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Space-vector state dynamic model of SynRM considering self- and cross-saturation and related parameter identification

Accetta, Angello and Cirrincione, Maurizio and Pucci, Marcello and Sferlazza, Antonino (2021) Space-vector state dynamic model of SynRM considering self- and cross-saturation and related parameter identification. IET Electric Power Applications, 14 (14). pp. 2798-2808. ISSN 1751-8660

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This study proposes a state formulation of the space-vector dynamic model of the Synchronous Reluctance Motor (SynRM) considering both saturation and cross-saturation effects. The proposed model adopts the stator currents as state variables and has been theoretically developed in both the rotor and stator reference frames. The proposed magnetic model is based on a flux versus current approach and relies on the knowledge of 11 parameters. Starting from the definition of a suitable co-energy variation function, new flux versus current functions have been initially developed, based on the hyperbolic functions and, consequently, the static and dynamic inductance versus current functions have been deduced. The dynamic inductance functions have been derived so to fulfill the reciprocity conditions. This study presents also a technique for the estimation of the parameters of the proposed magnetic model, which is based on stand-still tests without the need to lock the rotor. The identification process has been performed based on the minimization of a suitably defined error function including the difference between the measured and estimated stator fluxes. The proposed parameter estimation technique has been tested in both numerical simulation and experimentally on a suitably developed test set-up, permitting the experimental validation of the proposed model.

Item Type: Journal Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User: Fulori Nainoca - Waqairagata
Date Deposited: 23 Jan 2022 21:44
Last Modified: 23 Jan 2022 21:44

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