## 4 Results
### Data Collection and Preprocessing
During wind turbine operation, factors such as extreme weather, blade damage or soiling, and data processing errors can cause the SCADA system to generate a significant amount of anomalous data that does not accurately reflect the turbine's operational status and performance. To improve the accuracy of model training, it is essential to preprocess the SCADA data to ensure its accuracy and reliability before use. This study refined the original data by eliminating outliers and null values, retaining only valid values to ensure the accuracy and reliability of the data. In order to obtain high-quality SCADA data, this study conducted processing from multiple aspects, such as data integrity verification, outlier verification, sensor drift detection, and cross-sensor consistency. The main process is as follows:  
(1) Data integrity verification  
Data collection may be incomplete due to sensor failures or communication disruptions. In this study, the regular matching method was used to quickly identify null values (Null) in the original data. These null values were then either directly removed or filled using the linear interpolation method to complete the corresponding values, thereby ensuring the integrity of the data. (2) Handling of outliers  
During data collection and transmission, extreme weather conditions, complex electromagnetic environments, etc., can produce isolated data points that deviate significantly from the power curve, parameter thresholds, etc. These isolated points do not conform to the normal data distribution pattern and fluctuate randomly near the normal values. Such outliers do not represent abnormal conditions of the equipment operation and seriously interfere with the accurate judgment of the equipment operation status. To handle these outliers, this study uses the ideal wind speed power curve and parameter thresholds as benchmarks to identify and remove these anomalies, thereby ensuring data consistency. Taking the wind speed power curve of the wind turbine as an example, the ideal wind speed-power curve is an important indicator in the field of wind power and a key tool for evaluating wind turbine performance, as shown in Figure 13. The cut-in wind speed (Vim) represents the minimum wind speed threshold for the turbine startup. Vnrtd refers to the wind speed condition corresponding to the rated rotational speed, while Vrtd is the rated wind speed. The cut-out wind speed (Vout) represents the maximum wind speed for safe operation, and the rated power (Prtd) is the turbine's maximum designed power output. As the ambient wind speed gradually increases to the cut-in threshold, the wind turbine initiates the power generation procedure, with its output increasing proportionally to the wind speed. Once the rated power is reached, the system's output power remains constant. If the wind speed continues to rise to the cut-out threshold, the control system activates a protection mechanism to stop wind energy capture. However, in actual operation, due to factors such as environmental conditions and turbine status, the measured wind speed-power curve generally deviates from the theoretical curve. \begin{table}
\begin{tabular}{c c c c c} \hline
**Unit ID** & **Sampling Time** & **Fault Time** & **Fault Reason** & **Reason** \\ \hline
03 & 1 January 2024–31 &
\begin{tabular}{c} None \\ December 2024 \\ \end{tabular} & None & None & None \\
07 & 1 January 2024–31 &
\begin{tabular}{c} 19 October 2024/8:51 \\ December 2024 \\ \end{tabular} & Unplanned Downtime & Excessive Gerabox Oil Temperature \\ \hline \end{tabular}
\end{table} 
Table 3: Description of the turbine unit. Figure 14 shows the distribution of anomalies in the wind speed-power curve. A comparative analysis reveals significant deviations between the actual wind speed-power curve and the ideal model, with some data points abnormally distributed in a manner that contradicts with the aerodynamic characteristics and operational mechanisms of the wind turbine.