Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System

This paper presents the application of a modified fuzzy reasoning spiking neural P systems (MFRSN P system, for short) to fault diagnosis of metro traction power supply systems. In MFRSN P systems, three types of neurons are used to represent operation information of protection devices including pro...

ver descrição completa

Detalhes bibliográficos
Autores: He, Yangyang, Wang, Tao, Huang, Kang, Zhang, Gexiang, Pérez Jiménez, Mario de Jesús
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/107804
Acesso em linha:https://hdl.handle.net/11441/107804
Access Level:acceso abierto
Palavra-chave:Membrane Computing
Probabilistic fuzzy reasoning spiking neural P system
Metro traction power supply system
Fault diagnosis
id ES_d831e3d8a0ff3ecbd7f906ef2ee56c10
oai_identifier_str oai:idus.us.es:11441/107804
network_acronym_str ES
network_name_str España
repository_id_str
spelling Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P SystemHe, YangyangWang, TaoHuang, KangZhang, GexiangPérez Jiménez, Mario de JesúsMembrane ComputingProbabilistic fuzzy reasoning spiking neural P systemMetro traction power supply systemFault diagnosisThis paper presents the application of a modified fuzzy reasoning spiking neural P systems (MFRSN P system, for short) to fault diagnosis of metro traction power supply systems. In MFRSN P systems, three types of neurons are used to represent operation information of protection devices including protective relays and circuit breakers; a reasoning algorithm associated with MFRSN P systems is introduced to fulfill fault reasoning; fault diagnosis rules for metro traction power supply systems and their MFRSN P systems are described. Case studies show the feasibility and effectiveness of the presented method.Ministerio de Economía y Competitividad TIN2012-37434Romanian Academy, Section for Information Science and TechnologyCiencias de la Computación e Inteligencia ArtificialTIC193: Computación NaturalMinisterio de Economía y Competitividad (MINECO). España2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/107804reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRomanian Journal of Information Science and Technology (ROMJIST), 18 (3), 256-272.TIN2012-37434https://www.romjist.ro/content/cuprins18_3.htmlinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1078042026-06-17T12:51:07Z
dc.title.none.fl_str_mv Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
title Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
spellingShingle Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
He, Yangyang
Membrane Computing
Probabilistic fuzzy reasoning spiking neural P system
Metro traction power supply system
Fault diagnosis
title_short Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
title_full Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
title_fullStr Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
title_full_unstemmed Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
title_sort Fault Diagnosis of Metro Traction Power Systems Using A Modified Fuzzy Reasoning Spiking Neural P System
dc.creator.none.fl_str_mv He, Yangyang
Wang, Tao
Huang, Kang
Zhang, Gexiang
Pérez Jiménez, Mario de Jesús
author He, Yangyang
author_facet He, Yangyang
Wang, Tao
Huang, Kang
Zhang, Gexiang
Pérez Jiménez, Mario de Jesús
author_role author
author2 Wang, Tao
Huang, Kang
Zhang, Gexiang
Pérez Jiménez, Mario de Jesús
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
TIC193: Computación Natural
Ministerio de Economía y Competitividad (MINECO). España
dc.subject.none.fl_str_mv Membrane Computing
Probabilistic fuzzy reasoning spiking neural P system
Metro traction power supply system
Fault diagnosis
topic Membrane Computing
Probabilistic fuzzy reasoning spiking neural P system
Metro traction power supply system
Fault diagnosis
description This paper presents the application of a modified fuzzy reasoning spiking neural P systems (MFRSN P system, for short) to fault diagnosis of metro traction power supply systems. In MFRSN P systems, three types of neurons are used to represent operation information of protection devices including protective relays and circuit breakers; a reasoning algorithm associated with MFRSN P systems is introduced to fulfill fault reasoning; fault diagnosis rules for metro traction power supply systems and their MFRSN P systems are described. Case studies show the feasibility and effectiveness of the presented method.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/107804
url https://hdl.handle.net/11441/107804
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Romanian Journal of Information Science and Technology (ROMJIST), 18 (3), 256-272.
TIN2012-37434
https://www.romjist.ro/content/cuprins18_3.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Romanian Academy, Section for Information Science and Technology
publisher.none.fl_str_mv Romanian Academy, Section for Information Science and Technology
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421102120304640
score 15.300719

Registos relacionados