Optimized MPPT Control for PV Cell using Iterative Feedback Method and Fuzzy Logic

Energy losses in photovoltaic (PV) systems due to partial shading are a significant problem and thus result in overall efficiency losses. The research herein introduces a novel Maximum Power Point Tracking (MPPT) algorithm that combines Fuzzy Logic with Ant Colony Optimization (ACO) to offset such negative impacts. The system efficiently selects the best power point utilizing ACO to evaluate a number of operating points in real time, thus improving adaptability when irradiance and temperature trends are fluctuating. Fuzzy Logic then adjusts the tracking parameters, thus improving the accuracy of power extraction. The suggested system, implemented by an ATmega328 microcontroller, monitors solar panels, an energy storage battery, and a load. Experimental results illustrate improved performance compared to current MPPT mechanisms, showing improved efficiency in energy harvesting under partial shading. The contribution of the method lies in the hybrid optimization method, which significantly enhances the reliability and efficacy of PV systems and thus the advancement of renewable energy technology.