eprintid: 725
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/07/25
datestamp: 2023-11-09 15:48:52
lastmod: 2023-11-09 15:48:52
status_changed: 2023-11-09 15:23:02
type: conference_item
metadata_visibility: show
creators_name: Desta Zahlay, F.
creators_name: Rao, K.S.R.
title: Autoreclosure in extra high voltage lines using Taguchi's method and optimized neural networks
ispublished: pub
keywords: Artificial neural networks; Autoreclosure; EHV transmission line faults; Levenberg Marquardt algorithm; RPROP; Taguchi's method, Algorithms; Backpropagation; DC generators; Electric lines; Fast Fourier transforms; Feature extraction; MATLAB; Neural networks; Optimization; Transmission line theory, Electric fault location
note: cited By 5
abstract: This paper presents a method to discriminate a temporary fault from a permanent one 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 to extract 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. © 2009 IEEE.
date: 2009
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-65949118488&doi=10.1109%2fICCET.2009.171&partnerID=40&md5=2a8f020ea4d6cff2b0a70b5ed1d3a08e
id_number: 10.1109/ICCET.2009.171
full_text_status: none
publication: Proceedings - 2009 International Conference on Computer Engineering and Technology, ICCET 2009
volume: 2
pagerange: 151-155
refereed: TRUE
isbn: 9780769535210
citation:   Desta Zahlay, F. and Rao, K.S.R.  (2009) Autoreclosure in extra high voltage lines using Taguchi's method and optimized neural networks.  In: UNSPECIFIED.