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dc.date.accessioned2022-03-26T16:25:31Z
dc.date.available2023-03-27T22:45:49Z
dc.date.created2021-08-16T15:09:33Z
dc.date.issued2021
dc.identifier.citationDali, Ali Abdelmalek, Samir Bakdi, Azzeddine Bettayeb, Maamar . A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine. Renewable Energy. 2021, 172, 1021-1034
dc.identifier.urihttp://hdl.handle.net/10852/92970
dc.description.abstractThis paper proposes a novel Improved Maximum Power Point Tracking (IMPPT) algorithm to extract the maximum available power from a direct-drive Permanent-Magnet Synchronous Generator (PMSG) based standalone Small-scale Variable Speed Wind Turbine (VSWT). The proposed control scheme consists of an IMPPT algorithm to effectively improve the extracted power under various regimes of wind speed. The IMPPT constructs the reference value of the DC voltage for the DC bus. Moreover, a composite low-cost controller (LCC) is also proposed in order to improve the DC voltage tracking based on a new designed nonlinear state observer. The proposed nonlinear robust controller accounts for the overall system dynamics and nonlinear behavior. The objective is to improve the dynamic performance and ensure a good balance of energy conversion efficiency, robustness, cost efficiency, and a simple structure for practical implementation in wind energy conversion systems. Furthermore, the stability of the closed-loop system is analyzed and guaranteed through Lyapunov stability theory. Moreover, two scenarios are used for validation in Matlab/Simulink, including step change and stochastic profiles of wind speed. Simulation results verify the effectiveness and superiority of the IMPPT-LCC approach whereas comparisons with other techniques prove its superiority.
dc.languageEN
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleA new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine
dc.typeJournal article
dc.creator.authorDali, Ali
dc.creator.authorAbdelmalek, Samir
dc.creator.authorBakdi, Azzeddine
dc.creator.authorBettayeb, Maamar
cristin.unitcode185,15,13,25
cristin.unitnameStatistikk og Data Science
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.cristin1926379
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Renewable Energy&rft.volume=172&rft.spage=1021&rft.date=2021
dc.identifier.jtitleRenewable Energy
dc.identifier.volume172
dc.identifier.startpage1021
dc.identifier.endpage1034
dc.identifier.doihttps://doi.org/10.1016/j.renene.2021.03.083
dc.identifier.urnURN:NBN:no-95553
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0960-1481
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/92970/1/RENE_2021_AAM.pdf
dc.type.versionAcceptedVersion


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