This book discusses the application of digital twin (DT) in condition monitoring of offshore and onshore wind turbines, including a pertinent framework to explain critical component Condition Monitoring and Fault Diagnosis. Frequently used tools and enabling technologies for DT are briefly discussed while the associated benefits and challenges are analyzed. It identifies the key issues which need to be addressed in the wind energy industry to optimally benefit from DT.
Features:
- Exclusive title on application of DT in wind turbine condition monitoring
- Develops DT framework for condition monitoring of wind turbine
- Discusses industrial applications by wind turbine manufacturers and operators as case studies
- Explores the interface between DT technology and condition monitoring
- Extensively profiles recommendations for future research
This book is aimed at researchers and professionals in mechanical engineering, plant maintenance, wind engineering, and condition monitoring.
This book discusses the application of Digital Twin (DT) in condition monitoring of offshore and onshore wind turbines including a pertinent framework to explain critical component Condition Monitoring and Fault Diagnosis. Discussed the tools and enabling technologies for DT along with benefits and challenges.
1. Introduction
2. Evolution and Operation of Wind Turbine Technologies
3. Elements and Approaches to Condition Monitoring
4. Sensor Selection in
Condition Monitoring
5. Concept of Digital Twin Technology
6. Design Theories
and Application Platforms for Digital Twins
7. Applications of Digital Twin
Technology
8. Dimensions of Digital Twin and Its Enabling Technologies for
Wind Turbine Applications
9. Internet of Things, CyberPhysical Systems,
Digital Twins, and Artificial Intelligence in Wind Turbine Technology and
Condition Monitoring
10. Integrating Digital Twin, Virtual Reality, Augmented
Reality in Wind Turbine Condition Monitoring
11. Onshore and Offshore Wind
Turbine Technologies
12. Wind Turbine Mechanical Components
13. Failure
Analysis of Critical Wind Turbine Components
14. Condition Monitoring System
in Wind Turbines
15. Wind Turbine Failure Identification
16. Digital Twin for
Wind Turbine Condition Monitoring: Emerging Research Trend
17. Digital Twin
Case Studies in Wind Turbines
18. Digital Twins in Wind Turbine Condition
Monitoring: Barriers and Open Research Questions
Nkosinathi Madushele is a professional engineer registered with ECSA and holds a D.Eng. in Mechanical Engineering from the University of Johannesburg. He has industry and academic experience, having worked as a Junior Project Manager in construction and a Systems Engineer at ESKOM. He is currently the Head of the Department of Mechanical Engineering Science at the University of Johannesburg.
Obafemi O. Olatunji is a registered engineer, certified energy manager, and certified renewable energy professional with the Association of Energy Engineers. He holds a PhD in Mechanical Engineering focused on AI integration in energy systems. With ten years of experience in academia and industry, he is currently a program manager at UJ-PEETS, leading the energy and energy efficiency portfolio.
Paul A. Adedeji is an energy specialist at UJ-PEETS, focusing on AI and machine learning applications in renewable energy for resource prediction and condition monitoring. He holds a BSc. in Mechanical Engineering, MSc. in Industrial and Production Engineering, and a PhD. in Mechanical Engineering. He has published extensively on AI in wind and solar PV systems.
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