Optimal maintenance management of offshore wind turbines by minimizing the costs

Renewable and sustainable energy production systems offer promising perspectives for the future, as their production and maintenance prices decrease, and their efficiency and reliability increase, favouring the competitiveness of this industry. Thereby, wind energy is one of the most used and develo...

Descripción completa

Detalles Bibliográficos
Autores: Peinado Gonzalo, Alfredo, Benmessaoud, Tahar, Entezami, Mani, García Márquez, Fausto Pedro
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/36247
Acceso en línea:https://hdl.handle.net/10578/36247
Access Level:acceso abierto
Palabra clave:Maintenance
Wind farm
Offshore
Genetic Algorithms (GA)
Particle Swarm Optimization (PSO)
id ES_e4c02c8f638bfbf005abc34905c4e99e
oai_identifier_str oai:ruidera.uclm.es:10578/36247
network_acronym_str ES
network_name_str España
repository_id_str
spelling Optimal maintenance management of offshore wind turbines by minimizing the costsPeinado Gonzalo, AlfredoBenmessaoud, TaharEntezami, ManiGarcía Márquez, Fausto PedroMaintenanceWind farmOffshoreGenetic Algorithms (GA)Particle Swarm Optimization (PSO)Renewable and sustainable energy production systems offer promising perspectives for the future, as their production and maintenance prices decrease, and their efficiency and reliability increase, favouring the competitiveness of this industry. Thereby, wind energy is one of the most used and developed as renewable energy, since it is a cost-effective way to generate clean and sustainable energy. Wind energy is divided into onshore and offshore depending on the wind farm location. Offshore wind energy is increasing its use. However, the offshore industry requires more maintenance, which is also more complicated to do because of the environmental conditions. Setting the best maintenance strategy becomes a complicated optimization problem with several objectives and constraint functions. In this paper, a novel multi-objective optimization problem is defined and solved for real case studies by using Genetic Algorithms and Particle Swarm Optimization to minimize operational costs and maximize performance of the wind turbines. The results of both algorithms are compared considering several scenarios in a real case study. These results show a better performance of Particle Swarm Optimization for optimal cost achieved, and less computational cost to solve it. Finally, the influence of the model parameters is studied by performing a sensitivity study, that shows the importance of preventive maintenance and the reduction of corrective maintenance tasks.Elsevier202420242022info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10578/36247reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésSBPLY/19/180501/000102info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/oai:ruidera.uclm.es:10578/362472026-05-27T07:36:41Z
dc.title.none.fl_str_mv Optimal maintenance management of offshore wind turbines by minimizing the costs
title Optimal maintenance management of offshore wind turbines by minimizing the costs
spellingShingle Optimal maintenance management of offshore wind turbines by minimizing the costs
Peinado Gonzalo, Alfredo
Maintenance
Wind farm
Offshore
Genetic Algorithms (GA)
Particle Swarm Optimization (PSO)
title_short Optimal maintenance management of offshore wind turbines by minimizing the costs
title_full Optimal maintenance management of offshore wind turbines by minimizing the costs
title_fullStr Optimal maintenance management of offshore wind turbines by minimizing the costs
title_full_unstemmed Optimal maintenance management of offshore wind turbines by minimizing the costs
title_sort Optimal maintenance management of offshore wind turbines by minimizing the costs
dc.creator.none.fl_str_mv Peinado Gonzalo, Alfredo
Benmessaoud, Tahar
Entezami, Mani
García Márquez, Fausto Pedro
author Peinado Gonzalo, Alfredo
author_facet Peinado Gonzalo, Alfredo
Benmessaoud, Tahar
Entezami, Mani
García Márquez, Fausto Pedro
author_role author
author2 Benmessaoud, Tahar
Entezami, Mani
García Márquez, Fausto Pedro
author2_role author
author
author
dc.subject.none.fl_str_mv Maintenance
Wind farm
Offshore
Genetic Algorithms (GA)
Particle Swarm Optimization (PSO)
topic Maintenance
Wind farm
Offshore
Genetic Algorithms (GA)
Particle Swarm Optimization (PSO)
description Renewable and sustainable energy production systems offer promising perspectives for the future, as their production and maintenance prices decrease, and their efficiency and reliability increase, favouring the competitiveness of this industry. Thereby, wind energy is one of the most used and developed as renewable energy, since it is a cost-effective way to generate clean and sustainable energy. Wind energy is divided into onshore and offshore depending on the wind farm location. Offshore wind energy is increasing its use. However, the offshore industry requires more maintenance, which is also more complicated to do because of the environmental conditions. Setting the best maintenance strategy becomes a complicated optimization problem with several objectives and constraint functions. In this paper, a novel multi-objective optimization problem is defined and solved for real case studies by using Genetic Algorithms and Particle Swarm Optimization to minimize operational costs and maximize performance of the wind turbines. The results of both algorithms are compared considering several scenarios in a real case study. These results show a better performance of Particle Swarm Optimization for optimal cost achieved, and less computational cost to solve it. Finally, the influence of the model parameters is studied by performing a sensitivity study, that shows the importance of preventive maintenance and the reduction of corrective maintenance tasks.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10578/36247
url https://hdl.handle.net/10578/36247
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv SBPLY/19/180501/000102
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422619392999424
score 15.300719

Similares en LA Referencia