Hybrid Artificial Bee Colony and incremental conductance—Algorithm for enhanced MPPT in photovoltaic systems

The growing global demand for electricity necessitates efficient renewable energy solutions, with photovoltaic (PV) systems emerging as a prominent candidate. This study presents a novel hybrid Maximum Power Point Tracking (MPPT) algorithm that integrates the Artificial Bee Colony (ABC) optimization method with the Incremental Conductance (IC) technique, ensuring 100% accurate identification of the Global Maximum Power Point (GMPP) under partial shading conditions. Unlike standalone MPPT methods, the proposed approach leverages the exploratory capabilities of ABC for global search while utilizing IC for fast and precise tracking, achieving a convergence time of 0.37 s and minimal power oscillations of 2.7%. Experimental validation demonstrates the algorithm’s superior performance, attaining 100% efficiency, significantly outperforming standalone IC (74%) and ABC (99.5%) methods. The hybrid ABC-IC algorithm consistently tracks the GMPP, delivering 60 W under optimal irradiation (1000 W/m2) and surpassing conventional techniques such as P&O, FA, and PSO in terms of convergence speed, robustness, and adaptability to dynamic shading conditions. This innovative integration of bio-inspired and deterministic MPPT strategies offers a highly efficient and reliable solution for maximizing PV energy harvesting in real-world environments.