QP Formulation-Based Model Predictive Control (MPC) for Buck-Boost Converter-Fed Inverter-Based PV Systems

The integration of renewable energy sources, particularly photovoltaic (PV) systems, into modern power networks has created the need for advanced control strategies capable of handling uncertainties in solar irradiance and dynamic load conditions. This work presents the design and implementation of a Model Predictive Control (MPC) strategy for a PV-fed buck–boost inverter system, with a focus on voltage regulation. The proposed approach employs Quadratic Programming (QP)–based optimization to determine the optimal duty cycle of the converter, thereby ensuring smooth operation, accurate tracking, and enhanced power quality. The developed Simulink model is validated under diverse scenarios including sudden changes in irradiance and variable load conditions. Performance is benchmarked against Linear Programming (LP)–based MPC reported in the literature. Simulation results confirm that the QP-based MPC outperforms LP-based MPC by achieving lower overshoot (~2%), reduced DC voltage ripple, and higher efficiency (>99%). Furthermore, the smoother control signal reduces switching stress and enhances inverter output quality, with total harmonic distortion (THD) maintained below 2%. The findings establish QP-based MPC as a robust and efficient control strategy for PV inverter systems, offering improved transient and steady-state performance while addressing the practical challenges of renewable energy integration.