Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning

In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the in...

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Detalles Bibliográficos
Autores: Sierra-García, Jesús Enrique, Santos Peñas, Matilde
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/112245
Acceso en línea:https://hdl.handle.net/20.500.14352/112245
Access Level:acceso abierto
Palabra clave:Wind turbines
Pitch control
Neural networks
Unsupervised learning
Neuro control
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
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oai_identifier_str oai:docta.ucm.es:20.500.14352/112245
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spelling Performance analysis of a wind turbine pitch neurocontroller with unsupervised learningSierra-García, Jesús EnriqueSantos Peñas, MatildeWind turbinesPitch controlNeural networksUnsupervised learningNeuro controlInteligencia artificial (Informática)1203.04 Inteligencia ArtificialIn this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. A performance analysis of this neurocontrol strategy is carried out, showing the influence of the RBF parameters, wind speed, learning parameters, and control period, on the system response. The neurocontroller has been compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.WileyUniversidad Complutense de Madrid20202020-01-0120202020-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/112245reponame: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:20.500.14352/1122452026-06-02T12:44:21Z
dc.title.none.fl_str_mv Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
title Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
spellingShingle Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
Sierra-García, Jesús Enrique
Wind turbines
Pitch control
Neural networks
Unsupervised learning
Neuro control
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
title_short Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
title_full Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
title_fullStr Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
title_full_unstemmed Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
title_sort Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning
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 Wind turbines
Pitch control
Neural networks
Unsupervised learning
Neuro control
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
topic Wind turbines
Pitch control
Neural networks
Unsupervised learning
Neuro control
Inteligencia artificial (Informática)
1203.04 Inteligencia Artificial
description In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. A performance analysis of this neurocontrol strategy is carried out, showing the influence of the RBF parameters, wind speed, learning parameters, and control period, on the system response. The neurocontroller has been compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01
2020
2020-01-01
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/20.500.14352/112245
url https://hdl.handle.net/20.500.14352/112245
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 Wiley
publisher.none.fl_str_mv Wiley
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 15.228081

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