Show simple item record

dc.contributor.authorFernández Gauna, Borja
dc.contributor.authorFernández Gámiz, Unai
dc.contributor.authorGraña Romay, Manuel María
dc.date.accessioned2024-02-08T09:43:33Z
dc.date.available2024-02-08T09:43:33Z
dc.date.issued2016-12-30
dc.identifier.citationIntegrated Computer-Aided Engineering 24(1) : 27-39 (2017)es_ES
dc.identifier.issn1069-2509
dc.identifier.urihttp://hdl.handle.net/10810/65110
dc.description.abstractThe control of Variable Speed Wind Turbines (VSWT) to achieve optimal balance of power generation stability and rotor angular speed is impeded by the non-linear dynamics of the turbine-wind interaction and sudden changes of wind direction and speed. Conventional approaches to design VSWT controllers are not adaptive. However, the wind shear phenomenon introduces a strongly non-stationary environment that requires adaptive control approaches with minimal human intervention, i.e. very little supervision of the adaptation process. Reinforcement Learning (RL) allows minimally supervised learning. Specifically, Actor-Critic is designed to deal with continuous valued state and action spaces. In this paper we apply an Actor-Critic RL architecture to improve the adaptation of the conventional VSWT controllers to changing wind conditions. Simulation results on a benchmark VSWT model under strongly changing wind conditions show that Actor Critic RL approach with functional approximation provide great enhancement over state-of-the-art VSWT controllers.es_ES
dc.description.sponsorshipGIC participates at UIF 11/07 of UPV/EHU. The Computational Intelligence Group is funded by the Basque Government with grant IT874-13. Manuel Graña was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancementes_ES
dc.language.isoenges_ES
dc.publisherACMes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/316097
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.titleVariable Speed Wind Turbine Controller Adaptation By Reinforcement Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2017 IOSes_ES
dc.relation.publisherversionhttps://content.iospress.com/articles/integrated-computer-aided-engineering/ica531
dc.identifier.doi10.3233/ICA-160531
dc.contributor.funderFernandez-Gauna, Borja
dc.contributor.funderEuropean Commission
dc.departamentoesIngeniería Energéticaes_ES
dc.departamentoeuEnergia Ingenieritzaes_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record