A hybrid 3DSE-CNN-2DLSTM model for compound fault detection of wind turbines

In recent years, intelligent fault detection methods have achieved dramatic results for wind power generation. However, a majority of the available intelligent detected methods can only detect single faults. The supervisory control and data acquisition (SCADA) system is widely applied in wind turbines (WTs). SCADA data is a timestream of variable data. Considering spatial correlation and temporal correlation among SCADA data, this study proposes a WT compound fault detection model combining a three-dimensional squeeze-excitation convolutional neural network (3DSE-CNN), and a two-dimensional long and short-term memory network (2DLSTM). The proposed 3DSE-CNN-2DLSTM model consists of three parts: data preprocessing, spatiotemporal feature extraction, and fault detection. First, one-dimensional data is converted into a 2D image by a sliding window method. Then, in the proposed 3DSE-CNN model, the SE attention module is applied to enhance the convolutional channel information and extract spatial features. The proposed 2DLSTM model extracts spatiotemporal fusion features. Finally, the label of the fault category is obtained by argmax function. To validate 3DSE-CNN-2DLSTM, a 3 MW WT SCADA dataset from the south coast of Ireland is applied in the experiment. The proposed 3DSE-CNN-2DLSTM model achieves specific compound fault detection and improves detection accuracy.