EKF Digital Twinning of Induction Motor Drives for the Metaverse

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dc.contributor.authorEbadpour, Mohsen
dc.contributor.authorTalla, Jakub
dc.contributor.authorJamshidi, Mohammad
dc.contributor.authorPeroutka, Zdeněk
dc.date.accessioned2023-02-06T11:00:20Z
dc.date.available2023-02-06T11:00:20Z
dc.date.issued2022
dc.description.abstract-translatedThis paper presents a feasible state estimation of speed sensorless rotor field oriented controlled induction motor (IM) drive based on an accurate Extended Kalman Filter (EKF) digital twin model. Digital Twin is one of the attractive trends for the drive industries which provides physical assets over different operating scenarios in a cost-effective platform with no risk. The practical digital twin of the drive system for the Metaverse environment requires precise mathematical model of the motor, EKF algorithm, appropriate state controllers, and voltage source inverter. The quality of the state estimation with EKF strongly depends on input voltages which mainly come from the inverter. Unlike the previous researches which have adopted low precise ideal inverter model, in this study, a high performance EKF observer is employed based on the practical model of the inverter with rigorously considering the dead-time effects and voltage drops of switching devices. Therefore, operation of the EKF observer with digital twin model of the drive system is validated on a 4kW induction motor using simulation results acquired from MATLAB/Simulink software.en
dc.format6 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationEBADPOUR, M. TALLA, J. JAMSHIDI, M. PEROUTKA, Z. EKF Digital Twinning of Induction Motor Drives for the Metaverse. In Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika, ME 2022. Piscataway: IEEE, 2022. s. 398-403. ISBN: 978-1-66541-040-3cs
dc.identifier.doi10.1109/ME54704.2022.9983341
dc.identifier.isbn978-1-66541-040-3
dc.identifier.obd43937826
dc.identifier.uri2-s2.0-85146310531
dc.identifier.urihttp://hdl.handle.net/11025/51306
dc.language.isoenen
dc.project.IDEF18_053/0016927/Mobility Západočeské univerzity v Plznics
dc.publisherIEEEen
dc.relation.ispartofseriesProceedings of the 2022 20th International Conference on Mechatronics - Mechatronika, ME 2022en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.rights.accessrestrictedAccessen
dc.subject.translateddigital twinen
dc.subject.translatedextended kalman filter (EKF)en
dc.subject.translatedinduction motor (IM)en
dc.subject.translatedstate estimationen
dc.subject.translatedsensorless controlen
dc.titleEKF Digital Twinning of Induction Motor Drives for the Metaverseen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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