Title: EKF Digital Twinning of Induction Motor Drives for the Metaverse
Authors: Ebadpour, Mohsen
Talla, Jakub
Jamshidi, Mohammad
Peroutka, Zdeněk
Citation: EBADPOUR, 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: 978-1-66541-040-3
Issue Date: 2022
Publisher: IEEE
Document type: konferenční příspěvek
URI: 2-s2.0-85146310531
ISBN: 978-1-66541-040-3
Keywords in different language: digital twin;extended kalman filter (EKF);induction motor (IM);state estimation;sensorless control
Abstract in different language: This 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.
Rights: Plný text je přístupný v rámci univerzity přihlášeným uživatelům.
Appears in Collections:Konferenční příspěvky / Conference papers (RICE)
Konferenční příspěvky / Conference Papers (KEV)

Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/51306

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