M²PC for Quasi-Z-Source Inverter Fed by PV/ Battery Storage System-based Flying Squirrel Search Optimization (FSSO)

This paper proposes an advanced control framework for a photovoltaic (PV) system integrated with battery storage and a Quasi-Z-Source Inverter (QZSI), combining Flying Squirrel Search Optimization (FSSO)-based Maximum Power Point Tracking (MPPT) with Modulated Model Predictive Control (M²PC). The FSSO algorithm is employed to accurately and rapidly track the global maximum power point under varying irradiance conditions. Its performance is evaluated against conventional techniques, including Particle Swarm Optimization (PSO) and Perturb and Observe (P&O). The results demonstrate that FSSO achieves a tracking efficiency of 100%, outperforming PSO (97.89%) and P&O (97.16%), while ensuring faster convergence and reduced steady-state oscillations.The QZSI is regulated using M²PC, which predicts the future behavior of system currents and generates optimal control actions through a modulation stage combined with a cost function minimization process. Unlike conventional finite control set MPC, the proposed M²PC ensures a fixed switching frequency and improved harmonic performance. In the proposed architecture, the controller regulates the inductor and load currents according to the available PV power, while the DC-link capacitor voltage is independently controlled using a bidirectional buck–boost converter connected to the battery. This decoupled control strategy reduces control complexity and enhances overall system stability.Simulation results carried out in MATLAB/Simulink validate the effectiveness of the proposed approach, demonstrating improved dynamic response, enhanced power extraction capability, and robust performance under transient conditions. Overall, the integration of FSSO-based MPPT with M²PC-controlled QZSI significantly improves the efficiency, stability, and reliability of PV-battery energy conversion systems.