A deep generative model based on CNN-CVAE for wind turbine condition monitoring

Abstract

Auto-encoder (AE)-based condition monitoring (CM) methods for fault detection of wind turbines have received considerable attention due to their powerful feature extraction ability. However, traditional AE-based monitoring methods can only learn point-to-point features by minimizing reconstruction errors, which leads to a low sensitivity to anomaly data and weak robustness to noise data. To this end, we introduce a novel deep generative method based on the convolutional neural network (CNN)-conditional variational auto-encoder (CVAE). The key idea of CNN-CVAE is to unify the representation learning capacity of the CVAE and CNN. Specifically, CVAE can learn a probability distribution model by being trained on an anomaly-free supervisory control and data acquisition systems (SCADA) dataset; CNN and deconvolution operations are adopted for better time-series feature extraction and reconstruction performance. A statistical process control chart is applied to determine the alarm threshold. The effectiveness of the CNN-CVAE-based method is validated by datasets collected by SCADA installed in a commercial wind farm in China for impending blade breakage and gearbox failure. Abundant experiments with state-of-the-art deep learning-based CM methods are conducted, which indicate that our proposed method outperforms other methods in robustness, fault detection data sensitivity, fault warning time, and model parameters.