# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 3 Simulation Results and Discussion
### Comparison of Methods I, II, and III  
Table 9 presents a comprehensive overview of the results obtained from Methods I, II, and III. Notably, the validation accuracies of these methods exhibit minimal variance. However, it is evident that Method III outperforms the other two approaches, with Method I yielding the lowest accuracy. As elucidated in Section 2, Method I requires a longer training \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scaling Dataset** & **Training and Validation Dataset** \\
**(Historical Events)** & **(Simulations)** \\ \hline Transmission line configurations & Two-, three-, four-terminal & Two-terminal \\ Fault impedance (Ohms) & Unavailable & 0.1 to 10 \\ Fault distance (p.u.) & 0.037 to 0.81 & 0.005 to 0.81 \\ Fault inception angle (degrees) & 22.5 to 270 & 0 to 315 \\ Circuit breaker time delay (cycles) & 2 to 50 & 2 to infinity \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of event characteristics in datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Relay Brands** & **Sampling Rates (Samples per Cycle)** & **Number of Events** \\ \hline SEL & 4 & 12 \\ SEL and GE & 16 & 129 \\ Toshiba and Ingeteam & 24 & 75 \\ Ingeteam & 144 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sampling rates of commonly used relay brands in Taiwan power system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scaling per Cycles** & **4** & **16** & **24** & **144** & **188.5** \\ \hline Training accuracy (\%) & 99.54 & - & - & - \\ \hline Validation accuracy (\%) & 100.00 & 77.82 & 66.92 & 51.80 & 47.37 \\ \hline Testing accuracy (\%) & 91.67 & 91.47 & 65.33 & 64.28 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of data without resampling. time. Hence, for this study, Methods II and III emerge as more promising options. Both methods demonstrate competitive performance while offering potential time efficiency advantages compared to Method I. The testing accuracy exhibits a noticeable decrease compared to the training and validation accuracy across all methodologies. This disparity may be attributed to the omission of certain nonideal aspects present in the actual power system, which are not explicitly modeled in the simulations. Nonetheless, due to the constraints of limited historical data, relying on such data as the sole source for model training may not be advisable. To enhance the model's performance, there arises a need for a more comprehensive dataset or the incorporation of supplementary measurements beyond voltage and current. In this study, we investigate the performance of two models trained with different sampling rates: 16 samples per cycle (Model II) and 4 samples per cycle (Model III). The classification results of Methods II and III are presented in Tables 10 and 11, respectively. Notably, Method II achieves the highest validation accuracy for 16 samples per cycle. In comparison, Method III performs best at a sampling rate of 4 samples per cycle. Interestingly, despite the differences in requested sampling rates, the results do not show significant discrepancies between these models. This suggests that training the models with a sampling rate of four samples per cycle is sufficient for the fault type classification task. Consequently, Method III, which utilizes the lower sampling rate, emerges as a more promising choice. Additionally, it shows that detailed high-frequency information may have minimal impact on fault classification. This approach offers the advantage of reduced computation in both the inference and training processes while still achieving comparable performance in fault type classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resampling** & **Samples per Cycles** & **Validation Accuracy (\%)** & **Testing Accuracy (\%)** \\ \hline Unnecessary & 16 & 100.00 & 94.57 \\ \hline \multirow{4}{*}{Required} & 4 & 99.36 & 91.67 \\ \cline{2-4}  & 24 & 99.98 & 81.33 \\ \cline{1-1} \cline{2-4}  & 144 & 99.98 & 100.00 \\ \cline{1-1} \cline{2-4}  & 188.5 & 99.98 & - \\ \hline \hline \end{tabular}
\end{table}