## 5 Results and Discussion
### Fault Events Classification

Table 6: Performance of fault detection using SFS with ELM (five-fold cross-validation). \begin{table}
\begin{tabular}{l l l} \hline \multicolumn{1}{c}{**No. of Features**} & \multicolumn{1}{c}{**Selected Features**} & \multicolumn{1}{c}{**Accuracy (\%)**} \\ \hline
1 & F11 & 73.52 \\ \hline
2 & F11,F14 & 80.32 \\ \hline
3 & F11,F14,F3 & 82.29 \\ \hline
4 & F11,F14,F3,F7 & 85.38 \\ \hline
5 & F11,F14,F3,F7,F8 & 86.39 \\ \hline
6 & F11,F14,F3,F7,F8,F5 & 88.68 \\ \hline
7 & F11,F14,F3,F7,F8,F5,F12 & 88.23 \\ \hline
8 & F11,F14,F3,F7,F8,F5,F12,F10 & 89.90 \\ \hline
9 & F11,F14,F3,F7,F8,F5,F12,F10,F6 & 89.85 \\ \hline
10 & F11,F14,F3,F7,F8,F5,F12,F10,F6,F13 & 90.08 \\ \hline
11 & F11,F14,F3,F7,F8,F5,F12,F10,F6,F13,F9 & 90.20 \\ \hline
**12** & **F11,F14,F3,F7,F8,F5,F12,F10,F6,F13,F9,F11 & **91.50** \\ \hline
13 & F11,F14,F3,F7,F8,F5,F12,F10,F6,F13,F9,F1,F4 & 91.42 \\ \hline
14 & F11,F14,F3,F7,F8,F5,F12,F10,F6,F13,F9,F1,F4,F2 & 89.68 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of fault detection using SFS with ANN (five-fold cross-validation). A similar kind of observation can be seen for SVM and RF but with a little improvement in the performance (accuracy and dependability), such as 97.3% and 89.9% and 96.7% and 86.9%, respectively (refer to Tables 7 and 8 and Figures 7 and 8). However, the performance was significantly improved in the case of the KNN classifier. In the case of KNN (refer to Figure 9), out of 3744 internal DC faults events only 3599 numbers of events were truly detected as Class-1 having a dependability of 96.1%. However, the AC faults were recognized with 100% detection accuracy, whereas the external DC faults were detected with 95.5% accuracy. From this analysis, it can be analyzed that the overall maximum percentage accuracy in the case of KNN was found to be 98.82% (refer to Table 9), where only 4.1% of internal DC faults were incorrectly detected as external faults and 0.5% of other external DC faults were incorrectly detected as internal faults. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**No. of Features**} & \multicolumn{1}{c}{**Selected Features**} & \multicolumn{1}{c}{**Accuracy (\%)**} \\ \hline
1 & F3 & 68.7 \\ \hline
2 & F3,F2 & 70.1 \\ \hline
3 & F3,F2,F8 & 73.7 \\ \hline
4 & F3,F2,F8,F12 & 75.6 \\ \hline
5 & F3,F2,F8,F12,F6 & 78.7 \\ \hline
6 & F3,F2,F8,F12,F6,F1 & 83.6 \\ \hline
7 & F3,F2,F8,F12,F6,F1,F10 & 83.9 \\ \hline
8 & F3,F2,F8,F12,F6,F1,F10,F5 & 84.7 \\ \hline
9 & F3,F2,F8,F12,F6,F1,F10,F5,F11 & 87.8 \\ \hline
10 & F3,F2,F8,F12,F6,F1,F10,F5,F11,F4 & 89.9 \\ \hline
11 & F3,F2,F8,F12,F6,F1,F10,F5,F11,F4,F7 & 89.9 \\ \hline
12 & F3,F2,F8,F12,F6,F1,F10,F5,F11,F4,F7,F13 & 96.9 \\ \hline
13 & F3,F2,F8,F12,F6,F1,F10,F5,F11,F4,F7,F13,F14 & 97.1 \\ \hline
**14** & **F3,F2,F8,F12,F6,F1,F10,F5,F11,F4,F7,F13,F14,F9** & **97.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of fault detection using SFS with SVM (five-fold cross-validation). \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**No. of Features**} & \multicolumn{1}{c}{**Selected Features**} & \multicolumn{1}{c}{**Accuracy (\%)**} \\ \hline
1 & F4 & 68.8 \\ \hline
2 & F4,F3 & 68.1 \\ \hline
3 & F4,F3,F1 & 71.4 \\ \hline
4 & F4,F3,F1,F2 & 78.7 \\ \hline
5 & F4,F3,F1,F2,F7 & 78.7 \\ \hline
6 & F4,F3,F1,F2,F7,F5 & 81.6 \\ \hline
7 & F4,F3,F1,F2,F7,F5,F6 & 82.4 \\ \hline
8 & F4,F3,F1,F2,F7,F5,F6,F8 & 83.0 \\ \hline
9 & F4,F3,F1,F2,F7,F5,F6,F8,F9 & 89.0 \\ \hline
10 & F4,F3,F1,F2,F7,F5,F6,F8,F9,F10 & 88.9 \\ \hline
11 & F4,F3,F1,F2,F7,F5,F6,F8,F9,F10,F11 & 90.4 \\ \hline
12 & F4,F3,F1,F2,F7,F5,F6,F8,F9,F10,F11,F12 & 93.7 \\ \hline
13 & F4,F3,F1,F2,F7,F5,F6,F8,F9,F10,F11,F12,F13 & 96.4 \\ \hline
**14** & **F4,F3,F1,F2,F7,F5,F6,F8,F9,F10,F11,F12,F13,F14** & **96.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of fault detection using SFS with RF (five-fold cross-validation). In the studied data-mining framework, the authors tested the extracted fault dataset with a deep learning technique named 'DBM'.