Genetic algorithm and particle swarm applied in electric system optimization

Detalhes bibliográficos
Autor(a) principal: Costa, Heictor Alves de Oliveira
Data de Publicação: 2021
Outros Autores: Gomes, Larissa Luz, Costa, Denis Carlos Lima
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/18871
Resumo: This paper aims to present and run a composite model using Genetic Algorithm (GA) and Particle Swarm (PSO), with the assistance of parallel computing methods, to optimize the electrical distribution in a power grid based on an IEEE 14-bus system. The mathematical-computational modeling allows using the objective function to analyze the cost in relation to power or voltage as independent variables, and it is the bridge for the connection between the 2 implemented algorithms. The results presented in this article demonstrate that the methodology was implemented splendidly, in addition to obtaining an excellent computational cost and complying with the physical restrictions of network security, it also achieved global solutions in its optimization.
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spelling Genetic algorithm and particle swarm applied in electric system optimization Algoritmo genético y enjambre de partículas aplicado en la optimización del sistema eléctrico Algoritmo genético e enxame de partículas aplicados na otimização de sistema de elétrico Algoritmo GenéticoEnxame de PartículasComputação ParalelaOtimizaçãoRede elétrica.Genetic AlgorithmParticle SwarmParallel ComputingOptimizationElectrical network.Algoritmo genéticoComputación ParalelaMejoramientoRed eléctricaRed eléctrica.This paper aims to present and run a composite model using Genetic Algorithm (GA) and Particle Swarm (PSO), with the assistance of parallel computing methods, to optimize the electrical distribution in a power grid based on an IEEE 14-bus system. The mathematical-computational modeling allows using the objective function to analyze the cost in relation to power or voltage as independent variables, and it is the bridge for the connection between the 2 implemented algorithms. The results presented in this article demonstrate that the methodology was implemented splendidly, in addition to obtaining an excellent computational cost and complying with the physical restrictions of network security, it also achieved global solutions in its optimization.Este artículo tiene como objetivo presentar y ejecutar un modelo compuesto utilizando Algoritmo Genético (AG) y Enjambre de Partículas (PSO), con la ayuda de métodos de computación en paralelo, para optimizar la distribución eléctrica en una red eléctrica basada en un sistema IEEE de 14 buses. El modelado matemático-computacional permite utilizar la función objetivo para el análisis de costos en relación con la potencia o voltaje como una variable independiente, y es el puente para la conexión entre los 2 algoritmos implementados. Los resultados presentados en este artículo demuestran que la metodología se implementó de manera espléndida, además de obtener un excelente costo computacional y cumplir con las limitaciones físicas de la seguridad de la red, también logró soluciones globales en su optimización.Este artigo almeja apresentar e executar um modelo composto utilizando Algoritmo Genético (AG) e Enxame de Partículas (PSO), com auxílio de métodos da computação paralela, para otimizar a distribuição elétrica em uma rede energética baseada em um Sistema IEEE de 14 barras. A modelagem matemática-computacional permite utilizar a função objetivo para análise do custo em relação à potência ou tensão como variável independente, e é a ponte para a conexão entre os 2 algoritmos  implementados. Os resultados apresentados neste artigo demonstram que a metodologia foi implementada de forma esplêndida, além de obter excelente custo computacional e obedecer às restrições físicas de segurança da rede, também alcançou soluções globais em sua otimização.Research, Society and Development2021-08-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1887110.33448/rsd-v10i10.18871Research, Society and Development; Vol. 10 No. 10; e166101018871Research, Society and Development; Vol. 10 Núm. 10; e166101018871Research, Society and Development; v. 10 n. 10; e1661010188712525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/18871/16705Copyright (c) 2021 Heictor Alves de Oliveira Costa; Larissa Luz Gomes; Denis Carlos Lima Costahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCosta, Heictor Alves de OliveiraGomes, Larissa LuzCosta, Denis Carlos Lima2021-10-02T21:49:16Zoai:ojs.pkp.sfu.ca:article/18871Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:38:54.147055Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Genetic algorithm and particle swarm applied in electric system optimization
Algoritmo genético y enjambre de partículas aplicado en la optimización del sistema eléctrico
Algoritmo genético e enxame de partículas aplicados na otimização de sistema de elétrico
title Genetic algorithm and particle swarm applied in electric system optimization
spellingShingle Genetic algorithm and particle swarm applied in electric system optimization
Costa, Heictor Alves de Oliveira
Algoritmo Genético
Enxame de Partículas
Computação Paralela
Otimização
Rede elétrica.
Genetic Algorithm
Particle Swarm
Parallel Computing
Optimization
Electrical network.
Algoritmo genético
Computación Paralela
Mejoramiento
Red eléctrica
Red eléctrica.
title_short Genetic algorithm and particle swarm applied in electric system optimization
title_full Genetic algorithm and particle swarm applied in electric system optimization
title_fullStr Genetic algorithm and particle swarm applied in electric system optimization
title_full_unstemmed Genetic algorithm and particle swarm applied in electric system optimization
title_sort Genetic algorithm and particle swarm applied in electric system optimization
author Costa, Heictor Alves de Oliveira
author_facet Costa, Heictor Alves de Oliveira
Gomes, Larissa Luz
Costa, Denis Carlos Lima
author_role author
author2 Gomes, Larissa Luz
Costa, Denis Carlos Lima
author2_role author
author
dc.contributor.author.fl_str_mv Costa, Heictor Alves de Oliveira
Gomes, Larissa Luz
Costa, Denis Carlos Lima
dc.subject.por.fl_str_mv Algoritmo Genético
Enxame de Partículas
Computação Paralela
Otimização
Rede elétrica.
Genetic Algorithm
Particle Swarm
Parallel Computing
Optimization
Electrical network.
Algoritmo genético
Computación Paralela
Mejoramiento
Red eléctrica
Red eléctrica.
topic Algoritmo Genético
Enxame de Partículas
Computação Paralela
Otimização
Rede elétrica.
Genetic Algorithm
Particle Swarm
Parallel Computing
Optimization
Electrical network.
Algoritmo genético
Computación Paralela
Mejoramiento
Red eléctrica
Red eléctrica.
description This paper aims to present and run a composite model using Genetic Algorithm (GA) and Particle Swarm (PSO), with the assistance of parallel computing methods, to optimize the electrical distribution in a power grid based on an IEEE 14-bus system. The mathematical-computational modeling allows using the objective function to analyze the cost in relation to power or voltage as independent variables, and it is the bridge for the connection between the 2 implemented algorithms. The results presented in this article demonstrate that the methodology was implemented splendidly, in addition to obtaining an excellent computational cost and complying with the physical restrictions of network security, it also achieved global solutions in its optimization.
publishDate 2021
dc.date.none.fl_str_mv 2021-08-07
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/18871
10.33448/rsd-v10i10.18871
url https://rsdjournal.org/index.php/rsd/article/view/18871
identifier_str_mv 10.33448/rsd-v10i10.18871
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/18871/16705
dc.rights.driver.fl_str_mv Copyright (c) 2021 Heictor Alves de Oliveira Costa; Larissa Luz Gomes; Denis Carlos Lima Costa
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Heictor Alves de Oliveira Costa; Larissa Luz Gomes; Denis Carlos Lima Costa
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 10 No. 10; e166101018871
Research, Society and Development; Vol. 10 Núm. 10; e166101018871
Research, Society and Development; v. 10 n. 10; e166101018871
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
_version_ 1797052753685512192

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