Fault Identification, Classification, and Location on Transmission Lines Using Combined Machine Learning Methods

Abstract

This study develops a hybrid method to identify, classify, and locate electrical faults on transmission lines based on Machine Learning (ML) methods. Firstly, Wavelet Transform (WT) technique is applied to extract features from the current or voltage signals. The extracted signals are decomposed into eleven coefficients. These coefficients are calculated to the energy level, and the data of teen fault types are converted to the RGB image. Secondly, GoogLeNet model is applied to classify the fault, and Convolutional Neural Network (CNN) method is proposed to locate the fault. The proposed method is tested on the four-bus power system with the 220 kV transmission line via time-domain simulation using Matlab software. The conditions of the fault resistance random values and the pre-fault load changes are considered. The simulation results show that the proposed method has high accuracy and fast processing time, and is a useful tool for analyzing the system stability in the field of electricity.