Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Systems
- 1st Edition - October 1, 2025
- Imprint: Elsevier
- Authors: Majdi Mansouri, Abdelmalek Kouadri, Mansour Hajji, Mohamed Faouzi Harkat, Hazem N. Nounou, Mohamed N. Nounou
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 5 0 1 6 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 5 0 1 7 - 4
Intelligent Fault Detection and Diagnosis Techniques for Monitoring Wind and Solar Energy Systems provides innovative solutions for fault detection and diagnosis in renewa… Read more
Purchase options
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.
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
Researchers, advanced students, and faculty with an interest in operations, fault detection and diagnosis, and applications of deep learning and AI in solar energy, wind energy, or renewable energy in general, as well as those in control engineering or electrical engineering
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
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
- Edition: 1
- Published: October 1, 2025
- Imprint: Elsevier
- Language: English
MM
Majdi Mansouri
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.
Affiliations and expertise
Department of Electrical and Computer Engineering, Sultan Qaboos University, OmanAK
Abdelmalek Kouadri
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.
Affiliations and expertise
Institute of Electrical and Electronics Engineering University M'Hamed Bougara of Boumerdès, AlgeriaMH
Mansour Hajji
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.
Affiliations and expertise
Higher Institute of Applied Science and Technology of Kasserine, Kairouan University, TunisiaMH
Mohamed Faouzi Harkat
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.
Affiliations and expertise
Department of Electronics, Badji Mokhtar – Annaba University, AlgeriaHN
Hazem N. Nounou
Dr. Hazem N. Nounou is a Professor and Program Chair of the Electrical Engineering Program in the College of Science and Engineering at Hamad Bin Khalifa University (HBKU), in Qatar. Prior to joining HBKU, he was the Senior Associate Dean for Undergraduate Studies and Student Success and Professor of Electrical and Computer Engineering at Texas A&M University at Qatar. He received the B.S. degree (Magna Cum Laude) from Texas A&M University, College Station, in 1995, and the M.S. and Ph.D. degrees from Ohio State University, Columbus, in 1997 and 2000, respectively, all in electrical engineering. In 2001, he was a Development Engineer for PDF Solutions, a consulting firm for the semiconductor industry, in San Jose, CA. Then, in 2001, he joined the Department of Electrical Engineering at King Fahd University of Petroleum and Minerals in Dhahran, Saudi Arabia, as an Assistant Professor. In 2002, he moved to the Department of Electrical Engineering, United Arab Emirates University, Al-Ain, UAE. In 2007, he joined the Electrical and Computer Engineering Program at Texas A&M University at Qatar, Doha, Qatar. He was the holder of Itochu Professorship from 2015-2017. He published more than 200 refereed journal and conference papers and book chapters. He served as an Associate Editor and in technical committees of several international journals and conferences. His research interests include data-based control, intelligent and adaptive control, control of time-delay systems, and system monitoring, identification and estimation. Dr. Nounou is a senior member of IEEE.
Affiliations and expertise
College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarMN
Mohamed N. Nounou
Dr. Mohamed Nounou is a Professor of Chemical Engineering in the College of Science and Engineering at Hamad Bin Khalifa University (HBKU), in Qatar. Prior to joining HBKU, he was with Texas A&M University at Qatar (TAMUQ) for 18 years. He received his B.S. degree (Magna Cum Laude) from Texas A&M University, College Station in 1995, and his M.S. and Ph.D. degrees from the Ohio State University in 1997 and 2000, respectively, all in chemical engineering. Then, he joined PDF Solutions, Inc. until 2002 when he joined the chemical and petroleum engineering department at the United Arab Emirates University (UAEU). Then, he joined TAMUQ in 2006, where he made significant contributions and received numerous awards at the college and university levels. In teaching, he received the AFS College-level and University-level Distinguished Achievement Teaching Awards in 2011 and 2012, respectively. He also received the “TAMUQ Faculty Excellence Award” in 2013. He later received the “Chemical Engineering Faculty of the Year Award” five times in 2013, 2014, 2016, 2017, 2018, and 2021, and the “Student Government Association CHEN Educator of the Year Award” in 2016. In service, Dr. Nounou was appointed a chair of the “Aggie Life” program, which is a university-wide initiative that aims at enhancing the college life experience of freshman students at TAMUQ. He has been chairing student advising since joining TAMUQ and also chaired the ABET Committee during the first accreditation cycle in 2008, where the Program received full accreditation, and he received a recognition award for this role. He also helped establish a chapter of AIChE at TAMUQ in 2007, and later established and advised the Gamma Delta chapter of the Omega Chi Epsilon honor society at TAMUQ, the first chapter outside the US. As a result, he received the “Student Organization Advisor of the Year Award” in 2014 and 2016. Dr. Nounou is also an accomplished researcher in the area process systems engineering. He received research funding over $5M and published a book and more than 200 journal and conference papers and book chapters. He served as an associated editor for several journals. He is an associate editor for the "Network Modeling Analysis in Health Informatics and Bioinformatics". He is a senior member of AIChE and IEEE.
Affiliations and expertise
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar