# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 4 Case Analysis
### Abnormal Case Analysis and Effectiveness Verification  
Through checking the historical fault data of wind turbines, the yaw gear ring of Unit 3 of a wind farm was damaged on 6 October 2022. To verify the effectiveness of the model, it can be seen from the calculation of anomaly index \(e_{\text{AI}}\) of the yaw position that the value of \(e_{\text{AI}}\) was greater than 0.6 four times since September 2022 and reached the maximum value of 0.841 on 5 October. Therefore, it can be preliminarily judged that the yaw position has had an abnormal trend since September 2022. Due to the partial \"errors\" between the abnormal timing sequence initially screened and the actual anomaly, the verification of the field working conditions found that some \"anomalies\" were only affected by manual operation, resulting in data inconsistent with the actual situation, which is called \"abnormal false reporting\" in this paper. Therefore, the abnormal pattern of the yaw position was obtained through secondary feature construction of specific abnormalities. Figure 4 shows the situation of the yaw position under normal conditions. The vertical axis of Figure 4 displays the yaw position and the fluctuation of the yaw change rate, while the horizontal axis represents the length of the data segment.  
Figure 3: The prediction effect of the model.  
Figure 5 illustrates three distinct abnormal yaw position patterns identified by our model following extensive training on the big data platform. These patterns were corroborated through rigorous on-site inspections, revealing specific damage to various components of the yaw system under the conditions depicted by the time series data.  
Abnormal Pattern (a): This pattern is characterized by the yaw position being persistently fixed around \(-435^{\circ}\), with the yaw speed experiencing a sudden leap from \(0^{\circ}/\)s to approximately \(0.2^{\circ}/\)s. The diagnostic logic for this anomaly is based on the yaw position being static for an extended period, recurring more than twice within a month, each episode lasting over 60 s. Upon verification, it was determined that the yaw gear and the torsion cable switch small gear were excessively tight in their mesh, leading to an undue force on the yaw gear. This tight meshing not only increases friction and stress but also accelerates the wear and tear of the yaw gear, potentially leading to a complete failure over time.  
Abnormal Patterns (b) and (c): These patterns suggest issues such as potentiometer sticking or the wear of the resistance wire. The sticking of the potentiometer can cause the yaw drive to become unresponsive, while the wear of the resistance wire can lead to inaccurate position readings, both of which can disrupt the yaw system's alignment capabilities.  
The analysis of these patterns carries significant practical benefits for the operational management of wind turbines. Early detection and prompt resolution of these anomalies are crucial in preventing minor issues from developing into severe faults (Table 5). This proactive approach helps to avoid not only costly downtime but also expensive repair costs. Furthermore, the diagnostic strategy facilitates a more precise and efficient allocation of maintenance resources. With a clear understanding of when anomalies in the yaw system occur, maintenance crews can be better equipped to address these issues at the optimal times, ensuring that corrective actions are timely and effective. This enhances the overall reliability and longevity of wind turbine operations while minimizing the financial impact of maintenance activities.  
Figure 4: Yaw position under normal conditions.  
Figure 5: Yaw position under abnormal conditions.