Model Predictive Control based on Dynamic Voltage Vectors for Six-phase Induction Machines

Abstract

Model predictive control (MPC) has been recently suggested as an interesting alternative for the regulation of multiphase electric drives because it easily exploits the inherent advantages of multiphase machines. However, the standard MPC applies a single switching state during the whole sampling period, inevitably leading to an undesired x y voltage production. Consequently, its performance can be highly degraded when the stator leakage inductance is low. This shortcoming has been, however, mitigated in recent work with the implementation of virtual/synthetic voltage vectors (VVs) in MPC strategies. Their implementation reduces the phase current harmonic distortion since the average x y voltage production becomes null. Nevertheless, VVs have a static nature because they are generally estimated offline, and this implies that the flux/torque regulation is suboptimal. Moreover, these static VVs also present some limitations from the point of view of the dc-link voltage exploitation. Based on these previous limitations, this article proposes the implementation of dynamic virtual voltage vectors (DVVs), where VVs are created online within the MPC strategy. This new concept provides an online optimization of the output voltage production depending on the operating point, resulting in an enhanced flux/torque regulation and a better use of the dc-link voltage. Experimental results have been employed to assess the goodness of the proposed MPC based on DVVs.