# Defect Prediction for Capacitive Equipment in Power System
## 6 Conclusions  
Research on capacitive equipment defects primarily focuses on real-time online detection using various technologies. However, due to limited resources, it is impractical to install sensors on all capacitive equipment for real-time monitoring, which would require substantial manpower and resources. Our proposed method takes a different approach by integrating device information, geographical data, and substation information, utilizing machine learning techniques to explore the relationship between these factors and capacitive equipment defects. Subsequently, we developed machine learning models to predict whether current equipment is likely to develop defects and the severity of these defects. We applied WOE encoding from the finance domain to the analysis of capacitive equipment data, resulting in improved model performance. The experimental results demonstrate a significant enhancement in the performance of five algorithms with the use of WOE encoding. Upon comparison, the RF model employing WOE encoding emerged as the optimal defect occurrence prediction model and level prediction model. These findings are reliable and offer valuable insights for power grid companies in their production processes. In future endeavors, exploring the combination of various algorithms could augment the classification ability of the prediction model. Additionally, further experiments employing alternative machine learning algorithms or deep learning methods could yield an even more effective defect prediction model for capacitive equipment.  
Conceptualization, Q.P. and Z.Z.; resources, Q.P.; writing--original draft preparation, H.H.; writing--review and editing, H.H. and Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Science and Technology Project of Yunnan Province (grant number 202202 AD080004).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear & 0.44 & 0.43 & 0.44 & 0.42 \\ XGBoost & 0.61 & 0.60 & 0.61 & 0.60 \\ SVM & 0.55 & 0.54 & 0.55 & 0.54 \\ MLP & 0.61 & 0.61 & 0.60 & 0.61 \\ RF & 0.71 & 0.71 & 0.70 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the defect level prediction models based on one-hot encoding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Linear & 0.46 & 0.46 & 0.45 & 0.45 \\ XGBoost & 0.62 & 0.62 & 0.62 & 0.62 \\ SVM & 0.73 & 0.72 & 0.73 & 0.72 \\ MLP & 0.66 & 0.66 & 0.66 & 0.66 \\ RF & 0.78 & 0.79 & 0.78 & 0.78 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of the defect level prediction models based on WOE encoding.  
Conflicts of InterestAuthor [PERSON] was employed by the company Electric Power Research Institute of Yunnan Power Grid Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relation-ships that could be construed as a potential conflict of interest.