Extended Kalman filter based estimations for improving speed-sensored control performance of induction motors

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dc.authoridDemir, Ridvan/0000-0001-6509-9169
dc.contributor.authorYildiz, Recep
dc.contributor.authorBarut, Murat
dc.contributor.authorDemir, Ridvan
dc.date.accessioned2024-11-07T13:24:14Z
dc.date.available2024-11-07T13:24:14Z
dc.date.issued2020
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, an extended Kalman filter (EKF)-based estimation algorithm is presented to improve the speed-sensored control performance of induction motors (IMs). The proposed EKF-based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed-sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real-time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real-time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.
dc.identifier.doi10.1049/iet-epa.2020.0319
dc.identifier.endpage2479
dc.identifier.issn1751-8660
dc.identifier.issn1751-8679
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85096996550
dc.identifier.scopusqualityQ2
dc.identifier.startpage2471
dc.identifier.urihttps://doi.org/10.1049/iet-epa.2020.0319
dc.identifier.urihttps://hdl.handle.net/11480/14001
dc.identifier.volume14
dc.identifier.wosWOS:000595801000023
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInst Engineering Technology-Iet
dc.relation.ispartofIet Electric Power Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectKalman filters
dc.subjectfriction
dc.subjectangular velocity control
dc.subjectstators
dc.subjectnonlinear filters
dc.subjectinduction motors
dc.subjectsensorless machine control
dc.subjectmachine control
dc.subjecttorque control
dc.subjectrotors
dc.subjectspeed-sensor control performance
dc.subjectinduction motors
dc.subjectextended Kalman filter-based estimation algorithm
dc.subjectEKF-based estimation algorithm
dc.subjectstator stationary
dc.subjectrotor fluxes
dc.subjectrotor angular speed
dc.subjectload torque
dc.subjectrotor resistance
dc.subjectmagnetising inductance
dc.subjectsingle EKF algorithm
dc.subjectmeasured rotor speed
dc.subjectstationary axis components
dc.subjectmeasured stator currents
dc.subjectmeasured priori estimation values
dc.subjectposterior estimation ones
dc.subjecteighth order EKF algorithm
dc.subjectwide-speed range
dc.subjectdeveloped sixth order EKF
dc.subjectadditional estimations
dc.subjectimproved estimations
dc.titleExtended Kalman filter based estimations for improving speed-sensored control performance of induction motors
dc.typeArticle

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