Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm

Detalhes bibliográficos
Autor(a) principal: Escoto, Esau Figueroa [UNESP]
Data de Publicação: 2016
Outros Autores: Leao, Fabio Bertequini [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/162740
Resumo: This paper proposes a methodology based on the unconstrained binary programming (UBP) model and an Adaptive Genetic Algorithm (AGA) to solve the fault section estimation problem in power systems. The UBP model is formulated using the parsimonious set covering theory for associating the alarms of the protective relay functions informed by the SCADA (supervisory control and data acquisition) system and the expected states of the protective relay functions. The proposed AGA uses only two control parameters and it has automatic and dynamically calibrated recombination and mutation rates based on the saturation of the current population, having an immediate response to possible premature convergence to local optima. Test results for a part of South-Brazilian electric power system have shown that AGA presents robustness, efficiency and less processing time compared with others methods previously published.
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spelling Fault Section Estimation in Power Systems Using an Adaptive Genetic AlgorithmAdaptive Algorithm GeneticFault Section EstimationPower System ProtectionProtective RelayingThis paper proposes a methodology based on the unconstrained binary programming (UBP) model and an Adaptive Genetic Algorithm (AGA) to solve the fault section estimation problem in power systems. The UBP model is formulated using the parsimonious set covering theory for associating the alarms of the protective relay functions informed by the SCADA (supervisory control and data acquisition) system and the expected states of the protective relay functions. The proposed AGA uses only two control parameters and it has automatic and dynamically calibrated recombination and mutation rates based on the saturation of the current population, having an immediate response to possible premature convergence to local optima. Test results for a part of South-Brazilian electric power system have shown that AGA presents robustness, efficiency and less processing time compared with others methods previously published.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Sao Paulo State Univ, FEIS, Dept Elect Engn, Ilha Solteira, BrazilSao Paulo State Univ, FEIS, Dept Elect Engn, Ilha Solteira, BrazilIeeeUniversidade Estadual Paulista (Unesp)Escoto, Esau Figueroa [UNESP]Leao, Fabio Bertequini [UNESP]IEEE2018-11-26T17:29:44Z2018-11-26T17:29:44Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject52016 Ieee Power And Energy Society General Meeting (pesgm). New York: Ieee, 5 p., 2016.1944-9925http://hdl.handle.net/11449/162740WOS:000399937901042Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2016 Ieee Power And Energy Society General Meeting (pesgm)info:eu-repo/semantics/openAccess2021-10-23T21:44:23Zoai:repositorio.unesp.br:11449/162740Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:44:23Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
title Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
spellingShingle Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
Escoto, Esau Figueroa [UNESP]
Adaptive Algorithm Genetic
Fault Section Estimation
Power System Protection
Protective Relaying
title_short Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
title_full Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
title_fullStr Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
title_full_unstemmed Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
title_sort Fault Section Estimation in Power Systems Using an Adaptive Genetic Algorithm
author Escoto, Esau Figueroa [UNESP]
author_facet Escoto, Esau Figueroa [UNESP]
Leao, Fabio Bertequini [UNESP]
IEEE
author_role author
author2 Leao, Fabio Bertequini [UNESP]
IEEE
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Escoto, Esau Figueroa [UNESP]
Leao, Fabio Bertequini [UNESP]
IEEE
dc.subject.por.fl_str_mv Adaptive Algorithm Genetic
Fault Section Estimation
Power System Protection
Protective Relaying
topic Adaptive Algorithm Genetic
Fault Section Estimation
Power System Protection
Protective Relaying
description This paper proposes a methodology based on the unconstrained binary programming (UBP) model and an Adaptive Genetic Algorithm (AGA) to solve the fault section estimation problem in power systems. The UBP model is formulated using the parsimonious set covering theory for associating the alarms of the protective relay functions informed by the SCADA (supervisory control and data acquisition) system and the expected states of the protective relay functions. The proposed AGA uses only two control parameters and it has automatic and dynamically calibrated recombination and mutation rates based on the saturation of the current population, having an immediate response to possible premature convergence to local optima. Test results for a part of South-Brazilian electric power system have shown that AGA presents robustness, efficiency and less processing time compared with others methods previously published.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
2018-11-26T17:29:44Z
2018-11-26T17:29:44Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv 2016 Ieee Power And Energy Society General Meeting (pesgm). New York: Ieee, 5 p., 2016.
1944-9925
http://hdl.handle.net/11449/162740
WOS:000399937901042
identifier_str_mv 2016 Ieee Power And Energy Society General Meeting (pesgm). New York: Ieee, 5 p., 2016.
1944-9925
WOS:000399937901042
url http://hdl.handle.net/11449/162740
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2016 Ieee Power And Energy Society General Meeting (pesgm)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 5
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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