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Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

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Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

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dc.contributor.author Sierra-García, J. E. es_ES
dc.contributor.author Santos, M. es_ES
dc.date.accessioned 2021-10-05T06:57:03Z
dc.date.available 2021-10-05T06:57:03Z
dc.date.issued 2021-09-30
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/173784
dc.description.abstract [EN] Pitch control of wind turbines is complex due to the intrinsic non-linear behavior of these devices, and the external disturbances they are subjected to related to changing wind conditions and other meteorological phenomena. This difficulty is even higher in the case of floating offshore turbines, due to ocean currents and waves. Neural networks and other intelligent control techniques have been proven very useful for the modeling and control of these complex systems. Thus, this paper presents different intelligent control configurations applied to wind turbine pitch control. Direct pitch control based on neural networks and reinforcement learning, and some hybrid control configurations are described. The usefulness of neuro-estimators for the improvement of controllers is also presented. Finally, some of these techniques are used in an application example with a land wind turbine model. es_ES
dc.description.abstract [ES] El control del ángulo de las palas de las turbinas eólicas es complejo debido al comportamiento no lineal de los aerogeneradores, y a las perturbaciones externas a las que están sometidas debido a las condiciones cambiantes del viento y otros fenómenos meteorológicos. Esta dificultad se agrava en el caso de las turbinas flotantes marinas, donde también les afectan las corrientes marinas y las olas. Las redes neuronales, y otras técnicas del control inteligente, han demostrado ser muy útiles para el modelado y control de estos sistemas. En este trabajo se presentan diferentes configuraciones de control inteligente, basadas principalmente en redes neuronales y aprendizaje por refuerzo, aplicadas al control de las turbinas eólicas. Se describe el control directo del ángulo de las palas del aerogenerador y algunas configuraciones híbridas de control. Se expone la utilidad de los neuro-estimadores para la mejora de los controladores. Finalmente, se muestra un ejemplo de aplicación de algunas de estas técnicas en un modelo de turbina terrestre. es_ES
dc.description.sponsorship Ministerio de Ciencia, Innovación y Universidades: Proyecto MCI AEI/FEDER RTI2018- 094902-B-C21 es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Wind turbines es_ES
dc.subject Pitch control es_ES
dc.subject Intelligent control es_ES
dc.subject Neural networks reinforcement learning es_ES
dc.subject Turbinas eólicas es_ES
dc.subject Aerogeneradores es_ES
dc.subject Control del ángulo de las palas es_ES
dc.subject Control inteligente es_ES
dc.subject Redes neuronales aprendizaje por refuerzo es_ES
dc.title Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas es_ES
dc.title.alternative Neural networks and reinforcement learning in wind turbine control es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2021.16111
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094902-B-C21/ES/ANALISIS Y CONTROL DE UN DISPOSITIVO FLOTANTE HIBRIDO DE ENERGIA EOLICA Y MARINA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Sierra-García, JE.; Santos, M. (2021). Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial. 18(4):327-335. https://doi.org/10.4995/riai.2021.16111 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2021.16111 es_ES
dc.description.upvformatpinicio 327 es_ES
dc.description.upvformatpfin 335 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 18 es_ES
dc.description.issue 4 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\16111 es_ES
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades es_ES
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