Optimal placement of fault indicators using adaptive genetic algorithm

Detalhes bibliográficos
Autor(a) principal: Cruz, Hector Orellana [UNESP]
Data de Publicação: 2018
Outros Autores: Bertequini Leao, Fabio [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PESGM.2017.8273897
http://hdl.handle.net/11449/228540
Resumo: This work proposes the Adaptive Genetic Algorithm (AGA) to solve the problem of Fault Indicator (FI) placement in electric distribution systems to improve customer service quality. The AGA is developed to obtain the best configuration for the placement of FIs in the system reducing the annual cost of energy not supplied (CENS) and the annual FI placement investment cost (CINV). The AGA uses dynamically calibrated crossover and mutation rates based on the diversity of each population in the generation. The algorithm is tested using three electric distribution systems and the results shown that AGA is efficient, robust and adequate to placement of FI for improving the service quality in electric distribution systems.
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spelling Optimal placement of fault indicators using adaptive genetic algorithmAdaptive genetic algorithmElectric distribution systemsFault indicatorsService qualityThis work proposes the Adaptive Genetic Algorithm (AGA) to solve the problem of Fault Indicator (FI) placement in electric distribution systems to improve customer service quality. The AGA is developed to obtain the best configuration for the placement of FIs in the system reducing the annual cost of energy not supplied (CENS) and the annual FI placement investment cost (CINV). The AGA uses dynamically calibrated crossover and mutation rates based on the diversity of each population in the generation. The algorithm is tested using three electric distribution systems and the results shown that AGA is efficient, robust and adequate to placement of FI for improving the service quality in electric distribution systems.Department of Electrical Engineering FEIS Sao Paulo State University IlhaDepartment of Electrical Engineering FEIS Sao Paulo State University IlhaUniversidade Estadual Paulista (UNESP)Cruz, Hector Orellana [UNESP]Bertequini Leao, Fabio [UNESP]2022-04-29T08:27:19Z2022-04-29T08:27:19Z2018-01-29info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1-5http://dx.doi.org/10.1109/PESGM.2017.8273897IEEE Power and Energy Society General Meeting, v. 2018-January, p. 1-5.1944-99331944-9925http://hdl.handle.net/11449/22854010.1109/PESGM.2017.82738972-s2.0-85046349375Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Power and Energy Society General Meetinginfo:eu-repo/semantics/openAccess2022-04-29T08:27:19Zoai:repositorio.unesp.br:11449/228540Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T08:27:19Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Optimal placement of fault indicators using adaptive genetic algorithm
title Optimal placement of fault indicators using adaptive genetic algorithm
spellingShingle Optimal placement of fault indicators using adaptive genetic algorithm
Cruz, Hector Orellana [UNESP]
Adaptive genetic algorithm
Electric distribution systems
Fault indicators
Service quality
title_short Optimal placement of fault indicators using adaptive genetic algorithm
title_full Optimal placement of fault indicators using adaptive genetic algorithm
title_fullStr Optimal placement of fault indicators using adaptive genetic algorithm
title_full_unstemmed Optimal placement of fault indicators using adaptive genetic algorithm
title_sort Optimal placement of fault indicators using adaptive genetic algorithm
author Cruz, Hector Orellana [UNESP]
author_facet Cruz, Hector Orellana [UNESP]
Bertequini Leao, Fabio [UNESP]
author_role author
author2 Bertequini Leao, Fabio [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Cruz, Hector Orellana [UNESP]
Bertequini Leao, Fabio [UNESP]
dc.subject.por.fl_str_mv Adaptive genetic algorithm
Electric distribution systems
Fault indicators
Service quality
topic Adaptive genetic algorithm
Electric distribution systems
Fault indicators
Service quality
description This work proposes the Adaptive Genetic Algorithm (AGA) to solve the problem of Fault Indicator (FI) placement in electric distribution systems to improve customer service quality. The AGA is developed to obtain the best configuration for the placement of FIs in the system reducing the annual cost of energy not supplied (CENS) and the annual FI placement investment cost (CINV). The AGA uses dynamically calibrated crossover and mutation rates based on the diversity of each population in the generation. The algorithm is tested using three electric distribution systems and the results shown that AGA is efficient, robust and adequate to placement of FI for improving the service quality in electric distribution systems.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-29
2022-04-29T08:27:19Z
2022-04-29T08:27:19Z
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.2017.8273897
IEEE Power and Energy Society General Meeting, v. 2018-January, p. 1-5.
1944-9933
1944-9925
http://hdl.handle.net/11449/228540
10.1109/PESGM.2017.8273897
2-s2.0-85046349375
url http://dx.doi.org/10.1109/PESGM.2017.8273897
http://hdl.handle.net/11449/228540
identifier_str_mv IEEE Power and Energy Society General Meeting, v. 2018-January, p. 1-5.
1944-9933
1944-9925
10.1109/PESGM.2017.8273897
2-s2.0-85046349375
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.format.none.fl_str_mv 1-5
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_ 1803047310179958784

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