Abstract
The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).
Highlights
Nowadays, it is obligatory for world governments to integrate renewable energy resources into electrical power networks because of the energy crisis, climate change, environmental concerns, and political challenges
The efficacy of Gorilla Troops Optimization (GTO) was assessed with respect to diverse recent optimizers and other existing optimizers when employed on the SD model (SDM) and DD model (DDM) of various PV modules from the manufacturer’s datasheet; The quality of GTO was evaluated through various experiments and statistical analyses, where the experimental results showed that the GTO technique had better or competitive performance in comparison to recently developed optimizers
The root mean square error (RMSE) of GTO for the DDM of the Kyocera KC200GT PV module was compared to recently developed optimizers such as Equilibrium Optimizer (EO), Forensic-Based Investigation Optimizer (FBI), HEAP, Jellyfish Search (JFS), Marine Predator (MPA), and Enhanced MPA (EMPA), as shown in Figure 8, where 30 runs were performed to obtain the RMSE data for all recent optimizers
Summary
It is obligatory for world governments to integrate renewable energy resources into electrical power networks because of the energy crisis, climate change, environmental concerns, and political challenges. These models were operated with two sets of I-V data from the Kyocera KC200GT PV Module and the STM6-40/36 PV Module These data sets were selected to assess the performances of diverse methods of parameter extraction. Algorithm [50], and an Enhanced MPA (EMPA) [50]; For SDMs and DDMs, the fitness value and convergence characteristics were examined to measure the GTO performance in comparison to other optimizers. The efficacy of GTO was assessed with respect to diverse recent optimizers and other existing optimizers when employed on the SDMs and DDMs of various PV modules from the manufacturer’s datasheet; The quality of GTO was evaluated through various experiments and statistical analyses, where the experimental results showed that the GTO technique had better or competitive performance in comparison to recently developed optimizers.
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