# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 5 Conclusions  
Currently, most diagnostic methods for the yaw system focus on studying faults related to the yaw gearbox. However, there is a lack of relevant abnormal diagnostics for other faults such as insufficient yaw braking force, severe brake pad wear, and counter malfunctions leading to excessive torsion cable tension. Considering that the yaw system accumulates abnormal data in its relevant state parameters when components experience abnormalities, this paper proposes a data-driven abnormal diagnosis method of wind turbine yaw position based on the SCADA big data platform. To sum up, the following conclusions can be drawn.  
(1) Compared with traditional data dimensionality reduction methods, using the improved SIG-ReliefF algorithm for feature selection can effectively reduce the impact of redundant features on model performance, thereby improving the accuracy and reliability of the model.  
(2) The Adam optimizer performs parameter optimization on the Informer model combined with the two-stage attention mechanism (TSA), effectively improving the performance of the model when processing time series data. It shows significant advantages in practical applications of unit fault diagnosis and helps to achieve more accurate and timely fault prediction and diagnosis.  
(3) In the future, data-driven anomaly detection technology is expected to play a critical role in wind turbine fault diagnosis. Firstly, there is a continuous effort to enhance the accuracy and reliability of anomaly detection models. While machine learning and deep-learning-based methods have been utilized in diagnosing wind turbine faults, there remains potential for improving model performance through algorithm integration and model architecture optimization. Future research endeavors can focus on bolstering the precision and robustness of anomaly detection models. Secondly, there is a growing emphasis on integrating these models into practical applications and formulating comprehensive warning and maintenance strategies. By analyzing and identifying abnormal data in wind turbines, potential faults can be proactively identified, leading to the development of corresponding maintenance plans. Furthermore, leveraging accumulated fault data and experiences can optimize operational and maintenance processes, curtail operational costs, and enhance the reliability and efficiency of wind turbines. In summary, data-driven wind turbine anomaly diagnostic models hold substantial promise for practical application. Subsequent research will continue to refine algorithm and model performance, fortify data processing and analysis capabilities, and furnish more precise and reliable fault diagnosis and maintenance support for the wind energy industry.  
**Author Contributions:** Validation, X.H.; Writing--original draft, X.S.; Writing--review & editing, H.W.; Visualization, Y.C.; Supervision, H.W.; Project administration, H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Tianshan Talent Program, grant number 2022 TSYCJC0030, the Key Research and Development Program of the Autonomous Region, grant number 2022B03031, the Science and technology project of the Hami High-tech Zone, grant number HGX2023 KJXM008, and the central government will guide local science and technology development in 2024, grant number ZYYD2024 CG13.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wind Turbine** & & & & & & \\
**Number** & **Date** & **Start Time** & **End Time** & **Start Position** & **End Position** & **Abnormal Pattern** \\ \hline
12\# & 2022 November 4 & 06:48:33 & 06:51:07 & \(-8.62\) & 24.12 & a \\
51\# & 2022 December 21 & 09:47:04 & 09:49:04 & \(-7.69\) & \(-13.04\) & c \\
80\# & 2022 December 31 & 20:02:20 & 20:02:34 & 10.1 & 60.7 & b \\
12\# & 2022 November 4 & 06:48:33 & 06:51:07 & \(-8.62\) & 24.12 & a \\ \hline \hline \end{tabular}
\end{table}
Table 5: Example of abnormal diagnostic results.