Enhanced maximum power point tracking for photovoltaic systems: A Modified African Vulture Optimization (MAVO) algorithm approach

The growing need for renewable energy demands PV systems that can efficiently adapt to dynamic climate conditions. In this work, a novel Modified African Vulture Optimization (MAVO) algorithm for maximum power point tracking (MPPT) is presented that integrates structural algorithmic enhancements with adaptive duty-cycle prediction and addresses rapid irradiance and temperature changes, partial and complex shading, and load variations. Three main structural enhancements are made to the traditional AVO algorithm by MAVO: probabilistic leader selection, enhanced rotational flight, and elite solution retention. Additionally, predictive adjustments for duty cycle estimation under dynamic irradiance and load conditions are incorporated to minimize tracking time, settling time, and power loss. The MATLAB/Simulink environment was used to implement all cases, and the results were compared with some established optimization methods. Simulation results reflect that MAVO attains the supreme average tracking efficiency of 99.97%, the quickest tracking time (50–230 ms), and minimal steady-state oscillations (<0.6 W). Based on comparative, quantitative, and statistical analyses, MAVO outperforms other metaheuristic-based MPPT methods with up to 28% quicker tracking, 27% faster settling, and 3.5% higher power efficiency, demonstrating its robustness, reliability, and effectiveness. The outcomes ensure a reliable and computationally effective MPPT framework for PV systems functioning in abrupt situations. • A Modified African Vulture (MAVO) algorithm has been proposed for maximum power point (MPP) tracking. • Probabilistic leader selection, enhanced rotational flight, and elite solution preservation have been suggested. • Predictive duty-cycle tuning during dynamic variations in load and irradiance has been proposed. • Modifications improve the exploration–exploitation balance and reduce the tracking and settling time. • Successfully tracks the MPP under different scenarios, showing the robustness of the proposed system.