Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty se...

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Detalhes bibliográficos
Autores: Wang, Tao, Zhang, Gexiang, Zhao, Junbo, He, Zhengyou, Wang, Jun, Pérez Jiménez, Mario de Jesús
Formato: artículo
Estado:Versión enviada para evaluación y publicación
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:11441/116053
Acesso em linha:https://hdl.handle.net/11441/116053
https://doi.org/10.1109/TPWRS.2014.2347699
Access Level:acceso abierto
Palavra-chave:Electric power system
Fault diagnosis
Fuzzy production rules
Fuzzy reasoning
fuzzy reasoning spiking neural P system
Linguistic term
Trapezoidal fuzzy number
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spelling Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P SystemsWang, TaoZhang, GexiangZhao, JunboHe, ZhengyouWang, JunPérez Jiménez, Mario de JesúsElectric power systemFault diagnosisFuzzy production rulesFuzzy reasoningfuzzy reasoning spiking neural P systemLinguistic termTrapezoidal fuzzy numberThis paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.National Natural Science Foundation of China 61170016National Natural Science Foundation of China No. 61373047National Natural Science Foundation of China No. 61170030IEEE Computer SocietyCiencias de la Computación e Inteligencia ArtificialTIC193: Computación NaturalNational Natural Science Foundation of China2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/116053https://doi.org/10.1109/TPWRS.2014.2347699reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Power Systems, 30 (3), 1182-1194.611700166137304761170030https://ieeexplore.ieee.org/document/6887379info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1160532026-06-17T12:51:07Z
dc.title.none.fl_str_mv Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
title Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
spellingShingle Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
Wang, Tao
Electric power system
Fault diagnosis
Fuzzy production rules
Fuzzy reasoning
fuzzy reasoning spiking neural P system
Linguistic term
Trapezoidal fuzzy number
title_short Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
title_full Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
title_fullStr Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
title_full_unstemmed Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
title_sort Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems
dc.creator.none.fl_str_mv Wang, Tao
Zhang, Gexiang
Zhao, Junbo
He, Zhengyou
Wang, Jun
Pérez Jiménez, Mario de Jesús
author Wang, Tao
author_facet Wang, Tao
Zhang, Gexiang
Zhao, Junbo
He, Zhengyou
Wang, Jun
Pérez Jiménez, Mario de Jesús
author_role author
author2 Zhang, Gexiang
Zhao, Junbo
He, Zhengyou
Wang, Jun
Pérez Jiménez, Mario de Jesús
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
TIC193: Computación Natural
National Natural Science Foundation of China
dc.subject.none.fl_str_mv Electric power system
Fault diagnosis
Fuzzy production rules
Fuzzy reasoning
fuzzy reasoning spiking neural P system
Linguistic term
Trapezoidal fuzzy number
topic Electric power system
Fault diagnosis
Fuzzy production rules
Fuzzy reasoning
fuzzy reasoning spiking neural P system
Linguistic term
Trapezoidal fuzzy number
description This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/116053
https://doi.org/10.1109/TPWRS.2014.2347699
url https://hdl.handle.net/11441/116053
https://doi.org/10.1109/TPWRS.2014.2347699
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Power Systems, 30 (3), 1182-1194.
61170016
61373047
61170030
https://ieeexplore.ieee.org/document/6887379
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 IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
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
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score 15.301603

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