Classification methodology and feature selection to assist fault location in power distribution systems

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Autor principal: Juan José Mora Flórez
Formato: Artículo científico
Lenguaje:es
Publicado: Universidad de Antioquia 2008
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author Juan José Mora Flórez
author_facet Juan José Mora Flórez
contents Classification methodology and feature selection to assist fault location in power distribution systems Juan José Mora Flórez Germán Morales España Sandra Pérez Londoño Ingeniería power quality fault location support vectors distribution systems signal characterization A classification methodology based on Support Vector Machines (SVM) is proposed to locate the faulted zone in power distribution networks. The goal is to reduce the multiple-estimation problem inherent in those methods that use single end measures (in the substation) to estimate the fault location in radial systems. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the SVM classifier. Performance of the fault locator having several combinations of these features has been evaluated according to its capability to discriminate between faults in different zones but located at similar distance. An application example illustrates the precision, to locate the faulted zone, obtained with the proposed methodology in simulated framework. The proposal provides appropriate information for the prevention and opportune attention of faults,requires minimum investment and overcomes the multiple-estimation problem of the classic impedance based methods. 2008 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43004410 es http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.44
format Artículo científico
id redalyc_43004410
institution Redalyc
language es
publishDate 2008
publisher Universidad de Antioquia
spellingShingle Classification methodology and feature selection to assist fault location in power distribution systems
Juan José Mora Flórez
Ingeniería
power quality
fault location
support vectors
distribution systems
signal characterization
Classification methodology and feature selection to assist fault location in power distribution systems Juan José Mora Flórez Germán Morales España Sandra Pérez Londoño Ingeniería power quality fault location support vectors distribution systems signal characterization A classification methodology based on Support Vector Machines (SVM) is proposed to locate the faulted zone in power distribution networks. The goal is to reduce the multiple-estimation problem inherent in those methods that use single end measures (in the substation) to estimate the fault location in radial systems. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the SVM classifier. Performance of the fault locator having several combinations of these features has been evaluated according to its capability to discriminate between faults in different zones but located at similar distance. An application example illustrates the precision, to locate the faulted zone, obtained with the proposed methodology in simulated framework. The proposal provides appropriate information for the prevention and opportune attention of faults,requires minimum investment and overcomes the multiple-estimation problem of the classic impedance based methods. 2008 artículo científico 0120-6230 https://www.redalyc.org/articulo.oa?id=43004410 es http://www.redalyc.org/revista.oa?id=430 Revista Facultad de Ingeniería Universidad de Antioquia application/pdf Universidad de Antioquia Revista Facultad de Ingeniería Universidad de Antioquia (Colombia) Num.44
title Classification methodology and feature selection to assist fault location in power distribution systems
topic Ingeniería
power quality
fault location
support vectors
distribution systems
signal characterization
url https://www.redalyc.org/articulo.oa?id=43004410