# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 4 Results and Discussion  
Based on the analysis of sound waves in the time and frequency domains, experiments were done to see how well the proposed algorithms for finding arcing defects in switchgear worked. Figures 6 and 7 show the results of the experiments. They show the accuracy and loss in both domains during the training, validation, and testing stages. A total of 438 data samples were used to train and validate the 1 DCNN, LSTM, and 1D-CNN-LSTM models for detecting arcing and non-arcing conditions in the time domain and 160 samples in the frequency domain. The classifier with arcing in the time and frequency domains was able to accurately classify all test data into positive and negative categories. In the time domain, 438 samples were generated from simulations to represent arcing and non-arcing conditions, and 70% of these samples were randomly selected for training, while 15% each were used for model validation and testing. The same testing, validation, and training datasets were used for all methods, totaling 306 samples in the training phase. The results of multiple trials were compiled into confusion matrices, which show the accuracy and error rates for each phase. The validation and testing phases contained 66 samples each. In the 1D-CNN-LSTM model training phase, Table 5 shows that, out of the 306 data sets, 272 non-arcing instances and 34 arcing instances were accurately identified, resulting in an accuracy rate of 100% with a 0% error rate. On the other hand, both 1D-CNN and LSTM models detected 260 non-arcing cases and 43 arcing cases with a 99% accuracy rate and a 1% error rate, with three non-arcing cases mistakenly classified as arcing. \begin{table}
\begin{tabular}{l l l} \hline \hline  & \
\begin{tabular}{l} **Predictive Acing** \\ **Findings (0)** \\ \end{tabular} &
\begin{tabular}{l} **Predictive Non-Arcing** \\ **Findings (1)** \\ \end{tabular} \\ \hline Actual Arcing Findings (0) & TP & FP \\ \hline Actual Non-Arcing Findings (1) & FN & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 4: For arcing and non-arcing cases, a confusion matrix. Figure 6: The accuracy and loss curves are shown in the time domain for the 1D-CNN, LSTM, and 1D-CNN-LSTM models. Figure 7: The accuracy and loss curves are shown in the frequency domain for the 1D-CNN, LSTM, and 1D-CNN-LSTM models. In the time domain, 66 data sets were used during the testing phase of the 1D-CNN-LSTM model, which is shown in Table 7. The method correctly identified 56 non-arcing instances and 10 arcing instances, resulting in a 100% accuracy rate and a 0% error rate. In the case of the 1D-CNN model, 66 data sets were used, and the method successfully identified seven arcing instances and 58 non-arcing instances, with only one non-arcing instance being misclassified as an arcing fault. The accuracy rate for this model was 98.4%, with an error rate of 1.6%. The LSTM model, which also used 66 data sets, correctly identified 58 instances of non-arcing and seven instances of arcing. However, one instance of non-arcing was misclassified as an arcing fault. The accuracy rate for this model was also 98.4%, with an error rate of 1.6%, which is the same as the 1D-CNN model. In the frequency domain, 160 samples of arcing and non-arcing conditions were taken from simulations. The dataset was randomly split into 70% for training, 15% for validation, and 15% for model testing. The same testing, training, and validation datasets were used for each method, with a total of 112 cases for the training phase. The tables below show the true and false detections, as well as the best results from repeated training for the testing and validation phases. The training phase output matrix for the 1D-CNN-LSTM model in the frequency domain is shown in Table 8. Out of a total of 112 cases, 35 cases of arcing and 77 cases of non-arcing were correctly identified. The model achieved 100% accuracy and a 0% error rate. Similarly, the 1D-CNN model successfully identified 77 non-arcing instances and 35 arcing instances out of 112 sets of data.