Design and Implementation of Hybrid Adaptive Extended Kalman Filter for State Estimation of Induction Motor

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dc.authoridZerdali, Emrah/0000-0003-1755-0327
dc.contributor.authorOzkurt, Gizem
dc.contributor.authorZerdali, Emrah
dc.date.accessioned2024-11-07T13:25:24Z
dc.date.available2024-11-07T13:25:24Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractToday, induction motor (IM) is still the most popular electrical machine due to its robust and rare element-free structure, lower maintenance requirement, and cost-effective production. State estimation for this motor is the cornerstone for speed-sensorless control, fault-tolerant control, and fault diagnostics. Nonlinear Kalman filters, especially extended Kalman filters (EKFs), are the most preferred state and/or parameter estimation methods for IM. However, they require a stochastic system with complete process and measurement noise covariances for optimal estimations. These noise covariances, unknown or partially known in practice, vary under different operating conditions of the IM. To deal with this problem, various adaptive EKFs (AEKFs) have been proposed, which can compensate for the effect of varying noise covariances, but each approach has its own pitfalls. This article discusses the hybrid AEKF (HAEKF), which eliminates the problems of existing AEKFs. To demonstrate its effectiveness, the proposed HAEKF is compared qualitatively and quantitatively with existing AEKFs through simulation and experimental studies. Finally, improved estimation stability and performance are provided with the proposed HAEKF observer.
dc.identifier.doi10.1109/TIM.2022.3144729
dc.identifier.issn0018-9456
dc.identifier.issn1557-9662
dc.identifier.scopus2-s2.0-85123278574
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1109/TIM.2022.3144729
dc.identifier.urihttps://hdl.handle.net/11480/14682
dc.identifier.volume71
dc.identifier.wosWOS:000761251000052
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions on Instrumentation and Measurement
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectObservers
dc.subjectEstimation
dc.subjectCovariance matrices
dc.subjectMathematical models
dc.subjectAdaptation models
dc.subjectKalman filters
dc.subjectRotors
dc.subjectAdaptive extended Kalman filter (AEKF)
dc.subjectinduction motor (IM)
dc.subjectparameter estimation
dc.subjectspeed-sensorless control
dc.subjectstate estimation
dc.titleDesign and Implementation of Hybrid Adaptive Extended Kalman Filter for State Estimation of Induction Motor
dc.typeArticle

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