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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 Digital twin in wind turbine condition monitoring.
- Develops digital twin framework for condition monitoring of wind turbine.
- Discusses industrial applications by wind turbine manufacturers and operators as case studies.
- Explores the interface between the digital twin technology and condition monitoring.
- Extensively profiles recommendations for future research.
This book is aimed at researchers and professionals in mechanical engineering, plant maintenance, and condition monitoring.
List of contents
1. Introduction 2.
Evolution and Operation of Wind Turbine (WT) Technologies 3. Elements and Approaches to Condition Monitoring 4. Sensor Selection in Condition Monitoring 5. Concept of Digital Twin Technology 6. Digital Twins Design Theories and Application Platform 7. Applications of Digital Twin Technology 8. Dimensions of Digital Twin and its Enabling Technologies for Wind Turbine Applications 9. Internet of Things, Cyber-Physical 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 Tre 17. Digital Twin Case Studies in Wind Turbines 18. Digital Twins in Wind Turbine Condition Monitoring: Barriers and Open Research Questions
About the author
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.
