Intelligent Renewable Energy Systems: Integrating Artificial Intelligence Techniques and Optimization Algorithms
- Length: 500 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2021-12-14
- ISBN-10: 1119786274
- ISBN-13: 9781119786276
- Sales Rank: #8658554 (See Top 100 Books)
INTELLIGENT RENEWABLE ENERGY SYSTEMS
This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology.
Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion.
This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques.
This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library.
Audience
Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.
Cover Table of Contents Title Page Copyright Preface 1 Optimization Algorithm for Renewable Energy Integration 1.1 Introduction 1.2 Mixed Discrete SPBO 1.3 Problem Formulation 1.4 Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions 1.5 Optimum Placement of RDG and Shunt Capacitor to the Distribution Network 1.6 Conclusions References 2 Chaotic PSO for PV System Modelling 2.1 Introduction 2.2 Proposed Method 2.3 Results and Discussions 2.4 Conclusions References 3 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid 3.1 Introduction 3.2 Islanding in Power System 3.3 Island Detection Methods 3.4 Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods 3.5 Conclusion References 4 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment 4.1 Introduction 4.2 Grid Connected Solar PV System 4.3 Control Strategies for Grid Connected Solar PV System 4.4 Electromagnetic Interference 4.5 Intelligent Controller for Grid Connected Solar PV System 4.6 Results and Discussion 4.7 Conclusion References 5 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems 5.1 Introduction 5.2 Optimization and Control of HRES 5.3 Optimization Techniques/Algorithms 5.4 Use of GA In Solar Power Forecasting 5.5 PV Power Forecasting 5.6 Advantages 5.7 Disadvantages 5.8 Conclusion Appendix A: List of Abbreviations References 6 Integration of RES with MPPT by SVPWM Scheme 6.1 Introduction 6.2 Multilevel Inverter Topologies 6.3 Multilevel Inverter Modulation Techniques 6.4 Grid Integration of Renewable Energy Sources (RES) 6.5 Simulation Results 6.6 Conclusion References 7 Energy Management of Standalone Hybrid Wind-PV System 7.1 Introduction 7.2 Hybrid Renewable Energy System Configuration & Modeling 7.3 PV System Modeling 7.4 Wind System Modeling 7.5 Modeling of Batteries 7.6 Energy Management Controller 7.7 Simulation Results and Discussion 7.8 Conclusion References 8 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources 8.1 Introduction 8.2 Load and WTDG Modeling 8.3 Objective Functions 8.4 Mathematical Formulation Based on Fuzzy Logic 8.5 Solution Algorithm 8.6 Simulation Results and Analysis 8.7 Conclusion References 9 User Interactive GUI for Integrated Design of PV Systems 9.1 Introduction 9.2 PV System Design 9.3 Economic Considerations 9.4 PV System Standards 9.5 Design of GUI 9.6 Results 9.7 Discussions 9.8 Conclusion and Future Scope 9.9 Acknowledgement References 10 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm 10.1 Introduction 10.2 Micro Grid 10.3 Phasor Measurement Unit and Micro PMU 10.4 Situational Awareness: Perception, Comprehension and Prediction 10.5 Conclusion References 11 AI and ML for the Smart Grid 11.1 Introduction 11.2 AI Techniques 11.3 Machine Learning (ML) 11.4 Home Energy Management System (HEMS) 11.5 Load Forecasting (LF) in Smart Grid 11.6 Adaptive Protection (AP) 11.7 Energy Trading in Smart Grid 11.8 AI Based Smart Energy Meter (AI-SEM) References 12 Energy Loss Allocation in Distribution Systems with Distributed Generations 12.1 Introduction 12.2 Load Modelling 12.3 Mathematical Model 12.4 Solution Algorithm 12.5 Results and Discussion 12.6 Conclusion References 13 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers 13.1 Introduction 13.2 Design of Genetic Algorithm Based Controller for STATCOM 13.3 Design of Particle Swarm Optimization Based Controller for STATCOM 13.4 Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC 13.5 Conclusion References 14 Short Term Load Forecasting for CPP Using ANN 14.1 Introduction 14.2 Working of Combined Cycle Power Plant 14.3 Implementation of ANN for Captive Power Plant 14.4 Training and Testing Results 14.5 Conclusion 14.6 Acknowlegdement References 15 Real-Time EVCS Scheduling Scheme by Using GA 15.1 Introduction 15.2 EV Charging Station Modeling 15.3 Real Time System Modeling for EVCS 15.4 Results and Discussion 15.5 Conclusion References About the Editors Index End User License Agreement
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