Intelligent Renewable Energy Systems

Neeraj Priyadarshi, PhD, works in the Department of Energy Technology, Aalborg University, Denmark, from which he also received a post doctorate. He received his M. Tech. degree in power electronics and drives in 2010 from the Vellore Institute of Technology (VIT), Vellore, India, and his PhD from the Government College of Technology and Engineering, Udaipur, Rajasthan, India. He has published over 60 papers in scientific and technical journals and conferences and has organized several international workshops. He is a reviewer for a number of technical journals, and he is the lead editor for four edited books, including Scrivener Publishing. Akash Kumar Bhoi, PhD, is an assistant professor in the Department of Electrical and Electronics Engineering at Sikkim Manipal Institute of Technology (SMIT), India. He is also a research associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He is a member of several technical associations and is an editorial board member for a number of journals. He has published several papers in scientific journals and conferences and is currently working on several edited volumes for various publishers, including Scrivener Publishing. Sanjeevikumar Padmanaban, PhD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark and works with CTIF Global Capsule (CGC), Department of Business Development and Technology, Aarhus University, Denmark. He received his PhD in electrical engineering from the University of Bologna, Italy. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching. S. Balamurugan is the Head of Research and Development, QUANTS IS & Consultancy Services, India. He has authored or edited 40 books, more than 200 papers in scientific and technical journals and conferences and has 15 patents to his credit. He is either the editor-in-chief, associate editor, guest editor, or editor for many scientific and technical journals, from many well-respected publishers around the world. He has won numerous awards, and he is a member of several technical societies. Jens Bo Holm-Nielsen currently works at the Department of Energy Technology, Aalborg University and is head of the Esbjerg Energy Section. He helped establish the Center for Bioenergy and Green Engineering in 2009 and served as the head of the research group. He has served as technical advisor for many companies in this industry, and he has executed many large-scale European Union and United Nation projects. He has authored more than 300 scientific papers and has participated in over 500 various international conferences.

• Preface xv1 Optimization Algorithm for Renewable Energy Integration 1Bikash Das, SoumyabrataBarik, Debapriya Das and V Mukherjee1.1 Introduction 21.2 Mixed Discrete SPBO 51.2.1 SPBO Algorithm 51.2.2 Performance of SPBO for Solving Benchmark Functions 81.2.3 Mixed Discrete SPBO 111.3 Problem Formulation 121.3.1 Objective Functions 121.3.2 Technical Constraints Considered 141.4 Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions 171.5 Optimum Placement of RDG and Shunt Capacitor to the Distribution Network 181.5.1 Optimum Placement of RDGs and ShuntCapacitors to 33-Bus Distribution Network 251.5.2 Optimum Placement of RDGs and Shunt Capacitors to 69-Bus Distribution Network 291.6 Conclusions 33References 342 Chaotic PSO for PV System Modelling 41Souvik Ganguli, Jyoti Gupta and Parag Nijhawan2.1 Introduction 422.2 Proposed Method 432.3 Results and Discussions 432.4 Conclusions 72References 723 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid 79Soham Dutta, Pradip Kumar Sadhu, Murthy Cherikuri and Dusmanta Kumar Mohanta3.1 Introduction 803.1.1 Distributed Generation Technology in Smart Grid 813.1.2 Microgrids 813.3.1.1 Problems with Microgrids 813.2 Islanding in Power System 823.3 Island Detection Methods 833.3.1 Passive Methods 833.3.2 Active Methods 853.3.3 Hybrid Methods 863.3.4 Local Methods 873.3.5 Signal Processing Methods 873.3.6 Classifer Methods 883.4 Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods 893.4.1 Decision Tree 893.4.1.1 Advantages of Decision Tree 913.4.1.2 Disadvantages of Decision Tree 913.4.2 Artificial Neural Network 913.4.2.1 Advantages of Artificial Neural Network 933.4.2.2 Disadvantages of Artificial Neural Network 933.4.3 Fuzzy Logic 933.4.3.1 Advantages of Fuzzy Logic 943.4.3.2 Disadvantages of Fuzzy Logic 943.4.4 Artificial Neuro-Fuzzy Inference System 953.4.4.1 Advantages of Artificial Neuro-Fuzzy Inference System 953.4.4.2 Disadvantages of Artificial Neuro-Fuzzy Inference System 963.4.5 Static Vector Machine 963.4.5.1 Advantages of Support Vector Machine 973.4.5.2 Disadvantages of Support Vector Machine 973.4.6 Random Forest 973.4.6.1 Advantages of Random Forest 983.4.6.2 Disadvantages of Random Forest 983.4.7 Comparison of Machine Learning and Artificial Intelligence Based Island Detection Methods with Other Methods 993.5 Conclusion 99References 1014 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment 111Ritesh Tirole, R R Joshi, Vinod Kumar Yadav, Jai Kumar Maherchandani and Shripati Vyas4.1 Introduction 1124.2 Grid Connected Solar PV System 1134.2.1 Grid Connected Solar PV System 1134.2.2 PhotoVoltaic Cell 1144.2.3 PhotoVoltaic Array 1144.2.4 PhotoVoltaic System Configurations 1144.2.4.1 Centralized Configurations 1154.2.4.2 Master Slave Configurations 1154.2.4.3 String Configurations 1154.2.4.4 Modular Configurations 1154.2.5 Inverter Integration in Grid Solar PV System 1154.2.5.1 Voltage Source Inverter 1164.2.5.2 Current Source Inverter 1174.3 Control Strategies for Grid Connected Solar PV System 1174.3.1 Grid Solar PV System Controller 1174.3.1.1 Linear Controllers 1174.3.1.2 Non-Linear Controllers 1174.3.1.3 Robust Controllers 1184.3.1.4 Adaptive Controllers 1184.3.1.5 Predictive Controllers 1184.3.1.6 Intelligent Controllers 1184.4 Electromagnetic Interference 1184.4.1 Mechanisms of Electromagnetic Interference 1194.4.2 Effect of Electromagnetic Interference 1204.5 Intelligent Controller for Grid Connected Solar PV System 1204.5.1 Fuzzy Logic Controller 1204.6 Results and Discussion 1214.6.1 Generated EMI at the Input Side of Grid SPV System 1224.7 Conclusion 125References 1255 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems 131Megha Vyas, Vinod Kumar Yadav, Shripati Vyas, R.R Joshi and Ritesh Tirole5.1 Introduction 1325.2 Optimization and Control of HRES 1345.3 Optimization Techniques/Algorithms 1355.3.1 Genetic Algorithms (GA) 1365.4 Use of Ga In Solar Power Forecasting 1405.5 PV Power Forecasting 1425.5.1 Short-Term Forecasting 1435.5.2 Medium Term Forecasting 1445.5.3 Long Term Forecasting 1445.6 Advantages 1455.7 Disadvantages 1465.8 Conclusion 146Appendix A: List of Abbreviations 146References 1476 Integration of RES with MPPT by SVPWM Scheme 157Busireddy Hemanth Kumar and Vivekanandan Subburaj6.1 Introduction 1586.2 Multilevel Inverter Topologies 1586.2.1 Cascaded H-Bridge (CHB) Topology 1596.2.1.1 Neutral Point Clamped (NPC) Topology 1606.2.1.2 Flying Capacitor (FC) Topology 1606.3 Multilevel Inverter Modulation Techniques 1616.3.1 Fundamental Switching Frequency (FSF) 1626.3.1.1 Selective Harmonic Elimination Technique for MLIs 1626.3.1.2 Nearest Level Control Technique 1636.3.1.3 Nearest Vector Control Technique 1646.3.2 Mixed Switching Frequency PWM 1646.3.3 High Level Frequency PWM 1646.3.3.1 CBPWM Techniques for MLI 1646.3.3.2 Pulse Width Modulation Algorithms Using Space Vector Techniques for Multilevel Inverters 1676.4 Grid Integration of Renewable Energy Sources (RES) 1676.4.1 Solar PV Array 1676.4.2 Maximum Power Point Tracking (MPPT) 1696.4.3 Power Control Scheme 1706.5 Simulation Results 1716.6 Conclusion 176References 1767 Energy Management of Standalone Hybrid Wind-PV System 179Raunak Jangid, Jai Kumar Maherchandani, Vinod Kumar and Raju Kumar Swami7.1 Introduction 1807.2 Hybrid Renewable Energy System Configuration & Modeling 1807.3 PV System Modeling 1817.4 Wind System Modeling 1837.5 Modeling of Batteries 1857.6 Energy Management Controller 1867.7 Simulation Results and Discussion 1867.7.1 Simulation Response at Impulse Change in Wind Speed, Successive Increase in Irradiance Level and Impulse Change in Load 1877.8 Conclusion 193References 1948 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources 199Surajit Sannigrahi and Parimal Acharjee8.1 Introduction 2008.2 Load and WTDG Modeling 2048.2.1 Modeling of Load Demand 2048.2.2 Modeling of WTDG 2058.3 Objective Functions 2078.3.1 System Voltage Enhancement Index (SVEI) 2088.3.2 Economic Feasibility Index (EFI) 2088.3.3 Emission Cost Reduction Index (ECRI) 2118.4 Mathematical Formulation Based on Fuzzy Logic 2128.4.1 Fuzzy MF for SVEI 2128.4.2 Fuzzy MF for EFI 2138.4.3 Fuzzy MF for ECRI 2148.5 Solution Algorithm 2158.5.1 Standard RTO Technique 2158.5.2 Discrete RTO (DRTO) Algorithm 2178.5.3 Computational Flow 2198.6 Simulation Results and Analysis 2218.6.1 Obtained Results for Different Planning Cases 2238.6.2 Analysis of Voltage Profile and Power Flow Under the Worst Case Scenarios: 2308.6.3 Comparison Between Different Algorithms 2318.6.3.1 Solution Quality 2348.6.3.2 Computational Time 2348.6.3.3 Failure Rate 2348.6.3.4 Convergence Characteristics 2348.6.3.5 Wilcoxon Signed Rank Test (WSRT) 2368.7 Conclusion 237References 2399 User Interactive GUI for Integrated Design of PV Systems 243SushmitaSarkar, K UmaRao, Prema V, Anirudh Sharma C A, Jayanth Bhargav and ShrikeshSheshaprasad9.1 Introduction 2449.2 PV System Design 2459.2.1 Design of a Stand-Alone PV System 2459.2.1.1 Panel Size Calculations 2469.2.1.2 Battery Sizing 2479.2.1.3 Inverter Design 2489.2.1.4 Loss of Load 2499.2.1.5 Average Daily Units Generated 2499.2.2 Design of a Grid-Tied PV System 2509.2.3 Design of a Large-Scale Power Plant 2519.3 Economic Considerations 2529.4 PV System Standards 2529.5 Design of GUI 2529.6 Results 2559.6.1 Design of a Stand-Alone System Using GUI 2559.6.2 GUI for a Grid-Tied System 2579.6.3 GUI for a Large PV Plant 2599.7 Discussions 2609.8 Conclusion and Future Scope 2609.9 Acknowledgment 261References 26110 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm 267Kunjabihari Swain and Murthy Cherukuri10.1 Introduction 26810.2 Micro Grid 26910.3 Phasor Measurement Unit and Micro PMU 27010.4 Situational Awareness: Perception, Comprehension and Prediction 27210.4.1 Perception 27310.4.2 Comprehension 27410.4.3 Projection 28010.5 Conclusion 280References 28011 AI and ML for the Smart Grid 287Dr M K Khedkar and B RameshAbbreviations 28811.1 Introduction 28811.2 AI Techniques 29111.2.1 Expert Systems (ES) 29111.2.2 Artificial Neural Networks (ANN) 29111.2.3 Fuzzy Logic (FL) 29211.2.4 Genetic Algorithm (GA) 29211.3 Machine Learning (ML) 29311.4 Home Energy Management System (HEMS) 29411.5 Load Forecasting (LF) in Smart Grid 29511.6 Adaptive Protection (AP) 29711.7 Energy Trading in Smart Grid 29811.8 AI Based Smart Energy Meter (AI-SEM) 300References 30212 Energy Loss Allocation in Distribution Systems with Distributed Generations 307Dr Kushal Manohar Jagtap12.1 Introduction 30812.2 Load Modelling 31112.3 Mathematicl Model 31212.4 Solution Algorithm 31712.5 Results and Discussion 31712.6 Conclusion 341References 34113 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers 345Nagesh Prabhu, R Thirumalaivasan and M.Janaki13.1 Introduction 34613.2 Design of Genetic Algorithm Based Controller for STATCOM 34713.2.1 Two Level STACOM with Type-2 Controller 34813.2.1.1 Simulation Results with Suboptimal Controller Parameters 34913.2.1.2 PI Controller Without Nonlinear State Variable Feedback 34913.2.1.3 PI Controller with Nonlinear State Variable Feedback 35113.2.2 Structure of Type-1 Controller for 3-Level STACOM 35413.2.2.1 Transient Simulation with Suboptimal Controller Parameters 35713.2.3 Application of Genetic Algorithm for Optimization of Controller Parameters 35713.2.3.1 Boundaries of Type-2 Controller Parameters in GA Optimization 35913.2.3.2 Boundaries of Type-1 Controller Parameters in GA Optimization 36013.2.4 Optimization Results of Two Level STATCOM with GA Optimized Controller Parameters 36013.2.4.1 Transient Simulation with GA Optimized Controller Parameters 36113.2.5 Optimization Results of Three Level STATCOM with Optimal Controller Parameters 36213.2.5.1 Transient Simulation with GA Optimized Controller Parameters 36313.3 Design of Particle Swarm Optimization Based Controller for STATCOM 36413.3.1 Optimization Results of Two Level STATCOM with GA and PSO Optimized Parameters 36513.4 Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC 37113.4.1 Modeling of VSC HVDC 37113.4.1.1 Converter Controller 37413.4.2 A Case Study 37513.4.2.1 Transient Simulation with Suboptimal Controller Parameters 37613.4.3 Design of Controller Using GA and Simulation Results 37813.4.3.1 Description of Optimization Problem and Application of GA 37813.4.3.2 Transient Simulation 37913.4.3.3 Eigenvalue Analysis 37913.5 Conclusion 379References 38614 Short Term Load Forecasting for CPP Using ANN 391Kirti Pal and Vidhi Tiwari14.1 Introduction 39214.1.1 Captive Power Plant 39414.1.2 Gas Turbine 39414.2 Working of Combined Cycle Power Plant 39514.3 Implementation of ANN for Captive Power Plant 39614.4 Training and Testing Results 39714.4.1 Regression Plot 39714.4.2 The Performance Plot 39814.4.3 Error Histogram 39914.4.4 Training State Plot 39914.4.5 Comparison between the Predicted Load and Actual Load 40114.5 Conclusion 40314.6 Acknowlegdement 403References 40415 Real-Time EVCS Scheduling Scheme by Using GA 409Tripti Kunj and Kirti Pal15.1 Introduction 41015.2 EV Charging Station Modeling 41315.2.1 Parts of the System 41315.2.2 Proposed EV Charging Station 41415.2.3 Proposed Charging Scheme Cycle 41415.3 Real Time System Modeling for EVCS 41515.3.1 Scenario 1 41515.3.2 Design of Scenario 1 41815.3.3 Scenario 2 41915.3.4 Design of Scenario 2 42115.3.5 Simulation Settings 42215.4 Results and Discussion 42415.4.1 Influence on Average Waiting Time 42415.4.1.1 Early Morning 42515.4.1.2 Forenoon 42515.4.1.3 Afternoon 42615.4.2 Influence on Number of Charged EV 42615.5 Conclusion 428References 428About the Editors 435Index 437