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intelligent renewable energy systems integrating ai 2022

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Textbook in PDF format 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. Preface. Optimization Algorithm for Renewable Energy Integration. Introduction. Mixed Discrete SPBO. Problem Formulation. Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions. Optimum Placement of RDG and Shunt Capacitor to the Distribution Network. Conclusions. References. Chaotic PSO for PV System Modelling. Introduction. Proposed Method. Results and Discussions. Conclusions. References. Application of Artiϐicial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid. Introduction. Islanding in Power System. Island Detection Methods. Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods. Conclusion. References. Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment. Introduction. Grid Connected Solar PV System. Control Strategies for Grid Connected Solar PV System. Electromagnetic Interference. Intelligent Controller for Grid Connected Solar PV System. Results and Discussion. Conclusion. References. A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems. Introduction. Optimization and Control of HRES. Optimization Techniques/Algorithms. Use of GA In Solar Power Forecasting. PV Power Forecasting. Advantages. Disadvantages. Conclusion. Appendix A: List of Abbreviations. References. Integration of RES with MPPT by SVPWM Scheme. Introduction. Multilevel Inverter Topologies. Multilevel Inverter Modulation Techniques. Grid Integration of Renewable Energy Sources (RES). Simulation Results. Conclusion. References. Energy Management of Standalone Hybrid Wind-PV System. Introduction. Hybrid Renewable Energy System Conϐiguration &Modeling. PV System Modeling. Wind System Modeling. Modeling of Batteries. Energy Management Controller. Simulation Results and Discussion. Conclusion. References. Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources. Introduction. Load and WTDG Modeling. Objective Functions. Mathematical Formulation Based on Fuzzy Logic. Solution Algorithm. Simulation Results and Analysis. Conclusion. References. User Interactive GUI for Integrated Design of PV Systems. Introduction. PV System Design. Economic Considerations. PV System Standards. Design of GUI. Results. Discussions. Conclusion and Future Scope. Acknowledgement. References. Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm. Introduction. Micro Grid. Phasor Measurement Unit and Micro PMU. Situational Awareness: Perception, Comprehension and Prediction. Conclusion. References. AI and ML for the Smart Grid. Introduction. AI Techniques. Machine Learning (ML). Home Energy Management System (HEMS). Load Forecasting (LF) in Smart Grid. Adaptive Protection (AP). Energy Trading in Smart Grid. AI Based Smart Energy Meter (AI-SEM). References. Energy Loss Allocation in Distribution Systems with Distributed Generations. Introduction. Load Modelling. Mathematical Model. Solution Algorithm. Results and Discussion. Conclusion. References. Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers. Introduction. Design of Genetic Algorithm Based Controller for STATCOM. Design of Particle Swarm Optimization Based Controller for STATCOM. Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC. Conclusion. References. Short Term Load Forecasting for CPP Using ANN. Introduction. Working of Combined Cycle Power Plant. Implementation of ANN for Captive Power Plant. Training and Testing Results. Conclusion. Acknowlegdement. References. Real-Time EVCS Scheduling Scheme by Using GA. Introduction. EV Charging Station Modeling. Real Time System Modeling for EVCS. Results and Discussion. Conclusion. References. About the Editors. Index

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