# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 3 Conclusions  
This study presents a data mining-based algorithm for evaluating electrical equipment status in substations. The algorithm combines the a priori and Naive Bayes algorithms to assess the current state of electrical equipment. The implementation of GMM for unifying electrical equipment state feature data enables more comprehensive data utilization compared to alternative evaluation methods. The a priori algorithm efficiently processes  
Figure 11: Bar chart of performance metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **State** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{This method} & Normal & 93.06\% & 89.04\% & 90.28\% & 89.66\% \\ \cline{2-6}  & Attention & 91.20\% & 86.30\% & 87.50\% & 86.90\% \\ \cline{2-6}  & Abnormal & 97.22\% & 97.14\% & 94.44\% & 95.77\% \\ \hline \multirow{3}{*}{SVM} & Normal & 92.59\% & 86.84\% & 91.67\% & 89.19\% \\ \cline{2-6}  & Attention & 90.74\% & 87.14\% & 84.72\% & 85.91\% \\ \cline{2-6}  & Abnormal & 98.13\% & 97.14\% & 94.44\% & 95.77\% \\ \hline \multirow{3}{*}{KNN} & Normal & 92.59\% & 91.18\% & 86.11\% & 88.57\% \\ \cline{2-6}  & Attention & 90.74\% & 83.12\% & 88.89\% & 85.91\% \\ \cline{2-6}  & Abnormal & 96.76\% & 95.77\% & 94.44\% & 95.10\% \\ \hline \end{tabular}
\end{table} 
Table 8: Performance metrics for each method. large-scale data, while the Bayes classifier utilizes the derived association rules for classification. This approach enhances accuracy in conditional probability estimation and improves overall classification performance. The proposed method's applicability and effectiveness were assessed using diverse status monitoring data from operational substations. Results indicate the scheme's viability for practical implementation in substation environments. The feasibility of the proposed method was evaluated using statistical data from electrical equipment in a 110 kV substation of a power supply company. The evaluation process involved three steps: applying a Gaussian mixture model to analyze and discretize the GIS data, employing the a priori algorithm to derive frequent itemsets and association rules between electrical equipment quality and measurement data, and utilizing these rules to predict GIS quality through a Naive Bayes classifier. The method achieved a state prediction accuracy exceeding 95% when compared to the original dataset. Further evaluation using 110 kV arrester equipment data and a validity evaluation index demonstrated the superiority of the proposed classification method over other algorithms. Results showed that the proposed scheme outperforms single-category algorithms, demonstrating high accuracy and strong discriminative ability. The complexity of electrical equipment status monitoring data and multiple influencing factors present significant challenges. As datasets grow, the number of candidates itemset for deriving association rules increases exponentially, escalating computational complexity. Moreover, while numerous association rules may be generated, many could lack meaningful information, potentially complicating subsequent state classification. Future research will aim to optimize the model for applications in diverse and complex environments, focusing on addressing these identified challenges. In conclusion, this scheme exhibits considerable potential and practical value for evaluating the status of high-voltage electrical equipment in substations. Conceptualization, D.D. and Y.L. ; methodology, D.D. and Y.L. ; software, Y.L. ; validation, D.D. ; formal analysis, Y.L. ; data curation, D.D. ; writing--original draft preparation, D.D. ; writing--review and editing, Y.L. ; supervision, D.D. ; project administration, S.-K.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in this article, further inquiries can be directed to the corresponding authors.