relation: https://khub.utp.edu.my/scholars/397/
title: Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks
creator: Zahlay F., D.
creator: Rama Rao, K.S.
description: 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.
date: 2008
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Zahlay F., D. and Rama Rao, K.S.  (2008) Autoreclosure in extra high voltage lines using taguchi's method and optimized neural networks.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-63049089484&doi=10.1109%2fEPC.2008.4763325&partnerID=40&md5=dd91d2495da8df1555d58a1a79134682
relation: 10.1109/EPC.2008.4763325
identifier: 10.1109/EPC.2008.4763325