Multiple-Vector Direct Model Predictive Control for Grid-Connected Power Converters with Reduced Calculation Burden

Abstract

This paper proposes a multiple-vector direct model predictive control (MV-DMPC) scheme with reduced calculation burden for grid-connected power converters. The proposed control scheme is based on the discrete space vector modulation (DSVM) technique, where the real voltage vectors (VVs) of the converter are employed together with new virtual VVs to improve the steady-state performance of the proposed controller. Furthermore, in order to reduce the calculation burden of the proposed strategy, a deadbeat function is presented to directly compute the reference voltage vector from the demanded reference current/power. Then, the optimal real or virtual voltage vector is selected based on a certain cost function to apply in the next sampling instant. The performance of the proposed method is validated via simulation results and compared with that of the conventional DMPC and the well-known voltage oriented control (VOC) with proportional-integral (PI) controllers.