Rotor Resistance Estimation of Induction Motors With a Novel Innovation-Based Adaptive Extended Kalman Filter for Self-Tuning

dc.authorscopusid 55266224400
dc.authorscopusid 55924718000
dc.contributor.author Inan,R.
dc.contributor.author Bulent Ertan,H.
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2024-07-05T15:50:23Z
dc.date.available 2024-07-05T15:50:23Z
dc.date.issued 2023
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; Bulent Ertan H., Atilim University, Faculty of Engineering, Department of Mechatronics Engineering, Ankara, Turkey en_US
dc.description.abstract In this study a novel estimator is developed to identify the rotor resistance of the induction motor (IM) at standstill for self-tuning. For this purpose, an innovation-based adaptive extended Kalman (IAEKF) filter estimator is designed. IAEKF provides a more dynamic estimation compared to the conventional extended Kalman filter (EKF), as they have a mechanism where the system noise covariance matrix can be updated continuously, unlike conventional EKF. To increase estimation stability and also for position and amplitude information of the motor flux required for the dynamic control methods, stator stationary axis (-αβ) components of stator current and -αβ components of stator flux are estimated with rotor resistance by using the correlation between states and parameters defined as nonlinear inputs. The estimation performance of the proposed IAEKF algorithm is tested both in the simulation and on the real-time IM experimental setup at standstill. Simulation and real-time results show that the estimation achievement of the proposed IAEKF algorithm is quite impressive. © 2023 IEEE. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1109/ACEMP-OPTIM57845.2023.10287080
dc.identifier.isbn 979-835031149-5
dc.identifier.issn 1842-0133
dc.identifier.scopus 2-s2.0-85178144660
dc.identifier.uri https://doi.org/10.1109/ACEMP-OPTIM57845.2023.10287080
dc.identifier.uri https://hdl.handle.net/20.500.14411/4140
dc.institutionauthor Ertan, Hulusi Bülent
dc.language.iso en en_US
dc.publisher Transilvania University of Brasov 1 en_US
dc.relation.ispartof Proceedings of the International Conference on Optimisation of Electrical and Electronic Equipment, OPTIM -- 2023 International Aegean Conference on Electrical Machines and Power Electronics and 2023 International Conference on Optimization of Electrical and Electronic Equipment, ACEMP-OPTIM 2023 -- 1 September 2023 through 2 September 2023 -- Istanbul -- 194065 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 0
dc.subject adaptive extended Kalman filter en_US
dc.subject induction motor en_US
dc.subject rotor resistance en_US
dc.subject self-tuning en_US
dc.title Rotor Resistance Estimation of Induction Motors With a Novel Innovation-Based Adaptive Extended Kalman Filter for Self-Tuning en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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