Akademik Veri Yönetim Sistemi
Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.40, sa.1, ss.555-572, 2024 (SCI-Expanded)
Energy production with photovoltaic (PV) systems has an essential share among renewable energy sources. The energy efficiency of the panels varies between 11-28 %. The energy produced using PV systems is expected to provide maximum efficiency. Radiation and temperature values are the dominant atmospheric factors determining the amount of energy produced in solar energy systems. Various factors such as pollution on the panels, clouding in the sky and environmental factors cause the radiation values to which the panels are exposed to decrease. This condition is called partial shading (PSC). In PV arrays operating under different radiation values, one global maximum power point (GMPP) and more than one local maximum power point (LMPP) are formed. PV arrays must be operated in GMPP in PV systems operating under PSC to obtain maximum power output. For this purpose, different maximum power point tracker (MPPT) designs and optimization algorithms have been developed in the literature. This study uses different meta-heuristic search algorithms to track the maximum power point. The applied search algorithms are particle swarm optimization-gravitational search algorithm (PSO-GSA), grey wolf algorithm (GWO) and cheetah optimizer (CO) search algorithm, respectively. The mathematical model required for monitoring the maximum power point is written as code in the Matlab environment. The results obtained are compared with Matlab/Simulink and real-time measurement data. The performance and superiority of the proposed cheetah optimizer algorithm over the other tested algorithms is demonstrated using more than 15 different statistical methods.