This book discusses real applications of soft computing (SC) in power systems. These SC techniques, inspired by the human mind and biological behavior, have proven to be excellent tools to overcome the challenges faced in power systems and related areas. The techniques are robust and provide low-cost solutions while also offering an effective solution for studying and modeling the behavior of renewable energy generation, operation of grid-connected renewable energy systems, and sustainable decision-making among alternatives. The tolerance of SC techniques to imprecision, uncertainty, partial truth, and approximation makes them highly useful alternatives to conventional techniques. The rapid growth in SC techniques plays an important role in powerful representation, modeling paradigms and optimization mechanisms for solving power system issues such as power quality, reactive power control, oscillation and stability problems, renewable energy resource evaluation, design of energy efficiency systems, economic load dispatch problems or very different energy system applications in smart grids.

Preface xiii List of Figures xv List of Tables xxiii List of Contributors xxv List of Abbreviations xxvii 1 Introduction 1 1.1 Background and Motivation 2 1.2 Outline of Book 7 2 Soft Computing Techniques and Their Application in Power Systems 13 2.1 Introduction 13 2.2 Soft Computing Techniques 14 2.3 Fuzzy Logic System 15 2.3.1 Fuzzification 16 2.3.2 Rule base and inference mechanism 16 2.3.3 Defuzzification 16 2.4 Artificial Neural Networks (ANNs) 16 2.4.1 MLF neural networks 18 2.4.2 Radial basis function 20 2.4.3 Adaptive neuro-fuzzy inference system (ANFIS) 22 2.5 Support Vector Machine (SVM) 24 2.6 Nontraditional Optimization Techniques 25 2.6.1 Genetic algorithm 26 2.6.1.1 Initial population 26 2.6.1.2 Fitness function 26 2.6.1.3 Selection 27 2.6.1.4 Crossover 27 2.6.1.5 Mutation 27 2.6.2 Particle swarm optimization 29 2.6.3 Ant colony optimization 31 2.6.4 Grey wolf optimization 33 2.6.4.1 Social fitness hierarchy of optimal solutions: 34 2.6.4.2 Approaching and surrounding the prey: 35 2.6.4.3 Harassing prey 35 2.6.4.4 Attacking prey (exploitation) 35 2.6.4.5 Searching for prey (exploration) 36 2.7 Simulated Annealing 36 2.8 Conclusion 37 3 Load Flow Solution Algorithm for AC-DC Radial Distribution Systems in the Presence of Distributed Generation 43 3.1 Introduction 44 3.2 LF Algorithm for AC-DC Radial Distribution Systems 45 3.2.1 Converters modelling 46 3.2.1.1 Modelling of three-phase AC-DC bridge converters 46 3.2.1.2 Modelling of PWM AC-DC converter 48 3.2.1.3 Modelling of PWM DC-DC converter 49 3.2.2 Solution methodology 51 3.3 LF Algorithm for AC-DC Radial Distribution System with DGs 58 3.3.1 Case 1: The AC-DC distribution system with one Vdc node 59 3.3.2 Case 2: The AC-DC distribution system with two Vdc nodes 61 3.3.3 Case 3: The AC-DC distribution system with one PV node 64 3.3.4 Case 4: The AC-DC distribution system with both PV and Vdc buses 66 3.4 Results 70 3.4.1 Test system 1: 10 bus AC-DC distribution network 70 3.4.2 Test system 2: 15 bus AC-DC distribution network 73 3.5 Conclusion 79 4 Load Flow Studies in a Power System 83 4.1 Introduction 83 4.2 Power-Flow Problem 84 4.3 Power Flow Methods 86 4.3.1 Gauss⁰́₃Seidel (GS) method 86 4.3.2 Newton⁰́₃Raphson (NR) method 86 4.3.3 Other power flow methods 86 4.3.4 Load flow method using holomorphic embedding 87 4.3.5 Backward-forward sweep (BFS) method 87 4.3.6 Direct current (DC) power flow 87 4.4 Classification of Buses 87 4.4.1 Reference, slack or swing bus 88 4.4.2 Voltage controlled or generator bus (PV bus) 88 4.4.3 Load bus (PQ bus) 89 4.5 Power Flow with NR Method 89 4.6 Power System Losses 92 4.6.1 Transmission lines losses 92 4.6.2 Distribution system losses 92 4.7 IEEE 14-Bus System Power Flow with NR Method 93 4.7.1 Data for IEEE 14-bus test system 93 4.7.2 Case study: Determination of optimal location of DG unit using power flow 93 4.7.3 Results and discussion 95 4.8 Simulink for Power Flow Analysis 99 4.8.1 IEEE 5-bus model 99 4.8.2 Analysis of 5-bus model using Simulink 100 4.8.2.1 Advantages of Simulink power load flow analysis 104 4.8.2.2 Limitations of Simulink for power flow analysis 105 4.9 Conclusion 105 5 Novel Hybrid Swarm Intelligence and Cuckoo Search Based Microgrid EMS for Optimal Energy Scheduling 107 5.1 Introduction 108 5.2 Microgrid Modelling 110 5.2.1 Fuel-fired generators 112 5.2.2 PV 112 5.2.3 Wind 113 5.2.4 ESS 113 5.2.5 Constraints 114 5.3 Methodology⁰́₃Optimization Algorithms 114 5.3.1 Overview of particle swarm optimization (PSO) 115 5.3.2 Overview of modified CSA 117 5.3.3 Overview of HFPSOMCS 120 5.4 Case Studies and Results 122 5.4.1 First case study: Grid-connected mode 125 5.4.2 Second case study: Islanded mode 127 5.4.3 Third case study: Combined mode 129 5.5 Conclusion 132 6 Topological Investigations of Grid Integrated Renewable Energy Systems for Power Quality Improvement 137 6.1 Introduction 137 6.2 Types of Topologies and Their Modulation Approaches 139 6.2.1 H-Bridge-based inverter structures 139 6.2.2 Modulation strategies 140 6.2.2.1 Bipolar modulation 140 6.2.2.1.1 Advantages 141 6.2.2.1.2 Disadvantages 141 6.2.2.1.3 Analysis 141 6.2.2.2 Unipolar modulation 142 6.2.2.2.1 Advantages 143 6.2.2.2.2 Disadvantages 143 6.2.2.2.3 Analysis 143 6.2.2.3 Hybrid modulation 143 6.2.3 H5 structure 144 6.2.3.1 Advantages 145 6.2.3.2 Disadvantages 146 6.2.3.3 Analysis 146 6.2.4 HERIC structure 146 6.2.4.1 Advantages 147 6.2.4.2 Disadvantages 147 6.2.4.3 Analysis 148 6.2.5 Improved HERIC structure 148 6.2.5.1 Working of different modes 150 6.2.6 Parameter comparison 152 6.2.7 Comparison of losses in these topologies 153 6.2.8 System configuration under study 154 6.2.9 Control solutions for renewable energy system 155 6.3 Results and Discussion 157 6.3.1 Response of the internal signals 157 6.3.2 Response of system under insolation variations 157 6.3.3 Operation of system in DSTATCOM mode of operation 159 6.3.4 Operation of system under load disturbance 160 6.3.5 Response of system under sag and swell conditions of grid voltage 161 6.4 Conclusions 162 6.5 Future Works 163 7 HVDC System Issues and Related Solutions using Soft Computing 169 7.1 Introduction 170 7.2 HVDC Systems and Their Significance in a Smart Grid 171 7.2.1 HVDC modelling and control 175 7.2.2 Significance of HVDC in smart grid 178 7.2.2.1 Improving transient stability 179 7.2.2.2 Flexibility 180 7.2.2.3 Power restoration 180 7.2.2.4 Economic 180 7.3 Issues in HVDC Systems Due to Ongoing Modifications in Power System 181 7.3.1 Integration issues 182 7.3.2 Low power quality and electromagnetic interference (EMI) 182 7.3.3 Day-ahead operational strategy of HVDC to maximize accommodation of renewable energy between peak-valley load 183 7.3.4 Operation and control strategies 183 7.3.5 Security issues 184 7.3.6 Protection and diagnosis of HVDC system faults 184 7.4 Planning of HVDC Systems with Renewables 184 7.4.1 Capacity optimization to reduce curtailment of RES and underutilization of HVDC system 186 7.4.2 Virtual generator or load shedding options to minimize frequency instability 186 7.4.3 Fault ride-through during islanding via HVDC 186 7.4.4 Stability and safety threats due to sub-synchronous oscillation 186 7.5 Solutions using Soft Computing Techniques (SCT) 187 7.6 Real-time (RT) implementation and hardware-in-the-loop (HIL) simulations 196 7.6.1 Hardware-in-the-loop techniques for smart grids 197 7.7 Conclusion 201 8 Smart Grid: A Cyber⁰́₃Physical Infrastructure and Security 207 8.1 Introduction 207 8.2 Background 211 8.3 Cybersecurity Issues in Smart Grid 221 8.4 Intrusion Detections 223 8.5 Conclusion 227 9 Protection Challenges During Power Swing and Soft Computing Solutions 231 9.1 Introduction 231 9.2 Impact of Power Swing on Conventional Distance Protection 232 9.3 Detailed Analysis of Effect of Series Compensation Amid Power Swing 235 9.4 Protection Schemes Against Faults Amid Power Swing 237 9.4.1 Fault detection approaches during power swing 238 9.4.1.1 Cumulative sum (CUSUM)-based technique 239 9.4.1.2 Teager⁸́₂Kaiser energy operator (TKEO)-based technique 239 9.4.1.3 Park⁰́₉s transformation-based technique 240 9.4.2 Fault direction estimation techniques during power swing 242 9.5 Fault Section Identification during Power Swing 245 9.6 Conclusion 248 10 Conclusion 251 10.1 Introduction 251 10.1.1 Chapter 1 252 10.1.2 Chapter 2 252 10.1.3 Chapter 3 253 10.1.4 Chapter 4 253 10.1.5 Chapter 5 253 10.1.6 Chapter 6 254 10.1.7 Chapter 7 254 10.1.8 Chapter 8 255 10.1.9 Chapter 9 255 10.2 Conclusion 256 Index 257 About the Editors 259.

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