Real-time simulation of solar PV-based grid-supported converter operation using conductance-factor-driven control approach

This research presents a simple and flexible nonlinear control approach comprising a conductance factor (CF) for reference current estimation and a model predictive control (MPC) algorithm for reference current tracking in solar photovoltaic (SPV)-based power systems. This approach is implemented for an SPV-supported multifunctional converter (MFC) under dynamic operation scenarios. The CF approach directly estimates a clean, perfectly grid-synchronized source current reference based on closed-loop energy balance, whereas conventional approaches like DQ, p-q, Kalman, and Adaline are the most widely used methods applied to the load current. As a result, the CF approach avoids the significant phase delays, computational bottlenecks, and noise-tracking vulnerabilities that are present in load-current filtering methods. The aforementioned issues in both grid-supply and grid-injection modes, as well as source and load variations, are effectively handled by the proposed CF-based MPC. By taking into account the converter dynamics, the MPC algorithm reduced the need for PI controllers in grid-integrated power systems by producing the optimal switching signals for the corresponding converters. A real-time simulation setup on an OPAL-RT OP4510 is used to verify the proposed control strategy, which is implemented in the MATLAB/Simulink platform. Analyzed results show that the PV-supported MFC can effectively operate all its grid-supported multifunctional facilities, with improved active filtering efficiency. Moreover, in contrast to traditional techniques, the CF-MPC approach provides improved source current total harmonic distortion ( < 5%) under dynamic operation, meeting the IEEE 1547–2018 standard.