Applied Artificial Intelligence (AI) to Green Power Technology

Contents
Preface
Acknowledgments
Chapter 1
Energy Management and Artificial Intelligence
Abstract
Introduction
Energy Management
Overview
Objectives
Energy Management Process
Electric Grid and Energy Management
Artificial Intelligence
AI for Energy Management
Conclusion
References
Biographical Sketches
Chapter 2
Issues and Challenges of Latest Green Energy Technology Such as Fuel Cell, Waste to Energy and Application of AI
Abstract
Introduction
Fuel Cell
Artificial Intelligence Techniques
Artificial Neural Networks
Multi-Layer Perceptrons (MLPs)
Radial Basis Functions (RBF)
Fuzzy Logic
AI Applications in Renewable Energy
AI in Solar Energy
AI in Wind Energy
AI in Geothermal
Challenges in AI Techniques for Green Energy
Conclusion
References
Chapter 3
Voltage Improvement of Short Shunt Self-Excited Induction Generators Using Gravitational Search Algorithms and Genetic Algorithms
Abstract
Introduction
Literature Review
Problem Structure
Artificial Intelligence Techniques
Gravitational Search Algorithm (GSA)
Procedure to Be Followed for SEIG Operation
Genetic Algorithm (GA)
Steps to Be Followed for Genetic Algorithm in SEIG
Result and Discussion
Conclusion
Appendix
References
Chapter 4
Micro/Pico Hydropower Generation System Using Self-Excited Induction Generators and Applications of AI for Its Performance Improvement
Abstract
Introduction
Description of Micro/Pico Hydropower Generation System
Self-Excited Induction Generator: An Overview
Problem Formulation
Estimation of Hydro Capacity
Application of AI for Performance Improvement
Machine Learning
Deep Learning
Artificial Neural Network (ANN)
Fuzzy Logic
Adaptive Neuro-Fuzzy Interface System (ANFIS)
Conclusion
References
Chapter 5
An Investigation of Various Maximum Power Point Tracking Techniques Applied to Solar Photovoltaic Systems
Abstract
Introduction
Basics of Solar Energy
Solar Module Characteristics
Maximum Power Point Tracking Techniques
Perturb and Observe (P&O) Technique
P&O Based Multiple Power Sample MPPT Technique
Adaptive Perturb and Observe Technique
Incremental Conductance Method
Regulated Incremental and Conductance MPPT Technique
Variable Step Incremental Conductance Technique
Fractional Open Circuit Voltage Method (FOCV)
Semi-Pilot Cell FOCV MPPT Technique
Fractional Short Circuit Current (FSCC) Method
Soft Computing Techniques
Fuzzy Logic Control (FLC)
Artificial Neural Network (ANN) Control
Evolutionary Computing Control
Comparison between Various MPPT Techniques
Conclusion
References
Chapter 6
Fuzzy Logic-Based Maximum Power for Grid Connected PV Systems
Abstract
Introduction
A Variety of Renewable Energy Sources
Wind Power
Solar Power
Small Hydropower
Biomass
Geothermal
Trends of RES around the Globe
Solar Cell
Operating Principle
The Need of Renewable Energy
The Mathematical Equation for MPP
Literature Review
Simulation Models and Blocks
PV Modelling
Photovoltaic Cell Simulink Model in MATLAB
Effect of Load Mismatching
Boost Converter
Procedure for Designing a Boost Converter
Maximum Power Point Tracking Algorithms
A Study on MPPT Techniques
Algorithm for Fuzzy Logic
Detailed Information of Perturb and Observe Algorithm
Implementation Method
Result and Discussion
Results for PV System with Battery Integration by Using Fuzzy Logic Algorithm MPPT Techniques
Conclusion
Future Scope
References
Chapter 7
Different Reconfiguration Approaches for Photovoltaic Systems
Abstract
Introduction
Mathematical Modelling of Solar Cell
Various Modelling Topologies for Observing PSC Effects
Basic Connecting Topologies
Series-Parallel (S-P)
Bridge-Linked (B-L)
Total Cross-Tied (TCT)
Advanced Reconfiguration Topologies
Ken-Ken Reconfiguration (K-K)
Arithmetic Sequence Reconfiguration (AS)
L-Shape Reconfiguration (L-S)
Performance Indices under PSC
Global Maximum Power Point (GMPP)
Efficiency (Ƞ)
Fill Factor (FF)
% Power Loss (%PL)
Mismatch Loss (ML)
Execution Ratio (ER)
Result and Discussion
Global Maximum Power Point (GMPP)
Efficiency (Ƞ)
Fill Factor (FF)
% Power Loss (%PL)
Mismatch Loss (ML)
Execution Ratio (ER)
Comparison of TCT and L-S
Conclusion
References
Chapter 8
Implementation of Metaheuristic MPPT Approaches for a Large-Scale Wind Turbine System
Abstract
Introduction
System Description and Modeling
Wind Turbine Model
Maximum Power Point Tracking
WTS Maximum Power Point Tracking Algorithms
Grey Wolf Optimization Based MPPT Algorithm
Hybrid Particle Swarm Optimization with Grey Wolf Optimization Based MPPT
Whale Search Optimization Algorithm Based MPPT
Differential Squirrel Search Algorithm Based MPPT
Grasshopper Optimization Based MPPT
Experimental Assesment
Result and Discussion
Conclusion
References
Chapter 9
Wind Power Prediction Using Hybrid Soft Computing Models
Abstract
Introduction
Wind Power Prediction Techniques
Wavelet Transform (WT)
Adaptive Network-Based Fuzzy Inference System (ANFIS)
Dynamic Recurrent Neural Networks (DNNs)
NAR Neural Network
NARX Neural Network
Dynamic Particle Swarm Optimization (DPSO)
Wind Power Forecasting Using the Proposed Hybrid Technique
Wind Power Prediction Using Hybrid NAR/NARX Model
Conclusion
References
Chapter 10
Design Optimization of Inner Rotor Permanent Magnet Synchronous Machine Used in Wind Energy Conversion System Using Swarm Intelligence
Abstract
Introduction
Problem Formulation
Design Problem
Optimizing Techniques
Algorithm of GSA and GSA-PSO Technique
Result and Discussion
Conclusion
References
Chapter 11
A Novel Voltage Stability Index and Application of Machine Learning Algorithm for Assessment of Voltage Stability
Abstract
Introduction
The Existing Indices for Assessment of Voltage Stability
Line Stability Index (Lmn)
Fast Voltage Stability Index (FVSI)
New Voltage Stability Index (NVSI)
Proposed Modified Voltage Stability Index (MVSI)
Results and Comparative Analysis of MVSI vs Other Indices
IEEE 30 Bus System Results
Base Load Operating Condition
Heavy Active Loading Condition
Heavy Reactive Loading Condition
Heavy MVA Loading Condition
IEEE 57 Bus System Results
Base Load Operating Condition
Active Power Loading Condition
Reactive Power Loading Condition
IEEE 118 Bus System Results
Base Load Operating Condition
Active Power Loading Condition
Reactive Power Loading Condition
The Machine Learning Approach for Voltage Stability Assessment
The Exponential GPR Machine Learning Algorithm
Methodology
Results and Comparative Analysis of Exponential GPR vs NR Method MVSI Indices
Comparative Analysis
IEEE 30 Bus System
IEEE 57 Bus System
IEEE 118 Bus System
Conclusion
References
Biographical Sketches
Editors’ Contact Information
Index
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