Efficient finite control set-model predictive control for grid-connected photovoltaic inverters

Abstract

Finite control set-model predictive control (FCS-MPC) is a promising control scheme for power converters/inverters and electric drives due to its flexibility and simplicity. Using all the possible voltage vectors (VVs) generated by the inverter (7 VVs for 2-level inverter) and a discrete-time model of the system, the future behavior of the inverter can be predicted. Then, a certain cost function is employed for selecting the best switching action that will be applied in the next sampling interval (7 evaluations of the cost function are required). Accordingly, the main disadvantage of the traditional FCS-MPC scheme is the heavy calculation load. In this paper, an efficient FCS-MPC method for a grid-connected photovoltaic (PV) inverter is proposed in order to reduce the calculation burden of the traditional FCS-MPC. Based on the required reference current, the reference voltage vector (VV) is directly calculated. Then the sector where this reference VV is located is identified from its angle. Consequently, the cost function is evaluated only three times to obtain the optimal switching vector. The performance of the proposed efficient FCS-MPC is compared with the traditional FCS-MPC and voltage oriented control (VOC) schemes by simulation results for all operation conditions.