## 4 Results and Discussion
### Comparison of the Proposed Algorithm with the Deep Learning-Based Results  
The deep learning-based method was compared with the proposed algorithm for accuracy and tripping time. In [17], an artificial intelligence search algorithm and a genetic algorithm were employed to find the optimal relay setting coordination time. The result shows that the tripping time varied from 0.10 s to 0.69 s at different fault levels. The operating time varied from 0.28 s to 6.3 s at different fault locations. In [18], the Radial Bias Function Neural Network (RBFNN) to learn and detect short-circuit fault current was implemented in the microprocessor of a digital relay on a distribution feeder to detect short-circuit faults using inverter-based distributed energy resources. The offline training time was 0.414 s, the detection time was 0.0136 s, and the trip time was 0.5 s. In [19], a directional overcurrent relay was used at different setting groups to detect faults at various locations. The optimal coordination of directional overcurrent relay in clusters was obtained using a machine learning algorithm and a genetic algorithm, with heuristic adjustment, and the operating time was 497.4069 s. The tripping times were set at 0.282 s and 0.593 s for different clusters. In [20], a dual-path mixed-domain residual threshold network was used for fault diagnosis in bearings, the soft threshold function was employed as the nonlinear transformation layer, and dilated convolution was used to create a dual-path neural network to identify the critical features in the signal without using any signal denoising algorithms. The algorithm's accuracy was about 99.97% on Gaussian noise and 99.98% on real noise. This paper, on the other hand, focused on feature extraction at various \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Fault Types** & \begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} **Trip Time without** **Proposed Model** **(Seconds)** \\ \end{tabular} & \
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} **Trip Time with** **Proposed Model** **(Seconds)** \\ \end{tabular} &
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} **Trip Time with** **Proposed Model** **(Seconds)** \\ \end{tabular} \\ \hline L-G & 0.25 & 0.03 & 85.00 \\ \hline L-L-G & 0.38 & 0.05 & 99.87 \\ \hline L-L-L-G & 0.10 & 0.04 & 60.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage accuracy of the proposed model at bus A. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Fault Types** & \begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} **Trip Time without** **Proposed Model** **(Seconds)** \\ \end{tabular} & \
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} **Trip Time with** **Proposed Model** **(Seconds)** \\ \end{tabular} &
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} **Trip Time with** **Proposed Model** **in Accuracy** \\ \end{tabular} \\ \hline L-G & 0.10 & 0.03 & 70.00 \\ \hline L-L-G & 0.32 & 0.05 & 84.38 \\ \hline L-L-L-G & 0.20 & 0.04 & 80.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Percentage accuracy of the proposed model at bus B.  
noise levels and thresholds for machine learning training, but the difference lies in the direct application of the proposed model without the combination with other algorithms, as seen in [20], where the channel attention mechanism, spatial attention mechanism, and residual structure were all combined in the dual-path mixed-domain residual threshold network. The soft threshold function was used as the nonlinear transformation layer, and dilated convolution was used to make a dual-path neural network. This was done so that the signal's most important parts could be found without using algorithms to remove noise. When compared to the literature, the data extraction stage is simpler with the help of the threshold current and voltage and does not require another medium for the extraction of the threshold value; thus, it produces fast and accurate results for the relay setting without needing relay coordination.