Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules

One of the most significant barriers to broadening use solar energy is low conversion efficiency, which necessitates development novel techniques enhance equipment design. The correct modeling and estimation cell parameters are critical for control, design, simulation PV panels achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity become caught in some local optima. In study, Growth Optimization (GO) algorithm developed simulated from humans’ learning reflection capacities social growing activities. It based on mimicking two stages. First, procedure through people mature by absorbing information others. Second, examining one’s weaknesses altering aid improvement. estimating different modules, RTC France Kyocera KC200GT manufacturing technology modeling. Three present-day contrasted GO’s performance valley optimizer (EVO), Five Phases Algorithm (FPA), Hazelnut tree search (HTS) algorithm. results electrical properties systems due implemented GO technique. Additionally, technique can determine unexplained considering diverse operating settings varying temperatures irradiances. For module, achieves improvements 19.51%, 1.6%, 0.74% compared EVO, FPA, HTS PVSD 51.92%, 4.06%, 8.33% PVDD, respectively. proposed 94.71%, 12.36%, 58.02% 96.97%, 5.66%, 61.20%