Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units

Aimed at identifying the health state of wind turbines accurately by comprehensively using the change information in spatial and temporal scale of the supervisory control and data acquisition (SCADA) data, a novel condition monitoring method of wind turbines based on spatio-temporal features fusion of SCADA data by convolutional neural networks (CNN) and gated recurrent unit (GRU) was proposed in this paper. First, missing value complement and selection of variables with Pearson prod-moment correlation coefficient were applied to improve the effectiveness of SCADA data. Second, a deep learning model was constructed by the structures of CNN and GRU. The spatial features in SCADA data were extracted by CNN at every step, and the temporal features in the sequence of spatial features were extracted and fused by GRU. Third, the historical healthy SCADA data was used to train the normal behavior model. At last, the trained model received measured data and output the predicted values. The entire residual between the actual data and the predicted output was calculated to put into the exponential weighted moving average control chart for recognizing the condition of the wind turbine. The effectiveness and availability of the proposed method were proved in measured SCADA data experiments.