Data-driven decentralized algorithm for wind farm control with population-games assistance

In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines...

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Detalles Bibliográficos
Autores: Barreiro-Gómez, Julian, Ocampo-Martínez, Carlos, Bianchi, Fernando D., Quijano, Nicanor
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/202140
Acceso en línea:http://hdl.handle.net/10261/202140
Access Level:acceso abierto
Palabra clave:Data-driven control strategy
Wind turbines
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spelling Data-driven decentralized algorithm for wind farm control with population-games assistanceBarreiro-Gómez, JulianOcampo-Martínez, CarlosBianchi, Fernando D.Quijano, NicanorData-driven control strategyWind turbinesIn wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, there is a need to develop control strategies that maximize the energy captured from a wind farm. In this work, an algorithm that uses multiple estimated gradients based on measurements that are classified by using a simple distributed population-games-based algorithm is proposed. The update in the decision variables is computed by making a superposition of the estimated gradients together with the classification of the measurements. In order to maximize the energy captured and maintain the individual power generation, several constraints are considered in the proposed algorithm. Basically, the proposed control scheme reduces the communications needed, which increases the reliability of the wind farm operation. The control scheme is validated in simulation in a benchmark corresponding to the Horns Rev wind farm.This work has been partially funded by the Spanish projects SCAV (Ref. DPI2017-88403-R) and DEOCS (Ref. DPI2016-76493-C3-3-R) and the Colombian project ISAGEN Solución Energética Piloto La Guajira. J. Barreiro-Gomez gratefully acknowledges support from U.S. Air Force Office of Scientific Research under grant number FA9550-17-1-0259.Molecular Diversity Preservation InternationalMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Ministerio de Economía y Competitividad (España)Agencia Estatal de Investigación (España)Air Force Office of Scientific Research (US)IsagenConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020192020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/202140reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-88403-RDPI2017-88403-R/AEI/10.13039/501100011033DPI2017-88403-R/AEI/10.13039/501100011033info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493-C3-3-Rhttp://dx.doi.org/10.3390/en12061164Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2021402026-05-22T06:33:51Z
dc.title.none.fl_str_mv Data-driven decentralized algorithm for wind farm control with population-games assistance
title Data-driven decentralized algorithm for wind farm control with population-games assistance
spellingShingle Data-driven decentralized algorithm for wind farm control with population-games assistance
Barreiro-Gómez, Julian
Data-driven control strategy
Wind turbines
title_short Data-driven decentralized algorithm for wind farm control with population-games assistance
title_full Data-driven decentralized algorithm for wind farm control with population-games assistance
title_fullStr Data-driven decentralized algorithm for wind farm control with population-games assistance
title_full_unstemmed Data-driven decentralized algorithm for wind farm control with population-games assistance
title_sort Data-driven decentralized algorithm for wind farm control with population-games assistance
dc.creator.none.fl_str_mv Barreiro-Gómez, Julian
Ocampo-Martínez, Carlos
Bianchi, Fernando D.
Quijano, Nicanor
author Barreiro-Gómez, Julian
author_facet Barreiro-Gómez, Julian
Ocampo-Martínez, Carlos
Bianchi, Fernando D.
Quijano, Nicanor
author_role author
author2 Ocampo-Martínez, Carlos
Bianchi, Fernando D.
Quijano, Nicanor
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Ministerio de Economía y Competitividad (España)
Agencia Estatal de Investigación (España)
Air Force Office of Scientific Research (US)
Isagen
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Data-driven control strategy
Wind turbines
topic Data-driven control strategy
Wind turbines
description In wind farms, the interaction between turbines that operate close by experience some problems in terms of their power generation. Wakes caused by upstream turbines are mainly responsible of these interactions, and the phenomena involved in this case is complex especially when the number of turbines is high. In order to deal with these issues, there is a need to develop control strategies that maximize the energy captured from a wind farm. In this work, an algorithm that uses multiple estimated gradients based on measurements that are classified by using a simple distributed population-games-based algorithm is proposed. The update in the decision variables is computed by making a superposition of the estimated gradients together with the classification of the measurements. In order to maximize the energy captured and maintain the individual power generation, several constraints are considered in the proposed algorithm. Basically, the proposed control scheme reduces the communications needed, which increases the reliability of the wind farm operation. The control scheme is validated in simulation in a benchmark corresponding to the Horns Rev wind farm.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/202140
url http://hdl.handle.net/10261/202140
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-88403-R
DPI2017-88403-R/AEI/10.13039/501100011033
DPI2017-88403-R/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-76493-C3-3-R
http://dx.doi.org/10.3390/en12061164
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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