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dc.contributor.authorAlves, Erick Fernando
dc.contributor.authorNøland, Jonas Kristiansen
dc.contributor.authorMarafioti, Giancarlo
dc.contributor.authorMathisen, Geir
dc.date.accessioned2019-12-17T10:43:33Z
dc.date.available2019-12-17T10:43:33Z
dc.date.created2019-12-10T13:04:05Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-4878-6
dc.identifier.urihttp://hdl.handle.net/11250/2633589
dc.description.abstractThis paper investigates and implements a procedure for parameter identification of salient pole synchronous machines that is based on previous knowledge about the equipment and can be used for condition monitoring, online assessment of the electrical power grid, and adaptive control. It uses a Kalman filter to handle noise and correct deviations in measurements caused by uncertainty of instruments or effects not included in the model. Then it applies a recursive least squares algorithm to identify parameters from the synchronous machine model. Despite being affected by saturation effects, the proposed procedure estimates 8 out of 13 parameters from the machine model with minor deviations from data sheet values and is largely insensitive to noise and load conditions.nb_NO
dc.description.abstractOnline parameter identification of synchronous machines using Kalman filter and recursive least squaresnb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartofProceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019
dc.relation.urihttps://ieeexplore.ieee.org/document/8926707
dc.subjectElektriske maskinernb_NO
dc.subjectElectrical Machinesnb_NO
dc.subjectParameterestimeringnb_NO
dc.subjectParameter estimationnb_NO
dc.titleOnline parameter identification of synchronous machines using Kalman filter and recursive least squaresnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.subject.nsiVDP::Teknisk kybernetikk: 553nb_NO
dc.subject.nsiVDP::Technical cybernetics: 553nb_NO
dc.source.pagenumber7121-7128nb_NO
dc.identifier.doi10.1109/IECON.2019.8926707
dc.identifier.cristin1758837
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,20,0
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for elkraftteknikk
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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