Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maintenance to ensure their high availability and uninterrupted operation. Presenting the main concepts, state-of-the-art advances and case studies, this book approaches the topic by considering it as an integral part of the overall operation of any wind energy project. Linking the essential NDT subject with its sub disciplines, the book uses computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques to support analysis of prognostic problems with defined constraints and requirements.
This book is the first of its kind and will provide useful insights to industrial engineers and scientists, academics and students in the possibilities that NDT and condition monitoring technologies can offer.
1. Introduction to non-destructive testing and condition monitoring techniques in wind energy - p. Page ix 2. List of Illustrations - p. Page xi 3. List of Tables - p. Page xii 4. 1 : SCADA-based fault detection in wind turbines: data-driven techniques and applications - p. Page xii 5. 2 : Fault detection in small wind turbines using condition monitoring techniques and machine learning algorithms (a predictive approach) - p. Page 10 6. 3 : Prediction and classification of different wind turbine alarms using K-nearest neighbors - p. Page 28 7. 4 : Artificial neural networks applied for wind turbines maintenance management in unmanned aerial vehicle acoustic inspection case - p. Page 35 8. 5 : Quantitative interlink wear estimation method for the mooring chain - p. Page 45 9. 6 : Enhanced sparse representation-based intelligent recognition framework for fault diagnosis of wind turbine drive trains - p. Page 88 10. 7 : An optimal combined production and maintenance policies for a wind farm with environmental and operational considerations - p. Page 128 11. 8 : The valuation of geothermal power projects in Indonesia using real options valuation - p. Page 172 12. 9 : A robust multiple open-switch fault diagnosis approach for converter in wind energy system - p. Page 189 13. 10 : Condition monitoring in wind turbines: a review - p. Page 221 14. 11 : Artificial intelligence techniques and cloud computing for wind turbine pitch bearing fault detection∗ - p. Page 244 15. 12 : Alarms and false-alarm analysis by support vector machine in wind turbines - p. Page 273 16. 13 : Background, advancement, and applications of in situ structural health monitoring based on different modes of failure detection in composites: a review - p. Page 286 17. 14 : Numerical simulations of offshore wind farms considering accidental scenarios - p. Page 307 18. 15 : Multibody dynamic analysis of onshore horizontal-axis wind turbine - p. Page 345 19. 16 : Foundation monitoring system of offshore wind turbines - p. Page 393 20. Index - p. Page 441
Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy looks at the complex and critical components of energy assets and the importance of inspection and maintenance to ensure their high availability and uninterrupted operation. Presenting the main concepts, state-of-the-art advances and case studies, this book approaches the topic by considering it as an integral part of the overall operation of any wind energy project. Linking the essential NDT subject with its sub disciplines, the book uses computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques to support analysis of prognostic problems with defined constraints and requirements.
This book is the first of its kind and will provide useful insights to industrial engineers and scientists, academics and students in the possibilities that NDT and condition monitoring technologies can offer.
1. Introduction to non-destructive testing and condition monitoring techniques in wind energy - p. Page ix 2. List of Illustrations - p. Page xi 3. List of Tables - p. Page xii 4. 1 : SCADA-based fault detection in wind turbines: data-driven techniques and applications - p. Page xii 5. 2 : Fault detection in small wind turbines using condition monitoring techniques and machine learning algorithms (a predictive approach) - p. Page 10 6. 3 : Prediction and classification of different wind turbine alarms using K-nearest neighbors - p. Page 28 7. 4 : Artificial neural networks applied for wind turbines maintenance management in unmanned aerial vehicle acoustic inspection case - p. Page 35 8. 5 : Quantitative interlink wear estimation method for the mooring chain - p. Page 45 9. 6 : Enhanced sparse representation-based intelligent recognition framework for fault diagnosis of wind turbine drive trains - p. Page 88 10. 7 : An optimal combined production and maintenance policies for a wind farm with environmental and operational considerations - p. Page 128 11. 8 : The valuation of geothermal power projects in Indonesia using real options valuation - p. Page 172 12. 9 : A robust multiple open-switch fault diagnosis approach for converter in wind energy system - p. Page 189 13. 10 : Condition monitoring in wind turbines: a review - p. Page 221 14. 11 : Artificial intelligence techniques and cloud computing for wind turbine pitch bearing fault detection∗ - p. Page 244 15. 12 : Alarms and false-alarm analysis by support vector machine in wind turbines - p. Page 273 16. 13 : Background, advancement, and applications of in situ structural health monitoring based on different modes of failure detection in composites: a review - p. Page 286 17. 14 : Numerical simulations of offshore wind farms considering accidental scenarios - p. Page 307 18. 15 : Multibody dynamic analysis of onshore horizontal-axis wind turbine - p. Page 345 19. 16 : Foundation monitoring system of offshore wind turbines - p. Page 393 20. Index - p. Page 441
