# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 5 Discussion  
The CatBoost classifier produced exceptional results because its accuracy and precision were better than the other methods used in the literature. In another research study, the use of sparse representation classification with random dimensionality reduction projection technique was used to classify faults [38]. This method generated results ranging from 93.9%, 96.8% and 98.8% for 10 dB, 20 dB and 30 dB, respectively, which varied according to fault type [19]. In [14], S-transform and neural networks were used in fault classification, and the average accuracy was 99.6%. Still, the research of [14] was based on a three-phase fault in contrast to the four fault types used in this paper. The recursive neural network (RNN) was used in [19], and about 500 pieces of fault data were used, but the classifier failed to classify L-L and L-L-G fault types at 140 km. However, it was able to classify the fault in some distance with an accuracy of 98.67%, so the classifier's inability to classify all the different types of faults at different locations made it unsuitable as a fault classification technique. The CatBoost algorithm was proven to be a better machine learning tool in fault classification and detection for the training of data and is highly recommended for optimum, accurate results. Figure 8 shows a separate analysis of the performance of the CatBoost model where the single-phase and three-phase fault performs optimally with accuracy of 100% while the recall value was higher for the double phase-to-ground fault. The novelty of this paper is the use of the CatBoost classifier for transmission line fault classification. Table 7 enumerates some of its distinctive features over other machine-learning algorithms, including an overall result accuracy of 99.54% and an individual line fault accuracy of 100% in a three-face fault classification. The execution speed 58.5 s compared with that of the SVM. The model also handled a multi-dataset, combined multiple categorical features, and overcame gradient bias. It also prevented data overfitting and data pre-processing during training compared to techniques that use trial and error for parameter tuning, in contrast to other machine learning algorithms like SVM, K-NN, CNN and RNN. The Effect of Noise and Disturbance in the Proposed Algorithm  
Power quality disturbance (PQDs) and noise signals have adverse effects on fault classification in transmission line accuracy. During feature selection and extraction, it is necessary to consider noise and signal disturbance because of voltage swell, sag, interruption and flicker; transient oscillation; harmonics; and transient impulses. In the proposed model, noise signals and PQDs were considered and compared with other articles, and it was observed that the CatBoost classifier performed better, with accuracy remaining at 99.54%  
Figure 8: Performance of the different faults types. both in noise and noiseless signals. This showed that the method effectively reduced the effects of noise and disturbance on classification accuracy. In [39], the ANN technique was used for classification with a noiseless signal accuracy of 87.55% and 82.44% at 20 dB noise. In [40], the DWT was used for feature extraction, and the SVM was used for fault classification with an accuracy of 100% without disturbance and 98 and 95.6% accuracy at 30 and 20 dB noise, respectively. The novelty of the proposed method is the ability of the model to \"de-noise\" the signal for optimal performance, as seen in Figures 9 and 10. The current signal in the three phase-to-ground faults was de-noised for optimal model performance before it was integrated into the CatBoost classifier. In Figure 9 the base current rose to 30 per unit (PU) which caused a disturbance in the system but was reduced to 28 PU as seen in Figure 10. The process can continue to achieve a zero signal to noise ratio in the system. In addition, the power quality can be improved by this method for quality control, and online and offline fault classification with noise and noiseless data.