# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 4 Results and Discussion
The overall accuracy was 100% with a 0% error rate. For the LSTM model, all 112 sets of data were used, with 39 arcing instances and \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1D-CNN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**1D-CNN-LSTM**} \\ \cline{2-7}  & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** & **Corona** & **Non-Arcing** \\ \hline Actual Arcing & 4 & 0 & 4 & 0 & 10 & 0 \\ \hline Actual non-Arcing & 0 & 62 & 0 & 62 & 0 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation Phase Output Matrix: Time Domain Arcing Fault Classification. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**1D-CNN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**1D-CNN-LSTM**} \\ \cline{2-5}  & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** \\ \hline Actual Arcing & 7 & 0 & 7 & 0 & 10 & 0 \\ \hline Actual non- Arcing & 1 & 58 & 1 & 58 & 0 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Testing Phase Output Matrix: Time Domain Arcing Fault Classification. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1D-CNN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**1D-CNN-LSTM**} \\ \cline{2-7}  & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** \\ \hline Actual Arcing & 43 & 0 & 43 & 0 & 34 & 0 \\ \hline Actual non-Arcing & 3 & 260 & 3 & 260 & 0 & 272 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training Phase Output Matrix: Time Domain Arcing Fault Classification. 73 non-arcing instances being correctly identified. The accuracy of the LSTM model was 100%, with a 0% error rate. During the validation phase of the frequency domain analysis, the 1D-CNN-LSTM model used a total of 24 sets of data, successfully recognizing 15 non-arcing instances and nine arcing instances. The overall accuracy was 100% with no errors. Similarly, in the 1D-CNN model, 15 non-arcing instances and nine arcing instances were successfully identified, resulting in an accuracy of 100% with no errors. The LSTM model achieved an accuracy of 100% with no errors, correctly detecting five instances of arcing and 19 instances of non-arcing, using a total of 24 sets of data. Table 9 displays the results matrix for the validation phase. Table 10 shows the results matrix from the 24 sets of data used in the testing phase of the 1D-CNN-LSTM model in the frequency domain. The algorithm correctly identified 15 non-arcing instances and nine arcing instances, resulting in a 100% accuracy rate and a 0% error rate. For the 1D-CNN model, 24 sets of data were utilized, and it correctly identified 15 non-arcing instances and eight arcing instances. The overall accuracy rate was 95.8%, with a 4.2% error rate, where one instance of arcing was misclassified as non-arcing. The LSTM model also used 24 sets of data and correctly identified 15 non-arcing instances and 9 arcing instances. However, one instance of arcing was misclassified as non-arcing, resulting in a 95.8% accuracy rate and a 4.2% error rate. A thorough study was done using performance measures including accuracy, loss, sensitivity, dependability, and F1-measure in Tables 11 and 12 to assess the reliability of all three methods more rigorously. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1D-CNN**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**1D-CNN-LSTM**} \\ \cline{2-7}  & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** & **Arcing** & **Non-Arcing** \\ \hline Actual Arcing & 8 & 1 & 8 & 1 & 9 & 0 \\ \hline Actual non-Arcing & 0 & 15 & 0 & 15 & 0 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Testing Phase Output Matrix: Frequency Domain Arcing Fault Classification.