Open AccessBi‐subspace predictive current control of six‐phase PMSM drives based on virtual vectors with optimal amplitude
Author(s) -
Gonçalves Pedro F.C.,
Cruz Sérgio M.A.,
Mendes André M.S.
Publication year - 2019
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2019.0136
Subject(s) - control theory (sociology) , harmonics , subspace topology , total harmonic distortion , stator , torque , electromagnetic coil , amplitude , model predictive control , harmonic analysis , three phase , torque ripple , computer science , engineering , direct torque control , electronic engineering , voltage , physics , induction motor , electrical engineering , artificial intelligence , control (management) , quantum mechanics , thermodynamics
Owing to the low equivalent impedance of six‐phase machines in the secondary subspace, in which currents do not contribute to torque production, low‐order current harmonics with high amplitude are likely to appear in the stator currents, increasing the harmonic distortion and the losses in the machine. In the case of permanent magnet synchronous machines (PMSMs), the main causes of these harmonics are machine asymmetries, non‐linearities of the power converters and back‐electromotive force harmonics. The existing predictive control strategies based on virtual vectors for six‐phase machines only regulate the currents mapped into the fundamental subspace, responsible for flux/torque production and leave the currents mapped into the secondary subspace uncontrolled. This study proposes a novel bi‐subspace predictive control strategy for six‐phase PMSMs with optimal amplitude virtual vectors, which is able to effectively regulate the currents in both subspaces simultaneously, leading to a significant minimisation of the current harmonic distortion. Moreover, the proposed control strategy also allows operating the six‐phase PMSM with a current unbalance between the two sets of windings. Both simulation and experimental results confirm the much better performance of the proposed strategy in comparison with other state‐of‐the‐art predictive control strategies found in the literature.