Condition monitoring of wind turbine based on deep learning networks and kernel principal component analysis

Abstract

Condition monitoring is frequently hampered by the abominable working conditions of wind turbines (WTs). The present study proposes a new approach for condition monitoring of WT, which involves the convolutional neural network (CNN) that cascades to the long- and short-term memory network (LSTM) with kernel principal component analysis (KPCA). First, the density-based spatial clustering of applications with noise (DBSCAN) method was used to filter data from supervisory control and data acquisition (SCADA) to improve the effectiveness of the data. Then, the KPCA algorithm was applied for performance monitoring and fault prediction of WT by selecting input variables, and the KPCA–CNN–LSTM model was established. Finally, taking a wind farm as an example, the established prediction model was used to analyze multiple components of the WT. The experimental outcomes demonstrate that the proposed model can help not only in monitoring the state of the WT but also in predicting the abnormal operation state of the WT at an early stage, thereby verifying the effectiveness of the proposed method.