Experimental implementation of a fast dynamic response finite set control–model predictive control for voltage source converter in standalone microgrid

In this paper, a robust current control prediction strategy for a photovoltaic standalone three-phase two-level voltage source inverter is proposed to overcome the limitations of conventional controllers in microgrid applications. The main problem addressed is achieving accurate current tracking and power quality improvement under parameter uncertainties, external disturbances, and nonlinear operating conditions. This challenge is critical because stable and clean power delivery is essential for the reliability of standalone microgrids and their compliance with international standards. For this purpose, the discrete-time mathematical model of the system is first established. Second, an optimized two-step prediction horizon algorithm is designed. The adopted cost function allows to determine the minimum error between the predicted and reference load currents and, accordingly, select the optimal switching state while compensating delay and reducing repeated computations. To demonstrate the effectiveness of the proposed approach, various experimental tests are carried out. These experiments attest the superior performance of the Finite control set model predictive control (FSC-MPC) technique over the conventional PI control strategy in terms of the THD, reference tracking error, dynamic response, robustness under uncertainties of system parameters and external disturbances. Furthermore, the proposed FSC-MPC exhibits enhanced performance over advanced strategies, including the New Modified Discontinuous PWM Technique, the PWM-based approach with a novel switching algorithm, and the Asymmetrical Multi-Step Direct MPC, particularly in terms of THD, while ensuring its compliance with the IEEE Std 519-2014. These findings confirm that the proposed controller enhances current regulation and power quality in standalone microgrids, making it a promising candidate for practical deployment in renewable-based distributed generation systems.