# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 1 Introduction
The effectiveness of the EMPC strategy was ultimately validated using the XE112-2000 wind turbine model in the simulation software Bladed. Reference [16] utilized the GEZ gearbox fault diagnosis experimental platform to extract vibration experimental data. Through time-domain and frequency-domain analysis methods, the MATLAB software was used to effectively analyze the signals based on feature values and waveform-related information. The faulty signals were obtained by comparing them with the waveform and numerical parameters of normal signals, and this finally gives the fault result through the experiment. Some new diagnostic methods presented in the literature [7,17,18,19,20,21] have also provided new research directions for the diagnosis of faults in the yaw system. Most of the prevailing fault diagnosis approaches for yaw systems predominantly concentrate on the identification and categorization of yaw gear-related faults, yet there is a notable scarcity of relevant research regarding the identification of yaw system abnormalities through the anomalous jump of the yaw position. Undoubtedly, the precision of the yaw position serves as the bedrock for the unit to attain effective yaw control. In the actual operational trajectory of the wind turbine unit, there have been occurrences of unit cable twisting faults induced by abnormal yaw positions (cam gear leaping). Furthermore, the abnormality of the yaw position also mirrors certain yaw drive mechanical glitches, such as imprecise meshing due to gear wear, yaw motor malfunctions, and gear tooth breakage resulting from wear and tear. Drawing on the transient data from SCADA systems, this paper introduces a cutting-edge, data-driven diagnostic method specifically tailored for the abnormal yaw positions in wind turbines. This innovative approach not only identifies operational anomalies with high precision but also establishes an early warning system designed to predict and mitigate potential yaw system failures. By integrating historical data into a dynamic big data platform, the method undergoes continuous iteration and optimization, enhancing its diagnostic capabilities over time. The diagnostic process is streamlined into a seamless, closed-loop system that includes Data Collection, Anomaly Diagnosis and Early Warning, Verification and Investigation, Feedback and Model Refinement, and Event Handling. This method has proven to significantly reduce operational risks, enhance the safety and reliability of wind turbines, and lower maintenance costs. The model's exceptional performance is evidenced by a 92% accuracy rate in identifying anomalies over a three-month period, as demonstrated by the platform's operational results. The structure of this paper is as follows. In Section 2, the basic mechanism of the yaw system and its common fault types are briefly described, and we also explain the principle of the SIG-Relief algorithm, with yaw position being considered as the prediction parameter of the yaw system. In Section 3, the validity of SIG-Relief algorithm is verified. Additionally, based on the principle of the two-stage attention (TSA) mechanism, we optimize the structure of the Informer model and build the Adam-TSA-Informer anomaly identification model. In Section 4, the effectiveness of the model is validated through practical case studies. Finally, Section 5 provides a summary of the work.