The implementation of Archimedes optimization algorithm for solar charge controller-maximum power point tracking in partial shading condition

Abstract

Maximum power point tracking (MPPT) enhances the efficiency of solar photovoltaic (PV) systems by ensuring optimal power extraction under varying conditions. MPPT is implemented in solar charge controllers or hybrid inverters connected to PV arrays. The current-voltage (IV) curve, influenced by temperature and irradiance fluctuations, becomes more complex under partial shading, causing multiple local maxima and reducing efficiency. This study proposes an MPPT technique using the Archimedes optimization algorithm (AOA), a novel metaheuristic inspired by Archimedes' principle. The AOA-based MPPT integrates a DC/DC buck converter controlled by an STM32 microcontroller to address challenges in complex shading conditions. Comparative analysis demonstrates the AOA's superiority in achieving high efficiency and fast convergence. The AOA-based MPPT achieved an average efficiency of 93.17% across shading scenarios, outperforming PSO (87.04%) and non-MPPT systems (84.56%). It also exhibited faster average tracking times of 90.5 ms compared to PSO's 100.5 ms, ensuring robust and reliable performance. These results confirm the effectiveness of the AOA-based method in maximizing energy harvesting in real-world PV applications.