Condition Monitoring of Wind Turbine Generator Based on Transfer Learning and One-Class Classifier

The healthy operation conditions of wind turbines (WTs) with insufficient data need attention, but they face the problems of data imbalance and lack of labels. Aiming at the condition monitoring (CM) of these WTs, a CM method based on transfer learning and one-class classification (OCC) is proposed. This method uses the source WT data to help learn information about monitoring data from the target WT. First, the data of the target and source WTs are preprocessed to construct a training set. Second, to improve the TrAdaBoost algorithm for the OCC task, a novel weighting method based on an autoencoder (AE)—an unsupervised one-class classifier—is designed to assign weights for samples in the source and target domain dynamically and then the CM model of the target WT is established. Finally, its effectiveness is verified by using the monitoring data to detect the generator fault of the target WT. The comparison with the nontransfer method shows that the proposed method can significantly reduce the number of false alarms and can issue early warnings.