Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm

Icing significantly affects the performance of wind turbines in terms of power loss and structural degradation, and an effective blade icing diagnosis is the prerequisite to achieve the optimal control of wind turbines to mitigate such icing influence. However, current icing diagnostic methods lack consideration of fundamental icing physics and have limited generalizability to large-scale applications. To address such challenges, in the present study, we aim to propose an effective and robust blade icing diagnostic method for wind turbines. Specifically, hybrid features that fully consider both short-term and long-term icing influence are extracted based on the underlying icing physics. Such features are used to build a Stacked-XGBoost model (i.e., based on a combination of stacking ensemble learning algorithm and XGBoost machine learning algorithm) to achieve blade icing diagnosis. The proposed method is evaluated at two wind farms and further compared with three single algorithm-based models (i.e., random forest, support vector machine and XGBoost algorithms). The results show that the hybrid features significantly enhance the similarity between different datasets and the Stacked-XGBoost algorithm achieves a higher diagnostic accuracy and a better generalizability compared to the single-algorithm-based models.