Fault section estimation in power systems using an Adaptive Genetic Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/PESGM.2016.7741394 http://hdl.handle.net/11449/228253 |
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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Repositório Institucional da UNESP |
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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.Department of Electrical Engineering FEIS São Paulo State UniversityDepartment of Electrical Engineering FEIS São Paulo State UniversityUniversidade Estadual Paulista (UNESP)Escoto, Esaú Figueroa [UNESP]Leão, Fábio Bertequini [UNESP]2022-04-29T07:58:31Z2022-04-29T07:58:31Z2016-11-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PESGM.2016.7741394IEEE Power and Energy Society General Meeting, v. 2016-November.1944-99331944-9925http://hdl.handle.net/11449/22825310.1109/PESGM.2016.77413942-s2.0-85002488034Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Power and Energy Society General Meetinginfo:eu-repo/semantics/openAccess2022-04-29T07:58:31Zoai:repositorio.unesp.br:11449/228253Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T11:21:55.698291Repositório 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, Esaú 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, Esaú Figueroa [UNESP] |
author_facet |
Escoto, Esaú Figueroa [UNESP] Leão, Fábio Bertequini [UNESP] |
author_role |
author |
author2 |
Leão, Fábio Bertequini [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Escoto, Esaú Figueroa [UNESP] Leão, Fábio Bertequini [UNESP] |
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 |
2016-11-10 2022-04-29T07:58:31Z 2022-04-29T07:58:31Z |
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 |
http://dx.doi.org/10.1109/PESGM.2016.7741394 IEEE Power and Energy Society General Meeting, v. 2016-November. 1944-9933 1944-9925 http://hdl.handle.net/11449/228253 10.1109/PESGM.2016.7741394 2-s2.0-85002488034 |
url |
http://dx.doi.org/10.1109/PESGM.2016.7741394 http://hdl.handle.net/11449/228253 |
identifier_str_mv |
IEEE Power and Energy Society General Meeting, v. 2016-November. 1944-9933 1944-9925 10.1109/PESGM.2016.7741394 2-s2.0-85002488034 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Power and Energy Society General Meeting |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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 |
|
_version_ |
1803045809557602304 |