Condition Monitoring of Wind Turbines With Implementation of Interactive Spatio–Temporal Deep Learning Networks

Condition monitoring of wind turbines (WTs) is a crucial task to ensure efficient and safe operations. This article 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. First, the SCADA data are preprocessed by utilizing the missing values filling, the feature selection, and the normalization. Second, 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 the true value and the 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.