# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 1 Introduction  
An electrical power system consists of different interacting segments: generation, transmission, and distribution. The transmission line is an integral part since it transfers electricity from the generating station to the distribution network and to the end-user. These components are interconnected through the transmission lines, which are subject to faults and cannot be controlled manually except through advanced techniques [1]. When a fault occurs, significant damage is done to the power system's reliability, affecting power output and causing loss of installations, outages, and system collapse. It is imperative that a model be designed that can classify and locate a fault quickly and precisely so that it can be isolated and identified for fault protection and management. Fault classification is essential for protecting the network; therefore, measures must be taken to achieve maximum protection to avert system collapse and preserve energy output. Faults can be categorised as incipient or unpredictable [2]. Incipient faults are transient, while unpredictable faults occur due to human interference, lightning, and extreme weather, which directly affect the entire network. Researchers in recent years have been brainstorming the best way to protect transmission lines from faults, which must be classified according to type to isolate the line quickly and prevent system collapse [3]. However, feedback generated from fault classification can significantly assist in detecting a fault location so that power can be restored quickly [4]. The recent literature has discussed fault classification using machine learning: an artificial neural network (ANN) [5; 6; 7; 8; 9], support vector machine (SVM), [1; 2; 10], decision tree (DT) [11] and probabilistic neural network (PNN) [12]. All these methods have been used for the classification of faults. However, some, like the wavelet technique (WT), are helpful when time and frequency data are needed although this technique is sensitive to noise and harmonics, and requires a high sampling rate. It is time-consuming because getting a referred wavelet and the number of decompositions is done by trials. Although WT detects faults accurately and instantly, it is has trouble differentiating among various fault conditions [12]. WT and ANN are predominantly used for fault detection and classification [13]. Many hybrid methods have produced good results. S-transform and ANNs were used to classify faults on the transmission line. Though the ANN and SVM produced good results in identifying faults, they needed a large volume of data for training, making them complex to handle [14]. Furthermore, in [12], three ANN approaches were compared to each other for fault classification: PNN, Back Propagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). These methods produced accurate results, but they were used on faulty voltage and current signals and focused more on time and speed of execution of the training. Despite the fact that most of these methods have been used recently, there are some challenges, such as not being applicable for high-frequency signals and high computational complexity, as found in the Hilbert-Huang Transform (HHT) [15]. The convolutional neural network (CNN) is another technique used in fault classification that is accurate and fast, but the computational cost for offline analysis is relatively high [16]. Principal Component Analysis (PCA) in machine learning is a fast and simple method that reduces re-projection error and is immune to noise. It is also used to map data from multidimensional space to low-dimensional subspace to mitigate dimensionality and perceive the variance of the data in the best way possible.