Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted moving average (EWMA) analysis. In the training phase, FS is performed using correlation analysis to eliminate redundant features, and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis (SCADA) data to build a normal behavior model. In the online monitoring phase, an EWMA analysis is introduced to monitor the abnormal changes in features. A blade icing warning is issued when the monitored features continuously exceed the control limit, and the ambient temperature is below 0°C. This study uses data from three icing-affected wind turbines and one normally operating wind turbine for validation. The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines. © 2024, Tech Science Press. All rights reserved.