Semi-Supervised Blade Icing Detection Method Based on Tri-XGBoost

Abstract

Blade icing caused by low-temperature environments results in the degradation of wind turbine power performance. As there is no obvious influence on the performance of wind turbines in the early stage of blade icing, it is difficult to detect the early icing state, so there will be inaccurate labels in the process of data collection. To address these challenges, this paper proposes a novel semi-supervised blade icing detection method based on a tri-training algorithm. In the proposed method, extreme gradient boosting tree (XGBoost) is used as the base classifier. A tri-training algorithm is used to integrate three base classifiers and the integrated model generates a pseudo-label for unlabeled data. In addition, we introduce Focal Loss as the loss of the base classifier in the proposed model, which solves the problem of class imbalance caused by the fact that the wind turbine is operating under normal conditions in most cases. In order to verify the effectiveness of the proposed blade icing detection method, experiments are implemented on the collected Supervisory Control and Data Acquisition (SCADA) data. The experimental results show that the proposed method effectively improves the ability to identify blade icing. Compared with other methods, it has better classification performance, robustness, and generalization.