# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 1 Introduction
The Kernel Principal Component Analysis (KPCA) and the SVM were used for the real-time fault diagnosis of a high-voltage circuit breaker, whereas a sample reduction algorithm based on a similarity degree function was used to analyse the similarity among the samples to detect faults [17] and with the dynamic kernel principal component analysis (DKPCA) [18]. However, if the number of dimensions is greater than the number of data points, the convergence matrix is always large, making it difficult to obtain a convergence matrix for data that has varying properties and capabilities [16, 19]. Deep-learning diagnosis techniques, such as the wavelet packet distortion and a CNN [20], have also been used in fault classification. It applies the wavelet packet distortion to generate a faulty data sample, while the CNN is used to classify the fault into different categories. However, the wavelet packet function uses Daubechies wavelet (DB4) for extraction, which does not have a theoretical justification. The adaptive intraclass and interclass CNN (AIICNN) [21] was applied in the algorithm to enhance sample distribution differences by applying designed intraclass and interclass constraints. The 1-D CNN (1 dCNN) had an added activation function to enlarge the heterogeneous and reduce the homogeneous distance between samples for proper classification. A normalized conditional variational auto-encoder with adaptive focal loss (NCVAE-AFL) was also used to classify faults into different categories [22]. In [23], the CNN long short-term memory (CNN-LSTM) was used to identify and locate a fault using the frequency response analysis (FRA) to extract it. This method detected faults accurately and in a timely manner. Each of the methods mentioned above had disadvantages and limitations. Some of the main noticeable observations were the inability of most articles to explain fault classification extensively concerning fault clearing time, thereby making it difficult to isolate the fault or take on significant repairs within the shortest possible time. Moreover, discrete wavelet transform (DWT) and DT [24] had a limited time resolution capability and had a low performance for high-performance faults. Wavelet and Data mining [24], K-Nearest Neighbours (KNN) and Decision Tree [25], are limited to the fault classification technique but without considering the speed, accuracy and precision of the result. In [26], fault classification was not determined using the S-transform technique, and the effect of noise in the transmission line was not considered in the model [27]. Differential and Hibert-Huang transmission methods are expensive and have a no-fault direction. In addition, for fault classification,the mathematical morphology and recursive least-square (RLS) methods [28] involves using a mathematical morphology-based fault feature extraction scheme. This method has high calculation and technical standards that necessitate professional implementation. Researchers have widely used machine learning for its increased involvement of communication and computation in transmission systems [28]. Research shows that most techniques use a smaller dataset to train the algorithm, giving highly accurate results. They also use either a single phase-to-ground fault or a double phase-to-line fault data for training [15, 29, 30]. Another shortcoming of most of the methods is the inability to consider the inevitable noise and disturbance in the transmission line networks. This method will also address the effect of noise signals and disturbance and how they can be reduced or eliminated for optimal system performance and accuracy of results. Due to the shortcoming of the different algorithms and models discussed in the literature on fault classification, the CatBoost classifier algorithm is proposed for the training of fault data from single-phase, double-phase, and three phase-to-ground faults. Twelve different dataset types were used for fault classification and the CatBoost classifier was used to train the data.