Standstill Estimation of Stator Resistance of Induction Motors with Novel Innovation-Based Adaptive Extended Kalman Filter

dc.authorscopusid 55266224400
dc.authorscopusid 57350709400
dc.authorscopusid 55924718000
dc.contributor.author Inan,R.
dc.contributor.author Yirtar,M.Z.
dc.contributor.author Bulent Ertan,H.
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2024-07-05T15:45:59Z
dc.date.available 2024-07-05T15:45:59Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp Inan R., Isparta University of Applied Sciences, Faculty of Technology, Department of Electrical and Electronics Engineering, Isparta, Turkey; Yirtar M.Z., Middle East Technical University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Ankara, Turkey; Bulent Ertan H., Atilim University, Faculty of Engineering, Department of Mechatronics Engineering, Ankara, Turkey en_US
dc.description.abstract In this study, a method is developed to identify stator resistance of an induction motor (IM) at standstill in the self-tuning. An innovation-based adaptive extended Kalman filter (IAEKF) estimator in which the process noise is dynamically updated with an adaptive mechanism different from the conventional extended Kalman filter (EKF) is designed to estimate stator resistance with αβ- stator stationary axis components of stator current and αβ- components of stator flux of an IM. The reason for estimating the stator flux and stator current together with the stator resistance is to both increase the stability of the proposed estimator algorithm by using the correlation between the parameters and states in the non-linear inputs applied to the estimator and obtain the motor flux information needed by the control system. In the proposed IAEKF algorithm, a stator flux-based IM model is used for prediction purposes. The standstill estimation performance of the proposed novel IAEKF is tested with both sinusoidal and PWM power supplies, The real-time estimation results show the effectiveness and prediction accuracy of the proposed stochastic-based estimator. © 2021 IEEE. en_US
dc.identifier.citationcount 3
dc.identifier.doi 10.1109/OPTIM-ACEMP50812.2021.9590060
dc.identifier.endpage 444 en_US
dc.identifier.isbn 978-166540298-9
dc.identifier.scopus 2-s2.0-85119657275
dc.identifier.startpage 439 en_US
dc.identifier.uri https://doi.org/10.1109/OPTIM-ACEMP50812.2021.9590060
dc.identifier.uri https://hdl.handle.net/20.500.14411/3995
dc.institutionauthor Ertan, Hulusi Bülent
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 2021 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2021 and 2021 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2021 -- 2021 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2021 and 2021 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2021 -- 2 September 2021 through 3 September 2021 -- Brasov -- 173666 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 2
dc.subject adaptive extended Kalman filter en_US
dc.subject induction motor en_US
dc.subject standstill operation en_US
dc.subject stator resistance en_US
dc.title Standstill Estimation of Stator Resistance of Induction Motors with Novel Innovation-Based Adaptive Extended Kalman Filter en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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