# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 4 Conclusions  
This study employs a machine learning-based method to classify transmission line faults using three-phase voltage and current measurements, utilizing a Conformer model. The goal of this research is to develop a model that generalizes effectively without relying on extensive expert-driven feature extraction. Traditionally, feature extraction plays a crucial role in fault classification by isolating relevant and critical data from raw signals. However, the Conformer Convolution-Augmented Transformer model used in this study demonstrates a remarkable ability to autonomously identify key features. This capability significantly reduces the need for predefined feature extraction. Our classifier is trained on simulation data from a two-terminal transmission line and tested on historical data from the Taiwan power system. It is important to note that, typically, studies in the literature rely solely on simulation data. We collected 108 historical events across 10 fault types, which is generally insufficient for machine learning training purposes. Based on the analysis of these events, our training dataset is designed to cover various scenarios, including different fault types, fault impedances, fault distances, fault inception angle, and circuit breaker time delay. Key preprocessing techniques explored include window selection, normalization, and resampling rate. Our findings indicate that including pre-fault and fault states in the window selection is crucial, whereas the inclusion of the post-fault state has a minimal impact on the model's performance. Normalization involves dividing the measurements by the root mean square value of the first cycle in the pre-fault state. Although resampling is often overlooked in the literature, it is vital for data from the Taiwan power system where relay sampling rates vary from 4 to 188.5 samples per cycle. Resampling in the frequency domain effectively handles non-integer sampling rates, benefits from batch normalization, and enhances training efficiency. Our results show that resampling to 4 samples per cycle is adequate for fault classification, maintaining accuracy and reducing computational demands. This finding suggests that for our specific classification tasks, focusing on lower frequency data could be sufficient, challenging the conventional emphasis on high-frequency analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & \\ \hline
**Fault Types** & **Counts** & **Method III** & **ELM** \\ \hline AG & 85 & 96.47 & 74.12 \\ \hline BG & 64 & 96.88 & 82.81 \\ \hline CG & 38 & 97.37 & 84.21 \\ \hline ABG & 4 & 75.00 & 50.00 \\ \hline BCG & 3 & 100.00 & 100.00 \\ \hline CAG & 13 & 92.31 & 84.62 \\ \hline AB & 2 & 100.00 & 100.00 \\ \hline BC & 2 & 100.00 & 0.00 \\ \hline CA & 0 & – & – \\ \hline ABC & 19 & 68.42 & 78.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance of each fault type for Method III and ELM. Despite achieving a validation accuracy of 100%, the testing accuracy is lower at 88.88%, likely due to factors unaccounted for in the training dataset. This discrepancy underscores the need for more comprehensive historical data to better simulate real-world fault conditions and leverage the power of big data to enhance model performance. Moreover, we compared our method with the ELM proposed in 2023, and our method significantly outperformed the ELM in terms of testing accuracy. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed the study. [PERSON], [PERSON] and [PERSON] performed transmission line fault simulations. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the paper with input from all authors. All authors have read and agreed to the published version of the manuscript. This research was funded by National Atomic Research Institute grant number 112A019. Not applicable. The data are not publicly available due to the power company's ownership. The testing datasets presented in this article are not readily available because they are the property of the Taipower company.