Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems

In this paper, intuitionistic fuzzy spiking neural P (IFSNP) systems as a variant are proposed by integrating intuitionistic fuzzy logic into original spiking neural P systems. Compared with a common fuzzy set, intuitionistic fuzzy set can more finely describe the uncertainty due to its membership a...

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Autores: Peng, Hong, Wang, Jun, Ming, Jun, Shi, Peng, Pérez Jiménez, Mario de Jesús, Yu, Wenping, Tao, Chengyu
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2018
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/116055
Acceso en línea:https://hdl.handle.net/11441/116055
https://doi.org/10.1109/TSG.2017.2670602
Access Level:acceso abierto
Palabra clave:Power systems
Fault diagnosis
Spiking neural P Systems
Intuitionistic fuzzy set
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spelling Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P SystemsPeng, HongWang, JunMing, JunShi, PengPérez Jiménez, Mario de JesúsYu, WenpingTao, ChengyuPower systemsFault diagnosisSpiking neural P SystemsIntuitionistic fuzzy setIn this paper, intuitionistic fuzzy spiking neural P (IFSNP) systems as a variant are proposed by integrating intuitionistic fuzzy logic into original spiking neural P systems. Compared with a common fuzzy set, intuitionistic fuzzy set can more finely describe the uncertainty due to its membership and non-membership degrees. Therefore, IFSNP systems are very suitable to deal with fault diagnosis of power systems, specially with incomplete and uncertain alarm messages. The fault modeling method and fuzzy reasoning algorithm based on IFSNP systems are discussed. Two examples are used to demonstrate the availability and effectiveness of IFSNP systems for fault diagnosis of power systems. Case studies involve single fault, complex fault, and multiple faults with protection device failures and incorrect tripping signals.National Natural Science Foundation of China No. 61472328Chunhui Project Foundation of the Education Department of China Z2016143Chunhui Project Foundation of the Education Department of China Z2016148Research Foundation of the Education Department of Sichuan Province, China 17TD0034. Paper no. TSG-01301-2016IEEE Computer SocietyCiencias de la Computación e Inteligencia ArtificialTIC193: Computación NaturalNational Natural Science Foundation of ChinaChunhui Project Foundation of the Education Department of ChinaResearch Foundation of the Education Department of Sichuan Province, China2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/116055https://doi.org/10.1109/TSG.2017.2670602reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Smart Grid, 9 (5), 4777-4784.61472328Z2016143Z201614817TD0034. Paper no. TSG-01301-2016https://ieeexplore.ieee.org/abstract/document/7857789info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1160552026-06-17T12:51:07Z
dc.title.none.fl_str_mv Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
title Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
spellingShingle Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
Peng, Hong
Power systems
Fault diagnosis
Spiking neural P Systems
Intuitionistic fuzzy set
title_short Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
title_full Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
title_fullStr Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
title_full_unstemmed Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
title_sort Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems
dc.creator.none.fl_str_mv Peng, Hong
Wang, Jun
Ming, Jun
Shi, Peng
Pérez Jiménez, Mario de Jesús
Yu, Wenping
Tao, Chengyu
author Peng, Hong
author_facet Peng, Hong
Wang, Jun
Ming, Jun
Shi, Peng
Pérez Jiménez, Mario de Jesús
Yu, Wenping
Tao, Chengyu
author_role author
author2 Wang, Jun
Ming, Jun
Shi, Peng
Pérez Jiménez, Mario de Jesús
Yu, Wenping
Tao, Chengyu
author2_role author
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
Chunhui Project Foundation of the Education Department of China
Research Foundation of the Education Department of Sichuan Province, China
dc.subject.none.fl_str_mv Power systems
Fault diagnosis
Spiking neural P Systems
Intuitionistic fuzzy set
topic Power systems
Fault diagnosis
Spiking neural P Systems
Intuitionistic fuzzy set
description In this paper, intuitionistic fuzzy spiking neural P (IFSNP) systems as a variant are proposed by integrating intuitionistic fuzzy logic into original spiking neural P systems. Compared with a common fuzzy set, intuitionistic fuzzy set can more finely describe the uncertainty due to its membership and non-membership degrees. Therefore, IFSNP systems are very suitable to deal with fault diagnosis of power systems, specially with incomplete and uncertain alarm messages. The fault modeling method and fuzzy reasoning algorithm based on IFSNP systems are discussed. Two examples are used to demonstrate the availability and effectiveness of IFSNP systems for fault diagnosis of power systems. Case studies involve single fault, complex fault, and multiple faults with protection device failures and incorrect tripping signals.
publishDate 2018
dc.date.none.fl_str_mv 2018
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/11441/116055
https://doi.org/10.1109/TSG.2017.2670602
url https://hdl.handle.net/11441/116055
https://doi.org/10.1109/TSG.2017.2670602
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Smart Grid, 9 (5), 4777-4784.
61472328
Z2016143
Z2016148
17TD0034. Paper no. TSG-01301-2016
https://ieeexplore.ieee.org/abstract/document/7857789
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 15,300719

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