Parameter Estimation of Photovoltaic Models Based on Enhancement Moutain Gazelle Optimizer Algorithm

Accurate parameter estimation in photovoltaic (PV) system design and simulationis essential for optimizing performance. Traditional numerical, analytical,and hybrid methods often fail to deliver quick and precise results. This researchintroduces enhancements to three fundamental PV models (single-diode, doublediode,and three-diode) and employs the Improved Mountain Gazelle Optimizer(i_MGO) algorithm for parameter extraction. The innovative objective functionproposed aids in minimizing the discrepancy between calculated and measuredvalues. Rigorous experimental validation demonstrates that i_MGO outperformsexisting algorithms, achieving optimal parameter values with minimal rootmean square error (RMSE). The experimental findings illustrate that i_MGOperforms better than the following competing algorithms: Improved MountainGazelle Optimizer (i_MGO), Harris Hawks Optimization (HHO), LightningAttachment Procedure, Optimization Algorithm (LAPO), Sine Cosine Algorithm(SCA), Grey Wolf Optimizer (GWO), African Vultures Optimization Algorithm(AVOA), Hippopotamus Optimization Algorithm (HO), Electric Eel ForagingOptimization (EEFO), Synergistic Swarm Optimization Algorithm (SSOA),Coati Optimization Algorithm (COA), Gazelle Optimization Algorithm (GOA).This comparison demonstrates that the parameters extracted by i_MGO areoptimal, as the discrepancy between measured and calculated data is minimal.The optimal RMSE values for SDM, DDM, and TDM, as determined bythe proposed i_MGO algorithm, are 0.00081373, 0.00073908, and 0.00092975,correspondingly.