DC FieldValueLanguage
dc.contributor.authorDittmer, Antje-
dc.contributor.authorSharan, Bindu-
dc.contributor.authorWerner, Herbert-
dc.date.accessioned2023-01-25T16:22:00Z-
dc.date.available2023-01-25T16:22:00Z-
dc.date.issued2022-12-
dc.identifier.citationIEEE 61st Conference on Decision and Control (CDC 2022)de_DE
dc.identifier.isbn978-1-6654-6761-2de_DE
dc.identifier.urihttp://hdl.handle.net/11420/14669-
dc.description.abstractA novel adaptive Koopman based model predictive control (MPC) algorithm for wind farm control is presented. Using a data-driven Koopman approach the highly non-linear wake effects governing wind farm dynamics can be efficiently modelled. An update rule is presented to enable online learning only when new information is available. Moreover, to provide sufficient excitation of all relevant model frequencies in closed loop, a small test signal is superimposed on the control input while the Koopman model is updated. Simulation studies in the WFSim environment illustrate excellent accuracy for wind speed estimation with changing wind speed. In closed loop, the adaptive online update strategy tracks reference farm yield well, considerably outperforming recently presented non-adaptive schemes.en
dc.language.isoende_DE
dc.titleData-driven Adaptive Model Predictive Control for Wind Farms: A Koopman-Based Online Learning Approachde_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishA novel adaptive Koopman based model predictive control (MPC) algorithm for wind farm control is presented. Using a data-driven Koopman approach the highly non-linear wake effects governing wind farm dynamics can be efficiently modelled. An update rule is presented to enable online learning only when new information is available. Moreover, to provide sufficient excitation of all relevant model frequencies in closed loop, a small test signal is superimposed on the control input while the Koopman model is updated. Simulation studies in the WFSim environment illustrate excellent accuracy for wind speed estimation with changing wind speed. In closed loop, the adaptive online update strategy tracks reference farm yield well, considerably outperforming recently presented non-adaptive schemes.de_DE
tuhh.publisher.doi10.1109/CDC51059.2022.9992829-
tuhh.publication.instituteRegelungstechnik E-14de_DE
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage1999de_DE
tuhh.container.endpage2004de_DE
dc.relation.conferenceIEEE 61st Conference on Decision and Control, CDC 2022de_DE
dc.identifier.scopus2-s2.0-85146974849de_DE
datacite.resourceTypeArticle-
datacite.resourceTypeGeneralConferencePaper-
item.cerifentitytypePublications-
item.openairetypeinProceedings-
item.creatorOrcidDittmer, Antje-
item.creatorOrcidSharan, Bindu-
item.creatorOrcidWerner, Herbert-
item.creatorGNDDittmer, Antje-
item.creatorGNDSharan, Bindu-
item.creatorGNDWerner, Herbert-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.mappedtypeinProceedings-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
crisitem.author.deptRegelungstechnik E-14-
crisitem.author.deptRegelungstechnik E-14-
crisitem.author.orcid0000-0003-3456-5539-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik (E)-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik (E)-
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