Wind Turbine Fault Diagnosis Based on Feature Selection and Stacking Model Fusion with Small-scale Data.

Wind energy, as new energy, becomes important support in the low-carbon transformation of the power industry. However, in wind farms, wind turbine fault diagnosis based on small-scale data is a thorny problem. To this end, this paper advances a wind turbine fault diagnosis method given feature selection and stacking model fusion. For unbalanced data, smote oversampling method is used to the effective instance of small class data and increase its proportion. RFECV is used to rank the importance of features, and then the features with high correlation are deleted according to the thermal map to reduce the dimension and obtain the feature subset. Then, XGBoost and LightGBM models based on 6-fold cross-validation is used to train the filtered data. To further improve the stability and generalization ability, a stacking fusion model based on logistic regression is trained using logistic regression. The results of this experiment are compared to other traditional methods by accuracy, ROC, and other indicators. The experimental results show that the strategy used in this paper has more satisfactory accuracy results than the traditional methods and can be used in wind turbine fault diagnosis engineering. [ABSTRACT FROM AUTHOR]