Abstract
To maximize photovoltaic (PV) energy extraction, this study proposes a novel hybrid maximum power point tracking (MPPT) method that combines artificial neural networks (ANNs) with particle swarm optimization (PSO). The ANN-PSO controller is integrated within a PV-battery microgrid system and enables efficient tracking of the maximum power output while minimizing oscillations. The MPPT unit operates alongside a droop-controlled inverter to coordinate the power flow between the PV array and battery energy storage system (BESS), supporting dynamic transitions between grid-connected and islanded modes. The controller monitors the current and voltage at the point of common coupling (PCC) to ensure reliable power delivery. By enhancing the accuracy of irradiance estimations, the ANN-PSO approach improves tracking responsiveness and overall system efficiency. The proposed strategy is benchmarked against traditional and intelligent MPPT techniques, including Perturb & Observe, ANFIS-PSO, and PSO-SMC, under varying irradiance and load conditions. The performance is validated through detailed MATLAB/Simulink simulations. The results demonstrate superior tracking performance and faster, more stable microgrid operation, highlighting the controller's potential for efficient renewable energy integration. This work supports the advancement of intelligent, autonomous energy systems and contributes to the development of resilient, grid-interactive solar microgrids. This research supports SDG 7: Affordable and Clean Energy by enhancing the efficiency and integration of solar energy systems, and it aligns with SDG 9: Industry, Innovation, and Infrastructure through the development of advanced control strategies for smart power grids.