# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 4 Results and Discussion
\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 & 35 & 0 & 39 & 0 & 35 & 0 \\ \hline Actual non-Arcing & 0 & 77 & 0 & 73 & 0 & 77 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Training Phase Output Matrix: Frequency 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 & 35 & 0 & 39 & 0 & 35 & 0 \\ \hline Actual non-Arcing & 0 & 77 & 0 & 73 & 0 & 77 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Validation Phase Output Matrix: Frequency Domain Arcing Fault Classification. and 98.4%. However, the 1D-CNN-LSTM model outperformed both the 1D-CNN and LSTM models, achieving a 100% success rate in all three phases. In the frequency domain analysis, the success rates for the 1D-CNN and LSTM models in the training, validation, and testing phases were 100%, 100%, and 95.8%, respectively. On the other hand, the 1D-CNN-LSTM model achieved a 100% success rate in all three phases. The results of the analysis conducted in both the time and frequency domains showed that all three algorithms were successful in detecting arcing faults, with the 1D-CNN-LSTM model achieving the highest success rates of 100% in all phases. These findings demonstrate the potential of deep learning techniques in improving fault detection systems for switchgear. However, there is still room for improvement and future work in this area. One possible direction for future research is to explore the use of other types of sensors, such as temperature or vibration sensors, in combination with sound wave analysis for more accurate fault detection. Another area of future work could be to optimize the proposed models further to reduce the training time and computational resources required. Additionally, it may be useful to investigate the effectiveness of transfer learning and data augmentation techniques in improving the performance of the proposed models. Finally, this study provides a promising foundation for further research on the development of deep learning-based fault detection systems for switchgear. Conceptualization, S.P.K. and S.K.T. ; methodology, Y.A.M.A., C.T.Y., S.P.K. and S.K.T. ; software, Y.A.M.A. ; validation, Y.A.M.A., C.T.Y., S.P.K. and C.P.C. ; formal analysis, Y.A.M.A., C.T.Y. and S.P.K. ; investigation, Y.A.M.A., C.T.Y., S.P.K., Y.A.L.B., C.H.T., K.A. and C.P.C. ; resources, Y.A.M.A., C.T.Y., S.P.K., S.K.T. and C.P.C. ; data curation, Y.A.M.A., C.T.Y. and C.P.C. ; writing--original draft preparation, Y.A.M.A., C.T.Y. and S.P.K. ; writing--review and editing, Y.A.M.A., C.T.Y., S.P.K., S.K.T., C.P.C., K.A., Y.A.L.B. and C.H.T. ; visualization, S.P.K., C.T.Y., S.K.T., Y.A.L.B. and C.P.C. ; supervision, S.P.K., C.P.C. and C.T.Y. ; project administration, S.P.K. and S.K.T. ; funding acquisition, S.P.K. and S.K.T. All authors have read and agreed to the published version of the manuscript. This research was funded by 202101 KETTHA and BOLDREFRESH 2025 (J510050002 (IC6C)). Not applicable. This work was supported by Universiti Tenaga Nacional, BOLDREFRESH 2025 and AAIBE of Renewable Energy (ChRE) for providing all out-laboratory support. The authors declare no conflict of interest.