Model Predictive Control of Six-Phase Induction Motor Drives Using Virtual Voltage Vectors

Abstract

The most serious and recent competitor to the standard field oriented control for induction motors (IM) is the finite control set model predictive control (FCS-MPC). Nevertheless, the extension to multiphase drives faces the impossibility to simultaneously regulate the flux/torque and the secondary current components (typically termed $x - y$ in the literature). The application of a single switching state during the whole sampling period inevitably implies the appearance of $x - y$ voltage/currents that increase the system losses and deteriorate the power quality. These circulating currents become intolerably high as per the unit $x - y$ impedance and the switching frequency diminish. Aiming to overcome this limitation, this work suggests the integration of virtual voltage vectors (VVs) into the FCS-MPC structure. The VVs ensure null $x - y$ voltages on average during the sampling period and the MPC approach selects the most suitable VV to fulfill the flux/torque requirements. The experimental results for a six-phase case study compare the standard FCS-MPC with the suggested method, confirming that the VV-based MPC maintains the flux/torque regulation and successfully improves the power quality and efficiency.