## 6 Decision Algorithm
In contrast, the BPNN and PNN methods have an accuracy of 91.67% and 95%, respectively. In this study, the results of external faults showed that the PNN method gave the greatest accuracy. Therefore, it is most suitable for use in detecting external faults compared to the above methods. Finally, when analyzing the overall test results, including internal and external faults, it was found that the PNN method provided the most accurate fault detection. As shown in Figure 12, the detection accuracy result of the PNN method is up to 193.15% from 200%, while the BPNN and RBF methods have an overall accuracy of only 189.82% and 118.89%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Coil (\%)** & & **20** & **40** & **50** & **60** & **80** & **AVG** \\ \hline \multirow{8}{*}{**Case 1 LV Side** (\%)**} & & A & 100 & 100 & 100 & 100 & 100 & \\  & **BPNN** & B & 100 & 100 & 100 & 100 & 0 & 93.33 \\  & & C & 100 & 100 & 100 & 100 & 100 & \\ \cline{2-10}  & \multirow{4}{*}{**PNN**} & A & 100 & 100 & 100 & 100 & 100 & \\  & & B & 100 & 100 & 100 & 100 & 100 & 93.33 \\ \cline{2-10}  & & A & 100 & 100 & 100 & 100 & 100 & \\ \cline{2-10}  & **PNN** & B & 100 & 100 & 100 & 100 & 100 & 93.33 \\  & & C & 100 & 100 & 0 & 100 & 100 & 93.33 \\ \cline{2-10}  & \multirow{4}{*}{**RBF**} & A & 0 & 0 & 0 & 0 & 0 & \\  & & B & 0 & 0 & 0 & 0 & 0 & 0.00 \\ \hline \multirow{8}{*}{**Case 1 HV Side** (\%)} & & A & 100 & 100 & 100 & 100 & 100 & \\  & & B & 0 & 100 & 100 & 0 & 100 & 86.67 \\  & & C & 100 & 100 & 100 & 100 & 100 & \\ \cline{2-10}  & \multirow{4}{*}{**BPNN**} & A & 100 & 100 & 100 & 100 & 100 & \\  & & B & 100 & 100 & 100 & 100 & 100 & 100.00 \\ \cline{2-10}  & & C & 100 & 100 & 100 & 100 & 100 & 100 \\ \cline{2-10}  & \multirow{4}{*}{**RBF**} & A & 0 & 0 & 0 & 0 & 0 & \\  & & B & 0 & 0 & 0 & 0 & 0 & \\ \hline \multirow{8}{*}{**Case 2 LV Side** (\%)} & & A & 100 & 100 & 100 & 100 & 100 & \\  & & B & 100 & 100 & 100 & 100 & 100 & 100.00 \\ \cline{2-10}  & \multirow{4}{*}{**BPNN**} & A & 100 & 100 & 100 & 100 & 100 & \\  & & B & 100 & 100 & 100 & 100 & 100 & 100.00 \\ \cline{2-10}  & & C & 100 & 100 & 100 & 100 & 100 & \\ \cline{2-10}  & \multirow{4}{*}{**RBF**} & A & 0 & 0 & 0 & 0 & 0 & \\ \cline{2-10}  & & B & 0 & 0 & 0 & 0 & 0 & 0.00 \\ \cline{2-10}  & & C & 0 & 0 & 0 & 0 & 0 & \\ \hline \multirow{8}{*}{**Case 2 HV Side** (\%)} & & A & 100 & 100 & 100 & 100 & 100 & \\  & & B & 0 & 0 & 100 & 100 & 100 & 86.67 \\ \cline{1-1}  & & C & 100 & 100 & 100 & 100 & 100 & \\ \cline{1-1} \cline{2-10}  & \multirow{4}{*}{**RBF**} & A & 100 & 100 & 100 & 100 & 100 & \\ \cline{1-1}  & & B & 100 & 100 & 0 & 0 & 0 & 80.00 \\ \cline{1-1}  & & C & 100 & 100 & 100 & 100 & 100 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of external winding fault accuracy. In a comparison of the detection methods in Figure 13, the sum of detection accuracy between internal and external faults in each detection method is compared. The results show the percentage of accuracy in detecting internal and external faults and the sum of those percentage values for each detection method. The PNN method is the most accurate in both internal and external fault detection, providing an internal fault accuracy of 98.15% and an external fault accuracy of 95%. The sum of percentage accuracy is 193.15% of 200%. Subsequently, the accuracy of the PNN method was determined in each case of the fault-detection methods consisting of the maximum coefficient algorithm (Case 1) and the maximum ratio algorithm (Case 2), as shown in Figure 13. The maximum coefficients algorithm of the PNN method provides the highest accuracy, providing an overall accuracy of 196.67% of 200%, divided into the internal fault detection accuracy within 100% and the external fault detection accuracy within 96.67%. While the maximum ratio algorithm provides an accurate analysis of the PNN and BPNN methods that are closely related, the BPNN method is slightly greater. The overall accuracy of the BPNN method is 191.49%, and the PNN method is 189.63%.