Computationally Efficient Model Predictive Direct Power Control with Online Finite Set Model Inductance Estimation Technique for Grid-Connected Photovoltaic Inverters

Abstract

Model predictive direct power control (MP-DPC) is considered as a convenient control approach for grid-connected inverters, as it eliminates the need for internal control loops and external modulators. In this paper, a computationally efficient MP-DPC scheme is presented, which overcomes the main drawback of the traditional scheme by reducing the calculation burden. Based on the values of the reference and actual power, the reference voltage vector of the inverter can be calculated. By locating this reference voltage vector in the $\alpha - \beta$ reference frame, only three iterations are required. Hence, the cost function is optimized with a reduced calculation load. The dynamic behavior of the computationally efficient MP-DPC, traditional MP-DPC, and lookup table direct power control (LT-DPC) for grid-connected photovoltaic inverters is compared by simulation results. Furthermore, a novel online inductance estimation technique is proposed to enhance the robustness of the control scheme against any inductance variation.