Finite-Control-Set Model Predictive Direct Power Control of Grid-Connected Photovoltaic Inverters (مجلة الفاروق للعلوم, 2026)
This paper proposes a model predictive direct power control strategy for photovoltaic grid-connected inverters based on instantaneous power theory in a rotating d-q reference frame. This method shows a discrete power prediction model using sampled grid voltage and current, directly predicting the inverter's future active and reactive power without requiring an internal current control loop or a pulse width modulation stage. A finite control set model predictive control algorithm is used to evaluate the available inverter voltage vector. The optimal switching vector is selected by minimizing a cost function that defined as the sum of the absolute tracking errors between the predicted and reference active power and the reference active and reactive power. To verify the effectiveness of the proposed control method, simulation studies were conducted under different operating conditions, including step changes in active and reactive power and fluctuations in solar irradiance. The results show that the photovoltaic grid-connected inverter attains fast power point tracking with reduction in control complexity, and adequate steady-state and dynamic performance. These results verify that the proposed model predictive direct power control strategy is a feasible and effective solution for improving the dynamic response and control performance of photovoltaic grid-connected inverter systems.