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Erschienen in: Electrical Engineering 1/2021

12.08.2020 | Original Paper

Implicit predictive flux control for high-performance induction motor drives

verfasst von: Mahmoud A. Mossa, Silverio Bolognani

Erschienen in: Electrical Engineering | Ausgabe 1/2021

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Abstract

The paper aims to present a new formulation of a model predictive control for induction motor (IM) drives. The proposed control approach is based on utilizing the stator flux components as control variables; consequently, the cost function can be formulated in terms of only flux terms avoiding the use of weighting factor usually present in the model predictive direct torque control (MP DTC) techniques. The motor is modelled considering stator iron losses. An effective stator flux estimator is used to enhance the flux predictive control. A loss minimization criterion (LMC) is proposed in order to minimize the machine losses, especially at light load operation. In order to simplify the overall control scheme, the PWM strategy is eliminated and new model predictive flux control (MP FC) is implemented by the finite control set principle which enables the direct selection of voltage vectors among those (eight) delivered by the inverter. To prove the validity of the proposed MP FC, a detailed comparative study has been carried out between the performances of the IM drive under the classic MP DTC and the proposed MP FC control approaches (with and without the LMC). Dynamic performance of the drive is firstly tested by MATLAB/Simulink; then, a dSpace 1104 fast control prototyping board is used for the experimental verifications. The obtained results approve the improved dynamic performance of the IM drive under the proposed MP FC technique in comparison with the MP DTC.

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Metadaten
Titel
Implicit predictive flux control for high-performance induction motor drives
verfasst von
Mahmoud A. Mossa
Silverio Bolognani
Publikationsdatum
12.08.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Electrical Engineering / Ausgabe 1/2021
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-020-01081-9

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