Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Energy Systems offers innovative solutions that enable more accurate, timely, and efficient fault detection and diagnosis (FDD) processes, introducing advanced AI-based techniques and integrating deep learning, multiscale representation, and statistical analysis, in order to improve system reliability and performance, and reduce downtime and costs. The book begins by introducing fault detection and diagnosis, as well as the fundamentals of deep learning applications, in the context of renewable energy and specifically photovoltaic and wind turbine operations. In-depth chapters then cover data preprocessing techniques, feature extraction and selection methods, multiscale representation tools, deep learning model design and optimization, and integration of statistical methods with deep learning. Finally, case studies are presented and discussed, and the authors consider future directions and challenges in terms of fault detection and diagnosis within the renewable energy sector, emphasizing the role of AI and machine learning. This is a useful resource for all those with an interest in the operations, monitoring, and fault detection and diagnosis of photovoltaic systems and wind turbines, and applications of deep learning and AI in renewable energy, including researchers, advanced students, faculty, scientists, engineers, technicians, practitioners, 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