Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using hill climbing (HC)–artificial electric field algorithm (AEFA) considering DC/DC buck converter. The AEFA inspired by Coulomb’s law electrostatic force and has high speed optimization accuracy. Because the traditional HC method cannot perform global search instead performs local tracking, used for in proposed HC-AEFA. critical advantage HC-AEFA that it desirable performing searches. hybrid implemented to derive an MPP tuning converter duty cycle, objective function maximizing PV extracted power. Its capability evaluated compared with well-known particle swarm (PSO), standards, PSCs, radiation changes conditions. efficiency most challenging pattern (third pattern) HC-AEFA, HC, PSO obtained at 99.93, 90.35, 98.85, 99.80%, respectively. analysis population-based process different algorithms proved faster convergence lower iterations than other methods. So, superiority subjected patterns confirmed higher peak, fewer fluctuations, speed, dynamic Static-efficiency