Читать книгу Intelligent Renewable Energy Systems - Группа авторов - Страница 10

Preface

This book presents intelligent renewable energy systems integrating artificial intelligence techniques and optimization algorithms. The first chapter describes placement of distributed generation (DG) sources including renewable distributed generation (RDGs) such as biomass, solar PV, and shunt capacitor has been considered for the study purpose. The second chapter develops a new approach to chaotic particle swarm optimization (CPSO) technique. In the third chapter, comprehensive reviews of different artificial intelligence and machine learning techniques have been explicated. To bring out its advantages over other methods used in island detection, the traditional methods are first explained and then compared with artificial intelligence and machine learning island detection techniques. The performance of the intelligent controller is found to be good under steady conditions for grid connected photovoltaic systems and has been discussed in chapter four. Chapter five explains various uses of Genetic Algorithms (GA) and Solar PV forecasting are described; further, many stimulated algorithms which have been used in optimization, controlling, and methods of supervising of power for renewable energy analysis, which include hybrid power generation strategies are discussed. Chapter six presents the integration of 100 kW solar PV source to the 25 kV AC grid by using generalized r-s based SVPWM algorithm. Chapter seven aims to discuss the idea of hybrid system configuration, dynamic modeling, energy management, and control strategies. A multi-stage planning framework is proposed in chapter eight to divide the planning period into several stages so that investments can be made in each stage as per the requirements. A unique and a novel GUI is presented to design the entire solar PV systems has been discussed in Chapter nine. Chapter ten addresses micro-grid situational awareness using micro PMU. Role of AI & ML in smart grid entities such as Home Energy Management System (HEMS), Energy Trading, Adaptive Protection, Load Forecasting and Smart Energy Meter are presented in Chapter eleven. Chapter twelve presents a new method for energy loss allocation in radial distribution network (RDN) with distributed generationin the context of deregulated power system. Chapter thirteen presents the optimization of controller parameters for FACTS and VSC based HVDC. Chapter fourteen describes Short Term load forecasting for a Captive Power Plant Using Artificial Neural Network. Chapter fifteen defines Real-time EV Charging Station Scheduling Scheme by using Global Aggregator.

Neeraj PriyadarshiAkash Kumar BhoiSanjeevikumar Padmanaban S. BalamuruganJens Bo Holm-Nielsen Editors