Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)

Descripción del Articulo

This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tange...

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Detalles Bibliográficos
Autores: Puma, David W., Atoccsa, Brayan A., Molina, Y. P., Luyo, J. E., Ñaupari, Zocimo
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Tecnológica del Perú
Repositorio:UTP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.utp.edu.pe:20.500.12867/14086
Enlace del recurso:https://hdl.handle.net/20.500.12867/14086
Nivel de acceso:acceso abierto
Materia:Distribution network reconfiguration
Optimization
Power losses
Delta optimized value
https://purl.org/pe-repo/ocde/ford#2.02.01
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dc.title.es_PE.fl_str_mv Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
title Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
spellingShingle Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
Puma, David W.
Distribution network reconfiguration
Optimization
Power losses
Delta optimized value
https://purl.org/pe-repo/ocde/ford#2.02.01
title_short Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
title_full Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
title_fullStr Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
title_full_unstemmed Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
title_sort Distribution network reconfiguration optimization using a new algorithm hyperbolic tangent particle swarm optimization (HT-PSO)
author Puma, David W.
author_facet Puma, David W.
Atoccsa, Brayan A.
Molina, Y. P.
Luyo, J. E.
Ñaupari, Zocimo
author_role author
author2 Atoccsa, Brayan A.
Molina, Y. P.
Luyo, J. E.
Ñaupari, Zocimo
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Puma, David W.
Atoccsa, Brayan A.
Molina, Y. P.
Luyo, J. E.
Ñaupari, Zocimo
dc.subject.es_PE.fl_str_mv Distribution network reconfiguration
Optimization
Power losses
Delta optimized value
topic Distribution network reconfiguration
Optimization
Power losses
Delta optimized value
https://purl.org/pe-repo/ocde/ford#2.02.01
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.01
description This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ, into the HT-PSO, enhancing the curve’s adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. Comparative simulations conducted on 33- and 94-bus systems show an improvement in convergence, demonstrating a more exhaustive exploration of the search space than existing methods documented in the literature based on PSO and variations where functions are proposed for the rate of change of values.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2025-10-28T19:32:30Z
dc.date.available.none.fl_str_mv 2025-10-28T19:32:30Z
dc.date.issued.fl_str_mv 2024
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dc.identifier.issn.none.fl_str_mv 1996-1073
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12867/14086
dc.identifier.journal.es_PE.fl_str_mv Energies
dc.identifier.doi.none.fl_str_mv doi.org/10.3390/en17153798
identifier_str_mv 1996-1073
Energies
doi.org/10.3390/en17153798
url https://hdl.handle.net/20.500.12867/14086
dc.language.iso.es_PE.fl_str_mv eng
language eng
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dc.publisher.es_PE.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.es_PE.fl_str_mv Repositorio Institucional - UTP
Universidad Tecnológica del Perú
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spelling Puma, David W.Atoccsa, Brayan A.Molina, Y. P.Luyo, J. E.Ñaupari, Zocimo2025-10-28T19:32:30Z2025-10-28T19:32:30Z20241996-1073https://hdl.handle.net/20.500.12867/14086Energiesdoi.org/10.3390/en17153798This paper introduces an innovative approach to address the distribution network reconfiguration (DNR) challenge, aiming to reduce power loss through an advanced hyperbolic tangent particle swarm optimization (HT-PSO) method. This approach is distinguished by the adoption of a novel hyperbolic tangent function, which effectively limits the rate of change values, offering a significant improvement over traditional sigmoid function-based methods. A key feature of this new approach is the integration of a tunable parameter, δ, into the HT-PSO, enhancing the curve’s adaptability. The careful optimization of δ ensures superior control over the rate of change across the entire operational range. This enhanced control mechanism substantially improves the efficiency of the search and convergence processes in DNR. 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