Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/PMAPS53380.2022.9810640 http://hdl.handle.net/11449/242090 |
Resumo: | Currently, electric utilities have improved their operational performance using automated switches (ASWs). These devices make it possible to transfer loads remotely, restoring energy supply to the greatest number of consumers. However, utilities do not have precise criteria to define the number of ASWs and their respective locations. This paper proposes a binary particle swarm optimization (BPSO) to explore ASW allocation alternatives seeking the best solution that minimizes the system Mean Interruption Frequency Index (SAIFI), System Mean Interruption Duration Index (SAIDI) and Unsupplied Energy (ENS). A probabilistic methodology based on Monte Carlo Simulation is proposed to estimate SAIFI, SAIDI and ENS. An analysis of the BPSO parameters, such as inertia weight (w) and acceleration coefficients (c1, c2) is performed to select values suitable for application in electrical networks. The results show that the parameters c1 and c2 between 3.0 and 3.3 and w = 1 improve the behavior of the BPSO. |
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Repositório Institucional da UNESP |
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Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO ParametersAutomated switch placementdistribution systemparticle swarm optimizationservice restorationCurrently, electric utilities have improved their operational performance using automated switches (ASWs). These devices make it possible to transfer loads remotely, restoring energy supply to the greatest number of consumers. However, utilities do not have precise criteria to define the number of ASWs and their respective locations. This paper proposes a binary particle swarm optimization (BPSO) to explore ASW allocation alternatives seeking the best solution that minimizes the system Mean Interruption Frequency Index (SAIFI), System Mean Interruption Duration Index (SAIDI) and Unsupplied Energy (ENS). A probabilistic methodology based on Monte Carlo Simulation is proposed to estimate SAIFI, SAIDI and ENS. An analysis of the BPSO parameters, such as inertia weight (w) and acceleration coefficients (c1, c2) is performed to select values suitable for application in electrical networks. The results show that the parameters c1 and c2 between 3.0 and 3.3 and w = 1 improve the behavior of the BPSO.São Paulo State University (UNESP) Institute of Science and TechnologySão Paulo State University (UNESP) Institute of Science and TechnologyUniversidade Estadual Paulista (UNESP)Carneiro, Carlos Alberto [UNESP]Rossi, Andre Luis Debiaso [UNESP]Cebrian, Juan Carlos [UNESP]Morales-Paredes, Helmo Kelis [UNESP]2023-03-02T08:38:09Z2023-03-02T08:38:09Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/PMAPS53380.2022.98106402022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022.http://hdl.handle.net/11449/24209010.1109/PMAPS53380.2022.98106402-s2.0-85135058555Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022info:eu-repo/semantics/openAccess2023-03-02T08:38:09Zoai:repositorio.unesp.br:11449/242090Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:26:38.422443Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
title |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
spellingShingle |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters Carneiro, Carlos Alberto [UNESP] Automated switch placement distribution system particle swarm optimization service restoration |
title_short |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
title_full |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
title_fullStr |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
title_full_unstemmed |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
title_sort |
Automated Switch Placement in Power Distribution Systems: Analysis of Binary PSO Parameters |
author |
Carneiro, Carlos Alberto [UNESP] |
author_facet |
Carneiro, Carlos Alberto [UNESP] Rossi, Andre Luis Debiaso [UNESP] Cebrian, Juan Carlos [UNESP] Morales-Paredes, Helmo Kelis [UNESP] |
author_role |
author |
author2 |
Rossi, Andre Luis Debiaso [UNESP] Cebrian, Juan Carlos [UNESP] Morales-Paredes, Helmo Kelis [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Carneiro, Carlos Alberto [UNESP] Rossi, Andre Luis Debiaso [UNESP] Cebrian, Juan Carlos [UNESP] Morales-Paredes, Helmo Kelis [UNESP] |
dc.subject.por.fl_str_mv |
Automated switch placement distribution system particle swarm optimization service restoration |
topic |
Automated switch placement distribution system particle swarm optimization service restoration |
description |
Currently, electric utilities have improved their operational performance using automated switches (ASWs). These devices make it possible to transfer loads remotely, restoring energy supply to the greatest number of consumers. However, utilities do not have precise criteria to define the number of ASWs and their respective locations. This paper proposes a binary particle swarm optimization (BPSO) to explore ASW allocation alternatives seeking the best solution that minimizes the system Mean Interruption Frequency Index (SAIFI), System Mean Interruption Duration Index (SAIDI) and Unsupplied Energy (ENS). A probabilistic methodology based on Monte Carlo Simulation is proposed to estimate SAIFI, SAIDI and ENS. An analysis of the BPSO parameters, such as inertia weight (w) and acceleration coefficients (c1, c2) is performed to select values suitable for application in electrical networks. The results show that the parameters c1 and c2 between 3.0 and 3.3 and w = 1 improve the behavior of the BPSO. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-02T08:38:09Z 2023-03-02T08:38:09Z |
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/PMAPS53380.2022.9810640 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022. http://hdl.handle.net/11449/242090 10.1109/PMAPS53380.2022.9810640 2-s2.0-85135058555 |
url |
http://dx.doi.org/10.1109/PMAPS53380.2022.9810640 http://hdl.handle.net/11449/242090 |
identifier_str_mv |
2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022. 10.1109/PMAPS53380.2022.9810640 2-s2.0-85135058555 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022 |
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_ |
1808129069869432832 |