Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Energy Systems provides innovative solutions for fault detection and diagnosis in renewable energy systems. By leveraging advanced AI-based techniques such as deep learning, multiscale representation, and statistical analysis, this book aims to enhance system reliability, performance, and cost-efficiency. Readers will gain insights into the fundamentals of FDD processes tailored for photovoltaic and wind turbine operations. The book delves into data preprocessing techniques, feature extraction and selection methods, and optimization of deep learning models.
It also includes case studies and explores future directions for AI and machine learning in renewable energy, making it valuable for researchers, engineers, and policy makers.
1. Introduction - p. xv 2. Chapter One: Feature extraction and selection using reduced kernel PCA techniques - p. 1 3. Chapter Two: Renewable energy monitoring: a comprehensive literature review - p. 19 4. Chapter Three: Renewable energy monitoring: certain systems - p. 43 5. Chapter Four: Renewable energy systems monitoring: uncertain wind energy conversion systems - p. 73 6. Chapter Five: Renewable energy monitoring based on sensors' number optimization: certain and uncertain PV systems - p. 89 7. Chapter Six: Renewable energy monitoring under varying irradiance - p. 115 8. Chapter Seven: Renewable energy monitoring: implementation on hardware logic - p. 131 9. Chapter Eight: Conclusions and future research directions - p. 147 10. References - p. 151 11. Index - p. 165
Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Energy Systems provides innovative solutions for fault detection and diagnosis in renewable energy systems. By leveraging advanced AI-based techniques such as deep learning, multiscale representation, and statistical analysis, this book aims to enhance system reliability, performance, and cost-efficiency. Readers will gain insights into the fundamentals of FDD processes tailored for photovoltaic and wind turbine operations. The book delves into data preprocessing techniques, feature extraction and selection methods, and optimization of deep learning models.
It also includes case studies and explores future directions for AI and machine learning in renewable energy, making it valuable for researchers, engineers, and policy makers.
1. Introduction - p. xv 2. Chapter One: Feature extraction and selection using reduced kernel PCA techniques - p. 1 3. Chapter Two: Renewable energy monitoring: a comprehensive literature review - p. 19 4. Chapter Three: Renewable energy monitoring: certain systems - p. 43 5. Chapter Four: Renewable energy systems monitoring: uncertain wind energy conversion systems - p. 73 6. Chapter Five: Renewable energy monitoring based on sensors' number optimization: certain and uncertain PV systems - p. 89 7. Chapter Six: Renewable energy monitoring under varying irradiance - p. 115 8. Chapter Seven: Renewable energy monitoring: implementation on hardware logic - p. 131 9. Chapter Eight: Conclusions and future research directions - p. 147 10. References - p. 151 11. Index - p. 165
