Classification of Short Circuit Faults Occurring in Transmission Lines by Using Transient Current Signals, J48 And Naïve Bayes Machine Learning Algorithms

Abstract

In this study, a method has been used to classify the short circuit faults. In this method, the current values of three phases and zero, positive, and negative sequence current component values obtained through the application of modal transformation on the line currents have been used in order to obtain the classification features. In each case of fault, in order to ensure that the classification features remain within specific range of values, the current values of the three phases for one cycle after fault occurrence are reduced being divided by the biggest peak value among the current values of again these three phases. Similarly for each case of fault, the sequence current components for one cycle after fault occurrence are reduced being divided by the biggest peak value among the sequence current components. After the signals are reduced, the classification features are obtained using Root Mean Square (RMS) values of the three phase current signals, RMS values of the sequence current component signals and the proportions of these RMS values to each other. The classification features obtained are used with Naive Bayes and J48 machine learning methods to classify the short circuit faults occurring in transmission lines. While the Alternative Transients Program (ATP/EMTP) is used to model the transmission lines, the Waikato Environment for Knowledge Analysis (WEKA) program is used for Naive Bayes and J48 machine learning algorithms