Majdi Mansouri, Abdelmalek Kouadri, Mansour Hajji, Mohamed Faouzi Harkat, Hazem N Nounou, Mohamed N Nounou

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

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 250
• Erscheinungstermin: 1. Oktober 2025
• Englisch
• ISBN-13: 9780443450167
• ISBN-10: 0443450161
• Artikelnr.: 73493340

Autorenporträt

Dr. Majdi Mansouri is an Associate Professor, at the Department of Electrical and Computer Engineering, Sultan Qaboos University, in the Sultanate of Oman. A Senior Member of the IEEE, he received this Ph.D. degree in electrical engineering from the University of Technology of Troyes (UTT), France, in 2011, and the H.D.R. degree (accreditation to supervise research) in electrical engineering from the University of Orleans, France, in 2019. From 2011 to 2024, he held different research positions at Texas A&M University at Qatar, in Doha. Since September 2024, he has been with Sultan Qaboos University as an Associate Professor. Dr. Mansouri has authored more than 250 publications, as well as the book 'Data-Driven and Model-Based Methods for Fault Detection and Diagnosis' (Elsevier, 2020). His research interests include the development of model-based, data-driven, and AI-based techniques for fault detection and diagnosis.is a member of IEEE.

Inhaltsangabe

1. Introduction to Fault Detection and Diagnosis in Wind and Solar Energy
Systems
2. Fundamentals of Machine Learning, Deep Learning and Their Application in
Fault Detection and Diagnosis of Wind and Solar Energy Systems
3. Data Preprocessing Techniques for Fault Detection and Diagnosis of Wind
and Solar Energy Systems
4. Feature Extraction and Selection Methods for Fault Detection and
Diagnosis of Wind and Solar Energy Systems
5. Multiscale Representation Tools in Fault Diagnosis of Wind and Solar
Energy Systems
6. Deep Learning Model Design and Optimization for Fault Detection and
Diagnosis in Wind and Solar Energy Systems
7. Integration of Statistical Methods with Deep Learning for Fault
Detection and Diagnosis in Wind and Solar Energy Systems
8. Case Studies in Fault Detection and Diagnosis of Wind and Solar Energy
Systems
9. Future Directions and Challenges in Fault Detection and Diagnosis for
Wind and Solar Energy
10. Conclusions: Key Concepts in Fault Detection and Diagnosis for Wind and
Solar Energy