Fault classification and location using HS-transform and radial basis function neural network

Abstract

A new approach for protection of transmission lines has been presented in this paper. The proposed technique consists of preprocessing the fault current and voltage signal sample using hyperbolic S-transform (HS-transform) to yield the change in energy and standard deviation at the appropriate window variation. After extracting these two features, a decision of fault or no-fault on any phase or multiple phases of the transmission line is detected, classified, and its distance to the relaying point found out using radial basis function neural network (RBFNN) with recursive least square (RLS) algorithm. The ground detection is done by a proposed indicator ‘index’. As HS-transform is very less sensitive to noise compared to wavelet transform, the proposed method provides very accurate and robust relaying scheme for distance protection.