MLPNN and Ensemble Learning Algorithm for Transmission Line Fault Classification

Abstract

Recently, Bangladesh experienced a system loss of 11.11%, leading to significant power cuts, largely due to faults in power transmission lines. This paper proposes the XGBoost machine learning method for classifying electric power transmission line faults. The study compares multiple machine learning approaches, including ensemble methods (decision tree, random forest, XGBoost, CatBoost, and LightGBM) and the multilayer perceptron neural network (MLPNN), under various conditions. The power transmission system is modeled using Simulink and the machine learning algorithms. In the IEEE 3-bus system, all of the learning types achieve approximately 99% accuracy in imbalanced and noisy data states, respectively, except CatBoost and decision tree, in the classification of line to line, line to line to line, line to line to ground, line to ground types of faults, and no fault. However, although all of the methods gain significant accuracy, assessing the performance results indicates that the XGBoost model is the most effective for transmission line fault classification among the methods tested, as it showed the best accuracy in the imbalanced and noisy state’s classification of faults, contributing to the development of more reliable and efficient fault detection methodologies for power transmission networks.