Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/142949
Title: High-fidelity model identification for synchronous reluctance motor drives
Authors: Varvolik, Vasyl
Buticchi, Giampaolo
Wang, Shuo
Prystupa, Dmytro
Peresada, Sergei
Bozhko, Serhiy
Galea, Michael
Keywords: Electric motors, Synchronous
Reluctance motors
Electric motors -- Mathematical models
Torque -- Measurement
Electric power systems -- Simulation methods
Issue Date: 2023
Publisher: Institute of Electrical and Electronics Engineers
Citation: Varvolik, V., Buticchi, G., Wang, S., Prystupa, D., Peresada, S., Bozhko, S., & Galea, M. (2023). High-fidelity model identification for synchronous reluctance motor drives. IEEE Transactions on Energy Conversion, 38(4), 2623-2633.
Abstract: This article presents an accurate model identification method for the synchronous reluctance machine (SynRel), considering the nonlinear magnetic behavior and spatial harmonics. The availability of the high-fidelity magnetic model is essential for high-performance control and modeling the vibro-acoustic behavior of the electric drive, allowing to predict not only the average torque but also the torque ripple behavior versus rotor position. The magnetic coenergy variation is calculated based on the flux linkages in order to yield the torque ripple. During the identification, the current disturbances introduced by the spatial effects and the inverter nonlinearities are properly suppressed by the merged proportional-integral (PI) controller with a repetitive technique. Besides, the inverter nonlinearity and digital control effects are carefully addressed to improve identification accuracy. The effectiveness of the proposed identification method is demonstrated by the comparison of the finite element (FE) simulation results and experiments for two SynRel prototypes.
URI: https://www.um.edu.mt/library/oar/handle/123456789/142949
Appears in Collections:Scholarly Works - FacEngEE

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