# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology
### Preprocessing  
In our approach, we avoid relying on expert experimentation in the preprocessing stage. Instead, we address three key topics related to the input data: window selection, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **System Parameter** & **Values** \\ \hline Sources & Line to line voltage (kV) & 161 \\ \hline \multirow{5}{*}{Overhead transmission line} & Transmission line length (km) & 50 \\ \cline{2-3}  & Positive and zero sequence resistances (Ohms/km) & 0.0321, 0.3479 \\ \cline{2-3}  & Positive and zero sequence inductances (H/km) & 0.473 \(\times\) 10\({}^{-3}\), 1.370 \(\times\) 10\({}^{-3}\) \\ \cline{2-3}  & Positive and zero sequence capacitances (F/km) & 0.038 \(\times\) 10\({}^{-6}\), 0.038 \(\times\) 10\({}^{-6}\) \\ \hline \multirow{5}{*}{Underground cable transmission line} & Transmission line length (km) & 50 \\ \cline{2-3}  & Positive and zero sequence resistances (Ohms/km) & 0.00214, 0.2319 \\ \cline{1-1} \cline{2-3}  & Positive and zero sequence inductances (H/km) & 0.5676 \(\times\) 10\({}^{-3}\), 1.644 \(\times\) 10\({}^{-3}\) \\ \cline{1-1} \cline{2-3}  & Positive and zero sequence capacitances (F/km) & 7.6 \(\times\) 10\({}^{-8}\), 7.6 \(\times\) 10\({}^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of the system parameters used for data generation. Figure 3: Simulation model for a two-terminal transmission line using MATLAB Simulink. normalization, and resampling. Each of these topics plays a crucial role in preparing the data for fault classification, and we will provide a brief overview of each. Window selection: In fault events, there are typically three distinct states: pre-fault, fault, and post-fault, as illustrated in Figure 4. The post-fault state occurs after the fault has been cleared. In power systems, protective relays isolate the fault by opening circuit breakers once a fault is detected. As a result, the currents in Figure 4 decrease to zero after the fault is cleared. Our analysis shows that focusing exclusively on the pre-fault and fault states provides sufficient information to accurately categorize and identify faults in transmission lines. An adequately large window size that encompasses the relevant pre-fault and fault periods is crucial for capturing the necessary information for precise fault classification. Nonetheless, we have observed that some historical event data also include post-fault states. Consequently, our model is designed to successfully classify faults using data that either include or exclude post-fault states. Normalization: Normalization is a vital technique to standardize the input data, ensuring a consistent range or distribution. By removing variations in signal magnitude or amplitude, this process enables the model to receive a uniform and comparable input across different samples. As a fundamental step in data preprocessing for machine learning applications, normalization is crucial for enhancing the model's performance and accuracy. In this study, the normalization of phase voltage and current is carried out by dividing them by the root mean square value of the first cycle in the pre-fault state. The normalization process is illustrated in Figure 5.  
Resampling: Resampling involves adjusting the sampling rate of input data to meet the specific requirements of the classifier model. This adjustment is crucial when the collected data have a different sampling rate than that required by the fault classification classifier model. Resampling ensures the input data's consistency and compatibility with the model's processing demands. However, most studies in the literature rely solely on simulation-generated data, which typically do not require resampling due to their consistent and pre-adjusted sampling rates. In this study, we apply the resampling method proposed in [23]. The process of resampling the signal using the Fast Fourier Transform (FFT) is illustrated as follows:  
* Process the original signal using the Fast Fourier Transform (FFT) to obtain the signal spectrum in the frequency domain.