Whilst wind power is a promising alternative to wasteful and polluting fossil fuels, there are a number of issues that must be addressed.
Many difficulties lie in the maintenance of the ever increasing size of the blades, especially in offshore environments. The current industry standard of visual inspection is outdated and needs to be replaced with real-time and online monitoring. Vibration-based Structural Health Monitoring (VSHM) has been proposed as a potential solution to this problem. However, the presence of Environmental and Operational Variations (EOVs) causes VSHM methods to struggle to differentiate between damaged and undamaged observations. The Damage Sensitive Features (DSFs) measured from the wind turbine blades are heavily influenced by the EOVs and effort has to be made to mitigate their effects to ensure the damage detection is reliable. Through regression analysis, relationships can be established between the DSFs and measured Environmental and Operational Parameters (EOPs). Subsequently, EOP-normalised DSFs are created by the difference between the original DSFs and those predicted by the regression models.
The reliability in the predictions, beyond what they were trained with, is an extremely important but often overlooked aspect of regression design. Uncertainty can easily be introduced by overfitting model orders, including non-influential EOPs and by benign trends present in the training data. Through considered design, this work aims to address such issues through the application of a comprehensive nonlinear forward stepwise regression method for the purpose of monitoring an operational wind turbine blade.
The proposed methodology employs methods to remove collinear variables, identify the most influential EOPs, reduce model orders and determine which DSFs should be regressed.
The combination of these methods facilitates a compact regression basis, purged of as much uncertainty as possible. Lasso regression is used for comparison, as it is a similar and established type of stepwise regression.
Ultimately, reducing biases and overfitting through considered design will increase the robustness of the system, as well as increasing confidence in the decision making process
