%K Algorithms; Backpropagation; DC generators; Electric fault location; Electric lines; Electric power supplies to apparatus; Fast Fourier transforms; Feature extraction; MATLAB; Power transmission; Transmission line theory, Artificial neural networks; Autoreclosure; Back-propagation algorithm; EHV transmission; Levenberg Marquardt algorithm; Taguchi's method; Transmission line faults, Neural networks
%X This paper presents a method to discriminate the temporary faults from the permanent ones in an extra high voltage (EHV) transmission line so that improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with standard Error Back-Propagation, Levenberg Marquardt Algorithm and Resilient Back-Propagation training algorithms together with Taguchi's Method. The algorithms are developed using MATLAB� software. A range of faults are simulated on EHV modeled transmission line using SimPowerSytems�, and the spectra of the fault data are analyzed using fast Fourier transform which facilitates extraction of distinct features of each type of fault. For both training and testing purposes, the neural network is fed with the normalized energies of the DC component, the fundamental and the first four harmonics of the faulted voltages. The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively. © 2008 IEEE.
%O cited By 1; Conference of 2008 IEEE Electrical Power and Energy Conference - Energy Innovation ; Conference Date: 6 October 2008 Through 7 October 2008; Conference Code:75680
%L scholars397
%J 2008 IEEE Electrical Power and Energy Conference - Energy Innovation
%D 2008
%R 10.1109/EPC.2008.4763325
%T Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
%A D. Zahlay F.
%A K.S. Rama Rao
%C Vancouver, BC