive power in a UPQC [10]. The series APF reduces voltage sag/swell and grid voltage distortion [11]. In all of the above topologies, the DC link capacitor is a crucial component of the compensator. Deep voltage sag/ swell rectification and interruption are not achievable due to the restricted quantity of energy stored in a DC capacitor. Over the last decade, DGs including as photovoltaic (PV), and wind turbine have been progressively integrated into distribution networks [12]. However, the uses of RES result in poor PQ, necessitating the usage of power conditioners such as APFs to minimize these issues when nonlinear loads are present. These issues can be resolved by including RES as an active power source on a DC bus with an APF, which improves stability, voltage management, and power quality. Several studies have been conducted to combine RES with APFs in order to take use of both RES and APF benefits in order to offer high-quality energy to the grid. This chapter discusses the functioning of RES in conjunction with APFs. It is divided into three application-specific sections: ▪ ▪ ▪

Renewable energy sources-based shunt active filter, Renewable energy sources-based dynamic voltage restorer, and Renewable energy sources-based unified power quality conditioner.

Throughout this chapter, all topologies are given in terms of grid-connected systems. This chapter also includes an interesting part on UPQC-PV operation and control, which helps clarify the active filter technologies that are possible for inclusion in the DG framework.

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8.2 Renewable sources-based shunt active filter Due to the fact that a grid-connected photovoltaic (PV) system runs during the day and shuts down at night, the power system’s stability and solar system utilization are impacted. As a result, in order to maximize system usage, the photovoltaic system may also be employed to provide PQ enhancements. The authors in [13–17] suggested a three-phase three-wire shunt active filter (SAF) fed by PV arrays or by PV arrays plus storage systems for distribution system PQ enhancement [18,19]. The PV inverter acts as a SAF, mitigating harmonics, unbalances, reactive power, and supply voltage changes while simultaneously injecting the greatest amount of solar energy accessible to the grid. The PV is linked to the SAF through a DC/DC converter that extracts the MPPT [20]. Figure 8.1 illustrates the fundamental notion. The storage system, which acts as a critical grid backup during periods of high demand, is connected to the SAF through a bidirectional buck/boost converter, which manages the charging and discharging of the batteries. Ref. [21] connected the storage system directly to the photovoltaic array’s output terminals, while the boost converter regulates the DC link voltage. When solar radiation is not available, 3 Phase AC Mains

PCC

Zs Zs Zs

Load Linear Load RL XL

Lc

Lc

Lc

Nonlinear Load

DC link

+

DC/DC Boost Converter

S1

S1' S2'

vdc

V

Cdc

Cpv

S3'

I

S3

Photovoltaic Arrays

S2

Shunt Active Filter

–

+

Battery Banks

Bi-directional DC/DC Converter

–

Figure 8.1 PV-SAF topology

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the SAF may still be utilized to improve the quality of the power. When solar radiation is available, the PV system can provide electricity to the load while also mitigating power quality issues such as harmonics, unbalanced loads, and reactive power. Khadkikar et al. introduced an interline-photovoltaic (I-PV) system in [22, 23]. Two feeders are linked in the I-PV through a shunt-connected converter. Figure 8.2 illustrates an I-PV power system with a single line diagram. The I-PV scheme enables active and reactive power flow regulation, as well as energy management, in a multi-line system. This setup is comparable to the interline power flow controller (IPFC) in terms of construction. However, the I-PV system connects two feeders by shunt-connected back-to-back converters, whereas the IPFC system connects feeders via series-connected back-to-back converters. Due to the incorporation of nonlinear loads into the distribution system, the wind system’s primary function is not only to extract the highest amount of energy possible from the wind, but also to reduce PQ issues [24–26]. Because of their higher energy production efficiency, enhanced PQ, and dynamic characteristics during system failures, variable-speed wind systems are more common than fixedspeed wind systems. Variable speed wind systems are divided into two categories. Direct drive synchronous generators, which are totally separated from the grid through a back-to-back converter. The generator-side converter can be either a VSC or a diode rectifier, whereas the grid-side converter is a VSC. The second kind is the doubly fed induction generator (DFIG), which uses back-to-back converter as well. As seen in Figure 8.3, one terminal of the back-to-back converter supplies power to the rotor winding, while the other is linked to the electrical grid. In the rotor side, the system employs two back-to-back VSCs called grid side converter (GSC) and rotor side converter (RSC). PCC#1

Grid#1 L11

Is Vs AC

ShuntAC PWM Inverter Converter #2

SA SA2

SA1 T1

T2

DC

+

+

DC

Photovoltaic Arrays SD1

SD3

SD2

PCC#2 Grid#2

Photovoltaic Arrays

SB

L21 Is Vs

Figure 8.2 Interline PV (I-PV) system configuration proposed by [23]

Power electronics technology and applications Wind Turbine

203 Grid

DFIG

Rotor Side Converter AC

DC

Grid Side Converter DC Link

AC

DC DFIG Controller

Sensitive Load RL XL

Figure 8.3 Wind energy system with DFIG The existence of a nonlinear load increases harmonic current injection, resulting in a violation of the IEEE-519 PQ standard and an increased risk of power system equipment damage. Numerous studies have been undertaken on how to improve PQ through the use of wind energy systems. The GSC is used as a shunt APF in [27] to enhance power factor and adjust for THD. The RSC is used in [28] to compensate the reactive power and to filter out nonlinear load harmonics, while the GSC is responsible for active power distribution between the DC bus and the grid. In [29], the authors investigated flicker suppression in a DFIG-based wind turbine system using RSC control. The RSC regulates the speed of the DFIG, the reactive power of the stator, and harmonic current reduction, whilst the GSC regulates the active power flow between the DC bus and the grid. Gupta et al. [30] employed a hybrid APF to enhance the PQ of a nonlinear load supplied by DFIG and grid, as shown in Figure 8.4. The shunt APF eliminates higher order harmonics, whereas the passive filter eliminates the majority of dominant lower order harmonics. The authors of [31–33] examined a grid-connected wind energy system equipped with a permanent magnet synchronous generator (PMSG) capable of Wind Turbine

DFIG

Grid

Rotor Side Converter AC

Transformer

DC

Grid Side Converter

DC Link

DC DFIG Controller

AC

Sensitive Load RL XL

AC R L C

DC Hybrid Shunt Active Filter

Figure 8.4 Schematics of a parallel connected hybrid compensator system

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harmonic and reactive power adjustment at the point of common coupling (PCC). As seen in Figure 8.5, the suggested system consists of two main back-to-back converters coupled by a common dc-capacitor. The generator side converter is used to track the MPPT while the GSC is regulated to supply generated power while minimizing harmonics and reactive power at the PCC. Several energy storage technologies, such as superconducting magnetic energy storage (SMES), super capacitors (SC), flywheels, fuel cells, and batteries, can be included into the active power filter’s DC side [34]. The authors of [35] examined the combined operation of fuel cells with DSTATCOM for the purpose of improving PQ. As shown in Figure 8.6, a DC/DC converter is utilized to boost and Wind Turbine

Rotor Side Converter

PMSG

Grid Side Converter

AC DC Link

DC

Grid

AC

DC

Controller

Sensitive Load RL XL

Figure 8.5 Block diagram of PMSG-based variable speed wind system

Fuel Cell I 2e– Hydrogen (from fuel)

H2 Electrolyte

2e–

2H+

V

+

+ 2H+

2H+

Grid

Shunt Active Filter AC Vdc

2e–

DC/DC Boost Converter

Oxygen (air)

DC

+

1 –O 2 2

Anode

Heat H 2O + Water Cathode

Sensitive Load RL XL

Figure 8.6 DSTATCOM system with fuel cell

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Superconducting Magnetic Energy System LT/HT Superconducting Magnet

Helium/ Nitrogen

Two Quadrant DC/DC Converter

Shunt Active Filter AC DC link

Cryogenic Refrigerator

V

Liquid Helium/ Nitrogen

I

PCC

C1 C2

DC

Coupling Transformer

Pump Cryostat

Figure 8.7 Structure of the DSTATCOM-SMES

Flywheel Energy Storage System Axes of Rotation

Interface Converter

a

S3

S2

S1

S3'

S2'

S1'

c

Vacuum Pump

Protective Shield Lower Magnetic Bearings

DSTATCOM S3

S2

S1

S3'

S2'

S1'

PCC

Cdc

Udc

b Rotor

DC Bus +

Upper Magnetic Bearings Motor/ Generator

-

Coupling Transformer

Figure 8.8 Structure of the DSTATCOM/FESS

manage the fuel cell terminal voltage to fit the DC capacitor requirement of the VSC. Ref. [36] proposed the usage of a SMES system in conjunction with a DSTATCOM as a distributed energy storage for the purpose of stabilizing and controlling microgrids with wind power. As seen in Figure 8.7, the proposed system comprises of the DSTATCOM and a two-quadrant DC/DC converter serving as an interface to the SMES coil. Through a buck–boost converter control mode, this converter manages the power exchange between the SMES and the DSTATCOM. In [37, 38], a DSTATCOM is utilized in conjunction with a flywheel energy storage system to resolve issues caused by wind energy systems (Figure 8.8). The PMSM allows the power exchange between the flywheel and the interface.

8.3 Renewable sources-based dynamic voltage restorer The primary function of DVR is to maintain a constant voltage across the load independent of grid voltage sag, swell, imbalance, or distortion. The authors of [39] demonstrated the feasibility of DVR by installing a prototype on the existing power system. The suggested arrangement utilizes a VSC to inject a controlled voltage at the PCC through a series transformer. The authors of [40] investigated a PV-based DVR with the purpose of mitigating voltage sag and swell. As seen in Figure 8.9, the PV is linked to the DVR through a DC/DC converter, which is used to extract

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Power electronics for next-generation drives and energy systems: Volume 2 Load

Grid

Sensitive Load

Zs

RL

V’DVR

XL

Photovoltaic Arrays

I

DVR AC

LR

V

CR

VDVR

DC/DC Boost Converter

Lf

Lf

Cf

Cf

DC

Figure 8.9 DVR system with PV arrays

the MPPT [41]. The VSC is used to transfer active and reactive power from the PV to the load. Similarly, Ref. [42] presented a PV-driven three-phase four-wire hybrid DVR consisting of a DVR and an LC shunt passive filter. The proposed system adjusts for reactive power, neutral current, and voltage failures while mitigating current harmonics. The battery is connected directly to the terminals of the PV system, while a DC/DC converter is employed to fulfill the VSC’s dc bus voltage need. In [43], the authors developed a DVR that included PV as a means of supplying an alternate energy source to the DVR in order to enhance the wind farm’s functioning, as seen in Figure 8.10. The proposed system eliminates voltage sag and swell in the distributed system, ensuring that the terminal voltage connected to the wind turbine remains constant. Additionally, despite network voltage instability, wind generators can remain connected to the network and operate in a stable state by utilizing DVR. Ramasamy [44] suggested a DVR based on PV/battery units for improving the voltage quality in a microgrid. The proposed DVR’s primary components are a PV, two DC/DC boost converters (one to regulate the photovoltaic array’s output voltage in order to achieve MPPT, and the other to control battery

Wind Farm

Grid Transformer V’DVR

I

Photovoltaic Arrays

DVR AC

LR

V

CR

VDVR

DC/DC Boost Converter

DC

Figure 8.10 DVR with PV/wind system

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207

charging/discharging mode), a battery, and semiconductor switches, as illustrated in Figure 8.11. The suggested system might serve as a dynamic voltage restorer, a source of continuous power, and a micro-source. In [45], Chankhamrian et al. investigated the arrangement of a small hydro power plant connected to the grid via a VSC and a DVR-based three-level diode clamped VSC with PV arrays as an alternate DC source, as seen in Figure 8.12. Low pass filter is utilized to remove harmonics and reduce signal distortion from the DVR. Ramasamy et al. [46] investigated the hybrid PV/wind system as a DVR for voltage sag/swell reduction, interruption, and imbalanced power. The suggested DVR operates in four modes: DVR mode; UPS mode; energy saving mode; and maintenance mode or idle mode. Figure 8.13 depicts a schematic design of the suggested system. The interline-DVR (I-DVR) can be used to correct for voltage sag/swell due to the constraint of the DVR storage element [47]. The adjacent feeder provides the active power required for voltage restoration. RES and any storage devices can be connected through a shared dc bus to share the needed Grid

Microgrid

S1 S

Zs

Microgrid (Wind, PV)

V’DVR

S2

Is Vs

Cf

Energy Management System (EMS)

Lf AC PWM Converter

I

DC/DC Boost Converter

K2

V

K1 Battery Bank

XL

Converter

DC Photovoltaic Arrays

Load RL

AC PWM

DC DC/DC Boost Converter

Figure 8.11 DVR topology proposed by [44]

Load

Grid

Sensitive Load

Zs

RL XL

Lf

Is Vs

Cf

Rf

VDVR

Small Hydro Power Plant FLOW CONTROL TOWER

I Cpv

TERBINE TAIL WATER

DC

EXCAVATED TRENCH EXTENDED PRESSURE PIPE

Photovoltaic Arrays

AC

DC

PENSTOCK DRAFT TUBE

Figure 8.12 Proposed system by [45]

V

AC GENERATOR

DVR

Converter

DAM HEAD WATER

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Power electronics for next-generation drives and energy systems: Volume 2 Load#1 Load#2 Grid

B 22kV/400V S

Zs

V DVR

Wind Turbine

Cf Lf

DC/DC Boost Converter

Wound Field DC Generator

Sensitive Load

Transmission Line 5km Is Vs

PWM Converter

AC PWM Converter

AC

DC DC Photovoltaic Arrays

I

Charge Controller

R2

R1 Battery Bank

V

400V

DC/DC Boost Converter

DC Link 400 V

Figure 8.13 The conceptual diagram of PV/wind/battery-DVR system [46] power in the event that the neighboring feeder’s voltage sag mitigation is insufficient or both feeders are being subjected to simultaneous voltage sag [48]. Figure 8.14 illustrates the overall schematic for the I-DVR. The primary difficulty for DFIGs is operating in the presence of voltage sags and short circuits. Disconnection of the wind system may occur during a short circuit if the wind system is unable to support the voltage dip. Rapidly disconnecting a wind system may have an adverse effect on the grid, particularly with big wind farms. Additionally, the terminal voltage will fluctuate in response to changes in wind speed. When voltage oscillations exceed a certain threshold, they have a detrimental effect on the grid. In the event of voltage sag, the authors of [49, 50] recommended for the use of DVR to segregate the DFIG from the PCC. During system failures, the suggested arrangement adjusts for voltage variations; the DFIG will continue to operate normally in this condition. The authors in [51, 52] put an extra VSC between the traditional DFIG and the grid, which functioned as a DVR and improved the fault ride-through performance when a DFIG encounters a grid Grid#1 Zs Load# 2 DC Load

PWM Converter

K1

KPV

K2

KB

I

Lf AC

DC/DC Boost Converter

Photovoltaic Arrays

V

Cf

Is Vs

DC DC PWM Converter

AC

Lf

Grid#2

DC Bus

Cf

Zs

Load# 2 Is Vs

Figure 8.14 IDVR combined with a PV and battery [48]

Power electronics technology and applications

209

fault. As seen in Figure 8.15, the DVR and GSC share a same DC capacitor. Additionally, the series converter may give benefits such as a series active filter, reactive power compensation, and electronic isolator. Additionally, these three VSCs working will provide increased capacity for controlling the active and reactive power of the DFIG system, as well as suppressing the harmonic issue. Similarly, as illustrated in Figure 8.16, the third converter in [53] is linked in series with the stator windings of the DFIG-based wind turbine. Other designs have been developed that utilize a separate DVR for fault ride-through of a DFIG. The authors of [54] investigated the implementation of a separate DVR coupled to a wind-turbine-driven DFIG. The suggested arrangement compensates for the defective line voltage, allowing the DFIG wind turbine to operate normally. Figure 8.17 illustrates the suggested system. The authors in [55] investigated the integration of the DVR in the DFIG stator (Figure 8.18) to enhance the fault ridethrough capabilities of the grid-connected wind generator. Huang et al. [56] presented a unique shunt and series grid interface architecture for DFIG-based wind turbines. Figure 8.19 depicts the proposed system architecture, which includes a DFIG

0.69kV

10kV

Series Injection Transformer

Gear Box

DFIG Controller Rotor Side Converter DC

DC Link

Grid Side Converter AC

Grid

Filter AC

DC

AC

DC

Chopper

Figure 8.15 Scheme of the DFIG with a DVR [51, 52]

DFIG

Series Injection Transformer

PCC

DC

AC

Gear Box

AC

DC

AC Rotor Side Converter

DC Link

DC Grid Side Converter

Figure 8.16 Schematic DFIG with DVR in series with stator winding [53]

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Power electronics for next-generation drives and energy systems: Volume 2 DFIG

0.69kV

Gear Box

Grid

Rotor Side Converter DC Bus Grid Side Converter S2

S1

S3

S2'

S2

S1

S2'

S1'

DC Bus

Udc

S3'

+

+

S3

Cdc S3'

S1'

–

Cdc

–

DVR

Figure 8.17 DFIG wind system equipped with separate DVR

AC

DFIG

PCC

DC

Gear Box Rotor Side Converter DC

Grid Side Converter DC Link

AC

S Series Injection Transformer

AC

Grid

DC DFIG Controller

Figure 8.18 DFIG wind system equipped with separate DVR

SW2

DFIG

Grid

Gear Box

Braking Resistor

Series Injection Transformer

Rotor Side ConverterGrid Side Converter (RSC) (GSC) DC

DC Link

Filter

AC SW1

GSC Series Controller (Normal Condition)

Vdc

Voltage Regulator

V *dc

I *sh,d

Current Regulator

I*sh,q

AC

abc dq

Ish,dq abc dq

GSC Shunt Controller (Normal Operation)

SW3

DC 3-Phase PWM Generator

θs Ish,abc

3-Phase PWM Generator

dq abc

θg Vcom,abc Ig,abc

Voltage Regulator

Vcom,dq

V*com,dq

Compensation Voltage Calculation

Ig,dq

dq abc

Vg,abc θg

GSC Series Controller (Fault Condition)

Figure 8.19 DFIG wind system with the shunt/series GSC

Power electronics technology and applications

211

Bypass Switch

Grid

Load Sensitive Load RL XL

Storage System DC Bus

+ Vdc

DC/DC Boost Converter

Cdc

– 1st H-Bridge

2nd H-Bridge

nth H-Bridge

Figure 8.20 The multilevel inverter-based DVR proposed by [60] DFIG, RSC, and GSC, four reconfigurable switches, a series braking resistor, a shunt braking resistor, a low pass filter, and a series transformer. When a failure occurs, the GSC instantly switches to DVR mode, reducing voltage dips and guaranteeing that the stator voltage stays within the prescribed range. Cascade multilevel converters have gained popularity in recent years because to their benefits in high-voltage and low harmonic distortion [57–59]. The multilevel inverter-based DVR with controllable dc voltage is investigated in [60]. The DC/DC boost converter regulates the dc side voltage in accordance with the voltage sag value. The proposed configuration considerably improved the output voltage’s quality, reduced the amount of filtering required, and reduced switching losses. Figures 8.20 and 8.21 illustrate the proposed multilevel inverter-based DVR. Ref. [61] investigated the integration of rechargeable supercapacitors-based energy storage into the DVR structure. Through this combination, the supercapacitorsDVR system increases active power capability and the capacity to compensate for temporary voltage sags/swells independently. Supercapacitors are integrated into the DVR’s dc-link via a bidirectional DC/DC converter, which assists in supplying a stiff dc-link voltage. In [62], the authors described a DVR that utilized a matrix converter and flywheel energy storage to address the voltage sag issue, as shown in Figure 8.22. The suggested topology employed AC/AC converter as the grid interface, rather than a more common AC/DC/AC converter, which results in increased power density and system dependability. The permanent magnet synchronous generator-driven flywheel is chosen as the energy storage technology because it strikes an optimal balance between power density, efficiency, affordability, and environmental friendliness. The suggested architecture removes the dc side passive components, resulting in decreased maintenance and increased power density.

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Power electronics for next-generation drives and energy systems: Volume 2 Load

Grid

Sensitive Load RL

VD1

VDn

VD2

XL

nth H-Bridge

Cdc

2nd H-Bridge

+

–

DC Bus

1st H-Bridge

Vdc DC/DC Boost Converter

Storage System

Figure 8.21 Alternative topology for the DVR proposed by [61]

Load Grid Series Injection Transformer

Zs

Sensitive Load

VDVR

RL XL

Is Vs

Flywheel Energy Storage System Axes of Rotation

Motor/ Generator

Rotor

Vacuum Pump

Matrix Converter

Upper Magnetic Bearings

AC

L

a b c

C

Protective Shield

AC

Lower Magnetic Bearings

Filter

Figure 8.22 Architecture of the DVR system proposed by [62]

8.4 Renewable sources-based UPQC The UPQC is the most often employed APF in distribution systems for the purpose of mitigating current and voltage issues. Combining PQ enhancement with RES leads in more efficient use of UPQC and cost savings. A shunt APF mitigates current harmonics, imbalance, and reactive power in a UPQC. The series APF

Power electronics technology and applications

213

mitigates voltage sag/swell and grid voltage distortion. The authors of [63, 64] explored the configuration of a PV array using UPQC. The PV array is directly coupled or via intermediate DC/DC converter to the UPQC’s dc side, as shown in Figure 8.23. The shunt converter is responsible for tracking the MPPT and compensating for harmonics and reactive power. When the proposed topology is utilized for PV generating, the reference voltage is determined using the MPPT algorithm [65]. During voltage interruptions, the system adds value by powering the load. Additionally, the PV system’s output electricity can be transported in an interconnected or islanding manner. The bidirectional buck/boost DC/DC converter is used to manage the battery bank’s charging and discharging. In [66], the authors presented a system that combines PV arrays and UPQC. A multi-input single output (MISO) DC/DC converter connects the PV arrays to the dc link. The suggested system’s topology is shown in Figure 8.24. The MISO DC/DC converters are particularly suited for merging many dispersed generating systems with different power capacities and voltage magnitudes in order to get a well-regulated output voltage. The battery bank acts as a dc voltage maintainer, particularly during periods of insufficient solar radiation, such as evenings and overcast weather. In [67], the authors introduced a UPQC and associated control technique for helping large-scale PV power plant fault ride-through operation. The PV system layout consists of two identical PV arrays linked to a three-winding transformer. The transformers are then connected in parallel to a common bus. As indicated in Figure 8.25, the UPQC will be connected to both sides of step-up transformer. The proposed structure can achieve the following benefits: (1) the current flowing Load Sensitive Load

Series Injection Transformer

RL

DFIG Controller Rotor Side Converter DC

AC

I

Photovoltaic Arrays

DC/DC Boost Converter

XL

Grid Side Converter DC Link

AC

DC

Bidirectional DC/DC Converter

Battery Bank

V

Figure 8.23 General structure of PV-UPQC system

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Power electronics for next-generation drives and energy systems: Volume 2 S

Grid

Load Sensitive Load

S Series Injection S

RL

Transformer

DFIG Controller Rotor Side Converter DC

AC

Photovoltaic Arrays

I V

Photovoltaic Arrays

XL

Grid Side Converter DC Link

AC

DC

Multi-Input Single Output DC/DC Converter

Battery Bank

I

V

Figure 8.24 The PV-UPQC configuration proposed by [66] AC

Grid

275V/35KV PCC

35KV/110KV DC AC DC AC

AC DC

AC

275V/35KV DC Shunt Filter

DC Series Filter

AC DC

Figure 8.25 A UPQC configuration proposed by [67] through the DVR is reduced; (2) the output voltage of the shunt APF is reduced, and the measured voltage is nearly pure sinusoid after DVR compensation. Han et al. [68] studied the performance of a UPQC-distributed generating system. The series and the shunt APFs are coupled back-to-back through a dc link capacitor.

Power electronics technology and applications

215

As seen in Figure 8.26, the distributed generator is linked to the dc link via a diode rectifier. In both grid-connected and islanding modes, the proposed topology can mitigate the voltage sag/swell, interruption, harmonics, and reactive power. The authors in [69] suggested a combination of UPQC with supercapacitors for energy storage to improve the PQ in three-phase distribution systems, as seen in Figure 8.27. Voltage sag/swell, reactive power, harmonic, imbalance, and Feeder

PCC

Load

Zs

RL XL

AC

AC

DC Series Filter

DC Shunt Filter Diode Rectifier

Distributed Generation

Transformer

Figure 8.26 UPQC system with distributed generation [68]

Grid Zs

Other Loads Causing Voltage Sag

Load Bus Non-Linear Load

Rsf

Csf

Cpf Lsf

Rpf

Lpf

AC

AC

CDC

Sensitive Load RL XL

DC Series Filter

DC Shunt Filter

Supercapacitor

Figure 8.27 UPQC system interconnected with super-capacitor

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Power electronics for next-generation drives and energy systems: Volume 2

interruption can all be mitigated by the recommended arrangement. To manage the dc-link voltage, the supercapacitor is directly linked to the dc link of the threephase four-leg UPQC system. During normal operation, the converter charges the supercapacitor for energy storage and discharges it for energy release during the voltage interruption. To overcome the fuel cell’s poor dynamic response, Ref. [70] presented a hybrid energy storage device that combines a proton exchange membrane fuel cell (PEMFC) with a supercapacitor. The PEMFC is controlled to a constant voltage using a DC/DC converter and is capable of supplying the energy necessary by UPQC to perform its duties. The supercapacitor is connected directly to the dc bus through an inductor, which serves to limit the amount of current flowing through it. As illustrated in Figure 8.28, the hybrid storage system is then linked as the dc bus to the UPQC’s complete bridge back-to-back voltage source converters. The authors in [71] investigated the UPQC in conjunction with SMES. As seen in Figure 8.29, the UPQC is composed of a three-phase DC/AC converter for harmonic current compensation, three single-phase DC/AC converters for voltage sag/swell compensation, and interruption compensation. When disturbances such as sag/swell or interruptions occur, the SMES maintains the dc-link voltage at a fixed set point. Additionally, when a distribution system disturbance occurs, SMES supplies the needed power to the local load via the shunt converter.

Grid

Load Bus

Zs

Rsf

Csf

Cpf Rpf Lpf

Lsf

AC

AC

PWM Converter

CDC

DC Series Filter

PWM Converter

DC Shunt Filter

Fuel Cell

O2 Cathode

S1 LUC Udc

+ H Electrolyte

DC/DC Boost Converter

Super-Capacitor

H2 Anode

Figure 8.28 The UPQC supported by the hybrid storage system

Non-Linear Load

Power electronics technology and applications

217

Load Grid

Sensitive Load

RL XL

DC Bus +

S3

S2

S1

CC dcdc SMES S3'

S2'

S1'

-

Figure 8.29 The UPQC with SMES proposed by [71] The authors in [72] suggested and analyzed a compensation technique for a squirrel cage induction generator-based wind farm coupled to a week distribution network using UPQC. The UPQC’s primary functions are to regulate the wind farm’s terminal voltage and to correct the voltage differences at the PCC caused by system load fluctuations and pulsing wind farm output power. The series VSC regulates the wind farm’s terminal voltage by injecting an ac voltage in phase with the PCC voltage. While a shunt VSC is used to balance active and reactive power. In [73], a pitch-controlled fixed speed wind generator is combined with an IEEE 13 node test feeder through a current source converter based on UPQC. The shunt VSC are used to bring the power factor at the PCC to unity during normal operation. During a system malfunction, the UPQC’s series VSC injects an adequate regulated voltage to prevent the fixed speed induction generator (FSIG) from being disconnected, while the shunt VSC delivers additional reactive power. In [74], the authors performed case studies with right shunt-UPQC (RS-UPQC) and left shuntUPQC (LS-UPQC) to find an appropriate UPQC rating for the installed wind generating capacity. As seen in Figure 8.30, the system under discussion comprises of a fixed speed wind generator that is powered by a wind turbine and supplies electricity to a utility network. The authors in [75] reconfigured and controlled the DFIG back-to-back converters to operate similarly to a UPQC. Figure 8.31 Grid Bus

M

Grid Bus PCC

Z

Z

Z AC

Fault

DC

Z

AC

DC

Series Shunt Filter Filter

M PCC

Z

Wind Farm Based Fixed Speed Induction Generator

Z AC

AC

Fault

DC

DC

Shunt Series Filter Filter

Figure 8.30 RS-UPQC LS-UPQC [74]

Wind Farm Based Fixed Speed Induction Generator

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Power electronics for next-generation drives and energy systems: Volume 2 DFIG

Grid Bus

Gear Box

Rotor Side Converter

DC Bus

Grid Side Converter

+

S3

S2

S1

S3 Udc

S3'

S2'

S2

S1

S2'

S1'

Cdc S3'

S1' –

Figure 8.31 The wind energy system proposed in [75] illustrates the suggested system’s structure. The RSC regulates active and reactive power in accordance with the DFIG’s maximum power tracking curve. The GSC mitigates any balanced or unbalanced voltage sag/swell and establishes a stable operating point for the wind system during a grid side fault.

8.5 Dual UPQC-based OEW transformers (case study) The topology described in this section comprises of four series VSC linked to the grid through two transformers with open-end windings (OEWs) on the converter side [76]. The PV arrays are divided into two equal arrays that are linked to two dclinks of four VSC, as shown in Figure 8.32. The grid is linked to the OEW series VSCs through a transformer with an OEW on the converters’ side. Another transformer with an OEWs on the converters’ side connects the shunt VSCs in parallel with the grid, two isolated dc-links supply power to all four VSC. The shunt VSCs are responsible for transferring PV energy to the grid while also mitigating harmonics and reactive power. The series VSCs are utilized to minimize sag, swell, and voltage harmonics. The proposed topology is indented to provide a sinusoidal voltage to a sensitive/critical load that must be totally protected from sag/swell, distortion, and interruption. These loads typically consist of manufacturing businesses and critical service suppliers, such as medical facilities, airports, and telecommunications centers, where voltage interruptions might result in significant financial losses or human injuries. To accomplish these objectives, the OEW series VSCs function as voltage regulators, while the OEW shunt VSCs function as current controllers.

8.5.1

PV modeling and MPPT method

Figure 8.33 illustrates the corresponding model of a PV array. The PV array is made up of many series–parallel coupled solar cells. The fundamental equation for the PV array is provided by the following equation [77]:     qðV þ Rs I Þ V þ Rs I (8.1) 1  I ¼ Np Ipv;cell  Np I0;cell exp aNs kT Rp

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Sensitive Load

Grid

Sensitive Load

RL OEW Series Filter

OEW Shunt Filter Photovoltaic Arrays

AC

DC

AC

DC

Vdc#1

Photovoltaic Arrays

AC

DC

XL

AC

DC

Vdc#2

Figure 8.32 Proposed D-UPQC with OEW transformers

Practical PV array Ideal PV Cell

I

Rs

+ Ipv

Id

Rp

V –

Figure 8.33 The equivalent model of PV array where Ipv,cell and I0,cell are the current produced by the incident light and the leakage current of the diode, respectively. Np and Ns denote the number of parallel and series cells, respectively. q, k, and a are the electron charge, Boltzmann constant, and the diode ideality constant, respectively. T is the temperature of the cell. Table 8.1 shows the parameters of the PV module extracted from the data sheet. Figure 8.34 shows the I–V and P–V curves of the KC200GT PV module at different irradiance. Each PV array is made up of 29 modules linked in series. The MPPT algorithm’s objective is to ensure that maximum power is collected from the PV arrays regardless of solar insulation or temperature. This is accomplished by matching the Table 8.1 Parameters of the KC200GT PV array Imp (A)

Vmp (V)

Pmax (W)

Isc (A)

Voc (V)

Kv (V/K)

KI (A/K)

7.61

26.3

200

8.21 32.9 0.123 0.0032

Nss (No) 54

Rp (W)

Rs (W)

Io, n (A)

a

415.4 0.221 9.83108 1.3

Power electronics for next-generation drives and energy systems: Volume 2 200 180 160 140 120 100 80 60 40 20 0 0

9

P-V curve @1,000 W/m2 P-V curve @800 W/m2 P-V curve @600 W/m2 P-V curve @400 W/m2

8

X: 26.3 Y: 200.1

Module Current (A)

Module Power (W)

220

X: 0 Y: 8.211 X: 26.3 Y: 7.61

7 6 5 4 3

I-V curve @1,000 W/m2 I-V curve @800 W/m2 I-V curve @600 W/m2 I-V curve @400 W/m2

2 1

5

10 15 20 25 Module Voltage (V)

30

35

0

0

5

10 15 20 25 Module Voltage (V)

X: 32.89 Y: 0.004694

30

35

Figure 8.34 The characteristic curves of the PV module P&O MPPT Algorithm Calculate PV Output Power P(k)=V(k)*I(k)

P(k)>P(k-1)

No Complement slope sign

I

Yes V

V*

V*(k)=V*(k-1)+Φ×slop

P(k-1)=P(k)

Figure 8.35 P&O-based MPPT algorithm maximum point of the PV array to the operational voltage and current of the shunt VSCs. Because the perturb and observe approach is clear and simple to execute, it is a commonly used MPPT technique [3]. The P&O algorithm monitors the voltage and current of the photovoltaic plant, perturbs the operating voltage, and compares the power obtained at the prior and new voltages. The direction of the new perturbation is determined by the value of the measured power; if the measured power is greater at the latest voltage, the perturbation will be in the same direction; otherwise, it will be in the reverse direction as illustrated in Figure 8.35.

8.5.2

VSCs control

The PV system described in this section is symmetrical, with each dc link having similar current and voltage ratings and being delivered by two identical PV arrays.

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DC Links Controller Vdc2 ++

PWM

Vsd + LcωIfq – uq vdc dnd =

P&O MPPT

ud

Id0 Id* + ILdh – + + Iq* –+

I V

abc

IL1 IL2 Load IL3

ILd ILq dq0

vdc

uq

+–

Algorithm

– Vdc* +

Vdc u udc dc V^

Sin_Cos

S1 S1' S2 S2' S3 S3'

abc

Vsq + LcωIfd – uq

Converter#4

dq0

2 3

dnq =

Sin_Cos Converter#3 S1 S1' S2 PWM S2' S3 S3'

Vdc1

1/2

Vdc Ifq

Ifd

I fd V dc

Ifq

PLL

Vs1 Feeder Vs2 Vs3

abc

If1 If2 Shunt Filter If3 dq0

Figure 8.36 Control diagram of the shunt VSCs Due to the direct connection of the PV arrays to the VSCs, the MPPT extraction is conducted by the shunt VSCs. Due to the fact that both PV arrays are identical, a single MPPT can be considered, as with a single PV array. As a result, the same dclink reference was used in this work for OEW shunt VSCs. The control block diagram for the OEW shunt VSC is shown in Figure 8.36. The MPPT algorithm is used to track the reference dc voltage when the system is operated as a PV generator. When the system is used as an active filter, the reference voltage remains constant. To minimize the high-frequency components in the d–q reference frame, the d-axis load current is processed via a low pass filter. Negative d-axis harmonics are obtained by subtracting the d-axis load current from the filtered d-axis. The current required to maintain the dc-link voltage is calculated by the dc-link controller. Figure 8.37 illustrates the control diagram of the series VSCs. The fundamental frequency and unit amplitude signals are extracted from the source voltage using a phase locked loop. The abc to dq transformation is used to convert the three-phase load voltages to the dq frame, which can then be compared to the reference voltage’s d-axis. The error signal contains sag/swell and grid voltage harmonics.

8.5.3 Simulation results The suggested scheme is simulated in MATLAB/Simulink using the control mechanism depicted in Section 8.2. Table 8.2 summarizes the system’s primary parameters. The P&O method was employed to track the MPPT, and the DualUPQC was used to improve PQ. Figure 8.38 illustrates the dynamic behavior of the dc link voltages (VDC#1 and VDC#1) in the presence of an abrupt shift in the voltage reference, as requested by the MPPT algorithm or in response to a change

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Power electronics for next-generation drives and energy systems: Volume 2 Vdref S1 S1' S2 S2' S3 S3' S1 S1' S2 S2' S3 S3'

Converter#1

dq0 Verror

Vd

– +

abc

Vq

PWM abc

V0=0

V0

VL1 dq0 Feeder#1

Converter#2 PLL

Sin_Cos

Vs1

PWM

Figure 8.37 Control diagram of the series VSCs Table 8.2 Dual-UPQC parameters Parameters

Value

PV module power Total PV array (1 arrays) No. of PV modules per string Grid voltage PWM frequency

200 W 5,200 26 in series 415 V 5 kHz

DC Links Voltage (V)

800

400 200 0 0.5

PV System maximum power (w)

VDC1 VDC2 Vmpp

600

1

1.5

2

2.5

3

3.5

4

4.5

5

6,000 5,000

PPV2 PPV1

4,000 3,000 2,000 0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Time (s)

Figure 8.38 DC links voltages and PV arrays output power variation in the grid voltage. It is noted that the OEW shunt converter’s dc link voltages are kept at their respective reference levels. The suggested scheme is capable of successfully mitigating harmonics and reactive power. The shunt VSC current, load current, and grid current are shown in

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Shunt Filter Current (A)

Load Current (A)

Grid Current (A)

Figure 8.39 for a one phase; similar values may be displayed for the remaining phases. The load current has been regulated in this figure using shunt VSCs to make the grid current sinusoidal. Line current has a total harmonic distortion of less than 5%. Additionally, Figure 8.39 depicts the injected current of the shunt VSC, which is used to correct for the load’s current harmonics, allowing the grid current to be sinusoidal. Simultaneously, it transfers the PV’s energy to the load. The line current and grid voltage are in phase, as shown in Figure 8.40. As a consequence, the control algorithm is able to regulate the current injected into the grid at unity power factor by adjusting the reactive power injected by the shunt VSCs. 50 0 –50 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.2

0.3

0.4

0.5

0.6

0.7

0.8

50 0 –50 0.1 50 0 –50 0.1

Time (s)

Figure 8.39 Current harmonics compensation

400

Grid Voltage Grid Current

300 200 100 0 –100 –200 –300 –400 0.1

0.2

0.3

0.4

0.5

0.6

Time (s)

Figure 8.40 Reactive power compensation

0.7

0.8

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Power electronics for next-generation drives and energy systems: Volume 2

Figure 8.41 illustrates the active powers injected by OEW shunt converters and grid during voltage sag/swell occurrence. When voltage sags, the active power pumped into the load via OEW shunt converters decreases. When swell occurs, the proportion of OEW series converters increases, and a portion of the load power is injected via OEW shunt converters. Figure 8.42 illustrates the findings obtained during voltage sag with a voltage amplitude drop of up to 70%. As a result of the voltage injected in phase by the OEW series converter, the source voltage is u 2.5

Load Power OEW Shunt Converter power

Power (w)

2 1.5 1 0.5 0 –0.5 0.5

1

1.5

2

2.5 3 Times (s)

3.5

4

4.5

5

Figure 8.41 Active powers injected by OEW shunt converters

Grid Voltage (V)

400 200 0 –200 –400 2.4

2.6

2.8

3

3.2

3.4

3.6

2.6

2.8

3 Time (s)

3.2

3.4

3.6

Grid Voltage (V)

400 200 0 –200 –400 2.4

Figure 8.42 Current harmonics compensation

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dropped to 240 peak during voltage sag, while the load voltage remains constant at 340 peak. Figure 8.42 further illustrates the results achieved when a 30% overvoltage is applied. As can be seen, once an excess voltage condition is recognized, the D-UPQC operates to restore the load voltage to its nominal value.

8.6 Summary Distributed generation (DG) technologies, such as solar and wind, have caught the attention of energy sectors as a viable alternative source of electrical energy, whether grid-connected or islanding. The increased penetration of DGs results in a degradation of power quality, necessitating the employment of power conditioners like as active power filters to minimize these issues, while passive filters are unable to entirely mitigate power quality issues in the presence of nonlinear loads. This chapter discusses the various topologies for combining solar, wind turbine, and active power filter.

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Index

acceleration mode 10–11 active power filters (APFs) 200 Adaptive Neuro Fuzzy Inference System (ANFIS) 29 alarm mode 97 ant bee colony (ABC) optimizer 22, 29 ant colony (AC) optimizer 23, 29 Ant Colony Optimization (ACO) 101 Artificial Bee Colony (ABC) 101 artificial intelligence (AI) 23, 95 theoretical aspects of 98 universe of 99 Artificial Neural Network (ANN) 29 back to back (BTB) cascaded H-bridge converter 72 back-to-back (BTB) connected converter 70, 202 backward feature selection (BFS) 101 Bat Algorithm (BA) 29 batteries 204 Bayesian Networks 104 Bi-modal BBI 140–2 bi-modal fourth-order inverters derivation and working principle 145 basic requirements 146 comparison among SOBBI, topologies-1 and 2 inverters 155–8 fourth-order converters 145–6

merging two converters for bipolar output 147 new switching strategy-single mode inverter 159–63 possible combinations with fourthorder converters 150–1 schematics of two new inverter circuits 152 second-order buck–boost inverter 147–9 topology-1 (C´uk+SEPIC) 152–4 topology-2 (CSC-IL+SEPIC) 154–5 on topology-2 operation 158–9 bi-modal inverter (BMI) 147 Big Bang-Big Crunch 29 big data 104–5 bioinspired (BI) algorithms 23 boost-VSI (B-VSI) inverter 147 branch nodes 102 buck-boost inverter (BBI) 129 operating modes of 138 operating principle of 129 renewable energy applications of 129 common mode leakage current 132–7 power decoupling 130–2 state of the art operating modes of single-stage BBI 137–44 single-stage inverters 145

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buck–boost (BB) topologies 148 buck–boost voltage gain 146 capacitor switching 15–17 carbon footprints (CFP) 2–3, 5 cascaded failure 108 classical MPPT algorithms 23 climate change 97 common mode current 44 common mode leakage current (CMLC) 132–7 common-mode voltage (CMV) 43, 45 comprehensive system analysis challenges and opportunities 5–6 contributions 3–4 IEEE Standards 4 power-electronic controlled industrial motor at different operating conditions 11–14 grid connected/islanded and during transition mode of operation 7–9 on PV based generation plant 10–11 state of the art 4–5 test system description 6–7 computational intelligence methods in energy systems studies 103 big data 104–5 decision making 109 fault detection 109–10 operation and control 105–8 optimization 108 power flow by neural network 114–15 power systems 104–5 stability analysis 110–14 constant bias 114

continuous conduction mode (CCM) 160 control 98 scheme 175–6 techniques 5 theory 77 controller design 179–81 current loop design 181–2 current robust and feedforward controllers 187–8 dynamic modeling 181–2 model estimation 182–5 voltage controller design 186–7 conventional full-bridge TLI 45 bipolar SPWM modulation 45–8 unipolar SPWM modulation 48–51 conventional H-bridge inverter 55 converter-based generation (CIG) technologies 111 convertor topology 71–3 convolutional neural network (CNN) 104 convolutional sparse autoencoder (CSAE) technique 109 cuckoo search optimizer (CSO) algorithm 22, 29–30 cuckoo’s eggs 30 current controlled PWM (CCPWM) 71 current loop design 181–2 current robust and feedforward controllers 187–8 current-related temperature coefficient 172–3 data selection 100 DC–DC boost converter 26, 30 DC–DC converters 22 dead-time operation 153–4 deceleration mode 10–11

Index decision 102 decision tree (DT) 100, 102–3 decision-making approach 99 deep learning (DL) 99 deep reinforcement learning (DRL) 103 deep RNN (DRNN) 104 device count and stress 158 device-dependent sub-synchronous oscillations (DDSSO) 112 Differential Evolution (DE) algorithm 29 differential-mode voltage (DMV) 45 digital signal processor (DSP) 69 direct drive synchronous generators 202 discontinuous conduction mode (DCM) 140 distributed energy resources (DERs) 4 distributed generation (DG) 5, 200, 225 doubly fed induction generator (DFIG) 202 wind energy system with 203 doubly grounded topology 136 doubly or dual grounded inverters 136 doubly-fed acceptance generators (DFIG) 68, 74–5 control model 79 DSTATCOM system 204 DSTATCOM-SMES 205 DSTATCOM/FESS 205 dual UPQC-based OEW transformers 218 MPPT method 218–20 PV modeling 218–20 simulation results 221–5 VSCs control 220–1 dual-UPQC parameters 222 duty cycle 30

235

dynamic modeling 181–2 dynamic programming method 108 dynamic security assessment (DSA) 110 dynamic voltage restorer 205–12 economic resilience 2 electrical control systems 69 electricity industry 95 electromagnetic interference (EMI) 44, 133 energy harvesting scheme (EHS) 170 EV SRM drive 191–4 evaluation of the proposed control scheme 188 evolutionary/artificial intelligence (E/ AI) algorithms 23 factor analysis (FA) 101 fault detection techniques 109–10 feature selection 100 field programmable gate array (FPGA) 69–70 field-oriented control (FOC) 80 finite control-set model predictive control (FCS-MPC) 70 firefly algorithm (FA) 23 fixed speed induction generator (FSIG) 217 fixed-speed WT (FSWT) 68 flower pollination (FP) optimizer 22, 29 flywheels 204 forward feature selection (FFS) 101 fourth-order buck–boost converters 150 fourth-order converters 145–6 fuel cells 204 full-bridge DC bypassed inverter (FB-DCBP) 59–65

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circuit diagram of 60 CMV waveform of 63 DMV waveform of 64 equivalent circuit of operating mode of 61 equivalent circuit when clamping circuit conducting in 62 leakage current waveform of 64 modulation strategy 60 Fuzzy logic controller (FLC) 23, 29 gamma function 30 Gaussian Mixed Models (GMM) 101 genetic algorithm (GA) 22, 29, 101 German DIN VDE 0126-1-1 standard 44 global peak power (GPP) 22 governing equations 172 gravitational search algorithm (GSA) 29 greenhouse gas emissions 93 grey wolf (GW) optimizer 23, 29 grid connected common-node PV inverter 137 grid connected system 43 grid controller 26 grid frequency 44 grid side controller 79–80 grid side converter (GSC) 68, 70, 74, 79, 202 grid synchronization approach 26 grid-connected mode (GCM) 2, 7 grid-connected PV system 44 grid-connected wind energy systems 68 gross domestic production (GDP) 2 H5 inverter 51–5 DMV and CMV for different modes of operation of 54

equivalent circuits of operating modes of 52 leakage current waveform of 55 Harmony Search (HS) 29 HERIC inverter 55–9 DMV and CMV for different modes of operation of 57 equivalent circuits of operating modes of 56 Hierarchical Agglomerative Clustering (HAC) 101 high-frequency component 44 high-frequency transformer (HFT) 68 high-voltage direct current (HVDC) 98 Hill Climbing 28 hill-climb search (HCS) control 81 control principle 82 hybrid DL 104 hybrid energy storage system (HESS) 105 IEEE Standards 4 Incremental Conductance 28 industrial waste management 5 input current 158 intelligent electronics devices (IEDs) 2 intelligent systems technology 98 decision tree 102–3 neural network 101–2 support vector machine 103 interconnected M2V/V2M operations between PV powered DC microgrid 191 M2V charging operation 194 V2M discharging operation 193–4 interline power flow controller (IPFC) 202 interline PV (I-PV) system 202

Index interline-DVR (I-DVR) 207 interline-photovoltaic (I-PV) system 202 interpretation and validation 100 islanded mode (IM) 2, 7 islanding modes 200 k-nearest neighbor (k-NN) 100 KC200GT PV array 219 Le´vy multiplication coefficient 30 leakage current waveform 44, 64 left shunt-UPQC (LS-UPQC) 217 Levenberg–Marquardt algorithm 114–15 line development planning 96 load 158 load switching 15–17 long short-term memory (LSTM) 104 M2V charging operation 194 machine learning (ML) 93 dynamic security assessment 115 emergency and preventive control by 107 fault detection by 110 machine side converters (MSCs) 68, 70 MATLAB
/Simulink
54, 57, 63 maximum power point tracking (MPPT) 22, 68, 77–82, 131, 169, 218–20 maximum torque per ampere control (MTPA) 81 Metaheuristic algorithms 99 microcontroller (mC) 69 microgrid 2, 169 model estimation 182–5 model predictive control (MPC) 70 model selection 100

237

model use 100 modern energy systems 93 modern power systems challenges in 94–6 energy systems problems 96 control 98 operation 97 planning 96–7 modulation strategy 52 multi local power peaks (LPPs) 22 multi-input single output (MISO) 213 multilayer Feedforward NN (MFNN) 101 multilevel converters 5 nature-inspired MPPT algorithms 23, 28 classification of 28–9 proposed MPPT algorithms 29–33 negative half (CSC-IL) 154 neural network (NN) 100–2 Newton–Raphson (NR) method 111 non-ideal voltage gain 158 nonlinear method 81 open-end windings (OEWs) 218 operation and control 105–8 operational principle 159–61 optimization 108 P–V curve 27 parallel operation for resistive load and battery 189–90 for SRM-driven flywheel 190–1 parasitic capacitor 44 partial shading problem 23 causes and effects of 23–4 partial shadowing remediation 24–5 partial shadowing conditions (PSCs) 22

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Power electronics for next-generation drives and energy systems: Volume 2

partially shaded photovoltaic (PSPV) energy conversion system 23 particle swarm optimizer (PSO) algorithm 22, 29–32, 101 permanent magnet synchronous generator (PMSG) 68, 75–6, 80, 203 based variable speed wind system 204 perturb and observe (P&O) approach 22, 28, 32–3, 170 phase locked loop (PLL) 26 phasor measurement devices 2 phasor measurement units (PMUs) 104 photovoltaic (PV) array with interleaved boost converter 174 established interleaved boost converter 174 control scheme 175–6 power circuit 174–5 simulated PV array 174 photovoltaic (PV) array with plug-in single-phase AC source 188–91 photovoltaic (PV) cell equivalent circuit of 172 evaluation on MPPT and interleaving operations 176–7 governing equation for 172 I–V curves and effects on temperature and irradiance 173–4 parameter determination 172 current-related temperature coefficient 172–3 saturation current 173 series resistor 173 whole system operation 178–9 photovoltaic (PV) modeling 218–20 photovoltaic (PV) penetration level 11–14 photovoltaic (PV) powered microgrid 170

photovoltaic (PV) shading 11–14 photovoltaic (PV) system 128, 200–1 configuration of PV power conversion system under study 27–8 nature-inspired MPPT algorithms 28 classification of 28–9 proposed MPPT algorithms 29–33 partial shading problem 23 causes and effects of 23–4 partial shadowing remediation 24–5 standalone and grid-interconnected PV power conversion systems 25–7 physics/chemistry (P/C)-based algorithms 23 planning 96–7 plug-in energy harvesting mechanism with AC source 179 controller design 179–81 evaluation of the proposed control scheme 188 power circuit 179 PV array with plug-in single-phase AC source 188–91 point of common coupling (PCC) 7, 204 positive half (SEPIC) 154 power circuit 174–5, 179 power converter equipment 69 power decoupling 130–2 power electronic converters 68 power exchange 9 power flow by neural network 114–15 power quality (PQ) 83, 200 power signal feedback (PSF) control 81–2 power systems 104–5 power–voltage characteristic graph 22 preventive control 106

Index principal component analysis (PCA) 101 prognostics and health management (PHM) technique 109 proposed MPPT algorithms 29 cuckoo search optimizer algorithm 29–30 perturb and observe (P&O) approach 32–3 PSO algorithm 30–2 proton exchange membrane fuel cell (PEMFC) 216 Quad-modal BBI 138–40 random forest (RF) 100 recurrent neural network (RNN) 104 regression algorithms 100 reinforcement learning (RL) 101, 103 renewable energy sources based shunt active filter 201–5 based UPQC 212–18 dual UPQC-based OEW transformers 218 MPPT method 218–20 PV modeling 218–20 simulation results 221–5 VSCs control 220–1 dynamic voltage restorer 205–12 renewable energy sources (RESs) 2, 68 restoration mode 97 restorative control 97 right shunt-UPQC (RS-UPQC) 217 root-mean-square (RMS) 44 rotor side converter (RSC) 68, 79, 202 safe mode 97 saturation current 173 second-order buck–boost inverter (SOBBI) 147–9

239

DC values of states at zero crossing of 149 operational modes of 148 switching sequence for 148 security control 106 series resistor 173 shunt active filter (SAF) 201–5 sigmoid function 114 simulated annealing (SA) 29 simulated PV array 174 simulation results 221–5 single global global peak power (GPP) 22 single-mode BBI 142–4 single-mode inverter (SMI) 159 operating equations for 162 single-phase SMR 179 sinusoidal PWM (SPWM) 71 small wind turbines (SWT) 128 small-scale microgrids 5 smart grid paradigm 94 solar energy 96 solar PV (SPV) 6–7 solar PV micro-inverters 129 common mode leakage current 132–7 power decoupling 130–2 squirrel cage induction generators (SCIG) 68, 81 stability analysis 110–14 state of the art 4–5 operating modes of single-stage BBI 137–44 single-stage inverters 145 Static Var Compensators (SVC) 98 steady-state mode 10–11 stochastic programming method 108 sub-synchronous resonance (SSR) 112 super capacitors (SC) 204

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Power electronics for next-generation drives and energy systems: Volume 2

superconducting magnetic energy storage (SMES) 204 supervised learning 100 supervisory control and data acquisition (SCADA) 104 support vector machine (SVM) 100, 103 switching frequency 44 system parameter 57 temporary fault 15–17 three-phase line-frequency highpower transformer (LFT) 68 3-ph PWM converter 26 thyristor 72 tip speed ratio (TSR) control 74, 81 ´ uk+SEPIC) 152–4 topology-1 (C topology-2 (CSC-IL+SEPIC) 154–5 non-ideal voltage gain 158 training data 103 transformer less grid-connected inverter 43 need of 44 transformer less PV inverter 45 with clamping 59–65 conventional full-bridge 45–51 with decoupling 51–9 transformer less inverter (TLI) 44 tree growth 102 unconventional power electronic interface (UPEI) 70 unified power quality conditioner (UPQC) 200, 212–18 unipolar SPWM modulation 50 unsupervised learning 100–1 V2M discharging operation 193–4 variable-speed WTs (VSWTs) 68

vector control (VC) scheme 68 voltage controller design 186–7 voltage fluctuations 44 voltage gain 156–8 voltage gain of inverter 161–3 voltage source converters (VSCs) 68 control 220–1 voltage source inverters (VSI) 128 water cycle algorithm 29 wavelet transform (WT) 101 wind energy 68, 96 wind energy conversion systems (WECS) components in 69 control strategies 77–82 with hill climb search control 82 interconnection of 69 modeling of 73 different types of generators 74–6 modeling and control of DFIGbased 76 modeling and control of PMSGbased 77 MPPT 77–82 power converter 70 power quality 83 tip speed ratio control of 81 wind energy system 218 wind turbine generator systems (WTGS) 70 zero crossing distortion (ZCD) 146, 159 zero crossing states continuity 146 zero-order-hold (ZOH) method 181 zero-state filter 53 zero-voltage switching 57