Condition monitoring of wind turbines (WT) is a crucial task to ensure efficient and safe operations. This paper proposes a novel model, interactive spatio-temporal network (IST-Net), to extract features from supervisory control and data acquisition (SCADA) data effectively. The proposed model utilizes an interactive learning structure that combines uniform refine long and short-term memory network (URLSTM) and convolutional neural network (CNN) to extract spatio-temporal features. Firstly, the SCADA data is preprocessed by utilizing the missing values filling, the feature selection, and the normalization. Secondly, an IST-Net model is trained by the data of healthy state. The IST-Net can extract spatio-temporal information to represent the operation status of WT by the interactive learning process of two isometric sequences split from the original sequence. Finally, the trained model receives online data and calculated the residual between true value and predicted value. The root mean squared error (RMSE) of the residual is calculated as the monitoring indicator, and the exponentially weighted moving average (EWMA) control chart is utilized for threshold determination. Experimental results reveal that the proposed model has better capability in precise anomaly detection compared to existing methods.

Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments.