GA and PSO applied towind energy optimization

Autores
Alba, Enrique; Bilbao, Martín
Año de publicación
2009
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs.
Presentado en el X Workshop Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
CHC
geometric particle swarm optimization
Energía Eólica
Optimization
Heuristic methods
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/20883

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network_name_str SEDICI (UNLP)
spelling GA and PSO applied towind energy optimizationAlba, EnriqueBilbao, MartínCiencias InformáticasCHCgeometric particle swarm optimizationEnergía EólicaOptimizationHeuristic methodsIn this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI)2009-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf50-59http://sedici.unlp.edu.ar/handle/10915/20883enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:27:14Zoai:sedici.unlp.edu.ar:10915/20883Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:15.16SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv GA and PSO applied towind energy optimization
title GA and PSO applied towind energy optimization
spellingShingle GA and PSO applied towind energy optimization
Alba, Enrique
Ciencias Informáticas
CHC
geometric particle swarm optimization
Energía Eólica
Optimization
Heuristic methods
title_short GA and PSO applied towind energy optimization
title_full GA and PSO applied towind energy optimization
title_fullStr GA and PSO applied towind energy optimization
title_full_unstemmed GA and PSO applied towind energy optimization
title_sort GA and PSO applied towind energy optimization
dc.creator.none.fl_str_mv Alba, Enrique
Bilbao, Martín
author Alba, Enrique
author_facet Alba, Enrique
Bilbao, Martín
author_role author
author2 Bilbao, Martín
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
CHC
geometric particle swarm optimization
Energía Eólica
Optimization
Heuristic methods
topic Ciencias Informáticas
CHC
geometric particle swarm optimization
Energía Eólica
Optimization
Heuristic methods
dc.description.none.fl_txt_mv In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs.
Presentado en el X Workshop Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description In this article we analyze two kinds of metaheuristic algorithms applied to wind farm optimization. The basic idea is to utilize CHC (a sort of GA) and GPSO (a sort of PSO) algorithms to obtain an acceptable configuration of wind turbines in the wind farm that maximizes the total output energy and minimize the number of wind turbines used. The energy produced depends of the farm geometry, wind conditions and the terrain where it is settled. In this work we will analyze three study farm scenarios with different wind speeds and we will apply both algorithms to analyze the performance of the algorithms and the behavior of the computed wind farm designs.
publishDate 2009
dc.date.none.fl_str_mv 2009-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/20883
url http://sedici.unlp.edu.ar/handle/10915/20883
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
50-59
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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