EHV, HV and MV lines fault location using both RBF based SVM and SCALCG based neural network

摘要

An appropriate method for fault location on Extra High Voltage (EHV), High Voltage (HV) and Medium Voltage (MV) transmission lines using Support Vector Machine (SVM) is proposed in this paper. It relies on the application of SVM and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. This paper is proposing a new hybrid approach for fault location on EHV, HV and MV lines using Radial Basis Function (RBF) basis SVM and Scaled Conjugate Gradient (SCALCG) basis neural network method. Sample inputs are determined by MATLAB. The average error of fault location in 400 kV,110 kV and 10 kV with 150 km line is tested and the results prove that the proposed method is effective and reduce the error within a short duration of time using both RBF based SVM and SCALCG based neural network.