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 (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 250
• Erscheinungstermin: 1. Oktober 2025
• Englisch
• ISBN-13: 9780443450174
• Artikelnr.: 75661924

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.Dr. Abdelmalek Kouadri is a Professor of Electrical Engineering at the Institute of Electrical and Electronics Engineering, University M'Hamed Bougara of Boumerdès, in Algeria. He has more than 15 years of combined academic and industrial experience. His research interests relate to systems engineering and control, with emphasis on process modelling, monitoring, and estimation. Prof. Kouadri has published more than 80 refereed journal and conference publications as well as book chapters. He has served as a technical committee member of several international journals and conferences.Dr. Mansour Hajji is an Assistant Professor at the Higher Institute of Applied Science and Technology of Kasserine, Kairouan University, Tunisia, where he has been working since 2013. He received his Ph.D. degree in electrical engineering from the National Engineering School of Tunis (ENIT), Tunis, in 2013. Dr. Hajji is the author of several publications, and his current research interests include electrical machines, design and control, and machine learning techniques for fault detection and diagnosis.Dr. Mohamed Faouzi Harkat is a Professor in the Department of Electronics, at Badji Mokhtar - Annaba University, Algeria, which he joined in 2004. He received his Ph.D. degree from the Institut National Polytechnique de Lorraine (INPL), France, in 2003. From 2002 to 2004, he was an Assistant Professor at the School of Engineering Sciences and Technologies of Nancy (ESSTIN), France. Prof. Harkat has over twenty years of research and practical experience in systems engineering and process monitoring. He is the author of more than 100 refereed journal and conference publications, as well as book chapters, and has served as an Associate Editor and in technical committees of several international journals and conferences.

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