Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network

Authors:
K. Gayathri;N. Kumarappan
Affiliations:
Department of Electrical Engineering, Annamalai University, Annamalainagar 608 002, Tamilnadu, India;Department of Electrical Engineering, Annamalai University, Annamalainagar 608 002, Tamilnadu, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

An appropriate method for fault location on Extra High Voltage (EHV) transmission line 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 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 400kV and 150km 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.