maximum power point tracking controller for photovoltaic systems based on the weighted mean of vectors algorithm optimization algorithm under partial shading conditions and temperature changes

Under partial shading conditions (pscs), photovoltaic (pv) systems exhibit non-uniform p-v characteristic curves with multiple local maximum power points (lmpp). consequently, conventional maximum power point tracking (mppt) algorithms often fail to accurately track the global maximum power point (gmpp), becoming trapped at local maxima and resulting in power loss. this paper introduces a novel mppt controller based on the weighted mean of vectors (info) optimization algorithm to address these challenges. the info algorithm integrates three key operators: updated law, vector combination, and local search, offering advantages such as rapid convergence, high efficiency, and optimized power extraction under both pscs and temperature changes (tcs). to validate the effectiveness of the info algorithm under pscs and tcs, a partial shading platform featuring three pv modules with identical characteristics but varying radiation levels is utilized. simulation studies compare the info algorithm against traditional perturb and observe (p o), particle swarm optimization (pso), and overall distribution particle swarm optimization (od-pso) algorithms in terms of mppt performance. results demonstrate that the info algorithm achieves a 60% reduction in tracking time and a 0.02% increase in mppt controller efficiency compared to these methods. overall, the proposed info algorithm achieves a balanced approach between exploration and exploitation stages, enhancing robustness, efficiency, and reducing tracking time in mppt applications.