A Proof of Concept Study of Predictive Current Control for VSI-Driven Asymmetrical Dual Three-Phase AC Machines

Multiphase (more than three phases) drives possess interesting advantages over conventional three-phase drives. Over the last years, various topics related to the extension of the classical control schemes to these specifics drives have been covered in depth in literature, such as vector control of...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2009-06, Vol.56 (6), p.1937-1954
Main Authors: Barrero, F., Arahal, M.R., Gregor, R., Toral, S., Duran, M.J.
Format: Article
Language:English
Subjects:
AC machines
Alternating current
Analysis
asymmetrical dual three-phase induction machine
Asymmetry
Computational efficiency
Computational modeling
Cost function
Current control
Electric current control
Electric potential
Induction machines
Machine vector control
Mathematical analysis
Mathematical models
modeling
Multiphase
multiphase drives
Predictive control
Predictive models
Stator windings
Studies
Vectors (mathematics)
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Summary:Multiphase (more than three phases) drives possess interesting advantages over conventional three-phase drives. Over the last years, various topics related to the extension of the classical control schemes to these specifics drives have been covered in depth in literature, such as vector control of a six-phase induction machine with two sets of three-phase stator windings spatially shifted by 30 electrical degrees (also called asymmetrical dual three-phase ac machine). In this paper, a model-based predictive control (MBPC) for the current regulation of asymmetrical dual three-phase AC machines is analyzed. MBPC overcomes the difficulties of multiphase current control, avoiding complex controllers and modulation techniques, but at the expense of an increased computational cost. Simulation results are provided to examine the potential of the control method. The influence of the number of voltage vectors considered to evaluate the predictive model is studied, and different cost functions are analyzed. The computation time needed for the implementation of the control method is discussed to prove its real-time feasibility. Finally, experimental results are given to illustrate the capability of the control method.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2008.2011604