Elsevier Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Systems

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. Provides comprehensive methodologies for fault detection and diagnosis (FDD) that integrate AI with multiscale representation and statistical analysis Includes advanced feature extraction and selection techniques, helping readers to identify the most relevant features for accurate fault diagnosis while reducing model complexity Presents guidelines for data pre-processing, model optimization, and enhanced decision-making frameworks that leverage adaptive control strategies, enabling improved accuracy and efficiency