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...
| Authors: | ; ; ; |
|---|---|
| Format: | Article |
| Publication date: | 2022 |
| Country: | España |
| Institution: | Universidad de Castilla-La Mancha |
| Repository: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/36247 |
| Online Access: | https://hdl.handle.net/10578/36247 |
| Access Level: | Open access |
| Keyword: | Maintenance Wind farm Offshore Genetic Algorithms (GA) Particle Swarm Optimization (PSO) |
| _version_ | 1878741413873582080 |
|---|---|
| author | Peinado Gonzalo, Alfredo |
| author2 | Benmessaoud, Tahar Entezami, Mani García Márquez, Fausto Pedro |
| author2_role | author author author |
| author_browse | Benmessaoud, Tahar Entezami, Mani García Márquez, Fausto Pedro Peinado Gonzalo, Alfredo |
| author_facet | Peinado Gonzalo, Alfredo Benmessaoud, Tahar Entezami, Mani García Márquez, Fausto Pedro |
| author_role | author |
| collection | RUIdeRA. Repositorio Institucional de la UCLM |
| 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_e4c02c8f638bfbf005abc34905c4e99e |
| institution | Universidad de Castilla-La Mancha |
| instname_str | Universidad de Castilla-La Mancha |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:ruidera.uclm.es:10578/36247 |
| publishDate | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| reponame_str | RUIdeRA. Repositorio Institucional de la UCLM |
| spelling | Optimal maintenance management of offshore wind turbines by minimizing the costs Peinado Gonzalo, Alfredo Benmessaoud, Tahar Entezami, Mani García Márquez, Fausto Pedro Maintenance Wind farm Offshore Genetic 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. Elsevier https://hdl.handle.net/10578/36247 |
| 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 | 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_short | Optimal maintenance management of offshore wind turbines by minimizing the costs |
| title_sort | Optimal maintenance management of offshore wind turbines by minimizing the costs |
| topic | Maintenance Wind farm Offshore Genetic Algorithms (GA) Particle Swarm Optimization (PSO) |
| topic_facet | Maintenance Wind farm Offshore Genetic Algorithms (GA) Particle Swarm Optimization (PSO) |
| url | https://hdl.handle.net/10578/36247 |