Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics - Alanazi, Mohana; Alanazi, Abdulaziz; Memon, Zulfiqar Ali; Csaba, Mako; Mosavi, Amir (Frontiers in Energy Research, 2022)
This study aimed to investigate the global maximum power point tracking (MPPT) of a photovoltaic (PV) system under partial shading conditions (PSCs) through a hill climbing (HC)–artificial electric field algorithm (AEFA) by considering a DC/DC buck converter. The AEFA is inspired by the Coulomb's law of electrostatic force and has a high speed and optimisation accuracy. Because the traditional HC method is unable to perform global search tracking and instead performs local search tracking, the AEFA is used for a global search in the proposed HC-AEFA. The important advantage of the HC-AEFA is that it can perform optimal local and global search tracking. The proposed method is used to derive an MPP with tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power. To validate the proposed method, its capability was evaluated and compared with that of particle swarm optimisation (PSO). Here, the HC-AEFA and PSO methods were used considering the different standard and PSCs and radiation changes. The simulation results indicated that for different models, the proposed method achieved a better problem-solving performance than the other methods owing to its higher efficiency, global power peak, fewer fluctu-ations, and higher convergence speed.