Spatio-Temporal Feature Alignment Transfer Learning for Cross-Turbine Blade Icing Detection of Wind Turbines

Supervisory control and data acquisition (SCADA) data-based wind turbine blade icing detection has been widely studied due to its low cost and easy access. However, SCADA data often present severe class imbalance and thus challenge accurate icing detection. Moreover, since data distribution discrepancy exists in both spatio-temporal features of SCADA data from different wind turbines, the well-trained model has poor classification performance on new turbines. Building new models for different turbines is high-cost and time-consuming. Thus, model cross-turbine generalizability needs improvement. To solve these problems, a cross-turbine icing detection model is proposed based on the spatio-temporal alignment transfer learning method. Specifically, building an attention-based network to extract temporal and spatial features. Then, we apply maximum mean discrepancy (MMD) algorithms on shallow and deep networks to align spatio-temporal features of source and target domains. Besides, a self-adaptive weight (SAW) loss function is employed to address the class imbalance. Finally, we develop a loss weight assignment method based on analyzing the generated loss value variations with the number of training iterations for performance enhancement. The proposed method is evaluated on real SCADA datasets. Experiment results show our proposed transfer learning method significantly improves the model cross-turbine generalizability and classification performance.