Multi-objective grounding system optimisation using NSGA-II

This study investigates the optimisation of grounding infrastructure in substations by implementing the philosophy of the multi-objective algorithm NSGA-II Elite. A complete description of the operating scheme and the characteristic mechanisms that support the behaviour and development of optimal Pa...

Full description

Autores:
Lucero Tenorio, Miriam
Valcárcel Rojas, Angel C.
Tipo de recurso:
Article of journal
Fecha de publicación:
2024
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/13544
Acceso en línea:
https://doi.org/10.32397/tesea.vol5.n2.616
Palabra clave:
Step voltage
Touch voltage
Algorithm II (NSGA-II)
Grounding system
non-dominated Sorting Genetic
Optimization
Rights
openAccess
License
Miriam Lucero Tenorio, Angel C. Valcárcel Rojas - 2024
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oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/13544
network_acronym_str UTB2
network_name_str Repositorio Institucional UTB
repository_id_str
spelling Lucero Tenorio, MiriamValcárcel Rojas, Angel C.2024-12-24 00:00:002024-12-24 00:00:002024-12-24This study investigates the optimisation of grounding infrastructure in substations by implementing the philosophy of the multi-objective algorithm NSGA-II Elite. A complete description of the operating scheme and the characteristic mechanisms that support the behaviour and development of optimal Pareto solutions is provided. A detailed comparison was made with the optimisation method used in the GMAT program of Aplicaciones Tecnológicas, based on a semi-optimization process derived from the correlation of semi-precision optimisation solutions. The results show that multi-objective optimisation using NSGA-II results in a significant cost reduction compared to the semi-optimization method, although the computational time required to reach the final solution increases significantly. This approach allows a more adequate understanding of optimising the terrestrial substation grid. It highlights its ability to generate more cost-effective and performance-efficient solutions by carefully considering the computing time required.application/pdfengUniversidad Tecnológica de BolívarMiriam Lucero Tenorio, Angel C. Valcárcel Rojas - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/616Step voltageTouch voltageAlgorithm II (NSGA-II)Grounding systemnon-dominated Sorting GeneticOptimizationMulti-objective grounding system optimisation using NSGA-IIMulti-objective grounding system optimisation using NSGA-IIArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://doi.org/10.32397/tesea.vol5.n2.61610.32397/tesea.vol5.n2.6162745-0120Ramón Alfonso Gallego, Antonio Escobar, and Rubén Romero. Técnicas de optimización combinatorial. Universidad Tecnológica de Pereira, pages 19–77, 2006. [2] E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999. [3] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002. [4] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T Meyarivan. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, page 849–858. Springer Berlin Heidelberg, 2000. [5] L.E. Schrage. Optimization Modeling with LINDO. Duxbury Press, 1997.Transactions on Energy Systems and Engineering Applications5114https://revistas.utb.edu.co/tesea/article/download/616/421Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications220.500.12585/13544oai:repositorio.utb.edu.co:20.500.12585/135442025-09-16 09:15:14.599https://creativecommons.org/licenses/by/4.0Miriam Lucero Tenorio, Angel C. Valcárcel Rojas - 2024metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com
dc.title.spa.fl_str_mv Multi-objective grounding system optimisation using NSGA-II
dc.title.translated.spa.fl_str_mv Multi-objective grounding system optimisation using NSGA-II
title Multi-objective grounding system optimisation using NSGA-II
spellingShingle Multi-objective grounding system optimisation using NSGA-II
Step voltage
Touch voltage
Algorithm II (NSGA-II)
Grounding system
non-dominated Sorting Genetic
Optimization
title_short Multi-objective grounding system optimisation using NSGA-II
title_full Multi-objective grounding system optimisation using NSGA-II
title_fullStr Multi-objective grounding system optimisation using NSGA-II
title_full_unstemmed Multi-objective grounding system optimisation using NSGA-II
title_sort Multi-objective grounding system optimisation using NSGA-II
dc.creator.fl_str_mv Lucero Tenorio, Miriam
Valcárcel Rojas, Angel C.
dc.contributor.author.eng.fl_str_mv Lucero Tenorio, Miriam
Valcárcel Rojas, Angel C.
dc.subject.eng.fl_str_mv Step voltage
Touch voltage
Algorithm II (NSGA-II)
Grounding system
non-dominated Sorting Genetic
Optimization
topic Step voltage
Touch voltage
Algorithm II (NSGA-II)
Grounding system
non-dominated Sorting Genetic
Optimization
description This study investigates the optimisation of grounding infrastructure in substations by implementing the philosophy of the multi-objective algorithm NSGA-II Elite. A complete description of the operating scheme and the characteristic mechanisms that support the behaviour and development of optimal Pareto solutions is provided. A detailed comparison was made with the optimisation method used in the GMAT program of Aplicaciones Tecnológicas, based on a semi-optimization process derived from the correlation of semi-precision optimisation solutions. The results show that multi-objective optimisation using NSGA-II results in a significant cost reduction compared to the semi-optimization method, although the computational time required to reach the final solution increases significantly. This approach allows a more adequate understanding of optimising the terrestrial substation grid. It highlights its ability to generate more cost-effective and performance-efficient solutions by carefully considering the computing time required.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-24 00:00:00
dc.date.available.none.fl_str_mv 2024-12-24 00:00:00
dc.date.issued.none.fl_str_mv 2024-12-24
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.local.eng.fl_str_mv Journal article
dc.type.content.eng.fl_str_mv Text
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.eng.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.url.none.fl_str_mv https://doi.org/10.32397/tesea.vol5.n2.616
dc.identifier.doi.none.fl_str_mv 10.32397/tesea.vol5.n2.616
dc.identifier.eissn.none.fl_str_mv 2745-0120
url https://doi.org/10.32397/tesea.vol5.n2.616
identifier_str_mv 10.32397/tesea.vol5.n2.616
2745-0120
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.references.eng.fl_str_mv Ramón Alfonso Gallego, Antonio Escobar, and Rubén Romero. Técnicas de optimización combinatorial. Universidad Tecnológica de Pereira, pages 19–77, 2006. [2] E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999. [3] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002. [4] Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T Meyarivan. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, page 849–858. Springer Berlin Heidelberg, 2000. [5] L.E. Schrage. Optimization Modeling with LINDO. Duxbury Press, 1997.
dc.relation.ispartofjournal.eng.fl_str_mv Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv 5
dc.relation.citationstartpage.none.fl_str_mv 1
dc.relation.citationendpage.none.fl_str_mv 14
dc.relation.bitstream.none.fl_str_mv https://revistas.utb.edu.co/tesea/article/download/616/421
dc.relation.citationedition.eng.fl_str_mv Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv 2
dc.rights.eng.fl_str_mv Miriam Lucero Tenorio, Angel C. Valcárcel Rojas - 2024
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Miriam Lucero Tenorio, Angel C. Valcárcel Rojas - 2024
https://creativecommons.org/licenses/by/4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.eng.fl_str_mv application/pdf
dc.publisher.eng.fl_str_mv Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv https://revistas.utb.edu.co/tesea/article/view/616
institution Universidad Tecnológica de Bolívar
repository.name.fl_str_mv Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv bdigital@metabiblioteca.com
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