# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 3 Simulation Results and Discussion
### Summary of Historical Event Data  
A total of 108 events are considered, encompassing various fault types such as single line to ground faults (SLG), line to line faults (LL), double line to ground faults (2 LG), line to line faults (LLL), and line to line to line to ground faults (LLLG). These are detailed in Table 5. Historical events are utilized as testing data due to the limited occurrence of each fault type, suggesting that employing historical data for training purposes is feasible only with access to a more extensive dataset.  
An analysis of these events led to the generation of datasets that encompass a broader range of scenarios, with characteristics summarized in Table 6. The historical events include various transmission line configurations: 25 events from two-terminal lines, 76 from three-terminal lines, and 7 from four-terminal lines. However, our findings indicate that training with data from three-terminal or four-terminal transmission lines does not significantly affect testing accuracy. Consequently, we chose to generate simulation data only for two-terminal transmission lines for efficiency. In the historical data collected, fault impedance values are unavailable, whereas fault distances, FIAs, and circuit breaker time delays are recorded. In Table 6, fault distances are presented in per unit (p.u.) terms with reference to the transmission line length: a fault at the left end is labeled as 0 p.u., and at the right end as 1 p.u. Our training and validation datasets are specifically designed to emulate the potential faults within the Taiwan power system, ensuring that they cover the range of fault distances, FIAs, and time delays observed in the historical events.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training** & **Validation** & **Testing** \\ \hline Sources & Simulations & Simulations & Historical events \\ Number of events & 5038 & 851 & 108 \\ Number of terminals & 10,076 & 1702 & 230 \\  & 85\% & 15\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of training, validation, and testing datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault** & **Fault Type** & **Number of Events** \\ \hline Single line to ground faults (SLG) & AG & 41 \\  & BG & 29 \\  & CG & 20 \\ \hline  & AB & 1 \\ Line to line faults (LL) & BC & 1 \\  & CA & 0 \\ \hline  & ABG & 2 \\ Double line to ground faults (2 LG) & BCG & 1 \\  & ACG & 6 \\ \hline Line to line to line faults (LLL) & ABC & 4 \\ \hline Line to line to ground faults (LLLG) & ABCG & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fault types of historical events.  
In Taiwan, commonly used relay brands include SEL, Toshiba, GE, and Ingeteam. It is worth noting that the sampling rates for these relays can vary, ranging from 4 to 144 samples per cycle. As shown in Table 7, the largest number of events corresponds to a sampling rate of 16 samples per cycle.  
Data augmentation and transfer learning are powerful techniques for managing limited datasets in machine learning classification tasks. However, these methods must be carefully applied, as they can potentially amplify outliers or perpetuate biases present in the original data, especially when significant discrepancies exist between the simulated training data and real-world scenarios. Limited information on the events and the small number of available events have posed challenges in bridging the gap between simulated data and real-world measurements from the power system.