# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 1 Introduction  
Transmission line protective relaying is crucial to maintaining reliable power system operation, involving three core functions: detection, classification, and fault localization [1]. These functions play a vital role in safeguarding the overall stability and integrity of the power grid, ensuring a continuous and uninterrupted supply of electricity. This study aims to enhance fault classification accuracy using machine learning (ML) techniques. Fault classification provides valuable information that significantly aids in quickly estimating fault locations, leading to faster fault-clearing times and the rapid restoration of power service [2]. In real-life scenarios, relays and circuit breakers are deployed to monitor the status of transmission lines. However, actuating the relay is time-consuming and this operation is more efficient using ML [3]. ML-based techniques have garnered widespread acclaim in the domain of transmission line fault classification [2,4]. ML-based methods require substantial data to effectively train classifier models. While most studies rely on simulated data due to the scarcity of real-system fault data [5, 6, 7], our approach also incorporates historical event data from theTaiwan power system, comprising 108 events with varied fault scenarios. It is noteworthy that a laboratory physical system developed in [8] generates a relevant dataset. These studies generate data through simulation considering different fault impedances, fault locations, fault inception angles (FIAs), circuit breaker time delays, transmission line configurations, source impedances, and the transposition of transmission lines [5; 8; 9; 10; 11; 12; 13]. In this study, we train and validate our classifier model using simulated data but test it using historical event data collected from the Taiwan power system. The limited availability of historical data, particularly with some fault types represented by only a few cases, restricts our ability to use these data for training. Based on the analysis of the historical data, we generate various scenarios to emulate potential faults in the Taiwan power system more realistically. The input data for the classifier model comprise measurements of three-phase voltage and currents obtained from either the single end or double end of a multi-terminal transmission line. However, most studies require preprocessing via feature extraction prior to training. Feature extraction is crucial in classification by isolating relevant and critical data from raw signals. For fault classification purposes, most feature extraction processes involve transforming the input time series data into the frequency domain. Previous research has introduced feature extraction methodologies such as wavelet packet transformation (WT) [6; 7; 14; 15], multiwavelet packet transformation (MWT) [13], Hilbert-Huang Transform (HHT) [8], time series imaging [10], and mathematical morphology (MM) [9]. The precise classification of faults in transmission lines through WTs necessitates dynamically adjusting their parameters according to the prevailing power system topology [4]. Additionally, selecting an appropriate mother wavelet for analysis is imperative to address specific fault scenarios. This strategic choice ensures optimal extraction of relevant features from the signal, facilitating the accurate identification and characterization of faults within the transmission line network. Although HHT in [8] was used to eliminate the challenge of selecting a suitable mother wavelet for a WT, it is not applicable for wideband signals and requires high computational complexity. Though most studies focus on expert feature extraction, some studies apply time series raw data directly to their model [3; 5]. Designing a model adept at directly processing raw time series data allows us to harness the power of machine learning to autonomously discern and delineate crucial patterns and characteristics, thereby enhancing the accuracy of fault classification.