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 Garcia, Jesús Enrique, Santos, Matilde
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/6157
Acceso en línea:http://hdl.handle.net/10259/6157
Access Level:acceso abierto
Palabra clave:Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Ingeniería mecánica
Mechanical engineering
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spelling Deep learning and fuzzy logic to implement a hybrid wind turbine pitch controlSierra Garcia, Jesús EnriqueSantos, MatildeHybrid systemDeep learningFuzzy controlNeural networksPitch controlWind turbinesIngeniería mecánicaMechanical engineeringThis 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.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministry of Science, Innovation and Universities under MCI/AEI/FEDER Project Number RTI2018-094902-B-C21.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLESpringer202120212022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10259/6157reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésNeural Computing and Applications. 2022, V. 34, n. 13, p. 10503-10517https://doi.org/10.1007/s00521-021-06323-winfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094902-B-C21Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/61572026-05-28T07:56:11Z
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 Garcia, Jesús Enrique
Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Ingeniería mecánica
Mechanical engineering
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 Garcia, Jesús Enrique
Santos, Matilde
author Sierra Garcia, Jesús Enrique
author_facet Sierra Garcia, Jesús Enrique
Santos, Matilde
author_role author
author2 Santos, Matilde
author2_role author
dc.subject.none.fl_str_mv Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Ingeniería mecánica
Mechanical engineering
topic Hybrid system
Deep learning
Fuzzy control
Neural networks
Pitch control
Wind turbines
Ingeniería mecánica
Mechanical engineering
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
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/6157
url http://hdl.handle.net/10259/6157
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Neural Computing and Applications. 2022, V. 34, n. 13, p. 10503-10517
https://doi.org/10.1007/s00521-021-06323-w
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094902-B-C21
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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