Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subject...

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Detalles Bibliográficos
Autores: Sierra-García, Jesús Enrique, Santos Peñas, Matilde
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/108133
Acceso en línea:https://hdl.handle.net/20.500.14352/108133
Access Level:acceso abierto
Palabra clave:Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Inteligencia artificial (Informática)
3311.02 Ingeniería de Control
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oai_identifier_str oai:docta.ucm.es:20.500.14352/108133
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spelling Deep learning and fuzzy logic to implement a hybrid wind turbine pitch controlSierra-García, Jesús EnriqueSantos Peñas, MatildeHybrid systemDeep learningFuzzy controlNeural networksPitch controlWind turbinesInteligencia artificial (Informática)3311.02 Ingeniería de ControlThis work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.SpringerUniversidad Complutense de Madrid20212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/108133reponame: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:20.500.14352/1081332026-06-02T12:44:21Z
dc.title.none.fl_str_mv Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
title Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
spellingShingle Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
Sierra-García, Jesús Enrique
Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Inteligencia artificial (Informática)
3311.02 Ingeniería de Control
title_short Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
title_full Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
title_fullStr Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
title_full_unstemmed Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
title_sort Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control
dc.creator.none.fl_str_mv Sierra-García, Jesús Enrique
Santos Peñas, Matilde
author Sierra-García, Jesús Enrique
author_facet Sierra-García, Jesús Enrique
Santos Peñas, Matilde
author_role author
author2 Santos Peñas, Matilde
author2_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Inteligencia artificial (Informática)
3311.02 Ingeniería de Control
topic Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Inteligencia artificial (Informática)
3311.02 Ingeniería de Control
description This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
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/20.500.14352/108133
url https://hdl.handle.net/20.500.14352/108133
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 Springer
publisher.none.fl_str_mv Springer
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 15,81155

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