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dc.creatorD'Ambrosio, Danieles_ES
dc.creatorSchoukens, Johanes_ES
dc.creatorTroyer, T. Dees_ES
dc.creatorZivanovic, Miroslaves_ES
dc.creatorRunacres, Markes_ES
dc.date.accessioned2022-04-07T07:47:42Z
dc.date.available2022-04-07T07:47:42Z
dc.date.issued2022
dc.identifier.issn1752-1416
dc.identifier.urihttps://hdl.handle.net/2454/42669
dc.description.abstractThe increasing sophistication of wind turbine design and control generates a need for high-quality wind data. The relatively limited set of available measured wind data may be extended with computer generated data, for example, to make reliable statistical studies of energy production and mechanical loads. Here, a data-driven model for the generation of surrogate wind speeds is compared with two state-of-the-art time series models that can capture the probability distribution and the autocorrelation of the target wind data. The proposed model, based on the phase-randomised Fourier transform, can generate wind speed time series that possess the power spectral density of the target data and converge to their generally non-Gaussian probability distribution with an arbitrary, user-defined precision. The model performance is benchmarked in terms of probability distribution, power spectral density, autocorrelation, and nonstationarities such as the diurnal and seasonal variations of the target data. Comparisons show that the proposed model can outperform the selected models in reproducing the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.en
dc.description.sponsorshipThis work was partly supported by the Research Foundation Flanders (FWO) [grant number 74213/K231719N].en
dc.format.extent11 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherWileyen
dc.relation.ispartofIET Renewable Power Generation 2022;16:922–932en
dc.rights© 2022 The Authors. This is an open access article under the terms of theCreative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work isproperly cited.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectTurbine designen
dc.subjectData-drivenen
dc.subjecten
dc.titleData-driven generation of synthetic wind speeds: a comparative studyen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritzaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1049/rpg2.12394
dc.relation.publisherversionhttps://doi.org/10.1049/rpg2.12394
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© 2022 The Authors. This is an open access article under the terms of theCreative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work isproperly cited.
Except where otherwise noted, this item's license is described as © 2022 The Authors. This is an open access article under the terms of theCreative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work isproperly cited.

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