{"product_id":"intelligent-renewable-energy-systems-9781119786276","title":"Intelligent Renewable Energy Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Optimization Algorithm for Renewable Energy Integration 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBikash Das, SoumyabrataBarik, Debapriya Das and V Mukherjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Mixed Discrete SPBO 5\u003c\/p\u003e \u003cp\u003e1.2.1 SPBO Algorithm 5\u003c\/p\u003e \u003cp\u003e1.2.2 Performance of SPBO for Solving Benchmark Functions 8\u003c\/p\u003e \u003cp\u003e1.2.3 Mixed Discrete SPBO 11\u003c\/p\u003e \u003cp\u003e1.3 Problem Formulation 12\u003c\/p\u003e \u003cp\u003e1.3.1 Objective Functions 12\u003c\/p\u003e \u003cp\u003e1.3.2 Technical Constraints Considered 14\u003c\/p\u003e \u003cp\u003e1.4 Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions 17\u003c\/p\u003e \u003cp\u003e1.5 Optimum Placement of RDG and Shunt Capacitor to the Distribution Network 18\u003c\/p\u003e \u003cp\u003e1.5.1 Optimum Placement of RDGs and Shunt\u003c\/p\u003e \u003cp\u003eCapacitors to 33-Bus Distribution Network 25\u003c\/p\u003e \u003cp\u003e1.5.2 Optimum Placement of RDGs and Shunt Capacitors to 69-Bus Distribution Network 29\u003c\/p\u003e \u003cp\u003e1.6 Conclusions 33\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Chaotic PSO for PV System Modelling 41\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSouvik Ganguli, Jyoti Gupta and Parag Nijhawan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 42\u003c\/p\u003e \u003cp\u003e2.2 Proposed Method 43\u003c\/p\u003e \u003cp\u003e2.3 Results and Discussions 43\u003c\/p\u003e \u003cp\u003e2.4 Conclusions 72\u003c\/p\u003e \u003cp\u003eReferences 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid 79\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSoham Dutta, Pradip Kumar Sadhu, Murthy Cherikuri and Dusmanta Kumar Mohanta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 80\u003c\/p\u003e \u003cp\u003e3.1.1 Distributed Generation Technology in Smart Grid 81\u003c\/p\u003e \u003cp\u003e3.1.2 Microgrids 81\u003c\/p\u003e \u003cp\u003e3.3.1.1 Problems with Microgrids 81\u003c\/p\u003e \u003cp\u003e3.2 Islanding in Power System 82\u003c\/p\u003e \u003cp\u003e3.3 Island Detection Methods 83\u003c\/p\u003e \u003cp\u003e3.3.1 Passive Methods 83\u003c\/p\u003e \u003cp\u003e3.3.2 Active Methods 85\u003c\/p\u003e \u003cp\u003e3.3.3 Hybrid Methods 86\u003c\/p\u003e \u003cp\u003e3.3.4 Local Methods 87\u003c\/p\u003e \u003cp\u003e3.3.5 Signal Processing Methods 87\u003c\/p\u003e \u003cp\u003e3.3.6 Classifer Methods 88\u003c\/p\u003e \u003cp\u003e3.4 Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods 89\u003c\/p\u003e \u003cp\u003e3.4.1 Decision Tree 89\u003c\/p\u003e \u003cp\u003e3.4.1.1 Advantages of Decision Tree 91\u003c\/p\u003e \u003cp\u003e3.4.1.2 Disadvantages of Decision Tree 91\u003c\/p\u003e \u003cp\u003e3.4.2 Artificial Neural Network 91\u003c\/p\u003e \u003cp\u003e3.4.2.1 Advantages of Artificial Neural Network 93\u003c\/p\u003e \u003cp\u003e3.4.2.2 Disadvantages of Artificial Neural Network 93\u003c\/p\u003e \u003cp\u003e3.4.3 Fuzzy Logic 93\u003c\/p\u003e \u003cp\u003e3.4.3.1 Advantages of Fuzzy Logic 94\u003c\/p\u003e \u003cp\u003e3.4.3.2 Disadvantages of Fuzzy Logic 94\u003c\/p\u003e \u003cp\u003e3.4.4 Artificial Neuro-Fuzzy Inference System 95\u003c\/p\u003e \u003cp\u003e3.4.4.1 Advantages of Artificial Neuro-Fuzzy Inference System 95\u003c\/p\u003e \u003cp\u003e3.4.4.2 Disadvantages of Artificial Neuro-Fuzzy Inference System 96\u003c\/p\u003e \u003cp\u003e3.4.5 Static Vector Machine 96\u003c\/p\u003e \u003cp\u003e3.4.5.1 Advantages of Support Vector Machine 97\u003c\/p\u003e \u003cp\u003e3.4.5.2 Disadvantages of Support Vector Machine 97\u003c\/p\u003e \u003cp\u003e3.4.6 Random Forest 97\u003c\/p\u003e \u003cp\u003e3.4.6.1 Advantages of Random Forest 98\u003c\/p\u003e \u003cp\u003e3.4.6.2 Disadvantages of Random Forest 98\u003c\/p\u003e \u003cp\u003e3.4.7 Comparison of Machine Learning and Artificial Intelligence Based Island Detection Methods with Other Methods 99\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 99\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment 111\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRitesh Tirole, R R Joshi, Vinod Kumar Yadav, Jai Kumar Maherchandani and Shripati Vyas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 112\u003c\/p\u003e \u003cp\u003e4.2 Grid Connected Solar PV System 113\u003c\/p\u003e \u003cp\u003e4.2.1 Grid Connected Solar PV System 113\u003c\/p\u003e \u003cp\u003e4.2.2 PhotoVoltaic Cell 114\u003c\/p\u003e \u003cp\u003e4.2.3 PhotoVoltaic Array 114\u003c\/p\u003e \u003cp\u003e4.2.4 PhotoVoltaic System Configurations 114\u003c\/p\u003e \u003cp\u003e4.2.4.1 Centralized Configurations 115\u003c\/p\u003e \u003cp\u003e4.2.4.2 Master Slave Configurations 115\u003c\/p\u003e \u003cp\u003e4.2.4.3 String Configurations 115\u003c\/p\u003e \u003cp\u003e4.2.4.4 Modular Configurations 115\u003c\/p\u003e \u003cp\u003e4.2.5 Inverter Integration in Grid Solar PV System 115\u003c\/p\u003e \u003cp\u003e4.2.5.1 Voltage Source Inverter 116\u003c\/p\u003e \u003cp\u003e4.2.5.2 Current Source Inverter 117\u003c\/p\u003e \u003cp\u003e4.3 Control Strategies for Grid Connected Solar PV System 117\u003c\/p\u003e \u003cp\u003e4.3.1 Grid Solar PV System Controller 117\u003c\/p\u003e \u003cp\u003e4.3.1.1 Linear Controllers 117\u003c\/p\u003e \u003cp\u003e4.3.1.2 Non-Linear Controllers 117\u003c\/p\u003e \u003cp\u003e4.3.1.3 Robust Controllers 118\u003c\/p\u003e \u003cp\u003e4.3.1.4 Adaptive Controllers 118\u003c\/p\u003e \u003cp\u003e4.3.1.5 Predictive Controllers 118\u003c\/p\u003e \u003cp\u003e4.3.1.6 Intelligent Controllers 118\u003c\/p\u003e \u003cp\u003e4.4 Electromagnetic Interference 118\u003c\/p\u003e \u003cp\u003e4.4.1 Mechanisms of Electromagnetic Interference 119\u003c\/p\u003e \u003cp\u003e4.4.2 Effect of Electromagnetic Interference 120\u003c\/p\u003e \u003cp\u003e4.5 Intelligent Controller for Grid Connected Solar PV System 120\u003c\/p\u003e \u003cp\u003e4.5.1 Fuzzy Logic Controller 120\u003c\/p\u003e \u003cp\u003e4.6 Results and Discussion 121\u003c\/p\u003e \u003cp\u003e4.6.1 Generated EMI at the Input Side of Grid SPV System 122\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 125\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems 131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMegha Vyas, Vinod Kumar Yadav, Shripati Vyas, R.R Joshi and Ritesh Tirole\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 132\u003c\/p\u003e \u003cp\u003e5.2 Optimization and Control of HRES 134\u003c\/p\u003e \u003cp\u003e5.3 Optimization Techniques\/Algorithms 135\u003c\/p\u003e \u003cp\u003e5.3.1 Genetic Algorithms (GA) 136\u003c\/p\u003e \u003cp\u003e5.4 Use of Ga In Solar Power Forecasting 140\u003c\/p\u003e \u003cp\u003e5.5 PV Power Forecasting 142\u003c\/p\u003e \u003cp\u003e5.5.1 Short-Term Forecasting 143\u003c\/p\u003e \u003cp\u003e5.5.2 Medium Term Forecasting 144\u003c\/p\u003e \u003cp\u003e5.5.3 Long Term Forecasting 144\u003c\/p\u003e \u003cp\u003e5.6 Advantages 145\u003c\/p\u003e \u003cp\u003e5.7 Disadvantages 146\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 146\u003c\/p\u003e \u003cp\u003eAppendix A: List of Abbreviations 146\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Integration of RES with MPPT by SVPWM Scheme 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBusireddy Hemanth Kumar and Vivekanandan Subburaj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 158\u003c\/p\u003e \u003cp\u003e6.2 Multilevel Inverter Topologies 158\u003c\/p\u003e \u003cp\u003e6.2.1 Cascaded H-Bridge (CHB) Topology 159\u003c\/p\u003e \u003cp\u003e6.2.1.1 Neutral Point Clamped (NPC) Topology 160\u003c\/p\u003e \u003cp\u003e6.2.1.2 Flying Capacitor (FC) Topology 160\u003c\/p\u003e \u003cp\u003e6.3 Multilevel Inverter Modulation Techniques 161\u003c\/p\u003e \u003cp\u003e6.3.1 Fundamental Switching Frequency (FSF) 162\u003c\/p\u003e \u003cp\u003e6.3.1.1 Selective Harmonic Elimination Technique for MLIs 162\u003c\/p\u003e \u003cp\u003e6.3.1.2 Nearest Level Control Technique 163\u003c\/p\u003e \u003cp\u003e6.3.1.3 Nearest Vector Control Technique 164\u003c\/p\u003e \u003cp\u003e6.3.2 Mixed Switching Frequency PWM 164\u003c\/p\u003e \u003cp\u003e6.3.3 High Level Frequency PWM 164\u003c\/p\u003e \u003cp\u003e6.3.3.1 CBPWM Techniques for MLI 164\u003c\/p\u003e \u003cp\u003e6.3.3.2 Pulse Width Modulation Algorithms Using Space Vector Techniques for Multilevel Inverters 167\u003c\/p\u003e \u003cp\u003e6.4 Grid Integration of Renewable Energy Sources (RES) 167\u003c\/p\u003e \u003cp\u003e6.4.1 Solar PV Array 167\u003c\/p\u003e \u003cp\u003e6.4.2 Maximum Power Point Tracking (MPPT) 169\u003c\/p\u003e \u003cp\u003e6.4.3 Power Control Scheme 170\u003c\/p\u003e \u003cp\u003e6.5 Simulation Results 171\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 176\u003c\/p\u003e \u003cp\u003eReferences 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Energy Management of Standalone Hybrid Wind-PV System 179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRaunak Jangid, Jai Kumar Maherchandani, Vinod Kumar and Raju Kumar Swami\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 180\u003c\/p\u003e \u003cp\u003e7.2 Hybrid Renewable Energy System Configuration \u0026amp; Modeling 180\u003c\/p\u003e \u003cp\u003e7.3 PV System Modeling 181\u003c\/p\u003e \u003cp\u003e7.4 Wind System Modeling 183\u003c\/p\u003e \u003cp\u003e7.5 Modeling of Batteries 185\u003c\/p\u003e \u003cp\u003e7.6 Energy Management Controller 186\u003c\/p\u003e \u003cp\u003e7.7 Simulation Results and Discussion 186\u003c\/p\u003e \u003cp\u003e7.7.1 Simulation Response at Impulse Change in Wind Speed, Successive Increase in Irradiance Level and Impulse Change in Load 187\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 193\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources 199\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSurajit Sannigrahi and Parimal Acharjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 200\u003c\/p\u003e \u003cp\u003e8.2 Load and WTDG Modeling 204\u003c\/p\u003e \u003cp\u003e8.2.1 Modeling of Load Demand 204\u003c\/p\u003e \u003cp\u003e8.2.2 Modeling of WTDG 205\u003c\/p\u003e \u003cp\u003e8.3 Objective Functions 207\u003c\/p\u003e \u003cp\u003e8.3.1 System Voltage Enhancement Index (SVEI) 208\u003c\/p\u003e \u003cp\u003e8.3.2 Economic Feasibility Index (EFI) 208\u003c\/p\u003e \u003cp\u003e8.3.3 Emission Cost Reduction Index (ECRI) 211\u003c\/p\u003e \u003cp\u003e8.4 Mathematical Formulation Based on Fuzzy Logic 212\u003c\/p\u003e \u003cp\u003e8.4.1 Fuzzy MF for SVEI 212\u003c\/p\u003e \u003cp\u003e8.4.2 Fuzzy MF for EFI 213\u003c\/p\u003e \u003cp\u003e8.4.3 Fuzzy MF for ECRI 214\u003c\/p\u003e \u003cp\u003e8.5 Solution Algorithm 215\u003c\/p\u003e \u003cp\u003e8.5.1 Standard RTO Technique 215\u003c\/p\u003e \u003cp\u003e8.5.2 Discrete RTO (DRTO) Algorithm 217\u003c\/p\u003e \u003cp\u003e8.5.3 Computational Flow 219\u003c\/p\u003e \u003cp\u003e8.6 Simulation Results and Analysis 221\u003c\/p\u003e \u003cp\u003e8.6.1 Obtained Results for Different Planning Cases 223\u003c\/p\u003e \u003cp\u003e8.6.2 Analysis of Voltage Profile and Power Flow Under the Worst Case Scenarios: 230\u003c\/p\u003e \u003cp\u003e8.6.3 Comparison Between Different Algorithms 231\u003c\/p\u003e \u003cp\u003e8.6.3.1 Solution Quality 234\u003c\/p\u003e \u003cp\u003e8.6.3.2 Computational Time 234\u003c\/p\u003e \u003cp\u003e8.6.3.3 Failure Rate 234\u003c\/p\u003e \u003cp\u003e8.6.3.4 Convergence Characteristics 234\u003c\/p\u003e \u003cp\u003e8.6.3.5 Wilcoxon Signed Rank Test (WSRT) 236\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 237\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 User Interactive GUI for Integrated Design of PV Systems 243\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSushmitaSarkar, K UmaRao, Prema V, Anirudh Sharma C A, Jayanth Bhargav and ShrikeshSheshaprasad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 244\u003c\/p\u003e \u003cp\u003e9.2 PV System Design 245\u003c\/p\u003e \u003cp\u003e9.2.1 Design of a Stand-Alone PV System 245\u003c\/p\u003e \u003cp\u003e9.2.1.1 Panel Size Calculations 246\u003c\/p\u003e \u003cp\u003e9.2.1.2 Battery Sizing 247\u003c\/p\u003e \u003cp\u003e9.2.1.3 Inverter Design 248\u003c\/p\u003e \u003cp\u003e9.2.1.4 Loss of Load 249\u003c\/p\u003e \u003cp\u003e9.2.1.5 Average Daily Units Generated 249\u003c\/p\u003e \u003cp\u003e9.2.2 Design of a Grid-Tied PV System 250\u003c\/p\u003e \u003cp\u003e9.2.3 Design of a Large-Scale Power Plant 251\u003c\/p\u003e \u003cp\u003e9.3 Economic Considerations 252\u003c\/p\u003e \u003cp\u003e9.4 PV System Standards 252\u003c\/p\u003e \u003cp\u003e9.5 Design of GUI 252\u003c\/p\u003e \u003cp\u003e9.6 Results 255\u003c\/p\u003e \u003cp\u003e9.6.1 Design of a Stand-Alone System Using GUI 255\u003c\/p\u003e \u003cp\u003e9.6.2 GUI for a Grid-Tied System 257\u003c\/p\u003e \u003cp\u003e9.6.3 GUI for a Large PV Plant 259\u003c\/p\u003e \u003cp\u003e9.7 Discussions 260\u003c\/p\u003e \u003cp\u003e9.8 Conclusion and Future Scope 260\u003c\/p\u003e \u003cp\u003e9.9 Acknowledgment 261\u003c\/p\u003e \u003cp\u003eReferences 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm 267\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKunjabihari Swain and Murthy Cherukuri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 268\u003c\/p\u003e \u003cp\u003e10.2 Micro Grid 269\u003c\/p\u003e \u003cp\u003e10.3 Phasor Measurement Unit and Micro PMU 270\u003c\/p\u003e \u003cp\u003e10.4 Situational Awareness: Perception, Comprehension and Prediction 272\u003c\/p\u003e \u003cp\u003e10.4.1 Perception 273\u003c\/p\u003e \u003cp\u003e10.4.2 Comprehension 274\u003c\/p\u003e \u003cp\u003e10.4.3 Projection 280\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 280\u003c\/p\u003e \u003cp\u003eReferences 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 AI and ML for the Smart Grid 287\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDr M K Khedkar and B Ramesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 288\u003c\/p\u003e \u003cp\u003e11.1 Introduction 288\u003c\/p\u003e \u003cp\u003e11.2 AI Techniques 291\u003c\/p\u003e \u003cp\u003e11.2.1 Expert Systems (ES) 291\u003c\/p\u003e \u003cp\u003e11.2.2 Artificial Neural Networks (ANN) 291\u003c\/p\u003e \u003cp\u003e11.2.3 Fuzzy Logic (FL) 292\u003c\/p\u003e \u003cp\u003e11.2.4 Genetic Algorithm (GA) 292\u003c\/p\u003e \u003cp\u003e11.3 Machine Learning (ML) 293\u003c\/p\u003e \u003cp\u003e11.4 Home Energy Management System (HEMS) 294\u003c\/p\u003e \u003cp\u003e11.5 Load Forecasting (LF) in Smart Grid 295\u003c\/p\u003e \u003cp\u003e11.6 Adaptive Protection (AP) 297\u003c\/p\u003e \u003cp\u003e11.7 Energy Trading in Smart Grid 298\u003c\/p\u003e \u003cp\u003e11.8 AI Based Smart Energy Meter (AI-SEM) 300\u003c\/p\u003e \u003cp\u003eReferences 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Energy Loss Allocation in Distribution Systems with Distributed Generations 307\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDr Kushal Manohar Jagtap\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 308\u003c\/p\u003e \u003cp\u003e12.2 Load Modelling 311\u003c\/p\u003e \u003cp\u003e12.3 Mathematicl Model 312\u003c\/p\u003e \u003cp\u003e12.4 Solution Algorithm 317\u003c\/p\u003e \u003cp\u003e12.5 Results and Discussion 317\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 341\u003c\/p\u003e \u003cp\u003eReferences 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers 345\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNagesh Prabhu, R Thirumalaivasan and M.Janaki\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 346\u003c\/p\u003e \u003cp\u003e13.2 Design of Genetic Algorithm Based Controller for STATCOM 347\u003c\/p\u003e \u003cp\u003e13.2.1 Two Level STACOM with Type-2 Controller 348\u003c\/p\u003e \u003cp\u003e13.2.1.1 Simulation Results with Suboptimal Controller Parameters 349\u003c\/p\u003e \u003cp\u003e13.2.1.2 PI Controller Without Nonlinear State Variable Feedback 349\u003c\/p\u003e \u003cp\u003e13.2.1.3 PI Controller with Nonlinear State Variable Feedback 351\u003c\/p\u003e \u003cp\u003e13.2.2 Structure of Type-1 Controller for 3-Level STACOM 354\u003c\/p\u003e \u003cp\u003e13.2.2.1 Transient Simulation with Suboptimal Controller Parameters 357\u003c\/p\u003e \u003cp\u003e13.2.3 Application of Genetic Algorithm for Optimization of Controller Parameters 357\u003c\/p\u003e \u003cp\u003e13.2.3.1 Boundaries of Type-2 Controller Parameters in GA Optimization 359\u003c\/p\u003e \u003cp\u003e13.2.3.2 Boundaries of Type-1 Controller Parameters in GA Optimization 360\u003c\/p\u003e \u003cp\u003e13.2.4 Optimization Results of Two Level STATCOM with GA Optimized Controller Parameters 360\u003c\/p\u003e \u003cp\u003e13.2.4.1 Transient Simulation with GA Optimized Controller Parameters 361\u003c\/p\u003e \u003cp\u003e13.2.5 Optimization Results of Three Level STATCOM with Optimal Controller Parameters 362\u003c\/p\u003e \u003cp\u003e13.2.5.1 Transient Simulation with GA Optimized Controller Parameters 363\u003c\/p\u003e \u003cp\u003e13.3 Design of Particle Swarm Optimization Based Controller for STATCOM 364\u003c\/p\u003e \u003cp\u003e13.3.1 Optimization Results of Two Level STATCOM with GA and PSO Optimized Parameters 365\u003c\/p\u003e \u003cp\u003e13.4 Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC 371\u003c\/p\u003e \u003cp\u003e13.4.1 Modeling of VSC HVDC 371\u003c\/p\u003e \u003cp\u003e13.4.1.1 Converter Controller 374\u003c\/p\u003e \u003cp\u003e13.4.2 A Case Study 375\u003c\/p\u003e \u003cp\u003e13.4.2.1 Transient Simulation with Suboptimal Controller Parameters 376\u003c\/p\u003e \u003cp\u003e13.4.3 Design of Controller Using GA and Simulation Results 378\u003c\/p\u003e \u003cp\u003e13.4.3.1 Description of Optimization Problem and Application of GA 378\u003c\/p\u003e \u003cp\u003e13.4.3.2 Transient Simulation 379\u003c\/p\u003e \u003cp\u003e13.4.3.3 Eigenvalue Analysis 379\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 379\u003c\/p\u003e \u003cp\u003eReferences 386\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Short Term Load Forecasting for CPP Using ANN 391\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKirti Pal and Vidhi Tiwari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 392\u003c\/p\u003e \u003cp\u003e14.1.1 Captive Power Plant 394\u003c\/p\u003e \u003cp\u003e14.1.2 Gas Turbine 394\u003c\/p\u003e \u003cp\u003e14.2 Working of Combined Cycle Power Plant 395\u003c\/p\u003e \u003cp\u003e14.3 Implementation of ANN for Captive Power Plant 396\u003c\/p\u003e \u003cp\u003e14.4 Training and Testing Results 397\u003c\/p\u003e \u003cp\u003e14.4.1 Regression Plot 397\u003c\/p\u003e \u003cp\u003e14.4.2 The Performance Plot 398\u003c\/p\u003e \u003cp\u003e14.4.3 Error Histogram 399\u003c\/p\u003e \u003cp\u003e14.4.4 Training State Plot 399\u003c\/p\u003e \u003cp\u003e14.4.5 Comparison between the Predicted Load and Actual Load 401\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 403\u003c\/p\u003e \u003cp\u003e14.6 Acknowlegdement 403\u003c\/p\u003e \u003cp\u003eReferences 404\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Real-Time EVCS Scheduling Scheme by Using GA 409\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTripti Kunj and Kirti Pal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 410\u003c\/p\u003e \u003cp\u003e15.2 EV Charging Station Modeling 413\u003c\/p\u003e \u003cp\u003e15.2.1 Parts of the System 413\u003c\/p\u003e \u003cp\u003e15.2.2 Proposed EV Charging Station 414\u003c\/p\u003e \u003cp\u003e15.2.3 Proposed Charging Scheme Cycle 414\u003c\/p\u003e \u003cp\u003e15.3 Real Time System Modeling for EVCS 415\u003c\/p\u003e \u003cp\u003e15.3.1 Scenario 1 415\u003c\/p\u003e \u003cp\u003e15.3.2 Design of Scenario 1 418\u003c\/p\u003e \u003cp\u003e15.3.3 Scenario 2 419\u003c\/p\u003e \u003cp\u003e15.3.4 Design of Scenario 2 421\u003c\/p\u003e \u003cp\u003e15.3.5 Simulation Settings 422\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 424\u003c\/p\u003e \u003cp\u003e15.4.1 Influence on Average Waiting Time 424\u003c\/p\u003e \u003cp\u003e15.4.1.1 Early Morning 425\u003c\/p\u003e \u003cp\u003e15.4.1.2 Forenoon 425\u003c\/p\u003e \u003cp\u003e15.4.1.3 Afternoon 426\u003c\/p\u003e \u003cp\u003e15.4.2 Influence on Number of Charged EV 426\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 428\u003c\/p\u003e \u003cp\u003eReferences 428\u003c\/p\u003e \u003cp\u003eAbout the Editors 435\u003c\/p\u003e \u003cp\u003eIndex 437\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407151210839,"sku":"9781119786276","price":169.16,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/intelligent-renewable-energy-systems-9781119786276","provider":"Book 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