Power Electronics for Green Energy Conversion 9781119786481, 1119786487

Table of contents :
Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Green Energy Technology-Based EnergyEfficient Appliances for Buildings
1.1 Balance of System Appliances Needed for Green Energy Systems
1.1.1 Grid Interactive Inverters for Buildings with AC Wiring
1.1.2 Grid Interactive Inverter with No Battery Backup
1.1.3 Main Grid-Interactive Inverter (Hybrid Inverter)
1.1.4 DC-DC Converter for DC Building
1.1.5 Bidirectional Inverter
1.1.6 Battery Bank
1.2 Major Green Energy Home Appliances
1.2.1 DC Air Conditioners
1.2.2 DC Lighting
1.2.3 DC Refrigeration
1.2.4 Emerging Products for Grid Connected Homes and Businesses
1.2.5 Electrical Vehicle
1.3 Energy Savings Through Green Appliances
1.3.1 Appliance Scheduling
1.3.2 A Case Study of a Mid-Ranged Home with Green Home Appliances Versus Conventional Home Appliances: A Comparison of 1 Day Co
1.4 Conclusion
References
2 Integrated Electric Power Systems and Their Power Quality Issues
2.1 Introduction
2.2 Designing of a Hybrid Energy System
2.3 Classification of Hybrid Energy Systems
2.3.1 Hybrid Wind-Solar System
2.3.2 Hybrid Diesel-Wind System
2.3.3 Hybrid Wind-Hydro Power System
2.3.4 Hybrid Fuel Cell-Solar System
2.3.5 Hybrid Solar Thermal System
2.4 Power Quality Implications
2.4.1 Interruption
2.4.2 Undervoltage or Brownout
2.4.3 Voltage Sag or Dip
2.4.4 Noise
2.4.5 Frequency
2.4.6 Harmonic
2.4.7 Notching
2.4.8 Short-Circuit
2.4.9 Swell
2.4.10 Transient or Surges
2.5 Conclusion
References
3 Renewable Energy in India and World for Sustainable Development
3.1 Introduction
3.2 The Energy Framework
3.3 Status of Solar PV Energy
3.4 Boons of Renewable Energy
3.5 Energy Statistics
3.5.1 Coal
3.5.2 Natural Gas
3.5.3 Biofuels
3.5.4 Electricity
3.6 Renewable Energy Resources
3.7 Conclusion
Abbreviations
References
4 Power Electronics: Technology for Wind Turbines
4.1 Introduction
4.1.1 Overview of Wind Power Generation
4.1.2 Advancement of Wind Power Technologies
4.1.3 Power Electronics Technologies for Wind Turbines
4.2 Power Converter Topologies for Wind Turbines
4.2.1 Matrix Converter
4.2.2 Z Source Matrix Converter
4.3 Quasi Z Source Direct Matrix Converter
4.3.1 Principle of Operation
4.3.2 Modulation Strategy
4.3.3 Simulation Results and Discussion
4.4 Conclusion
References
5 Investigation of Current Controllers for Grid Interactive Inverters
5.1 Introduction
5.2 Current Control System for Single-Phase Grid Interactive Inverters
5.2.1 Hysteresis Current Controller
5.2.2 Proportional Integral Current Control
5.2.3 Proportional Resonant Current Control
5.2.4 Dead Beat Current Control
5.2.5 Model Predictive Current Control
5.3 Simulation Results and Analysis
5.3.1 Results in Steady-State Operating Mode
5.3.2 Results in Dynamic Operating Mode
5.3.3 Comparative Assessment of the Current Controllers
5.3.4 Hardware Implementation
5.4 Experimental Results
5.5 Future Scope
5.6 Conclusion
References
6 Multilevel Converter for Static Synchronous Compensators: State-ofthe Art, Applications and Trends
6.1 Introduction
6.2 STATCOM Realization
6.2.1 Two-Level Converters
6.2.2 Early Multilevel Converters
6.2.3 Cascaded Multilevel Converters
6.2.4 Summary of Topologies
6.3 STATCOM Control Objectives
6.3.1 Operating Principle
6.3.2 Control Objectives
6.3.3 Modulation Schemes
6.4 Benchmarking of Cascaded Topologies
6.4.1 Design Assumptions
6.4.2 Current Stress in Semiconductor Devices
6.4.3 Current Stress in Submodule Capacitor
6.4.4 Comparison of Characteristics
6.5 STATCOM Trends
6.5.1 Cost Reduction
6.5.2 Reliability Requirements
6.5.3 Modern Grid Codes Requirements
6.5.4 Energy Storage Systems
6.6 Conclusions and Future Trends
References
7 Topologies and Comparative Analysis of Reduced Switch Multilevel Inverters for Renewable Energy Applications
7.1 Introduction
7.2 Reduced-Switch Multilevel Inverters
7.3 Comparative Analysis
7.4 Conclusion
References
8 A Novel Step-Up Switched-CapacitorBased Multilevel Inverter Topology Feasible for Green Energy Harvesting
8.1 Introduction
8.2 Proposed Basic Topology
8.3 Proposed Extended Topology
8.3.1 First Algorithm (P1)
8.3.2 Second Algorithm (P2)
8.4 Operational Mode
8.4.1 Mode A
8.4.2 Mode B
8.4.3 Mode C
8.4.4 Mode D
8.4.5 Mode E
8.4.6 Mode F
8.4.7 Mode G
8.4.8 Mode H
8.4.9 Mode I
8.4.10 Mode J
8.4.11 Mode K
8.4.12 Mode L
8.4.13 Mode M
8.4.14 Mode N
8.4.15 Mode O
8.4.16 Mode P
8.4.17 Mode Q
8.5 Standing Voltage
8.5.1 Standing Voltage (SV) for the First Algorithm (P1)
8.5.2 Standing Voltage (SV) for the Second Algorithm (P2)
8.6 Proposed Cascaded Topology
8.6.1 First Algorithm (S1)
8.6.2 Second Algorithm (S2)
8.6.3 Third Algorithm (S3)
8.6.4 Fourth Algorithm (S4)
8.6.5 Fifth Algorithm (S5)
8.6.6 Sixth Algorithm (S6)
8.7 Modulation Method
8.8 Efficiency and Losses Analysis
8.8.1 Switching Losses
8.8.2 Conduction Losses
8.8.3 Ripple Losses
8.8.4 Efficiency
8.9 Capacitor Design
8.10 Comparison Results
8.11 Simulation Results
8.12 Conclusion
References
9 Classification of Conventional and Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Generation Systems
9.1 Introduction
9.1.1 Classification of MPPT Techniques
9.1.2 MPPT Algorithms Based on PV Side Parameters
9.2 MPPT Algorithms Based on Load Side Parameters
9.3 Conventional MPPT Algorithms
9.3.1 Indirect Techniques
9.3.2 Direct Techniques
9.4 Soft Computing (SC) MPPT Techniques
9.4.1 MPPT Techniques Based on Artificial Intelligence (AI)
9.4.2 Bioinspired (BI)-Based MPPT Techniques
9.5 Hybrid MPPT Techniques
9.5.1 Conventional with Conventional (CV/CV)
9.5.2 Soft Computing with Soft Computing (SC/SC)
9.5.3 Conventional with Soft Computing (CV/SC)
9.5.4 Other Classifications of Hybrid Techniques
9.6 Discussion
9.7 Conclusion
References
10 A Simulation Analysis of Maximum Power Point Tracking Techniques for Battery-Operated PV Systems
10.1 Introduction
10.2 Background of Conventional MPPT Methods
10.2.1 Perturb & Observe (P&O)
10.2.2 Incremental Conductance (IC)
10.2.3 Fractional Short Circuit Current (FSCC)
10.2.4 Fractional Open Circuit Voltage (FOCV)
10.2.5 Ripple Correlation Control (RCC)
10.3 Simulink Model of PV System with MPPT
10.4 Results and Discussions
10.4.1 (a) Simulation Results for P&O Method
10.4.2 (b) Simulation Results for Incremental Conductance (IC) Method
10.4.3 (c) Fractional Open Circuit Voltage (FOCV) Method
10.4.4 (d) Fractional Short Circuit Current (FSCC) Method
10.4.5 (e) Ripple Correlation Control (RCC)
10.4.6 (f) Performance Comparison
10.5 Conclusion
References
11 Power Electronics: Technology for Grid-Tied Solar Photovoltaic Power Generation Systems
11.1 Introduction
11.2 Grid-Tied SPVPGS Technology
11.2.1 Module Inverters
11.2.2 String Inverters
11.2.3 Multistring Inverters
11.2.4 Central Inverters
11.3 Classification of PV Inverter Configurations
11.3.1 Single-Stage Isolated Inverter Configuration
11.3.2 Single-Stage Nonisolated Inverter Configuration
11.3.3 Two-Stage Isolated Inverter Configuration
11.3.4 Two-Stage Nonisolated Inverter Configuration
11.4 Analysis of Leakage Current in Nonisolated Inverter Topologies
11.5 Important Standards Dealing with the Grid-Connected SPVPGS
11.5.1 DC Current Injection and Leakage Current
11.5.2 Individual Harmonic Distortion and Total Harmonic Distortion
11.5.3 Voltage and Frequency Requirements
11.5.4 Reactive Power Capability
11.5.5 Anti-Islanding Detection
11.6 Various Topologies of Grid-Tied SPVPGS
11.6.1 AC Module Topologies
11.6.2 String Inverter Topologies
11.6.3 Multistring Inverter Topologies
11.6.4 Central Inverter Topologies
11.7 Scope for Future Research
11.8 Conclusions
References
12 Hybrid Solar-Wind System Modeling and Control
12.1 Introduction
12.2 Description of the Proposed System
12.3 Model of System
12.3.1 Model of Wind Turbine
12.3.2 Dynamic Model of the DFIG
12.3.3 Mathematic Model of Filter
12.3.4 Medium-Term Energy Storage
12.3.5 Short-Term Energy Storage
12.3.6 Wind Speed Model
12.3.7 Photovoltaic Array Model
12.3.8 Boost Converter Model
12.4 System Control
12.4.1 Grid Side Converter GSC Control
12.4.2 Rotor Side Converter RSC Control
12.4.3 MPPT Control Algorithm for Wind Turbine
12.4.4 Two-Level Energy Storage System and Control Strategy
12.4.5 PSO-Based GMPPT for PV System
12.5 Results and Interpretation
12.6 Conclusion
References
13 Static/Dynamic EconomicEnvironmental Dispatch Problem Using Cuckoo Search Algorithm
13.1 Introduction
13.2 Problem Formulation
13.2.1 Static Economic Dispatch
13.2.2 Dynamic Economic Dispatch (DED)
13.3 Calculation of CO2, CH4, and N2O Emitted During the Combustion
13.3.1 Calculation of CO2
13.3.2 Calculating CH4 and N2O Emissions
13.4 The Cuckoo Search Algorithms
13.5 Application
13.5.1 Case I: The Static Economic Dispatch
13.5.2 Case II: The Dynamic Economic Dispatch
13.6 Conclusions
References
14 Power Electronics Converters for EVs and Wireless Chargers: An Overview on Existent Technology and Recent Advances
14.1 Introduction
14.2 Hybrid Power System for EV Technology
14.3 DC/AC Converters to Drive the EV
14.4 DC/DC Converters for EVs
14.4.1 Isolated and Nonisolated DC/DC Converters for EV Application
14.4.2 Multi-Input DC/DC Converters in Hybrid EVs
14.5 WBG Devices for EV Technology
14.6 High-Power and High-Density DC/DC Converters for Hybrid and EV Applications
14.7 DC Fast Chargers and Challenges
14.7.1 Fast-Charging Station Architectures
14.7.2 Impacts of Fast Chargers on Power Grid
14.7.3 Fast-Charging Stations Connected to MV Grid and Challenges
14.8 Wireless Charging
14.8.1 Short History of Wireless Charging
14.8.2 Proficiencies
14.8.3 Deficiencies
14.9 Standards
14.9.1 SAE J1772
14.9.2 IEC 62196
14.9.3 SAE J2954
14.10 WPT Technology in Practice
14.11 Converters
14.12 Resonant Network Topologies
14.13 Appropriate DC/DC Converters
14.14 Single-Ended Wireless EV Charger
14.15 WPT and EV Motor Drive Using Single Inverter
14.15.1 Problem Definition
14.15.2 Wave Shaping Analysis
14.15.3 Convertor System
14.15.4 WPT System and Motor Drive Integration
14.16 Conclusion
References
15 Recent Advances in Fast-Charging Methods for Electric Vehicles
15.1 Introduction
15.2 Levels of Charging
15.2.1 Level 1 Charging
15.2.2 Level 2 Charging
15.2.3 Level 3 Charging
15.3 EV Charging Standards
15.4 Battery Charging Methods
15.5 Constant Voltage Charging
15.6 Constant Current Charging
15.7 Constant Current-Constant Voltage (CC-CV) Charging
15.8 Multicurrent Level Charging
15.9 Pulse Charging
15.10 Converters and Its Applications
15.10.1 Buck Converter
15.10.2 Boost Converter
15.10.3 Interleaved Buck Converter
15.10.4 Interleaved Boost Converter
15.11 Design of DC-DC Converters
15.12 Results and Discussions
15.13 Conclusion
References
16 Recent Advances in Wireless Power Transfer for Electric Vehicle Charging
16.1 Need for Wireless Power Transfer (WPT) in Electric Vehicles (EV)
16.2 WPT Theory
16.3 Operating Principle of IPT
16.3.1 Ampere’s Law
16.3.2 Faraday’s Law
16.4 Types of Wires
16.4.1 Litz Wire
16.4.2 Litz Magneto-Plate Wire (LMPW)
16.4.3 Tubular Conductor
16.4.4 REBCO Wire
16.4.5 Copper Clad Aluminium Wire
16.5 Ferrite Shapes
16.6 Couplers
16.7 Types of Charging
16.7.1 Static Charging
16.7.2 Dynamic Charging
16.7.3 Quasi-Dynamic Charging
16.8 Compensation Techniques
16.9 Power Converters in WPT Systems
16.9.1 Primary Side Converter
16.9.2 Secondary Side Converter
16.9.3 Recent Novel Converter
16.10 Standards
16.11 Conclusion
References
17 Flux Link Control Modulation Technique for Improving Power Transfer Characteristics of Bidirectional DC/DC Converter Used in
17.1 Introduction
17.2 GDAB-IBDC Converter
17.2.1 Analysis and Modeling of GDAB-IBDC
17.3 FLC Modulation Technique
17.3.1 Modes of Operation of GDAB-IBDC Converter
17.3.2 Analytical Modeling of SPS and FLC Modulation
17.4 Dead Band Analysis of GDAB-IBDC Converter
17.5 Simulation and Results
17.6 Conclusion
References
Index
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Power Electronics for Green Energy Conversion

Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

Power Electronics for Green Energy Conversion

Edited by

Mahajan Sagar Bhaskar Nikita Gupta Sanjeevikumar Padmanaban Jens Bo Holm-Nielsen and

Umashankar Subramaniam

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep­ resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-­ ability or fitness for a particular purpose. No warranty may be created or extended by sales representa­ tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further informa­ tion does not mean that the publisher and authors endorse the information or services the organiza­ tion, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 9781119786481 Cover image: Pixabay.com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1

Contents Preface xvii 1 Green Energy Technology-Based Energy-Efficient Appliances for Buildings 1 Avanish Gautam Singh, Rahul Rajeevkumar Urs, Rajeev Kumar Chauhan and Prabhakar Tiwari Nomenclature 2 Variables 2 1.1 Balance of System Appliances Needed for Green Energy Systems 3 1.1.1 Grid Interactive Inverters for Buildings with AC Wiring 4 1.1.2 Grid Interactive Inverter with No Battery Backup 4 1.1.3 Main Grid-Interactive Inverter (Hybrid Inverter) 6 1.1.4 DC-DC Converter for DC Building 6 1.1.5 Bidirectional Inverter 10 1.1.6 Battery Bank 11 1.2 Major Green Energy Home Appliances 13 1.2.1 DC Air Conditioners 14 1.2.2 DC Lighting 15 1.2.3 DC Refrigeration 15 1.2.4 Emerging Products for Grid Connected Homes and Businesses 17 1.2.5 Electrical Vehicle 17 1.3 Energy Savings Through Green Appliances 18 1.3.1 Appliance Scheduling 20 1.3.2 A Case Study of a Mid-Ranged Home with Green Home Appliances Versus Conventional Home Appliances: A Comparison of 1 Day Consumption 23

v

vi  Contents 1.4 Conclusion References 2 Integrated Electric Power Systems and Their Power Quality Issues Akhil Gupta, Kamal Kant Sharma and Gagandeep Kaur 2.1 Introduction 2.2 Designing of a Hybrid Energy System 2.3 Classification of Hybrid Energy Systems 2.3.1 Hybrid Wind-Solar System 2.3.2 Hybrid Diesel-Wind System 2.3.3 Hybrid Wind-Hydro Power System 2.3.4 Hybrid Fuel Cell-Solar System 2.3.5 Hybrid Solar Thermal System 2.4 Power Quality Implications 2.4.1 Interruption 2.4.2 Undervoltage or Brownout 2.4.3 Voltage Sag or Dip 2.4.4 Noise 2.4.5 Frequency 2.4.6 Harmonic 2.4.7 Notching 2.4.8 Short-Circuit 2.4.9 Swell 2.4.10 Transient or Surges 2.5 Conclusion References

26 27 29 30 32 34 34 35 36 37 37 38 39 40 41 42 43 43 44 45 45 45 62 63

3 Renewable Energy in India and World for Sustainable Development 67 Kuldeep Jayaswal, D. K. Palwalia and Aditya Sharma 3.1 Introduction 67 3.2 The Energy Framework 68 3.3 Status of Solar PV Energy 73 3.4 Boons of Renewable Energy 75 3.5 Energy Statistics 76 3.5.1 Coal 76 3.5.2 Natural Gas 78 3.5.3 Biofuels 78 3.5.4 Electricity 80 3.6 Renewable Energy Resources 82

Contents  vii 3.7 Conclusion Abbreviations References

85 86 86

4 Power Electronics: Technology for Wind Turbines 91 K.T. Maheswari, P. Prem and Jagabar Sathik 4.1 Introduction 92 4.1.1 Overview of Wind Power Generation 93 4.1.1.1 India-Wind Potential 94 4.1.2 Advancement of Wind Power Technologies 95 4.1.3 Power Electronics Technologies for Wind Turbines 96 4.2 Power Converter Topologies for Wind Turbines 98 4.2.1 Matrix Converter 99 4.2.2 Z Source Matrix Converter 100 4.3 Quasi Z Source Direct Matrix Converter 104 4.3.1 Principle of Operation 104 4.3.2 Modulation Strategy 107 4.3.2.1 Closed Loop Control of QZSDMC 107 4.3.3 Simulation Results and Discussion 108 4.4 Conclusion 111 References 111 5 Investigation of Current Controllers for Grid Interactive Inverters Aditi Chatterjee and Kanungo Barada Mohanty 5.1 Introduction 5.2 Current Control System for Single-Phase Grid Interactive Inverters 5.2.1 Hysteresis Current Controller 5.2.2 Proportional Integral Current Control 5.2.3 Proportional Resonant Current Control 5.2.4 Dead Beat Current Control 5.2.5 Model Predictive Current Control 5.2.5.1 Analysis of Discretized System Model Dynamics 5.2.5.2 Cost Function Assessment 5.3 Simulation Results and Analysis 5.3.1 Results in Steady-State Operating Mode 5.3.2 Results in Dynamic Operating Mode 5.3.3 Comparative Assessment of the Current Controllers 5.3.4 Hardware Implementation

115 116 117 119 121 125 129 131 134 135 137 138 139 145 145

viii  Contents 5.3.4.1 Hardware Components 5.3.4.2 Digital Implementation 5.4 Experimental Results 5.5 Future Scope 5.6 Conclusion References 6 Multilevel Converter for Static Synchronous Compensators: State-of-the-Art, Applications and Trends Dayane do Carmo Mendonça, Renata Oliveira de Sousa, João Victor Matos Farias, Heverton Augusto Pereira, Seleme Isaac Seleme Júnior and Allan Fagner Cupertino 6.1 Introduction 6.2 STATCOM Realization 6.2.1 Two-Level Converters 6.2.2 Early Multilevel Converters 6.2.3 Cascaded Multilevel Converters 6.2.4 Summary of Topologies 6.3 STATCOM Control Objectives 6.3.1 Operating Principle 6.3.2 Control Objectives 6.3.3 Modulation Schemes 6.3.3.1 NLC 6.3.3.2 PS-PWM 6.4 Benchmarking of Cascaded Topologies 6.4.1 Design Assumptions 6.4.1.1 Y-CHB 6.4.1.2 ∆-CHB 6.4.1.3 HB-MMC 6.4.1.4 FB-MMC 6.4.2 Current Stress in Semiconductor Devices 6.4.3 Current Stress in Submodule Capacitor 6.4.4 Comparison of Characteristics 6.5 STATCOM Trends 6.5.1 Cost Reduction 6.5.2 Reliability Requirements 6.5.3 Modern Grid Codes Requirements 6.5.4 Energy Storage Systems 6.6 Conclusions and Future Trends References

147 150 151 153 154 155 159

160 164 164 168 170 174 175 175 176 179 181 181 187 187 190 191 193 196 198 201 205 209 209 212 215 216 217 218

Contents  ix 7 Topologies and Comparative Analysis of Reduced Switch Multilevel Inverters for Renewable Energy Applications Aishwarya V. and Gnana Sheela K. 7.1 Introduction 7.2 Reduced-Switch Multilevel Inverters 7.3 Comparative Analysis 7.4 Conclusion References

221 221 224 251 258 258

8 A Novel Step-Up Switched-Capacitor-Based Multilevel Inverter Topology Feasible for Green Energy Harvesting 265 Erfan Hallaji and Kazem Varesi 8.1 Introduction 266 8.2 Proposed Basic Topology 269 8.3 Proposed Extended Topology 270 8.3.1 First Algorithm (P1) 270 8.3.2 Second Algorithm (P2) 271 8.4 Operational Mode 272 8.4.1 Mode A 275 8.4.2 Mode B 275 8.4.3 Mode C 275 8.4.4 Mode D 276 8.4.5 Mode E 276 8.4.6 Mode F 277 8.4.7 Mode G 277 8.4.8 Mode H 277 8.4.9 Mode I 278 8.4.10 Mode J 278 8.4.11 Mode K 279 8.4.12 Mode L 279 8.4.13 Mode M 279 8.4.14 Mode N 280 8.4.15 Mode O 280 8.4.16 Mode P 281 8.4.17 Mode Q 281 8.5 Standing Voltage 282 8.5.1 Standing Voltage (SV) for the First Algorithm (P1) 283 8.5.2 Standing Voltage (SV) for the Second Algorithm (P2) 283 8.6 Proposed Cascaded Topology 283 8.6.1 First Algorithm (S1) 284

x  Contents 8.6.2 Second Algorithm (S2) 284 8.6.3 Third Algorithm (S3) 284 8.6.4 Fourth Algorithm (S4) 285 8.6.5 Fifth Algorithm (S5) 285 8.6.6 Sixth Algorithm (S6) 286 8.7 Modulation Method 286 8.8 Efficiency and Losses Analysis 287 8.8.1 Switching Losses 287 8.8.2 Conduction Losses 288 8.8.3 Ripple Losses 288 8.8.4 Efficiency 288 8.9 Capacitor Design 289 8.10 Comparison Results 291 8.11 Simulation Results 295 8.12 Conclusion 299 References 299 9 Classification of Conventional and Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Generation Systems Mohammed Salah Bouakkaz, Ahcene Boukadoum, Omar Boudebbouz, Nadir Boutasseta, Issam Attoui and Ahmed Bouraiou 9.1 Introduction 9.1.1 Classification of MPPT Techniques 9.1.2 MPPT Algorithms Based on PV Side Parameters 9.2 MPPT Algorithms Based on Load Side Parameters 9.3 Conventional MPPT Algorithms 9.3.1 Indirect Techniques 9.3.1.1 MPPT Based on Constant Voltage (CV) 9.3.1.2 Fractional Voltage (FV) Technique 9.3.1.3 Fractional Currents (FC) Technique 9.3.2 Direct Techniques 9.3.2.1 Hill Climbing (HC) Technique 9.3.2.2 Perturb & Observe (P&O) Technique 9.3.2.3 Incremental Conductance (IC) 9.4 Soft Computing (SC) MPPT Techniques 9.4.1 MPPT Techniques Based on Artificial Intelligence (AI) 9.4.1.1 Fuzzy Logic Control (FLC) Technique 9.4.1.2 Artificial Neural Network (ANN)

303

304 306 307 307 308 308 308 309 310 310 311 312 313 314 314 314 316

Contents  xi 9.4.1.3 Adaptive Neuro Fuzzy Inference System (ANFIS) 316 9.4.1.4 The Bayesian Network (BN) 317 9.4.2 Bioinspired (BI)-Based MPPT Techniques 317 9.4.2.1 Particle Swarm Optimization (PSO) 317 9.4.2.2 Whale Optimization Algorithm (WOA) 318 9.4.2.3 Moth-Flame Optimization (MFO) 322 9.5 Hybrid MPPT Techniques 322 9.5.1 Conventional with Conventional (CV/CV) 322 9.5.1.1 Fractional Current (FC) with Incremental Conductance (IC) 323 9.5.2 Soft Computing with Soft Computing (SC/SC) 323 9.5.2.1 Fuzzy Logic Control with Genetic Algorithm (FLC/GA) 323 9.5.3 Conventional with Soft Computing (CV/SC) 324 9.5.3.1 Hill Climbing with Fuzzy Logic Control (HC/FLC) 324 9.5.4 Other Classifications of Hybrid Techniques 325 9.6 Discussion 325 9.7 Conclusion 327 References 328 10 A Simulation Analysis of Maximum Power Point Tracking Techniques for Battery-Operated PV Systems 335 Pankaj Sahu and Rajiv Dey 10.1 Introduction 336 10.2 Background of Conventional MPPT Methods 339 10.2.1 Perturb & Observe (P&O) 340 10.2.2 Incremental Conductance (IC) 341 10.2.3 Fractional Short Circuit Current (FSCC) 342 10.2.4 Fractional Open Circuit Voltage (FOCV) 343 10.2.5 Ripple Correlation Control (RCC) 344 10.3 Simulink Model of PV System with MPPT 348 10.4 Results and Discussions 350 10.4.1 (a) Simulation Results for P&O Method 351 10.4.2 (b) Simulation Results for Incremental Conductance (IC) Method 356 10.4.3 (c) Fractional Open Circuit Voltage (FOCV) Method 361

xii  Contents 10.4.4 (d) Fractional Short Circuit Current (FSCC) Method 366 10.4.5 (e) Ripple Correlation Control (RCC) 371 10.4.6 (f) Performance Comparison 376 10.5 Conclusion 377 References 378 11 Power Electronics: Technology for Grid-Tied Solar Photovoltaic Power Generation Systems 381 K. Sateesh Kumar, A. Kirubakaran, N. Subrahmanyam and Umashankar Subramaniam 11.1 Introduction 382 11.2 Grid-Tied SPVPGS Technology 383 11.2.1 Module Inverters 384 11.2.2 String Inverters 385 11.2.3 Multistring Inverters 386 11.2.4 Central Inverters 386 11.3 Classification of PV Inverter Configurations 386 11.3.1 Single-Stage Isolated Inverter Configuration 387 11.3.2 Single-Stage Nonisolated Inverter Configuration 387 11.3.3 Two-Stage Isolated Inverter Configuration 388 11.3.4 Two-Stage Nonisolated Inverter Configuration 389 11.4 Analysis of Leakage Current in Nonisolated Inverter Topologies 390 11.5 Important Standards Dealing with the Grid-Connected SPVPGS 393 11.5.1 DC Current Injection and Leakage Current 393 11.5.2 Individual Harmonic Distortion and Total Harmonic Distortion 395 11.5.3 Voltage and Frequency Requirements 395 11.5.4 Reactive Power Capability 395 11.5.5 Anti-Islanding Detection 395 11.6 Various Topologies of Grid-Tied SPVPGS 396 11.6.1 AC Module Topologies 396 11.6.2 String Inverter Topologies 399 11.6.3 Multistring Inverter Topologies 405 11.6.4 Central Inverter Topologies 407 11.7 Scope for Future Research 415 11.8 Conclusions 415 References 416

Contents  xiii 12 Hybrid Solar-Wind System Modeling and Control 419 Issam Attoui, Naceredine Labed, Salim Makhloufi, Mohammed Salah Bouakkaz, Ahmed Bouraiou, Nadir Boutasseta, Nadir Fergani and Brahim Oudjani 12.1 Introduction 420 12.2 Description of the Proposed System 424 12.3 Model of System 425 12.3.1 Model of Wind Turbine 425 12.3.2 Dynamic Model of the DFIG 426 12.3.3 Mathematic Model of Filter 428 12.3.4 Medium-Term Energy Storage 429 12.3.5 Short-Term Energy Storage 429 12.3.6 Wind Speed Model 430 12.3.7 Photovoltaic Array Model 430 12.3.8 Boost Converter Model 432 12.4 System Control 433 12.4.1 Grid Side Converter GSC Control 434 12.4.2 Rotor Side Converter RSC Control 434 12.4.3 MPPT Control Algorithm for Wind Turbine 435 12.4.4 Two-Level Energy Storage System and Control Strategy 435 12.4.5 PSO-Based GMPPT for PV System 435 12.5 Results and Interpretation 438 12.6 Conclusion 445 References 445 13 Static/Dynamic Economic-Environmental Dispatch Problem Using Cuckoo Search Algorithm Larouci Benyekhlef, Benasla Lahouari and Sitayeb Abdelkader 13.1 Introduction 13.2 Problem Formulation 13.2.1 Static Economic Dispatch 13.2.2 Dynamic Economic Dispatch (DED) 13.3 Calculation of CO2, CH4, and N2O Emitted During the Combustion 13.3.1 Calculation of CO2 13.3.2 Calculating CH4 and N2O Emissions 13.4 The Cuckoo Search Algorithms 13.5 Application

453 454 455 455 456 457 457 458 459 460

xiv  Contents 13.5.1 Case I: The Static Economic Dispatch 13.5.2 Case II: The Dynamic Economic Dispatch 13.6 Conclusions References

463 465 470 471

14 Power Electronics Converters for EVs and Wireless Chargers: An Overview on Existent Technology and Recent Advances 475 Sahand Ghaseminejad Liasi, Faezeh Kardan and Mohammad Tavakoli Bina 14.1 Introduction 476 14.2 Hybrid Power System for EV Technology 477 14.3 DC/AC Converters to Drive the EV 478 14.4 DC/DC Converters for EVs 479 14.4.1 Isolated and Nonisolated DC/DC Converters for EV Application 479 14.4.2 Multi-Input DC/DC Converters in Hybrid EVs 480 14.5 WBG Devices for EV Technology 481 14.6 High-Power and High-Density DC/DC Converters for Hybrid and EV Applications 483 14.7 DC Fast Chargers and Challenges 484 14.7.1 Fast-Charging Station Architectures 484 14.7.2 Impacts of Fast Chargers on Power Grid 488 14.7.3 Fast-Charging Stations Connected to MV Grid and Challenges 489 14.7.3.1 SST-Based EV Fast-Charging Station 490 14.8 Wireless Charging 491 14.8.1 Short History of Wireless Charging 492 14.8.2 Proficiencies 493 14.8.3 Deficiencies 493 14.9 Standards 494 14.9.1 SAE J1772 494 14.9.1.1 Revisions of SAE J1772 495 14.9.2 IEC 62196 495 14.9.3 SAE J2954 497 14.10 WPT Technology in Practice 497 14.11 Converters 499 14.12 Resonant Network Topologies 501 14.13 Appropriate DC/DC Converters 501 14.14 Single-Ended Wireless EV Charger 502 14.15 WPT and EV Motor Drive Using Single Inverter 505 14.15.1 Problem Definition 507

Contents  xv 14.15.2 Wave Shaping Analysis 14.15.3 Convertor System 14.15.4 WPT System and Motor Drive Integration 14.16 Conclusion References 15 Recent Advances in Fast-Charging Methods for Electric Vehicles R. Chandrasekaran, M. Sathishkumar Reddy, B. Raja and K. Selvajyothi 15.1 Introduction 15.2 Levels of Charging 15.2.1 Level 1 Charging 15.2.2 Level 2 Charging 15.2.3 Level 3 Charging 15.3 EV Charging Standards 15.4 Battery Charging Methods 15.5 Constant Voltage Charging 15.6 Constant Current Charging 16.7 Constant Current-Constant Voltage (CC-CV) Charging 15.8 Multicurrent Level Charging 15.9 Pulse Charging 15.10 Converters and Its Applications 15.10.1 Buck Converter 15.10.2 Boost Converter 15.10.3 Interleaved Buck Converter 15.10.4 Interleaved Boost Converter 15.11 Design of DC-DC Converters 15.12 Results and Discussions 15.13 Conclusion References 16 Recent Advances in Wireless Power Transfer for Electric Vehicle Charging Sivagami K., Janamejaya Channegowda and Damodharan P. 16.1 Need for Wireless Power Transfer (WPT) in Electric Vehicles (EV) 16.2 WPT Theory 16.3 Operating Principle of IPT 16.3.1 Ampere’s Law 16.3.2 Faraday’s Law

507 510 512 513 513 519 519 520 520 520 522 523 524 525 526 527 528 529 530 532 533 534 535 536 538 542 543 545 546 546 550 551 551

xvi  Contents 16.4 Types of Wires 16.4.1 Litz Wire 16.4.2 Litz Magneto-Plate Wire (LMPW) 16.4.3 Tubular Conductor 16.4.4 REBCO Wire 16.4.5 Copper Clad Aluminium Wire 16.5 Ferrite Shapes 16.6 Couplers 16.7 Types of Charging 16.7.1 Static Charging 16.7.2 Dynamic Charging 16.7.3 Quasi-Dynamic Charging 16.8 Compensation Techniques 16.9 Power Converters in WPT Systems 16.9.1 Primary Side Converter 16.9.1.1 Unidirectional Charger 16.9.1.2 Bidirectional Charger 16.9.2 Secondary Side Converter 16.9.3 Recent Novel Converter 16.10 Standards 16.11 Conclusion References 17 Flux Link Control Modulation Technique for Improving Power Transfer Characteristics of Bidirectional DC/DC Converter Used in FCEVS Bandi Mallikarjuna Reddy, Naveenkumar Marati, Kathirvel Karuppazhagi and Balraj Vaithilingam 17.1 Introduction 17.2 GDAB-IBDC Converter 17.2.1 Analysis and Modeling of GDAB-IBDC 17.3 FLC Modulation Technique 17.3.1 Modes of Operation of GDAB-IBDC Converter 17.3.2 Analytical Modeling of SPS and FLC Modulation 17.4 Dead Band Analysis of GDAB-IBDC Converter 17.5 Simulation and Results 17.6 Conclusion References

552 552 552 552 553 553 553 554 556 556 558 559 560 564 565 565 566 567 567 567 570 570

573 574 575 576 580 582 583 589 591 598 598

Index 601

Preface Power electronics has emerged as one of the most critical technologies globally and will play a significant role in the conversion of the present power grid systems into smart grids. Applications like HVDC systems, FACTs devices, uninterruptible power systems, and renewable energy systems rely on advances in power electronic devices and control systems. Further, renewable energy needs continue to grow, and the complete departure of fossil fuels and nuclear energy is not unrealistic, thanks to power electronics. Therefore, power electronics’ increasingly critical role in the power sector industry remains paramount. This groundbreaking new volume aims to cover these topics and trends of power electronic converters, bridging the research gap on green energy conversion system architectures, controls, and protection challenges to enable wide-scale implementation. This book presents topics and trends in power electronic converters, bridging the research gap on green energy conversion system architectures and control and protection challenges to enable their wide-scale implementation. Green energy sources based power systems are providing a muchneeded solution for stationary as well as transportation based applications. Green energy sources combined with power electronics can handle low, high, and variable power requirements and provide a key element in stabilizing and managing the power when necessary. Green energy applications require two primary power electronics areas to be addressed: DC/DC converters for power regulation and DC/AC converters for connection to the primary grid apart from supplying load demands. DC microgrid energy distribution systems will probably encourage DC-DC power converter technology for green energy applications in interconnected power converters, isolated, non-isolated, resonant, multi-port, multilevel converters, high voltage gains, and medium/high voltage converters. Furthermore, this will assist in the addition of storage systems, which are necessary to manage the unpredictability of the green energy supply. Grid-independent systems require DC/AC converters to use green energy sources as the primary power source. Inverters are commonly used xvii

xviii  Preface to supply fixed AC power from DC sources such as fuel cells, solar panels, batteries, and variable AC sources like wind. Thus, advances in green energy technology require similar advances in power converter technology. Furthermore, the numerous experimental results and associated simulations contribute significantly to the book’s high quality. Chapter 1 provides an elaborated view of green energy appliances, which goes a long way in reducing the electricity bill and the dependency on the utility. It also presents the optimization of the grid through load scheduling that helps reduce the peak load, which is one of the major concerns of any grid. Chapter 2 provides the design of different modes of hybrid energy systems to understand their operation that is possible under various variable constraints and PQ assessment with different dynamic and static controlling mechanisms. Chapter 3 describes the renewable energy scenario for sustainable development in India and the world by describing various supporting factors. This chapter also describes the various policies of the International Energy Agency (IEA) for sustainable energy development through renewable energy. Chapter 4 proposes a quasi Z source direct matrix converter based direct-drive wind energy conversion system to control the output voltage under different wind speeds and loading conditions. Simulation results validate the advantage of the converter over conventional matrix converters. Chapter 5 presents the design and analysis of the hysteresis current controller, a proportional integral current controller, a proportional resonant current controller, a dead beat current controller, and a model predictive current controller and their comparative performance assessment. Chapter 6 presents a survey on the application of multilevel converters for STATCOM realization. The development and the current state-of-the-art STATCOM technology are critically presented and discussed. Chapter 7 reviews the recent state-of-the-art trends in reducedswitch multilevel inverters (RSMLI) for renewable energy applications. The chapter provides an exhaustive review of the latest trends in RSMLI for renewable energy applications, their challenges, a comparative analysis of various RSMLIs, and their future developments. Chapter 8 proposes a basic switched-capacitive multilevel inverter topology with the following advantages: numerous voltage levels, voltage-boosting capability, self-voltage balancing ability, and the inherent negative voltage-level generation  that reduces the number of switches bearing the maximum output voltage. In the suggested configuration, green energy sources with DC output voltage like photovoltaics, wind turbines (with rectified output voltage), fuel cells, and energy storage devices like batteries can be employed as the input sources. An assessment of different approaches for combining existing MPPT techniques to construct hybrid methods combining characteristics

Preface  xix from both domains is carried out in Chapter 9. An overview of the existing MPPT classification for the photovoltaic system is provided. The classification includes conventional, hybrid, and modern MPPT techniques for PV energy generation systems. Chapter 10 proposes a comparative study and simulation analysis of conventional maximum power points tracking techniques like perturb and observe, incremental conductance, fractional short circuit current and fractional open circuit voltage with a nonlinear control approach named “ripple correlation control.” Simulations have been carried out for various levels of solar irradiance and for step irradiance inputs at a constant temperature to show all MPPT techniques’ performance concerning tracking speed and oscillations in output. Chapter 11 presents an overview of the power electronic technologies employed for the grid-tied inverter configurations. Various inverter topologies under the different classes have been classified based on the power rating. Also, some of the important grid codes and standards for selecting an inverter are discussed. Finally, the future trends in the grid-tied inverter topologies are discussed to show the directions for researchers. Chapter 12, modelling, simulation, and control of a grid-connected hybrid solar-wind system with two-level energy storage under different climatic conditions. The system proposed in this chapter includes a wind turbine system equipped with a Doubly Fed Induction Generator DFIG, a photovoltaic (PV) system, a hybrid super capacitor-battery energy storage system, and controlled power electronics converters. The hybrid system is connected to the grid using a three-level inverter with hybrid supercapacitor batteries for energy storage. In order to maximize the power of the PV system, the Particle Swarm Optimization (PSO) algorithm that generates the optimal current of the PV system is applied. Chapter 13 presents a static and dynamic economic dispatch study in electrical power systems using the Cuckoo Search Algorithm (CSA). With the problem formulation and the Cuckoo Search Algorithm, a description of the western Algerian electrical power system is presented, followed by a discussion of the simulation results. Chapter 14 discusses the existing technology and recent advances in power electronic devices used in EVs and wireless charging. In Chapter 15, the recent developments of DC-DC converters and control strategies for various charging techniques. The modelling and design of DC fast-charging techniques for electric vehicles are proposed. The proposed method consists of various DC-DC converters as a power conditioning unit, and a suitable charge control scheme is employed. The performance of the conventional charging methods is compared with selected converters for DC fast-charging technique, and its feasibility for level 3 charging is addressed. Chapter 16 presents recent studies aimed at achieving bidirectional charging in inductive power transfer

xx  Preface and converters involved in fast-charging EV applications. This chapter will give a complete overview of the state-of-the-art couplers and their auxiliaries in different sections. Chapter 17 presents the mathematical modelling of the generic dual active bridge isolated bidirectional dc/dc converter (GDAB-IBDC) converter in both boost and buck modes. The flux link control modulation has been developed for the GDAB-IBDC converter. Mahajan Sagar Bhaskar Nikita Gupta Sanjeevikumar Padmanaban Jens Bo Holm-Nielsen Umashankar Subramaniam Editors

1 Green Energy Technology-Based EnergyEfficient Appliances for Buildings Avanish Gautam Singh1*, Rahul Rajeevkumar Urs2, Rajeev Kumar Chauhan3 and Prabhakar Tiwari4 *

Politecnico di Milano, Milan, Italy Northumbria University Newcastle, Newcastle, United Kingdom 3 Department of Electrical Engineering, Dayalbagh Educational Institute, Agra, Uttar Pradesh, India 4 Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India 1

2

Abstract

This chapter provides an elaborated view of green energy appliances, which goes a long way in reducing the electricity bill and the dependency on the utility. As the governments around the world are focusing on lowering the CO2 emission, green energy is penetrating the electrical market aggressively. These green appliances can be divided into two parts, wherein the first part includes the appliances that are fundamental units of the system and operates between source and the load like battery, charge controller, fuses, etc. The second part includes the daily household AC or DC appliances. The chapter is divided into various sections that provide the different perspective of appliances, like the mode of their operation or benefits of replacing AC appliances to DC appliances. Further, the chapter gives a quick view on balance of system for a typical islanded system that includes typical elements like batteries, charge controller, power conditioning devices, safety devices, meters, and instrumentation, along with the major home appliances, which consume maximum part of the daily energy consumption. The very next section presents the optimization of the grid through load scheduling that helps in reducing the peak load, which is one of the major concerns of any grid. The electrical appliances based on green energy contain few fundamental elements, which play a vital role in the flawless working of green energy grid; hence, the study of *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (1–28) © 2022 Scrivener Publishing LLC

1

2  Power Electronics for Green Energy Conversion those elements is the main objective of this chapter. Before the last section, efficiency analysis of fundamental elements of grid-like grid-tied inverters, battery bank, and solar charge controller are shown. In the last section, a case study based on daily load profile appliance, which will provide an overview of daily overall energy saving could be possible with green appliances in comparison with conventional appliances. Photovoltaic, wind turbines, biomass, fuel cell, etc., are renewable energy sources that could be taken as a source, but this chapter considers PV as an energy source and presents the work accordingly. Keywords:  DC appliances, DC-building, DC-microgrid, energy conversion, green energy home appliances, converters, energy efficient home appliances, battery efficiency

Nomenclature Acronyms PHEVs Plug-in Hybrid Electric Vehicles BMS Battery Management System MPPT Maximum Power Point Tracking MPTT Maximum Power Transfer Theorem NEC National Electric Code EV Electric Vehicle AC Air Conditioner, Alternating Current DC Direct Current BOS Balance of System PCUs Power Conditioning Units PV Photovoltaic PEC Power Electronic Converter BB Battery Bank CFL Compact Florescent Lamp LED Light Emitting Diode SOC State of Charge

Variables µ D Vo Io

Converter Efficiency Duty Cycle Output Voltage Output Current

Energy-Efficient Appliances for Buildings  3 Req RS RLOAD j, i PPV PL PB

Equivalent Resistance Series Resistance Load Resistance Range Indicator Photovoltaic Array Power Load Power Battery Power

1.1 Balance of System Appliances Needed for Green Energy Systems This section introduces the few fundamental power system appliances that are used in a green power system. In the process of installing a renewable energy system to the home whether grid-connected or islanded, an individual is required to invest in a few additional appliances that are also called as balance of system (BOS). These appliances are required to condition the electricity, and to safely transmit the power to the loads for utilization or store for future use. The islanded grid, which is not connected to a utility, the number of fundamental appliances a consumer needs to purchase depends upon the additional functionalities like energy trading system, appliance scheduler etc. In the most basic system, the power generated by the system is directly utilized by the load. Further, if a consumer wants to use power when the system is not generating power, a battery bank and charge controller is required. Depending upon consumers requirements, the balance of system appliances for the islanded system might be accountable to half of your total system costs. The system integrator will tell the consumer about the exact appliances required. Also, the balance of system for autonomous DC-grid includes charge controller, batteries, safety devices, power conditioning devices, meters, and instrumentation. The grid-connected system needs the balance of system appliances for flawless and safely transmission of power to the loads and to comply with utility grid connection requirements. The consumer will need power conditioning devices (PCUs), safety devices, meters, and instrumentation.

4  Power Electronics for Green Energy Conversion

1.1.1 Grid Interactive Inverters for Buildings with AC Wiring The grid-interactive inverters convert DC power from Photovoltaic array into AC synchronous power. To extract the maximum power from the PV, maximum power point tracking (MPPT) algorithms are used. This section focuses on inverters for grid-connected systems, net metering, including battery backup and non-battery backup inverters. The grid-tied models are divided into two parts one is micro-inverters other is central inverters [1].

1.1.2 Grid Interactive Inverter with No Battery Backup To convert the DC supply of PV plant into AC supply, central inverter is used. For reduction of transmission losses, central grid-tie inverters are designed, which run on high voltage DC inputs and allows relatively large PV array to connect in series. Micro-inverters are the alternative for the central inverters, which are attached to each PV module to optimize the module output power by tracking each module’s Maximum Power Point. Central inverters have a large range of input voltage that can reach up to 600VDC, this limit has set by the US National Electrical Code (NEC) and the maximum inverter input voltage is determined by a PV array total maximum open-circuit voltage (VOC). The wide range of input voltage allows PV system designers to have a broad selection of PV module configurations. The micro-inverters comparatively have low input voltage, they are compatible with most of the PV modules available in the market. The output voltage of inverter depends upon the voltage of the grid that inverter is synchronized with. Usually the output voltage of small commercial and residential buildings has 120/240VAC. Grid-tie inverters are programmed in that way if system switch to the islanded mode (mode in which home energy system perform autonomously with no connection to main grid) inverters automatically shut down. This prevents power from being transmitted to the grid during a power outage. a) The efficiency of central inverter The efficiency of a photovoltaic inverter is a function of input voltage and input power; a typical set of efficiency curves is shown in Figure 1.1. The efficiency of the inverter is higher, generally well around 90% when the irradiance level is high on PV array. The efficiency drops off sharply at low irradiance level [2]. The average operating efficiency of the inverter varies

Energy-Efficient Appliances for Buildings  5 1 0.95 Inverter efficiency

0.9 0.85 Rated DC voltage Lower DC voltage Higher DC voltage

0.8 0.75 0.7 0.65 0.6

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure 1.1  General inverter efficiency curve as a function of input DC voltage. The curve also shows the lower and higher voltage that is closes to limits of the voltage range [4].

with varying weather conditions. The peak efficiency (varies between 95% and 98% depending on manufacturer) and the weighted efficiency included in the technical information of central inverter of PV systems, both the elements are accounted for the operation at a different level of irradiance. In Europe, this weighted efficiency is known as Euro efficiency [3] and that can be represented in the equation given below:

Euro efficiency = 0.03eff.@5% + 0.06eff.@10% + 0.13eff.@20%

+ 0.1eff.@30% + 0.48eff.@50% + 0.2eff.@100%

b) Life of the inverter Most of the manufacturers recommend regular preventive maintenance of inverters throughout the lifetime to ensure minimum accidental repairing costs and maximize the life of inverter. PV inverters regular maintenance includes annual inspection and component replacement according to product specific maintenance schedules. Inverter service actions require check of insulated gate bipolar transistors, inverter board for discoloration, check of input DC and output AC for the sign of overheating damages, to check any corrosion and overheating of the cables and terminals, to check the appearance of both AC and DC surge suppressors for damage or burn marks etc. inspection need to be done by the expert in a recommended time period.

6  Power Electronics for Green Energy Conversion The inverters are expected to be replaced at least once during the lifetime of a residential PV system, i.e., 10 to 15 years. However, different inverter manufacturing companies claim that the life of central inverters is 10 to 20 years.

1.1.3 Main Grid-Interactive Inverter (Hybrid Inverter) The hybrid inverter converts DC power coming from battery bank and PV panels to AC power, which further feed to the loads or the main grid via net metering. The above-discussed inverter and this inverter have two major differences to their nonstorage counterparts: they have an in-built converter to convert the AC grid power to DC power for charging of the battery bank, on the other side, unlike most inverters compatible with battery bank, battery bank with inverter system does not include MPPT, this function is being performed by a charge controller located upstream. Thus, these machines perform as a bidirectional inverter. These inverters usually have low voltage inputs, which can either 24 or 48VDC. Because photovoltaic systems with backed battery bank include charge controller and battery bank powered inverter typically run at 24 or 48VDC voltage match the battery voltage/charge controller. They imply for grid-connected requirements by disconnecting from the main grid but do not shut down completely; they continue to convert power either from the PV panels or the battery bank to feed critical loads, same as an uninterruptible power supply. The efficiency of the hybrid inverter is lower than those that are not compatible with a battery bank. The different companies’ models come with different efficiency level, ranging from 90% to 95%, but modern inverters are compatible with battery bank available in the market, those have up to 98% highest efficiency and 94.5% weighted efficiency [4]. These types of inverters have more components than those that are not compatible with battery bank system; therefore, they are having higher probability of failing. The manufacturers generally offer 2 years of standard warranty and 5 years of extended warranty.

1.1.4 DC-DC Converter for DC Building A DC-DC converter is a solid-state device, which converts one voltage level into another level, and these are found in electronic devices or circuits. These converters generally have two standards types; Boost Converter (step-up voltage) and Buck Converter (step-down voltage) these could be the unidirectional or bi-directional converter. Figure 1.2 shows the possible placement of the DC-DC converter. Here, the converter work as DC-DC

Energy-Efficient Appliances for Buildings  7 DC-Home DC-DC Converter PEC-II Controller

Battery Bank

PEC-I

DC-BUS

Switch & P. Meter PV-Array

MPPT

LOAD

Figure 1.2  Schematic of a DC home with its basic elements like battery bank, DC-DC converter (power electronic converter [PEC]), controller, and load.

step-down converter, which step down the main DC bus voltage to 24VDC to supply energy for a load. The converter used for MPPT is a buck converter in most of the application, and in these converters, the duty cycle D is the ratio of “on” time to the total time, given by

D=



Vo V

(1.1)

Here, Vo and V are the output voltage and input voltage of the buck-­ converter, respectively. The residential loads and converters both treated as load on PV array. According to maximum power transfer theorem (MPTT), the equivalent resistance (load and converter) must equal to the series resistance Rs of the solar cell to achieve maximum power transfer. The equivalent load resistance of the converter from the model of the buck converter is given by,

( µ RLOAD ) D2

(1.2)

 µ RLOAD  I  D2 

(1.3)

Req =

Also,



V=

8  Power Electronics for Green Energy Conversion Here, µ is converter efficiency, RLOAD is load resistance, I is panel current, and D is duty cycle, which is adjusted to match the impedance Req to source impedance RS, from equation 1.3, Req is inversely proportional to the square of the duty cycle. According to the solar cell characteristic equation, the dependency of power and solar array current on the converter dusty cycle is given by the following equations (1.4 & 1.5)

qµRLOAD    − 1 I = I L − IO exp  2   AkTD  



(1.4)

and



P = IL

 µ RLOAD   µ RLOAD    qµRLOAD   −1 − IO exp 2  D   D 2    AkTD 2  



(1.5)

Equations 1.2 and 1.3 form the relationship with the power available from the panel and duty cycle. Once it is derived from the equation, i.e., the voltage of PV array is a function of duty cycle of the converter, or alternatively, equivalent resistance is also a function of duty cycle as these parameters might be used for tracking MPPT [5, 6]. The DC-DC converters are generally used for low-power and low-­ voltage applications. For the future perspective, DC-DC converters could be used in DC buildings as in high-power converter (1–5 kw) that require a high-voltage DC input with the output voltage of 24VDC. The efficiency of step-down converters is generally high with the efficiency around 95%. The efficiency curve of the existing DC-DC converters with the input DC power supply of 36 volt, 48 volt, 75 volt is shown in Figure 1.3. In general, the lifespan of high-power DC-DC converters is 4 to 5 years. Few manufacturers claim it up to 10 years. Evidently, a building installed with DC-DC converter in a stable condition could have significantly longer life, compared with other distributed equipment used in that building like BMS and appliance scheduler. A) Maximum Power Point Tracker (MPPT) Controller The MPPT charge controllers work on specialized algorithms that adjust the operating voltage of panel in real time so that energy harvested would be maximum. The MPPT controllers ensure the maximum energy utilization from the solar panel and PWM controllers ensures utilization of

Energy-Efficient Appliances for Buildings  9 100 90

Vin = 36V Vin = 48V Vin = 75V

Efficiency [%]

80 70 60 50 40 30

0

5

10

15

20

25

30

Load Current [A]

Figure 1.3  Power supply efficiency curve of Toshiba DC-DC converter [7].

the battery bank. The controller incorporated features of both PWM, and MPPT controllers will significantly improve the performance of battery-­ backed PV system. The MPPTs are generally included in grid-tie inverters with no battery backup (central and microinverters) and modern charge controller. The MPPT is also known as DC-DC optimizer and have similar attributes to the microinverters (simpler system design with MPPT and monitoring at module level). Thus, a wide range of operating for MPPTs exists, depending on the application. The input voltage range may vary from 12VDC to 380VDC for small charge controllers and grid-tie inverters, respectively. Capacities range from low power to several kW in DC-DC optimizers in large grid-tie inverters. The MPPT DC output voltage often ranges from 12 volt and 48 volt depending up on battery bank configuration. As per the various manufacturing companies, the efficiency range lies between 97.5% and 99.5%. Figure 1.4 shows the efficiency curve with respect to output power [7]. The failure of MPPT in grid-tie inverter is not a common occurrence, thus, the lifespan of MPPTs is expected to be longer than that of grid-tie inverters. Most of the DC-DC optimizers making manufacturer offers 20-25 years of warranty. B) Charge Controller Charge controllers are used in battery bank systems, purposely used for altering the current coming out or going into the battery bank. Therefore, a charge controller with MPPT performs the functions of both MPPT as well as current control. Charge controller used in the AC-building regulates

10  Power Electronics for Green Energy Conversion 24V Conversion Efficiency Curve

Conversion Efficiency (%)

99 98 97 96 95 94 93 92 91 90 89 88 0

200

400

600

800

1000

1200

1400

Charging Power(W) 33V

66V

98V

115V

Figure 1.4  The efficiency curves of MPPT controllers of 33V, 66V, 98V, 115V DC [7].

current to and from the battery bank further supplies to inverter with DC power. The DC-building charge controller interacts similarly with the battery but in this case, it supplies power directly to the load at high voltage DC. The operating parameter range of charge controller is same as MPPT, which is described above. The range of the battery current lies between 4.5A and 80A manufactured by the major charge controllers manufacturing companies. The efficiency of major high-end charge controller with MPPT ranges from 97% to 99%. Figure 1.4 shows the efficiency curve with respect to output power [7]. The lifecycle of the high-end charge controller is expected 16 to 25 years, which is same as the lifetime of the PV modules. However, according to the experts of this domain, the actual lifecycle of the product significantly shorter for certain models. The case of failure of the charge controller, the battery bank is often destroyed, and this can significantly increase the system repair cost.

1.1.5 Bidirectional Inverter As shown in Figure 1.2, the bidirectional inverter work for both rectify AC to DC power to supply from the grid to the building load and invert DC to AC power further fed back to the main grid whenever the excess of power from the PV and battery bank is available. In situation of feeding back of

Energy-Efficient Appliances for Buildings  11 excess power to the main grid, it is assumed that the MPPT is not included since this has in advance dealt with MPPT or the charge controller.

1.1.6 Battery Bank Electricity storage systems typically designed for situations where grid connected homes facing power outages frequently or in autonomous grids, the battery banks are used as an electricity back-up The batteries are charged through charge controller from the PV system and are discharged to the loads when utility power is not available or during nonsunny days. There are many recognized battery technologies available in the market including lithium cobalt oxides, nickel-cadmium, lithium-ion, nickel-metal hydride, etc. Table 1.1 given below shows the data that compare the different batteries. However, for industries or consumers, priority remains lead-acid batteries, because of its low maintenance, better performance, and low capital cost. Our discussion in this chapter focuses on lead-acid batteries since it is widely used [9]. The typical battery voltage in the PV system applications are 12V, 24V, or 48V, it depends on the load requirements that interact with battery bank, i.e., charge controller, inverters, and possibly the source and the capacity of battery bank required. Another parameter must be consider while considering Table 1.1  The efficiency and durability of various electrolyte-based batteries [8–10]. Cell chemistry

Efficiency (%)

Cycle durability

Lead-acid

70–90

500 typical, 800 max.

Nickel-iron

65–80

5000

Nickel-metal hydride

66

300–800

Lithium cobalt oxide

90

500–1000

Lithium iron phosphate

90

7000 to 80% capacity

Lithium manganese oxide

90

300–700

Lithium nickel cobalt aluminium oxide

90

1000–1500

Lithium nickel manganese cobalt oxide

90

5000

12  Power Electronics for Green Energy Conversion batteries for battery bank is C rating (example- 3 (C3), 5 (C5), 8 (C8), 10 (C10), 20 (C20) or 100 (C100)) as this gives the load capacity (rated current) of the battery also indicates about the discharging rate of the battery as given in Table 1.2. In the above given C ratings, the 10 (C10) is the most suitable battery for solar applications because of its fast charging and standard discharging rate, 150 AH battery of C10 rating will last for 10 hours on a load of 15 A. The capacity of the battery has been calculated through the mathematical approach; as the standard PV generation results are known and also the load profile of each house has been taken practically, thus in order to compensate and make the system autonomous, the battery nominal capacity depends on the dominating value of the charging and discharge current of the battery. Mathematically, it can be expressed as [11]:

µn = Max (Ibc,Max, Ibd,Max)

(1.6)

where Ibc,peak and Ibd,peak are the charging and discharging current of the battery bank. Where PPV is the generated power of solar panels; PL represents the load required for the system; and PB represents the power from the battery used for compensation process and to make the system autonomous. Depending on the power generated by the solar panel and the load requirement, the charging and discharging of the battery take place. For example [12], in a case of centralized PV system, each home has its own solar panel and a centralized battery bank is used. To calculate the battery capacity two major parameters are required, the total power generated

Table 1.2  Discharging rate and C rating of the battery [12].

Battery C rating

Nominal capacity of battery bank (Ampere hour)

Discharging rate (Ampere)

C3

150

50

C5

150

30

C8

150

18.75

C10

150

15

C20

150

7.5

C100

150

1.5

Energy-Efficient Appliances for Buildings  13 (PPVn) by PV array and the load demand. So, the net power generated by the panel in the centralized system is given by equation 1.7 as,



∑nj =1 PPVj (t ) = PPV 1 (t ) + PPV2 (t ) + PPV3 (t ) + PPV 4 (t )…+ PPVn (t ) (1.7)

and the load demand to the system can be given through the equation 1.8 as,



∑nj =1 PLj (t ) = PPL1 (t ) + PPL2 (t ) + PPL3 (t ) + PPL4 (t )…+ PLn (t ) (1.8) Hence, the battery power PB can be calculated as in equation 9,



PB (t ) = ∑nj =1 PPVj (t ) − ∑nj =1 PL j (t )

(1.9)

The efficiency of battery measured is round-trip efficiency; it is not constant but is a function of the battery’s state of charge (SOC), charging and discharging rate of battery, and operating temperature of the battery [12]. The lead-acid round-trip efficiency lies in 70% to 90% range [13]. Table 1.1 compares the efficiencies of various battery technologies along with typical cycle durability. The lifespan of lead-acid batteries majorly depends up on the various factors, such as high temperature (≥75%) environment, deep discharge cycles, or poor maintenance can significantly degrade the battery life. Typically, lead-acid batteries have 5 to 20 years of life, it depends on application and quality of lead plates used in it [8].

1.2 Major Green Energy Home Appliances The electrical appliance based on green energy could be AC or DC appliances, but in homes with DC distributed systems have advantage that rectification of AC power is not needed. However, with AC grids, rectification is done before transfer of the power to DC distribution systems, when the solar power is unavailable or unable to meet the overall demand of load. Most of the home appliances utilize DC power in AC power systems, so AC power is converter to DC power and, such appliances could be easily operated on direct DC supply, if their power requirements were standardized. Mainly DC appliances have three dominant electricity end-uses, i.e., lighting, cooling, and refrigeration, which approximately accounts to 40%

14  Power Electronics for Green Energy Conversion of total consumption of electricity in residential and commercial sector. For each of these, we provide the power characteristics and a comparison of their energy efficiencies with their AC counterparts in this section.

1.2.1 DC Air Conditioners Figure 1.5 shows the comparison of efficiencies of DC air conditioner vs AC air conditioner. The efficiency of DC products is higher than the AC products. There are many reasons behind off-grid power being costly, that is why there is an incentive to develop efficient products. DC air conditioners also have various inherent efficiency benefits. Nowadays, air conditioner technology uses vapor compression technology, which has DC motor as a pump. The compressor of refrigeration technology uses two types of motor drivers, one with controlled variable speed and constant speed. The controlled variable speed motor operates according to load and requirement of user, whereas constant speed compressors run all the time at the constant load and often faces overcooling. Therefore, the controlled variable speed compressors are more energy efficient (around 30% more efficient) than constant speed compressors. The controlled variable speed compressors generally use variable frequency drives. The variable frequency drives first convert the input AC power to DC power, then with the help of pulse width modulation (PWM), it produces the desired frequency. Nowadays, the modern variable-speed drives use brushless DC motors.

Energy Efficiency Ratio (EER)

25.00 20.00 15.00 DC Air Conditioners

10.00

AC Air Conditioners

5.00 4,000

14,000

24,000

34,000

Cooling Output BTU/h

Figure 1.5  Efficiency comparison of DC verses AC air conditioners [14].

Efficacy (lm/W)

Energy-Efficient Appliances for Buildings  15 100 90 80 70 60 50 40 30 20 10 0

DC CFL AC CFL

0

50

100

Power (W)

Figure 1.6  Efficiency comparison of AC and DC CFLs [14].

1.2.2 DC Lighting The DC lighting products have huge diversity of products having more manufacturing companies as compared with DC air conditioners manufacturing companies. The DC lighting significantly contributes to the DC appliance industry. In major, it includes products, like DC lamps; light emitting diode (LED), fluorescent or compact fluorescent light (CFL), etc. Figure 1.6, Figure 1.7, and Figure 1.8 or [6–8] show the efficiency comparison of CFL, LED, and DC fluorescent lamps with AC lamps of same kind. It is observed that the efficiency of CFL, LED, and DC-air conditioners is higher than the products with AC power input. The comparison of the efficiencies of AC and DC LEDs is given, which is based on the study done by the California Energy Commission (ECE) [14]. For lower luminosity bulbs, the efficiency of DC LEDs is higher than AC LEDs. The collected data of fluorescent lamps mostly focus on low-power DC lamps, while in higher-power DC lamps, the comparison of efficiency is quite difficult because of consistent trend in both AC and DC lamps.

1.2.3 DC Refrigeration Refrigerators are the one of the most common and heavy load appliances in household. Considering power efficiency, refrigerator’s efficiency mainly depends upon two factors: insulation and compressor technology. Same as air conditioners, refrigerators do have compressors and same condition of

16  Power Electronics for Green Energy Conversion 100 90 Efficacy (lm/W)

80 70 60 DC (including DC/AC-Hybrid) LED

50 40

AC LED

30 20 10 0 0

20

40 Power (W)

60

80

Figure 1.7  Comparison of AC and DC LEDs efficiency [14]. 160

Efficacy (lm/W)

140 120 100 80

DC Fluorescent

60

AC Fluorescent

40 20 0 0

20

40

60

80

100

120

Power (W)

Figure 1.8  Efficiency comparison of DC verses AC Fluorescent [14].

variable and constant load compressor apply to this appliance also. The variable speed compressors have larger efficiency because of driver controlled brushless DC motors. The efficiency also depends on capacity of refrigerator, with or without freezer. The refrigerators with upper compartment could have two compressors (one for refrigeration and another one is for freezer compartment), each allows to run on optimal efficiency. Still the market of DC refrigerators is weaker than the AC refrigerators. Figure 1.9 shows efficiency comparison between AC refrigerator model and DC refrigerator model of same capacity range, and this was calculated with refrigerator efficiency metric of kilowatt-hour data of every year

Energy-Efficient Appliances for Buildings  17 600

kWh/year

500 400 300

DC

200

AC

100 0 0

2

4

6

8

10 12 14 Capacity (ft3)

16

18

20

22

Figure 1.9  Energy usage comparison between AC and DC Refrigerator with freezer [14].

operation. Only double-compartment refrigerators powered with DC or AC were taken for comparison. Based on previous study, the products powered with DC consumes considerably less energy, on average, less than half of the energy consumed by AC products. While DC refrigerators with higher efficiency are costlier than AC with energy star symbol of same capacity. The reason behind this could be the advance of new technology, which reduces energy consumption and increased the cost.

1.2.4 Emerging Products for Grid Connected Homes and Businesses Nowadays, two major products are emerging in green technology for residential and commercial applications. The first one is electrical vehicles (EVs) and charging stations, the second one is electrical appliances designed for hybrid power system, which could operate both on AC and DC power. The current trend of DC products is not designed on standard voltage, also DC products tend to lie on the smaller side of the capacity range of mainstream AC products.

1.2.5 Electrical Vehicle Electrical vehicle and plug-in hybrid vehicle (PHEVs) industries are rapidly growing while the automobile manufacturers are manufacturing hybrid vehicles, which can run on both electric batteries and fuel. PHEVs in comparison to EVs, are more common and preferred by users because of current weak charging infrastructure. PHEVs users have option to run the vehicle on liquid fuel if battery system ran out of charging. Study shows

18  Power Electronics for Green Energy Conversion rapid growth globally in PHEV and EVs sales, the United States leading global sales in 2015 with more than one-third of the market. PHEVs and EVs would become a large future DC load and could also serve as potential battery storage for residential load. The current method to charge EVs is from rectifying AC, EVs charging infrastructure would be simpler as it would be integrated with high-voltage DC bus of the future DC house.

1.3 Energy Savings Through Green Appliances The information expresses the fact that savings from each DC appliance could make a large impact on power system and on environment as well. Households sector should adapt change to make an impactful step toward green future, because residential sector holds a large portion of power consumption. Green home appliances significantly contribute to the reduction of carbon emissions, as residential buildings are the second largest power-­ consuming sector. Energy labels, ecomarks, ecolabels, green marks, certified labels, corporate advertisements are some of the ways by which consumers can identify the green appliances. Various practices generally supported by government involvement are employed around the globe to grade the products based on their power consumption requirement. The one such practice adopted by USA is Energy Star labeling.

Table 1.3  Power savings of major home appliances with green home appliances about conventional home appliances.

Appliance

Average power consumption with conventional appliance

Power saving with green appliance

Refrigerator

725W

20%–30%

Cloth Washer

350W–500W

20%–50%

Dishwasher

1200W–2400W

25%

Television

100W–170W

25%

Air Conditioners

1000W

20%–50%

Water Heater

4500W–5500W

50%

Energy-Efficient Appliances for Buildings  19 According to Energy Star study in [14] if refrigerators with Energy Star certification are sold across US, then the cost of energy saving would be $1.4 billion per year and also a significant figure of 19 billion pounds annual emission of harmful greenhouse gas could be avoided, which is also equal to the emissions of 1.8 Million vehicles. Similarly, if all laundry machines are replaced with the Energy Star–certified laundry machines, then it results in savings of around 500 kWh of electric power, along with around 20 billion gallons of water and around 1.5 trillion BTUs of natural gas each year, moreover, tends to save electricity bill of worth approximately 250 million dollar per annum. Likewise, if only green appliance are utilized then it will lead to reduction of 2.5 billion pounds greenhouse gases could be achieved per annum, which is equivalent to the emissions from more than 200,000 gasoline-driven vehicle. Table 1.4  List of few more common green appliances with their average energy consumption per year of mid-range homes [14].

End use

End use subcategories

Consumption by appliance (Terawatt hour)

Lighting

Lighting-Incandescent

157.88

Lighting-Fluorescent

17.06

Lighting-Reflector

24.95

Lighting-Torchiere

10.31

Electric Heat Pumps

40.89

Central Air Conditioners

153.07

Room Air Conditioners

27.09

Geothermal Heat Pumps

1.13

Electric Water Heaters

130.02

Solar Water Heaters

0.34

Television

Colour Televisions and SetTop Boxes

108.13

Refrigerator

Refrigerators

107.23

Space heating

Water heating

(Continued)

20  Power Electronics for Green Energy Conversion Table 1.4  List of few more common green appliances with their average energy consumption per year of mid-range homes [14]. (Continued)

End use

End use subcategories

Consumption by appliance (Terawatt hour)

Space heating

Electric Heat Pumps

21.32

Electric Space Heaters other than Heat Pumps

53.35

Geothermal Heat Pumps

0.75

Clothes dryers

Electric Clothes Dryers

78.02

Personal computer

Personal Computers and Related Equipment

54.56

Furnace fans

Furnace Fans and Boiler Circulation Pumps

42.21

Cooking

Electric cooking equipment

31.52

Dishwashers

Dishwashers

26.71

Freezers

Freezers

22.89

Ceiling fans

Ceiling fans

18.19

Microwave oven

Microwave oven

13.15

Home audio

Home audio

11.57

Clothes washers

Clothes washers

9.56

Rechargeable electronics

Rechargeable electronics

8.98

spas

spas

8.27

Secondary space heating

Electrical Secondary space heating

7.80

Coffee maker

Coffee maker

4.10

1.3.1 Appliance Scheduling The new technology of smart grids allows users to utilize energy both by choosing the most suitable supplier and through scheduling of home

Energy-Efficient Appliances for Buildings  21 appliances according to constraints like electricity price, current load. In demand side management algorithm, meeting the customer demand and achieving the optimization target can be done by reducing peak load, reducing energy consumption, which are crucial aspects of green power system. The interconnection of home appliances with central controller is connected through Zigbee module network signals, which contains triggering signals of appliances and power consumption. The central controller monitors the vital parameter like current pricing, total load, and inform the consumer for the same. This can not only reduce electricity bill but also reduces appliance load during peak demand, the scheduling function could be performed over heavy computing server, mobile devices, while their logic runs in the controller. The process scheduling in real-time operating system and appliance scheduling task is quite similar. The execution of task on controller is time constraint, which is managed by real time scheduling. The appliances are modeled as tasks having start time, execution time, and off time. The major difference occurs in energy scheduling, the appliances can run on parallel connection if the total power at any time does not exceed the current capacity of the distribution wires. Scheduling scheme is algorithm in the home power management system. The appliances are controlled by the home controller according to the distributed grid condition and variable pricing. The power management system in the home is capable to take local decision to manage the home power utilization, and it also controls the demand according to price signals. The schedule can be produced and modified by the users according to the tasks set at any time. Lee et al. [15] show scheduling, where each appliance has its own power consumption characteristics according to its working or task. The classification of task is needed for the study. Tasks are categorized in classes as class 1 tasks must start suddenly after the task gets ready and cannot be ceased to the end. For instance, the hair drier starts suddenly as user starts using it. As the power utilization is fixed and there is none other way to adjust their operation, these kinds of task do not need to be scheduled. Class 2 tasks can start after they get ready to use, and their operation is not preemptive. Like a washing machine and dishwasher can start at any time if the task can be done within the defined time. And the last is class 3 task, which is like class 2 except they are preemptive. The EV charging comes under this class, the task can be delayed and resumed within the fixed time lap. The power load profile of each appliance is practical for characterizing the behavior of power consumption. As study presented in [16], the set

22  Power Electronics for Green Energy Conversion program of washing machine decides its load profile, the water temperature and the time duration of wash, which is preset by the user. Figure 1.10 shows power consumption profile of five tasks. The Task 1 gets ready at time 1 and utilize 7 times units. The duration of timeslot can be set according to the system’s needs on the computing time and the schedule graduality. In power scheduling, the time slot duration can be tens of minutes, for instance, 20 minutes. Tasks 1 to 4 cannot be preempted while task 5 can be preempted. Power consumption profile of each appliance is set to a defined duration of time slot. In fact, every appliance has its own time scale in its power demand behavior. After all, Figure 1.11 shows the power 16 14

Power

12 10 8 6 4 2 0 0

5

10

15

20

25

30

Time Task 1

Task 2

Task 3

Task 4

Task 5

Total

Figure 1.10  Power consumption profile of five tasks. 12 10

Power

8 6 4 2 0 0

10

5

15

20

30

25

Time Task 1

Task 2

Task 3

Task 4

Task 5

Total

Figure 1.11  Power consumption profile produced after appliance scheduling.

Energy-Efficient Appliances for Buildings  23 profile produced by the scheme for the task set given in Figure 1.10. Figure 1.11 indicates the achieved peak load reduction by up to around 25% after applying appliance scheduling.

1.3.2 A Case Study of a Mid-Ranged Home with Green Home Appliances Versus Conventional Home Appliances: A Comparison of 1 Day Consumption In a case study calculation of the electrical load is the initial step of study, analysis, and modelling of renewable power system to meet the demand of residential consumers. The statistics helps in determining the cost of the power system, the size, and the daily, yearly performance of the power system. The load profiles are made with recording power usage through all the appliances like television, washing machine, refrigerator, dryer, air conditioner, lights, etc. different appliance have different load profiles as few loads are continuous and few might be discontinuous or the usage is discrete. The appliances are categorized in six sections: lighting, kitchen, living room, laundry, heating/cooling system and room, power ratings, and the time duration of loads are summarized in the same. The energy consumption of appliances in 24 hours is calculated by multiplying power watt rating (W) to time duration during (h/day) along which the appliance Table 1.5  Daily consumption of each appliance. Conventional appliances data

Green appliances data

Watts

W h/day

Watts

Wh/day

Clothes washer

500

250

Clothes dryer*

2500

1250

1200

410

Iron

1000

80

Cable box

35

210

TV LCD(19”-35”)*

100

600

40

240

TV LCD (53”-61”)

170

1020

Appliances Laundry

Living room

(Continued)

24  Power Electronics for Green Energy Conversion Table 1.5  Daily consumption of each appliance. (Continued) Conventional appliances data

Green appliances data

Watts

W h/day

Watts

Wh/day

Satellite dish

25

600

Stereo receiver

450

450

Ceiling fan

120

720

Box fan

200

200

Central AC*

3800

38000

1850

5070

Water heater

4500

0.16

Incandescent/LED*

50

6000

12

1440

Incandescent/LED*

100

5000

18

900

100 100 200 1500

10 400 400 30

Electric oven*

1200

1200

1200

960

Microwave

1000

80

Freezerchest*(0.42m2)

200

1080

120

970

Fridge*(0.57m2)

150

1400

100

945

Toaster

1100

33

Dishwasher*

1800

2700

180

578

Coffee machine

1100

187

Appliances

Heating/Cooling

Lights

Rooms Printer Laptop Desktop computer Hair dryer Kitchen

(Continued)

Energy-Efficient Appliances for Buildings  25 Table 1.5  Daily consumption of each appliance. (Continued) Appliances Blender

Conventional appliances data

Green appliances data

Watts

W h/day

Watts

500

40

Wh/day

*Major green appliances

was active. The considered power rating is the average rating of appliances available in the market. Based on energy consumption calculations, it is decided to replace few major conventional appliances (air conditioner, clothes washer, TV, lighting, clothes dryer, fridge, oven, dishwasher) with the green appliances because these are consuming more than 1000 Wh/ day extra as compared with green appliances. The total energy consumption for a day of each major appliance both green as well as conventional is given in Table 1.4 and shown graphically in Figure 1.10, the total daily consumption of conventional and green appliances is 62.90 and 30.80 kWh/ day, respectively as given in Table 1.5. In addition to the daily energy consumption data as given in Table 1.6, the load profile of each appliance is required for the complete study. Figure 1.10

Table 1.6  Total per day energy consumption. Daily consumption of appliances

Conventional appliances (kWh/day)

Green appliances (kWh/day)

Lights

6.72

3.79

Rooms

1.5

0.738

Living room

3.045

2.265

Laundry

39.64

20.64

HVAC

11

2.34

Kitchen

1.0085

1.0085

Total (kWh/day)

62.91

30.78

26  Power Electronics for Green Energy Conversion

Appliances Energy Consumption (kWh)

7 6 5 4 3 2 1 0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (Hour)

Green Electrical Appliances

Conventional Electrical Appliances

Figure 1.12  Daily appliance energy consumption profiles.

shows a-day load profile of both cases with conventional appliances and other with green appliances. This residential building load profile possess 3 peaks: one is around 6- to 7 am, second one is at 12 pm, and the third one is around 6 pm evening as given in Figure 1.12.

1.4 Conclusion Reducing energy consumption in home could benefit in saving money, contributing to CO2 reduction and expands user’s energy security. DC or highly efficient devices, like DC refrigerators, DC air conditioners, DC lightings, and many more, help to reduce the electricity demand and improves the overall efficiency of home’s incorporated with these types of devices. As energy-efficient appliances utilize minimum amount of power in transition of energy also maximize the level of energy conversation. Similarly, the result shown in case study provided in this chapter: the energy consumption graph of a home with conventional and with green appliances (DC or highly efficient devices), could save up to 50% as compared to the case when conventional appliances are being used. In the future, the maximum energy savings would be from DC rather than conventional AC, which would be used for heavy loads only like space heating. EV charging and the battery integrated with solar electric power system required to achieve

Energy-Efficient Appliances for Buildings  27 high penetration of distributed Photovoltaic supply. Nowadays power sector is undergoing the emergence of DC power systems designed for general services, and new standards are being developed which is compatible with all residential and commercial loads. Combining both the energy savings of possible DC-based appliances, neglecting AC-to-DC conversion losses, the overall savings are more than 40%. It is concluded that if trends continue as anticipated, including the need of reduction of carbon emission, the energy advantages of green electrical appliances will expand.

References 1. Kim, Y., Modeling and analysis of a DC electrical system and controllers for implementation of a grid-interactive building. Energies, 10, pp. 1–21, 2017. 2. Peng, W., Baghzouz, Y., Haddad, S., Local load power factor correction by grid-interactive PV inverters. 2013 IEEE Grenoble Conference, Grenoble, pp. 1–6, 2013, doi: 10.1109/PTC.2013.6652412. 3. Ab. Rahman, A., Salam, Z., Shaari, S., Ramli, M.Z., Methodology to determine photovoltaic inverter conversion efficiency for the equatorial region. Appl. Sci., 10, 201, 2020. 4. Bletterie, B., Brundlinger, R., Lauss, G., On the characterisation of PV inverters’ efficiency introduction to the concept of achievable efficiency. Prog. Photovolt. Res. Appl., 19, 423–435, 2011. 5. Sivakumar, S., Jagabar Sathik, M., Manoj, P.S., Sundararajan, G., An assessment on performance of DC–DC converters for renewable energy applications. Renewable Sustainable Energy Rev., 58, 1475–1485, 2016. 6. Garbesi, K., Vossos, V., Shen, H., Maximizing Energy Savings from Direct-DC in U.S. Residential Buildings, In press, Lawrence Berkeley National Laboratory, 68, A, 223–231, 2014, ISSN 0378-7788, doi.org/10.1016/j. enbuild.2013.09.009. 7. Mirzaei, A., Forooghi, M., Ghadimi, A.A., Abolmasoumi, A.H., Riahi, M.R., Design and construction of a charge controller for stand-alone PV/battery hybrid system by using a new control strategy and power management. Sol. Energy, 149, 132–144, 2017. 8. May, G.J., Davidson, A., Monahov, B., Lead batteries for utility energy storage: a review. J. Energy Storage, 15, 145–157, 2018. 9. Sullivan, J.L. and Gaines, L., Status of life cycle inventories for batteries. Energy Convers. Manage., 58, 134–148, 2012. 10. Placke, T., Kloepsch, R., Dühnen, S., Lithium ion, lithium metal, and alternative rechargeable battery technologies: the odyssey for high energy density. J. Solid State Electrochem., 21, 1939–1964, 2017.

28  Power Electronics for Green Energy Conversion 11. Chauhan, R.K., Chauhan, K., Mehrotra, A., Agarwal, A., Subrahmanyam, B.R., Singh, A.G., Singh, D., Droop control-based battery management system for automated DC microgrid, in: Proc. 2020 International Conference on Contemporary Computing and Applications (IC3A), Lucknow, India, pp. 81–86, 2020. 12. Chauhan, R.K., Chauhan, K., Subrahmanyam, B.R., Singh, A.G., Garg, M.M., Distributed and Centralized Autonomous DC Microgrid for Residential Buildings: A Case Study. J. Build. Eng., Elsevier, 27, 1–6, January 2020. 13. McKenna, E., McManus, M., Cooper, S., Thomson, M., Economic and environmental impact of lead-acid batteries in grid-connected domestic PV systems. Appl. Energy, 104, 239–249, 2013. 14. Garbesi, K., Vossos, V., Shen, H., Catalog of DC appliances and power systems, Berkeley, escholarship, CA, Oct 2011. 15. Lee, J., Park, G.L., Kim, S.W., Kim, H.J., Sung, C.O., Power consumption scheduling for peak load reduction in smart grid homes. Proc. ACM SAC, Mar. 2011, pp. 584–588. 16. Derin, O. and Ferrante, A., Scheduling energy consumption with local renewable micro-generation and dynamic electricity prices. Proc. 1st Workshop Green Smart Embedded Syst. Technol.: Infrastruct. Methods Tools, 2010-Apr.

2 Integrated Electric Power Systems and Their Power Quality Issues Akhil Gupta1*, Kamal Kant Sharma2 and Gagandeep Kaur3 Electrical Engineering Department, I. K. Gujral Punjab Technical University Batala Campus, District Gurdaspur, Punjab, India 2 Electrical Engineering Department (UIE), Chandigarh University, Mohali, Punjab, India 3 Electrical Engineering Department, I. K. Gujral Punjab Technical University Main Campus, Kapurthala, Punjab, India

1

Abstract

With the increasing consumer electric power demand, centralized power plants have converted into decentralized power plants. But sometimes, due to numerous faults, there are problems of electric power production. This problem has especially been overcome by commissioning of hybrid electric power systems. By employing hybrid systems, it is possible to install a solar photovoltaic (PV) array and synchronize its generation with conventionally installed large-scale power generation systems. In recent times, it has been observed that hybrid system offers the best solution in energy production and harnessing good quality electric power at output. It is possible by operating two or more renewable energy sources (RESs) through effective and efficient controlling systems. Moreover, renewable energy (RE) sector has been supported by Indian Government in recent times. Additionally, numerous policies for granting financial aids are being framed in promoting research-based activities. Hybrid systems, thus, have become a reliable option for rural and remote areas in ensuring the availability of uninterrupted electric power. However, the integration of RESs may pose some immediate power quality (PQ) problems. However, due to integration of RES with conventional energy sources, turning into microgrid leads to PQ issues. These problems can be overcome by implementing power conditioning systems. This chapter deals with designing of different modes of hybrid energy systems in order to have an *Corresponding author: [email protected]; [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (29–66) © 2022 Scrivener Publishing LLC

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30  Power Electronics for Green Energy Conversion understanding of their operation that is possible under various variable constraints and PQ assessment with different dynamic and static controlling mechanisms. Keywords:  Hybrid energy, renewable energy, system integration, power quality, assessment

2.1 Introduction Hybrid system is a combination of two or more systems connected together in order to provide maximum deliverables in terms of energy and power. Moreover, it comprised of two or more energy systems to provide dominating and yield cooling, heating, and hot water to home buildings and industrial applications [1], depicted in Figure 2.1. In modern times, renewable energy sources (RESs) are built up employing fossil fuel and by latest installed renewable sources utilized by the use of nonrenewable sources. Since there are many RESs available, each type performs well under different climatic conditions. But due to unavailability of any of the source, performance of overall system is disturbed [2–4]. Employing hybrid system, if one source is unable to perform under climatic conditions as per requirement, the second source fulfills the requirement and provides better response. Hence, these hybrid systems are preferred for many years [3].

I Inverter DC to AC Grid PV panels

DC AC Battery R Rectifier AC to DC

Wind Turbine

Figure 2.1  Pictorial representation of a hybrid energy system.

MBR

Power Quality Assessment of DG Systems  31 Various sources have been used with different configuration and approached to develop a new terminology, which helps a system to operate faster and have better deliverables in terms of efficiency. With the hybrid system, various sources, which are identical in their operation or having same properties, can share same cluster requirements and overcome the necessity of additional manpower and other sophisticated equipments for storage specifications. Various case studies proved that in the hybrid system, more than one set of variables are not preferred over different set of variables in order to achieve a new set of outputs [5]. For example, in case of RES, only nonconventional energy sources together will increase order of a system but degree will remain same therefore, hybrid system will increase order and degree of system in such a way that balanced algorithms can be suppressed together to control the output in a range. Therefore, when different RESs are combined together and split into different arrays, then the terminology is termed as hybrid RES systems. Hybrid RES system also alter the myth that RES is not cost effective, most hybrid RESs can combine together conventional energy sources along with nonconventional energy sources resulting in microgrid and whole system will work as base power plant and peak power plant simultaneously [6]. These kinds of terminology provide enough space for emerging technologies to grow and be cost-effective for investors and easy for implementation at remote locations. Hybrid RES systems may be a combination of conventional and nonconventional energy sources [3, 7–9]. Hybrid systems also operate in different configurations: traditional and emerging modes. In the traditional mode, all sources connected together will operate together simultaneously and grid operate in such a manner will operate as base power plant but not economical as not more effective as compared with conventional energy sources. For example, if solar and wind systems are combined together and work simultaneously, both systems will work together but their inputs are variable in nature and intermittent and not reliable. On the other hand, if one of a system fails then other system also liable to fail resulting in collapse of a system. Other mode of operation is emerging mode and also termed as sequential mode. In sequential mode, all sources connected together operate not together but operate in a sequential manner. This kind of operation is better than previous one but only limited to operate as peak power plants and cost effective with load factor not equal to 100%. Due to these disadvantages, hybrid energy systems need to operate in a mode of sequential when both systems are of different nature like conventional and nonconventional energy sources, resulting in one of the systems operated as base power plant and other will operate as peak power plant.

32  Power Electronics for Green Energy Conversion In hybrid power systems, one source will add the system and creates as a backup for one of the systems and make hybrid system effective. Hybrid renewable energy (RE) systems are the composition of at least one conventional and other nonconventional energy source [10–12]. Due to these drawbacks, various researchers are forming and simulating integrated circuits comprises of nonconventional energy sources with tested sources in connected traditional grid codes and reregulated environment. In India, deregulated environment only limited to individual users or a single organization where as in other countries especially Europe, deregulated environment evaluated and analyzed with multi-attributes at different end consumers together. These possibilities lead to better network and higher value of reliability [13]. In remote and isolated areas, the applications of hybrid energy systems are more widely preferable than that of grid connected systems [14, 15]. Along with in distributed generation or micro-grids, the application of hybrid systems has been popular, since few years and has become great concern [3]. Various studies are archived and analyzed on basis of different operating characteristics and evaluate significant parameters in the combined operation. Certain parameters, which seem to be dominating in RES, are not relevant in conventional energy sources; despite of this, many parameters are common and studied with operation of microgrid following grid requirements and requisite detailing for the hybrid systems [7]. The advancements in power electronic converter technology and automatic controllers indicate continuous improvement in system performance of hybrid system. These changes in hybrid systems make them more reliable, efficient and economical [16–18].

2.2 Designing of a Hybrid Energy System Hybrid systems are complex systems with many sets of variables have uncommon branches but connected with a string and having parallel attributes. The main concern is suitability and reliability in terms of processing unit, processed and unprocessed parameters along with size of a system. Hybrid system size depicts the number of computations and iterations required for a stipulated time and processing time for evaluate optimum result. Hybrid system also involves feedback (close and open) to obtain desired output with real and reactive power in limited range so that frequency mismatching and voltage stability issues can be accessed well in time. Moreover, when the number of resources are connected with

Power Quality Assessment of DG Systems  33 different operating characteristics together (likewise hydro and wind) then power quality (PQ) is a problem developed and selection is one of requisite to solve an issue before creating a suitable hybrid system. In hybrid system, the number of devices, which control real and reactive power control and provide desired output with controlled deliverables, are commonly termed as power conditioning devices and also use of Flexible AC Transmission Systems (FACTS) devices with power electronic interfaces. Permutations and combinations adhered to a system on suitable indices depends upon availability of land and resources connected to make planned hybrid system [19]. RES also gained importance in hybrid system due to its many advantages and a major agreement signed between developing and developed countries and termed as Kyoto protocol, which signify that greenhouse gases and emission of harmful gases should be avoided and benefit of carbon credits evaluated in dollars per unit to be paid to developing countries by developed entities. These kinds of agreements and interactive knowledge sharing between these two entities across globe make researchers to make simulation models of hybrid systems acquiring environmental and economic benefits. These parameters help to design proper modeling of hybrid RE system [1]. Some guidelines are to be followed for designing of hybrid energy system: • • • •

Type of RE to be installed. Number and estimated capacity of RE unit. Energy storage system, if it is required. System is standalone or traditional (grid code) mode.

Before making simulation models and advancement of new technology penetrating into traditional system invokes planning of hybrid system in software implementation and availability of resources are analyzed for finding congestion and available transfer capability considering RES for selected location. RES also depends on climate changes as they are natural resources and dependent linearly or nonlinearly on a scale of 4.5 out of 5. Therefore, the climate conditions play a very efficient role for attaining particular decision. Potential benefits of a hybrid energy system are high reliability, long and sustainable, low-cost services, free from pollution, clean and green energy, efficient, and easy to install.

34  Power Electronics for Green Energy Conversion

2.3 Classification of Hybrid Energy Systems There are many possible configurations of hybrid energy systems, which perform under the influence of numerous climatic conditions [20, 21]. These hybrid energy systems are classified below: • • • • •

Hybrid wind-solar system Hybrid diesel-wind system Hybrid wind-hydropower system Hybrid fuel cell- solar system Hybrid solar-thermal system

2.3.1 Hybrid Wind-Solar System Hybrid systems are complex system, and many issues are associated with it but they are developed considering the availability and need of use for a particular type of land. Researchers simulate hybrid systems contain RES as wind and solar due to their abundance in nature and interdependent in nature but both of these systems are intermittent in nature not able to fulfill load duration curve and not cater the load uniformly. As a result, both systems cannot operate individually as both will not able to withstand capital cost incurred alone but integrated system leads to PQ issues and voltage sag and voltage swell for a large duration of time. Although wind and solar systems, combined together, are only used at coastal regions or

wind turbine

DC Loads PV Panels Battery Inverter AC Loads Control Panel

Figure 2.2  Pictorial representation of distributed generation systems.

Power Quality Assessment of DG Systems  35 for a small population where load is utmost fixed and can be stimulated in advance [17]. Schematic of a typical hybrid solar-wind system is depicted in Figure 2.2. Various issues pertain to hybrid system contain wind and solar that need to be considered: solar irradiance, solar orientation, wind speed annually, total output simulated, temperature level and type of land suitability [18]. Both systems need to cater load of end users and must be tested in islanding mode or grid connected mode so that whenever any of the system fails, second systems must fulfill minimum requirements and grid connected mode should be available as both systems are unpredictable. Grid codes must be evaluated for a specific region where hybrid systems are planned in consideration with energy requirements. Although, hybrid system must follow grid code requirements and operation should be in parallel so that voltage stability should not be affected and extra energy must be supplied to batteries considering economic aspects.

2.3.2 Hybrid Diesel-Wind System Power generation is also possible by employing a hybrid system through wind and diesel energy source. There are two strategies followed: first one is to operate diesel engine continuously and second one is to operate the diesel engine on selected time interval on a time scale of 4 out of 5 or on immediate basis when required. Important components required are load, generators with power electronic controller, diesel engine, and battery energy storage system [13]. Schematic of a typical hybrid wind-diesel system is depicted in Figure 2.3. In hybrid system comprised of diesel and Bus Bar Wind Turbine wind

Induction Generator

Wind System Diesel Engine

Synchronous Generator

Consumer Loads

Static Var Compensator

Dump Loads Diesel Generator set

Figure 2.3  Schematic diagram of a typical hybrid diesel wind system.

36  Power Electronics for Green Energy Conversion wind at the same scale, both operate as peak power plant, and the main objective to use both systems economically is so that deliverables in terms of output and efficiency must be cost-effective, and optimization must be controlled enough with less number of iterations. These kinds of systems are used at remote locations and for small population of island or at remote places where electrification is required considering security concerns and economic aspects.

2.3.3 Hybrid Wind-Hydro Power System This kind of system commonly used for generation of electricity at a larger scale. These kinds of system that perform practically very rare as hydropower are limited to a certain consideration like catchment area, dam, and reservoir but wind availability at a particular speed is a constraint and vary place to place. Both systems are land specific and subjected to various constraints, which cannot be overcome by placing alternative at all. The most promising advantage of this kind of hybrid system is a combination of base power plant and peak power plant in form of hydro power plant acts as base power plant and wind power plant behave as peak power plant addressing a need of systems complementing each other. Pumped off storage plant can be implemented with a provision of both systems will operate in peak mode and land constraints still exists [19]. Schematic of a typical hybrid wind hydropower system is depicted in Figure 2.4. Upper Reservoir

Pumping

Wind farm Wind energy

Lower Reservoir

Generating Pump

Electricity during pumping

Pumping

Electricity during generating

End-use

Control Station

Generating Turbine

Figure 2.4  Schematic diagram of a typical hybrid wind hydropower system.

Power Quality Assessment of DG Systems  37

Batteries PV Panels

Charge Controller

SOFC

AC Inverters

Load

Figure 2.5  Schematic diagram of a typical hybrid fuel cell-solar system.

2.3.4 Hybrid Fuel Cell-Solar System Taking hybrid fuel cell-solar system, input power varies with time continuously, because sunlight irradiance continues changes. This terminology comprises of different configurations having distributed generation sources in a form of solar Photovoltaic (PV) cell, a proton exchange membrane fuel cell and power conditioning devices. System is incorporated in consideration with environmental conditions and changing value of parameterization evolution for desired deliverables. Electricity is produced by a PV cell to meet consumer demand [20]. When there is any problem regarding generation due to low solar irradiance, the proton exchange membrane fuel cell is used to maintain reliability of system. It is a combined system and both systems will act a catalyst to dive one another. One important feature of a system is their less complexity but having a drawback of less reliability indices. It also decreases environmental impact on system. Power conditioning unit is a bidirectional power converter, which changes from DC energy into AC energy. It also becomes rectifier and changes AC power into DC power. Hence, power conditioning unit behaves as a heart of hybrid PV and fuel-cell system. This hybrid system avoids usage of fuel-cell and improves voltages of the entire system on large scale. Schematic of a typical hybrid fuel cell-solar system is depicted in Figure 2.5.

2.3.5 Hybrid Solar Thermal System Another hybrid system can be made by using solar PV and thermal system [22]. In this system, performance of entire system can be enhanced by temperature of plate and solar irradiance. This gives us both thermal

38  Power Electronics for Green Energy Conversion

Solar irradiance

Auxiliary heater DHW (6-11%) Heating (30-90%

PV-T panel Cooling (1-60% Ref. cycle / HP Pump

Thermal storage

Control system

Power to other needs

Figure 2.6  Schematic diagram of a typical hybrid solar thermal system.

efficiency and electrical efficiency. Both efficiencies define system performance at different climatic conditions. Since optical concentration technologies are employed widely with latest technologies and can come to solar thermal system for producing thermal energy storage for industrial application purposes. These kinds of system complement each other but a major drawback is the conversion technologies, which are remote and limited and cannot be accessed by end user very easily and from investor side, technologies are not user friendly. Among all popular concentrating solar thermal systems like parabolic trough collectors, linear Fresnel reflector systems, central receiver systems, and dish systems are utilized in various applications [21–24]. Schematic diagram of hybrid solar-thermal system is depicted in Figure 2.6.

2.4 Power Quality Implications PQ is an aggregate of voltage, frequency deviation and improves reliability indices of a system. PQ relates with zero tolerance towards distorted and disturbed supply. In other words, good value of PQ reflects stability and continuous system with enhanced system security. PQ related with clean system to meet industrial requirements of zero disturbance supply [25]. There are various issues, which are related with PQ problems. Various PQ problems occur instantaneously or randomly extinct; brief description problems and causes of PQ are listed below:

Power Quality Assessment of DG Systems  39

2.4.1 Interruption As depicted in Figure 2.7, waveform for interruption suggests that there is a lag of some period of time, which is a dead time and can lead to outage. When value of voltage less than 0.10 times than the actual value due to sudden or prolonged disturbances, then it leads to loss of system indices and known as interruption (for small period of time) or a blackout (for prolonged loss of system indices). Interruptions are being segregated on the basis of different time cycles of occurrence and clearing time. It has been seen that momentary interruption lasted for 3 cycles, and starting time is considered at an extinct interval of 30 cycles depending on fundamental frequency of a system, on the other hand, temporary interruption is an extension of momentary condition and lasted a duration of 1 minute and the problem lies with sustained oscillation, which can cause problems, like harmonics, and produces heating effect in a system with more thermal insulation level. Although reduction in instantaneous voltage in a network leads to interruption or collapse of a system and even leads to shut down in case of long-lasting interruptions. Sometimes, voltage sag (reduction in voltage) is also considered be interruption but it is a kind of reduction in voltage below, which an equipment failed to deliver. In Figure 2.7, the waveform is represented in terms of voltage; as 90% of PQ issues sustained for voltage as current (load) depends upon functional description of equipment. It has been evident from different case studies that PQ rises exponential in time and related with voltage stability; however, interruptions are quite typical and share less than 4% of total causes of problems of concern while on the other hand, voltage dip like sag or increase in instantaneous voltage

Figure 2.7  Waveform for PQ issue: interruption.

40  Power Electronics for Green Energy Conversion like swell share more than 95 % of total problem occurred and responsible of system shut down mechanism. Interruptions also lead to outage and sometimes blackout, which is not even partial shading but total collapse of a system [26–28]. If a system fails for a very short time interval, it is said to be known as an interruption; but if it is sustained for a longer interval, it is summed as outage and failure of a system. Interruptions are of few cycle phenomena and lead to blackout, whereas brownout occurs due to partial effect of interruption and can be revived upon clearance of fault, which can also occur due to PQ issue.

2.4.2 Undervoltage or Brownout Undervoltage is a normally defined and reduction in voltage below than 0.90 of its actual voltage impressed on system lasted more than 60 sec. This kind of phenomenon is sometimes misunderstood as brownout, which is not actually a case as this term is not used technically. In a centralized power system, due to increased load or high demand at receiving end, different utilities imply a technique to overcome demand issues, then undervoltage can occur and termed as brownout. The word brownout also confused with blackout where system loses its premature value and leads to outage. Brownout is intentional practice, whereas blackout is forced compulsion on system due to adverse effect of faults or prolonged interruptions. The phenomenon of undervoltage is normally seen or significant whenever a new system or device is added and load increases and reduction in voltage occurs. Besides this, if voltage falls to particular value, then it is categorized as undervoltage. Problem of undervoltage leads to malfunctioning of various appliances or equipment as undervoltage protection is failed in 90% cases due to dominant behavior of heavy current. Undervoltage is not user control and even though utilities have their standards of ±5%, but in adverse weather conditions, this value touches as low as ±10% range. Below this ±10%, problem of undervoltage starts. This problem can be seen during increase of demand initially due to starting of heavy load or during peak demand operating conditions. This is usually in morning or in afternoon time. In Figure 2.8, the waveform is represented in terms of voltage, as 90% of PQ issues sustained for voltage as current (load) depends upon functional description of equipment. Brownout is different from voltage sag, in such a way that it is a part of outage, if brownout persists for some time, then it can also lead to failure of a system, which can be the cause in terms of voltage sag.

Power Quality Assessment of DG Systems  41

Figure 2.8  Waveform of a brownout.

2.4.3 Voltage Sag or Dip Voltage control in power system is being classified and categorized into different phenomena with different terminologies derived by various researchers of different origin. It has been observed that sag and dip used by American and British research researchers respectively differs in their quantitative efforts ranging from reduction in voltage level from 10% to 90% of nominal value of voltage depicted for time cycle from 0.5 cycles to 60 sec. It has been seen that various outcomes due to parametric reduction in PQ assessment are different and different mitigation techniques. However, every PQ problem must be analyzed properly before developing its mitigation technique. It also differs from Brownout as that lasts for more than 60 sec. Voltage sag is a major problem experienced by end users, arises in transmission and distribution system. As this phenomenon is severe and occur on transmission system, it can travel thousand miles and effect end users, which are not directly influenced by system problem. Normally, it has been seen that whenever large equipment started or sudden load is added in an existing system, voltage sag is experienced, even a customer experiencing undervoltages, voltage sag can have detrimental effects. In Figure 2.9, the waveform is represented in terms of voltage as 90% of PQ issues sustained for voltage as current (load) depends upon functional description of equipment. According to IEEE standard 1159-1995, sags and swell with value of interruptions are classified in terms of instantaneous and temporary interruptions. It has been seen that instantaneous value of voltage sag and voltage swell has been found dominant in a range of 1/2 cycle to 30 cycles of fundamental frequency, but the value of momentary sag and swell has been

42  Power Electronics for Green Energy Conversion

Figure 2.9  Waveform of a voltage sag.

found considerable significant in a range of 30 cycles to 3 sec in consideration with the value of temporary sag and swell, which lasted for a long time duration and extended momentary sag and swell to 3 sec. It has been evident from time duration that sag and swell are critical from duration point of view that interruptions long lasting leads to disturbance and induces instability in a system. Interruptions are majorly classified as momentary and temporary, in which temporary lasted for long time duration, whereas momentary lasted for few cycles and effect switching characteristics.

2.4.4 Noise Noise termed as frequency problem and produces distortion and can leads to overheating. Disturbances due to noise can be caused by power

Figure 2.10  Waveform of a noise signal.

Power Quality Assessment of DG Systems  43 electronic interference, protective equipments. This is an external distortion, which can normally occur due to external sources interruption or frequency mismatch at transmitter or receiver levels in terms of voltage and power. In Figure 2.10, dark lines represent the change of voltage and current with a change of frequency. It has been seen that noise is developed by external cause and occurs due to poor regulation in a system that can occur due to interference with other carrier lines or not having sufficient amount of power separation among them. It is also evident from Figure 2.10 that noise signals can be segregated on basis of voltage amplitude and phasor orientation.

2.4.5 Frequency In India, operating frequency is 50 Hz, with a fact that when different generators are connected in a system with different speeds with their reference speed, then with a slight change in frequency can lead to instability and generate oscillations of smaller magnitude, which can also lead to problems in terms of PQ. As discussed, frequency change also leads to instability, this can be rotor or angle instability, which can halt generator and able to halt a system.

2.4.6 Harmonic Harmonics are a repeated distortions and discontinuous waveform forming an integral part of frequency generated by power electronic devices and nonlinear loads. As depicted in Figure 2.11, harmonics are termed as

Figure 2.11  Waveform of a harmonic.

44  Power Electronics for Green Energy Conversion integral multiple of frequency in segregating odd and even harmonics in such a way that cancelling effect can be dominant. Harmonics are normally developed by electronic devices or due to resonance effect where attenuation is high and amplitude is reducing keeping voltage profile constant. Various electronic interfaces involved in conditioning of power at load end played a significant role in producing harmonics. Harmonics leads to distorted sinusoidal waveform that reduces reliability and overheating in a system. Power Electronic interface also a main cause of harmonics as they have mixture of DC and AC supply and interconnections at PCC also reduce the periodicity of a system. Harmonics are dependent of frequency and an integral part of frequency not a fractional value therefore, whenever a system subjected to harmonics. Harmonics are also dependent on various issues like electronic interference, white noise, and short noise. Various interruptions have different definitions and followed different standards and can be segregated into category of voltage instability and waveform distortion keeping steady and faulty state at large. It has been studied and observed from various researches that voltage instability has been a critical effect for time duration having more than 1 minute but waveform distortions are tendency of occurrence in case of steady state. Voltage imbalance is one of most important reason for instability along with nonuniform variations due to change in phases and unequal voltages, which also induces interharmonics in between a system and odd harmonics, which cannot be cancelled out and having paramount effect on system stability as added offset values and constant power supply is affected and can lead to interruption for longer period. The main reason for interruptions is change of voltage amplitude rather than current amplitude and load variation. Variation of load can affect dynamic stability of a system.

2.4.7 Notching Notching is a negative disturbance and lasts for less than 30 sec but provides DC value. This kind of phenomenon occurs in large electronic switches and power conditioners are used. This also occurs due to improper functioning of equipment at large. Notching is related with time cycle issue as depicted in Figure 2.12, therefore, it is independent from magnitude of voltage waveform and dependent on tie variation and subjected to effective voltage characteristics. Notching is a small voltage deviation but can affect any electronic equipment drastically that lasts for few cycles predominantly. Notching is a problem concerned with voltage variations for less than a minute, which also reveal that for a very short duration during starting, this problem of notching can persist.

Power Quality Assessment of DG Systems  45

Figure 2.12  Waveform of notching.

2.4.8 Short-Circuit A short-circuit involves not a frequent effect and primarily depends upon a failure of a protection system. Short-circuit of system can be due to overvoltage, overloading, insulation failure, and increase of capacity and ratings of machines not in prescribed limits. Short circuit is not considered as a PQ problem but it occurs due to melting, overheating or primarily due to voltage deviation from its safety limits. As voltage magnitude effects current magnitude, insulation failure occurs that leads to failure of protection system. In short, short-circuit is after effect of PQ problem arises due to voltage or current deviation leads to malfunctioning of equipment.

2.4.9 Swell Voltage swell is problem related with increase in voltage magnitude subjected to 0.11 times of its actual voltage impressed on a system. Voltage swell occurs between 30 sec and 60 sec and not as frequent as voltage sag (see Figure 2.13). This problem of voltage swell is common as this only happens when load is switched off or thrown off, in this case, overvoltage protection is required. This is a problem subjected to 1.5 times the effective voltage with decrease in load current adherently due to switching of heavy loads or can be dominated by inductive devices or equipments.

2.4.10 Transient or Surges These transients normally occur during switching or starting off load, therefore, at initial time or closing of an event. These are almost surges (in terms of voltages), these can also be represented in form of current (see Figure 2.14),

46  Power Electronics for Green Energy Conversion

Figure 2.13  Waveform of voltage swell.

but 90% of the surges almost occur on form of voltage. The elements resistance, inductance, and capacitance decide the growth and decay of waveform subjected with time. Various causes of poor PQ are deviation in voltage profile and spectrum, sudden change in load, faults, insulation failure and communication interference. Performance of the given system is also decided by the presence of continuity, flickering, deviation in frequency, type of power electronic interface and its switching characteristics. PQ is a problem that arises from load variations. Different loads on which PQ depends are arcing and nonlinear loads, unplanned reduction of loads, inductive nature of load, atmospheric condition, instability and zone unprotected system. Symptoms of a PQ problem: The prominent and the most common symptoms, which led to the PQ problems in an electric system are: • • • • • • •

Failure of control system driven by electronics Increased corona loss Failure of low voltage appliances Capacitor banks failure Faulty readings Outages Interference with communication circuits

Power Quality Assessment of DG Systems  47

Figure 2.14  Waveform of a transient surge. Outages, 6% Spikes, 7%

Swells, 31%

Sags, 56%

Figure 2.15  Qualitative analysis of PQ problems.

PQ problems are also depicted by percentage shown in Figure 2.15. PQ has been segregated as voltage instability and waveform distortion. Various solutions have been researched and optimal solution has been proposed for individual problem of PQ. Voltage sag can be improved by

48  Power Electronics for Green Energy Conversion UPS (Uninterruptible power supply) or by using constant voltage transformer or FACTS devices, which can inject reactive power into a system by Dynamic Voltage Restorer (DVR). It has been formulated that interruption of voltage in a system can be controlled by UPS by providing constant voltage amplitude to a load connected [29–32]. There is also a problem associated with increases in voltage that must be mitigated by UPS or any kind of power conditioner equipment, which can be able to inject or absorb reactive power in a system. During switching times, unwanted voltage and current flow in a system lead to heavy in rush current and able to damage moving mechanism or effect dynamic stability can be mitigated by using shunt connected FACTS devices are readily available as SVC and controlled by sophisticated controller. Various solutions indicated that problem of notching is required to be mitigated by using power electronic circuit with use of resistor and capacitor in a capacity of snubbers circuits. Snubbers circuits also used as protection mechanism for voltage imbalance and power electronic interface against nonlinear increase in voltage and current with respect to time. Mitigation of PQ Problems PQ can be summarized as balance of active and reactive power, however reactive power being controlled by voltage profile and active power has been controlled by managing frequency variations in defined limits. It has been seen that PQ problems are mainly due to change in voltage across load and terminals connected; therefore, compensation is required in terms of absorbing or generating reactive power. Reactive power compensation can be series and shunt mechanism depending upon the parameters involved and desired deliverables and segregated on use of different types of techniques and elements like Inductor and capacitor as filter for antialiasing problems as passive elements and pulse modulation correction methods as active methods of compensation. Pulse Width Modulation Topologies There are various other methods that can also be used other than compensation techniques, which are described below: • Separate feeders for sensitive loads • Circuit breakers used should be rated on basis of harmonic content • Surge arrestors • Minimization of voltage drop • Use of bundled conductors

Power Quality Assessment of DG Systems  49 Power Quality Events

Transmission Distribution Distributed Resources

Power Quality Interface

Assure Grid Adequacy

Develop Enhanced Interface Devices

Issue

Develop Codes and Standards

Develop Advanced Distributed Resources

EndUse Devices

Make EndUse Devices Less Sensitive

Solutions

Figure 2.16  Mitigation techniques with solutions.

Mitigation techniques are used for specific PQ problems and can be broadly classified as shown in Figure 2.16. Dynamic Voltage Restorer (DVR) DVR is commonly known as dynamic voltage restorer and used for compensating inter harmonic levels and shown in Figure 2.17 (a). It is a series device and boosts active power and suppresses reactive power from utility. It provides controlled voltage and maintains profile pre and post-fault conditions. There are various problems that pertain to voltage sag operational

Injection Transformer L1

Source DC Link

Load LC Filter

C1 Inverter

(a) Im VS=VLOAD δ Φ

VDVR

VS,Sag Re

ILOAD (b)

Figure 2.17  (a) Schematic of a DVR. (b) Compensating vector diagram.

50  Power Electronics for Green Energy Conversion conditions (before sag occurs) and before fault occurs due to fault current with varying loads [33–35]. Its operation is shown in vector diagram as shown in Figure 2.17 (b). There are various sag detection techniques, which are described below: • • • •

Peak value method Root mean square method Fourier transform Space vector method

Root mean square, peak value method, and Fourier methods are used for linearization of problem and termed as active technique in such a way those controllers can be subjected to different standards of PQ. Space vector method is termed as passive technique and employed nonlinear controllers. Controllers have been used for the detection of voltage sag indices depending on parametric approach and problem involved in solving various differential equations [36]. These controllers are used for removing external component and superimposing harmonic content, they can also be used as harmonic filters. It has been seen that linear elements are required to access the system and reduce antialiasing formation in consideration of superposition so that harmonics especially odd harmonics can be reduced in a significant manner. Representation of a harmonic filter is shown in Figure 2.18. Distribution Static Compensators (DSC) Power system comprises of generation, transmission and distribution terminologies out of which distribution has maximum number of losses and having significant frequency deviation, which leads to voltage imbalance and also acts as a catalyst for poor voltage regulation. The operation of DSC deals with reduction in values of harmonics by injecting different pulses

Load(s) Network

Polluting load(s)

Network

Passive filter(s)

Active filter

Figure 2.18  Schematic of a harmonic filter.

Power Quality Assessment of DG Systems  51 with same fundamental frequency out of phase in order to remove odd and even harmonics existing in a system. However, harmonics are somehow important in various applications; the objective of DSC to reduce the unwanted harmonics and increase quality of system in terms of THD as per IEEE and CIGRE standards. CIGRE deals with industrial applications; on the other hand, IEEE-1519 standard caters a load in domestic applications with real power implications [37, 38]. CIGRE deals with real and reactive power compensation with THD incorporated with different switching characteristics. The schematic representation shown in Figure

non linear loads

DC to AC converter (injects anti harmonic pulses) (a)

+

= 0

(b)

Figure 2.19  (a) Loads representation. (b) Linearization of DSC.

52  Power Electronics for Green Energy Conversion 2.19 (a). Results of distributed static compensator can be shown in Figure 2.19 (b). Constant Voltage Transformers These are devices that improve voltage stability indices along with keeping voltage amplitude in defined limits with variation of load dynamics. Constant voltage transformers (CVTs) are used to improve switching characteristics and maintain load terminal voltage curve in a defined way so that voltage dip does not lead to collapse of a system. It has been seen that resonance effect along with saturation of core used in transformer must be avoided so that hysteresis and eddy current losses should be reduced and does not have steep hysteresis curve. The main objective of using CVT is to mitigate problems of saturation in nonlinear devices and reduce risk of interruption. CVT also have various applications in power electronics dominant areas. A schematic diagram with schematic components of CVT is shown in Figure 2.20. Isolation Transformers PQ issues are having paramount effect in case of AC loads relatively as compared with DC loads. In AC loads with AC supply with conversion mechanism, three types of disturbances occur like distortion, harmonics and noise. Noise is an external signal and can be overlapped with switching pattern of loads; therefore, isolation transformers are being used in order to isolate different loads, which are sensitive by different types of transients connected to load having dynamic characteristics and noise due to unregulated power supply. It has been observed that different connections of transformer across primary and secondary also act as a catalyst to reduce burden of harmonics and amount of circulating current flowing across core of transformer [39, 40]. Various connections are being used, but delta- star connection depicting delta at primary side signify high

Mains

Load

Resonate capaciitor Ressonate winding

Figure 2.20  Constant voltage transformer.

Power Quality Assessment of DG Systems  53 Shield

Load

Mains

Ground

Figure 2.21  Electric circuit of an isolation transformer.

voltage and star connect at secondary side signify low voltage side reduces the risk of grounding effect and excessive current producing resonance effect in transformer. Isolation transformer representation can be shown in Figure 2.21. Transient Voltage Surgé Supressors (TVSS) TVSS are used to reduce transients and ability to transmit near the loads; it normally comprises of load, which reduces and have nonohmic resistance. They reduce excessive voltage for prevention purposes. Noise Filters Noise is an external signal with high frequency characteristics such that it can generate higher value of current and voltage and effect terminal voltages at load ends. It has been observed that noise provides fluctuations and notching with a feature of ant aliasing mechanism and reduces the error of high frequency component superimposing low frequency component. In those cases; low frequency component would be redundant and avoided and can cause distortions in a power system comprising of different machines connected together in various configurations. Various case studies have been validated by results of connecting inductor and capacitor in such a way that low-impedance and high-impedance paths are created in an order that high-frequency components are surpassed and noise frequencies can be eliminated. Noise can be of various order and different types like white noise and external noise but customized filters are used to eliminate noise at different levels. High pass filters are being incorporated to eliminate noise signals in a range of kHz. Static VAR Compensators Static reactive compensators are compensating devices, which comprises different combination of capacitors to improve voltage profile and keep

54  Power Electronics for Green Energy Conversion V

V L

V

Max. L

C

IL

IS

IC

Min. L

L

C

V

V

Slope KS

Slope KS IL (a) Controllable reactor

V

Capacitive 0 Inductive IS

IC (b) Fixed capacitor

(c) SVS

Figure 2.22  Characteristics of a typical SVC.

regulation in safety limits by reducing flickering and reduce interference in communication. They are used for improving power transfer capability, improve stability, reducing low power oscillations and voltage bifurcation. The characteristics of SVC are shown in Figure 2.22. Unified Power Quality Conditioner (UPFC) UPFC is a type of FACTS device that has a distinct feature of shunt, series compensation along with shifting mechanism. This device termed as UPFC comprises of combination of static compensator that is connected in series and shunt device, which is used to regulate voltage at terminals due to insufficient reactive power. It provides two different kinds of mechanism with series and parallel structure and connected with DC capacitor, which actively controls flow of active power into a transmission network. It has been used extensively for shifting of series voltage and controlling shunt voltage and can be classified into a category where dynamic stability PCC

Series AF

Figure 2.23  Schematic of a UPFC.

Shunt AF

Power Quality Assessment of DG Systems  55 is being controlled along with dynamic voltage profile. It is quite expensive and used as a protection strategy in which control mechanism is not proper and insufficient to keep voltage disturbances in a defined limit. It has an ability to provide reactive power compensation by absorbing or injecting in a shunt mode and gives flexibility to a user to define constrained limits [39–42]. The structured diagram of UPFC has shown in Figure 2.23. It provides accurate amplitude and phase of voltage to terminals of network to enhance stability of a system. The main objective is to compensate voltage instability indices along with sequencing of current and harmonic reduction. It also has an ability to be connected at point of coupling in distribution system and have a flexibility to be used as a combination of compensator and regulator. Compensator is being connected in series but regulator connected as feedback mechanism preferred to be connected in parallel configuration to have desired deliverables. Magnetic Synthesizers Magnetic synthesizers are used to synthesize waveform with inductive load connected with capacitor storage requirements. They have been used extensively for three-phase systems with energy transfer mechanism with voltage isolated network. The only objective is to incorporate a system with less value of voltage sag and induces stability with reduction in harmonics. History of Harmonics Power system termed as complex system comprises of various devices of different configuration and structure. Various devices subjected to different frequency characteristics but aligned with a fundamental frequency of a system with linear or integrated approach. With use of various devices customized at load end and exponential increase in use of electronic devices leads to a problem, which can be severe in switching loads and limit a use of sophisticated equipments and designated as Harmonics. Harmonics were investigated and discovered in late nineteenth century but not taken as an outmost priority due to less voltage profile of equipment and less penetration of Electrical supply system in day-to-day life. Harmonics were considered as unwanted voltage and current but not able to differentiate between transients and noise, which also have the same characteristics. It has been observed that with the use of electronic devices in power system for compensation of reactive power and maintaining voltage profile of a system at large forces researchers to make benchmark models for the mitigation of harmonics in a system, which has their presence in a system since too long. There are various cases in which harmonics plays

56  Power Electronics for Green Energy Conversion an important role in defining sustainability indices and making structured ergonomics of a power system considering other essential parameters intact. Various loads have been incorporated and investigated to find the sources of harmonics and their effect on stability of a system. Loads plays an important role in defining dynamic characteristics of a system as current carrying capacity and insulation level are subjected to load behavior. Nonlinear loads like controller-based mechanism, various drives to control speed of motor, electronic equipments maintaining structured behavior of electrical equipments used in conjunction for reducing a size and increasing efficiency like ballasts, UPS, conversion mechanism of different supply integration system. It has been observed that nonlinear loads are sources of reactive power and subjected to instability with frequency variations considering value of fundamental frequency in predefined limits. Linear Loads Linear loads termed as balanced load based on impedance value of a system. In balanced load, all phases of a system need to be equally loaded such that current and voltage relationship not disturbed. Practically, linear loads are quite difficult to use as it follows two principles like superposition and homogeneity. Principle of homogeneity and superposition related with output and input structure, even though; with replacement of input and output, system characteristics must not be changed. It has been observed that these types of loads incorporate independent and dependent parameter proportionally and utilized total amount of energy. Various types of linear loads are used in domestic use like heaters and other purely resistive elements. The major disadvantage of using linear loads in a system is that a consumer needs to pay extra amount of money for a same type of consumption; it can be understood from the fact that whenever a voltage is not sufficient to drive a load and termed as distorted but current would Load of onlv “linear” elements

current

voltage

Figure 2.24  Representation of a linear load.

.

. . . . . . . . . . . . . . . .

sinewave

. . .

.

. R1 . .

.

.

. .

. . V1 . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . . . . .

. . .

.

. . . R2 . . . . .

.

Power Quality Assessment of DG Systems  57 be significant, and electricity bill is generated. This drawback can be overcome by using regulated voltage so that flow of circulating current can be avoided. Representation of a linear load is shown in Figure 2.24. Nonlinear Loads These types of loads are variable in nature, and current is not changing in the same proportion of applied voltage. Every voltage must be AC supply and sinusoidal in nature but current drawn by load is not periodic in nature and does not follow a sinusoidal structure and overheating occurs in a system. The amount of current has been considered and calculated from the instant of cycle starting and ends at point of instant. It has been observed that nonsinusoidal current drawn in case of nonlinear load is much higher as compared with current drawn in resistive load and causes distortion of varying frequency, which can be applied and effect the dynamics of load. Various types of loads incorporated power electronic interfaces in a form of electronic switches and variable frequency configurations of motors incorporating electronic structures in form of drives and arc furnaces. All types of loads, which have nonlinear characteristics, depict a relationship between voltage ad current considered as sources of voltage and current distortion. These types of distortion are part of external disturbances and can incorporate harmonics as a function of fundamental frequency and classified as single-phase and three-phase harmonics depending upon structured environment and types of supply used in a system. Single-Phase and Three-Phase Load Harmonics Harmonics can be developed by various types of systems incorporating different phase structure. It has been investigated that single-phase sources develop odd harmonics like 1st-order and 3rd-order harmonics. Odd harmonics effect cannot be cancelled out and multiple order harmonics are also termed as triplen and depend on phase system structured as R, Y and B structure formation. Single-phase loads like domestic appliances along with electronic devices produce single-phase harmonics or can be seen as odd harmonics in such a way that at low-voltage application with neutral point, harmonics’ effect exponentially increased rather than wiped out. Odd harmonics are being segregated as 1st, 3rd, 5th, 7th, and so on but on the other hand, triplens are multiple of three and can be segregated as 3rd, 9th, 15th, and so on. It has been observed that triplens are very dangerous in power system containing 4 wire systems and induces instability. These types of harmonics produce unbalanced system by creating overload at neutral point and causes faulty and circulating current to flow [38]. While considering triplen, harmonics also divided into voltage and current

58  Power Electronics for Green Energy Conversion

Fundamental h3 h5 h7 Sum

Amplitude

HARMONICS

Figure 2.25  Effect of rectifier pulses on harmonics.

harmonics, and it has been studied that single-phase sources produces current harmonics keeping fundamental frequency intact, on the other hand three-phase sources produces 5th and 7th order harmonics along with small amount of harmonics of higher order having a value more than ten and it has been seen from various case studies that harmonics having lesser order has more impact as compared to higher order harmonics in terms of voltage and current standards. Two methods are used out of various techniques to evaluate severity of distortion in a system and effect of unwanted voltage and current of defined order. The methods used in evaluation are crest factor and value of THD. Crest factor is ratio of peak value to effective value of wave keeping in mind about fundamental frequency. Crest factor decides structured mechanism of waveform and provide information about steepness; on the other value, THD must be less than 5% as per IEEE and CIGRE standards defining PQ operational value of conduct. A wave having a crest factor less than 1.4 tends to be flat topped. A crest factor greater than 1.4 indicates a voltage that tends to be pointy and having sinusoidal waveform with value of 1.414 signify no distortion present in waveform [33]. Nonlinear loads have dominant effect on quality of power supply and therefore, type of withdrawal current get distorted and becomes nonsinusoidal. The effect of rectifier pulses on various types of harmonics is depicted in Figure 2.25. Causes of Harmonics Harmonics are type of external disturbances in a system; however, they are frequency-dependent and mostly governed by use of electronic devices like UPS, nonlinear loads, and speed control of variable machines, converters and workstations. Speed controls of machines are being designed on basis of various types of converters used and reactances in a system.

Power Quality Assessment of DG Systems  59 Harmonics also having a resonance effect on a system, whenever inductive reactance and capacitive reactance are the same and maximum frequency characteristics are governed by resistive load only. Various structured circuits have been designed with the help of transformer and capacitive element and significant amount of circulating current flows and leads to distorted voltage waveform. System has nonlinear characteristics with nonlinear load and connected as impedance of all phases of a system such that harmonic current flows in a system subjected to different harmonic voltages and sum up to fundamental voltage of fundamental frequency. As a result, different voltages are being seen in a single waveform with different phase differences and lead to different voltage drops and create overheating and induce frequency and voltage instability at large [36]. The value of voltages developed by harmonic depends on the impedance connected and signify amount of voltage in consideration with system inherent parameters. Reduction in harmonic current reduces penalty losses and helps in increasing annual saving and can be given by the following equation. dollar hrs days   annual _ saving =  Total _KW _ saving ⋅ ⋅ KWh day year  



dollar   + Total _KW _ saving ⋅12months   KW _demandchange   Months + Total _PFpenalty _ saving



Harmonic Components Harmonics are divided into different components by their properties (ranking number). The component class shows the rotational direction of the phase of the harmonic with respect to the fundamental frequency. It can be further classified into various components on the basis of impedance connected and type of connection in various systems and defined as zero, positive and negative sequences, which further define range of frequency and order of harmonics in odd and even groups. They are elaborated as follows: • Zero-sequence harmonics: 3, 6, 9, 12. • Positive sequence: 1, 4,7,10, 13. • Negative sequence: 2,5,8,11,14.

60  Power Electronics for Green Energy Conversion If n=1, 4, 7, 10, 13, and so on, the phase order is the same in consideration with fundamental wave and direction is kept same, this kind of behavior is termed positive and governed by positive sequence. If n=2, 5, 8, 11, and so on, the phase order is opposite to fundamental waveform and termed as negative sequence. It can be well understood from the elaborated view that negative and positive sequence will provide zero magnitude. If n=3, 6, 9, 12, and so on, the phase angle in all phases is the same, or harmonics do not rotate. Methods to Reduce Harmonics Various methods are available to reduce harmonics significantly by using variable frequency drives for speed control or addition of equipment or FACTS devices for compensation and classified into primary methods and secondary methods. Some of primary methods are being listed below: • Power System Design: Power system designing is a critical factor in reducing harmonics by reducing a nonlinear load to its maximum value of 30% keeping transformer rating in consideration. However, power factor correctors are also installed in order to create resonance condition, which keep resistive load intact and reduce passive elements contribution to zero and keep nonlinear load and probability of harmonics reduces to 15%. It has also been observed that potential designing can reduce the probability of harmonics to maximum extent. • Isolation transformers: This is one of primary method used for reducing probability of harmonics caused by ground faults. The main objective to use isolated transformers to increase or decrease voltage to match up with existing terminal voltage so that ground reference terminal can be created and circulated current is avoided. This method is normally employed in drive control where number of electronic devices are used with high-power ratings are used. • Line reactors: Line reactors are cost effective solution as compared with transformers and used for drives in which ac-dc converter or rectifier terminology has been used. This method is useful in standard range keeping harmonics limit from 1.5% to 7.5% and not suitable for faults with more circulating current. This method is best suitable for variable load and variable frequency environment. Harmonics trap filters: This method is only used with an objective to

Power Quality Assessment of DG Systems  61 trap certain type of harmonics with defined order. This is a targeted method with designed nonlinear ratio to reduce a value of circulating current with more filtration. It has also been observed that harmonic trap filters are used to eliminate odd harmonics with designated value of 3rd, 5th, 7th, 11th value so that power factor correction can also be made. IEEE-519 Standard PQ is an important parameter for delivering sustainability indices in system. There are various standards developed and established, but in a year of 1981, IEEE provides a framework and reference for controlling reactive power compensation in case of electronic interfaces and allowed levels of THD along with voltage and current distortion keeping in consideration of penetration of nonlinear loads at distribution level. This standard is applicable to high and low and high voltage installations in power sharing mode. This standard was given a name of IEEE-519 and revised in a year of 1993 with recommended practices and required framework for reducing values of harmonics in power system. This standard is kept mandatory to follow PQ standards in high and low voltage installation along with power factor improvement. Various current and voltage limits are shown in Table 2.1.

Table 2.1  IEEE standard voltage and current limits. Bus voltage at PCC

Individual harmonics

THD

69kV and below

3.0%

5.0%

69.001kV–161kV

1.5%

2.5%

above 161kV

1.0%

1.5%

ISC/IL

n < 11

11≤nVmpp

V ref(k) = Vref(k-1)+dV

Figure 9.9  Fractional voltage flowchart.

Yes

Vref(k) = Vref(k-1)-dV

Classification of MPPT Methods for PV Systems  311 Start

Sense Isc, I(k)

Impp= Ki *Isc

No

Yes

I(k)>Impp

Iref(k) = Iref(k-1)+dI

Iref(k) = Iref(k-1)-dI

Figure 9.10  Fractional current flowchart.

and observe (P&O), or hill-climb search (HCS), which are mathematical optimization techniques used to seek for the local optimum point of a considered function.

9.3.2.1 Hill Climbing (HC) Technique Hill climbing (HC) gets its name from the “hill-shaped” form of the function space [31]. If the algorithm starts at the local optimum (top of the hill), and it moves in any direction, then it will go down. Anywhere else, any axis may be chosen as long as it moves, it will be getting closer to the MPP. Hill climbing fails to track the MPP when the function space contains “local maxima.” A local MPP is a small hill whose top is not as high as the global MPP. If a local MPP of HC is found, the algorithm is trapped there. Any little movement in any direction will make things temporarily worse. The condition to be verified at the maximum power is given as follows:



S=

∆P / ∆d P /d

(9.3)

where S denotes to perturbation step size. The HC flowchart is shown in Figure 9.11.

312  Power Electronics for Green Energy Conversion Start

Sense V (k),I(k) ∆P= P(k)-P(k-1) ∆D= D(k)-D(k-1)

No

Yes

Yes

∆P>0

∆D>0

∆D>0

No Dref(k) = Dref(k-1)-dD

Yes

No

Dref(k) = Dref(k-1)+dD

Dref(k) = Dref(k-1)-dD

Dref(k) = Dref(k-1)+dD

Figure 9.11  HC flowchart.

9.3.2.2 Perturb & Observe (P&O) Technique This method is based on the perturbation of a control variable in small change and observing the target function’s resulting response until the slope becomes zero. The perturbation and observation method’s operating process illustrates the movement of the steady-state operating point of the system; the perturbation step-size and the observation time step-size are the considered parameters to be adjusted [32]. The perturbation and observation method with fixed step-size (P&O-F) is one of the most used direct MPPT methods. The output power of the generator is monitored and the inverter input voltage or one of the converter variables are perturbed, such as duty cycle, input current, or input voltage. The condition to be verified at the maximum power is given as follows:



∆Ppv =0 ∆Vpv



(9.4)

where Vpv and Ppv are the PV module voltage and power respectively. The P&O flowchart is shown in Figure 9.12.

Classification of MPPT Methods for PV Systems  313 Start Sense V (k),I(k) Calculate ∆P= P(k)-P(k-1) Yes

∆P= 0 No

Yes

Yes

∆P>0

∆V>0

∆V>0

No Vref (k) = Vref (k-1)-dV

Vref (k) = Vref (k-1)+dV

Yes

No Vref (k) = Vref (k-1)-dV

Vref (k) = Vref (k-1)+dV

Figure 9.12  Perturb-and-observe flowchart.

9.3.2.3 Incremental Conductance (IC) This technique has been discovered for the first time in 1983. The final version of the classical incremental conductance computational flowchart was presented in 1995. In this technique, the controller measures incremental changes in current and voltage of the PV array in order to be able to predict a voltage change effect. This technique requires additional computation time in the controller; however, it can track more rapidly changing conditions when compared with the P&O method. It is similar to the P&O algorithm for the fact that it can generate steady-state oscillations in the produced power. The condition to be verified at the maximum power is given as follows [33]:

dI I + =0 dV V

(9.5)

where I is the PV array current. The incremental conductance flowchart is given in Figure 9.13.

314  Power Electronics for Green Energy Conversion Start Sense V(k),I(k) Calculate ∆V= V(k)-V(k-1) ∆I = I(k)- I(k-1) ∆V= 0 Yes

No ∆I/∆V=-I/V No

∆I= 0 No

∆I/∆V>-I/V

∆I> 0

No Vref (k) = Vref (k-1)-dV

Yes

Yes

Yes Yes

No

Vref (k) = Vref (k-1)+dV

Vref (k) = Vref (k-1)-dV

Vref (k) = Vref (k-1)+dV

Figure 9.13  Incremental conductance flowchart.

9.4 Soft Computing (SC) MPPT Techniques Soft-computing (SC) is an approach used to construct computational intelligent systems [34], it encompasses a group of unique methodologies which provide flexible information processing characteristics able to solve complex optimization problems as shown in Figure 9.14.

9.4.1 MPPT Techniques Based on Artificial Intelligence (AI) 9.4.1.1 Fuzzy Logic Control (FLC) Technique Initially, the theory of fuzzy logic (FL) has been known to be a well-­established technique. Fuzzy logic has been used as application for the first time in 1970

Soft computing Methods AI Based

FLC

ANN ANFIS BN

BI Based

PSO

MFO

Figure 9.14  Soft computing technological grouping.

WOA

Classification of MPPT Methods for PV Systems  315 for decision support in medicine, and steam boiler fuzzy regulation in 1975 by Mamdani. A major uncertainty treatment has been initiated by fuzzy set theory, which results to advanced information systems formalization. It also has a considerable impact on modern automatic classification techniques and the renewal of some decision support approaches [35]. The words of the current language are used in fuzzy logic as values of truth, for this reason is called linguistic logic in which a combination of symbolic and digital data processing can be found. Intelligent diagnosis may be designed using fuzzy logic-based methods based on natural language expressed knowledge. It is also human reasoning not rigid calculations modeled approach. In fact, it is characterized by a reasoning mode which is more intuitive than conventional logic. The use of sets rules and membership functions called “fuzzy sets” allows users to better understand physical phenomena, difficult and inaccurate to model otherwise. Industrial control systems use frequently the fuzzy logic. The resulting control law is often effective and does not require significant ­theoretical-based developments. The structure of fuzzy logic-based control is shown in Figure 9.15.

E( k ) =



P(k ) − P(k − 1) V (k ) − V (k − 1)

(9.6)

∆E(k) = E(k) − E(k−1)

(9.7)

Rule base Fuzzy set Fuzzy rules Output scaling gain

Input scaling gains TABLEAU 1: LES RÈGLES D’INFÉRENCE

E

∆E Fuzzification

ECE

NG NP

ZE

NG

ZE

ZE

PG

PG PG

NP

ZE

ZE

PP

ZE PP

ZE

PP

ZE

ZE

ZE NP

PP

NP

NP

NP

ZE ZE

PG

NG NG NG ZE ZE

PP PG

Inference engine

Figure 9.15  Fuzzy logic control structure.

∆D

Defuzzification

316  Power Electronics for Green Energy Conversion

9.4.1.2 Artificial Neural Network (ANN) Artificial neural network are composed of simple elements (usually adaptive) massively connected in parallel with hierarchical organization. They try to memic biological nervous systems in its interaction with objects of the real world. In addition, ANN performs algebraic function of its inputs. The ANN may be modeled by using a graph-oriented and by using connections for the interconnection of neurons and the information exchange. The calculation will be carried out in a cooperative, parallel and distributed and approach [36]. The neural network is essentially characterized by its topology which consists of the choice of the transfer function and learning modes and type of interconnection. The neurons that construct the network characterize the structure of the model, a distributed representation in which each neuron contributes. They can be used in industrial equipment supervision problems. The fault tolerance properties of neural networks is defined by their capacity to maintain their processing ability in the presence of network damage and execute the assigned task in the presence of information with errors. The structure of an ANN is shown in Figure 9.16.

9.4.1.3 Adaptive Neuro Fuzzy Inference System (ANFIS) Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented as part of adaptive networks fuzzy inference systems which uses a hybrid learning technique. Its architecture adjusts the human experts fuzzy rules in order to describe the input/output relationship of complex systems. ANFIS is known to give better trajectory tracking, dynamic control, signal processing and nonlinear approximation.

Input Layer

Hidden Layer

Output Layer

1 2

1

2 w2,i

T

1

i

Figure 9.16  Artificial neural network structure.

wi,1

G

Vmp

Classification of MPPT Methods for PV Systems  317 In addition, when ANN and ANFIS are compared, ANFIS algorithm gives lower mean percentage error when compared with the one generated by ANN. ANFIS is also characterized by a better performance and a learning capacity that is faster than ANN. In the case of using of the largest number of inputs, ANFIS has the property of increasing the forecasting data accuracy [37, 38]. ANFIS is a combination of data-driven and knowledge-oriented methods. In the case of prognostics based on ANFIS, data-driven method, a stream from multidimensional data to the output is noted. The ANFIS structure is shown in Figure 9.17.

9.4.1.4 The Bayesian Network (BN) The Bayesian network (BN) is a technique used to model multiple random variables probability distribution which is used in information fusion [39]. Bayesian Networks are used in literature to detect the correct movement direction of the actual output voltage of PV arrays. In order to verify the objective of operating at the MPP, the Bayesian Network flowchart is shown in Figure 9.18.

9.4.2 Bioinspired (BI)-Based MPPT Techniques 9.4.2.1 Particle Swarm Optimization (PSO)

Inputmf

Rule

Outputmf

Input

G Output

Vmp

T

Figure 9.17  Adaptive neuro-fuzzy inference system structure.

318  Power Electronics for Green Energy Conversion S

A1

A10

Left side observation nodes

A11

A20

Right side observation nodes

Figure 9.18  Bayesian network structure.

PSO is a method of the large family of swarm intelligence and which mimics the social behavior and collaboration between individuals often referred to as particles. The analysis of the environment and the neighborhood describes the social behavior and, therefore, constitutes an optimum search method by analyzing the trends of neighboring particles. Each particle has the objective to optimize its chances by following the tendency of its neighborhood, which it moderates by its own experience. The algorithm is usually initialized randomly and possible solutions are randomly positioned in the objective function search space. The PSO central concept is that particles have acceleration in the direction of the best solutions [40]. They iteratively assess the suitability of candidate solutions and have history of their best fitness value. Each particle has the property to communicate with the particles around it and has a memory of its best visited solution. The particles move at each iteration considering their best position and the neighbors best position. The objective is to change their trajectory so that they converge to the optimum. The processes of intelligence and movement in a dynamic system result in an optimal operating point. The PSO priorities cooperation over competition and without selection. A particle, which has poor performance at a given iteration, is preserved in case it will find a best solution. According to some literature results, this technique does not have problems found in other evolutionary techniques. The flowchart of PSO is shown in Figure 9.19(a).

9.4.2.2 Whale Optimization Algorithm (WOA) This technique tries to mimic the bubble net hunting algorithm of humpback whales presented in [41, 42]. The biggest of all whales is called Humpback which has a spiral bubble-net feeding special hunting mechanism. By generating distinctive bubbles in a circular trajectory, the whales

Classification of MPPT Methods for PV Systems  319

Start

PSO Initialization

i=1

Calculate the fitness value of particle i

Better individual fitness value!

Yes

Update Pbest

Iter = Iter+1

No

Better global fitness value!

Yes Update Gbest

No All particles evaluated

No

Next particle

Yes

Update particle and velocity

Iter < Max_Iter

Yes

No

End (a)

Figure 9.19  Flowchart (a) PSO, (b) WOA, (c) MFO. 

(Continued)

320  Power Electronics for Green Energy Conversion

Start Initialize the whales population, ub, Ib and Max_iter

i=1 Adjust Whales Calculate the fitness value of each search agent

F = the best search agent X* = the best search agent

No

Yes

i=i+1

j=j+1

Is p=I? Yes

Update the position of current search agent by Eq.(2)

Update the position of current search agent by Eq.(9.8)

Yes Is i0

∆P>0

Fuzzy rules TABLEAU 1: LES RÈGLES D’INFÉRENCE ECE NG NP ZE PP PG

Fuzzification

Dref(k) = Dref(k-1)+dD

ZE

ZE

PG PG PG

NP

ZE

ZE

PP

ZE PP

ZE

PP ZE

ZE

ZE NP

PP

NP NP

NP ZE ZE

PG

NG NG NG ZE ZE

Inference engine

Defuzzification

Yes

∆D>0

No Dref(k) = Dref(k-1)-dD

NG

Yes

No Dref(k) = Dref(k-1)-dD

Dref(k) = Dref(k-1)+dD

Figure 9.23  Hill climbing with fuzzy logic control [19].

9.5.4 Other Classifications of Hybrid Techniques In [47], the authors have proposed other classification of hybrid techniques as shown in Figure 9.24. A conceptual map (C-Map) has been used as a suggested pattern followed in order to combine two MPPT techniques into a Hybrid MPPT method. Moreover, a proposed list of uninvestigated hybrid combinations has been suggested in order to explore further potential enhancement.

9.6 Discussion In order to select, the more adapted MPPT method (as shown in comparison of MPPT techniques in Table 9.1), we should take into consideration the speed of the tracking, low oscillation around steady-state operating point and a good performance of the different climatic condition changes. In uniform climatic condition, the conventional methods are good enough to be implemented in real application. However, they suffer under rapid change of irradiance and from oscillation around Maximum Power Point (MPP). Moreover, they are not reliable under partial shading conditions because of their inability to find the Globle MPPT (GMPPT).

326  Power Electronics for Green Energy Conversion Hybrid MPPT Techniques Can be classified into Sequential Two-Stage Hybrid MPPT Techniques

Simultaneous MPPT Techniques

Can be categorized into Not-Intelligent Hybrid MPPT Techniques

Intelligent Hybrid MPPT Techniques Includes

Can be divided as Offline-Hill Climbing Sequential Techniques

Other Two-Stage Non-Intelligent Techniques

Includes

Includes

Includes

FSCC+P&O, FSCC+IC, FOCV+P&O and FOCV+IC

RCC+Constant Voltage

LE, CSAM, Power Increment

Estimation and Revision

FLC Based Techniques

P&O and IC

PSO Based Techniques

DE and PSO

ANN Based Techniques, GSA and SA

GSA and PSO

Figure 9.24  Another classification of hybrid techniques based on Conceptual Map (C-Map) [47].

Artificial Intelligence (AI) and bioinspired (BI) methods considered as modern methods in MPPT algorithms. The AI method is inspired from the human intelligence and it relies on the estimation to track the optimum operating point of PV system. When collecting a big data in a real PV system under uniform conditions, the estimations are more accurate in determining the optimum operating point under different weather changes. Yet, the method suffers the same problems as the conventional methods in partial shading conditions. The BI-based methods, which are inspired by biological phenomena in nature is replicated under partial shading condition (as those given in Table 9.2) when multi-peaks exist in PV curve. Hence, the tracking mechanism uses a multi-agent strategy in the search space. However, the drawback of this approach is the premature convergence due to the population lack of diversity. As mentioned above, hybrid methods can be combined using techniques from the same or different category. But the best combination is settled between the bioinspired/bioinspired methods by selecting the advantages of each one as illustrated in recent application. The drawback of this approach is the complexity of the algorithm and the high cost of the implementation of real implementation.

Classification of MPPT Methods for PV Systems  327 Table 9.1  Comparison of MPPT technique.

DIR ( A-I ) ( B-I )

Soft computing

Conventional

INDIR

MPPT techniques

Complexity

Efficiency

Convergence speed

CV

Low

Low

Low

FV

Low

Low

Medium

FC

Medium

Low

Medium

HC

Low

Low

Low

P&O

Medium

Low

Medium

INC

Medium

High

Fast

FLC

High

High

Fast

ANN

High

High

Fast

ANFIS

High

High

Fast

BN

High

High

Fast

PSO

Medium

Very High

Medium

WAO

Medium

Very High

Medium

MFO

Medium

Very High

Medium

9.7 Conclusion Extracting the maximum power of the PV system is recently becoming the spotlight in the solar energy sector. While PV power capacity increases continuously with new installed PV stations, MPPT control techniques keep developing. The paper gives a classification of conventional and modern MPPT techniques for PV energy generation systems. The assessment of different possible combination in order to construct hybrid methods have been discussed in this paper. Based on this review, it can be concluded that the bioinspired MPPT algorithms, especially, when these methods have been hybridized between them by taking advantage of each one, will help to construct a perfect tracker for the MPPT search problem.

328  Power Electronics for Green Energy Conversion Table 9.2  GMPPT technique. No

GMPPT

Introduced on

01

Bacterial foraging optimization [48]

2002

02

Grey wolf optimization [49]

2014

03

Artificial bee colony optimization [50]

2005

04

Cat swarm optimization [51]

2007

05

Bat optimization [52]

2010

06

Jaya optimization [53]

2016

07

Firefly optimization [54]

2007

08

Memetic Salp swarm optimization [55]

2019

09

Dragonfly optimization [56]

2015

10

Spider monkey optimization [57]

2014

11

Chiken swarm optimization [58]

2014

12

Artificial fish swarm optimization [59, 60]

2002

13

Cuckoo Shearch optimization [61]

2009

14

Butterfly optimization [62]

2018

15

Shuffled Frog Leaping optimization [63]

2006

16

Ant Colony optimization [64]

2006

17

Ant Lion Optimization [65]

2015

18

Spotted Hyena Optimization [66]

2017

19

Emperor Penguin Optimization [67]

2018

20

Teaching learning-based optimization [68]

2011

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330  Power Electronics for Green Energy Conversion 15. Necaibia, S., Kelaiaia, M.S., Labar, H., Necaibia, A., Djelailia, O., Merad, F., Practical implementation of a proposed MPPT control strategy to mitigate inaccurate responses for photovoltaic systems. Int. J. Electr. Eng. Inform., 10, 4, 765–782, 2018. 16. Liu, L., Meng, X., Liu, C., A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renew. Sust. Energ. Rev., 53, 1500–1507, 2016. 17. Rizzo, S.A. and Scelba, G., ANN based MPPT method for rapidly variable shading conditions. Appl. Energy, 145, 124–132, 2015. 18. Bouakkaz, M.S., Boukadoum, A., Boudebbouz, O., Bouraiou, A., Boutasseta, N., Attoui, I., ANN based MPPT Algorithm Design using Real Operating Climatic Condition. Proc. 2020 Int. Conf. Math. Inf. Technol. ICMIT 2020, 159–163, 2020. 19. Bouakkaz, M.S., Boukadoum, A., Boudebbouz, O., Attoui, I., Boutasseta, N., Bouraiou, A., Fuzzy Logic based Adaptive Step Hill Climbing MPPT Algorithm for PV Energy Generation Systems. 2020 Int. Conf. Comput. Inf. Technol. ICCIT 2020, 2020. 20. Pakkiraiah, B. and Durga Sukumar, G., Enhanced Performance of an Asynchronous Motor Drive with a New Modified Adaptive Neuro-Fuzzy Inference System-Based MPPT Controller in Interfacing with dSPACE DS-1104. Int. J. Fuzzy Syst., 19, 6, 1950–1965, 2017. 21. Boutasseta, N., Ramdani, M., Mekhilef, S., Fault-tolerant power extraction strategy for photovoltaic energy systems. Sol. Energy, 169, 594–606, 2018. 22. Boutasseta, N., Bouakkaz, M.S., Bouraiou, A., Necaibia, A., Attoui, I., Fergani, N., Practical Implementation of Computational Algorithms for Efficient Power Conversion in Photovoltaic Energy Generation Systems. 2020 Int. Conf. Comput. Inf. Technol. ICCIT 2020, 2020. 23. Bouakkaz, M.S., Boukadoum, A., Boudebbouz, O., Fergani, N., Boutasseta, N., Attoui, I., Bouraiou, A., Necaibia, A., Dynamic performance evaluation and improvement of PV energy generation systems using Moth Flame Optimization with combined fractional order PID and sliding mode controller. Sol. Energy, 199, 411–424, 2020. 24. Jiang, L.L., Nayanasiri, D.R., Maskell, D.L., Vilathgamuwa, D.M., A hybrid maximum power point tracking for partially shaded photovoltaic systems in the tropics. Renew. Energy, 76, 53–65, 2015. 25. El-Helw, H.M., Magdy, A., Marei, M., II, A Hybrid Maximum Power Point Tracking Technique for Partially Shaded Photovoltaic Arrays. IEEE Access, 5, 11900–11908, 2017. 26. Ramli, M.A.M., Twaha, S., Ishaque, K., Al-Turki, Y.A., A review on maximum power point tracking for photovoltaic systems with and without shading conditions. Renew. Sust. Energ. Rev., 67, 144–159, 2017. 27. Subudhi, B. and Pradhan, R., A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE T. Sustain. Energ., 4, 1, 89–98, 2013.

Classification of MPPT Methods for PV Systems  331 28. Prasetyono, E., Mohammad, L., Murdianto, F.D., Performance of ACOMPPT and constant voltage method for street lighting charging system. Int. Rev. Electr. Eng., 15, 3, 235–244, 2020. 29. Vijayakumari, A., A non-iterative MPPT of PV array with online measured short circuit and open circuit quantities. J. King Saud. Univ. Eng. Sci., 33, 176–185, 2021. 30. Baimel, D., Tapuchi, S., Levron, Y., Belikov, J., Improved fractional open circuit voltage MPPT methods for PV systems. Electronics, 8, (3), 2019. 31. Motahhir, S., El Hammoumi, A., El Ghzizal, A., The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm. J. Clean. Prod., 246, 1–17, 2020. 32. Mamarelis, E., Petrone, G., Spagnuolo, G., A two-steps algorithm improving the P&O steady state MPPT efficiency. Appl. Energy, 113, 414–421, 2014. 33. Elgendy, M.A., Zahawi, B., Atkinson, D.J., Assessment of the incremental conductance maximum power point tracking algorithm. IEEE T. Sustain. Energ., 4, 1, 108–117, 2013. 34. Basha, C.H. and Rani, C., Different conventional and soft computing MPPT techniques for solar PV systems with high step-up boost converters: A comprehensive analysis. Energies, 13, 2, 1–27, 2020. 35. Liu, C.L., Chen, J.H., Liu, Y.H., Yang, Z.Z., An asymmetrical fuzzy-logiccontrol-based MPPT algorithm for photovoltaic systems. Energies, 7, 4, 2177–2193, 2014. 36. Ruchira, Patel, R.N., Sinha, S.K., Comparison of ANN-based MPPT controller and incremental conductance for photovoltaic system, in: Lecture Notes in Electrical Engineering, vol. 476, pp. 295–305, 2019. 37. El-Zoghby, H.M. and Bendary, A.F., A Novel Technique for Maximum Power Point Tracking of a Photovoltaic Based on Sensing of Array Current Using Adaptive Neuro-Fuzzy Inference System (ANFIS). Int. J. Emerg. Electr. Power Syst., 17, 5, 547–554, 2016. 38. Bouakkaz, M.S., Boukadoum, A., Boudebbouz, O., Boutasseta, N., Attoui, I., Fergani, N., Bouraiou, A., Necaibia, A., Anfis-based maximum power point tracking using genetic algorithm tuned fractionaal-order proportional-­ integral-derivative controller and on-site measured climatic Data. Int. J. Energy Clean Environ., 22, 4, 63–82, 2021. 39. Keyrouz, F., Enhanced Bayesian Based MPPT Controller for PV Systems. IEEE Power Energy Technol. Syst. J., 5, 1, 11–17, 2018. 40. Liu, Y.H., Huang, S.C., Huang, J.W., Liang, W.C., A particle swarm optimization-based maximum power point tracking algorithm for PV systems operating under partially shaded conditions. IEEE T. Energy Conver., 27, 4, 1027–1035, 2012. 41. Mirjalili, S. and Lewis, A., The Whale Optimization Algorithm. Adv. Eng. Software, 95, 51–67, 2016.

332  Power Electronics for Green Energy Conversion 42. Diab, A.A.Z., MPPT of PV system under partial shading conditions based on hybrid whale optimization-simulated annealing algorithm (WOSA), in: Green Energy and Technology, pp. 355–378, 2020. 43. Mirjalili, S., Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl. Based Syst., 89, 228–249, 2015. 44. Mohapatra, A., Nayak, B., Das, P., Mohanty, K.B., A review on MPPT techniques of PV system under partial shading condition. Renew. Sust. Energ. Rev., 80, 854–867, 2017. 45. Labeeb, K., Shankar, S., Ramprabhakar, J., Hybrid MPPT controller for accurate and quick tracking. 2016 IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. RTEICT 2016 - Proc, pp. 1533–1537, 2017. 46. Larbes, C., Aït Cheikh, S.M., Obeidi, T., Zerguerras, A., Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renew. Energy, 34, 10, 2093–2100, 2009. 47. Batarseh, M.G. and Zater, M.E., Hybrid maximum power point tracking techniques: A comparative survey, suggested classification and uninvestigated combinations. Sol. Energy, 169, 535–555, 2018. 48. Passino, K.M., Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Syst., 22, 3, 52–67, 2002. 49. Mirjalili, S., Mirjalili, S.M., Lewis, A., Grey Wolf Optimizer. Adv. Eng. Software, 69, 46–61, 2014. 50. Karaboga, D., An idea based on Honey Bee Swarm for Numerical Optimization. Tech. Rep. TR06, Erciyes Univ., 10, 06, 1–10, 2005. 51. Chu, S.A., Tsai, P.W., Pan, J.S., Cat swarm optimization, in: Lecture Notes in Computer Science (including Subseries Lecture Notes in Artifial Intelligence Lecture Notes Bioinformatics), LNAI, vol. 4099, pp. 854–858, 2006. 52. Yang, X.S., A new metaheuristic Bat-inspired Algorithm, in: Studies in Compututational Intelligence, vol. 284, pp. 65–74, 2010. 53. Rao, R., Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput., 7, 1, 19–34, 2016. 54. Yang, X.S., Firefly algorithms for multimodal optimization, in: Lecture Notes in Computer Science (including Subseries Lecture Notes in Artifial Intelligence Lecture Notes Bioinformatics), LNCS, vol. 5792, pp. 169–178, 2009. 55. Yang, B., Zhong, L., Zhang, X., Shu, H., Yu, T., Li, H., Jiang, L., Sun, L., Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. J. Clean. Prod., 215, 1203– 1222, 2019. 56. Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl., 27, 4, 1053–1073, 2016. 57. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M., Spider Monkey Optimization algorithm for numerical optimization. Memetic Comput., 6, 1, 31–47, 2014.

Classification of MPPT Methods for PV Systems  333 58. Meng, X., Liu, Y., Gao, X., Zhang, H., A new bio-inspired algorithm: Chicken swarm optimization, in: Lecture Notes in Computer Science (including Subseries Lecture Notes in Artifial Intelligence Lecture Notes Bioinformatics), vol. 8794, pp. 86–94, 2014. 59. Li, X.L., Shao, Z.J., Qian, J., An optimizing method based on autonomous animals: fish-swarm algorithm. Syst. Eng. Theory Pract., 22, 32–38, 2002. 60. Li, X.L., A new intelligent optimization-artificial fish swarm algorithm, Dr. thesis, Zhejiang University, Zhejiang, China, 2003. 61. Yang, X.S. and Deb, S., Cuckoo search via Lévy flights. 2009 World Congr. Nat. Biol. Inspired Comput. NABIC 2009 - Proc, pp. 210–214, 2009. 62. Arora, S. and Singh, S., Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput., 23, 3, 715–734, 2019. 63. Eusuff, M., Lansey, K., Pasha, F., Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Eng. Optim., 38, 2, 129–154, 2006. 64. Dorigo, M., Birattari, M., Stutzle, T., Ant colony optimization. IEEE Comput. Intell. M., 1, 4, 28–39, 2006. 65. Mirjalili, S., The ant lion optimizer. Adv. Eng. Software, 83, 80–98, 2015. 66. Dhiman, G. and Kumar, V., Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Software, 114, 48–70, 2017. 67. Dhiman, G. and Kumar, V., Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowl. Based Syst., 159, 20–50, 2018. 68. Rao, R.V., Savsani, V.J., Vakharia, D.P., Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. CAD Comput. Aided Des., 43, 3, 303–315, 2011.

10 A Simulation Analysis of Maximum Power Point Tracking Techniques for Battery-Operated PV Systems Pankaj Sahu* and Rajiv Dey

*

Dept. of ECE, SoET, BML, Munjal University, Gurugram, India

Abstract

In the past few decades renewable energy resources became popular due of the fast and continuous exhaustion of fossil fuels and oil resources and since last decade pollution is becoming a threat to humanity, therefore, huge research has been done in the field of green energy, which is still going on. Solar, wind and water are some green energy resources, by which electricity can be generated. Among these available green energy resources, solar energy has emerged as one of the most impactful renewable energy resources which has the potential to replace conventional energy sources. Solar energy is a free and constant source of renewable energy which is harvested using environment-friendly Photovoltaic (PV) systems. Extensive research has been done on photovoltaic materials, but due to certain limitations of materials, the efficiency of solar cells is around 18-23%. However, photovoltaic system also suffers from some drawbacks e.g. during the day time, level of solar insolation is non-uniform moreover, increase in the environmental temperature degrades the power transfer to the load. Therefore, the power generated by solar panel is not same all the time and due to this each and every time the Maximum Power Point (MPP) of photovoltaic system gets changed. Solar panel can deliver maximum power to the load for a certain value of voltage VMPP and current IMPP. This point is known as Maximum Power Point (MPP). All these factors make output power generated by a solar panel costly; therefore, it is reasonable to improve its performance via power converters. These converters have many advantages in solar PV system, like can provide isolation between PV *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (335–380) © 2022 Scrivener Publishing LLC

335

336  Power Electronics for Green Energy Conversion panel and load and can be used to extract maximized power. Moreover, converters can be made more effective with Maximum Power Point Tracking (MPPT) algorithms. MPPT algorithms are integrated with power electronic converter which can deliver maximum power by controlling its duty cycle. This work presents a comparative study and simulation analysis of conventional maximum power point tracking techniques like perturb and observe, incremental conductance, and fractional short circuit current, fractional open circuit voltage with a nonlinear control approach named as ripple correlation control. Simulations have been carried out for various level of solar irradiance i.e. 1000 W/m2, 800 W/m2 and for step irradiance inputs at a constant temperature of 25°C, to show the performance of all MPPT techniques with respect to tracking speed as well as presence of oscillations in output. Keywords:  Photovoltaic (PV), maximum power point tracking (MPPT), ripple correlation control (RCC), hill climbing (HC), perturb & observe (P&O), incremental conductance (IC), fractional open circuit voltage (FOCV), fractional short circuit current (FSCC)

10.1 Introduction Sustainable energy resources became highly demanded and necessity, due to the fast depletion of fossil fuels and oil resources and amount of harmful carbon emission by conventional energy sources. Solar, wind, and water are some examples, which are very popular for renewable energy harvesting. Among the available green energy resources, solar energy has emerged as one of the most impactful renewable energy resources which has the potential to replace conventional energy sources. Solar energy is a free and constant source of renewable energy which is harvested using environment-friendly photovoltaic systems. A general block diagram of photovoltaic energy harvester is shown in Figure 10.1. However, this photovoltaic energy harvester also suffers from some drawbacks, e.g., during day time, level of solar insolation is nonuniform moreover, increase in the environmental temperature degrades the power transfer to the load. Therefore, the power generated by solar panel is not same all the time and due to this each and every time the Maximum Power Point (MPP) of photovoltaic system gets changed [1]. Solar panel can deliver maximum power to the load for a certain value of voltage VMPP and current IMPP. This point is known as Maximum Power Point (MPP) as shown in Figure 10.2.

Analysis of MPPT Techniques for PV Systems  337 Since the output power generated per watt by solar panel is too high, it is reasonable to improve its performance via power converters. These converters have many advantages in solar PV system like it can provide isolation between PV panel and load, extract maximum power. Moreover, converters are effective means for implementing MPPT technology [2]. MPPT algorithms are integrated with power electronic converter, which can deliver maximum power by controlling its duty cycle. There are two types of available charge controllers; (i) pulse width modulation (PWM) and (ii) maximum power point tracking (MPPT) [3]. In photovoltaic systems, MPPT-based charge controller is preferred over PWM as a typical PWM charge controller can only be able to regulate the output voltage of a Photovoltaic (PV) array, not the current, but in MPPT controller both the voltage and current can be regulated. The block diagram of a batteryoperated PV system is shown in Figure 10.1. Many research works have been done on MPPT in the last few decades, such as conventional methods, like perturb and observe (P&O) [1–11], incremental conductance [12–15], hill climbing [1] have been deployed, and a large number of modifications for these techniques has been proposed in the literature. The main shortcomings of P&O, HC & IC is that these methods can only track the maximum power when the irradiation conditions are uniform, and it fails to track MPP when there is a partial shading or cloudy environmental condition. Further, these methods suffer from poor convergence, slow tracking speed, and high steady-state oscillations (ripples). Therefore, to track the MPP under partial shading conditions, conventional techniques need to be

Q

L

ipv

+ vpv

+ C D1

Co

d(t) PV Panel

MPPT Controller

Figure 10.1  Representation of single stage battery-operated PV system.

Load Battery Ro

Vo

338  Power Electronics for Green Energy Conversion

Current (A)

8

IMPP

1 kW/m2

6 4 2 0

0

5

10 15 Voltage (V)

25

PMAX 1 kW/m2

150 Power (W)

VMPP 20

100 50 0

0

5

10

15

VMPP 20

25

Voltage (V)

Figure 10.2  Current and power of the PV panels versus voltage.

upgraded. Due to which, different modified versions of P&O have been proposed in the literature, such as adaptive P&O with fixed step size, variable step size, etc. [2, 3]. Additionally, soft computing/evolutionary algorithm-based approaches [15, 16] have also been emerged along with the conventional MPPT methods, such as particle swarm optimization-based MPPT algorithm [16], which is useful in finding the global MPP when there are multiple PV arrays with large area, having advantages like they can handle nonlinearity and coherent skill to reach global optimal point. Another category of MPPT techniques have been developed, which calculates the MPP based on a priori data, without continuously tracking of PV panel voltage and current [22–24]. The data generally take values from voltage-current curves for various irradiances and temperatures. Fractional open circuit voltage (FOCV) [22, 23] and fractional short circuit current (FSCC) [22–24] comes under this category. These techniques are popular due to low cost of implementation, as it requires only one sensor for measurement [25]. In FOCV only voltage sensor is needed and current sensor is not required, whereas in FSCC, only current sensor is needed, no requirement of voltage sensor. The racking performance of FOCV is better than FSCC, but it still not up to the mark. Another advantage of FOCV is that it requires less computational load to perform tracking. Eventhough the

Analysis of MPPT Techniques for PV Systems  339 above alterations have enhanced the performance of MPPT charge controllers; however, it is not sufficient enough for all the environmental conditions [5, 7, 21, 26–29]. This gives a motivation to the researchers to find some alternative ways to improve the performance of MPPT charge controllers under partial shading conditions. On the other hand, new era of MPPT algorithms in PV system have emerged when evolutionary RCC algorithm was first applied for MPP tracking [17, 18]. Due to its ability of handle nonlinear problems, it has opened up new scopes of opportunities. Among these ripple correlation control (RCC) method has been well received by the research community because of its simplicity, robustness, and ripple removal characteristics [17–21, 27–29]. Ripple correlation control (RCC provides a solution to the steady state oscillations, which is mainly caused by the internal ripples present in the system due to the presence of power electronic circuitry. Since P&O, IC, and HC methods do not consider these ripples as internal perturbations, due to which the algorithm consider them as external perturbations. As a result, the output voltage and current oscillates around MPP, in turn power also oscillates. These spikes or ripples are harmful for the battery, as it reduces its operating life span [27–29]. In this work, RCC MPPT with buck converter for battery-connected load has been implemented and its simulation results have been compared with P&O, IC, FSCC, and FOCV MPPT methods. The organization of the paper is as follows: Section 10.1 gives introduction of MPPT and various existing methods available in literature. Section 10.2 provides mathematical formulation of P&O, IC, FSCC, FOCV, and RCC techniques, along with formulation of buck converter used in the simulation. In section 10.3, parameters specifications are given along with photovoltaic system diagram. Simulation results and discussions for abovementioned techniques have been provided in section 10.4, followed by conclusion in section 10.5.

10.2 Background of Conventional MPPT Methods In this section, a brief overview of the various MPPT methods along with detailed mathematical formulation has been discussed. All the methods used in simulation and comparison purpose have been presented along with their flowcharts and algorithms.

340  Power Electronics for Green Energy Conversion

10.2.1 Perturb & Observe (P&O) The P&O [1–11] method utilizes an additional perturbation of current or voltage array to check the status, whether the system has reached the nominal value of voltage or current. If the power output increases when voltage is changed in a particular direction of perturbation then it means that MPP will be obtained in that particular direction of perturbation and if the power output decreases for the same voltage perturbation then the MPP can be found by reversing the direction of perturbation. Although this method is easy to implement and is cost effective, the overall system is never able to attain stability because apart from adding external perturbation to it the perturbation due to the environmental changes and the inherent generated perturbation affects its stability, since this method does not consider internal ripples, which causes perturbations (oscillations) in output. This method works on the concept of continuous observations of PV array output power for the input perturbations, due to changing current or voltage of PV array. The algorithm continuously modifies the reference voltage or current based on the previous value of power until reaches the MPP [6, 7]. Figure 10.3 shows the relation between power with respect to duty cycle as per the input perturbations. When dP/dV > 0 and the operating voltage of PV array is perturbed in a specific direction, it known that perturbation moves the operating point of PV array to the MPP. P&O method will then continue to perturb the PV voltage in the same direction. PV output Power MPP

Steady state operation

Possible starting points

Duty cycle

Figure 10.3  Power vs duty cycle for P&O technique.

Analysis of MPPT Techniques for PV Systems  341 When dP/dV < 0, the perturbation moves the operating point of PV array away from the MPP, and the P&O method reverses the direction of the perturbation [4, 7].

10.2.2 Incremental Conductance (IC) Another algorithm to locate the MPP is incremental conductance (IC) [12–15]. In this algorithm, a relationship between power and voltage is used to make the derivative of power with voltage zero. This algorithm performs well under rapidly changing environmental conditions, but expense of increased response times due to complex hardware and software requirements increases the computation time of MPPT.



dP d(VI ) dI dI dI = =I +V = I +V dV dV dV dV dV

(10.1)

Equation (10.1) represents the derivative of PV output power with voltage, the PV system will operate at MPP when this equation is equal to zero i.e., as shown in equation (10.2);



dP dI I dI = 0 ⇒ I +V =0⇒− = dV dV V dV

(10.2)

Comparing the instantaneous change in conductance, dI/dV and instantaneous conductance of PV array I/V in equation 10.2, the position of operating point in relation to maximum power point can be revealed. Equation (10.3) shows the position of operating point at different values of dP/dV as shown in Figure 10.4.



        

dP > 0, for V < VMPP dV dP = 0, for V = VMPP dV dP < 0, for V > VMPP dV

(10.3)



342  Power Electronics for Green Energy Conversion dP/dV=0

P

MPP

dP/dV>0

dP/dV>0

V

Figure 10.4  P-V conductance slope.

10.2.3 Fractional Short Circuit Current (FSCC) Another category of MPPT techniques, which calculates the MPP based on a priori data, without continuously tracking of PV panel current [22–24]. These data are taken from voltage-current curves for various irradiances and temperatures. Fractional short circuit current (FSCC) technique is popular due to the low cost of implementation, as it requires only one sensor for current measurement; and voltage sensor is not required. In this technique, the nonlinear characteristics of PV system are modeled using mathematical equations or numerical approximations by considering the environmental conditions and degradation level of PV panel. Based on these parameters, a mathematical relation between IMPP and ISC is constructed as follows:

IMPP = KPV ISC

(10.4)

where the constant KPV is the current factor, which is calculated based on the panel V-I curves, and varies in the range of 0.64–0.85 Iact is the actual current [24]. Typically, this value is specified in the panel’s datasheet. To estimate the IMPP voltage using the FSCC technique, the short circuit current is first measured and then multiplied by the current factor KPV.

Analysis of MPPT Techniques for PV Systems  343

10.2.4 Fractional Open Circuit Voltage (FOCV) This technique also calculates the MPP based on a priori data, without continuously tracking of PV panel voltage. The data generally take values from voltage-current curves for various irradiances and temperatures. Fractional open circuit voltage (FOCV) [22–24] technique is popular due to the low cost of implementation, as it requires only one sensor for voltage measurement; and current sensor is not required. Another advantage of this technique is and less computational load to perform tracking. FOCV algorithm periodically takes the samples the open-circuit voltage of the cell, VOC, in order to estimate VMPP by a linear estimation law and, finally, to set the PV cell around the estimated VMPP. This MPPT technique is based on a relationship, which exists between the open-circuit voltage VOC and the peak power voltage VMPP. The flowchart of FSCC algorithm has been shown in Figure 10.5.

VMPP = KPV VOC

(10.5)

where the constant KPV is the voltage factor, which is calculated based on the panel V-I curves, and varies in the range of 0.78–0.92 and Vact is the

Start Measure short circuit current Isc of solar panel IMMP = KPV ISC

Measure current of solar panel Iact

Yes

Iact > IMPP

Decrease Iact

Figure 10.5  Flowchart of FSCC algorithm [22–24].

No

Increase Iact

344  Power Electronics for Green Energy Conversion Start Measure open circuit voltage VOC of solar panel VMPP = KPV VOC

Measure voltage of solar panel Vact

Yes

Vact > VMPP

Decrease Vact

No

Increase Vact

Figure 10.6  Flowchart of FOCV algorithm [22–24].

actual voltage [23]. Typically, this value is specified in the panel’s datasheet. To estimate the VMPP voltage using the FOCV technique, the open circuit voltage is first measured and then multiplied by the voltage factor KPV. The measured open-circuit voltage VOC gives an estimation of the VMPP voltage, and this VOC voltage factor stays almost constant for changing irradiance and temperature. The open circuit voltage VOC is sampled periodically by fleetingly disconnecting the load. The duration and frequency of this sampling course of action directly control the accuracy of the estimated VMPP, where high-frequency and/or large duty cycles improve the estimation accuracy, but also increase the power loss. The flowchart of FOCV algorithm is shown in Figure 10.6.

10.2.5 Ripple Correlation Control (RCC) Ripple correlation control [17–21, 27–29] is a nonlinear control approach applicable to power electronic circuits. It makes use of voltage, current, or power ripple and correlates this with switching functions to affect control. It correlates the ripple content derivative of power with the derivatives of either voltage or current. The RCC solves major problems through less

Analysis of MPPT Techniques for PV Systems  345 complex implementation. The most important factors and advantages of the RCC are the simple circuit implementation, fast computation/simulation time, there is no need for external perturbation like in P&O and IC, to generate ripple contents, converges asymptotically to the object and its converging rate can be tuned by the controller gain. RCC is a method that is used to calculate the duty cycle which provides maximum power, which will be supplied to the gate of switching circuits to maintain MPP. The main advantage of RCC is that it uses inherent ripples that occur due to the power electronic elements, i.e., the DC-DC convertor used in the PV system. Through the correlation of the time-based derivative of voltage and power the RCC tries to identify whether this correlation is greater than zero i.e., to the left of the MPP, or less than zero, i.e., to the right of the MPP, or exactly zero, i.e., equal to MPP [27–29].

dpPV dt  dpPV dt dp   PV dt  



dv PV > 0 when VPV < VMPP dt dv PV < 0 when VPV > VMPP dt dv PV = 0 when VPV = VMPP dt

(10.6)



The general equation used to find the ripple content is as:



  x(t ) = x (t ) − x (t )

(10.7)

where x(t) is the general quantity that can be array current, voltage or power which contains both the ripple, as well as moving average component. x(t ) is the ripple content, whereas x (t ) is the average component. From equation (10.7), we can easily find out the ripple content of voltage, current, and power. Figure 10.1 represents the RCC that has been implemented to find out the duty cycle. This RCC is different from [17, 18], as it has low pass filter instead of high pass filters as in [21]. In this work RCC mentioned in [27–29] is implemented for battery-operated systems for output power of around 100 watts. The work differs from [21], in terms of load which is battery in this work and converter type, which is buck converter, instead of

346  Power Electronics for Green Energy Conversion boost converter used in [21] for grid connected load. From Figure 10.7, it is evident that the error is equal to the product of the ripple content of both the power and voltage. Where v(t) is PV voltage, i(t) is PV current, p(t) is PV power, v (t ) is filtered DC voltage, p(t ) is filtered DC power, v(t ) is voltage ripple, p(t ) is power ripple, V * (t) is reference voltage obtained through first proportional integral (PI) controller and δ is optimal duty cycle obtained through second PI controller. The final equation of the error e(t) in RCC can be expressed as

 dp(t )  2  e(t ) = p(t ) × v(t ) = v2 (t )  + v (t )i (t )  dv(t ) 



(10.8)

Error e(t) is directly proportional to the magnitude of dp/dv as the average value v2 (t ) i (t ) is zero over a cycle. e(t) represents the distance from the MPP. When the output is on the left of MPP, the average value of error is positive. When output is on the right of MPP the average value of error is negative. When the output is at the MPP, the average value of error is zero. From Figure 10.7, it can be anecdotal that when the error signal passes through the first PI controller, it generated the reference signal, and the difference of this reference signal & PV array voltage v(t) acts as an input for the second PI controller that gives us the load angle δ, which is used to generate the PWM signal to control the switching of buck convertor. The DC-DC voltage converter is a power electronic device, which is used for conversion of one level of DC to some other level DC, with regulation in current. There are three distinguish types of converters available, in accordance with the requirements, these are, buck-boost converter, buck and boost converters. The main function of the buck converter is to

v(t)

i(t) v(t)

LPF

p(t)

LPF

+

v(t)

v(t)

–

~ v(t) ~ p(t)

p(t) –

~ ~ p(t)v(t) e(t)

+

Figure 10.7  RCC block of the MPPT Controller [27–29].

PI

V*(t)

– +

PI

δ

Analysis of MPPT Techniques for PV Systems  347 increase the voltage in accordance with the need with increment in the current. In this work, a buck converter is utilized, due to low voltage battery charging requirements [27–29]. A typical representation of buck converter circuit has been shown in Figure 10.8. There are two modes of operation in a buck converter, when the switch M is closed, and the input power is supplied to the load through inductor and diode is reverse biased. In this mode the inductor current IL increases at the rate of [6].

di L v L v in − v 0 = = , t [0,Dt] dt L L



(10.9)

When the switch M is opened, then the stored energy of inductor is supplied to the load through the diode. During this mode diode is forward biased. In this mode the inductor current decreases or discharges at the rate of,

di L v L − v 0 = = , t[Dt,T] dt L L



(10.10)

Equation (10.11) represents the average value of the output voltage. The average inductor current over the switching cycle in steady state is zero.

v0 =



1 T

∫

T

0

v 0 (t)dt =

1 T

Q

∫

Dt

0

v in (t)dt = Dv in

L

+

+ d(t)

Vin

(10.11)



Load Battery

D1 Co

Figure 10.8  Electrical model of DC-DC buck converter [6, 27].

Ro

Vo

348  Power Electronics for Green Energy Conversion Therefore;

v0 = Dvin

(10.12)

The required minimum value of inductance for continuous mode of operation is.



v 0 (1 − D) 2I0 fPWM

L=

(10.13)

Where D is the duty cycle, given by;



D=

t on t on + t off

(10.14)

The capacitor value of the buck converter can be calculated by;



C=

i0 4 VfPWM

(10.15)

10.3 Simulink Model of PV System with MPPT The Simulink model schematic of the PV system that is used for simulations is depicted in Figure 10.9, which represents a PV solar panel connected to a resistive load through a dc-dc buck converter with a variant subsystem of MPPT controller, by which any of these four implemented MPPT algorithms, i.e., P&O, IC, FOCV, and RCC, can be chosen. The PV panel consists of 36 cells in series, i.e., ncells = 36. The temperature for all the simulation has been selected as 25°C, as standard temperature. All the algorithms have been implemented, and simulation has been performed to check their response for different irradiance levels. Figure 10.10 represents the complete internal architecture of RCC block used in simulation. The PV parameters used in Simulink simulation model of this work are given in Table 10.1 [27]. It has the values of open circuit voltage, short circuit current, values of series, and parallel resistance of PV model, and

Analysis of MPPT Techniques for PV Systems  349 Step Irradiance

Discrete 0.0001 s.

[PWM]

gate

Irradiance

powergui1

Vout +

Vin +

+ -i Measurement Block

Irradiance Vsolar

Vin -

Vsolar

+

+

DC-DC Buck Converter Load

V+

Isolar

Ci

I_Buck + v -

V_Buck

Isolar V-

PV Solar Array

Figure 10.9  Simulink model of PV system used in simulation.

Vsolar

1 1+0.0015z-1

1/3

-

+ -

Isolar

100

×

1 1+0.0015z-1

× -

+

PI(z)

+

PI(z)

D

Discrete PI Controller 2

P

[PWM] PWM

v*t

Discrete PI Controller 1

Figure 10.10  MPPT variant blocks and Simulink implementation of RCC MPPT controller [27–29].

all other important parameters related to PV panel modeling. Similarly, the all-component parameters of the buck converter used in Simulink are given. The value of inductor has been selected such that it will not produce ripples. Also, the value of DC link capacitor C is selected to minimize ripples. This DC link capacitor with low value of resistance acts as a low pass filter, which eliminates high frequency oscillations. The components parameters of RCC block are also provided in Table 10.1. It consists of two proportional integral (PI) controllers, first one is the part of RCC block and second one is outside of RCC block. Both PI blocks need proper tuning for optimized results accordingly. Simulation has been performed for three different types of variations, fist one is at 1000 W/m2 at 25°C, second at 800 W/m2 at 25°C, and third is for step variation as shown in Figure 10.11.

350  Power Electronics for Green Energy Conversion Table 10.1  Simulation specifications [27]. S. no.

Parameter name

Value

1

Number of cells in series

ncells = 36

2

Open circuit voltage

Voc = 21.6 V

3

Short circuit current

Isc = 7.34 A

4

Series resistance

Rs = 1.54 Ω

5

Parallel resistance

Rp = 50.23 Ω

6

Quality factor of diode

N = 1.5

7

DC link Capacitor connected at input

Cdc = 100 µF

8

Inductance value of buck converter

L = 23 µH

9

Capacitance value of buck converter

C = 120 µF

10

Switching Frequency

fsw= 2*106

11

Sample Time

0.0001 Sec

12

Diode Resistance

RON = 0.001Ω

13

Low Pass Filters Time constant used inside RCC MPPT block

τ = 1.5 ms

14

PI controller used inside RCC MPPT block

Kp = 400 KI = 5.5

15

PI controller used outside RCC MPPT block

Kp = 5e-9 KI = -0.009

10.4 Results and Discussions All the simulations have been performed in MATLAB/SIMULINK 2018. In this work, buck-converted PV system having 36 cells in series has been used for a 15-V output at 1000 W/m2 at 25°C, with 2 Ω resistive load. First, the irradiation level is set at 1000 W/m2 at 25°C, which is standard test conditions (STC), and results of voltage, current and power for all the

Analysis of MPPT Techniques for PV Systems  351 1000

Irradiance (Watts/m2)

900 800 700 600 500 400 300

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.11  Step variation of irradiance used in simulation to represent cloudy weather conditions.

methods, i.e., P&O, IC, FOCV, FSCC, and RCC have been provided. Then the results are obtained for 800 W/m2 irradiance level at 25°C. After this simulation has been performed for step variation of irradiance as shown in Figure 10.11.

10.4.1 (a) Simulation Results for P&O Method This subsection provides voltage, current and power outputs obtained by the simulation using conventional P&O MPPT method. From Figures 10.12 to 10.20, it can be clearly seen that outputs of P&O MPPT are oscillatory, which is due to the fact that the algorithm takes internally generated ripples as external perturbations and tends to shift the MPP again and again, which in results oscillatory output. Basically, these internally generated ripples are the part of the PV system and introduced due to the use of power electronics converters. Therefore, the steady-state performance of P&O method is highly affected by oscillation problem. Output of the photovoltaic system is dependent on the level of solar irradiance; from the figures, it can be seen that according to the level of solar irradiance all parameters are changing. In the process of maintaining MPP, P&O algorithm introduces some oscillations.

352  Power Electronics for Green Energy Conversion 18 16

Voltage (V)

14 12 10 8 6 4 2 0 0

16.2 16 15.8 15.6 15.4 15.2 15 0.016

0.01

0.018

0.02

0.02

0.03

0.022

0.024

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.12  Output voltage of P&O MPPT for 1000 W/m2 irradiance level at 25°C.

15

14.4

10 Voltage (V)

14.2 14 13.8 13.6

5

13.4 13.2 13

0

0

0.01

0.008

0.02

0.01

0.012 0.014

0.03

0.016

0.018

0.04 0.05 0.06 Time (seconds)

0.02

0.07

0.08

0.09

0.1

Figure 10.13  Output voltage of P&O MPPT for 800 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  353 16 14

Voltage (V)

12 10 8 6 4

12 11.8 11.6 11.4 11.2

2

11 0.052

0 0

0.01

0.054

0.02

0.056

0.03

0.058

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.14  Output voltage of P&O MPPT for step irradiance level at 25°C.

8 7

Current (A)

6

8.1

5

8

4

7.9 7.8

3

7.7

2

7.6

1 0 0

7.5 0.015

0.01

0.02

0.03

0.02

0.04 0.05 0.06 Time (seconds)

0.025

0.07

0.08

0.09

0.1

Figure 10.15  Output current of P&O MPPT for 1000 W/m2 irradiance level at 25°C.

354  Power Electronics for Green Energy Conversion 7

Current (A)

6 7.2

5

7

4

6.8

3

6.6

2

6.4 0.02

1 0

0

0.01

0.02

0.021 0.022

0.03

0.023

0.024

0.025

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.16  Output current of P&O MPPT for 800 W/m2 irradiance level at 25°C.

8 7

Current (A)

6 5

6.2

4

6

3 5.8

2

5.6 0.045

1 0

0

0.01

0.05

0.02

0.03

0.055

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

Figure 10.17  Output current of P&O MPPT for step irradiance level at 25°C.

0.1

Analysis of MPPT Techniques for PV Systems  355 140 120

Power (Watts)

100

128

80

126

60

122

124

120

40

118 116

20 0

114 0.015

0

0.01

0.02

0.02

0.03

0.025

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.18  Output power of P&O MPPT for 1000 W/m2 irradiance level at 25°C.

100

Power (Watts)

80 100

60 95

40 90

20 0

85 0.005

0

0.01

0.02

0.01

0.03

0.015

0.04 0.05 0.06 Time (seconds)

0.02

0.07

0.08

0.09

0.1

Figure 10.19  Output power of P&O MPPT for 800 W/m2 irradiance level at 25°C.

356  Power Electronics for Green Energy Conversion 120

Power (Watts)

100 80 75

60 70

40 65

20 0

60 0.045

0

0.01

0.05

0.02

0.055

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.20  Output power of P&O MPPT for step irradiance level at 25°C.

10.4.2 (b) Simulation Results for Incremental Conductance (IC) Method This subsection provides voltage, current, and power outputs obtained by the simulation using conventional IC MPPT method. From Figures 10.21 to 10.29, like P&O method IC also suffers from oscillatory outputs, which is due to the fact that this algorithm also takes internally generated ripples as external perturbations and tends to shift the MPP again and again, which in results oscillatory output. Basically, these internally generated ripples are the part of the PV system and introduced due to the use of power electronics converters. Again, simulation have been performed for all three cases of irradiance and results of voltage, current and power have been obtained. Moreover, the steady-state and transient performance of IC method is better than the conventional P&O method. From the simulation results, it is evident that the tracking effectiveness of IC MPPT is slightly better than the P&O MPPT strategy, but the problem of oscillations is appearing in both the cases, i.e., steady state (when no change in solar irradiance) and transient state (when level of solar irradiance changes).

Analysis of MPPT Techniques for PV Systems  357 16 14 16

Voltage (V)

12

15.8

10

15.6

8

15.4

6

15.2 15 0.01

4

0.012

0.014

0.016

0.018

0.02

2 0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.21  Output voltage of IC MPPT for 1000 W/m2 irradiance level at 25°C.

16 14

Voltage (V)

12 10 8 6 4 2 0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.22  Output voltage of IC MPPT for 800 W/m2 irradiance level at 25°C.

358  Power Electronics for Green Energy Conversion 18 16 14 Voltage (V)

12 10 8 6 4 2 0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.23  Output voltage of IC MPPT for step irradiance level at 25°C.

8 7 Current (A)

6

8

5

7.9

4

7.8 7.7

3

7.6

2

7.5 0.01

0.015

0.02

1 0 0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.24  Output current of IC MPPT for 1000 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  359 8 7

Current (A)

6 5

7.2

4

7

3

6.8

2 6.6 0.01

1 0 0

0.01

0.02

0.015

0.03

0.02

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.25  Output current of IC MPPT for 800 W/m2 irradiance level at 25°C.

8 7 Current (A)

6 5 4 3 2 1 0 -1

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

Figure 10.26  Output current of IC MPPT for step irradiance level at 25°C.

0.1

360  Power Electronics for Green Energy Conversion 120

Power (Watts)

100 80 60 40 20 0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.27  Output power of IC MPPT for 1000 W/m2 irradiance level at 25°C.

100

Power (Watts)

80 60 40 20 0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.28  Output power of IC MPPT for 800 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  361 120

Power (Watts)

100 80 60 40 20 0

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.29  Output power of IC MPPT for step irradiance level at 25°C.

10.4.3 (c) Fractional Open Circuit Voltage (FOCV) Method This subsection provides voltage, current, and power outputs obtained by the simulation using conventional FOCV MPPT method. From Figures 10.30 to 10.38, it can be seen that like P&O and IC methods, it does not suffer much from oscillatory outputs. Although this method suffers from tracking problem, as it takes a fraction of input to achieve MPP, with delayed output response, which are the drawbacks of this method. Also, output suffers from oscillation problem in transient conditions, when irradiance level gets changed. It can be seen in Figures 10.32, 10.35, and 10.38, where oscillations are in outputs, whenever transient occurs. Moreover, the method is having advantages over P&O and IC, as it requires only one voltage sensor for input, as well as tracking problem can be compensated as per low output requirements settings. The FOCV performance is also better than the conventional FSCC method, since FOCV is based on voltage fraction input, whereas FSCC method depends upon current input fraction. FOCV technique requires only one voltage sensor, which makes the technique popular. It actually compares input PV voltage with fraction of PV voltage, due to which it is able to eliminate ripples from output up to certain extent with high PWM, but the tracking problem persists. The ripple performance is improved due to the use of PI controller in FOCV.

362  Power Electronics for Green Energy Conversion 16 14

Voltage (V)

12

15

10 10

8 6

5

4 0

2 0

0

0.01

4

6

0.02

8

0.03

10 ×10-4

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.30  Output voltage of FOCV MPPT for 1000 W/m2 irradiance level at 25°C.

15

15

Voltage (V)

10

10

5

5

0 0.5

0 0

0.01

0.02

1

0.03

1.5

0.04 0.05 0.06 Time (seconds)

2 ×10-3

0.07

0.08

0.09

0.1

Figure 10.31  Output voltage of FOCV MPPT for 800 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  363 16 14

Voltage (V)

12 10 8 6

16

15

14 10

12

4

5

2

0 4

0 0

10 6

0.01

8

0.02

8

10 ×10-4

0.03

0.08

0.0805

0.04 0.05 0.06 Time (seconds)

0.07

0.081

0.08

0.09

0.1

Figure 10.32  Output voltage of FOCV MPPT for step irradiance level at 25°C.

8

Current (A)

7 6

8

5

6

4

4

3

2

2

0 0

0.5

1 0 0

0.01

0.02

0.03

1

1.5 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.33  Output current of FOCV MPPT for 1000 W/m2 irradiance level at 25°C.

364  Power Electronics for Green Energy Conversion 7 6

Current (A)

5 6

4

4

3

2

2

0

1 0 0

0.01

0.02

0

0.03

0.5

1

1.5

2 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.34  Output current of FOCV MPPT for 800 W/m2 irradiance level at 25°C.

8 7

Current (A)

6 5 4

8 7.6

3

7.4

2

7

1 0 0

8

7.8

7 6

7.2

5 0.02

0.01

0.0205

0.02

0.03

0.021

4

0.08 0.0805 0.081 0.0815

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.35  Output current of FOCV MPPT for step irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  365 120 130

Power (Watts)

100

120

80

110

60

100

40

90

20

80 0

0 0

0.01

1

0.02

0.03

2

3

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.36  Output power of FOCV MPPT for 1000 W/m2 irradiance level at 25°C.

100 90

Power (Watts)

80

100

70

80

60 50

60

40

40

30

20

20

0 0.5

10 0

1

1.5

2 ×10-3

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.37  Output power of FOCV MPPT for 800 W/m2 irradiance level at 25°C.

366  Power Electronics for Green Energy Conversion 140 120

Power (Watts)

100 80 60 40

130 120

100

110

90 80

100

20 0 0

90 0.0195

0.01

70 0.02

0.0205 0.021

0.02

0.03

60 0.06

0.04 0.05 0.06 Time (seconds)

0.0605

0.07

0.061

0.08

0.09

0.1

Figure 10.38  Output power of FOCV MPPT for step irradiance level at 25°C.

10.4.4 (d) Fractional Short Circuit Current (FSCC) Method This subsection provides voltage, current and power outputs obtained by the simulation using conventional FSCC MPPT method. From Figures 10.39 to 10.47, it can be seen that like P&O and IC methods, it does not suffer much from oscillatory outputs. Although, this method suffers from tracking problem, as it takes a fraction of input to achieve MPP, with delayed output response, which are the drawbacks of this method. Also, output suffers from oscillation problem in transient conditions, when irradiance level gets changed. Moreover, the method is having advantages over P&O and IC, as it requires only one voltage sensor for input, as well as tracking problem can be compensated as per low output requirements settings. This method is much similar to FOCV, only change is FSCC requires a current sensor, as in FOCV voltage sensor is needed. Also, the output performance of FSCC is less as compared to FOCV method. Figure 10.41 shows the output voltage obtained through FSCC MPPT method, in which it is evident that during transitions of irradiance from one level to another i.e. cloudy weather conditions, the problem of oscillations

Analysis of MPPT Techniques for PV Systems  367 16 14

Voltage (V)

12

15

10 10

8 6

5

4 0 0

2 0

0

0.01

1

0.02

0.03

3 ×10-3

2

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.39  Output voltage of FSCC MPPT for 1000 W/m2 irradiance level at 25°C.

12

Voltage (V)

10

12 10

8

8

6

6 4

4

2

2 0 0

0

0.01

0

0.02

1

0.03

2

3

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.40  Output voltage of FSCC MPPT for 800 W/m2 irradiance level at 25°C.

368  Power Electronics for Green Energy Conversion 16 14

Voltage (V)

12 10 8

15

6

10

4

5

2

0 0

0 0

12 10

0.01

8 1

3

2

6 0.079 0.08 0.081 0.082 0.083

×10-3

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.41  Output voltage of FSCC MPPT for step irradiance level at 25°C.

7 8

Current (A)

6 5

6

4

4

3

2

2

0

1 0

0

0.01

0

0.02

1

0.03

2

3

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.42  Output current of FSCC MPPT for 1000 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  369 6 6

5 Current (A)

5

4

4 3

3

2

2

1 0 0

1 0 0

0.01

0.02

1

0.03

3

2

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.43  Output current of FSCC MPPT for 800 W/m2 irradiance level at 25°C.

8 7

Current (A)

6 5 4

8

3

6

2

4

1

2

0

6 5 4

0 0

0

2

0.01

0.02

4 ×10-3

0.03

3

0.08

0.082

0.04 0.05 0.06 Time (seconds)

0.084

0.07

0.08

0.09

0.1

Figure 10.44  Output current of FSCC MPPT for step irradiance level at 25°C.

370  Power Electronics for Green Energy Conversion

100 120

Power (Watts)

80

100 80

60

60 40

40

20

20 0

0 0

0

0.01

1

0.02

2

0.03

3

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.45  Output power of FSCC MPPT for 1000 W/m2 irradiance level at 25°C.

70

Power (Watts)

60

80

50

60

40 40

30

20

20 0 0

10 0

0

0.01

0.02

1

0.03

2

3

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.46  Output power of FSCC MPPT for 800 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  371 120 60 50

100

40

Power (Watts)

30

80

20

0.08

0.081

0.082

60 100

40

80 60

20 0

0

0

2

0.01

4 ×10-3

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.47  Output power of FSCC MPPT for step irradiance level at 25°C.

appears, which disappears in steady state or constant irradiance condition. FSCC is much affected by change in irradiance as it’s input which is PV current, is very much affected by change in irradiance. FSCC technique is also one of MPPT techniques, which requires one current sensor only. It compares the fraction of input current with PV input current. Steady state oscillations for high PWM are eliminated as shown in Figures 10.39 to 10.47, but performance of tracking is not up to the mark.

10.4.5 (e) Ripple Correlation Control (RCC) This subsection presents voltage, current, and power outputs obtained by the simulation using RCC MPPT method. From Figures 10.48 to 10.56, it can be seen that, unlike P&O and IC method, it does not suffer from oscillatory outputs. Also, the method has good tracking performance among all existing methods, as it utilizes internally generated ripples as perturbations to attain MPP. The only drawback is some oscillatory performance in transient, i.e., cloudy weather conditions, which can be further improved

372  Power Electronics for Green Energy Conversion 15

15

Voltage (V)

10 10

5

5 0 0

0

0

0.01

0.02

1

0.03

2

3 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.48  Output voltage of RCC MPPT for 1000 W/m2 irradiance level at 25°C.

12 10

12 10

Voltage (V)

8

8 6

6

4 2

4

0

0

1

2 0 0

0.01

0.02

0.03

2

3 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.49  Output voltage of RCC MPPT for 800 W/m2 irradiance level at 25°C.

Analysis of MPPT Techniques for PV Systems  373 16 14

Voltage (V)

12 10 15

8

12

14 11

6

13

4

12

2

11

0

0

10

0.02

0.01

9 0.06

0.021 0.022 0.023

0.02

0.03

0.062

0.04 0.05 0.06 Time (seconds)

0.07

0.064

0.08

0.09

0.1

Figure 10.50  Output voltage of RCC MPPT for step irradiance level at 25°C.

8 7 8

Current (A)

6

7 6

5

5

4

4

3

3 2

2

1

1 0 0

0 0

0.01

2

1

0.02

0.03

3

4

5 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figures 10.51  Output current of RCC MPPT for 1000 W/m2 irradiance level at 25°C.

by using some adaptive control strategy with RCC, uttermost accuracy needed [27–29]. From the figures, it is evident that RCC MPPT is eliminating oscillations in steady state (when level of solar irradiance is constant, 1000 W/ m2 for Figure 10.48 and 800 W/m2 for Figure 10.49), as well as in transient state (when level of solar irradiance is changing, Figure 10.50). Also, the

374  Power Electronics for Green Energy Conversion 6

Current (A)

5

6 5

4

4 3

3

2

2

1

1 0

0

0.01

0 0

1

0.02

0.03

2

3

4

5 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.52  Output current of RCC MPPT for 800 W/m2 irradiance level at 25°C.

8 7

Current (A)

6 5 8

4 3

4

2

2

1

0 0

0

6

6

0

0.01

5 4 2

0.02

4 ×10-3

0.03

3

0.08 0.081 0.082 0.083

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

Figure 10.53  Output current of RCC MPPT for step irradiance level at 25°C.

0.1

Analysis of MPPT Techniques for PV Systems  375 100

Power (Watts)

120 100

80

80

60

60 40

40

20

20 0 0

0.01

0 0

1

0.02

0.03

2

3

4 ×10-3

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.54  Output power of RCC MPPT for 1000 W/m2 irradiance level at 25°C.

70 60 80

Power (Watts)

50

60

40

40

30

20

20

0

0

1

10 0

2

3

4 ×10-3

0

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.55  Output power of RCC MPPT for 800 W/m2 irradiance level at 25°C.

376  Power Electronics for Green Energy Conversion 120 60

Power (Watts)

100

50

80

40

60

0.06 0.062 0.064

100 90

40

80 70

20 0

60

0

0.02

0.022

0.01

0.02

0.03

0.04 0.05 0.06 Time (seconds)

0.07

0.08

0.09

0.1

Figure 10.56  Output power of RCC MPPT for step irradiance level at 25°C.

tracking performance is better than all other techniques discussed previously. It is basically eliminating the ripples contents by comparing the derivative of power with derivative of voltage. With this way RCC is able to completely eliminate steady state oscillations (see Figures 10.48–10.49), as well as transient oscillations (see Figure 10.50).

10.4.6 (f) Performance Comparison A summary of performance comparison between all mentioned MPPT methods has been provided in Table 10.2. From the simulation results and Table 10.2, it can be clearly seen that RCC MPPT outperforms all the existing MPPT methods, in all technical aspects. From the Table 10.2, it is evident that RCC MPPT not only performs well under steady state, also it performs well in transient state in terms of ripple contents at output. Also, it is having good performance for tracking speed with high accuracy. Based on the simulation studies, in general, the following performance comparison can be given:

Analysis of MPPT Techniques for PV Systems  377 Table 10.2  Performance comparison. S.no.

Performance parameters

Techniques P&O

IC

FSCC

FOCV

RCC

1

Steady State Oscillations

Yes

Yes

No

No

No

2

Transient Oscillations

Yes

Yes

Yes

Yes

No

3

Tracking Speed

Slow

Slow

Slow

Slow

Fast

4

Tracking Accuracy

Medium

Medium

Low

Low

High

5

Sampling Rate

Low

Low

Low

Low

High

6

Complexity

Complex

Complex

Simple

Simple

Complex

7

Parameter Tuning

No

No

Yes

Yes

Yes

8

Stability

No

Yes

No

No

Very Stable

9

Efficiency

High

High

High

High

High

10

Cost

Medium

Medium

Low

Low

High

10.5 Conclusion This work presents a simulation analysis and comparative study between various MPPT techniques with same parameters. PV panel, buck converter and all the MPPT algorithms have been implemented to obtain simulation results. This comparative analysis present here gives a performance comparison of RCC MPPT over other conventional MPPT methods like P&O, IC, FOCV and FSCC. The main shortcoming of conventional maximum power point tracking (MPPT) techniques like P&O and IC is that they suffered from very high steady state oscillations, due to internally generated power electronics circuitry ripples, which causes change in MPP, results

378  Power Electronics for Green Energy Conversion in oscillation in output. Also, these methods are incapable of tracking MPP under varying environmental conditions, i.e., change in irradiance or temperature. The problem of oscillations is improved in FOCV and FSCC methods, but like P&O and IC, these methods also suffer from the drawbacks of poor tracking speed, poor convergence. Ripple Correlation Control (RCC) is a nonlinear MPPT technique, which provides solutions of these drawbacks with ease of implementation, robustness and ripple removal characteristics. The simulation results show that both the methods P&O and incremental conductance have oscillations in current output and hence power output in all three case, i.e., 1000 w/m2, 800 w/m2 and step irradiance levels; whereas, fractional open circuit voltage and fractional short circuit current suffer from these oscillations in case of step response. On the other hand, ripple correlation control MPPT does not has any sort of oscillations neither in current output, nor in power output, which is a good sign for longer battery life. Basically, these ripples were internal, generated due to use of power electronic converters, and RCC utilizes these internally generated ripples as perturbations to attain MPP, and hence, able to eliminate them. However, the performance of RCC in transient phase can be further improved by using suitable adaptive control strategy if more accuracy is needed.

References 1. Femia, N., Petrone, G., Spagnuolo, G., Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron., 20, 4, 963–73, 2005. 2. Pandey, A., Dasgupta, N., Mukerjee, A.K., High-performance algorithms for drift avoidance and fast tracking in solar mppt system. IEEE Trans. Energy Convers., 23, 681–9, 2008. 3. Abdelsalam, A.K., Massoud, A.M., Ahmed, S., Enjeti, P.N., High-performance adaptive perturb and observe MPPT technique for photovoltaic-based micro grids. IEEE Trans. Power Electron., 26, 4, 1010–21, 2011. 4. Elgendy, M.A., Zahawi, B., Atkinson, D.J., Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE T. Sustain. Energ., 3, 1, 21–33, 2012. 5. Banu, I.V. et al., Comparative analysis of the perturb-and-observe and incremental conductance MPPT methods. 8th IEEE International Symposium on Advanced Topics In Electrical Engineering (ATEE), 2013. 6. Zainuri, M.A.A.M., Radzi, M.A.M., Soh, A.C., Rahim, N.A., Development of adaptive perturb and observe-fuzzy control maximum power point tracking

Analysis of MPPT Techniques for PV Systems  379 for photovoltaic boost dc–dc converter. IET Renew. Power Gen., 8, 2, 183–94, 2014. 7. de Brito, M.A.G., Galotto Jr., L., Sampaio, L.P., de Azevedo e Melo, G., Canesin, C.A., Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans.Ind. Electron., 60, 3, 1156–657, 2013. 8. Salam, Z., Ishaque, K., Lauss, G., The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions. Appl. Energy, 119, 228–36, 2014. 9. Kollimalla, S.K. and Mishra, M.K., A novel adaptive P&O MPPT algorithm considering sudden changes in the irradiance. IEEE Trans. Energy Convers., 29, 3, 602–10, 2014. 10. Elgendy, M.A., Zahawi, B., Atkinson, D.J., Operating characteristics of the P&O algorithm at high perturbation frequencies for standalone PV systems. IEEE Trans. Energy Convers., 30, 1, 189–98, 2015. 11. Ahmed, J. and Salam, Z., An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Appl. Energy, 150, 97–108, 2015. 12. Liu, F., Duan, S., Liu, F., Liu, B., Kang, Y., A Variable step size INC MPPT method for PV systems. IEEE Trans. Ind. Electron., 55, 7, 2622–8, 2008. 13. Safari, A. and Mekhilef, S., Simulation and hardware implementation of incremental conductance MPPT with direct control method using Cuk converter. IEEE Trans. Ind. Electron., 58, 4, 1154–61, 2011. 14. Hsieh, G.-C., Hsieh, H.-I., Tsai, C.-Y., Wang, C.-H., Photovoltaic powerincrement-aided incremental-conductance MPPT with two phased tracking. IEEE Trans. Power Electron., 28, 6, 2895–911, 2013. 15. Prasanth Ram, J., Sudhakar Babu, T., Rajasekar, N., Renewable and Sustainable Energy Reviews. Renew. Sust. Energ. Rev., 67, 826–847, 2017. 16. Mao, M., Duan, Q., Zhang, L. et al. Maximum Power Point Tracking  for Cascaded PV-Converter Modules Using Two-Stage Particle Swarm Optimization. Sci. Rep., 7, 9381, 2017. https://doi.org/10.1038/s41598017-08009-7. 17. Midya, P. et al., Dynamic Maximum Power Point Tracker for Photovoltaic Applications. IEEE Power Electronics Specialists Conference, 1996. 18. Krein, P.T., Ripple Correlation Control, With Some Applications. IEEE International Symposium on Circuits and Systems (ISCAS), 1999. 19. Khanna, R., Zhang, Q., Stanchina, W.E., Reed, G.F., Maximum Power Point Tracking Using Model Reference Adaptive Control. IEEE Trans. Power Electron., 29, 3, 1490–1499, 2014. 20. Costabeber, A., Carraro M., Zigliotto, M., Convergence Analysis and Tuning of a Sliding-Mode Ripple-Correlation MPPT, in: IEEE Transactions on Energy Conversion, vol. 30, no. 2, pp. 696-706, June 2015, doi: 10.1109/ TEC.2014.2371873. 21. Srinivas, Ch. L.S. and Sreeraj, E.S., A Maximum Power Point Tracking Technique Based on Ripple Correlation Control for Single Phase Photovoltaic

380  Power Electronics for Green Energy Conversion System with Fuzzy Logic Controller. Energy Procedia, 90, 69–77, 2016, ISSN 1876-6102, https://doi.org/10.1016/j.egypro.2016.11.171. 22. Bharath, K. et al., Design and Implementation of Improved Fractional Open Circuit Voltage Based Maximum Power Point Tracking Algorithm for photovoltaic Applications. Int. J. Renew. Energy Res., 7, 3, 1108–1113, 2017. 23. Baimel, D., Tapuchi, S., Levron, Y., Belikov, J., Improved Fractional Open Circuit Voltage MPPT Methods for PV Systems Electronics, 8, 3, 321, 2019. https://doi.org/10.3390/electronics8030321. 24. Nadeem, A. et al., Online fractional open-circuit voltage maximum output power algorithm for photovoltaic modules. IET Renew. Power Gen., 14, 2, 188–198, 2020. 25. J. G. Moorthy, S. Manual, S. Moorthi, and P. Raja, Performance analysis of solar PV based DC optimizer distributed system with simplified MPPT method, SN Appl. Sci., vol. 2, 2, 2020, doi: 10.1007/s42452-020-2010-2. 26. Bollipo, R.B., Mikkili, S., Bonthagorla, P.K., Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation, IET Renew. Power Gener., 14, 9, 1433–1452, 2020, doi: 10.1049/iet-rpg.2019.1163. 27. Sahu, P., Sharma, A., Dey, R., Ripple Correlation Control Maximum Power Point Tracking for Battery Operated PV Systems: A Comparative analysis. 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Vancouver, BC, Canada, pp. 1–6, 2020, doi: 10.1109/ IEMTRONICS51293.2020.9216414. 28. Sahu, P., Sharma, A., Dey, R., A Simulation Analysis of Ripple Correlation Control MPPT for Low Power PV Systems. JoARB, 7, 3, 14–19, 2020. 29. Sahu, P., Sharma, A., Dey, R., A Comparative analysis of Maximum Power Point Tracking Techniques for Battery Operated PV Systems at Diferent Temperatures. Am. J. Sci. Eng., 1, 4, 22–31, December 2020, in AJSE Publishers.

11 Power Electronics: Technology for Grid-Tied Solar Photovoltaic Power Generation Systems K. Sateesh Kumar1*, A. Kirubakaran2, N. Subrahmanyam2 and Umashankar Subramaniam3

*

Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, India 2 Department of Electrical Engineering, National Institute of Technology, Warangal, India 3 Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia

1

Abstract

Solar photovoltaic power generation systems (SPVPGS) have been progressing at an exponential rate to meet the increased energy demands due to dwindling conventional energy sources. Switching to SPVPGS helps to reduce environmental pollution, global warming and climate change. Grid-tied inverters are the potential candidates for the effective interface of renewable energy sources and the utility grid in distributed generation systems. Due to the absence of energy storage elements the size, cost, and reliability of the grid-tied inverters take up more than 95% of share in the PV market. Therefore, the motive of this chapter is to give an overview of the power electronic technologies employed for the grid-tied inverter configurations. Various inverter topologies under the different classes have been classified based on the power rating. Also, some of the important grid codes and standards are discussed for the selection of an inverter for SPVPGS. Further, the most popular and often-used power electronic converter topologies for the grid-tied PVPGS, namely, AC module, string, multistring, and central inverters are described in detail. Comparison of the topologies based on component count, leakage current, merits and demerits are presented

*Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (381–418) © 2022 Scrivener Publishing LLC

381

382  Power Electronics for Green Energy Conversion to give an informed choice to the reader. Finally, the future trends in the grid-tied inverter topologies are discussed to show the directions for researchers. Keywords:  Distributed generation, solar photovoltaic power generation systems, central, multistring, string and AC module inverters, single-stage and two-stage grid-tied inverter configurations, isolated and nonisolated inverter topologies, common mode voltage, leakage current

11.1 Introduction In the present scenario, renewable power generation is emerging to meet the increased energy demands due to dwindling conventional energy sources. The massive scale of fossil fuels has a huge impact on environmental pollution, global warming, and climate change. In such circumstances, people are showing much interest in renewable power generation. There are wide varieties of renewable energy sources like solar energy, wind energy, energy from fuel cell, hydroelectric energy, energy from biomass, etc. In recent years, solar photovoltaic (SPV) power generation is growing at a rapid rate as per surveys by international energy agencies (IEA). The growth of SPV-based power generation is exponential as evident in Figure 11.1 [1]. This is mainly due to the fall in costs of PV materials for PV cells and modules, use of affordable and superior technology in manufacture of PV panels resulting in improved solar PV efficiencies, technology development in the production of large scale manufacturing of PV modules, deployable at any location with suitable placement of PV arrays, GWp

GLOBAL EVALUATION OF PV INSTALLATIONS

600

500

400 300

200

100

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 11.1  Global evaluation of PV installations [1].

Power Electronics: Technology for Grid-Connected PV Systems  383 improvement of semiconductor technology, and associate inverter or converter topologies [2]. Thus, the investment in the SPV segment is a safe and proven option over other sources of renewable energy. Solar Photovoltaic Power Generation Systems (SPVPGS) figure among the most promising solution to meet world energy requirements. As per the renewable energy status report [3], the total capacity of 583.5 GW solar power-generating units had been installed by the end of 2019. The SPVPGS are classified into two groups based on the output connection, such as standalone and grid-tied. In the standalone SPVPGS, lithium-ion and lead-acid batteries are generally used as storage devices, which reduce the reliability of PV systems, increase the overall operation and maintenance cost and also require additional controllers for charging and discharging. In grid-tied SPVPGS, batteries are absent, because of which, the entire SPV power is fed to the grid for transmission. Moreover, the grid-tied SPV systems are cost-effective and low maintenance than standalone SPV systems. Therefore, grid-tied SPVPGS command more than 95% of the total share in the SPV market vis-à-vis standalone systems [4–6]. Nevertheless, existing PV technologies, such as polycrystalline or monocrystalline semiconductor-based PV modules can only achieve a maximum efficiency of 15% and 18%, respectively. Therefore, transferring power effectively for various applications in agricultural, residential and grid-­ connected systems becomes challenging due to changes in solar irradiation, changes in operating temperatures, partial shading and grounding issues. A lot of investigation has been going on in terms of PV material design, power electronic interfacing circuits and control schemes. Finding solutions to achieve minimum device counts, minimum leakage current and reduced control complexity will enhance the overall reliability and efficiency of the SPVPGS. In this context, various grid-tied SPVPGS have been developed to convert the direct current (DC) generated from the PV source to alternating current (AC) as per the standards and specifications of the grid. However, the need for lower leakage current, reduced device count, and better quality of output waveforms have inspired further research on this arena. Different technologies employed for achieving SPVPGS include PV inverter topologies, leakage current issue in nonisolated inverters, and standards of gridtied PV inverters and these figures in the forthcoming sections.

11.2 Grid-Tied SPVPGS Technology In general, the grid-tied SPVPGS are classified into four important categories named as a (i) module, (ii) string, (iii) multistring, and (iv)

384  Power Electronics for Green Energy Conversion DC

Multiple PV Strings

DC Bus

PV

Cell

PV PV String Cell

DC

Multiple PV Strings

PV

Cell

PV

PV

DC

Cell

PV

Cell

Cell

PV

DC

Cell

AC

(a)

PV

Cell

PV

Cell

PV

Cell

DC

PV

Cell

PV

Cell

PV

Cell

Cell

DC

DC module converter

AC 1-Ø or 3-Ø

AC bus power < 0.5 KW rating

DC

PV

DC

AC

1-Ø

DC

< 1 KW

(b)

DC AC

1-Ø or 3-Ø 1-10 KW

(c)

DC AC

Central inverter

AC module inverter

DC

Cell

String inverter

DC

DC

PV

Cell

PV

Cell

DC

PV

Cell

PV

Cell

DC

DC PV

PV

Cell

Multi-String inverter

PV

Cell

(d)

AC

3-Ø

1-Ø or 3-Ø 10-100 KW

DC

100-850 KW

(e)

Figure 11.2  Grid-tied PVPGS technologies [8]: (a) AC module, (b) DC optimizer, (c) string inverter, (d) multistring inverter, and (e) central inverter.

central inverters based on the maximum power point tracking (MPPT) and power-generating capacity as shown in Figure 11.2 and the same is compared in Table 11.1. The detailed description of all the SPVPGS are as follows [7, 8].

11.2.1

Module Inverters

Module inverters have been developed to mitigate problems like partial shading, voltage mismatch, and uneven aging. Generally, they are arranged at the backside of each PV panel due to their tiny size and compact design and are known as microinverter. The microinverters provide the highest flexibility for the extension of PV power generation through the simplest modular structure. Further, these inverters are subdivided into two categories, such as AC module and DC-optimizer, as shown in Figures 11.2(a) and (b) respectively. In the AC module application, a single PV panel is integrated with the grid through high voltage gain inverter or with an additional DC-DC converter as shown in Figure 11.2(a). Conversely, in the DC optimizers, each PV module is built with an independent DC-DC converter to boost the low PV voltage into higher voltage and to enable the module-level MPPT. Further, the output from all DC-DC converters forms a high-voltage DC bus that is connected to a

Power Electronics: Technology for Grid-Connected PV Systems  385 Table 11.1  Overview of grid-tied PVPGS. Inverter type

Module

String

Multistring

Central

Power range

< 1 KW

< 10 KW

< 500 KW

< 850 KW

Devices

Mosfet

Mosfet, IGBT

Mosfet, IGBT

IGBT

MPPT Type

Module

Large string

Small string

Array

Converter efficiency

Lowest (up to 96.5%)

High (up to 97.8%)

High (up to 98%)

Highest (up to 98.6%)

Positive features

* Flexible/Modular * Easy installation * Plug-play type * Highest MPPT efficiency

* Reduced DC wiring * Transformerless (very common) * Good MPPT efficiency

* Flexible/ Modular * Low cost for multiple string systems * High MPPT efficiency

* Simple structure * Highest converter efficiency * Reliable

Negative features

* High cost per watt * Two-stage system is mandatory * High losses

* High component count * One string, one inverter

* Two-stage is mandatory

* Needs blocking diodes * Poor MPPT performance * Not flexible

Examples

Siemens SMIINV215R60 and Power One aurora MICRO-0.3-I

Danfoss DLX 4.6 and ABB PVS 300

SMA SB5000TL and SATCON Solstic

SMA MV Power Platform and 1.6 Siemens SINVERT PVS630

central grid-tied inverter. In all the microinverters, the number of power converters in a fully distributed system increases due to their per module architecture. As there is a possibility of module-level MPPT, low PV system installation effort, easy monitoring, and failure detection, module inverters are useful for development at complex roof structures, small systems, or a combination of different roof orientations.

11.2.2

String Inverters

The string inverter is depicted in Figure 11.2(c), where a single string of PV modules is connected to the inverter for interfacing the PV power into the grid. It can be categorized into single-stage or two-stage depending on the introduction of boost stage like DC-DC converter. String inverter

386  Power Electronics for Green Energy Conversion registers less MPPT efficiency in comparison with the AC module inverter under partial shading conditions. Conversely, for the SPVPGS, these configurations have more efficiency and lower cost for the same power rating. The string inverters have been preferred for small as well as medium scale rooftop PV applications.

11.2.3

Multistring Inverters

To improve the flexibility and MPPT performance of the string inverter, the multistring inverter has been developed and is shown in Figure 11.2(d). In this system, the strings are divided into smaller groups and connected through an independent DC-DC boost stage, and all such groups are then fed to a single central inverter. The additional DC-DC boost stage is used to attain better MPPT performance and to boost the output PV voltage to attain high DC-link voltage. Thus, the overall energy extraction from the PV source is improved and the control complexity is reduced in comparison with the string inverters. The multistring inverters are suitable for both medium and large-scale PV power plants due to the reduced effect of partial shading and voltage mismatching.

11.2.4 Central Inverters Figure 11.2(e) illustrates the central inverter, where a single inverter is interfaced with a whole PV array to the grid. The PV array is composed of series-connected PV modules (called a string) to produce the desired higher voltage. Series and parallel combination arrangements are available to increase the current capacity. A blocking diode is essential in each string to block the reverse flow of current under partial shading or when voltage mismatch occurs. Also, only a global MPPT is possible in this configuration because the whole PV array is cascaded with a single central inverter, which leads to the lowest MPPT efficiency of all SPVPGS. However, these configurations are most popular for large scale SPVPGS with the inherent features of simple structure, reliable, and efficient converter.

11.3 Classification of PV Inverter Configurations From the above discussion, it is observed that either a single central inverter (i.e., single-stage) or an inverter with a front-end DC-DC converter (i.e., two-stage) is commonly used in the grid-tied PVPGS. Generally, an additional DC-DC converter is used before the inverter for extracting maximum

Power Electronics: Technology for Grid-Connected PV Systems  387 power from the PV source and for boosting the PV voltage from a low value to a higher value. The number of PV modules that are connected in series is fewer in a two-stage inverter in comparison with the single-stage inverter to overcome the effects due to partial shading and module mismatch. On the other hand, the two-stage inverter requires more number of passive components and power electronic devices for boosting and inversion operations, which results in increased component count and reduced efficiency. Since both of the single-stage and two-stage inverters have their own merits and demerits, and therefore they are optimally selected based on the power-generating capacity. In both cases, galvanic isolation from the leakage current is a major challenge to tackle in terms of safety and reliability issues of grid-tied SPVPGS. To address this issue, PV inverters are further subclassified into four groups based on the placement of transformer position and they are explained as follows [9–12].

11.3.1 Single-Stage Isolated Inverter Configuration This topology comprises a DC-AC inverter and a line frequency transformer (LFT) as shown in Figure 11.3. The LFT step-up converters low voltage to medium voltage and also provides the electrical isolation from the PV source to the grid. It is the earliest and most popular topology used in almost all central inverter-based SPVPGS. However, the use of bulky LFT leads to low power density, low efficiency, higher power losses, and increased cost of the overall system.

11.3.2

Single-Stage Nonisolated Inverter Configuration

The single-stage nonisolated inverter configuration is commonly known as transformerless inverter (TLI) configuration. To overcome the drawbacks associated with bulky transformer, it is eliminated from the power circuit

DC-AC Inverter PV Cell

Filter

LFT

Cdc

PV Source

Figure 11.3  Single-stage isolated inverter configuration [8].

Grid

388  Power Electronics for Green Energy Conversion DC-AC Inverter

Filter

PV Cdc Cell

PV Source

Grid

Figure 11.4  Single-stage nonisolated inverter configuration [8].

as shown in Figure 11.4. On the other hand, it is directly fed to the grid due to the absence of transformer. Consequently, leakage current flows from the grid to PV module due to the production of CMV fluctuations in the inverter and from the existence of PV parasitic capacitances. This results in an increase in electromagnetic interference (EMI), total harmonic distortion (THD), and also reducing reliability and operational safety. To overcome these drawbacks, several types of transformerless inverter topologies and different pulse width modulation (PWM) schemes have been proposed in the literature. Thus, a negligible amount of leakage current is attained without the use of a transformer in the power circuit while also achieving the highest power density and efficiency. Therefore, these inverter topologies have drawn plenty of attention from researchers, industries, and policymakers for designing and developing grid-tied string and multistring SPVPGS.

11.3.3 Two-Stage Isolated Inverter Configuration In this category, an LFT or a HFT is placed between the PV panel and the utility grid for providing isolation from the leakage current as shown in Figure 11.5. Generally, an LFT is placed at the grid side while HFT is placed on the DC side as shown in Figures 11.5(a) and (b) respectively. The use of a transformer in either way for realizing the galvanic isolation has a great impact on power conversion efficiency and power density of the grid-tied PV system. Moreover, the cost, size, and weight of the transformer will increase in proportion to the power rating of the system. In comparison with the LFT-based isolation, HFT-based isolation offers reduced size, weight, and cost of the overall power conversion system. However, the incorporation of HFT in the DC-DC converter stage increases the number of power

Power Electronics: Technology for Grid-Connected PV Systems  389 DC-AC Inverter

DC-DC Converter

Filter

LFT

Cdc

PV Cell

PV Source

Grid

DC-link

(a) Isolated DC-DC Converter

DC-AC Inverter

HFT

Filter

Cdc

PV Cell C PV PV Source

DC-link

Grid

(b)

Figure 11.5  Two-stage isolated inverter configuration with [8]: (a) LFT and (b) HFT.

processing stages, which further results in further reduction in efficiency. Typically, the inverter topologies with HFT in the power conversion stage are used in module inverters.

11.3.4 Two-Stage Nonisolated Inverter Configuration In this topology, both LFT and HFT are eliminated to reduce the power losses, size, weight, and cost of the overall PV power conversion system as depicted in Figure 11.6. Moreover, the removal of the transformer from the power conversion system improves efficiency and power density.

DC -AC Inverter

DC -DC Converter

PV Cell

PV Source

Filter

Cdc

DC -link

Figure 11.6  Two-stage nonisolated inverter topology [8].

Grid

390  Power Electronics for Green Energy Conversion Hence, these inverter configurations are more popular for multistring and AC module applications. However, the removal of the transformer causes a flow of leakage current from the grid to PV source. It is a challenge to address the problem of leakage current and voltage amplification in the grid-tied SPVPGS, without the use of a transformer for the researchers and industrial units to obtain the benefits of high efficiency and high power density.

11.4 Analysis of Leakage Current in Nonisolated Inverter Topologies Based on the literature survey, the advantages derived by the elimination of transformer from the power conversion stage have drawn more attention of the researchers, where the PV inverter is directly connected to the grid because of the absence of transformer. In this context, a resonant circuit have been touted as a possibility with parasitic capacitances among PV panel and ground, filter inductors, and grid impedance. This results in leakage current flowing between the PV panel and grid through PV parasitic capacitances (CPV). The flow of leakage current increases EMI, THD, and power losses while reducing the reliability and operational safety. Generally, the leakage current path is created among PV modules and ground due to the existence of parasitic capacitance (CPV) between the aluminium frame and solar cell, glass and solar cell of the PV module as shown in Figure 11.7. The value of CPV is mainly dependent on the surface area of the PV cell, the distance between the PV cell and frame, atmospheric

PV module Aluminium frame Glass Encapsulant-EVA Solar cells Encapsulant-EVA Back sheet CPV

g

Figure 11.7  Parasitic capacitance of the PV module.

Junction box

Power Electronics: Technology for Grid-Connected PV Systems  391 conditions, dust, humidity, etc. Thus, the value of CPV is not constant, and it varies with respect to the environmental conditions and other factors. The typical value of CPV varies between several picofarads and few microfarads. The current flowing through the CPV is called leakage current. Moreover, the magnitude of leakage current flowing depends mainly on the value of CPV and variations in the total common-mode voltage (VTCMV) as shown in (11.1).

ileakage = CPV



dVTCMV dt

(11.1)

The analysis for finding the total voltage variations in CMV is explained as follows: the typical structure of nonisolated inverter configuration with most significant stray elements is illustrated in Figure 11.8, where P and N denote the positive and negative terminals of the input PV source respectively, while A and B are the output terminals of the inverter respectively. As per the definitions, the common-mode voltage “VCM” and differential-mode voltage “VDM” are expressed in terms of inverter terminal voltages VAN and VBN as follows:

VDM = VAN – VBN VCM =



P

IPV

VPV

(11.2)

VAN + VBN 2

Inverter

A

Cdc

(11.3)

Filter

ig

VAB

vg

B

N

Rg

CPV ileakage

g

Figure 11.8  Typical structure of nonisolated inverter configuration with most significant stray elements.

392  Power Electronics for Green Energy Conversion From the analysis given in ref [13], it is concluded that the VCM may have a clear impact on the leakage current production. The additional CMV (VCM-DM), which is transformed from the VDM, is derived as follows:



VCM − DM = VDM ∗

L 2 − L1 2 ∗ ( L 2 + L1)

(11.4)

From (11.4), the VDM with asymmetrical filter inductors (L1 ≠ L2) results in the additional VCM-DM. The total high-frequency common-mode voltage VTCMV can be evaluated thus:

 

VTCMV = VCM + VCM − DM =

VAN + VBN VAN − VBN L 2 − L1 + (11.5) 2 2 ( L 2 + L1)

If, L1=0, VTCMV is simplified by



VTCMV =

VAN + VBN VAN − VBN + = VAN 2 2

(11.6)

If, L2=0, VTCMV is simplified by



VTCMV =

VAN + VBN VAN − VBN + = VBN 2 2

(11.7)

where the inverters are asymmetrical inductor configurations (i.e., L1=0 or L2=0), an important condition to eliminate the leakage current is that the terminal voltage of the inverter (VAN or VBN) needs to be kept constant. If, L1=L2=L/2, VTCMV is simplified by



VTCMV =

VAN + VBN = VCM 2

(11.8)

In symmetrical inductor-based configuration (L1=L2), VTCMV has no variations i.e. VCM to be maintained constant to eliminate the leakage current. Usually, these high-frequency oscillations depend on the topology and their corresponding PWM scheme employed for the switching operation. In this context, several solutions have been provided in the literature

Power Electronics: Technology for Grid-Connected PV Systems  393 either by improving the structure of the topology or by the PWM scheme to maintain constant VTCMV.

11.5 Important Standards Dealing with the Grid-Connected SPVPGS The main objective of the SPV inverter is to extract maximum power from the PV module and inject sinusoidal current in the grid. To ensure better system performance and reliable operation, the interfacing inverter should fulfil the standards of PV systems and the grid. The standards are normally imposed by different international regulation authorities, such as the Institute of Electrical and Electronics Engineers (IEEE), the International Electro-technical Commission (IEC), and the Association for Electrical, Electronic, and Information Technologies (VDE). In the case of grid-­ connected operation of SPV inverters, these standards deal with some important aspects, such as DC current and leakage current injection limits, individual harmonic distortion and total harmonic distortion (THD) levels, voltage and frequency ranges for regular operations, reactive power capability, anti-islanding and reconnection settings [14–19]. In the section that follows, the standards are further explored in detail.

11.5.1 DC Current Injection and Leakage Current As shown in Table 11.2, the IEEE 1547, IEC61727, EN61000-3-2, and VDE01260-1-1 standards place restrictions on maximum allowable DC current injection into the grid. Normally, the effect of core saturation in distribution transformer can be ignored by reducing the DC current injection. For example, as per the IEEE1547 standard the DC current injection should be less than 0.5% of rated output current, whereas VDE 01260-1-1 allows a maximum DC current of 1 A. But, reducing the DC current injection precisely below the standard is quite difficult to achieve because of the exciting circuit of the inverter. In the isolated inverter topologies, the DC current injection into the grid is limited by the transformer. This is, therefore, a serious concern in the nonisolated inverter topologies. The other most important requisite for nonisolated inverter topologies is the flow of leakage current. The parasitic capacitance formed between the PV module and ground creates a path to leakage current and it increases EMI, THD, and power losses, and also reduces the reliability and operational safety. Because of all these effects, various standards have

394  Power Electronics for Green Energy Conversion Table11.2  Summary of the standards dealing with grid-tied inverters. IEEE 1547

IEC 61727

EN61000-3-2

VDE01260-1-1

Nominal Power

30 KW

10 KW

16A*230V=3.7 KW

-

Maximum THD (%)

5.0%

5.0%

-

-

DC current injection

< 0.5% of rated output current

< 1% of rated output Current

< 0.22A

< 1A; max. trip time 0.2 sec

Power factor

-

0.90 at 50% of rated power

-

-

Voltage deviations

88%–110% (97 V–121 V)

85% - 110% (196 V - 253 V)

-

85% - 110% (196 V - 253 V)

Frequency deviations

59.3 Hz – 60.5 Hz

50 ± 1 Hz

-

47.5 Hz – 50.2 Hz

Leakage currents

-

-

-

Average current (mA)

Time (s)

30

0.3

60

0.15

100

0.04

300 (peak)

0.3

Anti-islanding detection

Detection and isolation within 2 s

Disconnect within 2 s

-

Disconnect within 5 s

been imposed on the PV inverters, which deal with the magnitude of leakage current flowing through the PV module. One of the standards among them is VDE 01260-1-1. As per this standard, the magnitude of leakage current should not exceed 300 mA (peak). Thus, different control schemes and topologies have been introduced in the nonisolated PV inverter topologies to suppress the magnitude of DC current injection and the leakage current below the grid standards.

Power Electronics: Technology for Grid-Connected PV Systems  395

11.5.2 Individual Harmonic Distortion and Total Harmonic Distortion To regulate the undesirable effects due to THD of PV inverters, the international regulations, such as IEEE 1547, IEC 61727, and IEC61000-3-2 standards, impose a limit on THD as shown in Table 11.2. According to these limits, the harmonic content in the injected current should be lower than 5%. The THD of the injected current can be minimized by designing suitable filter circuit, improving the modulation techniques applied to the inverter and also by employing multilevel inverter concepts. Some of the most significant harmonic contents specified by the IEEE 1547 standard are described in reference [14].

11.5.3 Voltage and Frequency Requirements Voltage and frequency requirements are among the essential requirements of the inverter output, which must be under the desired level for better performance. The standard EN 50106 describes the voltage characteristics of electricity in distribution system. As per the standard, it should not exceed 1 KV and 35 KV for low and medium voltage in RMS range respectively. The allowable frequency variations in both low voltage and medium voltage ranges is limited to 1%.

11.5.4

Reactive Power Capability

To allow the high penetration of PV power into the grid, the next generation of PV inverters should provide enough reactive power support similar to that provided by conventional power plants. The international standard shows the direction for handling capacity of reactive power for grid connected PV inverter. As per VDE-AR-N-4105 standard, the inverter rating below 3.68 KVA should be capable of operating in the range of 0.95 lagging to 0.95 leading. Therefore, the newly designed PV inverter should provide reactive power support with better quality of output waveforms.

11.5.5 Anti-Islanding Detection Isolation of the PV inverter from power dispatching is very much important during the grid trips. If there is no monitoring of islanding operation, hazardous situations can arise for both humans and equipment. The IEEE 1547, IEC 61727, and VDE01260-1-1 standards specifically state that there

396  Power Electronics for Green Energy Conversion should not be any islanding condition, which if present should be identified and isolated in fewer than less than 5s. From the discussions so far, it is apparent that the design of an inverter for the grid-connected SPVPGS should meet the abovementioned standards. Hence, the researchers, industries, and research and development sectors have been focussing on this area to explore different possible solutions by designing novel inverter configurations with improved PWM techniques. Some of the most popular grid-tied inverters under different configurations (as explained in section 11.2) are presented in the following section. Further, a thorough comparison has been formulated in terms of component count, number of stages, merits, demerits and applications to identify the a suitable configuration.

11.6 Various Topologies of Grid-Tied SPVPGS The evaluation of SPV inverter technology, determined by the growth in the installed power capacity, has lead to the development of various types of grid-tied inverter topologies in practice. In this section, various popular SPV inverter topologies for AC-module, string, multistring and central will be analyzed [13].

11.6.1

AC Module Topologies

AC module is the combination of a single PV module with a grid-tied inverter. To meet the high DC voltage requirement, the inverter consists of either a high step-up transformer or an embedded voltage boost circuit. Some solutions for the AC module inverter topologies are presented in Figures 11.9 to 11.13, and they are described as follows [14]: The topology illustrated in Figure 11.9 is the traditional AC module topology, which comprises the cascaded connection of a boost converter with a full-bridge (F-B) inverter. The front-end converter attains the MPPT and voltage boosting. The F-B inverter controls the output voltage converter, grid interconnection and active or reactive power control. In order to enhance the efficiency a variation of this configuration has been presented in reference [15] as shown in Figure 11.10. This configuration has a soft-switched boost converter that attains both zero voltage and zero current switching in all the switches. Thus, the efficiency of the converter is improved in comparison with the traditional boost converter. However, the abovementioned two topologies require a very low duty ratio to achieve the voltage as per the grid standards. Also, it requires an additional

Power Electronics: Technology for Grid-Connected PV Systems  397 Lb

S3

S1

C1

Sb

Cdc

Filter

S2

ig vg

S4

Figure 11.9  Two-stage nonisolated boost converter with full-bridge inverter [14].

Db

Lb

Dr1

Sr1 C1

CP Cr

Dr2

S3

S1

Lr

Sr2

ig vg

Filter

SP S2

S4

Figure 11.10  Two-stage nonisolated soft-switched boost converter with full-bridge inverter [15].

S1

S3

C2 Filter

C1

Q1

S2

S4

Figure 11.11  Two-stage isolated flyback converter with full-bridge inverter [16].

ig vg

398  Power Electronics for Green Energy Conversion L2

S1

Q3

Q1

L1

S3

C2 C3

C1

Q2

Filter

S2

Q4

ig vg

S4

Figure 11.12  Two-stage isolated H-bridge-based boost converter with full-bridge inverter [5]. L1

S1

S3

C2

C1

Cdc

Filter

S2

ig vg

S4

L2

Figure 11.13  Single-stage nonisolated impedance source-based full-bridge inverter [17].

isolation transformer or an electromagnetic interface (EMI) filter to limit the leakage current magnitude due to PV parasitic capacitance. To overcome the abovementioned drawbacks, an interleaved flyback converter is developed by Siemens and it is depicted in Figure 11.11 [16]. The front-end flyback converter performs the functions of MPPT, voltage boosting and isolation from the leakage current. The higher switching frequency operations of the flyback converter result in the reduction in the size of the transformer. Another commercially available AC module topology as developed has been by Enecsys is illustrated in Figure 11.12, which includes H-bridge stage with a high frequency isolation transformer and

Power Electronics: Technology for Grid-Connected PV Systems  399 a diode bridge rectifier. The H-bridge stage on the input DC side has better power conversion efficiency in comparison with the flyback converter. All the above-mentioned topologies are examples of two-stage AC module inverters, where both active and passive component count is increased significantly to obtain the voltage boosting. Moreover, these inverters are associated with reduced efficiency due to an increase in the number of power stages. In addition, the use of classical voltage source inverter and current source inverter demands large DC-link capacitance and large value of input inductance respectively which should be immune to shoot through problems if more than one switch conducts simultaneously on the same leg. This necessitates a single-stage power conversion with better-input voltage regulation and increased efficiency as a reliable solution for PV applications using impedance network with voltage source inverter [17] as shown in Figure 11.13. In recent years, different single-phase and three-phase Z-source inverter topologies have been proposed with lower switch count to improve the efficiency and to reduce the cost. But, the issue of leakage current should be considered in these topologies due to the absence of isolation from the grid to PV panel.

11.6.2

String Inverter Topologies

String inverter is a combination of multiple PV modules with a single gridtied inverter. All the PV modules are connected in series to meet the DC voltage requirement. Some of the classical solutions for the string inverter topologies are presented as follows: The inverter shown in Figure 11.14 is a traditional and simple LFT-based F-B topology. An LFT is placed between the inverter output and the grid to

S1

S3 L1 ig

C1

vg

S2

S4

Figure 11.14  Isolated full-bridge-based string inverter [19].

400  Power Electronics for Green Energy Conversion provide isolation from the leakage current magnitude, and also for voltage boosting as well. However, the use of a transformer has several demerits (i.e., increased size and increased power losses), making this configuration less popular in string inverter applications. The transformerless inverter topologies get rid of the LFT by distributing the filter inductor equally into the line and neutral wires of the grid [18, 19]. In olden days a full-bridge (F-B) inverter with bipolar PWM (BPWM) and unipolar PWM (UPWM) schemes with symmetrical filter inductor-based topologies were very popular [20]. In the case of BPWM, two-level voltage (+VDC, -VDC) is generated by switches S1 and S4 during positive half-cycle, S2 and S3 during negative half-cycle. Also, it generates constant VTCMV and less leakage current. However, the terminal voltage (VAB) step is twice the input DC voltage, which results in increased THD, EMI noise and switching losses. On the other hand, in the case of F-B inverter with UPWM, the VAB step is halved to reduce the EMI noise, THD and inductor size. In addition, high-frequency oscillations in VTCMV, the UPWM scheme fails to reduce the leakage current. Therefore, both the switching schemes with traditional F-B configuration fail to fulfill the grid standards. One of the critical reasons for high-frequency oscillations in VTCMV with the UPWM scheme is due to the fact that either positive or negative terminal of the DC source is connected to the freewheeling path during zero voltage state. For simplicity in representations, the PV module is modeled as an equivalent DC source throughout this subsection. To overcome the problems associated with conventional F-B with UPWM and BPWM schemes, a decoupling network is introduced to the F-B inverter using additional circuitry as shown in Figure 11.15. Based on DC Decoupling

P

S1

S3 L1

Vdc

Cdc

A B

ig

AC Decoupling

vg g

S2 N

S4

DC Decoupling

Figure 11.15  Full-bridge inverter with decoupling network.

L2

Power Electronics: Technology for Grid-Connected PV Systems  401 this concept, many topologies and patented solutions have been proposed in the literature to achieve the advantages of both BPWM and UPWM schemes (i.e., higher efficiency and the lower leakage current). In DC decoupling-based inverter topologies, an additional decoupling network is placed between the DC source and F-B module to provide isolation during the freewheeling state. One such solution is proposed and patented by SMA solar technology. Switch S5 is inserted between the positive terminal and F-B module to act as a decoupling network, which is the well-known H5 TLI as depicted in Figure 11.16(a) [21]. The operation is almost similar to the F-B inverter except for zero level, where switch S5 is turned-OFF to P S5 S3

S1

L1 Vdc

A

Cdc

ig vg

B

g S2

S4

L2

N (a) H5 Inverter [21]

P S5 S1

S3 L1

Vdc

A

Cdc

ig vg

B

g S2

S4

L2

S6 N (b) H6-I inverter [22]

Figure 11.16  DC Decoupling-based transformerless inverter topologies. (Continued)

402  Power Electronics for Green Energy Conversion P S5 S3

S1

L1 Vdc

A

Cdc

ig

S6

vg

B

g S2

L2

S4

N (c) H6-II inverter [23] P S5 S3

S1

L1 Vdc

A

Cdc

ig vg

B

g

S6 S2

S4

L2

N (d) H6-III inverter [23].

Figure 11.16 (Continued)  DC Decoupling-based transformerless inverter topologies.

provide isolation from the grid. However, switch S5 operates at a double frequency in comparison with other switches, which results in nonuniform power losses. Also, it increases the burden of heat-sink design and reduces the power density. To balance the power losses occurring due to decoupling switch, one more switch S6 is placed between the negative terminal and F-B module known as H6 TLI and it is depicted in Figure 11.16(b) [22]. However, the power losses increase due to the conduction of four switches during power transferring modes. To reduce the conduction losses, position of the S6 can be changed, as shown in Figures 11.16(c) and (d), which goes by the name of improved H6 TLI topologies [23]. The operations of all these topologies are quite

Power Electronics: Technology for Grid-Connected PV Systems  403 similar except for the freewheeling period. Moreover, the loss distribution of the decoupling switches also balanced by improving the topology structure. Conversely, the same functions can also be achieved with AC-based decoupling inverter topologies, where the decoupling network is employed between the F-B module and filter components to provide isolation during the freewheeling period. The HERIC (highly efficient and reliable inverter concept) was first formulated in 2003 [24], which is formed by inserting two active switches (S5 and S6) between the F-B module and the symmetrical filter inductors, as shown in Figure 11.17(a). Conduction of the P L1 S1

ig

S3 S5

A Vdc

vg

Cdc S6

B

g S2

S4

L2

N (a) HERIC inverter [24]

P

S1

S2 A

Vdc

Cdc

S3

L1 ig

B D1

D2

vg

S4 g L2

S5

S6

N (b) H6 inverter [25]

Figure 11.17  AC decoupling-based transformerless inverter topologies. (Continued)

404  Power Electronics for Green Energy Conversion P S3 S1

Vdc

S4

D1

L1

A

ig

Cdc

vg

B D2

S2

S5

S6

g L2

N (c) Hybrid-bridge inverter [26] P

S1

Vdc

Cdc

S3

S6

S5

L1 ig

A S2

B

vg

S4

g L2

N (d) H6-IV inverter [27]

Figure 11.17  (Continued) AC decoupling-based transformerless inverter topologies.

switches S5 and S6 depends on the grid voltage polarity during the freewheeling period. If grid voltage polarity is positive, the switch S5 and the body diode of S6 will conduct, else the switch S6 and body diode of S5 will conduct to decouple the source from the grid. Conduction losses of the HERIC inverter are lower in comparison with the DC decoupled topologies because only two devices are in conduction at any operating state. Based on a similar principle, some of the other AC decoupled-based topologies are derived as illustrated in Figures 11.17(b) and (c). An H6 TLI topology is proposed in [25], where AC side decoupling is achieved by conducting switch S4 and diode D1 or switch S3 and D2 during the freewheeling period based on the grid voltage polarity. Further, a hybrid-bridge [26]

Power Electronics: Technology for Grid-Connected PV Systems  405 (H-B) TLI is derived based on H6 TLI. The placement of bi-­directional switch is the only difference between the H6 and H-B topologies. Hence, the PWM schemes, operating modes, power losses and leakage current analysis are quite similar. Another AC decoupled TLI topology is proposed in [27], by changing the position of a bidirectional switches as shown in Figure 11.17(d). But, the switching and conduction losses of all three derived topologies are higher compared with HERIC inverter due to more number of switches being in conduction during the power transferring modes. In comparison with the full-bridge inverter topology, the AC decoupling-based topologies produces lower leakage current due to the absence of high-frequency oscillations in VTCMV (similar to the DC decoupling).

11.6.3 Multistring Inverter Topologies As explained above, the multistring inverter comprises several strings and each string has a dedicated front-end boost converter. The front-end boost converter serves the functions of voltage boosting and MPP tracking. Later, all the converters are connected in parallel to form a high voltage DC bus for providing input to the inverter. Thus, two-stage power conversion system is essential in multistring PV systems. Some of the popular single-phase and three-phase multistring inverter topologies are illustrated in Figures 11.18 to 11.20 [5, 28]. String inverter topologies may also be used in multistring

VC2

S1

Filter

ig vg

N VC1

S2

Figure 11.18  Topology for multistring PV applications with two-level inverter and traditional boost converter [5].

406  Power Electronics for Green Energy Conversion inverter configurations. Similar to the string inverters, the same structures of the transformer and transformerless topologies are applied for asymmetrical and symmetrical filter inductors. SMA introduced a multistring PV inverter with a conventional boost converter and a half-bridge inverter for single-phase PV applications, which figures in Figure 11.18. The halfbridge inverter is not quite popular due to its increased filter size and THD. One other topology is developed by the integration of conventional boost

S3 VC2

D1

S4 Filter

D2

ig vg

S5

VC1 S6

Figure 11.19  Topology for multistring PV applications with 3L-NPC inverter and traditional boost converter [5].

L2 Q1

Q3

S3

C3

C1 Q2

S1

Q4

S2

Filter

ig vg

S4

Figure 11.20  Isolated switched-mode boost converter with F-B inverter for multistring PV applications [28].

Power Electronics: Technology for Grid-Connected PV Systems  407 converter with 3L-NPC inverter and it is shown in Figure 11.19. Further, to improve the voltage gain and to provide the isolation from the leakage currents an HFT-based two-stage multistring inverter is presented in [29] and it is illustrated in Figure 11.20. Unfortunately, the total number of power processing stages increases, leading to lower power density and higher power losses.

11.6.4 Central Inverter Topologies Central PV inverters are primarily used to interface large PV plants to the grid. Most of the topologies are with the LFT based to step-up the voltage and to isolate the PV strings with the grid. The most practical central inverter is the two-level voltage source inverter with three halfbridge legs connected to a single DC-link as shown in Figure 11.21. Other recent approaches in central inverter configurations are three-level NPC inverters and T-type inverter as shown in Figures 11.22 and 11.23 respectively. The inverter structure is simple and efficient. However, the blocking diodes (which are connected in series, to bypass the current under fault condition), are affected by partial shading and voltage mismatches between each string, thereby resulting in reduction overall efficiency of the PV plant [2, 5, 29].

VC2

S1

Filter N VC1

S2

Figure 11.21  Two-level half-bridge-based central inverter [5].

N

RY B

408  Power Electronics for Green Energy Conversion

S3 VC2

D1

S4

N

Filter D2

N

S5

RY B

VC1 S6

Figure 11.22  Three-level NPC-based central inverter [5].

S1

VC2 S4

N

Filter

N

S3

RY B

S2 VC1

Figure 11.23  Three-level T-type-based central inverter [5].

A detailed comparison of different grid-tied inverter topologies for various applications is made in Table 11.3. From the comparison, it is observed that either a single central inverter (i.e., single-stage) or an inverter with front-end DC-DC converter (i.e., two-stage) is used in grid-connected PVPGS. Generally, an additional DC-DC converter is used prior to the inverter for extracting maximum power from the PV source and for boosting the low PV voltage to higher DC voltage. The number of PV modules connected in series is fewer in two-stage inverter in comparison with the single-stage inverter to overcome the drawbacks arising from partial

M

5

7

5

9

Topology

Figure 11.9 [14]

Figure 11.10 [15]

Figure 11.11 [16]

Figure 11.12 [5]

6

1

3

1

D

Device count

Two-stage

Two-stage

Two-stage

Two-stage

No. of stages

HFT

HFT

Absent

Absent

Transformer

Table 11.3  Comparison of the various grid-tied inverter topologies.

+ High gain + Negligible leakage currents

+ High gain + Negligible leakage currents

+ Reduced converter losses

+ Simple + Traditional + Robust

Merits

- More power conversion stages - Higher component count

- More power conversion stages - Higher power losses

- Limited gain - Leakage current

- Limited gain - Lower efficiency - Leakage currents

Demerits

(Continued)

Single-phase AC module

Single-phase AC module

Single-phase AC module

Single-phase AC module

Application

Power Electronics: Technology for Grid-Connected PV Systems  409

M

4

4

5

Topology

Figure 11.13 [17]

Figure 11.14 [19]

Figure 11.16. (a) [21]

0

0

0

D

Device count

Single-stage

Single-stage

Single-stage

No. of stages

Absent

LFT

Absent

Transformer

+ Lower component count

+ Simple + Traditional

+ High gain + Reduced power conversion stages + Reduced component count

Merits

Table 11.3  Comparison of the various grid-tied inverter topologies. (Continued)

- Un balanced copper losses-

- Lower power density - Higher power losses - Leakage currents

- Leakage currents - Increased control complexity

Demerits

(Continued)

Single-phase String

Single-phase String

Single-phase AC module

Application

410  Power Electronics for Green Energy Conversion

M

6

6

6

6

6

Topology

Fig. 11.16. (b) [22]

Fig. 11.16. (c) [23]

Fig. 11.16. (d) [23]

Fig. 11.17. (a) [24]

Fig. 11.17. (b) [25]

2

0

0

0

0

D

Device count

Single-stage

Single-stage

Single-stage

Single-stage

Single-stage

No. of stages

Absent

Absent

Absent

Absent

Absent

Transformer

+ Efficient

+ Highly efficient

+ Balanced copper losses + Reduced power losses than H5

+ Balanced copper losses + Reduced power losses than H5

+ Balanced copper losses

Merits

Table 11.3  Comparison of the various grid-tied inverter topologies. (Continued)

- More component count than Heric

- One switch extra than H5 inverter

-

-

- Comparatively H5 power losses are more-

Demerits

(Continued)

Single-phase String

Single-phase String

Single-phase String

Single-phase String

Single-phase String

Application

Power Electronics: Technology for Grid-Connected PV Systems  411

M

6

6

3

13

Topology

Figure 11.17. (c) [26]

Figure 11.17. (d) [27]

Figure 11.18 [5]

Figure 11.19 [5]

6

0

0

2

D

Device count

Two-stage

Two-stage

Single-stage

Single-stage

No. of stages

Absent

Absent

Absent

Absent

Transformer

+ Negligible leakage currents + Simple + Robust

+ Simple + Robust

+ Efficient

+ Efficient

Merits

Table 11.3  Comparison of the various grid-tied inverter topologies. (Continued)

- Higher DC-link voltage requirement - Balancing issues of DC-link capacitors

- Higher harmonics - Leakage currents

- More component count than Heric

- More component count than Heric

Demerits

(Continued)

Three-phase Multistring

Single-phase Multistring

Single-phase String

Single-phase String

Application

412  Power Electronics for Green Energy Conversion

M

8

6

12

Topology

Figure 11.20 [28]

Figure 11.21 [5]

Figure 11.22 [5]

6

0

4

D

Device count

Single-stage

Single-stage

Two-stage

No. of stages

LFT

LFT

HFT

Transformer

+ Simple + Negligible leakage current

+ Simple + Negligible leakage currents

+ Negligible leakage currents + High gain

Merits

Table 11.3  Comparison of the various grid-tied inverter topologies. (Continued)

- Balancing issues of DC-link capacitors - Higher power losses due to LFT

- Higher harmonics - Higher voltage stress - Higher power losses due to LFT

- More number of power conversion stages - Higher power losses

Demerits

(Continued)

Three-phase Central

Three-phase Central

Single-phase Multistring

Application

Power Electronics: Technology for Grid-Connected PV Systems  413

12

Figure 11.23 [5]

0

D

Single-stage

No. of stages LFT

Transformer + Balanced DC-link voltages + Negligible leakage currents

Merits -

Demerits

Three-phase Central

Application

* M, Mosfet. **D, diode. *** HFT, high-frequency transformer. **** LFT, line frequency transformer. *****CMV, common mode voltage. A single multistring is considered for the evaluating the component count.

M

Topology

Device count

Table 11.3  Comparison of the various grid-tied inverter topologies. (Continued)

414  Power Electronics for Green Energy Conversion

Power Electronics: Technology for Grid-Connected PV Systems  415 shading and module mismatch. Whereas the two-stage inverter requires several passive components and power electronic devices for boosting and inversion operations, which results in increased component count and reduced efficiency. Since both of the single-stage and two-stage inverters have their own merits and demerits, they need to be optimally chosen on the basis of power-generating capacity. In both cases, galvanic isolation from the leakage current is a major challenge to solve in terms of safety and reliability issues of grid-connected PVPGS.

11.7 Scope for Future Research The PV industry has recorded exceptional growth in the last 10 years, also becoming a significant alternative to meet the future electricity demand. With continuous fall in the prices of equipment and technology and increase in the efficiency of the PV panels, the power electronic interface configurations need to be upgraded with more standards and requirements. This is especially in respect of development of PV-based microgrid applications, where application of such high standards would be helpful. To meet these standards, the converter topologies should be designed with lower losses, lower harmonics, high power density, reliability, and installation cost. In recent times, multilevel inverters have become popular for gridtied SPVPGS because of their significant advantages, such as high quality of output power, low THD, reduced voltage stress of the switches, and reduced filter size with increased modularity. It offers many benefits in nonisolated SPV inverter topologies such as reduced leakage current at various ranges of kilowatt at low voltage for residential applications. On the other hand, newly developed wide-band gap power semiconductor devices, such as Sic and GaN, offer lower power losses, high-power density, and high reliability in comparison with the conventional Si-based SPV inverters. Thus, the trend is moving toward the development of SiCbased SPV inverters [30, 31].

11.8 Conclusions Grid-tied inverters are promising candidates for the successful interface of renewable energy sources in the distributed power generation systems. This chapter provided an overview of the power electronic technology used in grid-tied SPV applications. Various configurations employed for

416  Power Electronics for Green Energy Conversion the grid-tied SPVPGS, such as AC module, string, multistring, and central inverters, have been described in detail. Moreover, various state-of-the art power electronic circuits were classified based on the number of power processing stages, component count, merits, demerits and applications. Finally, some of the most popular and practically used grid-tied inverters have been described to give an informed choice to users for the selection of inverter for SPV applications.

References 1. Waldau, A.J., Snapshot of Phtotvoltaics-Febraury 2019. Energies, 12, 5, 769– 776, Feb 2019. 2. Romero-cadaval, E., Spagnuolo, G., Franquelo, L.G., Grid-Connected Photovoltaic Generation Plants. IEEE Trans. Ind. Electron. Mag., 7, 6–20, Sep. 2013. 3. REN21, Renewables 2019: Global status report, REN21 Secretariat, MDPI, Paris, Jul. 2019. 4. Islam, M., Mekhilef, S., Hasan, M., Single phase transformerless inverter topologies for grid-tied photovoltaic system: A review. Renewable Sustainable Energy Rev., 1, 45, 69–86, May 2015. 5. Kouro, S., Leon, K.I., Vinnikov, D., Franquelo, L.G., Grid-connected photovoltaic systems: An overview of recent research and emerging PV converter technology. IEEE Ind. Electron. Mag., 9, 1, 47–61, Mar. 2015. 6. Freddy, T.K., Rahim, N.A., Hew, W.P., Che, H.S., Comparison and analysis of single-phase transformerless grid-connected PV inverters. IEEE Trans. Power Electron., 29, 10, 5358–69, Dec. 2013. 7. Jana, J., Saha, H., Bhattacharya, K.D., A review of inverter topologies for single-phase grid-connected photovoltaic systems. Renewable Sustainable Energy Rev., 1, 72, 1256–70, May 2017. 8. Kjaer, S.B., Pedersen, J.K., Blaabjerg, F., A review of single-phase grid-­ connected inverters for photovoltaic modules. IEEE Trans. Ind. Appl., 41, 5, 1292–1306, Oct. 2005. 9. Kamran, Z., Khan, I., Uddin, W., Sathish Kumar, P., Busarello, T.D.C., Ahmad, I., Kim, H.J., A review on recent advances and future trends of transformerless inverter structures for single-phase grid-connected photovoltaic systems. Energies, 11, 8, 1–33, Aug. 2018. 10. Barater, D., Lorenzani, E., Concari, C., Franceschini, G., Buticchi, G., Recent advances in single-phase transformerless photovoltaic inverters. IET Renewable Power Gener., 10, 2, 260–273, Feb. 2016. 11. Yang, Y., Prasad, E., Blaabjerg, F., Wang, H., Wide-scale adoption of photovoltaic energy: Grid code modifications are explored in the distribution grid. IEEE Ind. Appl. Mag., 21, 5, 21–31, Jun. 2015.

Power Electronics: Technology for Grid-Connected PV Systems  417 12. Chen, W., Yang, X., Zhang, W., Song, X., Leakage current calculation for PV inverter system based on a parasitic capacitor model. IEEE Trans. Power Electron., 31, 12, 8205–8217, Jan. 2016. 13. Sonti, V., Jain, S., Agarwal, V., Bhattacharya, S., Terminal voltage analysis for the transformerless PV inverter topologies in a single-phase system. IET Renewable Power Gener., 13, 15, 2723–39, Aug. 2019. 14. Meneses, D., Blaabjerg, F., Garcia, O., Cobos, J.A., Review and comparison of step-up transformerless topologies for photovoltaic AC-module application. IEEE Trans. Power Electron., 28, 6, 2649–2663, Nov. 2012. 15. Kim, Y.H., Ji, J.G., Won, C.Y., Jung, Y.C., Photovoltaic parallel resonant dc-link soft switching inverter using hysteresis current control, in: 25th Annual IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2275–2280, Feb. 2010. 16. Li, Q. and Wolfs, P., A review of the single phase photovoltaic module integrated converter topologies with three different DC link configurations. IEEE Trans. Power Electron., 23, 3, 1320–1333, May. 2008. 17. Rahul, J.R., Das, C.K., Kirubakaran, A., Somasekhar, V.T., Impedance Source-based Multilevel Inverter: A State-of-the-Art Review. J. Circuits Syst. Comput., 29, 2030011-1 to 2030011-31, Mar. 2020. 18. Özkan, Z. and Hava, A.M., Classification of Grid Connected Transformerless PV Inverters with a Focus on the Leakage Current Characteristics and Extension of Topology Families. J. Power Electron., 15, 1, 256–267, Jan. 2015. 19. Li, W., Gu, Y., Luo, H., Cui, W., He, X., Xia, C., Topology review and derivation methodology of single-phase transformerless photovoltaic inverters for leakage current suppression. IEEE Trans. Ind. Electron., 62, 7, 4537–4551, Jul. 2015. 20. Sonti, V., Jain, S., Agarwal, V., Bhattacharya, S., Terminal voltage analysis for the transformerless PV inverter topologies in a single-phase system. IET Renewable Power Gener., 13, 15, 2723–39, Aug. 2019. 21. Khan, M.N., Forouzesh, M., Siwakoti, Y.P., Li, L., Kerekes, T., Blaabjerg, F., Transformerless Inverter Topologies for Single-Phase Photovoltaic Systems: A Comparative Review. IEEE J. Emerging Sel. Top. Power Electron., 8, 1, 805– 835, Apr. 2019. 22. Victor, M., Greizer, F., Bremicker, S., Hübler, U., Inventors, SMA Solar Technology AG, assignee, 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. United States patent US 7, 411, 802, 2008, Aug. 2008. 23. Yang B, B., Li, W., Gu, Y., Cui, W., He, X., Improved transformerless inverter with common-mode leakage current elimination for a photovoltaic grid-­ connected power system. IEEE Trans. Power Electron., 27, 2, 752–62, Jun 2011. 24. Schmidt, H., Christoph, S., Ketterer, J., Current inverter for direct/alternating currents, has direct and alternating connections with an intermediate power

418  Power Electronics for Green Energy Conversion store, a bridge circuit, rectifier diodes and a inductive choke. German Patent DE10. 2003, 221(592):A1, Dec. 2003. 25. Ji, B., Wang, J., Zhao, J., High-efficiency single-phase transformerless PV H6 inverter with hybrid modulation method. IEEE Trans. Ind. Electron., 60, 5, 2104–2115, May. 2013. 26. Cui, W., Luo, H., Gu, Y., Li, W., Yang, B., He, X., Hybrid-bridge transformerless photovoltaic grid-connected inverter. IET Power Electron., 8, 3, 439–446, May 2014. 27. P. Knaup, International Patent Application. Pub No. WO 2007/048420 A1 3, May 2007. 28. Meinhardt, M. and Cramer, G., Multi-string-converter: The next step in evolution of string-converter technology, in: Proc. 9th Eur. Power Electronics and Applications Conf, Aug. 2001. 29. Nema, S., Savitha, R.K., Agnihotri, G., Inverter topologies and control structure in photovoltaic applications: A review. J. Renewable Sustainable Energy, 3, 1, 012701–724, Jan. 2011. 30. Ando, Y., Oku, T., Yasuda, M., Shirahata, Y., Ushijima, K., Murozono, M., Comparative study of SiC-and Si-based photovoltaic inverters, in: AIP Conference Proceedings, Jan. 2017, vol. 1807, No. 1, p. 020020. 31. Huusari, J. and Casini, P., Evolving solutions: Technology trends and design targets for next-generation photovoltaic inverters, in: ABB review, pp. 1–15.

12 Hybrid Solar-Wind System Modeling and Control Issam Attoui1*, Naceredine Labed2, Salim Makhloufi2, Mohammed Salah Bouakkaz3, Ahmed Bouraiou4, Nadir Boutasseta1, Nadir Fergani1 and Brahim Oudjani1 *

Research Center in Industrial Technologies CRTI P.O. Box 64, Cheraga, Algeria 2 University of Adrar, Adrar, Algeria 3 Department of Electrical Engineering, Laboratoire d’électrotechnique de Skikda “LES”, Université du 20 Août 1955, Skikda, Algeria 4 Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), Adrar, Algeria

1

Abstract

In the world, the enormous usage of fossil fuels, e.g., coal, oil, and gas, is the source of both air pollutants and greenhouse gases, thus provoking serious problems for air quality, public health, and climate. Renewable energy sources can provide a good solution to overcoming these problems. However, the renewable energies are one of the energy sources that their energy production are unpredictable in changing climate conditions, which provoke a real problem of limited rate of penetration when this energy is injected in the electrical network. Nevertheless, the penetration of renewable energy in the grid can be enhanced by the improvement of the energy management and control techniques. This chapter presents modeling, simulation and control of grid-connected hybrid solar–wind system with two level energy storage under different climatic conditions. The system proposed in this paper includes wind turbine system equipped by a Doubly Fed Induction Generator DFIG, photovoltaic (PV) system, hybrid supercapacitors-battery energy storage system and controlled power electronics converters. The hybrid system is *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (419–452) © 2022 Scrivener Publishing LLC

419

420  Power Electronics for Green Energy Conversion connected to the grid using a three-level inverter with hybrid supercapacitorsbatteries energy storage. In order to maximize the power of PV system, the Particle Swarm Optimization PSO algorithm that generates the optimal current of the PV system is applied. The results of simulation are demonstrated using MatLabSimulink through different climatic conditions. Keywords:  PV system, variable speed wind turbine, two-level energy storage system, GMPPT control, hybrid system

12.1 Introduction The photovoltaic and wind turbines are two intermittent sources of energy and are considered as the two main types of renewable energy that are connected to the grid [1, 2]. The hybridization between the photovoltaic energy, the wind energy and the energy storage can provide an effective solution for stabilizing and managing the power of the grid when necessary. The optimization problem of these systems should be resolved to determine the necessary number and size of PV and WT. This problem can be resolved using solar radiation and wind speed data that are measured for the long term [3]. However, these data are not always available. For this reason, many artificial intelligence procedures, like fuzzy logic and genetic algorithms, are applied [4]. In addition, in the literature, other optimization performance indicators, such as cost of energy [5], net present value [6] and energy expected not supplied [7], are proposed and applied. The addition of energy storage systems can also increase the production of the electrical energy under wind speed, solar irradiation, temperature and grid demand variations using controlled DC/DC converters. The wind energy conversion systems have the great potential compared to other renewable-energy sources [8–12]. The major technological interests for this system reside on maintenance procedures optimization, the improvement of the production capacity and control stability [13]. As the wind turbine systems are generally equipped with the DFIG for variablespeed operation [14, 15], the wind turbine system used in this chapter contains a wind turbine based on a DFIG. This machine is connected to the grid via its stator without the use of any converter, while the rotor is connected to the grid through 2 three-level, neutral point clamped NPC converters and filter. The grid-side converter is controlled in order to regulate the electrical power generated by the proposed hybrid energy system to the grid. While the rotor-side converter is controlled in order to regulate the stator reactive power of the DFIG exchanged with the grid

Hybrid Solar-Wind System Modeling and Control  421 and also the DFIG speed in order to produce the maximum power from the proposed system. The PV system can convert the absorbed photon energy into electrical energy by exhibiting the photovoltaic effect by means of semiconducting materials. This energy attracts important attention as one of the most used renewable energy sources for its availability and other numerous advantages. However, the generated power output from PV system needs to be controlled using a nonlinear control mechanism to track its maximum power point. This can be achieved by the use of a power electronics converter, such as boost converter that is capable of implementing high-speed and high-accuracy control strategies. The objective of the control procedure is to ensure the maximum energy production of the PV system and transfer this energy effectively to the grid, particularly in partial shading (PS) condition. Under such condition, the P-V characteristic of the PV systems has multiple local maximum power points. This characteristic depends also on the temperature and irradiance that will make it highly nonlinear and consequently the purpose to guarantee an optimal performance of the PV system when rapid weather conditions occur will be more difficult. In this chapter, the PV system is controlled using the Particle Swarm Optimization PSO algorithm-based maximum power point tracking (MPPT) that efficiently improves the energy conversion under uniform irradiation and partial shading conditions by searching the global maximum power point (GMPP). Lead-acid battery is appropriate for storing energy and longtime of use and is characterized by a high-energy density. But this battery has a slow speed of charging and discharging and a limited lifespan. On the other hand, supercapacitors are characterized by a long lifetime and higher power density, but a lower energy density. Based on these characteristics, a hybrid energy storage system based on lead-acid batteries and supercapacitors is proposed. In this system, the voltage of the DC bus is controlled at the same time using the Lead-acid batteries and the supercapacitors simultaneously. The contribution of each storage system depends on its characteristic. To increase the lifespan of the battery, the batteries’ current slopes are limited to decrease transient stresses at peak power. In this case, the supercapacitors ensure the peak power response. The storage system size depends on the maximum power mismatch between the available powers of the solar and wind energy systems and the required grid power. The two-level energy storage system is connected to the DC link of the DFIG converters using a two-quadrant DC/DC converter. The control strategy of the two-level energy storage system is applied to control the DC-link voltage with bi-directional DC-DC converters. In the literature, a lot of

422  Power Electronics for Green Energy Conversion research efforts have been done to the hybrid PV-wind energies and many researchers have presented several challenges and their possible solutions [1, 16–74]. The main challenges for solar PV and wind systems with possible solutions or mitigation are summarized in Table 12.1. This chapter is organized as follows: the different parts of the hybrid system are described in section 12.2. The wind turbine model, DFIG model, the PV system model and the two-level energy storage system are described in section 12.3. The proposed control strategies of the grid-connected hybrid solar– wind system with two-level energy storage are presented in section 12.4.

Table 12.1  Main challenges/possible solutions or mitigations for hybrid solarwind systems. No.

Challenges

Solutions

References

01

Fluctuations of the frequency and voltage due to rapid variations in the load active power, the wind speed and the solar radiation.

Regulation of the voltage and frequency of the system.

[1, 16, 17, 27, 38, 49, 60, 71–74]

02

Harmonic from power electronic devices and nonlinear devices.

Switching control based on PWM technique of the converter and suitable filters.

[18–23]

03

Intermittent energy impacts on grid safety

Accurate planning and forecasting system. Regression analysis method and algorithms to predict weather patterns, solar radiation and wind speed.

[24, 25]

Increase or decrease in the system operator’s switchable electricity output to deal with any deficit/surplus in renewable energy production.

[26]

(Continued)

Hybrid Solar-Wind System Modeling and Control  423 Table 12.1  Main challenges/possible solutions or mitigations for hybrid solarwind systems. (Continued) No.

Challenges

Solutions

References

Advanced fast response devices, such as control of automatic generation and dynamic transmission system for AC

[28]

04

Grid Synchronization

Method can be based on phaselocked loop or detecting the grid voltages zero crossing or using a hybridization between filters and nonlinear transformation.

[29–35]

05

Fault detection and diagnosis

Fault detection and diagnosis of PV systems using statistical-based methods, Artificial intelligence-based methods, model-based methods, methods based on signal processing, electrical characterization, infrared (IR)/thermal imaging, visual inspection, ultrasonic inspection, image of electroluminescence

[36, 37, 39–45, 75]

Fault detection and diagnosis of WT systems using model-free and model-based methods for fault detection and diagnosis. Robust observer and filter

[44, 46–48, 50–55]

Conventional methods: Perturb and Observe (P&O), Hill Climb Searching (HCS)

[56–59, 61, 62]

Soft computing methods: AI or BI, such as ANFIS, PSO, radial basis function network RBFN, Regression Neural Network (GRNN)

[62–70, 76, 77]

06

Power maximizing of the hybrid solar-wind systems using MPPT controller.

424  Power Electronics for Green Energy Conversion The  simulation results are presented and discussed in section 12.5 and, finally, conclusions are drawn in section 12.6.

12.2 Description of the Proposed System In the literature, there are many topologies for PV-wind hybrid systems. A basic component of this system can be seen from Figure 12.1. The output voltages of the PV system, wind turbine and storage system can be joined on the DC side using separate DC/DC and AC/DC converters. Then these systems can be connected to the grid using one common DC/AC inverter to make available the required grid power even if only one source is available. Therefore, the PV and wind energy sources that considered as current sources can exchange their powers with the grid. Using this configuration, the DC/DC converter of the storage system can control the DC bus voltage. The storage system is charged when there is an extra power from the renewable energy sources and discharged when there is shortage of power. The individual converters can be used to maximize the power of the PV and wind turbine systems. Furthermore, the renewable energy sources can inject their powers directly to the grid through their individual DC/AC and AC/DC-DC/AC converters. The proposed hybrid solar/wind/Lead-acid batteries/supercapacitors power generation system presented in Figure 12.2 comprises of a PV array, a wind turbine system, a supercapacitors bank, a battery bank, one unidirectional DC/DC converter, two bidirectional DC/DC converters, two MPPT controllers, a control unit, and two tree level DC/AC inverters.

Solar Energy

DC/AC DC/DC

Wind Energy

AC/AC AC/DC

Storage

Bidirectional Converter

AC / DC Bus

AC/AC AC/DC DC/AC DC/DC

Figure 12.1  Basic components of PV–wind hybrid system.

AC / DC Bus

AC/DC Load

Grid

Hybrid Solar-Wind System Modeling and Control  425 AC 50 HZ Grid

Wind Turbine Blade Gearbox

Pg , Qg

Ps , Qs

Ωt Taer

DFIG

Ωmec Tg Pr Qr

Wind Turbine

RSC Control

PGSC QGSC

PWM 3L_Inverter (Rotor Side)

DC

Filter Lf Rf

PWM 3L_Inverter (Grid Side)

GSC Control

PESS QESS

DC/DC

Boost converter

DC/DC

GMPPT Control

PV system

Two-Level ESS Control

DC/DC

Converter II

Converter I

Medium-term Storage (Lead-Acid batteries)

Short -term Storage (Supercapacitors)

Figure 12.2  The proposed system.

12.3 Model of System 12.3.1 Model of Wind Turbine The model of the wind turbine is presented as follows [55]:





Taer

GTg

Pw

GPw

tr

mec

0.5C p ( , )ARvw3 /

 116  C p (λ , β ) = 0.22  − 0.4 β − 0.5 e  λi 

12.5 λi



tr



(12.1)

(12.2)

−1



1 0.035  λi =  − 3   λ + 0.08β β + 1 

(12.3)

where Pw, ρ, and AR are, respectively, the aerodynamic power; air density and the area of the wind turbine. Cp, υw, and λ are, respectively, the power

426  Power Electronics for Green Energy Conversion coefficient, the wind speed and the Tip Speed Ratio TSR. λi, β, and ωtr are, respectively, the middle variable; the pitch angle and the mechanical angular velocity. Taer, Tg, G, and ωmec are, respectively, the output torque of the wind turbine; the driving torque of the DFIG, the gear ratio and the DFIG mechanical angular velocity.

12.3.2 Dynamic Model of the DFIG The differential equations of the stator and rotor windings of the DFIG described in dq reference frame rotating by angular speed ω are [78, 79]:

 dϕ ds − ω sϕ qs  Vds = Rsids +  dt ,  ϕ d qs  Vqs = Rsiqs + + ω sϕ ds  dt

 dϕ dr − (ω s − ω )ϕ qr  Vdr = Rr idr + dt    Vqr = Rr iqr + dϕ qr + (ω s − ω )ϕ dr  dt



(12.4) where the subscripts r and s represent rotor and stator quantities, respectively. The subscripts d and q are adopted for three phases quantities in d and q reference frame, respectively. The symbols V and i represent voltages and currents and φ denotes flux linkages. R represents the resistance. Ωmec, ωs are, respectively, the generator chaft speed and the stator pulsation. ω = P Ωmec and P are, respectively, the electrical speed and the number of pair pole. Relationships between magnetic fluxes and stator/rotor currents are:



 ϕ ds = Lsids + Lmidr   ϕ qs = Lsiqs + Lmiqr

,

 ϕ dr = Lr idr + Lmids   ϕ qr = Lr iqr + Lmiqs

(12.5)



where the subscript m relates to the magnetizing inductance. L represents the inductance. The equation of electromagnetic torque Tem-DFIG is expressed as:

Tem−DFIG = P(φdsiqs − φqsids)

(12.6)

The active and reactive powers at the stator (Ps, Qs), the rotor (Pr, Qr) are defined as:



Hybrid Solar-Wind System Modeling and Control  427

 Ps = Vdsids + Vqsiqs ,   Qs = Vqsids − Vdsiqs



 Pr = Vdr idr + Vqr iqr   Qr = Vqr idr − Vdr iqr



(12.7)

The mechanical equation is given by:

Tem− DFIG − Tg = J t



dΩmec + ft Ωmec dt

(12.8)

where Jt and ft are, respectively, the total inertia in shaft and the viscous friction coefficient. If the stator flux of the DFIG is oriented along with d reference frame synchronous coordinate,

 Vqs = Vs = ω sϕ s  ϕ ds = ϕ s   ϕ ds = 0 



(12.9)



When the stator resistance is ignored, the active and reactive power of the stator, the electromagnetic torque and the rotor voltages of the DFIG are described as in Eqs. 12.10 and 12.11. Therefore, the model of the DFIG for control design can be simplified as in Figure 12.3.

Lm Ls

Tem–DFIG–ref +

–

KpTem +

p

Lr

+

L2

- Lm

+ –

1

– +

–

Rp + p Lr –

L2m

i qr

–P

Ls

Lm Ls

Tem–DFIG фs

L2

ωs Lr - m Ls

i dr

S

i qr

1/ ωs

L2m

K pQs +

K iQs p

– +

Vs2 ωs Ls

ωs Lr - L s

S –

+

+

Simplified model for control design

фs

s

Qs Qs–ref

Ls +

ωs p

фs

KiTem

Tem–DFIG

Ωmec

Lm

1

+ +

R p + p Lr –

Figure 12.3  Simplified model of DFIG for control design.

L2m Ls

– i dr

Lm Ls

Vs

+ +

Qs

428  Power Electronics for Green Energy Conversion



 L Ps = − m iqr  Ls   Lm ϕs  Qs = Vs L − Vs L idr s s   ϕs    Tem− DFIG = −  PLm L  iqr s 

(12.10)



   L2m  L2m  didr  Vdr = Rr idr +  Lr −  − sω s  Lr −  iqr   Ls  Ls  dt     L L2  L2  diqr  Vqr = Rr iqr +  Lr − m  − sω s  Lr − m  idr + s m Vs   Ls Ls  Ls  dt   ω −ωr s= s  ωs 

(12.11)



where s is the DFIG slip. ωr is the rotor pulsation.

12.3.3 Mathematic Model of Filter The filter can be described by the following equations:



 diaf + Vas  Vaf = − R f iaf − L f dt   dibf + Vbs  Vbf = − R f ibf − L f dt   dicf  Vcf = − R f icf − L f dt + Vcs 

(12.12)



The filter can be written in a rotating reference frame by using Park transformation:

Hybrid Solar-Wind System Modeling and Control  429



 didf + ω s L f iqf + Vds  Vdf = − R f idf − L f  dt   Vqf = − R f iqf − L f diqf − ω s L f idf + Vqs  dt

(12.13)



where the subscript f relates to the filter quantity.

12.3.4 Medium-Term Energy Storage The battery voltage is given by [80]:

Ubat = Eb – Rbib

(12.14)

where Ubat is the battery voltage, Eb is the no load voltage, ib is the battery current, and Rb is the battery internal resistance. The State of Charge SOC of the battery can be calculated as follows [81]:



 ∫ dt  SOS (%) = 100  1 − ib   Q 

(12.15)

where Q is the maximum battery capacity, in Ah.

12.3.5 Short-Term Energy Storage The total voltage of the supercapacitor bank, the equivalent resistance Rsc, the total supercapacitor capacitance (Csc) and the maximum current through the supercapacitor bank Isc_max described as follows:



Vsc _ max = nsVcell , I sc _ max =

Psc

Vsc _ max

np I n n p = sc _ max , Rsc = s Rcell , Csc = Ccell I cell np ns       

, np =

n I sc _ max , Rsc = s Rcell , C np I cell (12.16)

where ns is number of SC cells in each string, np the number of series strings in parallel, Vcell is supercapacitor voltage, Rcell is the equivalent series

430  Power Electronics for Green Energy Conversion resistance (ESR), Icell is the individual series string current, and Psc is the supercapacitor bank active power. The energy of the supercapacitor Esc is calculated by

Esc = CscVsc2



(12.17)

and the supercapacitor bank capacitance is calculated using [82]:



Csc =

2 Pctd , Pctd = Esc (Vsc _ nom − Vsc _ max )2

(12.18)

where, Vsc_nom and Vsc_min are, respectively, the bank nominal voltage and lower voltage limit. The SOC of the super capacitor can be calculated by:



 Vsc2 (C /2)   Esc  = 100 SOC (%) = 100   V 2   Esc _ max  sc _ max (C /2) 

(12.19)

12.3.6  Wind Speed Model The wind speed profile can be modeled as in (12.20) [83]:

Vv (t) =10 + 0.6 [0.2sin (0.1047 t) + 2sin (0.2665 t)        + sin (1.2930 t) + 0.2sin (3.6645 t)] (12.20) 12.3.7 Photovoltaic Array Model A PV system can assemble many solar cells and each one of them consists of a p-n junction. In the literature, solar cells can be modeled with different

Iph

Ido Vdo

Figure 12.4  Solar cell electrically model.

Ir

I

Rsh

Rss V

Hybrid Solar-Wind System Modeling and Control  431 proposed manner [84–90]. Figure 12.4 shows the electrical equivalent circuit of a solar cell used in this chapter where the output current and voltage of this cell is expressed in (12.21) and (12.22).



I = I ph − I do −

Vdo   q.Vdo   Vdo = I ph − I 0  exp −1 −  n.k.T   Rsh (12.21)  Rsh

V = Vdo – RssI

(12.22)

where the output current of solar cell is represented by (12.23):



  q    V + Rss I  (V + Rss I ) − 1 −  I = I ph − I 0  exp     Rsh  (12.23)  n.k.T If the resistances are neglected, (12.23) can be simplified to (12.24).



  q   V − 1 I = I ph − I 0  exp   n.k.T  

(12.24)



The open-circuit voltage Voc is represented by (12.25) when the circuit is opened (I = 0)



 n.k.T   I ph   n.k.T   I ph  In + 1 ≈  In  Voc =    q   I 0   q   I 0



(12.25)

The short-circuit current Isc is given by (12.26) when the circuit is shorted (V = 0) and Id is neglected.

I sc = I =

I ph Rs    1 + R  sh

(12.26)

The output power P is given by (12.27).



V   P = VI =  I ph − I do − do  V  Rsh 

(12.27)

432  Power Electronics for Green Energy Conversion Iph

Ido

Ido

Ir I

Ido

Ir I

Iph

Rss

Rss

(a)

Ido Vdo

V

Rsh

Vdo

Ido Vdo

Rsh

Vdo

Iph

Iph

Rss

Rsh

Vdo

Iph

Ir I

Iph

Ido Vdo

Ir I

Rss

Rsh

Bd

Ir I Rsh

Rss

Ir I Rsh

Rss

V

Bd

Bd

(b)

Figure 12.5  Model of PV array in series connection. (a) without bypass diodes, (b) bridged by bypass diodes.

where I and V are the output current and voltage, respectively. Iph, I0, and Id are photocurrent, reverse saturation current and average current through diode, respectively. n, q and T are diode factor, reverse electron charge (q = 1.6*10−19 ) and solar arrays panel temperature, respectively. Rss and Rsh are the intrinsic series resistance of the solar cell and the equivalent shunt resistance of the solar array, respectively. A PV panel can be made using two or more modules. More PV panels are able to increase the electricity production. The PV system is composed by many series modules (known as an array) as shown in Figure 12.5 (a). The connection of the PV generator in series forces all modules to operate at the same current, the shaded cell leads to formation of hot spots. This problem can be overcome by the installation of bypass diodes (Figure 12.5 (b)). The bypass diodes can preserve more of module power in partial shading condition. For economic reasons, the bypass diodes are used for all cells, and even if one cell is shaded, the maximum power point can be moved to another place.

12.3.8 Boost Converter Model A boost converter steps up voltage from its input to its output. A capacitor, an inductor, and a filter are generally used at the output to reduce the ripple of the output voltage. This converter is shown in Figure 12.6. The relationship between the input and output voltages (Vin, Vdc) in steady state condition can be written as:

Hybrid Solar-Wind System Modeling and Control  433 Li

Diode

PWM

νin

Ci

i

di Source

νdci

Load

Figure 12.6  Boost converter.

Vdc 1 = Vin 1 − D



(12.28)

where D is the duty cycle.

12.4 System Control The proposed hybrid solar/wind/Lead-acid batteries/supercapacitors power generation system included control blocs (MPPT control, DFIG control and two-level energy storage system control, PSO-based MPPT controller for PV system) is shown in Figure 12.7. The main purpose of the energy storage system is to stabilize the dc link voltage at its nominal value. The PV and wind turbine systems produce the maximum power-based MPPT algorithms and when the produced power of these systems is higher than the demand power by the grid, the energy Ωm , Tm

Ps ,

Pg , Qg

Qs

DFIG

Rotor Side Control

+ –

Tem–DFIG – + Tem–DFIG–ref

Ωmec νw

PWM 3L_Inverter I (Rotor Side)

VDC

Filtre

PWM 3L_Inverter II (Grid Side)

QGSC–ref –

DC/DC

d

MPPT Control for PV and wind systems

iPV VPV

Figure 12.7  System control scheme.

DC/DC

ESS

PESS QESS

+

QGSC PGSC–ref

–

Storage Control

Qs

PGSC QGSC

Pr Qr

Grid Side Control

Qs–ref

+

PGSC VDC–ref

–

+

VDC

434  Power Electronics for Green Energy Conversion storage system stores the extra power. When the produced power of PV and wind turbine systems is lower than the demand power by the grid, the energy storage system generates the extra power.

12.4.1 Grid Side Converter GSC Control The control of the GSC is adopted in order to regulate of the active power flow between the proposed hybrid system and the grid and to regulate the reactive power flow between the GSC and the grid, as shown in Figure 12.8. The active and reactive powers at the grid side converter (PGSC, QGSC) can be expressed as:

PGSC = Vdsidf + Vqsiqf ,  QGSC = Vqsidf – Vdsiqf

(12.29)

The relation between VDC, PGSC, PESS and Pr can be expressed in (12.30). 2 1 d(VDC ) Pr − PGSC − PESS = C 2 dt

as:

(12.30)

For a constant dc-link voltage, the grid active power (Pg) can be expressed

Pg = PGSC + Ps = Pr – PESS + PS

(12.31)

12.4.2 Rotor Side Converter RSC Control The control diagram of RSC shown in Figure 12.9 is implemented in order to control the electromagnetic torque and the stator reactive power of the DFIG. PGSC-ref

Pg-ref +

-

+ Ps

Vqf-ref

PI

-

-

-ωs Lf idf+Vqs

PGSC QGSC-ref

0

+ QGSC

+

-

Figure 12.8  Control diagram of GSC.

-

Transform

Vbf-ref

Grid Side Converter (GSC)

Vcf-ref

+

ωs Lf iqf

VRPWM

dq/abc

Vdf-ref

PI

Vaf-ref

VDC (θs)

Vaf

Vbf Vcf

Hybrid Solar-Wind System Modeling and Control  435 Tem-DFIG-ref + Tem-DFIG

Qs

+

(ωs-ωr)Lmфs /Ls

Qs-ref +

Vdr-ref

PI

PI -

+

-

+

idr s ωs (Lr - L2 m/Ls)

dq/abc Transform

Vqr-ref

+

VRPWM

Vbr-ref

Rotor Side Converter (RSC)

Vcr-ref

-

iqr s ωs (Lr - L2 m/Ls)

Var-ref

Var Vbr Vcr

VDC (θs – θr)

Figure 12.9  Control diagram of RSC.

12.4.3 MPPT Control Algorithm for Wind Turbine For maximizing the extracted power by the wind turbine, the Tem-DFIG of the DFIG is regulated in order to maximize the Cp using PI controllers. The rotor speed can be controlled by adjusting the Tem-DFIG. The optimal mechanical power Pmec-opt can be expressed as:



3  ρπ R5   Ωmec  2 Pmec −opt = − (C p _ max / λCp   _ max )   2   2 

(12.32)

12.4.4 Two-Level Energy Storage System and Control Strategy Figure 12.10 shows the proposed energy storage control procedure. The super capacitors ensure the peak power response when the battery current slope is limited. This limitation is made to reduce of peak power transient stresses for optimize the lifespan of battery. The SOC control algorithm for this system is presented in Figure 12.11.

12.4.5 PSO-Based GMPPT for PV System The artificial intelligence and soft computing have been used in different industrial application like in [91–100]. In which, the PSO algorithm is a new approach that was first presented by James Kennedy and Russell Eberhart in 1995 [101]. It offer flexible information processing abilities for solving complex optimization problems [102]. The PSO-based MPPT technique is designed to overcome the problem of the classical tracking algorithms that does not have the capacity for global optimization. In fact,

436  Power Electronics for Green Energy Conversion VDC Lead - Acid batteries ib

Rb

Vb

d1‘ Lb

Eb

PESS

ST1

iESS

ST3

iS

VB

ST2

Supercapacitors d1

ST4 LSC

RSC CSC

1– d1

1– d1‘

PWM lb-max ib-min

– Current slope limitation

VDC-ref +

–

PI

PI

+

– is–max

–1

+

+ +

PI

is–min+ –

(Pr – PGSC )

VDC

Figure 12.10  Two-level energy storage system and its associated proposed control.

No SOC%

SOC% > SOCmin%

imax=0 , imin= imin-nom yes

SOC% < SOCmax% No

Discharge STOP yes

imax= imax-nom , imin= imin-nom

imin=0 , imax= imax-nom

Charge STOP

Figure 12.11  SOC control of the two-level energy storage system.

this technique use the animal behavior (bird flocks) mathematical interpretations in order to resolve complex optimization problem and it is characterized by a simple implementation and an acceptable performance for finding the global solution when multiextremum functions are presented [102]. The PSO algorithm preserves the particles, where each one represents a candidate solution. The particle position is changed according to the best particle in a neighborhood, Pbest, as well as the best solution establish by all particles, gbest. The particle position, xi, is adjusted by.

Xi(t + 1) = Xi(t) + Vi(t + 1) where the velocity component, Vi, represents the step size.

(12.33)

Hybrid Solar-Wind System Modeling and Control  437 Position after update Xi (t+1)

Vi (

t+ 1)

ce en flu l in

ci a So

( Global best ) Gi(t)

r Cu

ren

ce

luen

f nal in Perso Vi (t) ion ot tm

Pi(t) ( Personal best )

Xi (t)

Position before (a) update Start PSO Initialization

i=1 Calculate the fitness value of particle i

Yes

Better individual fitness value!

Update Pbest

Iter = Iter+1

No Yes

Better global fitness value

Update Gbest

No No

All particles evaluated

Next particle

Yes Update particle and velocity

Iter < Max_Iter

Yes

No

End (b)

Figure 12.12  PSO algorithm, (a) movement of particles in the optimization process, (b) flowchart.

438  Power Electronics for Green Energy Conversion The velocity is calculated by

Vi (t + 1) = wVi (t ) + c1r1 ( Pi (t ) − Xi (t )) + c2r2 (Gi (t ) − Xi (t ))

(12.34)

where xi represents the particle position of i; the velocity of the particle at point i is denoted by vi; the number of iteration is indicated by t; the inertia weight is denoted by w, r1 and r2 are uniformly distributed random variables between zero and one; and the cognitive and social coefficients are denoted by C1, C2, respectively. The best position of the ith particle found so far is represented by the variable Pbest, i, and the storage of the best position of all particles is denoted by gbest. Figure 12.11 (a) shows the motion of the particle in the optimization process. The flowchart of the PSO algorithm for application is given in Figure 12.12. The proportional-integral-derivative (PID) controller is commonly used in different industrial control applications [103]. It gives continuously a correction value based on the error between the reference and measured values of the PV current signals, as shown in Figure 12.13. In this study, the PID controller is used in order to track the PV current reference signal for kept the operating point of the PV system at GMPP that is produced by the PSO algorithm.

12.5 Results and Interpretation The results of simulation of the hybrid proposed system that is connected directly to the grid are presented using Matlab/Simulink. The reactive powers (Qs-ref , QGSC-ref ) are imposed equal to zero. The active power of the grid (Pg-ref) is imposed equal to −2 MW. The main parameters of the proposed system are summarized in Table 12.2. The PSO-PID controller

+

[Ipv]

PID(s) PID Controller

D

P PWM

[Vpv] PSO based on M PPT

S

Figure 12.13  Implemented MPPT technique using PSO algorithm-based PID controller.

C = 30000e−6 F imax-nom = 972 A Lb = 4.1 mH

Vb = 900 V

Rb = 0.0097 ohm

Vb-max = 979 V

Rf = 0.0015 ohm

Rr = 0.021 ohm

P=2

Pn = 840 KW

fs = 50 Hz

ft = 7.1e−2 N.m

Number of blades = 3,

Lm = 13.528 mH

Us=690 V

Medium -term Storage (lead–acid battery)

VDC = 1800 V

Back-to-back VSC and filters

Ls = 13.732 mH

Pn=1.5 MW

Doubly-fed Induction Generator

Jt = 50 kg.m2

R = 47 m

WT mechanical model

Table 12.2  Main parameters of the proposed hybrid system. λcp-max = 8.1

(Continued)

SOCmax / SOCmin% = 90/20 %

imin-nom = −972 A

Lf = 2 mH

Lr = 13.703 mH

Rs = 0.012 ohm

G = 90

Hybrid Solar-Wind System Modeling and Control  439

200 (+10%/- 5%)

maximum power (W)

7.61

Current (A)

Maximum

26.3

Voltage (V) 8.21 / 32.9

Current (A)

Voltage (V)

Short circuit

54

Number of cells

imin-nom = −888 A

imax-nom = 888 A

Rsc = 0.025 ohm

Lsc = 4.1 mH

PV module characteristics at STC (Kyocera KC200GT)

Csc = 59 F

V(0) = 900 V

Pn = 800 KW

Esc = 6 MJ

Short -term Storage (Supercapacitor)

WT mechanical model

Table 12.2  Main parameters of the proposed hybrid system. (Continued)

Np = 7

3.18e−3

Current

−1.23e−1

Voltage

Temperature coefficient (A/°C)

Ns = 334

SOCmax / SOCmin%= 95/15 %

440  Power Electronics for Green Energy Conversion

Hybrid Solar-Wind System Modeling and Control  441

60 60 50 50 40 40 30 30 20 20 10 10 0 0 0 0

I-V Curve of PV Array System I-V Curve of PV Array System

Power Power (KW) (KW)

60 60 50 50 40 40 30 30 20 20 10 10 0 0 0 0

200 200

400 600 800 1000 Voltage (V) 400 600 800 1000 (a) (V) Voltage (a) I-V Curve of PV Array System I-V Curve of PV Array System

1200 1200

Power Power (KW) (KW)

Current Current (A) (A)

Current Current (A) (A)

is tested under MATLAB/Simulink using the Kyocera KC200GT PV panel. The number of the PV strings of the PV array (Np=7); the number of the PV modules of a PV string (Ns=35). In this chapter, PSC are considered to validate the robustness of the proposed controllers. P–V and I–V curves under uniform irradiance and partial shading conditions considered in this work are presented in Figure 12.14. The PV system under partial shading fault (98 modules exposed to 1000 W/m², 98 modules exposed to 600 W/m² and 49 modules exposed to 200 W/m²). The PV system can deliver a maximum power of 490 KW at 1000 W/m² sun irradiance. The boost converter is used to increase the voltage output of PV to 1800 V. The wind speed profile and also the slip of the induction generator are shown in Figures 12.15 (a) and (b), respectively. Figure 12.15 (c) shows the turbine speed and its optimal speed of reference that maximize the output power of the wind turbine using the MPPT control strategy adopted in this work. The wind turbine speed follows its reference perfectly. Figure 12.16 (a) shows

200 200

400 600 800 Voltage (V) 400 600 800 Voltage (c) (V)

(c)

1000 1000

1200 1200

P-V Curve of PV Array System P-V Curve of PV Array System

50 50 40 40 30 30 20 20 10 10 0 0 0 0

200 200

30 30 25 25 20 20 15 15 10 10 5 5 0 0 0 0

200 200

400 600 800 1000 1200 Voltage (V) 400 600 800 1000 1200 (b) (V) Voltage P-V Curve of (b) PV Array System P-V Curve of PV Array System

400 600 800 Voltage (V) 400 600 800 Voltage (d) (V)

(d)

Figure 12.14  P–V and I–V curves; (a, b) under uniform irradiance conditions; (c, d) under partial shading fault.

1000 1200 1000 1200

12

0.2

10

0

8 0

S

Wind speed (m /s)

442  Power Electronics for Green Energy Conversion

5

10

15 20 Time (s)

25

-0.2 0

30

5

10

(a)

15 20 Time (s)

25

30

(b)

Ωmec (red/s)

180 160 Ωmec-ref

140 0

5

10

15 Time (s)

20

25

30

(c)

Figure 12.15  (a) Profile of the wind speed. (b) Slips of the DFIG. (c) Turbine rotational speed.

the electromagnetic torque Tem-DFIG and its reference Tem-DFIG-ref . The controller reaches a good performance while Tem-DFIG follows its reference perfectly. Figure 12.16 (b) shows the rotor voltage and current waveforms. Figure 12.16 (c) shows the stator reactive power of the DFIG and Figure 12.16 (d) shows the GSC reactive power. Figures 12.17 (a) and (b) give, respectively, the mechanical power of the wind turbine generator and also the stator active power of the generator. The DC voltage of the DC link that is nearly constant is shown in Figure 12.18. Figure 12.19 illustrates the simulation results of the PV system under partial shading conditions using different MPPT techniques like fixed duty cycle, incremental conductance method and PSO algorithm. The results confirm that the MPPT controller-based PSO algorithm can track the GMPP of the PV system under PSC.

Hybrid Solar-Wind System Modeling and Control  443 (a)

× 104

Tem-DFIG (N . m)

-0.5

-1 Tem-DFIG-ref -1.5

0

5

10

Var (V) and iar (A)

(b)

15 Time (s)

20

30

Var (V)

iar(A)

2000

25

0 -2000 0

(c)

5

10

15 Time (s)

20

25

30

5

10

15 Time (s)

20

25

30

5

10

15 Time (s)

20

25

30

Qs (W)

× 105

0

0

(d) QGSC (w)

2

× 105

0 -2

0

Figure 12.16  (a) DFIG electromagnetic torque. (b) DFIG rotor voltage and current. (c) DFIG stator reactive power. (d) GSC reactive power.

444  Power Electronics for Green Energy Conversion

P mec (w)

(a)

× 106

-0.5 -1 -1.5 -2 -2.5

0

P s (w)

(b)

5

10

15 Time (s)

20

25

30

5

10

15 Time (s)

20

25

30

× 106

-0.5 -1 -1.5 -2 -2.5

0

Figure 12.17  (a) Wind mechanical power. (b) Stator active power of the DFIG. 1820 1815 1810 V DC (V)

1805 1800 1795 1790 1785 1780

0

5

10

15 Time (s)

20

25

30

Figure 12.18  DC voltage waveform of the DC bus. 30

D = 0.5

MPPT based incremental conductance method

PV Power (KW)

25

MPPT based PSO method

20 15 10 5 0

Connection of the PV system at t=0.5 s 0

0.1

0.2

0.3

0.4

0.5 Time (s)

0.6

0.7

0.8

0.9

Figure 12.19  The electrical power of the PV system using several MPPT techniques.

Hybrid Solar-Wind System Modeling and Control  445

12.6 Conclusion In this chapter, the proposed system architecture and control strategy provides interesting possibilities for the management and the control of the two intermittent sources of energy (photovoltaic and wind turbine) and the connection of these systems to the grid. Simulation results show that the closed-loop grid-connected hybrid solar–wind system with two-level energy storage achieves favorable dynamic performance and robustness under different climatic conditions. The performance of the PSO algorithm is tested and the simulation results demonstrate acceptable convergence, accuracy and steady state oscillations. The PSO method may be enhanced by the combination with other algorithms-based MPPTs. The two-level energy storage system can ensure the power stability between PV system, wind turbine and the demands, thus increasing the availability of the whole system.

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13 Static/Dynamic EconomicEnvironmental Dispatch Problem Using Cuckoo Search Algorithm Larouci Benyekhlef1*, Benasla Lahouari2 and Sitayeb Abdelkader3

*

Department of Electrical Engineering, Faculty of Applied Sciences, University Kasdi Merbah Ouargla, Street Ghardaia, Ouargla, Algeria 2 Department of Electrical Engineering, U.S.T.O, Oran El M’naouar, Oran, Algeria 3 Renewable Energy Development Center, CDER, Ghardaïa, Algeria

1

Abstract

In this paper, the economic dispatch problem (EDP) is defined as determining the output power generated by the unit or units to supply the specified load in a manner that will minimize the volume of gas combusted (VGC). It is a kind of management for electrical energy in the power system in way to operate their generators as economically as possible. This problem is solved under some equality and inequality constraints. The equality constraints are the active power flow balance equations, while the inequality constraints are the minimum and maximum power output of each unit. The voltage levels and security are assumed to be constant. This paper presents a static and dynamic economic dispatch study in electrical power systems using the Cuckoo Search Algorithm (CSA). This method has been tested on the western Algerian electrical power system. The study results are quite encouraging showing the good applicability of CSA for EDP. After a theoretical introduction of the problem formulation and the Cuckoo Search Algorithm, a description of the western Algerian electrical power system is presented, followed by a discussion of the simulation results. Keywords:  Economic dispatch problem, static and dynamic economic dispatch, volume of gas combusted, Cuckoo Search Algorithm *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (453–474) © 2022 Scrivener Publishing LLC

453

454  Power Electronics for Green Energy Conversion

13.1 Introduction The economic dispatch problem (EDP) is defined as the determination of the output power generated by one or more units in order to deliver a certain load in a manner that will minimize the volume of gas combusted (VGC). This is a way of managing the electrical energy of a power system to operate the generator as economical as possible. This problem is resolved under certain inequality and equality constraints. The inequality constraint is the maximum and minimum power output of each unit, and the equality constraints are the balance equations of real power flow. The security and voltage level are assumed to be constant. This chapter describes the study of static and dynamic economic dispatch in power systems using Cuckoo Search Algorithm (CSA). This method was tested on the western Algerian power system. Natural gas is becoming an increasingly important fuel in the generation of electricity. It is one of the main combustion fuels in Algeria. It is majorly used to generate commercial and industrial electricity to generate heat and steam in industrial processes and to heat commercial and residential space. The combustion of this fuel for these activities produces emissions of various greenhouse gases, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Since the change in the US Clean Air Act of 1990 and the implementation of parallel regulations in other countries around the world, energy providers have been required to reduce the emission of harmful pollutants. Measures taken to generate clean energy include installing an exhaust gas cleaning system, replacing old heat generators with cleaner and more efficient ones, modifying existing production and incineration systems, load management, low switching to exhaust fuel, dispatching of emissions [1–3]. In our study to decrease the emissions of various greenhouse gases (environmental dispatch), we minimize the VGC (economic dispatch). In the energy control center, economics dispatch are made to determine the optimum output of thermal generating unit so that the power balance criteria are met, the operating limits of the unit are satisfied and the amount of fuel is minimized. In our study, VGCs are minimized under static and dynamic conditions. The static economic environment finds the optimal output of the generator for fixed load demand at a particular time. The dynamic environmental economic dispatch problem is the most accurate formulation of the practical economic dispatch problem carried out at the energy control centers at specified time intervals, with the objective of

Static/Dynamic EED Problems Using CSA  455 scheduling generators most economically with changing load projections. This problem is normally solved for discrete time intervals defined over the entire dispatch period, which is normally 1 day consisting of 24 h [4, 5].

13.2 Problem Formulation 13.2.1 Static Economic Dispatch At a power plant, each generator has an input/output curve. The fuel input is as a function of output [6–8]. The volume of gas burned by the generator i can be expressed as a polynomial function of power generated.



Fi (PGi ) ai PGi2

bi PGi

ci (Nm3 /hr )

(13.1)

Where: P: is real power output, and ng: is the number of generators including the slack bus, ai, bi and ci are the coefficients of the i-th unit of the volume of gas combusted. The Economic Dispatch Problem can be mathematically represented as:



 Min  F = 

ng

∑ i =1

 Fi ( PGi ) 

(13.2)

Under the following equality and inequality constraints: Equality constraint: Total power generation should cover the load demand PD and th transmission loss of system PL. Therefore, ng

∑P

Gi



i =1

− PD − PL = 0

(13.3)



Inequality constraints: Generation power should be within the minimum output and the maximum output.



PGimin ≤ PGi ≤ PGimax i = 1,2,.., ng

(13.4)

456  Power Electronics for Green Energy Conversion where PGimin and PGimax are lower and upper bounds, respectively, for power outputs of the ith generating unit.

13.2.2 Dynamic Economic Dispatch (DED) The objective of DED is to determine the generation level for all units which minimize the gas flow rate (in Nm3/hr) used for electricity generation over a dispatch period, while satisfying a set of constraints. We shall consider a simple form of DED problem involving two types of constraints, the load demand balance in terms of equality constraints, and generation capacity in terms of inequality constraints. It can be formulated as follows [9]: T

t Gi

G( P ) =

ng

∑∑ F (P ) i

t Gi

t =1 i =1

(13.5)



Fi ( PGit ) is the volume of gas combusted corresponding to ith generator output power PGi at time t, and is expressed as:



Fi ( PGit ) = ci + bi P t G1 + ai P t 2G1 ( Nm3 /hr )

(13.6)

where is the real power output (MW) of ith generator corresponding to time period t and T the number of intervals in the scheduled horizon. During the minimization process, some equality and inequality constraints must be satisfied. In this process, an equality constraint is called a power balance and an inequality constraint is called a generation capacity constraint [10]. Power balance constraint: This constraint is based on the principle of equilibrium that the total generation at any time interval “t” should satisfy the load demand at the interval “t” and transmission loss. This constraint is mathematically expressed as [11]: ng

∑P

t Gi



i =1

− Pdt − PLt = 0

(13.7)



where PGit and Pdt are the power demand and transmission loss in MW at time period t, respectively. The generator constraints: The output power of each generating unit has a lower and upper bound so that it lies in between these bounds. This constraint is represented by a pair of inequality constraints as follows [12].

Static/Dynamic EED Problems Using CSA  457



PGimin ≤ PGit ≤ PGimax i = 1,2,.., ng

(13.8)

13.3 Calculation of CO2, CH4, and N2O Emitted During the Combustion The emission gas of CO2, CH4, and N2O are released during the combustion of fossil fuels, but CO2 accounts for the majority of greenhouse gas emissions from most fixed combustion units that produce electricity, heat, and/or steam. Measured against the global warming potential, CO2 typically accounts for more than 99% of greenhouse gas emissions from the steady combustion of fossil fuels. The approach required to estimate CO2 emissions is very different from the approach required to estimate CH4 and N2O emissions [13].

13.3.1 Calculation of CO2 The fuel analysis approach to estimate CO2 emissions involves determining a carbon content of fuel combusted and applying that to the amount of fuel burned to get CO2 emissions. Equation (13.9) presents an overview of the default fuel analysis approach.

Emissions CO2 n

=

∑ Fuel .HC .C .FO . COC(m(m.w.w) ) i

i =1

i

i

i

i

(13.9)



Fueli: Mass or volume of fuel type i combusted. energy  HCi: Heat content of fuel type i   masse or volume of fuel  Ci: Carbon content coefficient of fuel type i  masse C   energy  FOi: Fraction oxidized of fuel type i. COi(m.w): Molecular weight of CO2. C(m.w): Molecular weight of Carbon. The steps involved with estimating CO2 emissions with the fuel analysis approach are [14–16]:

458  Power Electronics for Green Energy Conversion Step 1: Determine the amount of fuel combusted. This can be based on fuel receipts, purchase records, or through direct measurement at the combustion device. Step 2: Convert the amount of fuel combusted into energy units. The amount of fuel burned is measured in physical units (mass and volume). To apply the default carbon content coefficients, this must be converted to the amount of fuel used in energy units. The calorific value of the purchased fuel is well known and provided by fuel suppliers as it is directly related to the production or useful value of the fuel. Step 3: Estimate the carbon content of the fuel used. To estimate the carbon content, each fuel is given multiple energy contents by fuel-specific carbon content coefficients (mass C/energy). Step 4: Estimate the carbon emitted. When the fuel burns, most of the carbon is eventually oxidized to CO2 and released into the atmosphere. To account the small percentage that remains trapped in the ash without being oxidized, multiply the carbon content by the percentage of carbon oxide. Step 5: Convert to CO2 emitted. To obtain total CO2 emitted, multiply carbon emissions by the molecular weight ratio of CO2 (m.w.44) to carbon (m.w.12) (44/12).

13.3.2 Calculating CH4 and N2O Emissions For both pollutants (CH4 and N2O), the source category depends on the level of detail achieved in the analysis of fuel consumption data. CH4 and N2O emissions depend not only on the characteristics of the fuel but also on the type of combustion technology, combustion characteristics and control technology. At the lowest level of detail, emissions can be calculated by knowing the type of fuel. A more detailed approach uses fuel types and sectors (utilities, industrial applications, etc.). At the highest level of detail, the calculation uses information about the type of fuel and the particular type of combustion equipment [16]. The basic calculation procedure for estimating CH4 and N2O emissions from stationary combustion is represented by Equation (13.10) [14–16].

EmissionsPS = APS.EFPS p = Pollutant (CH4 or N2O). S = Source Category. A = Activity Level (incoming energy) (TJ).

(13.10)

Static/Dynamic EED Problems Using CSA  459 EF = Emission Factor (kg/TJ). The steps involved with estimating methane and nitrous oxide emissions are: Convert the amount of fuel combusted into energy units (as described in step 2); CH4 and N2O emissions are calculated by multiplying the Activity Data by the Emission Factor.

13.4 The Cuckoo Search Algorithms Cuckoo Search Algorithm (CSA) is one of the latest nature-inspired metaheuristic algorithms, developed in 2009 by Xin-She Yang and Suash Deb [17–19]. CSA is a population-based search procedure that is used as an optimization tool, in solving complex, nonlinear and nonconvex optimization problems [20, 21]. This algorithm is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds [17]. The authors describe their algorithm with the three idealized rules [22]: Each cuckoo lays one egg at a time, and dumps its egg in randomly chosen nest; The best nests with high quality of eggs (near to optimal value) will carry over to the next generations; The number of available host nests is fixed, and the egg laid by a cuckoo is discovered by the host bird with a probability pa € [0, 1]. These eggs (away from optimal value) are thrown away or the nest is abandoned, and a completely new nest is built, in a new location. Where each egg in a nest represents a solution and a cuckoo egg represents a new solution. Based on these three rules, the basic steps of the Cuckoo Search Algorithm can be summarized as the pseudo code shown in Figure 13.1 [22–24]. The general system-equation of the CSA is based on the general systemequation of the random-walk algorithms, which is given in equation 13.11 [25–27]:



Xtj+1 = Xtj + α ⊕ Lévy(λ )

(13.11)

where t indicates the number of the current generation and j is the step size. If is too large, then the new solution generated will be too far away

460  Power Electronics for Green Energy Conversion Begin Objective function f(x), x=(x1,…,xp)t Generate initial population of n host nests xi (i=1, … n) While the stop criterion not satisfied (tFj) then replace j by the new solution End if A fraction (pa) of worse nests are abandoned and new ones are built Rank the solutions and find the current best End while RESULTS End.

Figure 13.1  The pseudo code of CSA.

from the old one or even jump out of the bounds. However, if it is too small, the change will be too small to be significant or slow the search procedure. The product means entrywise multiplications. The initial values of the j-th attribute of the i-th solution are determined by using equation 13.12,



Xtj = Mini + random( Maxi + Mini )

(13.12)

where Mini and Maxi are the minimum and maximum limits of the parameter to be optimized.

13.5 Application The test system used in this article is a 220-kV Western Algerian power system. Figure 13.2 shows a single-line connection diagram for this system. The system consists of three production units (“Mersat El Hadjadj” power plant (Node 1) and “Ravin Blanc” power plant (Node 4)). And the power plant. “Tiare” (Node 3)), quadratic cost function. Detailed data for this system can be found at [28]. This power system is interconnected by 17 power lines. Table 13.1 shows the operating limits and fuel cost coefficients for this system [29, 30].

Static/Dynamic EED Problems Using CSA  461 1 4

2

12

5

10 6

9 8

11

3 7 13

14

Figure 13.2  Topology of the power system in western Algeria.

For determining the amount of fuel consumed in one hour, it is necessary to convert the flow from Nm3/h to m3/h. The law of Boyle-MariotteLussac permits us to write, at equal pressures [9]: Nm3 /h 273



m3 /h 273 c

(13.13)



which becomes for C° = 20°: m3/h=1.07 Nm3/h. The fuel consumed (in m3/hr) equations for the three generators becomes:

Table 13.1  Generation limits and fuel cost coefficients of the western Algerian electrical power system. Unit

1

2

3

Pmax (MW)

30

10

25

Pmin (MW)

510

70

420

ai (Nm3/MW²h)

0.85

1.70

0.40

bi (Nm3/MWh)

150

250

75

ci (Nm3/h)

2000

3000

850

462  Power Electronics for Green Energy Conversion

F1 ( PGt1 ) = 1864 + 139.8 P t G1 + 0.7922 P t 2G1 F2 ( PGt 2 ) = 2796 + 233 P t G 2 + 1.5844 P t 2G 2

F3 ( PGt 3 ) = 792.2 + 69.9 P t G 3 + 0.3728 P t 2G 3

(13.14)



Being of type H, The Algerian gas has the following characteristics [15]:



 HCi = 49.6GJ /ton  3  Density ρ = 0.78 Kg /m  Ci = 155 Kg /GJ   FOi = 0.995   EFCH 4 = 4 g /GJ  EFNO 2 = 2.5 g /GJ  CO2 (m.w ) = 44 and C(m.w ) = 12.



by multiplying the system equations given by (13.14) by the density ρ, we obtain the following three equations in ton/hr:

F1 ( PGt1 ) = (1453.92 + 109.044 P t G1 + 0.617916 P t 2G1 ).10−3 F2 ( PGt 2 ) = (2180.88 + 181.740 P t G 2 + 1.235832 P t 2G 2 ).10−3

F3 ( PGt 3 ) = (617.916 + 54.5220 P t G 3 + 0.290784 P t 2G 3 ).10−3



Simulations were carried out using Matlab computational environment, on an Acer Intel Core 2 i5-3210M (2.5 GHz) with 4 GB total memory. For all simulations cases, the Cuckoo Search Algorithm is allowed to run 100 searches (Kmax = 100) for a discovery rate of alien eggs/solutions of 0.25 (pa=0.25) and a number of nests of 70. The optimal solutions are computed for two different cases:

Static/Dynamic EED Problems Using CSA  463

13.5.1 Case I: The Static Economic Dispatch For our test system, the total volume of gas combusted given by (13.2) is optimized and the CO2, CH4, and NO2 emissions are calculate using equations (13.9) and (13.10) and these results are compared to their initial solutions performed by Newton-Raphson algorithm (without optimization) and reported in Table 13.2. The total system demand for the 11 load buses is 782 MW. According to Table 13.2, the volume of the gas combusted, the CO2, CH4, and N2O emissions are 18.4% lower than those found by considering the initial operating state based on the load flow without optimization, respectively. The daily CO2, CH4, and N2O emissions optimized are about 14844 tons/day and around 18188 tons/day for the initial operating state based on the load flow without optimization, which represents gains of 3344 tons/ day, of emissions quantity. The proposed CSA-based convergence profiles of the best solution for the volume of the gas combusted and emissions, as presented in Figures 13.3, 13.4, and 13.5 show better convergence characteristics yielded by the proposed algorithm.

Table 13.2  CSA solution of static economic dispatch/ emission dispatch. Control variable

Without optimization

With optimization

PG1 (MW)

464,54

325,34

PG2 (MW)

60,00

69,80

PG3 (MW)

280,00

416,87

VGC (ton/h)

242,34

197,65

CO2 emission (ton/h)

679,72

554,73

CH4 emission (ton/h)

48,08

39,24

N2O emission (ton/h)

30,05

24,52

464  Power Electronics for Green Energy Conversion 320

Fuel gas volume (ton/h)

300 280 260 240 220 200 180 0

10

20

30

40

50 60 Iterations

70

80

90

100

110

Figure 13.3  Convergence characteristic for fuel gas volume minimization.

900

CO2 emissions (ton/h)

850 800 750 700 650 600 550 0

20

40 60 Iterations

80

100

Figure 13.4  Convergence characteristic for CO2 emissions minimization.

From the results, it is inferred that, the volume of the gas combusted and emission are nonconflicting objectives. Emissions have minimum values when volume of gas combusted is minimized.

Static/Dynamic EED Problems Using CSA  465 65

N2O and CH4 emissions (Kg/h)

60 55 50 CH4

45 40 35

N2O

30 25 0

20

40 60 Iterations

80

100

Figure 13.5  Convergence characteristic for N2O and CH4 emissions minimization.

13.5.2 Case II: The Dynamic Economic Dispatch Figure 13.6 (February 3, 2003) shows the daily load curve. Figure 13.7 shows the values of the load scale factor (LSF). The maximum value is 1.35 for the maximum load of 782 MW. The detailed results of proposed algorithm are given in Table 13.3, and the optimal power dispatch of units over a 24-hour time horizon is shown in Table 13.3.

Daily load (MW)

800 700 600 500 400 0

2

4

Figure 13.6  Daily load curve.

6

8

10 12 14 16 Time stage (hr)

18

20

22

24

466  Power Electronics for Green Energy Conversion 1,4 1,2

LSF

1,0 0,8 0,6 0,4 0,2 0,0

0

2

4

6

8

10 12 14 16 Time stage (hr)

18

20

22

24

Figure 13.7  Load scaling factor.

Table 13.3  Detailed results of CSA. Time (hr)

PG1 (MW)

PG2 (MW)

PG3 (MW)

PL (MW)

VGC (Nm3/h)

VGC (ton/h)

1

106,5

24,3

311,67

10,96

100788,1

73,47

2

105,5

28,5

300,23

10,68

98201,25

71,59

3

113,1

12,4

307,8

11,2

98127,21

71,53

4

88,99

19,4

295,52

10,22

88544,79

64,55

5

95,37

31,9

318,28

11,92

102028,4

74,38

6

125,7

22,2

340,71

14,12

116531,9

84,95

7

145,1

48

394,15

20,29

153323,1

111,77

8

159,9

44,8

407,88

21,88

163338,8

119,07

9

153,8

46,3

419,07

22,99

165917

120,95

10

145,1

48

394,15

20,29

153323,1

111,77

11

132,6

39,3

385,08

18,23

141377,6

103,06

12

130,9

33

373,76

17,1

134108,8

97,76

12,5

130,3

26,5

375,68

17,18

132271,5

96,42

13

123,2

38,3

364,71

16,26

129949,1

94,73 (Continued)

Static/Dynamic EED Problems Using CSA  467 Table 13.3  Detailed results of CSA. (Continued) Time (hr)

PG1 (MW)

PG2 (MW)

PG3 (MW)

PL (MW)

VGC (Nm3/h)

VGC (ton/h)

14

123,2

38,3

364,71

16,26

129949,1

94,73

14,5

122

36,3

364,07

16,18

128444,3

93,63

15

126,6

23,7

369,46

16,56

127676,5

93,07

16

126,6

23,7

369,46

16,56

127676,5

93,07

17

130,3

26,5

375,68

17,18

132271,5

96,42

18

204,1

67,8

419,93

25,95

198852,7

144,96

19

325,3

69,8

416,87

30,12

271308,4

197,78

20

295,5

61,3

418,57

28,67

247634,9

180,52

21

233,4

64,8

417,68

26,44

211620,2

154,27

22

163,8

43

417,97

23

168457,4

122,8

23

132,2

29,2

351,6

15,15

125229,7

91,29

24

107,4

29,5

314,54

11,72

103776,4

75,65

The variations of the real generated optimal power and the total fuel gas volume with respect to the time stage are illustrated, respectively, in Figures 13.8 and 13.9.

Real generated optimal power (MW)

450 400

PG3

350 300 250 200 PG1

150 100

PG2

50 0 0

2

4

6

8

10 12 14 16 Time stage (hr)

Figure 13.8  Real generated optimal power.

18

20

22

24

468  Power Electronics for Green Energy Conversion 200

Fuel gas volume (ton/h)

180 160 140 120 100 80 60 0

2

4

6

8

10 12 14 16 Time stage (hr)

18

20

22

24

Figure 13.9  Fuel gas volume.

The total quantity of CO2, N2O, and CH4 emitted are shown in Figures 13.10 and 13.11 and detailed in Table 13.4. From the results, it is inferred that the combustion of natural gas releases very small amounts of methane and nitrous oxide and lower levels of carbon dioxide.

550

CO2 emissions (ton/hr)

500 450 400 350 300 250 200 150

0

2

4

Figure 13.10  CO2 emissions.

6

8

10 12 14 16 Time stage (hr)

18

20

22

24

Static/Dynamic EED Problems Using CSA  469

CH4 and N2O emissions (Kg/hr)

40 35 30 25

CH4

20

N2O

15 10 5

0

2

4

6

8

10 12 14 16 Time stage (hr)

18

20

22

24

Figure 13.11  CH4 and N2O emissions.

Table 13.4  Detailed CO2, N2O, and CH4 emissions. Time (hr)

CO2 emission (ton/h)

N2O (kg/hr)

CH4 (kg/hr)

1

206,08

9,11

14,58

2

200,79

8,88

14,20

3

200,64

8,87

14,19

4

181,04

8,00

12,81

5

208,61

9,22

14,76

6

238,27

10,53

16,85

7

313,49

13,86

22,17

8

333,97

14,76

23,62

9

339,24

15,00

24,00

10

313,49

13,86

22,17

11

289,07

12,78

20,45

12

274,21

12,12

19,40

12,5

270,45

11,96

19,13 (Continued)

470  Power Electronics for Green Energy Conversion Table 13.4  Detailed CO2, N2O, and CH4 emissions. (Continued) Time (hr)

CO2 emission (ton/h)

N2O (kg/hr)

CH4 (kg/hr)

13

265,70

11,75

18,79

14

265,70

11,75

18,79

14,5

262,62

11,61

18,58

15

261,05

11,54

18,47

16

261,05

11,54

18,47

17

270,45

11,96

19,13

18

406,58

17,97

28,76

19

554,73

24,52

39,24

20

506,33

22,38

35,81

21

432,69

19,13

30,61

22

344,44

15,23

24,36

23

256,05

11,32

18,11

24

212,19

9,38

15,01

13.6 Conclusions In this paper, the Cuckoo Search Algorithm based on the interesting breeding behavior, such as brood parasitism of certain species of cuckoos, has been successfully introduced to solve the static/dynamic environmental-economic dispatch problem consisting of western Algerian electrical power system. The computing time of Cuckoo Search Algorithm is not important because the number of iterations required to stop the process is very low. Therefore, CSA is acceptable and applicable for static/ dynamic environmental-economic dispatch problem solution. Further extensions of CSA should be considered to include more objective functions and constraints for more realistic problems, as well as other standard dataset and test questions. From the simulation results, we can see that the volume of the gas combusted and emissions is reduced by considering the static economic dispatch/emission dispatch. These results

Static/Dynamic EED Problems Using CSA  471 are very clear, as we have shown that CO2 emissions can be limited to at least 25 tons/hour by minimizing the total volume of gas burned. Recall that CO2 is a notorious greenhouse gas and has a life expectancy of 100 years in the atmosphere.

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472  Power Electronics for Green Energy Conversion 13. Indirect CO2 Emissions from the Consumption of Purchased Electricity, Heat, and/or Steam, in: A WRI/WBCSD GHG Protocol Initiative calculation tool, Guide to calculation worksheets v 1.2, 2007. 14. M. J. Moran, H. N Shapiro, D. D Boettner and M. B Bailey., Fundamentals of engineering thermodynamics. Chapter 9, pp. 530, John Wiley & Sons, 2010. 15. Gicquel, R. and Rivière, P., Fiche-guide de TD sur le calcul des émissions de CO2 dans les cycles à combustion, MINES ParisTech, France, 2006. 16. Direct Emissions from Stationary Combustion Sources, Climate Leaders, United States Environmental Protection Agency, Ariel Rios Building, Office of Acquisition Solutions, N.W ,USA, 2020. 17. Gandomi, A.H., Xin-She, Y., Talatahari, S., Alavi, A.H., Metaheuristic Applications in Structures and Infrastructures, Elsevier, 2013. 18. Majumder, A. and Laha, D., A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times. Swarm Evol. Comput., 28, 131–143, 2016, http://dx.doi.org/10.1016/j. swevo.2016.02.001. 19. Yang, X.-S., Cuckoo search and firefly algorithm: overview and analysis, pp. 1–26, Springer, Cham, 2014, DOI: 10.1007/978-3-319-02141-6_1. 20. Chandrasekaran, K. and Simon, S.P., Multi-objective unit commitment problem using Cuckoo search Lagrangian method. Int. J. Eng. Sci. Technol., 4, 89–105, 2012. 21. Fong, S., Opportunities and challenges of integrating bio-inspired optimization and data mining algorithms, in: Swarm Intelligence and Bio-Inspired Computation, pp. 385–402, Elsevier, 2013, DOI : http://dx.doi.org/10.1016/ B978-0-12-405163-8.00018-1. 22. Xin-She, Y. and Suash, D., Cuckoo Search via Lévy Flights. World Congress on Nature & Biologically Inspired Computing, pp. 210–214, 2009. 23. Larouci, B., Benasla, L., Belmadani, A., Rahli, M., Cuckoo search algorithm for solving economic power dispatch problem with consideration of facts devices. Sci. Bull., Ser. C, 79, 1, 43–54, 2017. 24. Aydin, I., Fuzzy integral and cuckoo search based classifier fusion for human action recognition. Adv. Electr. Comput. Eng., 18, 1, 3–11, 2018. 25. Sanajaoba, S. and Fernandez, E., Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy System. Renewable Energy, 96, 1–10, 2016, DOI: 10.1016/j.renene.2016.04.069. 26. Shehab, M., Khader, A.T., Al-Betar, M.A., A survey on applications and variants of the cuckoo search algorithm. Appl. Soft Comput., 61, 1041–1059, 2017, http://dx.doi.org/10.1016/j.asoc.2017.02.034. 27. Ouaarab, A., Ahiod, B., Yang, X.S., Improved and discrete cuckoo search for solving the travelling salesman problem, in: Cuckoo Search and Firefly Algorithm, pp. 63–84, Springer, Cham, 2014, DOI: 10.1007/978-3-319-02141-6_4. 28. Benasla, L., Rahli, M., Belmadani, A., Benyahia, M., Study of an economic dispatching: application to the western Algerian network. ICES’06, Oum El Bouaghi, Algeria, 2006.

Static/Dynamic EED Problems Using CSA  473 29. Rahli, and Tamali, M., The optimal distribution of the active powers of the western Algerian network by linear programming. Proceedings of the Second International Seminar on Energetic Physics, SIPE’2, Bechar, Algeria, pp. 19–24, 1994. 30. Rahli, M., Benasla, L., Tamali, M., Optimization of the production of active energy from the western Algerian network using the SUMT method. Proceedings of the Third International Seminar on Energetic Physics, SIPE’3, Bechar, Algeria, pp. 149–153, 1996.

14 Power Electronics Converters for EVs and Wireless Chargers: An Overview on Existent Technology and Recent Advances Sahand Ghaseminejad Liasi*, Faezeh Kardan and Mohammad Tavakoli Bina

*

K. N. Toosi University of Technology, Tehran, Iran

Abstract

By increasing concerns about global warming, air pollution, and fossil fuel cost, most countries have regulated laws to reduce the exploitation of fossil fuels and to replace green energy resources. Electric vehicles (EVs) and hybrid EVs are mainly considered as the best alternatives for conventional internal combustion vehicles. These vehicles use electricity, which is extracted from the stored energy in their batteries, fuel cells (FC), and supercapacitors (SC). Since these energy storages can be charged using renewable energy resources, EVs are typically considered as green vehicles. However, charging these kinds of vehicles requires additional power electronics facilities because the voltage and frequency of the electricity network are not the same as the voltage and frequency demanded by EVs. Therefore, power electronic converters are of the highest importance as obligatory interfaces between electricity networks and EVs. In addition to financial requirements, obligatory standards force manufacturers to produce high-efficiency productions. Thus, advanced technologies must be used to improve EVs and their charging systems. Specifically, high-power density converters and WBG technologies are expected to be utilized in EV-related industries extensively. Developed converters have to provide proper energy for EVs’ batteries from the grid and green energy resources and convert the energy in the batteries in a way that the EVs’ motors can work in their best mode, with the least power loss and the highest quality. For this sake, various power electronic converters are required to perform different tasks. In this regard, in this chapter, some of the main challenges are reviewed and the main solutions for them are introduced. Specifically, this chapter discusses *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (475–518) © 2022 Scrivener Publishing LLC

475

476  Power Electronics for Green Energy Conversion existent technology and recent advances in the area of power electronic devices used in EVs and wireless charging. Keywords:  Electric vehicles, renewable energy resources, power electronic converters, high-power density converters, WBG technologies

14.1 Introduction Today, electric vehicles (EVs) and hybrid EVs (HEVs) are being proposed to overcome environmental issues, in which their performance depends on electrical energy sources, power electronic converters, and motors. To achieve high-power quality and appropriate output voltage and current for EVs, proper power electronic converters are required. Due to the EVs’ constraints, reliability, weight, size, efficiency, and electromagnetic interference in the power electronic converters are the crucial parameters in EV applications. Thus, power electronic converters play a tremendous role in the advancement of EV technologies that should meet some design considerations in EV applications, such as high efficiency, low weight and small size, and low EMI. Also, the development of EVs chargers, such as fast chargers, makes some problems that impact supply power quality. Therefore, in recent years, researchers are encouraged to propose advanced technologies to satisfy the EVs’ limitations and also to improve the grid-related charging challenges. In this regard, in this chapter, the general concept of power electronics converters and the selection of them for EV applications are reviewed. Moreover, the existing challenges for traditional converters used in EVs are discussed, and the developed technologies, which are introduced to overcome the existent obstacles, are investigated. Also, the recent advances in EV fast chargers and the related challenges are explained. Then, wireless charging for EVs is explained. In addition to general explanations, a quick overview on the history of wireless charging is provided and the advantages and disadvantages of this approach are discussed. Afterward, the most common standards are introduced and explained. Finally, some main challenges of wireless charging are described and solutions are provided.

Power Electronics Converters for EVs and Chargers  477

14.2 Hybrid Power System for EV Technology In recent years, hybrid power systems, which are combinations of green energy sources, are utilized in EVs due to the high reliability, high efficiency, and high quality. In these HEVs, the FC, battery, and supercapacitors are typically applied as green energy sources [1]. Figure 14.1 shows a hybrid power system for EV technology. Power-electronic converters are as an interface in hybrid power systems to the drivetrain of the EVs. Therefore, proper DC/DC and DC/AC converters are essential to drive the EV with hybrid energy sources to meet the high-power quality and high efficiency. Figure 14.1 shows the EVs drive system structure. This EV’s drive system consists of hybrid power sources, DC/DC converters, DC/AC converter, and an electrical motor. DC/AC converters are used to convert the DC power to a proper AC power required by the motor in EV [2]. Usually, a two-level DC/AC converter is applied in the industry but due to the large voltage harmonics in the two-level DC/AC converters, multilevel converters are used instead of two-level converters in high-power applications that harmonic voltage is low in the output of it. However, high number of switching elements, complex power circuit, and high cost are the main drawbacks of the multilevel converters that encourage researchers to propose developed multilevel converters to overcome the existing challenges in EVs’appilications [3].

Supercapacitors

Battery

DC/DC

DC/DC

DC-link

Fuel cell

DC/AC

DC/DC

Figure 14.1  Hybrid power systems for EV technology.

EM

478  Power Electronics for Green Energy Conversion

14.3 DC/AC Converters to Drive the EV As shown in Figure 14.1, the DC-link via DC/AC converter integrates the EV’s motor where the required AC power by the motor is achieved by this converter. Developed multilevel converters are the best choice for EV’s drive system due to their low harmonic contents. Therefore, proposing advanced multilevel converters with low complexity, low cost, and high efficiency are of special importance. Such converters are needed for EV applications to satisfy the proper power level with high-power quality for EVs. Paper [4], has proposed a novel higher energy density switched-­ capacitor (SC) boost converter (SC) for hybrid EV drives which can be used in DC/AC and AC/DC power conversion stages. The proposed structure of the SC power converter is indicated in Figure 14.2 which includes a switched-capacitor circuit and inverter. This converter provides unique features compared to traditional voltage source converter such as doubling the linear modulation area, high-power density, low cost. Also, in this converter, the output voltage and current are regulated by unified control of both the switched-capacitor part and the inverter. Elsied et al. [2] propose an advanced five-level T-type converter for EV with high efficiency to overcome the T-type converter issues since the T-type converter has more harmonic content however the proposed converter has the lowest harmonic contents in comparison with two-level and three-level converters.

+ -

C

SC Converter M

Figure 14.2  Proposed switched-capacitor converter [4].

Power Electronics Converters for EVs and Chargers  479

14.4 DC/DC Converters for EVs DC/DC converters are obligatory interfaces between sources and EVs battery to provide the power required of the EV battery. Energy sources in Hybrid power systems like fuel cell (FC), PV, and the Supercapacitors are coupled to DC bus by DC/DC converters, and also EV battery is charged by these converters. The output voltage in DC/DC converters is controlled via tuning the switches’ duty ratio. The soft-switching converters are proposed in studies for EV applications that can operate in low switching losses and thus achieve the high switching frequency (SF) that leads to high-power density. Most of the existing EVs require high-ratio DC/DC converters due to the low variable voltage of the green energy resources, such as FC. Therefore, various kinds of DC/DC converters are presented in the articles to attain a high conversion ratio. In the FC applications, A fullbridge (FB) DC/DC converter is the common converter which is used for high-power transmission and electrical isolation. Low input and output currents and also small voltage ripples are the main features of this topology.

14.4.1 Isolated and Nonisolated DC/DC Converters for EV Application Typically, to isolate the input from the output in EV applications, transformer-based (high-frequency transformer) converters are used. The most conventional isolated DC/DC converters, such as FB, half-bridge (HB), forward, fly-back, and push-pull DC/DC converters are proposed in papers which can be applied as bidirectional DC/DC converters with a high ratio. However, the electrical isolation causes large power losses that result in the isolated converters having a lower power density in comparison with the nonisolated power converters. Therefore, nonisolated power converters with an appropriate protection system that ensure the safe and reliable operation can attain high-power density. In reference [5], different kinds of nonisolated DC/DC converters are studied for electric transportation applications which these converters have been proposed in [5–9]. These nonisolated converters are indicated in Figure 14.3.

480  Power Electronics for Green Energy Conversion + Vo + Co

C1

C2 V o

Vin Vin -

(1)

(2) +

+

+

Vin

C3

C1

Vo

C1 C2

Vin C2

C3

C4

-

-

-

(3) + Vin

(4)

C3

C1

+ Vo + Vin

C1

C2

C3

C4

-

C2

+ Vo -

C4

-

(5)

(6)

Figure 14.3  (1) Four-stage interleaved boost converter [5]. (2) Two-stage flying capacitor boost converter [6]. (3) Multilevel buck converter [10]. (4) Four-level vertical cell interleaved converter [5]. (5) Two-level boost converter [7]. (6) Three-level flying capacitor converter [8].

14.4.2 Multi-Input DC/DC Converters in Hybrid EVs When the power is delivered to the load by each energy source via a single input DC/DC converter in hybrid technologies, the overall system efficiency and reliability are reduced. Therefore, the concept of multiple input DC/DC converters (MIC) that combine various energy sources in

Power Electronics Converters for EVs and Chargers  481 one power circuit for hybrid electric systems is caught attention. The main advantages of these converters are low cost, compactness, and bidirectional power flow for the storage element. However, system complexity, higher power losses, higher-cost, and a higher number of power devices are the main drawbacks of common MICs. Therefore, developed MICs are required instead of traditional MICs for hybrid power systems. The MICs usually are classified as isolated and nonisolated MICs. Many different types of MICs for hybrid applications have been proposed in papers [11–15]. In [16], new multi-input nonisolated DC/DC converters have been proposed. In this study, six traditional nonisolated DC/DC converters such as buck, boost, buck-boost, Zeta, Cuk, and SEPIC have been applied to design multi-input converters. However, the main drawback of these converters is the lower voltage gain which is not adequate for high voltages applications. Traditional nonisolated MICs can transmit power to the load either individually or simultaneously. However, they are not appropriate for high-power applications due to their low voltage gain. Therefore, isolated converters are the best choice to achieve high voltage gain. In such converters, the high voltage gain can be gained by regulating the transformer turn ratio. Also, the transformer in these converters provides galvanic isolation. However, isolated converters are high cost and complex compared to nonisolated converters. Hence, in recent years, high step-up nonisolated MICs for hybrid applications to minimize the whole system cost and complexity are proposed in papers. Moreover, the power sources in multi-input converters should be able to power to the load and also, charge or discharge the battery individually or simultaneously to be more reliable.

14.5 WBG Devices for EV Technology The successful penetration of EVs into the markets depends on the development of power electronic systems. Si devices could not cope with the advancements of power electronics and have some obstacles in the development of high technologies in power electronics. High losses and limited switching speed are the main obstacles of Si devices that can limit the system power density. Therefore, in recent years wide-band-gap (WBG) devices with many advantages over Si devices are proposed for the advancement of power converters by researchers which can be applied for EV applications. These devices have a wider band compared to Si devices that is why they are called WBG Semiconductors. The features of SiC and GaN in comparison with Si are indicated in Figure 14.4 [17–19]. Power density and efficiency are the important parameters in EV applications that the overall power density and efficiency in power converters

482  Power Electronics for Green Energy Conversion

Electron breakdown field (MV/cm)

Energy band gap (eV) 5 4 3 2

Thermal conductivity (W/cm-K)

1 0 Saturated electron drift velocity (×10^7 cm/s)

Dielectric constant

Electron mobility (×2×10^3 cm^2/V-s)

Si 6H-SiC 4H-SiC GaN

Figure 14.4  Features of Si, SiC, and GaN [17–19].

depend on the number of switching devices, size of passive elements, and cooling system, since the power density increases by reducing the power losses and minimizing the weight. Also, minimizing power losses result in decreasing the size of the cooling system. Therefore, the converters’ power density is increased by minimizing power losses. Furthermore, current and voltage ripples are related to the SF of the converter. Thus to gain the benefits of power density and low current and voltage ripples, the converter switches should operate at a higher SF. However, operating the power electronic converter at a higher SF will result in more switching losses. The most significant aspect to minimize the switching losses at the same frequency is the proper selection of the material of semiconductor devices. Wide-band gap (WBG) semiconductors are the best choice for high-frequency (HF) operation and lower switching losses. These devices can act at a high SF and operating temperature which these characteristics can minimize the volume, weight, and size of the cooling system and the size of the passive elements [17, 18]. Therefore, switching characteristics of the WBG devices allow designers to make a trade-off either to increase efficiency at the same SF or minimize the passive component size and weight by increasing the SF. Also, WBG devices, such as SiC and GaN, instead of Si can be used for high-power converters because of their higher switching speed, lower output capacitance, and lower reverse recovery loss for EVs application. Hence, in recent years, research about WBG devices that are called “next generation of semiconductor devices” and proposing novel topologies of

Power Electronics Converters for EVs and Chargers  483 the power converters using them to increase the efficiency and power density are of particular importance [20, 21]. Among the WBG devices, GaN has some advantages over SiC. GaN devices generally are applied for power supplies operate at 200 to 900 V. While SiC generally is used in high voltage applications such as 900 up to 15,000 V. However, SiC devices can operate in higher temperatures, but GaN has some benefits compared to the SiC devices such as Low-Threshold Gate Voltage and a Higher rate of voltage changes with respect to time that means fast switching operation.

14.6 High-Power and High-Density DC/DC Converters for Hybrid and EV Applications Power losses and stress on the devices in high-power applications are the crucial issues in the DC/DC converters; therefore, the cooling system may not be able to manage the generated heat, and due to high voltage and current stress, the system requires bulky passive components that result in large volume and weight. Nonetheless, the size of the passive components can be reduced by high SF, however, switching losses increase by higher SF. Thus, proposing a soft-switching topology to reduce the switching losses is important. Therefore, a DC/DC converter that is able to act at a higher SF as well as be able to meet the power-density requirements, can be an appropriate converter for high-power applications. The important factor for such converters is high-power density that the target applications require. Recently, different types of high-power DC/DC converters for hybrid electric applications have been suggested that they show considerable growth in this emerging area [20–23]. Achieving high-power density is one of the main problems of highpower DC/DC converters for electric appilications. Also, isolated converters has low power density in comparison to nonisolated power converters due to the huge magnetic elements with significant amounts of power losses. Thus to attain high-power-density, advanced nonisolated power converters that have appropriate protection part are suggested by researchers. In [22], a new interleaved DC/DC converter has been proposed for EVs that results in higher efficiency and power density. Also, as mentioned in the previous section, the use of WBG devices can increase the power density of power converter due to their characteristics. Therefore, novel high-power-density power converters with WBG devices are proposed for EV applications [20–23].

484  Power Electronics for Green Energy Conversion

14.7 DC Fast Chargers and Challenges Due to the limited capability of onboard chargers, off-board Dc fast chargers are proposed for EVs for long-distance trips that can rapidly refill the charge in an EV battery. The power rating for such chargers varies from 50 kW up to 350 kW, which provide recharging EVs within half an hour. In the DC fast-charging stations, DC power can be delivered to the EV battery by an isolated power converter. The typical structure of DC fast chargers consists of three-phase AC/ DC and DC/DC power conversion steps. The conventional DC fast charger power stages are indicated in Figure 14.5. In the AC/DC stage, AC/DC rectifier converts the AC input voltage into an intermediate DC voltage, and also, this stage consists of a power factor correction (PFC) part, that satisfies the needed power quality of the grid. Also, the DC/DC step converts the intermediate DC voltage into the regulated DC voltage needed by the EV battery, as well this stage makes isolation between the grid and EV. The characteristics of the available highpower DC-fast chargers in the market are indicated in Table 14.1 [24–28].

14.7.1 Fast-Charging Station Architectures EV fast-charging architectures are classified into two configurations: common AC-bus structure and common DC-bus structure. A Common AC-bus system is indicated in Figure 14.6. In this system, the system connects to the medium voltage grid throw a step-down transformer. in this system, DC/DC and DC/AC converters provide the connection of green energy sources and energy storage systems to the LV grid. Also, the energy storage systems can be used in this system with the aim of providing the stability of the grid. The AC-connected system has some

A B C Isolated DC/DC stage Input Filter

AC/DC Rectifier

PFC

DC/AC

AC/DC HF TR

Figure 14.5  Typical structure of dc fast chargers power stages.

Output Filter

0.95

CHAdeMO/CCS 1998 × 980 × 525

>0.99

95

150–920

375/500

150

CHAdeMO 1.2

2103 × 1170 × 770

350

Full Load Power Factor

Efficiency [%]

Output Voltage Range (DC) [V]

Output Current [A]

Output Power [kW]

Charging Connector

Dimensions (H×W×D) [mm]

Weight [kg]

700

475

500

920

98.5

Three -phase 480

Three-phase 400

Input Voltage Range (AC) [V]

Tritium veefil PK

Tesla terra HP

Fast charger

400

2079 × 998 × 852

CHAdeMO/CCS

150

300

170–550

94

0.99

Three -phase 400

Delta ultra fast

Table 14.1  Characteristics of DC-fast chargers in the market [24–28].

600

1800 × 600 × 600

ChadeMO/Combo-1

50

120

50–500

93

0.98

Three -phase 480

EFAECE-QC45

400

2000 × 930 × 850

CHAdeMO/CCS

120

300

170–500

93

0.93

Three -phase 480

EVTec espresso

Power Electronics Converters for EVs and Chargers  485

486  Power Electronics for Green Energy Conversion PV

MV Grid

MV/LV

ENERGY STORAGE

DC/DC

DC/DC

DC/AC

DC/AC

AC/DC

AC/DC

DC/DC

DC/DC

Figure 14.6  An AC-coupled charging station [29].

advantages, such as availability of the converter technology, protection devices, and approved standards but it has some drawbacks, such as reactive power control requirement and the need to have more power electronic converters which can decrease the system efficiency and also cause high complexity [29]. Figure 14.7 shows a common DC-bus system. In this system, a transformer and an AC/DC converter provide the connection of the grid to the charging station that the transformer delivers power at low voltage to an AC/DC step; afterward, DC/DC converters meet the power needed by each station subsystems. The low number of power conversion stages is the main advantage of the DC-coupled system that makes it more economical than the AC-coupled system. Moreover, the total system efficiency is improved due to the fewer power conversion steps and the synchronization issues are removed. The features of the Common AC-bus and Common DC-bus systems are shown in Table 14.2 [19].

Power Electronics Converters for EVs and Chargers  487 PV

MV Grid

ENERGY STORAGE

DC/DC

DC/DC

MV/LV AC/DC

DC/DC

DC/DC

Figure 14.7  A DC-coupled charging station [29].

Table 14.2  Features of the AC- coupled and DC-coupled systems [19]. Parameter

AC-coupled system

DC-coupled system

Technical Maturity

High

Low

Efficiency

Low

High

Control Complexity

High

Low

Operation at grid abnormality

No

Yes

Availability

High

Low

Complexity of Protection systems

Low

High

Conversion Stages

High

Low

Cost

High

Low

488  Power Electronics for Green Energy Conversion

14.7.2 Impacts of Fast Chargers on Power Grid Charging EVs A great amount of energy is required for charging EVs. The problem gets worse when the number of EVs, which are connected to the grid, is increased because by increasing the number of EVs, the required power also increases. Considering the point that thousands of EVs can be added to the cities monthly, it can be inferred that this power demand increases every month. The power demand increases so rapidly while the capacity of power plants cannot increment. Therefore, it is essential to add some additional power plants to comply with the power demand. However, founding new electricity power generation units is very expensive. Thus, founding several power plants in a month to meet the additional power demand caused by EVs is not sensible. On the other hand, we must pay attention to the point that the daily load profile is not a plat line, but it is a curve with valley and peaks. Valleys represent the hours that the power demand is less than average amount and peaks illustrate the hours that the power demand is much greater than the usual power demand. Contemplating this point, a solution can come out to cope with this problem. If we enforce EVs to be charged during off-peak hours or the valleys, not only do not add a load that cannot be supplied by existent power plants, but also helps to flatten the load profile. Fast chargers require high-power during the day, such chargers can affect supply power quality. In fact, overloading and voltage variations may occur. Therefore, there is a need to control the power demand of the station during peak consumption hours [30]. One approach to eliminate the destructive effects of fast chargers is the controlling of PF and bidirectional power flow by the station [31]. Generally, the use of energy storage systems is proposed for fast-charging stations to reduce the harmful influences of high-power charging for the grid. In this way, the energy storage system provides the power needed to sustain the continuous charging process of the EV battery when the battery power required not being extracted from the grid. Therefore these systems can flat the power demand from the grid during the charging process of the battery. Moreover, because of the fluctuating feature of green energy resources, the energy storage systems are used for hybrid energy powered EV to ensure balancing supply and demand. Therefore the energy storage systems are an important part of the fast-charging stations that satisfy the stable operation of the grid in the long term. Recently, pumped hydroelectric storage systems [32], compressed air [32], flywheels [32, 33], thermal energy storage in molten salt [32, 34], electrochemical storage in batteries [32, 33], have been presented as energy storage systems that among these

Power Electronics Converters for EVs and Chargers  489 systems, the battery-based systems are more suggested due to their higher energy and power density [32]. Such storage energy systems are coupled to DC bus by power converters.

14.7.3 Fast-Charging Stations Connected to MV Grid and Challenges As mentioned in the previous section, DC fast-charging stations connect to a three-phase system via a LF distribution transformer. Therefore, the the system weight and cost are increased due to the use of the LF transformer. Also, at a low voltage grid, fast chargers require massive conductors and protection systems. One way to overcome such issues is the direct connection of the fast chargers to the MV grid without a LF transformer. Figure 14.8 shows the concept of an ultrafast-charging station directly connecting to a MV line. This concept includes a nonisolated AC/DC converter in order to couple the DC bus to the medium voltage bus. Also to charge the EV battery, an isolated DC/DC converter has been used. Moreover, energy storage systems such as flywheels, compressed air storage, and battery systems have been coupled to the DC bus by power converters.

Photovaltaic

Fuelcell

AC/DC

DC/DC

DC/DC

AC/DC

DC/AC

DC/AC

DC/DC

DC/DC Transfer Unit

MV AC grid

Grid interface

LV AC distribution

MV DC bus Energy

Connector

Flyweel

Compressed air

Battery

EV Battery

Figure 14.8  Ultrafast-charging station concept with a medium voltage DC bus suitable for large electric charging stations [31].

490  Power Electronics for Green Energy Conversion Also, green energy sources e.g. Photovoltaic and Fuelcell which are coupled to DC bus by DC/DC converters provide energy to EVs. In such concepts, multilevel converters are proposed due to some of their advantages like lower harmonics, robust operation, and low switching losses [31].

14.7.3.1

SST-Based EV Fast-Charging Station

The common approach for the fast chargers domain is the use of a s­ olid-state transformer (SST) to enable the direct connection of fast ­chargers to a medium-voltage grid. The SST replaces both the LF transformer stage and the AC/DC conversion step in DC fast-charging stations that leads to the power-density is improved in this system [29]. The configuration of the SST based station is indicated in Figure 14.9. This structure provides some benefits, such as galvanic isolation via a HF transformer, unidirectional or bidirectional power flow, active and reactive power regulation, and PFC. Also, in this approach, the number of conversion stages has decreased. The use of WBG devices in SST-based converter is the preferred option due to their lower switching and conduction losses that lead to an increase in the system efficiency and smaller cooling system. Also, they can operate

PV

MV Grid

SST

ENERGY STORAGE

DC/DC

DC/DC

HF TR AC/DC

DC/DC

DC/DC

Figure 14.9  A SST based EV FC station structure [29].

DC/DC

Power Electronics Converters for EVs and Chargers  491 Reliability 5 4 3

Cost/KW

2 1 0

Control complexity

Technical maturity

Fault protection

Flexibility LF transformer-based Fast charger SST-based Fast charger

Figure 14.10  The comparison of features between the LF transformer-based fast chargers and SST-based fast chargers [19].

at higher SF that provides a decrement of passive elements size, thus minimizing the overall system size and weight. Researchers are conducting research to develop this concept for the multi-megawatt SST-based Fast chargers. Figure 14.10 shows a comparison of features between the LF transformer-based fast chargers and SSTbased fast chargers [19]. Reliability concerns are the main challenges of MV grid-connected fast chargers, almost 35% of faults in power system related to power electronic devices, therefore replacing transformers with power electronic devices for the grid connection influence the system reliability. One of these concerns is the lack of fast-acting protection systems to protect the grid faults such as overvoltages and short circuits. The conventional MV grid circuit breakers can act against a fault current in several tens of milliseconds, while to interrupt the MV connected power electronic devices, the breaker must act against the fault in several hundred microseconds. Solid-state circuit breakers and hybrid breakers can act with these speeds that are underdeveloped currently.

14.8 Wireless Charging Wireless charging is a kind of wireless power transfer (WPT), which utilizes electromagnetic induction to supply a portable device. Wireless charging

492  Power Electronics for Green Energy Conversion is applicable for a wide range of powers, from small devices, such as smart phones, to large EVs. The main privilege of wireless charging is that the portable device can be located near a charging pad to be charged without requiring to be exactly aligned or use electrical connector. In this approach, power is transferred using inductive coupling. Based on Faraday’s law, the AC current, which passes thru the primary windings, induces an inductive current in the secondary coil. In fact, according to Oersted’s law, while an object with electric charge moves, a magnetic field will be produced around it. And according to Faraday’s law, altering magnetic field produces electromotive force, which finally results in the inductive current in the secondary coil. Ultimately, a rectifier converts the oscillating current into DC current and voltage, which can charge EV’s batteries. When the distance between two windings increases, the efficiency of the system decreases because the leakage flux increments. In this case, a resonant inductive coupling can be exploited to solve this issue. To create a resonant circuit, a capacitor must be added to both primary and secondary induction windings to form two LC circuits, which creates a unique resonance frequency (RF). The RF matches the AC current flowing thru the winding. The selected frequency depends on the distance to achieve the highest possible efficiency. While the explained principles seem to be simple, implementing this structure is not easy. There are various theoretical and practical challenges. The main challenge is that we need to transfer HF high-power (HFHP) via a transformer, which requires very fast and very efficient (near ideal) elements. Another challenge is the weight and cost of the WPT system. These challenges and some common solutions will be explained in later sections.

14.8.1 Short History of Wireless Charging While the first WPT approach was proposed in 1894 [35], due to the great approval of internal combustion cars, using EVs and their wireless charging devices was forgotten [36]. About a century later, in 1972, a car power by wireless power supply was proposed at the University of Auckland [36]. Five years later, “electromagnetically coupled battery charger” was registered as a patent, which described the application of wireless charging for headlamp batteries that were mostly used in mines. The first practical application of wireless charging in the USA backs to the date that Bolger and Kirsten tried to power an EV using WPT approach. For this sake, they implemented a circuit which worked at 180 Hz and was able to transfer 20 kW power. In later years, in California, engineers were working on powering public transformation vehicles using WPT. In 1980s, the first bus,

Power Electronics Converters for EVs and Chargers  493 which was powered via WPT, run in California. Analogous productions were produced in Germany and France in those years. In 2000s, resonant coupling attracts more attention. Using this approach, power transformation with higher efficiency and for longer distances was made possible. Providing commercial versions of WPT based on resonant coupling can be considered as a major step in WPT. The proficiencies and deficiencies of the existing WPT systems can be abridged as below.

14.8.2 Proficiencies • Protected connections: since the charging is performed wirlessly, no connections or wires are physically available, which makes the decreases the possibility of short circuit happening [35]. • Low infection risk: while the power transfer is carrying out via magnetic field, infection hazards are avoided. • Durability: In wired charging, charger connections must be pluged and unpluged for evergy charging attempt, but in wireless charging, no plug/unplug is required, which increases life time if the charging devices. • Automated wireless charging of EVs permits more frequent charging, which resultantly extends driving range. • Reliability: Wireless charging can be performed automatically, which is not dependant to driver or any other operator; therefore, higher reliability can be expected. • Charge in motion: While wired charging requires long stops at charging stations or at homes, wireless charging of EVs at HP level provides nonstop charging or in-motion charging, which is also called dynamic charging.

14.8.3 Deficiencies • Slow charging: Because of lower rate of power transfer and efficiency in comparison to the wired charging mathos, longer time is needed to charge EV’s battery completely using wireless charging. • Expensive: Wireless charging needs additional power electronics converters and also coils, which results in more comprehensive and expensive structure in comparison to the wired charging methods.

494  Power Electronics for Green Energy Conversion • Low efficiency: Wireless charging is not as efficient as wired charging methods. Novel strategies decrease WPT losses via employing advanced coils, new control techniques, higher frequencies, and optimized power electronics drive systems. Therefore, using advanced technologies, higher efficiency and higher WPT can be achieved with lower cost and using reduced size chargers. We will explain and discuss some of these techniques in the next sections.

14.9 Standards Various standards are defined for WPT systems. Two main standard sets defined for wireless charging are: Qi and PMA. While these standards are similar, they have some variations in connection protocols and nominal frequencies. It is worth to mention that some devices that are compatible with one of these standards may not be compatible with the other one. Qi is an interface standard, which is released by the WPC for WPT. It was the most famous standard in 2017 and over 200 million devices were manufactured based on this standard. In 2012, IEEE announced the beginning of PMA under IEEE SAIC [37]. The Society of Automotive Engineers (SAE) defines the obligatory constraints required for wired and wireless charging of EVs. The International Electrotechnical Commission (IEC) also has published some standards associated with EVs and their charging infrastructure. Moreover, each country has adopted these standards with their national standard system. For example, ISIRI 12103-7 discusses special enclosure used for EVs charging stations. In the following subsections, we will abridge some of the major standards associated with EVs and their charging system.

14.9.1 SAE J1772 SAE J1772, which is also called J plug, is a standard for connectors of charging infrastructures for EVs in North America. This standard is published by the SAE [38]. This standard mainly covers the generic aspects of physical and operational necessities. Under this standard, the structure of charging stations follows almost the same rules and assures the minimum functionality and performance.

Power Electronics Converters for EVs and Chargers  495

14.9.1.1 Revisions of SAE J1772 The very first incentive, which made the SAE to develop SAE J1772 came from California. Four different charging types are defined by SAE J1772 for charging EVs. These four charging types include two AC and two DC charging levels. Details of these charging levels are given in the Table 14.3 [39]. It is worth mentioning that according to the appendix of SAE J1772, AC level 3 charging mode is also defined, but has been never used. At this charging Level, charging can be performed up to 96 kW at a rated voltage between 208 and 240 V whilst 400 A is defined as the maximum current. It must be mentioned that the third charging level is merely defined for AC charging and no DC level 3 is defined.

14.9.2 IEC 62196 As stated earlier, IEC 62196 is very similar to SAE J1772, which supports the standards associated with the connectors of EVS charging system. The development of IEC 62196 is done by technical committee of SC 23H at IEC. EVs manufactured under IEC 61851 and IEC 62752, must be powered by the connectors that support IEC 62196 standards. Most EV connectors manufactured under IEC 62196 series deliver extra contacts, which reinforce special functions related to EVs charging. For example, charging merely must be carried out when the EV is connected and is not moving. Various parts of this standard are adapted with national and regional standards of countries. For example, some parts have been released under European standards (EN 62196) and Institute of Standards and Industrial Table 14.3  Details of charging levels [39]. Charge level

Voltage (V)

Maximum current (A)

Maximum power (kW)

AC Level 1

120

12

1.44

16

1.92

AC Level 2

208–240

≤ 80

Up to 19.2

DC Level 1

50–1000

80

80

DC Level 2

50–1000

400

400

496  Power Electronics for Green Energy Conversion Research of Iran (ISIRI). Similar standards are defined by SAE for EVs in the USA. This standard series include the following parts: • IEC 62196-1, which covers general requirements • IEC 62196-2, which covers compatibility of dimensions and interchangeability requirements for alternative current pin IEC 62196-3, which covers compatibility of dimensions and interchangeability requirements for direct current pin. The first part of IEC 62196 was released in 2003. The first version was applicable to the connectors of booth AC and DC charging infrastructure for voltages up to 690 V and up to 400 A. In the second version, which was released in 2011, maximum voltage for DC charging increased to 1.5 kV. In the third version, which was released in 2014, general explanation of a “combined interface” as exploited by the combined charging system (CCS). CCS is a standard for charging EVs, which employs both Combo1 and Combo2 connectors. These types of connectors are extensions of the IEC 62196 Type 1 and Type 2 connectors, with two extra DC contacts to permit DC fast charging. There are various manufacturers all around the world; therefore, it seems rationale to expect having a wide variaty of connectors for connecting chargers to the EV. While cominication system, which is required for exchanging information between the EV and the charger, is usually selected from the currently eistent communication system. Thus, in spite of the connectors, communication exploited for charging is the same all around the world. Mainly, two kinds of communication are defined: • Basic signaling (BS) is carried out by a PWM signal that is sent over the control pilot contact (CP) according to IEC 61851-1. Due to safety reasons, this communication is exploited. As an instance, the connector must be plugged in before the charging cable is energized. • High-level communication (HLC) is performed by modulating a HF signal over the CP to transfer more comprehensive data, which can be used for other purposes such as load balancing. HLC is based on the other standards like DIN SPEC 70121.

Power Electronics Converters for EVs and Chargers  497

14.9.3 SAE J2954 The SAE J2954 standard sets up a series of requirements that introduces principle for electromagnetic compatibility, minimum working conditions and safety for WPT. The charachtersitics explained in this standard are based on the charging charachteristics introduced in SAE J1772 for EV charging. The defined charachtersistics maintain both at-home and at-charging-station wireless charging. There are some hints that say the EVs, which are able to be charged wirelessly under SAE J2954, have to be able to be charged via wired chargers under SAE J1772. It is worth mentioning that only surface mounted instllation is supported in this standard and buried installation is not included. This must be mentioned that bidirectional power transfer (from EV to grid, which is also called V2G, and from grid to EV, which is also known as G2V), which is a controversial topic in conductive charging, is not considered in this standard. However, the bi-directional power transfer may be included in the future versions. If this happen, it could provide the power system with a great oppurtuinity for contributing to demand response programs.

14.10 WPT Technology in Practice The concept of wireless power transformation has attracted many scientist’ attentions and the related technologies are developed to be exploited in various applications such as clinical devices, wireless charging systems, Maglevs, etc. [40]. Amongst these applications, charging EVs wirelessly seems to one of the most brilliant applications, which can cope with the existent problems and disadvantages of current charging infrastructure [41]. Efficiency, as one of the most crucial aspects, has been studied by many researchers both in designing the control system and designing physical elements to minimize the overall costs (which includes implementation cost, operation cost and maintenance cost) [42]. In addition, some control approaches have been suggested to improve the efficiency by enhancing the frequency of the primary side inverter and equivalent load resistance. To obtain the latter one, beside conventional rectifiers, a DC-DC converter can be put at the secondary side to adjust the equivalent load impedance by the duty cycle [43]. Substituting the rectifier and the DC-to-DC converter by only one rectifier, decrements losses and the complexity of the system. Furthermore, it helps to have wide range of load

498  Power Electronics for Green Energy Conversion control and voltage adjustment [44]. In this method, parameters’ data must be known. To cope with this problem, a tracking approach based on searching algorithms is suggested in [45], to avoid the necessity of knowing this information. However, this method suffers from slow dynamic charging; therefore, the optimum frequency cannot be identified exactly. The mutual inductance of wireless power transformation systems affects the optimum value of the equivalent resistance. When EV is driven on the charging path, the mutual inductance and coupling factor, which are dependant on the secondary winding position, change; therefore, dynamics of wireless charging changes. Some studies on indicating the position of have been conducted [46]. According to these studies, employing a position recognition technique requires additional hardware. Furthermore, because of high speed of EVs, the system must perform rapidly for EV dynamic charging. Moreover, various heights of EVs results in various air gaps, which influences the coupling factor. Thus, a strategy is required to calculate mutual inductance in real time. Although primary parameters of wireless power transformation systems, such as compensation capacitor, coil inductance and coil resistance can be practically determined in the time of regular repair and maintenance, C2 and equivalent load impedance depend on EVs’ system and cannot be determined by primary system designer. Therefore, these variations must be taken into account for designing the circuit, which means the system cannot be optimized for each type of EV, but instead, the system must be designed in a way to have a trade off so that it could work with various types of EVs. In other words, the efficiency of the system will not be maximum [47]. Therefore, in practice, the changes of the aforementioned parameters decreases the overall efficiency when the rated values are exploited in the control system. Moreover, if electrical power is going to be directly transferred to the battery or electric motor, voltage regulation for load is vital for wireless power transformation system because these devices are sensitive to voltage and supplying them with a voltage other than their nominal voltage may damage them or decrease their life span. The simplest answer that comes to mind to overcome this problem may be adjusting duty cycle or frequency to properly regulate the voltage of the load (battery or electric motor), but it must be noted that using these approaches reduces the wireless power transformation efficiency, which is not desired. To solve this problem, a good approach can be controlling duty cycle of primary inverter switches, which can be carried out using a PI controller. However, the unsolicited transient response of PI may cause some additional issues. Hence, this approach must be taken into account with cautious considerations.

Power Electronics Converters for EVs and Chargers  499

14.11 Converters Currently, tens of resonant compensation circuits are able to work as pulsating voltage source. Amongst these, about 20 of them are employed for WPT because all resonant elements of them engage in resonance. These circuits mainly utilize FB or HB topologies that are well-known rectifiers. However, the main disadvantage of them is remarkable leakage inductances which constraints the transferred power. No need to say, this malfunctionality of these type of converters, decreases the efficiency of the whole system. Hence, these structures exploit extra resonant circuits with several elements to use all magnetic field capacity to attenuate switching losses. Since adding additional elements increases the total cost of the system, it is not desirable. Thus, some other approaches are suggested to cope with this problem. In the following, to avoid additional costs, we will descibe that how multi–DC-DC converters can be employed that use resonant compensation network requiring no additional resonant tank. It is worth to mention that while this approach provides cheaper system, it must follow all aforementioned standards. The idea behind WPT can be easily demonstrated only by a primary winding connected to an AC voltage source, a magnetic link and a secondary winding. The second part, which links the primary and secondary windings is usually determined via coupling factor. Theerefore, the inducn tive voltage at the secondary winding can be stated as: V2 = k 2 V1 , where n1 (k < 1). The simplified principals of WPT is illustrated in Figure 14.11. According to the figure, the system can be modeled as a transforer and its leakage inductances. Needless to say, smaller coupling factor results in larger leakage inductance, and consequnetly, smaller voltage at output of the secondary winding. Moreover, this coefficicnet decreases by increasing the distance between two coils. In addition, when the distance increases, parasitic resistance increments and decreases the efficient of the system. Lleak1

n1 : n2

Lleak2

+ V1

+ V2

L1

– Figure 14.11  WPT Transformer model.

Ideal transformer

–

500  Power Electronics for Green Energy Conversion The topology used for WPT is highly dependant on the application of WPT. Both voltage and current sources can be exploited as input of the system. If the capacitor is selected large enough, the capacitor placed in parallel with resonant circuit maintains the voltage almost constant; therefore, it is contemplated as a voltage source. If the the inductor is chosen large enoughe, it would be a correct assumption if we contemplate the inductor placed in series with resonant circuit as a current source. While current source is selected as the input source, remarkable power must be provided at the input because the resonant circuit and the input are located in series, but when voltage source is selected to be exploited as the input source, a large current circulates in the parallel resonant circuit. At the second side, the parallel resonant circuit is appropriate for applications with higher voltages because of the existence of the voltage source. As illustrated in Figure 14.12, the quality factor is remarkable for both resonant circuits. Thus, the resonant impedance must be remarkable for series circuit. However, small

|G|(ω)

1

against

ωx Q=1 Q =3 Q =5 Q =7 Q =8 Q = 10

0.6

|G|(ω)

against

ωx Q=1 Q = 1.5 Q=2 Q = 2.5 Q=3 Q = 3.5

|G|(ωx)

|G|(ωx)

0.8

5.5 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5

0.4 0.2 0 10-1

100

101

0.5

(1)

against

ωx

0.5

|G|(ωx)

Q = 0.3 Q = 0.6 Q = 0.9 Q = 1.2 Q=3 Q = 12

|G|(ωx)

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2

|G|(ω)

1

1.5 (3)

2

2.5 3

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 10-1

|G|(ω)

1 (2)

1.5

against

ωx

2

Q = 0.2 Q = 0.3 Q = 0.5 Q = 0.7 Q=1 Q = 15

100 (4)

Figure 14.12  The gain of resonant tank against normalized frequency for different Q factors: (1) series (2) parallel, (3) series-parallel and (4) LLC resonant converters [49].

101

Power Electronics Converters for EVs and Chargers  501 impedance is preferred for parallel circuits. Therefore, since the resonant impedance is less than the load impedance, parallel resonant circuit is appropriate for applications with higher voltages. Since the load at the secondary side is a converter that must fed the EV motor or battery, the load impedance is not fixed. Therefore, impedance matching is mandatory to attain optimal condition. The coupling coefficient varies by distance alternations. This variation lead to changes in power transfer rate of the whole apparatus. In addition, leakage inductance plays a pivotal role in recognizing the total efficieny of the apparatus. Conventionally, for isolation purposes, coupled magnetic circuit was employed in buck-boost topology. However, the stored energy in the coupled winding core constrains the maximum WPT [48].

14.12 Resonant Network Topologies It is well known that incrementing the SF can significantly decrease the dimensions of passive elements in power electronics converters. However, it must be taken into consideration that increasing the SF results in higher power loss. Another approach to decrement the size of the converter, instead of incresing the SF, is changing the topology of the system and reducing the huge elements of the circuit. For EV charger applications, resonant topologies are attracting more attention because resonant tanks can nullify the leakage inductance at RF [49]. A CCL resonant strucrure at the first side provides great equivalent impedance at secondary side to maintain high current [50]. LCC resonant topologies for EV wireless chargers are suggested to provide a compressed system at decreased coil size [51]. Zero voltage switching can be attaiend while the working beyond the RF and zero current switching can be obtained when it is working below the RF. While parallel resonant converter (PRC) suffers from large circulating current at the RF, series resonant converter (SRC) enjoys low circulating current. Boost resonant converters are known for their soft-switching features.

14.13 Appropriate DC/DC Converters Four DC/DC converters are illustrated in Figure 14.13, which use the resonant compensation circuits without extra resonant tank. These topologies are appropriate for WPT to EVs because they comply the required

502  Power Electronics for Green Energy Conversion L1

n1 : n2

L2

(1)

(2)

(3)

(4)

Figure 14.13  Typical isolated converters: (1) Ćuk, (2) Zeta, (3) SEPIC, (4) P5 [51].

technical and financial constraints. Moreover, they do not have a dc flux bias. The conventional approaches based on SEPIC and Zeta converters employ a magnetic link for the inductor. However, the inductor’s core cannot get rid of flux bias due to the dc current. Therefore, since the flux bias results in additional power loss, this topology is not desired. To block the DC current, split capacitor is employed in P5 and isolated cuk topologies. During steady state, it is expected that the average current passes thru the capacitor and average voltage over the inductor are zero because if these average values are nonzero values, the circuit will not reach its steady-state condition and the voltage and current of capacitor and inductor would change over time. P5 topology enjoys DC-free current, which maintains zero average voltage and is desired to be used in WPT structure. However, the isolated cuk structure has zero-frequency component, which is not desired. To cope with this problem, in some methods, large capacitors have been exploited to suppress the unwanted effects.

14.14 Single-Ended Wireless EV Charger In general, to charge an EV wirelessly, the AC power of the electrical network must be rectified using a rectifier to provide the DC voltage of DC link. Then, the available energy in DC link will be converted to HF AC power. This HF power will be transferred from the primary coil, which is located at charger side, to the secondary coil, which is installed at the bottom of EV. Finally, another rectifier circuit is required at secondary

Power Electronics Converters for EVs and Chargers  503 side to convert the HF voltage to DC voltage, which can be fed to the EV’s battery. Other auxiliary circuits, such as power factor corrector, can also be employed to improve the quality of the whole system and decrease the undesired impacts on the electricity network [52]. A typical single-ended resonant inverter, which can be employed in WPT is demonstrated in Figure 14.14. The primary side comprises a simple rectifier with an LC filter at its output port (L3C3) and a power switch, which works with C1 and L1 under the resonance condition. Using zero voltage switching technique, the resonant inverter causes low switching loss, which is preferred in comparison to other inverters. The resonance happens in the second side by L2 and C2. The diode at the secondary side performs the rectifying task and C4 and L are considered as the output CL filter, which attenuate the voltage ripple and HF distortions. Finally, this rectified the voltage [53]. Fundamentals of single-ended inverter is illustratd in Figure 14.15. Figure 14.15 depicts the circuit diagram illustrates the voltage and current of the switch. The resonant circuit works when the switch is off and current rotates in the L1C1 loop. The current through and voltage over these elements are the same and can be expressed as below. Once the voltage over the capacitor reaches the DC voltage, the switch turns on. Meanwhile, the antiparallel diode of the switch turns on.

vC1 = vC0 cos(ω0t + φ0)

(14.1)

iL1 = iL0 sin(ω0t + φ0)

(14.2)

L3

C1

L1

Vin

C3

Primary side

Figure 14.14  Typical single-ended resonant inverter.

C2

C4

Secondary side

VB

504  Power Electronics for Green Energy Conversion L3

iL1 C1

Vin1 D

C3

L1

Vsw

vc3

isw Vsw

isw

Figure 14.15  Single-ended inverter: circuit diagram (left), and waveform (right) [53].

vsw = vC3 + vC1

(14.3)

isw = 0

(14.4) 2



T  With vC 0 = vC 3  ON  + 1  2L1 



i L 0 = vC 0 Z

ω0 = 1



0

(14.5)



(14.6)

L1C1



Z0 = L1 C 1



ϕ 0 = tan −1 Z0iL 0 v

(

(14.7)

C3

)

(14.8)

where the parameters are defined below: Vc

Capacitor voltage

iL

Inductor current

isw

Switch current

Z

Impedance

ω0

Angular frequency

Vsw

Switch voltage

φ0

Initial phase

Wloss and PLoss

Energy and power loss

Power Electronics Converters for EVs and Chargers  505 The inductor current flow thru the primary winding (L1). Meanwhile, while the switch is conducting, the switch current increases. The switch can be controlled using its gate pulses, which can be controlled precisely via high speed micro controllers. To achieve low power loss, the switching can be performed using zero voltage switching (ZVS) scheme. Using this technique, the power loss decreases because a majority of power loss is due to switching loss. Switching loss can be calculated as below.





Wloss =

∫

t1 +ton

t1

v sw isw +

Ploss =

∫

t2 +toff

t2

Wloss ton + toff

v sw isw





(14.9)

(14.10)

According to the above equations, if switching happen when the voltage across the switch is passing zero (Vsw=0), ideally, the power loss will be equal to zero. ZVS follows this principal. However, due to practical limitations, this values will not be exactly equal to zero in practice. According to this description and considering the figure above, clearly, we can observe soft-switching using ZVS approach to reduce power loss. To scrutinize the function of the circuit, we need to investigate the circuit under all working conditions. Therefore, various conditions (for example, when the switch is conducting and the diode is off) must be considered separately. For this aim, each condition is depicted in Figure 14.16. Moreover, the related equations for voltage and current of the main elements are brought beside the schematic of each mode.

14.15 WPT and EV Motor Drive Using Single Inverter Although HF and LF inverters are commonly exploited at the first and second sides of WPT system, this structure decreases system’s efficiency. To solve this problem, a multitask inverter can be employed at the primary side. Using this structure not only enhances the system’s efficiency, due to omitting additional inverter, but also results in a decrement in system implementation cost by eliminating large DC capacitor. In the following section, we are going to present the basic principals and scrutinize this structure [54].

506  Power Electronics for Green Energy Conversion Mode I Primary side

Vin

C3

C1

νL2

Pick-up side C4

C2

L1 L2

iL2

VB

= k

VB = L4

dt

C1

C3

C2

L1 L2

C4

νL2

C3

C1

L1 L2

C2

C4

VB

Primary side

C3

dt

+k

L 1 L2

diL4

diL2

dt

diL1

L1 L2

= –C2

νL1

= L1

diL2

dt

+ VB

dt

iL2

iL1

Mode IV

C1

L1 L2

dt

+ L2

diL2

dt

dνL2

dt

Pick-up side

SW

Vin

diL1

= L4

νL2 = k

Mode III

Vin

+k

dt

VB

SW

Primary side

dt

Pick-up side Vin = L1

Vin

diL2

diL4

Mode II Primary side

+ L2

dt diL1

Vin = L1

dt

dνL2

= –C2

SW

diL1

L1 L2

L1 L2

= –C1

diL1

dt

+k

diL2

dt

dνL1

dt

=k

L1 L2

νL2 = k

L1 L2

νL2

L 1 L2

diL1

dt

+ L2

diL2

dt

Pick-up side

C2

C4

VB

SW

Figure 14.16  Operating modes of the EV charger [52, 53].

iL1 = –C1

dνL1 dt

diL1 dt

+ L2

diL2 dt

Power Electronics Converters for EVs and Chargers  507

14.15.1 Problem Definition As explained in the previous section, to obtain HF current, resonant converters can be exploited in WPT systems, which results in higher efficiency and enhancing power transfer capacity. A common WPT system is illustrated in Figure 14.17, which includes an S-PWM-based inverter and the magnetic link. The resonant circuit employed in this system is similar to the circuit explained in the last section. Because of resonant circuit, merely the first harmonic of HF square voltage of the inverter output (Vinv) will be achieved in primary side of the magnetic link. Therefore, we would have HF voltage in the load voltage (VL). The waveforms are shown in Figure 14.17. Obviously, VL is a HF bipolar voltage. Nevertheless, a motor, requires a sinusoidal LF voltage near its rated frequency, as indicated by Vac in Figure 14.17. Thus, VL have to be converted to Vac. Conventionally, using two power electronics converters, the AC input is converterd to DC voltage and, using an inverter, the DC voltage is converted to the required AC output voltage. However, this method requires two converters. Therefore, in the following we are going to introduce an approach to eliminate additional converters.

14.15.2 Wave Shaping Analysis The arithmatic analysis in this section is intended to convert VL to the desired voltage to feed the motor as shown in Figure 14.17. Using the circuit model shown in Figure 14.18, VL is obtained mathematically. In this Vref mi 1/fs Ts* Th* Career

1 S-PWM

Gate drives +

Vdc

Vinv -

DC-Link

θ0=Th/2 θ1

Full bridge inverter

Resonant circuit

Resonant circuit

1/fh = Th*

Vinv Primary

EM

Magnetic Link

VL(n-1) VLn

θn

Vac

VL

VL0

Vm

VL Secondary

Vac Secondary

Figure 14.17  Illustration of dissimilarity of generated voltage and the desirable voltage: the generated voltage by WPT (left), and ideal voltage needed for supplying the motor (right) [54].

508  Power Electronics for Green Energy Conversion Cp I

p

Iinv

Lp

R1

Is

-jωMIS

Vinv

Ls

R2 Cs

jωMIP

IP R1 VL

Zp

RL

Vinv(1)

Rref=ω2M2/R2

Zp Off-board (under the road)

On-board (on the EV)

Figure 14.18  An overview of WPT model (left), and equivalent circuit seen from the primary side (right) [54].

model a variable load is shown as a RL load. To attain the maximum possible efficiency, which is not dependant on the load, the series-parallel compensator topology is required to obtain an optimal frequency of the system [41]. Because of variable duty cycle, the generated square pulse wave is neither an even function nor an odd function. Thus, we have to apply furrier transform to a square pulse voltage to obtain the magnitude of the 1st harmonic of xth voltage pulse for each of even and odd parts:





b1. X = Vdc ⋅

2 − 2cos(2π fhθ X ) π

(14.11)

2sin(2π fhθ X ) π

(14.12)

a1. X = Vdc ⋅

where f h and Vdc represent harmonic frequency and DC voltage, respectively. Therefore, the xth pulse voltage, Vinv(X), can be estimated as:

Vinv(X) ≅ a1X cos(2π f ht) + b1X sin (2π f ht)

(14.13)

where





θX =

(

1 1 − mi sin π X 2n 2 fh n=

fh 4 fs



)

(14.14)

(14.15)

Power Electronics Converters for EVs and Chargers  509 Contemplating natural oscillations, the inverter voltage (Vinv(t)) can be expresses as below:

  kT   (k + 1)T   ∞ Vinv1 (t ) = ∑ ∞k=1 U  t −  − U  t −  ∗ ( ∑ X =0 VLX [U (t − XT ) − U (t − ( X + 1)T )] +  2 2    T T      ∑ ∞k=1 VLX U  t − XT −  − U  t − ( X + 1)T −   .  4 4    

(14.16)

 In the above equations, U(t) represents the step function. Rewriting this equation, the output voltage of inverter can be expressed as below:



Vinv1(t ) =

2Vdc π    π  sin 2π fht − mi sin(2π f st ) cos mi sin(2π f st )     π 2 2

(14.17)

Assuming mi=1 and simplifying the equation:



Vinv1(mi =1) =

2Vdc π π    cos sin(2π f st ) sin 2π fht − sin(2π f st )     π 2 2

(14.18)

Utilizing the resonant circuit in first and second sides at RF as the 1st harmonic order of the inverter voltage, the equivalent model of the system can be obtained. The equivalnet circuit reffered to the first side is depicted in Figure 14.18. Rref is the equivalent resistance seen from primary side. Using this figure, no-load voltage of the system output will be stated as below:



VL (t ) =

M .Vinv (1) Cs ( R1 R2 + ω 02 M 2 )

(14.19)

According to the equation above, clearly, the output voltage depends on Vinv, which does not depend on the load impedance. According to (14.18), VL(t) is a sinusoidal voltage waveform consistent of two terms: a high RF (fh) wave and a low reference frequency (fs) wave. Where fs is the reference

510  Power Electronics for Green Energy Conversion voltage frequency which is required to feed the motor by the inverter. From the two last eqautions, it can be infered that we can create the LF voltage for motor drive and filter HF variations via only one onverter. For this sake, we have to use the following equation, which amends the reference voltage.

Vconv

 VL + VL ,   2 =  VL − VL ,  2

t≤

Ts 2

Ts ≤ t ≤ Ts 2

(14.20)



where Ts represents the switching time period. The resultant waveform is shown in Figure 14.19. Obviously, the waveform includes HF oscillations. Thus, the second term must get rid of the oscillations. To avoid these HF oscilations, we can employ a low pass filter (LPF). Using an inductance (Lf) as a filter, the HF current components will be omitted or significantly attenuated. Since we had modeled the motor as a vaiable RL load, we can calculate the transfer function (H(ω)) as follows:

H (ω ) =

RL

(14.21)

2 L

R + (ω Lr )2

Needless to say, HF current cannot pass through this filter.

14.15.3 Convertor System To carry out the explained power conversion, a bidirectional AC-to-AC chopper with an appropriate control strategy is required to convert VL to VL(n-1)

VL0

Vm

VLn

VL(n-1)

VL0

VLn

VL Pulsating waveform

Vconv

Vm

Converted waveform

Figure 14.19  Pulsating waveform (left) and desired voltage for the motor (right) [54].

Power Electronics Converters for EVs and Chargers  511 Vconv. Using this approach, a HF 1st- harmonic order of the voltage with LF alteration is supplied to the motor. This is a simple approach which can overcome the aforementioned proble. Since the input of this circuit is AC voltage and the output is AC voltage too, no additional rectifier or inverter is required. As explained in the previous sections, the magnetic link merely transfers HF power because its impedance agains LF power is significant, but using the AC/AC chopper, in addition to HF voltage, LF voltage, which is required for supplying electric motor can be provided. The chopper converter is illustrated in Figure 14.20. The chopper includes two switches. Each of these switches consist of two BJT switches and their antiparallel diodes. Regarding the converter’s switching states, different working conditions are defined for the electric motor: 1)  Power Mode In power mode, S1 is conducting and S2 is not conducting. Thus, power flows through the red line shown in Figure 15.21 (1) and (2) to feed the electric motor. The power path of the secondary side compensator is shown by blue line in the same figure. 2)  Bypass Mode In bypass mode, in contrast to the previous mode, the upper switch is not conducting and the lower switch is conducting. This mode presents a path for the current of the electric motor windings to circulate. Figure 14.21 (3) and (4) demonstrates the current flow in this mode.

S1 + ILoad

VL

+

S2

Vconv -

Figure 14.20  The AC/AC converter used for motor drive.

LL RL

512  Power Electronics for Green Energy Conversion

S1

S1

+

+

Iload

VL

+

-

LL

Vconv

S2

-

Iload

VL

+

-

Vconv

S2

RL

(1)

LL

-

RL

+

LL

(2)

S1

S1 +

+

Iload

VL

+

-

Vconv

S2

(3)

Iload

VL LL RL

-

Vconv

S2

-

RL

(4)

Figure 14.21  Current flow for (1) direct power mode, (2) reverse power mode, (3) direct bypass mode, (4) reverse bypass mode.

14.15.4 WPT System and Motor Drive Integration Using this approach, no communication signal is required to be transmitted to the secondary because the LF voltage required to feed the electric motor is supplyied by the same chopper. In other words, the sinusoidal LF reference voltage is put in a PWM with a HF carrier to generate a HF signal for the first side converter. Then, the converter carries out the following functions: First, a HF square wave voltage is provided with a particular modulation to transfer energy thru the coupled magnetic link. Second, the converter drives the motor with the LF sinusoidal voltage. Figure 14.22 shows a general overview of the integrated system. First, the primary converter generates a HF variations related to the reference rotor speed (ω r* ). Then, at the secondary side, after flowing thru WPT, the desired voltage will be generated. Due to power quality requirements, the converted voltage (Vconv) has to include the least possible harmonic distortions (which is known as THD).

Power Electronics Converters for EVs and Chargers  513 ωr*

Wave shaping

Motor Drive

ωr

WPT

Figure 14.22  A general overview of the integrated system.

An effective factor on THD is modulation index. For small indices, THD is larger. Thus, it is suggested to use large modulation index (for example, greater than 0.7).

14.16 Conclusion EVs are going to be the main transportation vehicles in near future; therefore, their performance must be seriously taken into account. There are different aspects that must be included for a thorough investigation. The impact of EVs on the electricity network required infrastructure, efficiency are among these aspects. However, before considering these aspects, it is required to discuss design challenges. Therefore, in this chapter, different types of power electronic converters, which are required in EVs, are introduced. Then, WBG technology for EVs is explained. DC fast chargers and the related challenges also explained. Afterwards, wireless charging is introduced. Providing a short overview of its history, advantages and disadvantages of wireless charging is presented. Finally, after reviewing some of the standards related to EVs wireless charging, some of the challenges of wireless charging are discussed and their solutions are explained.

References 1. Bauman, J. and Kazerani, M., An analytical optimization method for improved fuel cell-battery - Ultracapacitor powertrain. IEEE Trans. Veh. Technol., 58, 7, 3186–3197, 2009, doi: 10.1109/TVT.2009.2014843. 2. Elsied et al., Efficient Power-Electronic Converters for Electric Vehicle Applications, 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), 2015, pp. 1–6, doi: 10.1109/VPPC.2015.7352941. 3. Salem, A., Ahmed, E.M., Orabi, M., Ben Abdelghani, A., Novel three-phase multilevel voltage source inverter with reduced no. of switches, in: IREC 2014 - 5th International Renewable Energy Congress, 2014, doi: 10.1109/ IREC.2014.6827007.

514  Power Electronics for Green Energy Conversion 4. Janabi, A., Member, S., Wang, B., Member, S., Switched-Capacitor Voltage Boost Converter for Electric and Hybrid Electric Vehicle Drives. IEEE Trans. Power Electron., 1, 1, 2019, doi: 10.1109/TPEL.2019.2949574. 5. Du, Z.S., High Density High Power DC-DC Converter Architectures for Future Electric Transportation Applications. 2019 IEEE Energy Convers. Congr. Expo., pp. 5862–5869, 2019. 6. Qian, W., Cha, H., Peng, F.Z., Tolbert, L.M., Member, S., 55-kW Variable 3X DC-DC Converter for Plug-in. 9. IEEE Transactions on Power Electronics, 27, 4, 1668–1678, April 2012. 7. Kreutzer, O., Gerner, M., Billmann, M., Maerz, M., A 3 . 6 kV full SiC fuel cell boost converter for high power electric aircraft. 2018 IEEE Transp. Electrif. Conf. Expo, pp. 220–225, 2018. 8. Stillwell, A., Pilawa-podgurski, R.C.N., Street, N.W., A 5-Level Flying Capacitor Multi-Level Converter with Integrated Auxiliary Power Supply and 5-Level FCML Converter. IEEE Transactions on Power Electronics, 34, 2932–2938, 2017. 9. Novel high efficiency multilevel DC-DC boost converter topologies and modulation strategies. IEEE Conference Publication, [Online]. Available: https://ieeexplore.ieee.org/document/6020604. [Accessed: 30-Nov-2020]. 10. Costa, L.F., Mussa, S.A., Barbi, I., Multilevel Buck / Boost-type DC-DC Converter for High Power and High Voltage Application. IEEE Transactions on Industry Applications, 9994, 6, 3931–3942, 2014, doi: 10.1109/ TIA.2014.2313715. 11. Kardan, F., Alizadeh, R., Banaei, M.R., A New Three Input DC/DC Converter for Hybrid PV/FC/Battery Applications. IEEE J. Emerg. Sel. Top. Power Electron., 5, 4, 1771–1778, Dec. 2017, doi: 10.1109/JESTPE.2017.2731816. 12. Jianwu, Z., Wei, Q., Liyan, Q., Jiao, Y., An isolated multiport DC-DC converter for simultaneous power management of multiple different renewable energy sources. IEEE J. Emerg. Sel. Top. Power Electron., 2, 1, 70–78, 2014, doi: 10.1109/JESTPE.2013.2293331. 13. Ding, Z., Yang, C., Zhang, Z., Wang, C., Xie, S., A novel soft-switching multiport bidirectional dc-dc converter for hybrid energy storage system. IEEE Trans. Power Electron., 29, 4, 1595–1609, 2014, doi: 10.1109/TPEL.2013.2264596. 14. Chen, C.W., Liao, C.Y., Chen, K.H., Chen, Y.M., Modeling and controller design of a semiisolated multiinput converter for a hybrid PV/wind power charger system. IEEE Trans. Power Electron., 30, 9, 4843–4853, Sep. 2015, doi: 10.1109/TPEL.2014.2367594. 15. Zeng, J., Qiao, W., Qu, L., An isolated three-port bidirectional DC-DC converter for photovoltaic systems with energy storage, in: Conference Record IAS Annual Meeting (IEEE Industry Applications Society), 2013, doi: 10.1109/ IAS.2013.6682520. 16. Liu, Y.C. and Chen, Y.M., A systematic approach to synthesizing multi-input DC-DC converters. IEEE Trans. Power Electron., 24, 1, 116–127, 2009, doi: 10.1109/TPEL.2008.2009170.

Power Electronics Converters for EVs and Chargers  515 17. Ronanki, D., Singh, S.A., Williamson, S.S., Comprehensive Topological Overview of Rolling Stock Architectures and Recent Trends in Electric Railway Traction Systems. IEEE Trans. Transp. Electrif., 3, 3, 724–738, Sep. 2017, doi: 10.1109/TTE.2017.2703583. 18. She, X., Huang, A.Q., Lucia, O., Ozpineci, B., Review of Silicon Carbide Power Devices and Their Applications. IEEE Trans. Ind. Electron., 64, 10, 8193–8205, Oct. 2017, doi: 10.1109/TIE.2017.2652401. 19. Ronanki, D., Kelkar, A., Williamson, S.S., Extreme fast charging technology— prospects to enhance sustainable electric transportation. Energies, 12, 19, 1–17, 2019, doi: 10.3390/en12193721. 20. Whitaker, B., Barkley, A., Cole, Z., Passmore, B., Martin, D., McNutt, T.R., Lostetter, A.B., Lee, J.S., Shiozaki, K., A High-Density, High-Efficiency, Isolated On-Board Vehicle Battery Charger Utilizing Silicon Carbide Power Devices. IEEE Trans. Power Electron., 29, 5, 2606–2617, 2014. 21. Abdelrahman, A.S., Erdem, Z., Attia, Y., Youssef, M.Z., Wide Bandgap Devices in Electric Vehicle Converters: A Performance Survey. Can. J. Electr. Comput. Eng., 41, 1, 45–54, 2018, doi: 10.1109/CJECE.2018.2807780. 22. Yuan, Z., Wang, J., Yuan, X., Zhang, Q., High Efficiency and High Power Density Interleaved DC-DC Converter for Electric Vehicles. 2019 22nd Int. Conf. Electr. Mach. Syst. ICEMS 2019, pp. 1–5, 2019, doi: 10.1109/ ICEMS.2019.8922149. 23. Ahmed, M.R. and Li, Y., A low-cost, high-power-density DC-DC converter for hybrid and electric vehicle applications. 2019 21st Eur. Conf. Power Electron. Appl. EPE 2019 ECCE Eur., pp. P.1–P.8, 2019, doi: 10.23919/ EPE.2019.8914879. 24. 200 kilometers in 8 minutes: ABB’s fast chargers power the e-mobility revolution, [Online]. Available: https://new.abb.com/news/detail/4996/ABBs-fastchargers-power-the-e-mobility-revolution. Zurich, Switzerland, 2018-06-08, published by ABB. [Accessed: 05-Dec-2020]. 25. QC45 Fast Charging Station, Electric Mobility, Madrid, Spain, 2020 [Online]. Available: https://electricmobility.efacec.com/ev-qc45-quick-charger/. [Accessed: 05-Dec-2020]. 26. 350kW, Tritium, Brisbane, Australia, 2020, [Online]. Available: https://tritiumcharging.com/product/pk-350/. [Accessed: 04-Dec-2020]. 27. Solutions - EV Charging Solutions - EV Charging Solutions, Delta EMEA, Atlanta, GA, USA, 2019 [Online]. Available: http://www.delta-emea.com/ Solutions/CategoryListT1.aspx?CID=03&SID=319&hl=en-GB&Name=EV ChargingSolutions. [Accessed: 04-Dec-2020]. 28. EVTEC AG, espresso&charge. Switzerland, 2020. [Online]. Available: https:// www.evtec.ch/en/products/espressoandcharge-usp/. [Accessed: 04-Dec-2020]. 29. Srdic, S. and Lukic, S., Toward Extreme Fast Charging: Challenges and Opportunities in Directly Connecting to Medium-Voltage Line. IEEE Electrif. Mag., 7, 1, 22–31, 2019, doi: 10.1109/MELE.2018.2889547.

516  Power Electronics for Green Energy Conversion 30. Liasi, S.G. and Golkar, M.A., Electric vehicles connection to microgrid effects on peak demand with and without demand response, in: 2017 25th Iranian Conference on Electrical Engineering, ICEE 2017, pp. 1272–1277, 2017, doi: 10.1109/IranianCEE.2017.7985237. 31. F.T. Jauch, Medium Voltage AC-DC Converter Systems for Ultra-Fast Charging Stations for Electric Vehicles. No. 23280, 2016. 32. Chen, H., Cong, T.N., Yang, W., Tan, C., Li, Y., Ding, Y., Progress in electrical energy storage system: A critical review. Prog. Nat. Sci., 19, 3, 291–312, 10-Mar-2009, doi: 10.1016/j.pnsc.2008.07.014. 33. Ribeiro, P.F., Johnson, B.K., Crow, M.L., Arsoy, A., Liu, Y., Energy Storage systems for Advances Power Applications. Proc. IEEE, 89, 12, 1744–1756, 2001, doi: 10.1109/5.975900. 34. Nemecek, J.J., Simmons, D.E., Chubb, T.A., Demand sensitive energy storage in molten salts. Sol. Energy, 20, 3, 213–217, Jan. 1978, doi: 10.1016/0038-092X(78)90099-3. 35. M. Hutin and L. Maurice, TRANSFORMER SYSTEM FOR ELECTRIC RAILWAYS. US527857A, 23-Oct-1894. 36. Treffers, M., History, current status and future of the wireless power consortium and the Qi interface specification. IEEE Circuits Syst. Mag., 15, 2, 28–31, Apr. 2015, doi: 10.1109/MCAS.2015.2418973. 37. Global Industry Leaders Aim to Redefine Power in 21st Century as Smart and Wireless with Formation of the Power Matters Alliance, News, Boise, Idaho, USA, 2012, [Online]. Available: https://eepower.com/news/global-industryleaders-aim-to-­redefine-power-in-21st-century-as-smart-and-wireless-withformation-of-the-power-matters-alliance/. [Accessed: 10-Dec-2020]. 38. J1772: SAE Electric Vehicle Conductive Charge Coupler, SAE International, Warrendale, PA, USA, 2001, [Online]. Available: https://www.sae.org/standards/content/j1772_200111/. [Accessed: 10-Dec-2020]. 39. J1772A: SAE Electric Vehicle and Plug in Hybrid Electric Vehicle Conductive Charge Coupler, SAE International, Boise, Idaho, USA, 2017, [Online]. Available: https://www.sae.org/standards/content/j1772_201710/. [Accessed: 10-Dec-2020]. 40. Patil, D., McDonough, M.K., Miller, J.M., Fahimi, B., Balsara, P.T., Wireless Power Transfer for Vehicular Applications: Overview and Challenges. IEEE Trans. Transp. Electrif., 4, 1, 3–37, Dec. 2017, doi: 10.1109/TTE.2017.2780627. 41. Hasanzadeh, S. and Vaez-Zadeh, S., Efficiency analysis of contactless electrical power transmission systems. Energy Convers. Manage., 65, 487–496, Jan. 2013, doi: 10.1016/j.enconman.2012.07.007. 42. Li, S. and Mi, C.C., Wireless power transfer for electric vehicle applications. IEEE J. Emerg. Sel. Top. Power Electron., 3, 1, 4–17, Mar. 2015, doi: 10.1109/ JESTPE.2014.2319453. 43. Lovison, G., Sato, M., Imura, T., Hori, Y., Secondary-side-only simultaneous power and efficiency control for two converters in wireless power transfer

Power Electronics Converters for EVs and Chargers  517 system, in: IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, pp. 4824–4829, 2015, doi: 10.1109/IECON.2015.7392855. 44. Huang, Z., Wong, S.C., Tse, C.K., An Inductive-Power-Transfer Converter with High Efficiency Throughout Battery-Charging Process. IEEE Trans. Power Electron., 34, 10, 10245–10255, Oct. 2019, doi: 10.1109/ TPEL.2019.2891754. 45. Fu, W., Zhang, B., Qiu, D., Study on frequency-tracking wireless power transfer system by resonant coupling, in: 2009 IEEE 6th International Power Electronics and Motion Control Conference, IPEMC ‘09, pp. 2658–2663, 2009, doi: 10.1109/IPEMC.2009.5157857. 46. Shin, Y., Hwang, K., Park, J., Kim, D., Ahn, S., Precise vehicle location detection method using a wireless power transfer (WPT) system. IEEE Trans. Veh. Technol., 68, 2, 1167–1177, Feb. 2019, doi: 10.1109/TVT.2018.2885942. 47. Zakerian, A., Vaez-Zadeh, S., Babaki, A., Maximum Efficiency Control of a Wireless EV Charger with On-Line Parameter Calculation, in: 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer, WoW 2019, pp. 85–88, 2019, doi: 10.1109/WoW45936.2019.9030616. 48. Williams, B.W., Transformer Isolated Buck-Boost Converters. J. Renew. Energy Sustain. Dev., 2, 2, 112–125, 2016. 49. Elkhateb, A., Adam, G., Morrow, D.J., DC-to-DC Converter Topologies for Wireless Power Transfer in Electric Vehicles, in: IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, pp. 1665–1669, 2019, doi: 10.1109/IECON.2019.8927144. 50. Jin, L., Song, K., Zhu, C., Wei, G., Lu, R., Dong, S., A CCL topology based mid-range power transfer system for low voltage side equipments on power lines, in: 2017 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer, WoW 2017, pp. 9–12, 2017, doi: 10.1109/WoW.2017.7959356. 51. Li, W., Zhao, H., Li, S., Deng, J., Kan, T., Mi, C.C., Integrated LCC Compensation Topology for Wireless Charger in Electric and Plug-in Electric Vehicles. IEEE Trans. Ind. Electron., 62, 7, 4215–4225, Jul. 2015, doi: 10.1109/TIE.2014.2384003. 52. Viola, F. and Longo, M., On The Strategies for the Diffusion of EVs: Comparison between Norway and Italy | Viola | International Journal of Renewable Energy Research (IJRER). Int. J. Renew. Energy Res., 7, 3, 1376– 1382, 2017. 53. Murakami, A., Omori, H., Kimura, N., Morizane, T., A New Type of Wireless Electric Vehicle Charger with Miniaturized Single-Ended High PowerFactor Converter, in: 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018, pp. 555–560, 2018, doi: 10.1109/ ICRERA.2018.8566860. 54. Babaki, A., Vaez-Zadeh, S., Zakerian, A., Jafari, A., Analysis and Control of Wireless Motor Drives with a Single Inverter in Primary Side. IEEE Trans. Energy Convers., 36, 1–1, Sep. 2020, doi: 10.1109/tec.2020.3026072.

15 Recent Advances in Fast-Charging Methods for Electric Vehicles R. Chandrasekaran*, M. Sathishkumar Reddy, B. Raja and K. Selvajyothi

*

Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Tamil Nadu India

Abstract

In this chapter, the recent developments of DC-DC converters and control strategy for various charging techniques are discussed. The modeling and design of DC fast-charging techniques for electric vehicles is proposed. The proposed method consists of various DC-DC converters as a power conditioning unit, and a suitable charge control scheme is employed. The performance of the conventional charging methods is compared with selected converters for DC fast-charging technique and its feasibility for level 3 charging is addressed. The proposed model is validated through simulation study conducted in MATLAB/Simulink environment for the given EV battery. Keywords:  CC-CV charging, multicurrent charging, fast charging, DC-DC converters

15.1 Introduction In the last two decades, the electric powered transportation is emerging in all expanses, such as jet, electric loco motives, boats, and electrical vehicles (EVs). The usage of lithium ion batteries (LiB) are increased in transportation due to its considerable specific energy density and specific power density compared to other secondary batteries. Thus, this reduces the battery *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (519–544) © 2022 Scrivener Publishing LLC

519

520  Power Electronics for Green Energy Conversion pack size and hence the weight [1]. This salient feature of lithium ion battery is very much attractive for developing portable vehicles like 2-wheelers (2W) and 3-wheelers (3W). The LiBs exist in the market for several years, and it is used in different hand held devices such as computers, laptops, cell phones, etc. Now, it is tremendously used in HEVs and EVs. Still, the implementation in EVs has been restricted due to suspicious over safety, increased overall cost, and the time taken to charge the battery pack. Safety concerns arise mainly from thermal runaway. Charging period is one of the significant restrictions of EV transportation innovation, as it must be quicker compared with the process of fuel filling in internal combustion engine (ICE) vehicles.

15.2

Levels of Charging

The typical charging strategies of EVs have been arranged in three levels [2] based on the capacity and the duration of charging as follows:

15.2.1 Level 1 Charging Level 1 charging [5] is a slow charging scheme which has the input voltage 120 V/230 V AC from supply mains in homes and commercial places. The schematic representation of level 1 charging is shown in Figure 15.1. The charging current ranges [3] between 15 and 20 A. The power rating is in the range between 1 and 2 kW, and it takes 8 to 16 h to complete the charging of the battery. Figures 15.2 and 15.3 show the connectors employed in this charger. At one end, NEMA 5–15 R/20 R connector is used and at other ends SAE J1772 type connector [4] is used. SAE J1772 Type-1connector provides on board AC and DC charging. This charging method is most suitable for all types of consumers, and the charging can be done in their homes without any additional hardware requirements.

15.2.2 Level 2 Charging Level 2 charging [5] is the appropriate on board charging strategy providing both AC and DC for individual and common places like offices and malls. This method of charging can be attained either from 240 V, single-phase supply or from a 410 V, three-phase supply. Figure 15.4 shows the schematic representation, and Figure 15.5 shows corresponding connector used in this charging method. The charging current is limited to a maximum of 40 A and 80 A [6] in case of charging circuit from a single-phase

Advances in Fast-Charging Methods for EVs   521

NEMA 5-15

1 o o o

Figure 15.1  Level 1 charging.

Figure 15.2  NEMA 5–15 R/20R connector.

Figure 15.3  SAE J1772 type 1 connector.

supply and three-phase supply, respectively. SAE J1772 type-2 standard is proposed by SAE International and develops the connectors for level 2 charging. Sometimes, when DC charging alone is needed, Tesla connector is used. The power transfer capability of this type of charger is between 7.7 and 25.6 kW and takes 4 to 8 hours to complete the charging.

522  Power Electronics for Green Energy Conversion

J1772

Tesla

Figure 15.4  Level 3 charging and its connectors.

Figure 15.5  SAE J1772 type 2 connector.

15.2.3 Level 3 Charging Level 3 charging is an off board DC quick charger kept outside the EVs. Figure 15.6 shows schematic representation of this charging method. These charging stations do charge the vehicles up to 80% within 10 to 15 minutes and resembles the commercial fuel pump stations. The time required for full charge is depends on the type of battery used in vehicle and its power requirements. The Level 3 charging has an AC to DC conversion in its first level and the rectified DC is regulated by DC-DC converters [7]. The input supply is from a three-phase circuit mains with voltage of 208 V/600 V AC, which can handle a charging current up to 200 A. The combined charging system (CCS), Japanese CHAdeMO [8] in Figure 15.7 and Tesla super charger standards describes the design parameters of level 3 charging. The CCS system is an improved version of the type 2-J1772 connector. It is modified with two additional power contacts for the quick charging. For its Tesla, supercharger uses a modified version of the type 2-J1772 connector.

Advances in Fast-Charging Methods for EVs   523

CCS

CHAdeMO

Tesla

Figure 15.6  Level-3 charging and its connectors.

Figure 15.7  CHAdeMO connector (50 kW DC).

15.3 EV Charging Standards The various EV charging standards have been developed in recent years. A global standard IEC 62196 was established by the International Electrotechnical Commission (IEC), and it acts as an authority of standards [9] for a various charging standards. The global standard governs the power ratings and type of communication interfaces. There are separate standards for describing the structural and electrical specifications of the particular accessories. The SAE J1772 standard governs the AC and DC charging regulations in North America and VDE-AR-E 2623-2-2 standard governs 1Φ and 3ΦAC charging regulations of Europe and finally JEVS G105-1993, also called as CHAdeMogoverns high-power DC charging in Japan and it is a globally accepted. The maximum power output and charging current ratings and design principle codes given by SAE* and IWC* is given in Table 15.1.

524  Power Electronics for Green Energy Conversion Table 15.1  EV Charging station standards. Charging levels

Max. power (kW)

Charging current (A)

I

4−7.5

16

II

8−15

32

III

60−120

250

Quick charging (DC)

100−200

400

I

2

16

II

20

18

III

above 20

 

I

40

80

II

90

200

III

240

400

62.5

125

Standard—IEC AC charging

Standard—SAE AC charging

DC charging

CHAdeMo Quick charging (DC)

SAE* -Society of Automotive Engineers (SAE), IWC* - Infrastructure Working Council

15.4 Battery Charging Methods The recharging of EV batteries have been developed with numerous schemes and can be seen in various levels of implementations. These schemes decide the waveform profile and the currents or voltage levels required during EV charging, whereas the type of the energy source and

Advances in Fast-Charging Methods for EVs   525 its establishments determines how the energy is transferred to vehicle. The various methods [10] available for charging a battery are: 1. 2. 3. 4. 5.

Constant voltage charging Constant current charging Constant current constant voltage charging Pulse current charging Multi current level charging

To analyze the performance of above mentioned charging methods the battery parameters given in Table 15.2 is considered. In this section the study is performed under the ideal conditions of the converter.

15.5

Constant Voltage Charging

In this method, the terminal voltage of the battery is kept constant as the maximum voltage that should be appeared across the battery terminals. However, the battery a constant power device draw a charging current, which gradually decreases till it attains maximum capacity or in other words 100% SoC. The performance parameters of the battery like SoC, Charging Current and power during CV charging method is as shown in Figure 15.8. The variation of these parameters with respect to time in seconds is demonstrated. In this method, the specified battery takes 2436 s to attain 100% SoC. This method is suitable for batteries that are having lower voltage ratings [11]. The temperature of the battery is within limits while charging. Table 15.2  Battery parameters chosen for the study. S.no

Parameter

Specifications

1

Open Circuit Voltage

55.2 V

2

Nominal Voltage

51.2 V

3

Current

40 Ah

4

Cut Off Voltage

42 V

5

Temperature

25oC

6

Initial SoC

20%

526  Power Electronics for Green Energy Conversion CV CHARGING RESPONSE SoC %

100 50

Current (A)

Voltage (V)

0

500

1000

1500

2000

2500

0

500

1000

1500

2000

2500

0

500

1000

1500

2000

2500

0

500

1000

1500 Time (Sec)

2000

2500

50

0 100 50 0 5000

Power (W)

0

0

Figure 15.8  Variation of SoC, current, and power during CV charging.

The battery requires a longer time to complete the charging. Consider 3.3 V, 2.3 Ah LiFePO4 batteries have an energy of 7.59 Wh. The maximum output voltage of this cell is 3.748 V. As the terminal voltage of the battery increases the charging current drawn by the battery reduces so as to maintain the power at that instant a constant.

15.6 Constant Current Charging In this charging method, the current supplied for charging the battery is kept constant till the battery voltage reaches its fully charged value. The steady current supplied to the battery at this stage causes overheating may damage and deteriorates the lifetime of the battery. The performance of the battery in this method of charging is illustrated as shown in Figure 15.9. When a charging current of 80A (2C) is applied to the battery the terminal voltage gradually grows along with SoC and power. When SoC reaches 100%, the battery voltage can be seen as its open circuit voltage (OCV).

Advances in Fast-Charging Methods for EVs   527 CV CHARGING RESPONSE SoC %

100 50

Voltage (V)

0 0

400

800

1200

1600

2000

0

400

800

1200

1600

2000

0 0 100

400

800

1200

1600

2000

400

800

1200 Time (Sec)

1600

2000

60 58

Current (A)

Current (A)

10000 5000

50 0

0

Figure 15.9  Variation of SoC, current, and power during CC charging.

Actually, the battery voltage need to be regulated to its nominal voltage. In CC charging method, the specified battery takes 1230 s to attain 100% SoC as shown in Figure 15.9. Though this method takes minimum time to charge the battery, the temperature exceeds the nominal values and deteriorates the battery life because of constant heavy current.

15.7 Constant Current-Constant Voltage (CC-CV) Charging This method of charging is also called as “voltage controlled charging” and is an amalgamation of both CC and CV charging methods. In this charging method, the charger draws a constant current from the supply mains until the battery voltage reaches a particular value (say 80% of its final value). Then the terminal voltage is kept constant so that the current is reduced gradually until the charging is completed. The variation of battery parameters SoC, charging current and terminal voltage with respect to time in

528  Power Electronics for Green Energy Conversion CC-CV CHARGING RESPONSE SoC %

100 50

Current (A)

Voltage (V)

0

400

800

1200

1600

2000

0

400

800

1200

1600

2000

0

400

800

1200

1600

2000

0

400

800 1200 Time (Sec)

1600

2000

58 56 100 50 0

Power (W)

0

60

5000

0

Figure 15.10  Variation of SoC, current, and power during CC-CV charging.

seconds is illustrated through Figure 15.10. A steady current of 80A is applied to charge the battery until it attains 0.8 times OCV. Corresponding to this voltage, an SoC of approximately 20% only is attained by the battery. By this method, the battery attains 100% SoC within 1440 s. The chemical stabilization process is too slow, and this limits the C-rate of the lithium-ion batteries, so this method is not suitable in quick charging of EV batteries [12]. To overcome the battery polarization issue, the CC-CV method has been further developed, including multiple level current charging scheme, and it reduces the charging time of the battery.

15.8 Multicurrent Level Charging The battery charging is done by multiple constant current to reach 100% SoC with minimum time. In beginning of the charging process battery pack is charged by constant current (CC) method, this will continued till the battery voltage reaches upper threshold voltage and the charging is

Advances in Fast-Charging Methods for EVs   529 MULTI CURRENT CHARGING RESPONSE SoC %

100 50

Current (A)

Voltage (V)

0

0

500

1000

1500

2000

0 100

500

1000

1500

2000

0

500

1000

1500

2000

0

500

1000 1500 Time (Sec)

2000

64 62 60 58 56

50 0

Power (W)

5000

0

Figure 15.11  Variation of SoC, current, and power during multicurrent level charging.

terminated at this level. By this method, the cell can be charged without voltage saturation. The major benefit of this method is the charging time and voltage stress on the battery is reduced. The control circuit is intended to acquire the SoC and voltage of the battery being charged and based on the acquired values the charging current is shifted from one level to other level. Here, a steady current of 80A is supplied to the battery till 80% of the rated voltage is attained. Further, the charging current is reduced in steps, such as to reduce the charging current until 100% SoC is reached. Figure 15.11 shows the battery attained 100% SoC in about 1510 s.

15.9 Pulse Charging In this method of charging, the charging current is not continuous like above said methods. This method sends high-frequency current pulses to the battery to reduce the charging time and battery heating. The efficiency

530  Power Electronics for Green Energy Conversion of the charging process is improved due to the complete ion diffusion in the electrode is enhanced by the rest period of the charging pulse. Let us consider the 1st-order RC model of the battery as shown in Figure 15.12, the charging impedance of the battery is given by:

Z Charge  = R 0 +



R1 1 + jωR 1C1

(15.1)



The charging impedance is mainly depends internal resistance R0, as well as the polarization time resistance and time constant of the battery. During charging, the battery behaves like a capacitance. If the charging carried out through high switching frequency, the internal impedance of the battery will reduce the corresponding charging time of the battery also reduced. The appropriate duty ratio and switching frequency in pulse charging method improves lifetime compared to constant current-constant voltage charging method. In the other side if the switching frequency and duty cycle is not selected properly, it can rigorously shrink the life cycles of the battery, therefore, the pulse charging method should be designed with suitable charging parameters. The variation of battery parameters SoC, Charging current and terminal voltage with respect to number of samples is shown in Figure 15.13. A pulsed current of average value 40A is applied to charge the battery till it attains 60% of its OCV. Corresponding to this voltage SoC is approximately 20% only. By this method, the battery attains 100% SoC in around 2400 s.

15.10 Converters and Its Applications The power electronic converter is an energy control unit which includes the electronic, magnetic, and electrochemical components, which regulates the C1 ib

Ro

i1 U (SoC, T)

Figure 15.12  R-C model of battery.

R1 V

Advances in Fast-Charging Methods for EVs   531 PULSE CHARGING RESPONSE SoC %

100 50

Current (A)

Voltage (V)

0 0

1000

1500

2000

2500

500

1000

1500

2000

2500

500

1000

1500

2000

2500

500

1000 1500 Time (Sec)

2000

2500

50

0 0 100 50 0 -50 0

3000 Power (W)

500

200 100 0 0

Figure 15.13  Variation of SoC, current, and power during pulse charging.

energy transfer in electro mechanical devices. In an electric vehicle (EV) the wheels are propelled by the traction motors powered by the batteries, ultracapacitor or by the engine. So it is necessary to control the motor in required speed-torque conditions by the power drawn from the source as per the need of the motor. The powertrain must be able to transfer power bidirectional between the battery and the wheels. In DC-DC converter-based charging circuits, the battery charging current is regulated by varying the conduction period of the switch [13]. The ratio between the ON time to the total period of the gate pulse U is referred to as the duty cycle D. The value of D expresses the average value of U over the interval of the pulse T, as represented in Figure 15.14. Here, the converters are presumed to working in continuous conduction mode (CCM), which indicates the charging current of the battery is always continuous and the current carried by the inductor is always greater than 0. In this section, the circuit topologies and design of buck, boost, interleaved buck, and interleaved boost converters suitable for charging the specified EV battery are presented.

532  Power Electronics for Green Energy Conversion U 1

Time 0

DT

T

T+DT

Figure 15.14  Gate signal U for the switching devices in the converter.

15.10.1 Buck Converter The circuit topology for the Buck converter [14] with ideal switches is represented in Figure 15.15. The Buck converter regulates the input voltage and the output voltage is always less than the input voltage VOUT< VIN. When S1is ON, VL = VIN − VOUT, and when S1 is OFF, VL = −VOUT. For the total time period “T” of the pulse, the average voltage VL across the inductor “L” is zero for steady state condition. If VL is not equal to zero for the period “T” the converter does not attain the steady state. From (15.2), the voltage conversion ratio VIN and VOUT in the steady state is obtained as in (15.3), where D is the duty cycle of S1.



VL =

1  T

∫

DT

0

VIN − VOUT DT +

 − VOUT DT   = 0  DT

∫

T

D.VIN = VOUT

(15.2) (15.3)

As the range of duty cycle is between 0 (S1always open) and 1 (S1 always closed), the output voltage VOUT varies between 0 and VIN, under ideal conditions, such as all the circuit elements are perfect, and there is no ON state and OFF state losses within the converter and the source. In such conditions, the input power in (15.4) is equal to the output power in (15.5) under U

L

S

Vin

Figure 15.15  Buck converter.

i + VL

FD

iOUT

C

+ VOUT -

R

Advances in Fast-Charging Methods for EVs   533 steady state. The performance parameters of the converter and the current conversion ratio are given by (15.4), (15.5), (15.6) and (15.7).

PIN = VIN * IIN

(15.4)

POUT = VOUT * IOUT

(15.5)

PIN = POUT

(15.6)



(15.7)

D * IOUT = IIN L and C Values Volt second equation. Derivation

15.10.2 Boost Converter The circuit topology of the Boost (Step-up converter) is given in Figure 15.16. It is assumed that the circuit is operating under steady-state condition and the input voltage is an ideal source. The operation of the converter is as follows, when switch S1 is closed the diode D is reverse biased and the voltage across the inductor VL starts to increase and becomes equal to the source (VIN). Then, when S1 is off, energy accumulated during ON period in the inductance is discharged through the filter capacitor C and the voltage across the inductor is VL = VIN − VOUT. This process transfers the energy from source to load with an output voltage VOUT higher than the VIN. This type of converter is required in electric power train to regulate the energy between the low voltage battery pack to the traction inverter which is operating with voltage greater than source voltage. Over the time period “T” the average value of VL is equal to 0 under steady-state operation. From (15.8), the transformation ratio between VIN and VOUT in the steady state is given by relation (15.9), where D is the duty cycle of S1

L

i + VL

U

Vin

Figure 15.16  Boost converter.

iOUT

FD S

C

+ VOUT -

R

534  Power Electronics for Green Energy Conversion

VL =



1  T

∫

DT

0

VIN DT +

 VIN − VOUT DT  = 0  DT

∫

T

VIN = (1 – D) VOUT

(15.8) (15.9)

The current conversion ratio of the boost converter under steady state is given by:

IOUT = (1 – D) IIN

(15.10)

15.10.3 Interleaved Buck Converter The interleaved buck converters are used to reduce the ripple current achieved with high voltage conversion ratio. The structure of the interleaved converter [12, 15] is shown in Figure 15.17 is simple and the converter design requires the switches which is rated more than the source voltage because of the voltage stress. This causes an increase in cost, conduction losses, forward voltage drop and reverse recovery. If the conventional buck converter is operated under hard switching, then the efficiency becomes poor. To achieve the higher efficiency, it is required to split the output current into two parallel paths, which reduces the conduction losses of the switch. Operating the converter at high switching frequency, increases the transient behavior of the converter and power density. But the high switching frequency causes the increase in switching losses, and it affects the conversion efficiency so the selection of switching frequency must be made appropriately. In interleaved topologies, the converters are

U

L1

S1 U’

L2

S2

iL1 + VL1 iL2

+ VL2 -

Vin D1

D2

Figure 15.17  Interleaved buck converter.

iOUT C

+ VOUT -

R

Advances in Fast-Charging Methods for EVs   535 connected in parallel to share the power flow to the loads, thus the current carried by the inductor is halved in a two-phase interleaved converter, and it reduces the size and volume of filter elements. The control strategy for multiphase interleaved converter should be designed in focus to reduce the ripples in input current, output current, and output voltage. The working of the interleaved converter is detailed in various modes as below: Mode I Switch S1- ON & S2 - OFF L1 charges and L2 discharges and freewheels through the diode D2. The corresponding expressions for inductor voltages are given as

VL1 = VIN − VOUT

(15.11)

VL2 = − VOUT

(15.12)

Mode II Switch S1- OFF & S2 - OFF L1 and L2 discharges and freewheels through D1 and D2. The equations are,

VL1 = VL2 = − VOUT Mode III Switch S1- OFF & S2 - ON L2 charges and L1 discharges and freewheels through the diode D1.

VL1 = VIN − VOUT

(15.13)

VL2 = − VOUT

(15.14)

The design expressions for the circuit elements are given as



VOUT =D VIN

(15.15)

15.10.4  Interleaved Boost Converter The circuit topology of 2-phase interleaved boost converter is represented in Figure 15.18. The circuit contains two switches (S1 and S2) and two

536  Power Electronics for Green Energy Conversion L1

L2

iL1

D1

+ VL1 iL2

iOUT

D2

+ VL2 U

Vin

S1

C

U’

+ VOUT -

R

S2

Figure 15.18  Schematic diagram of interleaved boost circuit.

diodes (D1 and D2). The switches and diodes are connected in parallel for each phase. To reduce the size and volume of filter components, a suitable phase-shift control strategy need to be implemented [16]. The duration of each gate pulse can be obtained from T / (M × N), where T is time period of the switching pulse, M is the number of phases, and N is the number of switches connected in parallel per phase.

15.11 Design of DC-DC Converters The design procedure for these converters is available in many literature, and the design equations are tabulated as in Table 15.3. The optimal design Table 15.3  Design equations for DC-DC converters. Converter

Inductance (H)

Capacitance (F)

Duty cycle

Buck DC-DC Converter

L=

Vout (1 − Dmin )  ∆i L f s

C=

∆ Il 8 f s ∆Vout 

D=

Boost DC-DC Converter

L=

Vin  D ∆i L f s

C=

DIout f s Vout

D =1−

Interleaved 2-Phase Buck DC-DC Converter

L=

(Vin − Vout )D ∆i L f s

C=

∆ Il 8 f s ∆Vout 

D=

Interleaved 2-Phase Boost DC-DC Converter

L phase =

Vin  D ∆i L f s

C=

DIout f s Vout

D =1−

Vout Vin Vin Vout

Vout Vin Vin Vout

Advances in Fast-Charging Methods for EVs   537 of components is chosen based on the operating conditions like input and output specifications. The following requirements are considered critical [17, 18] while designing DC/DC converter circuits for any applications: • • • •

Stable operation, Efficiency, Low output ripple, Converter dynamics.

These properties can be improved to some extent by changing the DC/ DC converter modules and its auxiliary parts. The circuit parameters of Table 15.4  Specification of components and parameters usedin DC-DC converters under study.

Symbols Parameters

Buck converter

Boost converter

Interleaved buck converter

Interleaved boost converter

Vin

Input voltage (V)

120

24

120

24

Vout

Output voltage 60 (V)

60

60

60

fsw

Switching frequency (kHz)

20

20

20

20

ILmax

Inductor current (A)

80

80

80

80

∆IL

Inductor current ripple (5% of ILmax) (A)

4

4

4

4

Output ripple (V)

6

6

6

6

N

Number of phases

1

1

2

2

P

Output Power (kW)

2.4

2.4

2.4

2.4

D

Maximum Duty Cycle

0.5

0.6

0.5

0.6

L

Inductor (µH)

375

120

375

120

C

Capacitor (µF) 20.8

40

20.8

40

∆Vout

538  Power Electronics for Green Energy Conversion selected DC-DC converter topologies for EV battery fast-charging application is presented in Table 15.4 with a continuous conduction mode of operation. These design values are the essential components for experimental validation of DC-DC converters.

15.12 Results and Discussions The converters designed with abovementioned specifications and parameters are simulated in Matlab-SIMULINK for charging a2-wheeler EV battery. The schematic of SIMULINK model of the buck converter-based fast charging is shown in Figure 15.19. A constant pulse current of 80A is supplied to the battery through the buck converter till the battery attain 80% SoC. In Figure 15.20 for an incremental change in SoC from 20% to 20.2%, the battery takes about 3.8 s. A switching frequency 20 kHz is chosen for this study. The schematic of SIMULINK model of the boost converter-based fast charging is shown in Figure 15.21. A constant pulse current of 40A (1C) is supplied to the battery through the converter till the battery attain 80% SoC. In Figure 15.22 for an incremental change in SoC from 20% to 20.2%, the battery takes about 4 s. This indicates that with 2C charging, the battery will take 2 s for 0.2% increase in SoC. The schematic of SIMULINK model of the interleaved buck ­converter-based fast charging is shown in Figure 15.23. A constant pulse current of 80A is supplied to the battery through the converter till the

D

P

g

DC Source 100V

S

D

PWM

+

+

+

L

S D

C

+

SOC (%) Current (A)

-

Voltage(V)

Battery Pack 4S, 4*12.8V,40Ah

Charge Controller SOC (%)

D Charging Current (A)

Figure 15.19  Buck converter Simulink model.

Advances in Fast-Charging Methods for EVs   539

SoC %

20.4 20.2 20

Current (A)

100 0 -100 -200

Voltage (V)

BUCK CONVERTER CHARGING PERFORMANCE

60 40 20 0

SOC (%)

0

1

2

3

4

5

6

7

8

9

10 Current(A)

0

1

2

3

4

5

6

7

8

9

10 Voltage

0

1

2

3

4

5 6 Time (Sec)

7

8

9

10

Figure 15.20  Buck Converter charging performance.

P

PWM 20kHz

+

+

+

D

+

C

S

SOC (%) Current (A)

-

Voltage (V)

s

DC Source 100V

D

L

g

D

Battery Pack 4S, 4*12.8V,40Ah Charge Controller SOC (%)

D Charging Current (A)

Figure 15.21  Boost converter Simulink model.

battery attain 80% SoC. In Figure 15.24 for an incremental change in SoC from 20% to 20.2% the battery takes about 3.7 s. The schematic of SIMULINK model of the interleaved boost converter-­ based fast charging is shown in Figure 15.25. A constant pulse current of 80A is supplied to the battery through the converter till the battery 80% SoC. In Figure 15.26 for an incremental change in SoC from 20% to 20.2%, the battery takes about 1.7 s with reduced voltage ripple and charging time. Table 15.5 shows the charging time of the battery with various converters in DC fast-charging technique.

540  Power Electronics for Green Energy Conversion

SoC %

BOOST CONVERTER CHARGING PERFORMANCE 20.2 20.1

Current (A)

0 -10 -20 -30 -40

Voltage (V)

20

60 40 20

SOC (%)

0

1

2

3

4

5

6

7

8

9

10 Current(A)

0

1

2

3

4

5

6

7

8

9

10 Voltage(V)

0 0

1

2

3

4

5 6 Time (Sec)

7

8

9

10

Figure 15.22  Boost converter charging performance.

D

P

g D

S

g D DC Source + 24V

+

L2

S2

PWM 20kHz

+

S

+

L1

S1 D1

Charge Controller SOC (%)

D Charging Current (A)

Figure 15.23  Interleaved buck converter Simulink model.

SOC (%) Current (A)

-

C

D2

+

Voltage (V)

Battery Pack 4S, 4*12.8V,40Ah

SoC %

Advances in Fast-Charging Methods for EVs   541 20.6 20.4

INTERLEAVED BUCK CONVERTER CHARGING PERFORMANCE SOC (%)

20.2

Voltage (V)

Current (A)

20

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

1

2

3

4

5

6

7

8

9

10 Current(A)

1

2

3

4

5

6

7

8

9

10 Voltage (V)

0 0

1

2

3

4

5 6 Time (Sec)

7

8

9

Figure 15.24  Interleaved buck converter charging performance.

D

P +

PWM 20kHz

L2

D2

L1

D1

D g

+

C

S2

+

SOC (%) Current (A)

-

Voltage (V)

Battery Pack 4S, 4*12.8V,40Ah

S

S1 S

DC Source + 24V

D g

+

Charge Controller SOC (%)

D Charging Current (A)

Figure 15.25  Interleaved boost converter Simulink model.

10

542  Power Electronics for Green Energy Conversion INTERLEAVED BOOST CONVERTER CHARGING PERFORMANCE SoC %

21 20.5 20 Current (A)

SoC (%)

0

2

3

4

5

6

7

8

9

0

10 Current (A)

-50 -100 0

Voltage (V)

1

1

2

3

4

5

6

7

8

9

54

10 Voltage (V)

52 50 0

1

2

3

4

5 6 Time (Sec)

7

8

9

10

Figure 15.26  Interleaved boost converter charging performance.

Table 15.5  Comparison of DC fast charging time for various converters.

S. no

Converter topology

Charging time (s) (change in SoC from 20% to 20.2 %)

1.

Buck Converter

3.8

2.

Boost Converter

4.0

3.

Interleaved Buck Converter

3.7

4.

Interleaved Boost Converter

1.7

15.13 Conclusion In order to enhance green transportation, the global market focuses onto electric vehicles and required infrastructure development. At present, the charging time of the battery is the key issue in adopting this ecosystem. In this chapter, a study is performed to confirm the feasibility of DC fastcharging techniques for EVs. The performance of the proposed charging method is verified using various converters, and interleaved boost converter is found to be faster in charging and ripple free.

Advances in Fast-Charging Methods for EVs   543

References 1. Khan, W., Ahmad, A., Ahmad, F., Alam, M.S., A Comprehensive Review of Fast Charging Infrastructure for Electric Vehicles A. Smart Sci., 0477, 1–15, 2018. doi: 10.1080/23080477.2018.1437323. 2. Collin, R., Miao, Y., Yokochi, A., Enjeti, P., Jouanne, A., Advanced electric vehicle fast-charging technologies. Energies, 12, 1839, 2019. 10.3390/ en12101839. 3. Krishnamurthy, Mr. V., Varma, R., Tribhuvan, S., Shaikh, A., Automatic battery charger. Int. J. Eng. Res. Technol. (IJERT), 03, 05, May 2014. 4. CSB Energy Technology Co., Ltd., Charge Method, 2020. http: https:// www.csb-batter y.com.tw/english/03_support/01_support_detail. php?fid=1&id=29&page=5&action=page. 5. Chlebis, P., Tvrdon, M., Havel, A., Baresova, K., Comparison of standard and fast charging methods for electric vehicles. Adv. Electr. Electron. Eng., 12, 2014. 10.15598/aeee.v12i2.975. 6. Suarez, C. and Martinez, W., Fast and Ultra-Fast Charging for Battery Electric Vehicles – A Review, October, 2019, doi: 10.1109/ECCE.2019.8912594. 7. Kumar, S. and Usman, A., A Review of Converter Topologies for Battery Charging Applications in Plug-in Hybrid Electric Vehicles, pp. 1–9, 2018. 8. Andromeda, T. et al., Design of DC Fast Charging Buck Converter for LFP Battery on Electric Car, pp. 258–262, 2019. 9. Pinto, J.G. et al., Power Electronics Converters for an Electric Vehicle Fast Charging Station with Energy Storage System and Renewable Energy Sources. EAI Endorsed Trans. Energy Web, 7, 25, 1–10, 2020. 10. Zgheib, R., Kamwa, I., Al-haddad, K., Comparison between Isolated and NonIsolated DC/DC Converters for Bidirectional EV Chargers, . 11. Jacobson, B., Review of High Power Isolated Bi-directional DC-DC Converters for PHEV/EV DC Charging Infrastructure, pp. 553–560, 2011. 12. Joseph, A. and Francis, J., Design and simulation of two phase interleaved buck converter. Int. J. Adv. Res. Electr. Electron. Instrum. Eng., 4, 1, 8–15, 2015. 13. Khalili, S., Farzanehfard, H., Esteki, M., High step-down DC-DC converter with low voltage stress and wide soft-switching range. IET Power Electron., 13, 3001–3008, 2020. 10.1049/iet-pel.2019.1577. 14. Ho, C. N., Breuninger, H., Pettersson, S., Escobar, G., Serpa, L. A., Coccia, A., Practical design and implementation procedure of an interleaved boost converter using SiC diodes for PV applications. IEEE Trans. Power Electron., 27, 6, 2835–2845, June 2012, doi: 10.1109/TPEL.2011.2178269. 15. Kabalo, M., Paire, D., Blunier, B., Bouquain, D., Simoes, M., Miraoui, A., Experimental evaluation of four-phase floating interleaved boost converter design and control for fuel cell applications. Power Electron. IET, 6, 215–226, 2013. 10.1049/iet-pel.2012.0221.

544  Power Electronics for Green Energy Conversion 16. Sakka, M. A., Mierlo, J. V., Gualous, H., DC/DC converters for electric vehicles, in: Electric Vehicles- Modelling and Simulations, IntechOpen, London, United Kingdom, 2011 [Online]. Available: https://www.intechopen.com/ chapters/19583 doi: 10.5772/17048 17. Next Green Car Ltd., EV connector types, 2022, https://www.zap-map.com/ charge-points/connectors-speeds/ 18. Pod Point, EV Charging Connector Types and Speeds, 2022, https://podpoint.com/guides/driver/ev-connector-types-speed

16 Recent Advances in Wireless Power Transfer for Electric Vehicle Charging Sivagami K.1*, Janamejaya Channegowda2 and Damodharan P.1

*

Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, India 2 Ramaiah Institute of Technology, Bengaluru, India

1

Abstract

This article places emphasis on the various transmitter and pickup coil structures, demonstrating different magnetic materials, wires, and shields at both transmitting and receiving end, their interoperability, geometries, which has improved the coefficient of coupling, misalignment tolerance, and high-power transfer efficiency in contactless charging of an electric vehicle. Three modes of charging, namely static, dynamic, and quasidynamic charging, will briefly be discussed here. A general inductive power transfer (IPT) system consisting of an inverter, resonant tank network on either side, couplers, a rectifier with filter and load is covered in significant detail. This chapter describes standards and safety issues concerned with WPT. Reference designs using single-coil geometries in the SAE standards TIR J2954 for the diverse power stages and implementation aspects of compensation networks are covered. Finally, the chapter presents recent studies that are aimed to achieve bidirectional charging in WPT and converters involved in fast-charging EV applications. Thus, this chapter will give a complete overview on the state-of-the-art couplers and its auxiliaries in different sections. Keywords:  WPT, EV, couplers, power converters, compensation

*Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (545–572) © 2022 Scrivener Publishing LLC

545

546  Power Electronics for Green Energy Conversion

16.1 Need for Wireless Power Transfer (WPT) in Electric Vehicles (EV) Worldwide efforts are underway to reduce emissions of greenhouse gases. All modes of transportation are being electrified to combat climate change. The obstacles for prevalent penetration of electric vehicles (EVs) are range anxiety, slow, and frequent recharging compared with IC engine-based vehicles. Wired charging is inefficient in few scenarios, it is estimated that losses incurred due to wires is about 20% to 30% of total input power [1]. Wireless charging of EVs has several benefits over wired charging. With technology progressions in energy storage with various battery systems and power converters, wireless charging could aid mass adoption of EVs. Traditional conductive power transfer offers cost effective and viable solutions in most applications but wireless power transfer (WPT) has benefits in harsh environments. WPT offers safety and convenience over conductive charging. Recent advances in power electronic converters have helped commercialise WPT in fields, such as communication, medical domain, domestic gadgets, etc. Among all use cases, transportation sector has grabbed attention all over the world due to government norms and policies for the year 2030. WPT technology is improving at a rapid rate and will play an important role in charging infrastructure all across the globe. Different levels of wired charging that are currently available are: Level 1 charging is home outlet, 230 V, 15A, 2.5 kW Level 2 charging is AC 16 to 30 A, 230 V, 3.3 kW–7.2 kW Level 3 charging is DC fast charging, 100 A, 480 V, 50–60 kW [2]. Wired charging does not have universal chargers currently. Chargers currently available for charging EVs are shown in Table 16.1. All the chargers have unique design and hence not compatible. Therefore, WPT technology is projected to undergo significant advancement to solve this issue. All available global plugs are detailed in Figure 16.1 as in [2].

16.2 WPT Theory Charging by WPT system can also be called as contactless charging or plugless charging, which would help break down charging impediments due to mechanical contact for electric vehicles. Operating principle

Recent Advances in Wireless Power Transfer for EV Charging  547 Table 16.1  Chargers available worldwide. USA - Japan Tesla DC

Europe Tesla DC

Combo 1: Combined AC & DC

Combo 2: Combined AC & DC

LI

L2/N

CS

CP PE (a) PE

LI

USA - Japan - Europe Chademo

PP

PE

N L3

L2 PP

(b) PE

L1 CS

CP N

CP

DC+

DC-

CP

CP

L1

L2/N

China GB/T

(c) PE

L3 DC+

L2 DC(d)

PP

GND Start/stop1

NC En./Dis. DC+

DC+

DC-

PD CAN-L

(e) CC1 CC2

CAN-H Start/stop2 (f)

CAN-H

CAN-L

DC+

DC-

aux+

DC-

DC+

DC-

auxPE (g)

(h)

Figure 16.1  Global plugs [2]. (a) SAE J1772; (b) IEC 62196-2 Type2; (c) CCS/Combo (US/Japan); (d) CCS/Combo (Eur.); (e) CCS/Combo (DC); (f) CHAdeMO; (g) GB/T 20234-3; (h) Tesla Supercharger.

548  Power Electronics for Green Energy Conversion involves high-frequency electromagnetic energy distributed over space to the receiver coil from a transmitter coil (termed as couplers) by energizing them with high current at a high frequency. This transmission aided by couplers is at the heart of wireless charging. Nikola Tesla, the forerunner in the field of wireless transmission of electrical power did his initial experiments on radiative transmission at the Tesla tower, Colorado in 1981. In 1894, M. Hutin and co-founder M. Table 16.2  Timeline of WPT as in [3].

18th century Wireless Power Transfer by Tesla

1937 The vacuum tube oscillator transmitspower inductively to light a bulb

1891 Demonstration of Capacitive PowerTransfer in a lecture by Tesla

1899 Experiment in resonant inductive transfer at Colorado Springs

1964 Microwave powered Helicopter in flight 60ft above a transmitter antenna. 1968 Solar power satellite

2007 Demonstration of WPT by Marin Soljacic, MIT

2011 Nissan leaf Prototype inductive electriccar charging system at Tokyo Auto show

Recent Advances in Wireless Power Transfer for EV Charging  549 LeBlanc proposed a method by using an alternating current (AC) generator of low frequency to power an Electric Vehicle, but this first EV lost its boom due to the dawn of internal combustion engines (ICE) [2]. Progress of wireless power transfer through years is showed in Table 16.2, as in [3]. Wireless power transfer has gained momentum in the past decade due to the invention of power semiconductor devices, which are capable of operating at 100 s of kHz to MHz frequency range as well. These devices have accelerated advancements in plugless power transfer systems. WPT systems comprise of transmitter and pickup coils, the gap between these is typically in the range greater than 15 cm, for transit buses [4]. Broad classification of WPT is shown in Figure 16.2. And its simple case is as follows: –– Electromagnetic coupling WPT –– Uncoupled or radiative WPT Near-field WPT is of electromagnetic type and uses kilohertz frequency magnetic or electric fields. Thus, it is called as inductive power transfer (IPT) when high-frequency magnetic field is used, and when electric field is used, it is capacitive power transfer (CPT). CPT finds its applications in small domestic appliances like charging of mobile phone, laptop, medical devices, etc. Uncoupled or radiative WPT is termed as far-field WPT, and this is used in radio. Table 16.3 compares radiative and nonradiative wireless power transfer technologies adapted from [5].

WPT technologies Main categories

Near-field

Far-field

Mechanical

Acoustic

Electromagnetic radiations

Rotational magnets

Sound waves

Microwave

Magnetic gear

Acoustic

PowerElectromagnetic fields transfer form Magnetic field

Inductive

Magnetic resonant

Electric field

Capacitive

Laser

End-user application

Figure 16.2  Classification of WPT technologies.

Radio-wave

550  Power Electronics for Green Energy Conversion Table 16.3  Comparison of contactless charging technologies. Microwave transfer (MPT)

Capacitive power transfer (CPT)

Inductive power transfer (IPT)

Radiation characteristics

Radiated

Nonradiated

Nonradiated

Coupling system

Microwave source, waveguide, Tx, Rx antenna

Capacitive coupling system

Inductive coupling system

Capacity

µW, mW–kW

mW–3 kW

mW–kW

Distance (Tx to Rx)

meter–kilometer

20 kHz) is employed to excite the Tx. Coil. Mostly, half bridge, full bridge, or push-pull converter are used to power a WPT system. Ultimately, the battery is to be charged under constant

Recent Advances in Wireless Power Transfer for EV Charging  565

S1

S1

C2

Primary coil L1

Secondary coil L2 C2 DP1 DP2

Vin

C0 CP S2

DN1

DN2

+ V out

S2

Figure 16.9  Current source inverter as in [18].

voltage and/or constant current mode aiding maximum power transfer by matching source and load impedances

16.9.1 Primary Side Converter In the current source inverter (CSI) topology, proposed in [18], short-­ circuit protection against inverter fault is attained inherently in this topology, as the short-circuit current is limited by the DC-link inductor as shown in Figure 16.9. Parallel resonance is realized by connecting a capacitor across the coil. The reactive current component flows within the resonant tank and through the switching circuit, thus rendering a smaller current rating for the switching devices. A charger is categorised as unidirectional or bidirectional based on the direction of power flow.

16.9.1.1 Unidirectional Charger In these types of chargers, only grid to vehicle (G2V) power transfer happens. Matrix converters are the widely seen topology for a glib translation of grid frequency to high-frequency current that is provided to the transmitting coil, whilst the basic H-bridge converter is the conventional and common topology when conversion takes place in two stages. As the switches are under high stress in high-power applications, like charging an EV, matrix converters are not the right choice. For high-power applications, Multilevel inverters find their presence to rise the power capacity of plugless charging schemes. The cascaded multilevel converter is implemented for increased capacity as shown in Figure 16.10 [19].

566  Power Electronics for Green Energy Conversion

16.9.1.2 Bidirectional Charger A representative Bidirectional-IPT system employs two coordinated fullbridge converters functioning at fixed duty cycles to drive the transmitting and receiving magnetic couplers. The quantity of converters can be reduced by engaging matrix converters. They generate high-frequency AC from grid frequency. System performance terms, such as reliability, cost, size, weight, and efficiency, are improved when the energy storage elements are taken off in the DC link. Some of the recently introduced matrix AC-AC converters are half-bridge converters, full-bridge and AC-AC buck converters. These types of converters add the competence of bidirectional power flow, which is used in grid integration, widely known as vehicle-togrid (V2G) [20], one such topology is shown in Figure 16.11. S1

S2 CP

+ –

E

S3

S4

S5

S6

S7

S8

CS LS

LP

RL

+ –

E

Figure 16.10  Cascaded 5 level inverter [19]. ICpa SpaT VCpa Cpa

V

I

Lpa

S pbT Lpi Lpb

Cpb

Vp

VCpb Vpi

V

Cp

M VCp

Lsi VCs Cs

S pbB

Figure 16.11  Schematic of boost active bridge [20].

S

S

Vsi

S

Vsb

Csb Lsb

Vs

Csa

Lsa

S

V

V

Recent Advances in Wireless Power Transfer for EV Charging  567

16.9.2 Secondary Side Converter Typically, the pickup coil is mounted beneath the automobile, and it converts the time-varying flux to high-frequency ac. This is then converted to a dc supply, using a rectifier and dc-dc chopper which is used by the on-board batteries. Various AC/DC converters are available, as shown in Tables 16.8 and 16.9, which show available isolated DC/DC converters. These aids in fast charging too. Other nonisolated DC/DC fast charging converters include boost converter, interleaved Boost converter, Threelevel boost converter and flying capacitor converter [21].

16.9.3 Recent Novel Converter A single-stage IPT converter, which is proposed in reference [22], as shown in Figure 16.12, can operate in maximum-efficiency and constant-power mode during the charging process at a faster rate. This converter design incorporates a switch-controlled capacitor (SCC) and a semiactive rectifier (SAR) at the pickup side. Moreover, in this design, soft switching is also achieved. Table 16.8 and Table 16.9 consolidates converters for fast charging applications.

16.10 Standards Several national and international organizations are developing standards, guidelines, specifications and recommended practices of inductive charger for different vehicles and operating environment, such as Society of Automotive Engineering (SAE), International Electrotechnical Commission (IEC), the Japan Automobile Research Institute (JARI), (International Organization for Standardization) ISO, Underwriters Laboratories (UL), and the National Technical Committee of Auto Standardization (NTCAS). Some of these standards describe the system configurations for different power levels and airgaps, such as SAE J2954 and IEC 61980-1, while others provide guidelines for system data communication, such as IEC/TS 61980-2. A brief summary for most of the released standards for EV inductive stationary charging technology is presented in Table 16.10 as presented in [16].

568  Power Electronics for Green Energy Conversion Table 16.8  Various AC/DC converter for fast charging [21]. Converter

Complexity

Bidirectional Converters Low

Fig. PWM Converter Moderate

Fig. NPC Converter Non-Bidirectional Converters Moderate

Fig. Vienna Converter Low

Fig. Buck-type Converter

Recent Advances in Wireless Power Transfer for EV Charging  569 loosely coupled transformer Q1

Q3

CP iP

+ V1 –

vP

RP,w

LP Q2

iS

iP M

RS,w

switch-controlled capacitor CSCC vSCC

C1

Da Qa

C2

seme-active rectifier D5

Db

iSCC

Qb

Cf

vS iS

φ

D6

LS

Q4

Zeq = Req + jXeq

IO

D7

Q6

D8

Q8 θ

Figure 16.12  Schematic of the CP charging in [22].

Table 16.9  Isolated DC/DC converters for fast charging [21]. Converter Bidirectional Converters

Remarks Wide output range.

Fig. DAB Converter Low controllability under wide ZVS range.

Fig. CLLC Converter Non-Bidirectional Converters Simple, high losses, hard to realize ZVS.

Fig. PSFB Converter Limited controllability. ZVS on primary and ZCS on secondary.

Fig. LLC Converter

VO

570  Power Electronics for Green Energy Conversion Table 16.10  Summary of various standards [16]. Standards

Portrayal

SAE J2954

Wireless power transfer for lightduty plug-in/Electric vehicles and alignment methodology.

IEC 61980

Electric vehicle wireless power transfer (WPT) systems. Part 1: General requirements Part 2: Specific requirements for communication between electric vehicle (EV) and infrastructure with respect to WPT systems. Part 3: Specific requirements for the magnetic field power transfer systems.

ISO/AWIPAS 19363

Electrically propelled road vehicles. Safety and interoperability requirements.

UL2750

Wireless charging equipment for electric vehicles.

16.11 Conclusion This chapter demonstrated various magnetic materials, wires, coil structures at both transmitting and receiving end. Geometries to improve coefficient of coupling and high-power transfer efficiency in plugless charging of an EV were discussed along with three modes of charging. A general inductive power transfer (IPT) system comprising power converters, tank circuits were covered in detail. Finally, the chapter presented novel topologies aiming G2V and V2G charging. Converters involved in fast charging of EV batteries were compared. Thus, this chapter gave an overview on the state-of-the-art couplers and its auxiliaries in different sections.

References 1. Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J.D., Fisher, P., Soljačić, M., Wireless power transfer via strongly coupled magnetic resonances. Science, 317, 5834, 83–6, 2007.

Recent Advances in Wireless Power Transfer for EV Charging  571 2. Khaligh, A. and D’Antonio, M., Global Trends in High-Power On-Board Chargers for Electric Vehicles. IEEE T. Veh. Technol., 68, 4, 3306–3324, April 2019. 3. Patil, D., McDonough, M.K., Miller, J.M., Fahimi, B., Balsara, P.T., Wireless Power Transfer for Vehicular Applications: Overview and Challenges. IEEE Trans. Transp. Electrification, 4, 1, 3–37, March 2018. 4. Lee, S.-H. and Lorenz, R.D., Development and Validation of Model for 95% Efficiency, 220 W Wireless Power Transfer over a 30cm Air-gap. IEEE Trans. Ind. Appl., 47, 6, 2495–2504, Nov.-Dec. 2011. 5. Shinohara, N., Wireless Power Transfer -Theory, technology and applications, in: IET Energy Engineering 112, The Institution of Engineering and Technology, September 2018. 6. Mohameda, A.A.S., Shaiera, A.A., Metwallya, H., Selem, S.I., A comprehensive overview of inductive pad in electric vehicles stationary Charging. App. Energy, 262, 114584, 2020, https://doi.org/10.1016/j.­apenergy.2020.114584. 7. Bosshard, R., Kolar, J.W., Mühlethaler, J., Stevanovi´c, I., Wunsch, B., Canales, F., Modeling and Pareto optimization of inductive power transfer coils for electric vehicles. IEEE J. Emerg. Sel. Top. Power Electron., 3, 1, 50–64, Mar. 2015. 8. Budhia, M., Covic, G.A., Boys, J.T., Design and optimization of circular magnetic structures for lumped inductive power transfer systems. IEEE Trans. Power Electron., 26, 11, 3096–3108, Nov. 2011. 9. Budhia, M., Covic, G., Boys, J., A new IPT magnetic coupler for electric vehicle charging systems, in: Proc. 36th Annu. Conf. IEEE Ind. Electron. Soc. (IECON), Glendale, AZ, USA, pp. 2487–2492, Nov. 2010. 10. Budhia, M., Boys, J.T., Covic, G.A., Huang, C.-Y., Development of a single-­ sided flux magnetic coupler for electric vehicle IPT charging systems. IEEE Trans. Ind. Electron., 60, 1, 318–328, Jan. 2013. 11. Covic, G.A., Kissin, M.L.G., Kacprzak, D., Clausen, N., Hao, H., A bipolar primary pad topology for EV stationary charging and highway power by inductive coupling, in: Proc. IEEE Energy Convers. Congr. Expo, Phoenix, AZ, USA, pp. 1832–1838, Sep. 2011. 12. Kim, S., Zaheer, A., Covic, G., Boys, J., Tripolar pad for inductive power transfer systems, in: Proc. 40th Annu. Conf. IEEE Ind. Electron. Soc. (IECON) Dallas, TX, USA, pp. 3066–3072, Oct./Nov. 2014. 13. Moon, S., Kim, B.-C., Cho, S.-Y., Ahn, C.-H., Moon, G.-W., Analysis and design of a wireless power transfer system with an intermediate coil for high efficiency. IEEE Trans. Ind. Electron., 61, 11, 5861–5870, Nov. 2014. 14. Moon, S. and Moon, G.-W., Wireless power transfer system with an asymmetric four-coil resonator for electric vehicle battery chargers. IEEE Trans. Power Electron., 31, 10, 6844–6854, Oct. 2016. 15. Ahmad, A., Alam, M.S., Chabaan, R., A Comprehensive Review of Wireless Charging Technologies for Electric Vehicles. IEEE Trans. Transp. Electrification, 4, 1, 38–63, March 2018.

572  Power Electronics for Green Energy Conversion 16. Vincent, D., Huynh, P.S., Azeez, N.A., Patnaik, L., Williamson, S.S., Evolution of Hybrid Inductive and Capacitive AC Links for Wireless EV Charging—A Comparative Overview. IEEE Trans. Transp. Electrification, 5, 4, 1060–1077, Dec. 2019. 17. Vu, V., Phan, V., Dahidah, M., Pickert, V., Multiple Output Inductive Charger for Electric Vehicles. IEEE Trans. Power Electron., 34, 8, 7350–7368, Aug. 2019. 18. Samanta, S. and Rathore, A.K., Analysis and design of current-fed (L)(C) (LC) converter for inductive wireless power transfer (IWPT), in: Proc. IEEE Energy Convers. Congr. Expo. (ECCE), Montreal, QC, Canada, pp. 5724– 5731, Sep. 2015. 19. Hao, H., Covic, G.A., Boys, J.T., A parallel topology for inductive power transfer power supplies. IEEE Trans. Power Electron., 29, 3, 1140–1151, Mar. 2014. 20. Huynh, P.S., Vincent, D., Azeez, N.A., Patnaik, L., Williamson, S.S., Performance Analysis of a Single-Stage High-Frequency AC-AC Buck Converter for a Series-Series Compensated Inductive Power Transfer System. 2018 IEEE Transportation Electrification Conference and Expo (ITEC), Long Beach, CA, pp. 347–352, 2018. 21. Tu, H., Feng, H., Srdic, S., Lukic, S., Extreme Fast Charging of Electric Vehicles: A Technology Overview. IEEE Trans. Transp. Electrification, 5, 4, 861–878, Dec. 2019. 22. Huang, Z., Lam, C., Mak, P., da Silva Martins, R.P., Wong, S., Tse, C.K., A Single-Stage Inductive-Power-Transfer Converter for Constant-Power and Maximum-Efficiency Battery Charging. IEEE Trans. Power Electron., 35, 9, 8973–8984, Sept. 2020. 23. Tejeda, A., Kim, S., Lin, F.Y., Covic, G.A., Boys, J.T., A Hybrid Solenoid Coupler for Wireless Charging Applications. IEEE Trans. Power Electron., 34, 6, 5632–5645, June 2019, doi:10.1109/TPEL.2018.2867430. 24. Castillo-Zamora, I.U., Huynh, P.S., Vincent, D., Perez-Pinal, F.J., RodriguezLicea, M.A., Williamson, S.S., Hexagonal Geometry Coil for a WPT HighPower Fast Charging Application. IEEE Trans. Transp. Electrification, 5, 4, 946–956, Dec. 2019. 25. Li, S., Li, W., Deng, J., Nguyen, T. D., Mi, C. C., A double-sided LCC compensation network and its tuning method for wireless power transfer. IEEE Trans. Veh. Technol., 64, 6, 2261–2273, June 2015.

17 Flux Link Control Modulation Technique for Improving Power Transfer Characteristics of Bidirectional DC/DC Converter Used in FCEVS Bandi Mallikarjuna Reddy, Naveenkumar Marati*, Kathirvel Karuppazhagi and Balraj Vaithilingam *

Valeo India Private Limited, Chennai, India

Abstract

In the fuel cell (FC) hybrid electric vehicle, bidirectional dc/dc converter is essential to protect the battery from the inrush current, which is developed during deceleration mode. The battery is used in the FC vehicle to assist in the acceleration and deceleration modes of vehicle. Without integration with battery, FC does not have energy storage capability in deceleration mode. The dc bus bar in the FC vehicle is integrated with the battery through bidirectional converter. Isolation is required between the DC bus bar and battery to protect the battery from inrush current. To present one such bidirection converter, the authors of this manuscript have presented a flux link control (FLC) modulation of generic dual active bridge isolated bidirectional dc/dc converter (GDAB-IBDC). Though in the literature single-phase shift (SPS) modulation IS used in GDAB-IBDC, it has concerns with higher circulating current, nonlinear power transfer characteristics and lower efficiency. The proposed flux link control modulation has been derived from the SPS modulation to reduce circulating current, and enhance utilization factor of transformer and improve power transfer efficiency. In this manuscript, the mathematical modeling of GDAB-IBDC converter has been done in both boost and buck mode of operation. The flux link control (FLC) modulation has developed for the GDAB-IBDC converter. The analysis and modeling of SPS and FLC modulation have been done and converter simulated on PSCAD platform. The results of SPS and FLC modulation of GDAB-IBDC converter are presented and compared. *Corresponding author: [email protected] Mahajan Sagar Bhaskar, Nikita Gupta, Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen and Umashankar Subramaniam (eds.) Power Electronics for Green Energy Conversion, (573–600) © 2022 Scrivener Publishing LLC

573

574  Power Electronics for Green Energy Conversion Keywords:  Fuel cell, hybrid electric vehicle (HEV), flux link control (FLC), phase-shift modulation, DC-DC converter, GDAB-IBDC

17.1 Introduction With the adamant of severe regulations related to global warming, fuel economy, and conventional energy limitations; electric, hybrid, and FC vehicles have been drawing attention from vehicle manufacturers and governments [1–3]. Especially, proton exchange membrane (PEM) [4–7] based FCHEV has been gaining attention in this decade due to increasing concern about pollution and subsequent issues regarding environment [8, 9]. Even though, development of powertrain is difficult in FC vehicle and keeping on board hydrogen tank is a challenge [10]. The advanced dc/dc converters have been used in FC vehicles for performance enhancement [11]. The generic dual active bridge isolated bidirectional dc/dc converter (GDAB-IBDC) for renewable energy applications has proposed [12–16]. The GDAB-IBDC converter has gained popularity during recent years due to its modular structure [17–20]. The GDAB-IBDC converter consists of two full bridges (H1, and H2), high-frequency link power conversion system (HFLPCS), and one inductor (L), two capacitors (C1, and C). The GDAB-IBDC converter with SPS modulation has two level voltages across primary and secondary of isolation transfer (VPrim, and VSec) [21]. Due to this, soft switching techniques like zero voltage switching (ZVS) and zero current switching (ZCS) are not possible in the GDAB-IBDC converter [22]. The conventional modulations techniques has been using for enhancement of performance of the GDAB-IBDC converter [23, 24]. The conventional modulations techniques has been developed based on the constant frequency and variable flux link modulation [25–27]. Because of these concepts, utilization factor of the isolation transformer used in GDAB-IBDC is lower. To enhance the utilization factor of isolation transformer, need to develop new modulation technique, which can hold utilization factor more than 0.8 [28]. The FLC modulation technique has been developed for the GDABIBDC converter to enhance the utilization factor (λ) of isolation transformer and efficiency of power transfer of the GDAB-IBDC converter. The GDAB-IBDC converter has been discussed in the subsequent section [29, 30].

FluxLinkControlModulationTechniqueforEnhancementofBidirectionalDC/DCConverter  575 In this article, the organization of the article discussed in six sections. The literature survey of the bidirectional converters and its modulation techniques is discussed in Section 17.1. The mathematical modelling and analysis of the GDAB-IBDC converter has been presented in the Section 17.2. The Proposed modulation of GDAB-IBDC converter has been analysed and designed for enhancement of utilization factor of the isolation transformer is presented in Section 17.3. The dead band analysis, simulation, and results are described in the Section 17.4 and Section 17.5 respectively. Lastly, conclusions are presented in Section 17.6.

17.2 GDAB-IBDC Converter The GDAB-IBDC converter has been shown in the Figure 17.1. It has two full-bridges (H1, and H2), which consists of eight switching devices (S1, S2, S3, S4, S5, S6, S7, and S8), one high-frequency link power conversion system (HFLPCS), one inductor (L), and two capacitor (C1, and C). The switching pattern (FLC modulation) of converter has been developed and provided for gating every switch of the converter. A 900 displacement is kept between two bridges of converter (G1 and Q1) due to inductor (L). The phase shift between voltages across primary (VPrim) and voltage across secondary (Vsec) Bridge (H2) iRC

Bridge (H1)

iOut iC

iC1

D1 G3

S1

S3

Battery

G1

V1

D3

IL L

C1

S2

S5

S7

VSec

D4

S4

Figure 17.1  Circuit diagram of GDAB-IBDC converter.

VOut RLoadd

Np:NS D2 G4

K3

C

VPrim

G2

K1 Q3

RL HFL PCSs

VL

RC1

Q1

RC Q2 S6

K2 Q4 S8

K4

576  Power Electronics for Green Energy Conversion GDAB-IBDC Bridge H1

iL

HFSW (VPrim)

L

Bridge H2

HFSW (VSec)

V1

VOut

Figure 17.2  Block diagram of GDAB-IBDC converter.

of isolation transformer is 90 degrees due to FLC modulation. This converter does not have soft switching operation due to the unavailability of resonant network (inductor, L and capacitor, C) around isolation transformer. In the GDAB-IBDC converter, eight switching signals are required to drive the converter. These switching signals are developed by the FLC modulation technique. The FLC modulation technique has been discussed in the later sections of this chapter. The block diagram of GDAB-IBDC converter is shown in Figure 17.2. The high-frequency square wave (HFSW) primary voltage (VPrim) is developed by full-bridge (H1) and provided to the isolation transformer. Due to inductor (L), the outer phase shift is responsible for transfer power from bridge (H1) to bridge (H2). The high-frequency square wave (HFSW) secondary voltage (VSec) is developed by the isolation transformer and provided to the full-bridge (H2). The current (iL) flows through the inductor (L) from bridge (H1) to bridge (H2) as shown in Figure 17.2. This converter modulated by the FLC modulation technique to achieve better performance. In the subsequent section, the analysis and modelling of converter has been discussed.

17.2.1 Analysis and Modeling of GDAB-IBDC The GDAB-IBDC converter is the interface between battery and dc bus bar in FC vehicles. During charging, converter works in the buck mode and during discharging the converter works in the boost mode. The converter boosts the voltage by circuit phenomena as well as by isolation transformer. The primary voltage (Vprim) is higher than secondary voltage (Vsec) in buck mode of operation as shown in Figure 17.3 (a). The primary voltage (Vprim) is lesser than the secondary voltage (VSec) in the boost mode of converter as shown in Figure 17.3 (b). The primary voltage (Vprim) and secondary

FluxLinkControlModulationTechniqueforEnhancementofBidirectionalDC/DCConverter  577 VPrimVSec 60

90 Ts/2

40

VPrim VSec

20

VPrim

60

Ts/2

30

IL

IL 0

0

-20

-30

-40

Outer Phase Shift (θ)

-60 0

Time(s)

Ts

-60 Outer Phase Shift (θ) -90 0

(a) Buck Mode

VSec

Time(s)

Ts

(b) Boost Mode

Figure 17.3  Ideal waveforms of primary voltage of isolation transformer (VPrim), and secondary voltage (VSec) and inductor current (iL) of GDAB-IBDC converter (a) Buck mode, (b) Boost mode.

voltage (Vsec) and current (iL) through inductor (L) in the buck mode and boost mode are shown in Figure 17.3. The two phase shifted wave forms, VPrim (t) and VSec (t), generate a nonzero net voltage, VL (t), across inductor L and given in Eq. 17.1.

VL(t) = VPrim(t) – VSec(t)

(17.1)

where VL (t), Vprim (t), and Vsec (t) are voltage across inductor (L), primary voltage of isolation transformer, and secondary voltage of isolation transformer respectively. The voltage (VL (t)) across the inductor ‘L’, which is essential for the exchange of power from V1 to VOut and current (iL) flowing through the inductor ‘L’ is calculated and given Eq. 17.2.



1 iL (t ) = iL (0) + VL (t ).dt L

(17.2)

where iL (t), and iL (0), V1, VOut are instantaneous currents through the inductor ‘L’, and initial current in the inductor ‘L’, battery voltage or input voltage, and output voltage respectively. The voltages, V1 and VOut, thus generate or receive the respective instantaneous primary and secondary powers and given in Eq. 17.3 and Eq. 17.4, respectively.

578  Power Electronics for Green Energy Conversion

PPrim(t) = VPrim(t).iL(t)

(17.3)

PSec(t) = VSec(t).iL(t)

(17.4)

where Pprim (t), PSec (t) are primary side power of isolation transformer, and secondary side power of isolation transformer, respectively. The average power over one switching cycle TS, is calculated for the primary side of GDAB-IBDC converter and given in Eq. 17.5.

2 PPr im = TS



TS /2

∫V

Pr im

0

(17.5)

(t ).iL (t )

where TS=1/fS , is switching frequency. The GDAB-IBDC converter has higher power transfer capability with respect to outer phase shift (θ) at primary voltage of isolation transformer (VPrim) 58 volts as shown in Figure 17.4. The power transfer of converter is increased with increasing the primary voltage of isolation transformer (VPrim). The power transfer verses outer phase shift with different primary voltages of isolation transformer like 52 V, 54 V, 56 V, and 58 V, as shown in Figure 17.4. In the subsequent section, the analysis of GDAB-IBDC converter has been discussed in the boost mode.

Power transfer (p. u.)

1.0

0.5

0 VPrim=52V VPrim=54V VPrim=56V VPrim=58V

0.5

1.0 -200

-150 -100

-50 0 50 100 Outer phase shift (θ)

150

200

Figure 17.4  Power transfer verses outer phase shift as a function of primary voltage of isolation transformer (VPrim).

FluxLinkControlModulationTechniqueforEnhancementofBidirectionalDC/DCConverter  579 A) Boost mode In the boost mode, Vprim (t) is lesser than the Vsec (t) as shown in Figure 17.3 (b). The voltage of V1 is lesser than VOut, power flow from VOut to V1. The inductor current (iL (t)) is calculated in boost mode and given in Eq. 17.6.

iL (t )

0 t

TS 2

iL (0)

VPrim VS ec .TS . L 2.

iL (0) 0 t

.

TS 2.

VPrim VS ec .t L VPrim VS ec . t L

TS 2. TS t 2

.TS 2..

.

(17.6)



where θ is outer phase shift between the two bridges (H1, and H2). The instantaneous voltage of VPrim (t), instantaneous inductor current iL (t), and instantaneous voltage of VSec (t) satisfied the half wave symmetry over one switching cycle (TS) i.e. instantaneous voltages across isolation transformer (VPrim, and VSec) and current in the inductor (iL) are equal and opposite to the voltages and current of 0 < t < TS/2 and are given in Eq. 17.7, Eq. 17.8 and Eq. 17.9, respectively.



 T  VPrim t + S = −VPrim (t )  2

(17.7)



 T  VS ec t + S = −VS ec (t )  2

(17.8)



 T  iL t + S = −iL (t )  2

(17.9)

where t is the instantaneous time in seconds. In the subsequent section, the analysis of GDAB-IBDC converter in the buck mode is discussed.

580  Power Electronics for Green Energy Conversion B) Buck mode The primary voltage of isolation transformer, Vprim (t) is higher than the secondary voltage of isolation transformer, Vsec (t) as shown in Figure 17.3 (a). When voltage of V1 is higher than VOut, power can flow from V1 to VOut. The inductor current (iL (t)) is calculated in the buck mode and given in Eq. 17.10.

iL (t )

0 t

TS 2

iL (0) iL (0) 0 t

.

TS 2.

VPrim VS ec L T . S 2. TS t 2

VPrim VS ec t L TS VS ec VPrim 2. L

t

.TS 2.

(17.10)



The initial current of inductor (iL (0)) can be calculated and given in Eq. 17.11.



iL (0) =

1 π | Vsec .θ | +(Vprim − Vsec )   X 2 

(17.11)

where X is the reactance of inductor L. The GDAB-IBDC converter does not have the resonant network around the high-frequency link power conversion system to get three level voltages of Vprim (t) and Vsec (t). Without three level voltages of Vprim (t) and Vsec (t), is not possible to implement soft switching techniques in the GDAB-IBDC converter. The GDAB-IBDC converter has only one inductor (L) to transfer power from full-bridge (H1) to full-bridge (H2). The FLC modulation has been implemented on the GDAB-IBDC converter to compare with the SPS modulation in terms of utilization factor of isolation transformer, power transfer characteristics and efficiency. In the subsequent section, FLC modulation technique is discussed.

17.3 FLC Modulation Technique The FLC modulation technique has been derived from the SPS modulation in the GDAB-IBDC converter to enhance the efficiency of power

FluxLinkControlModulationTechniqueforEnhancementofBidirectionalDC/DCConverter  581 transfer. The flux linkages (ψ) and switching frequency (fsw) are modulated by using FLC modulation. The flux linkages (ψ) and switching frequency (fsw) are also modulated in the single phase shift (SPS), but frequency is almost constant and flux linkages (ψ) have been decreased with increase of outer phase shift (θ). The utilization factor (λ) of isolation transformer has been decreases with increase of outer phase shift (θ) in SPS modulation. Moreover, the power transfer characteristics of GDAB-IBDC are non linear for SPS modulation. By using FLC modulation, the flux linkages (ψ) have been kept constant, and frequency (fsw) has been decreased as outer phase shift (θ) increases. The switching signals (G1, G2, G3, G4, Q1, Q2, Q3, and Q4), voltages across primary and secondary of isolation transformer (VPrim, and VSec), magnetic current (iµ) and inductor current (iL) of GDAB-IBDC converter for FLC modulation technique are shown in Figure 17.5. The primary and secondary voltages of isolation transformer (VPrim, and VSec) are two level voltages. In the subsequent section, the modes of operation of GDAB-IBDC converter by using FLC modulation have been explained.

G1 G2 G3 G4 Q1 Q2

D

Q3 Q4 Vprim iµMax iµ Vsec iL

iµMin iLMax

iLMin Time Period (Ts) Mode Mode Mode Mode 1 2 3 4 0 DTS TS/2 3TS/4 TS

Time (us) D = Outer phase shif tratio TS = Switching time Time (s)

Figure 17.5  Waveforms of FLC modulation of GDAB-IBDC converter.

582  Power Electronics for Green Energy Conversion

17.3.1 Modes of Operation of GDAB-IBDC Converter The modes of operation of GDAB-IBDC converter with FLC modulation technique are shown in Figure 17.6. The four modes of operation are presented based on the outer phase shift ratio (D) as shown in Figure 17.5. The four modes of GDAB-IBDC converter are discussed as follows: In mode 1, the switches S1, S4, S6, and S7 are ON state, and switches S2, S3, S5, and S8 are OFF state as shown in Figure 17.6 (a). The primary voltage of isolation transformer (VPrim) is in positive level. The secondary voltage of isolation transformer (VSec) is either negative or positive. Both the voltages of isolation transformer (Vprim, and VSec) are two level voltages are shown in Figure 17.5. The magnetic current (iµ) starts increasing from minimum value (iµMin) and reaches zero. The inductor current (iL) starts increasing from minimum value (iLMin). In mode 2, the switches S1, S4, S5, and S8 are in ON state, and switches S2, S3, S6, and S7 are in OFF state as shown in Figure 17.6 (b). The primary voltage IRC IOut IC D1G3

S1

S3

D3 OFF State iL L

C1

S7 OFF State

RL

C

Q2

D4

(a)

K2

S6

Q 4

V1

S8 OFF State

S1 OFF S3 State

iL L

C1

VL

D4

OFF State

RC Q 2

(c)

K2

Q 4

S6 OFF S8 State

C1

K4

K1 Q3

S5

S7

VL VPrim

RL

D2G4

K3

OFF State C

VOut

VSec RC

Np:NS Q2

D4

S4

(b)

K2Q4

K4

S6 OFF S8 State IRC

IOut IC

G1

OFF State

VSec

L

IOut

K3

C

Q1

VOut

V1

D1G3

D3

S1 OFF S3 State

iL L

C1

VL VPrim

Q1

K1 Q3

S5

S7 OFF State

HFL PCSs

RL

C VSec

S2

D2G4 S4

Q2

D4

OFF State

S6

(d)

VOut

RC

Np:NS G2

K3

RLoad

S4

S7

IC

D3

iL HFL PCSs

RLoad

S2

S5

RL

Np:NS D2G4

K1 Q3

HFL PCSs

VPrim

G2

Q1

S3

S2

Battery

Battery

V1

D3

S1

G2

IC D1G3

D1G3

OFF State

K4

IRC

G1

G1

IOut

RLoad

VOut

RC

Np:NS

D2G4

K3

VSec

S4

S2

S5 HFL PCSs

OFF State G2

K1 Q3

RLoad

VL VPrim

Q1

Battery

Battery

V1

G1

IRC

OFF State

K2Q4

K4

S8 OFF State

Figure 17.6  Modes of operation of GDAB-IBDC converter for outer phase shift ratio (D > TS/4), the intervals are mentioned in Figure 17.5. (a) Mode 1: 0 ≤ t ≤ DTS. (b) Mode 2: DTS ≤ t ≤ TS/2. (c) Mode 3: TS /2 ≤ t ≤ 3TS/4. (d) Mode 4: 3TS/4 ≤ t ≤ TS.

FluxLinkControlModulationTechniqueforEnhancementofBidirectionalDC/DCConverter  583 of isolation transformer (VPrim) is either negative or positive. The secondary voltage of isolation transformer (VSec) is in positive level. The magnetic current (iµ) has been increases from previous value and reached maximum value (iµMax). The inductor current has been increases from previous value and reached maximum value (iLMax) as shown in Figure 17.5. In mode 3, the switches S2, S3, S5, and S8 are in ON state, and switches S1, S4, S6, and S7 are in OFF state as shown in Figure 17.6 (c). The primary voltage of isolation transformer (VPrim) is either negative or positive. The secondary voltage of isolation transformer (VSec) has been in positive level. The magnetic current (iµ) starts decreases from maximum value (iµMax) and reaches zero. The inductor current (iL) has been decreases from maximum value (iLMax) as shown in Figure 17.5. In mode 4, the switches S2, S3, S6, and S7 are in ON state, and switches S1, S4, S5, and S8 are in OFF state as shown in Figure 17.6 (d). The primary voltage of isolation transformer (VPrim) has been in negative level. The secondary voltage of isolation transformer (VSec) is either negative or positive. The magnetic current (iµ) has been decreases from previous value. The inductor current (iL) has decreased from previous value and reached the negative maximum value (iLMax) as shown in Figure 17.5. In the next section, the analysis and modeling of FLC modulation technique have been derived from the SPS modulation technique.

17.3.2 Analytical Modeling of SPS and FLC Modulation In SPS modulation, cross-connected switch pairs in both full bridges are switched in turn to generate phase shifted square waveform with 50% duty ratio to the transformer’s primary and secondary sides. The power flow between two bridges depends upon outer phase shift (θ). The switching frequency (fsw) is maintained constant and flux linkages (ψ) are decrease with increase in the outer phase shift (θ). There is no scope for reduction of switching losses. The utilization factor of isolation transformer is decreases with increase in outer phase shift (θ). The power flow is mainly based on primary (VPrim), secondary (VSec) voltages, reactance of inductor (XL) of isolation transformer and outer phase shift (θ) of converter. The expression for power flow between bridges (H1, and H2) is derived and given in Eq. 17.12.



P=

VPr imVSec θ (π − |θ |) 2π 2 f Sw L

where fsw, is the switching frequency.

(17.12)



584  Power Electronics for Green Energy Conversion (A) Inductor current (iL) The current flowing through inductor (L) depends on the reactance of inductor (XL) and isolation transformer. The current flowing through inductor (iL) has two portions, which are useful current and circulating current. Useful current means that which will go to load and circulating current means that which will flow between bridges (H1, and H2). The current flowing through inductor (iL) is calculated and given in Eq. 17.13.

  V + VS ec  iL (0) +  Pr im ω Sw t ,   ω sw L   iL (ω Sw t ) =   iL (|θ |) +  VPr im − VSec  (ω Sw t − |θ |),  ω L   Sw 

 0 ≤ ω Sw t