Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine

Among all the existing possibilities within the renewable energies field, wind energy stands out due to the significant expansion of offshore turbines installed in coastal and deep-sea areas. Although the latter represent considerable energy generation potential due to their larger size and location...

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Detalles Bibliográficos
Autores: Ramírez, Alejandro, Tomás-Rodríguez, María, Sierra-García, Jesús Enrique, Santos Peñas, Matilde
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/113279
Acceso en línea:https://hdl.handle.net/20.500.14352/113279
Access Level:acceso abierto
Palabra clave:Floating offshore wind turbine
Semi-active structural control
Tuned mass damper (TMD)
Meta-heuristic optimization
Genetic algorithms (GA)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/113279
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spelling Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbineRamírez, AlejandroTomás-Rodríguez, MaríaSierra-García, Jesús EnriqueSantos Peñas, MatildeFloating offshore wind turbineSemi-active structural controlTuned mass damper (TMD)Meta-heuristic optimizationGenetic algorithms (GA)Inteligencia artificial (Informática)1203.04 Inteligencia ArtificialAmong all the existing possibilities within the renewable energies field, wind energy stands out due to the significant expansion of offshore turbines installed in coastal and deep-sea areas. Although the latter represent considerable energy generation potential due to their larger size and location in areas of strong winds, they are exposed to harsh environmental disturbances, particularly waves, causing these structures to experience vibrations, increasing in this way fatigue, reducing efficiency, and leading to higher maintenance and operational costs. In this work, vibration reduction is achieved using two structural control systems for a 5 MW barge-type floating offshore wind turbine (FOWT), tuned via a metaheuristic method, with genetic algorithms (GAs). Firstly, the standard deviation of the Top Tower Displacement (TTD) is used as a cost function in the GA to optimize a passive Tuned Mass Damper (TMD), resulting in a vibration suppression rate of 34.9% compared to a reference standard TMD. Additionally, two semi-active structural control systems based on a gain scheduling approach are proposed. In one of the approaches, the TMD parameters are optimized based on the amplitude of oscillations, achieving a suppression rate of 45.4%. In the second approach, the TMD parameters are optimized in real time for the identified wave frequencies, demonstrating superior performance for medium-high frequencies compared to the other TMDs.MdpiUniversidad Complutense de Madrid20242024-12-0520242024-12-05journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/113279reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1132792026-06-02T12:44:21Z
dc.title.none.fl_str_mv Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
title Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
spellingShingle Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
Ramírez, Alejandro
Floating offshore wind turbine
Semi-active structural control
Tuned mass damper (TMD)
Meta-heuristic optimization
Genetic algorithms (GA)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
title_short Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
title_full Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
title_fullStr Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
title_full_unstemmed Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
title_sort Metaheuristic optimized semi-active structural control approaches for a floating offshore wind turbine
dc.creator.none.fl_str_mv Ramírez, Alejandro
Tomás-Rodríguez, María
Sierra-García, Jesús Enrique
Santos Peñas, Matilde
author Ramírez, Alejandro
author_facet Ramírez, Alejandro
Tomás-Rodríguez, María
Sierra-García, Jesús Enrique
Santos Peñas, Matilde
author_role author
author2 Tomás-Rodríguez, María
Sierra-García, Jesús Enrique
Santos Peñas, Matilde
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Floating offshore wind turbine
Semi-active structural control
Tuned mass damper (TMD)
Meta-heuristic optimization
Genetic algorithms (GA)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
topic Floating offshore wind turbine
Semi-active structural control
Tuned mass damper (TMD)
Meta-heuristic optimization
Genetic algorithms (GA)
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
description Among all the existing possibilities within the renewable energies field, wind energy stands out due to the significant expansion of offshore turbines installed in coastal and deep-sea areas. Although the latter represent considerable energy generation potential due to their larger size and location in areas of strong winds, they are exposed to harsh environmental disturbances, particularly waves, causing these structures to experience vibrations, increasing in this way fatigue, reducing efficiency, and leading to higher maintenance and operational costs. In this work, vibration reduction is achieved using two structural control systems for a 5 MW barge-type floating offshore wind turbine (FOWT), tuned via a metaheuristic method, with genetic algorithms (GAs). Firstly, the standard deviation of the Top Tower Displacement (TTD) is used as a cost function in the GA to optimize a passive Tuned Mass Damper (TMD), resulting in a vibration suppression rate of 34.9% compared to a reference standard TMD. Additionally, two semi-active structural control systems based on a gain scheduling approach are proposed. In one of the approaches, the TMD parameters are optimized based on the amplitude of oscillations, achieving a suppression rate of 45.4%. In the second approach, the TMD parameters are optimized in real time for the identified wave frequencies, demonstrating superior performance for medium-high frequencies compared to the other TMDs.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-05
2024
2024-12-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/113279
url https://hdl.handle.net/20.500.14352/113279
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Mdpi
publisher.none.fl_str_mv Mdpi
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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