Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter

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dc.authoridDemir, Ridvan/0000-0001-6509-9169
dc.contributor.authorYildiz, Recep
dc.contributor.authorDemir, Ridvan
dc.contributor.authorBarut, Murat
dc.date.accessioned2024-11-07T13:31:57Z
dc.date.available2024-11-07T13:31:57Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, a novel extended Kalman filter (EKF)-based observer is designed to increase the number of estimated states and parameters of the induction motor (IM). To perform the online estimations of stationary axis components of stator currents and rotor fluxes (i(sa), i(sb), f(ra), and f(rb)) as well as rotor mechanical speed (?(m)), which are required for direct vector control (DVC) systems along with the load torque (t(L)), rotor resistance (R-r), magnetizing inductance (L-m), and the reciprocal of the total inertia of the system (?(T) = 1=JT), the proposed EKF uses the measured phase currents and voltages together with the measured rotor speed. To estimate all of the five states (i(sa), i(sb), f(sa), f(sb), and ?(m)) plus four parameters (t(L), R-r, L-m, and ?(T)), the proposed EKF-based observer does not include a switching operation nor a hybrid structure, which is a common approach in the literature for online state and parameter estimations of IMs and results in design complexity and computational load increase. In simulation studies, the estimation performance of the proposed EKF is tested and verified under the variations of t(L), R-r, L-m, and ?(T) in DVC systems that perform the speed and position controls of IM. The obtained results confirm the satisfying tracking performances and thus better control achievements of the speed and position controlled IM drives in this paper. Moreover, the proposed EKF and the EKF without ?(T)-estimation are compared in the position control system to demonstrate the importance of the ?(T) estimation. In the comparison, nearly 10 times less mean square error (MSE) is obtained in the estimations t(L), R-r, L-m, and the magnitude of the rotor flux for the proposed EKF. Finally, the proposed EKF algorithm is tested and verified in real-time experiments with a challenging speed reversal scenario causing nonlinear variations in both t(L) and R-r.
dc.identifier.doi10.1177/01423312231160582
dc.identifier.endpage2738
dc.identifier.issn0142-3312
dc.identifier.issn1477-0369
dc.identifier.issue14
dc.identifier.scopus2-s2.0-85153113007
dc.identifier.scopusqualityQ2
dc.identifier.startpage2725
dc.identifier.urihttps://doi.org/10.1177/01423312231160582
dc.identifier.urihttps://hdl.handle.net/11480/15144
dc.identifier.volume45
dc.identifier.wosWOS:000970477300001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofTransactions of the Institute of Measurement and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectInduction motor
dc.subjectstate and parameter estimation
dc.subjectextended Kalman filter
dc.subjectspeed
dc.subjectposition control
dc.titleOnline estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter
dc.typeArticle

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