A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

The necessity for clean and sustainable energy has shifted the sector’s interest in renewable sources. Photovoltaics (PV) is most popular source because sun ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, insolation conditions. Based these factors, Maximum Power Point Tracking (MPPT) a mechanism formulated as an optimization problem adjusting to deliver maximum load. Under full conditions, varying solar panel temperatures, loads MPPT convex problem. when surface partially shaded, multiple peaks are created versus voltage (P-V) curve making non-convex. Unfortunately, all strategies under partial shading applied previous works, from traditional techniques Machine Learning recently proposed Nature-inspired algorithms, were either computationally expensive or/and led extensive losses. To this end, work presents algorithm that builds upon metaheuristic algorithms reduce their complexity further mitigate losses during tracking. Our experimental results demonstrated converges faster point lower tracking compared two very