Ensemble Learning-Based Wind Turbine Fault Prediction Method with Adaptive Feature Selection

In this paper we present a wind turbine (WT) fault detection method based on ensemble learning, WT supervisory control and data acquisition (SCADA) is used for model building. In feature selection process, random forest algorithm is applied to get the feature importances,this is much convenient compared with general feature selection by experience, also more accurate result is obtain. In model building,SVM based bagging algorithm is used, compared to individual SVM,out method is much faster and again with a better result.