## 5 Conclusions  
In this study, the coefficients and behavior of DWTs were analyzed for fault classification using various AI methods. Fault signals in a 115-kV hybrid transmission line were simulated using EMTP/ATPDraw software. Then, the signals comprising three-phase current and zero sequence were decomposed by the WT of MATLAB software. The coefficients from the DWT had different values depending on the position and angle at which the fault occurred. When a phase fault occurred, the values of the coefficients were high, and the values at zero sequence were high when short circuit to ground fault occurred. Using the behavior of DWT coefficients, previous studies designed algorithms for fault classification, but the algorithms had less accuracy. Therefore, this study used AI methods to increase the performance and accuracy of fault classification. ANN and SVM were chosen for fault classification because they are well-known for data classification. Two types of ANN were applied: PNN and BPNN. The 1080 datapoints were divided into three parts. Half of the data were applied for training the AI models. Data validation was performed using 25% of the data, and the case study used 25% of the data. Normalized values (0-1) of the coefficients of phase ABC and zero sequence were \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Fault**} & \multicolumn{3}{c}{**AI Type (Sending End)**} & \multicolumn{3}{c}{**AI Type (Receiving End)**} \\  & **PNN** & **BPNN** & **SVM** & **PNN** & **BPNN** & **SVM** \\ \hline SLG & 100\% & 99.86\% & 100\% & 100\% & 100\% & 100\% \\ DLG & 100\% & 99.72\% & 100\% & 100\% & 100\% & 100\% \\ LL & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\
3P & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ Average & 100\% & 98.88\% & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of accurate results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Additional Case**} & \multicolumn{3}{c}{**AI Type (Sending End)**} & \multicolumn{3}{c}{**AI Type (Receiving End)**} \\  & **PNN** & **BPNN** & **SVM** & **PNN** & **BPNN** & **SVM** \\ \hline \multicolumn{6}{c}{Vary ground resistivity} \\ (1–100 ohm) 6 Case & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ \multicolumn{6}{c}{Vary load (70–120\%)} \\ \multicolumn{6}{c}{10 Case} & 50\% & 50\% & 100\% & 50\% & 100\% & 100\% \\ Average & 68.75\% & 68.75\% & 100\% & 68.75\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of accurate results (additional case). input into the algorithms for classification. The results of all AI for fault classification in the case study show that PNN and SVM were the most accurate. For the additional cases with all AI models for fault classification, it can be seen that the SVM was found to be more accurate than PNN and BPNN. Because of complex information, AI methods are required for fault classification because of the high flexibility and because all fault data do not need to be input for training. PNN and BPNN showed errors in the case of different loads, and controlling the load in real-time cases is difficult. However, the SVM provided 100% accuracy for both types of data (case study and additional cases). In terms of computation time, the result was that the SVM is suitable for fault classification because it is flexible and no training is required. Conceptualization, J.K. and A.N. ; methodology, J.K.; software, J.K.; validation, J.K. and A.N. ; formal analysis, J.K.; investigation, J.K.; resources, A.N. ; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, A.N. ; visualization, J.K.; supervision, A.N. ; project administration, A.N. ; funding acquisition, A.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors wish to gratefully acknowledge financial support for this research from [PERSON]'s Institute of Technology Laddrabang Research fund, Thailand.