# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 2 Methodology
### Case Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Label** & **Parameters** & **Classification** \\ \hline \multirow{3}{*}{L1} & \multirow{3}{*}{Opening and closing time (Test)} & Closing \textless{} 70, opening \textless{} 35, synchronism \textless{} 2\% & Normal (1A) \\ \cline{3-4}  & & 70 \textless{} closing \textless{} 90, 35 \textless{} opening \textless{} 45, synchronism \textless{} 4\% & Attention (IB) \\ \cline{3-4}  & & Closing \textgreater{} 70, opening \textgreater{} 35, synchronism \textgreater{} 4\% & Abnormal (1C) \\ \hline \multirow{3}{*}{L2} & \multirow{3}{*}{Circuit resistance (Test)} & \textless{}130 & Normal (2A) \\ \cline{3-4}  & & [130, 160] & Attention (2B) \\ \cline{3-4}  & & \textgreater{}160 & Abnormal (2C) \\ \hline \multirow{3}{*}{L3} & \multirow{3}{*}{SF\({}_{6}\) moisture content (Test)} & \textless{}90 & Normal (3A) \\ \cline{3-4}  & & [90, 150] & Attention (3B) \\ \cline{3-4}  & & \textgreater{}150 & Abnormal (3C) \\ \hline \multirow{3}{*}{L4} & \multirow{3}{*}{Gas pressure (Online monitoring)} & \textgreater{}0.68 & Normal (4A) \\ \cline{3-4}  & & [0.65, 0.68] & Attention (4B) \\ \cline{3-4}  & & \textless{}0.65 & Abnormal (4C) \\ \hline \multirow{3}{*}{L5} & \multirow{3}{*}{Gas pressure (Inspection)} & \textgreater{}0.68 & Normal (5A) \\ \cline{3-4}  & & [0.65, 0.68] & Attention (5B) \\ \cline{3-4}  & & \textless{}0.65 & Abnormal (5C) \\ \hline \multirow{3}{*}{L6} & \multirow{3}{*}{External contact temperature (Inspection)} & \textless{}20 & Normal (6A) \\ \cline{3-4}  & & [20, 50] & Attention (6B) \\ \cline{3-4}  & & \textgreater{}50 & Abnormal (6C) \\ \hline \multirow{3}{*}{L7} & \multirow{3}{*}{Ambient temperature (Inspection)} & \textless{}10 & Moderate temperature (7A) \\ \cline{3-4}  & & [10, 30] & High temperature (7B) \\ \cline{3-4}  & & \textless{}10 & Low temperature (7C) \\ \hline \multirow{3}{*}{L8} & \multirow{3}{*}{Ambient humidity (Inspection)} & \textless{}30, 80 & Moderate (8A) \\ \cline{3-4}  & & \textgreater{}80 & Moist (8B) \\ \cline{3-4}  & & \textless{}30 & Dry (8C) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: High voltage electrical equipment quantification items. Naive Bayes classification calculates the posterior probability of a sample belonging to a specific category based on available evidence. The sample is then assigned to the category with the highest posterior probability. This discussion focuses solely on probability parameters, as the Naive Bayes classifier does not require learning structural parameters. The trained Bayes classifier processes the test dataset by determining the posterior probability of states based on association rules between mined states and metrics (Equations (13) and (14)). Using the data mining model to determine the a priori probability. The model's effectiveness is validated by comparing its detection criteria, based on the attributes of derived rules, with the state parameters of the test sample set. Figure 6 illustrates the prediction results of the test set. Figure 6 compares the predictive evaluation model with the actual dataset. In this figure, horizontal axis represents the labeling of test samples, and vertical axis represents the quality of the electrical equipment. The symbol '-' denotes equipment quality recorded in data samples, '1' represents the results obtained by using the fusion proposed in this paper of the a priori algorithm with Naive Bayes classification. And 'o' represents the results obtained using a priori algorithm alone. Comparative analysis reveals that the proposed algorithm achieves an accuracy of 96.68%, surpassing the a priori algorithm's accuracy of 89.21%. These results indicate strong consistency with the training dataset, im