Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention–Informer Algorithm

In response to the problems that abnormal yaw position causes during the yawing process—on the one hand leading to the accumulation of yaw position errors, affecting the accuracy of yawing to the wind or safety due to excessive cable twisting, and on the other hand, with the phenomena of frequent position jumps or frequent short-term position maintenance generating certain yaw errors, affecting the stability of yaw control, thus resulting in a high occurrence frequency of yaw system failures and high operation and maintenance costs—a data-driven fault diagnosis method is proposed to give early warnings for abnormal conditions of the yaw position of the wind turbine unit. Firstly, for the massive data in the SCADA (Supervisory Control and Data Acquisition) system, the ReliefF feature algorithm based on standardized interaction gain (Standardized Interaction Gain and ReliefF, SIG–ReliefF) is used for accurately identifying and screening the characteristic parameters that have a greater impact on the yaw system failure of wind turbines. The advantage of this method lies in its ability to effectively consider the correlation between features and retain the relevant features and interaction features of yaw system failures to the greatest extent. Then, an Informer yaw position prediction model is established, combined with the two-stage attention mechanism (two-stage attention and Informer, TSA–Informer), and the distribution of residuals is statistically analyzed through the sliding window method to determine the fault threshold. Finally, the validity and accuracy of the proposed method are verified through examples, and through comparison with other algorithms, it is verified that it has better abnormal early warning performance. Relevant conclusions can provide a reference for the fault diagnosis of the actual yaw system.