A new condition-monitoring method based on multi-variable correlation learning network for wind turbine fault detection

Abstract

Reasonable and in-depth analysis of the supervisory control and data acquisition (SCADA) dataset can improve the accuracy and reliability of anomaly detection in wind turbines. In this paper, a multi-variable correlation learning network named the attention mechanism temporal convolutional network–gated recurrent unit (AMTCN-GRU) is proposed to extract the multidirectional features of SCADA data for wind turbine condition monitoring. First, the parameters with greater relevance to the prediction target are selected as input parameters of this method. Meanwhile, the cabin vibration signal contains the transient characteristics of the operating system. If the component connected to the cabin fails, the vibration signal will change immediately. Then, the vibration parameter is selected as one of the inputs. In this paper, a novel AMTCN model is proposed to enhance the feature extraction capability, which is constituted by a convolutional block attention mechanism embedded to the TCN’s residual block structure. The extracted features can be weighted again to make the output more relevant to the predicted target. GRU is performed to construct the connections of feature and output for the condition prediction of the wind turbine. Finally, it is proven that the proposed method can accurately and reliably realize anomaly detection in wind turbines by analyzing the SCADA data of the actual wind farm.