Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*
Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854
- 11 June 2024
Abstract 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… More >