# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 5 Simulation Results and Discussion
### Noise Resistance Test and Algorithm Comparison Analysis  
In this paper, MCloFormer-ECA, MCECA-MobileNetV2, MCNN-CloFormer, and MCECA-ECANET are used to compare with the proposed algorithm. MCECA-CloFormer adopts the early fusion strategy, MCloFormer-ECA adopts the late fusion strategy, and the model structure is shown in Figure 10a. MCNN-CloFormer is a model that does not use ECA's attention mechanism, and the model structure is shown in Figure 10b.  
In order to test the anti-noise ability of the algorithm, 300 groups of samples were randomly selected from the NES test set for testing, including 100 groups of arc high-resistance grounding samples. Different degrees of Gaussian white noise were added to test the noise resistance of the algorithm. The test results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Fault** & \begin{tabular}{c} **Fault** \\ **Location** \\ \end{tabular} & \begin{tabular}{c} **Fault Ground** \\ **Resistance** \\ \end{tabular} & \
\begin{tabular}{c} **Initial Fault** \\ **Phase Angle** \\ \end{tabular} &
\begin{tabular}{c} **Test Content** \\ **Test Content** \\ \end{tabular} \\ \hline \multirow{2}{*}{parameter} & L\({}_{1}\), L\({}_{2}\), & 15\%, 35\%, 75\% & 10 \(\Omega\) & 0\({}^{\circ}\) & Effect of fault location \\  & L\({}_{3}\), L\({}_{4}\) & 50\% & 2 \(\Omega\), 200 \(\Omega\), 4000 \(\Omega\) & 0\({}^{\circ}\) & Effect of fault ground resistance \\  & 50\% & 10 \(\Omega\) & 20\({}^{\circ}\), 45\({}^{\circ}\), 160\({}^{\circ}\) & Effect of Initial fault phase angle \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test scenario settings.  
Figure 9: Visualization results.  
As can be seen from Table 7, the accuracy rate of all algorithms decreased at a noise intensity of 10 dB, with the accuracy rate of MCECA-MobileNetV2 decreasing to 96.33%. However, based on the performance of the algorithms at various noise intensities, MCECA-CloFormer, MCLoFormer-ECA, and MCECA-ECANet demonstrated better anti-noise capability than other algorithms.  
Rapid and accurate fault line selection can significantly reduce maintenance time and ensure power supply reliability. Therefore, the fault line selection time of the above algorithms were compared. Table 8 shows the time required by the different models to process a single fault.  
It can be seen that the time required for line selection of lightweight models MCECA-CloFormer, MCNN-CloFormer, and MCECA_MobileNetV2 was much shorter than other algorithms.  
By integrating the data from Tables 7 and 8, it is evident that while MCECA-MobileNetV2 and MCNN-CloFormer demanded shorter durations for fault line identification, they exhibited weaker noise tolerance capabilities. Conversely, MCECA-ECANet and MCLoFormer-ECA, while demonstrating superior resilience against noise interference, necessitated longer periods for line selection. Consequently, upon consolidating these dual metrics, the proposed algorithm, MCECA-CloFormer, manifests a prominent advantage, excelling in both efficiency and noise robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SNR = 40 dB** & **SNR = 30 dB** & **SNR = 20 dB** & **SNR = 10 dB** \\ \hline MCECA-CloFormer & 100\% & 100\% & 100\% & 99.33\% \\ MCloFormer-ECA & 100\% & 100\% & 99.67\% & 98.33\% \\ MCNN-CloFormer & 99.00\% & 99.00\% & 99.00\% & 98.33\% \\ MCECA-MobileNetV2 & 98.00\% & 97.67\% & 98.00\% & 96.33\% \\ MCECA-ECANet & 100\% & 100\% & 100\% & 99.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of line selection accuracy under different noise intensities.  
Figure 10: Structure diagram of models. **(a)** MCLoFormer-ECA. **(b)** MCNN-CloFormer.