01993nas a2200241   4500000000100000000000100001008004100002260001200043653001700055653002500072653002300097653001100120653001600131100001600147700001800163700002100181245012000202856008100322300001200403490000600415520131600421022001401737       2021                            d  c06/202110aOptimization10aParameter Estimation10aRenewable energies10aEnergy10aDiode Model1 aHegazy Rezk1 aJouda Arfaoui1 aMohamed R. Gomaa00aOptimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_15.pdf  a145-1550 v63 aThe performance of a solar photovoltaic (PV) panel is examined through determining its internal parameters based on single and double diode models. The environmental conditions such as temperature and the level of radiation also influence the output characteristics of solar panel. In this research work, the parameters of solar PV panel are identified for the first time, as far as the authors know, using hybrid particle swarm optimization (PSO) and grey wolf optimizer (WGO) based on experimental datasets of I-V curves. The main advantage of hybrid PSOGWO is combining the exploitation ability of the PSO with the exploration ability of the GWO. During the optimization process, the main target is minimizing the root mean square error (RMSE) between the original experimental data and the estimated data. Three different solar PV modules are considered to prove the superiority of the proposed strategy. Three different solar PV panels are used during the evaluation of the proposed strategy. A comparison of PSOGWO with other state-of-the-art methods is made. The obtained results confirmed that the least RMSE values are achieved using PSOGWO for all case studies compared with PSO and GWO optimizers. Almost a perfect agreement between the estimated data and experimental data set is achieved by PSOGWO.  a1989-1660