Due to high failure rates and long downtimes of wind turbines during the last decades, research has been initiated to develop methods for effective failure and maintenance prediction. Failures and life time of wind turbine systems are commonly predicted by data-driven and statistics-based approaches. However, statistical approaches have the drawback to be less precise than model-based approaches using physical equations and calculations. Therefore, in this thesis load-based prediction approaches are proposed, providing the advantage that the failure or remaining useful life can already be predicted at a stage where the real system still runs at or close to normal conditions and system degradations cannot be measured yet. To apply the load-based prediction approaches, physics-based models are developed that are based on analytical equations and can handle measurements of operational and environmental conditions. Further, the load-based prediction approaches can be used for system design and maintenance but also for system reliability. This is exemplarily demonstrated in this thesis for a number of wind turbine power train components, e.g. shafts, gear boxes, bearings, couplings and transformers. The thesis also shows that the maintenance intensity and reliability of any system (here wind turbine power trains) are directly related to their design. This means that the observation of high maintenance activities and low system reliability indicates that system loads and therefore, also the system design, are not properly considered. Finally, the load-based prediction methods proposed in this thesis are not only applicable to wind turbines and the renewable energy grid, but also to industrial or automotive (e.g. electrical car) power trains, or any other energy system. Moreover, as loads are a generic concept and any system is exposed to them, load based prediction methods (for system design, maintenance and reliability) can be developed for and applied to any system. In this sense, this thesis provides a template for developing load-based prediction approaches.
|Qualification||Doctor of Philosophy|
|Award date||3 Jul 2020|
|Place of Publication||Enschede|
|Publication status||Published - 3 Jul 2020|