A comparative study of hybrid stochastic algorithms and objective function formulations for extracting parameters of the three-diode photovoltaic model

This work investigates the feasibility and accuracy of applying hybrid meta-heuristic approaches and alternative objective formulations for optimally determining the parameters of the three-diode photovoltaic (3D-PV) model under a variety of meteorological situations. In addition, the Berndt-hall-hall Hausman technique is modified to boot its converge rate by incorporating the 2nd and 4th convergences to improve its stability, discovering the feature space broadly, and precisely providing initial root solutions utilizing robust adaptive arithmetic optimization algorithm (RaAOAAdmBHHH). A variety of statistical matrices are employed to exhibit the superiority of the algorithms in terms of accuracy and convergence rate. The performance of the hybrid algorithms and different objective function designs are evaluated using real experimental data points distributed on under wide range of weather conditions. The experimental findings indicated that the RaAOAAdmBHHH model can, with a comparatively small number of iterations, determine the 3D-PV model's parameters at any state of weather circumstances. Prior to this, it is possible to use the RaAOAAdmBHHH model with confidence to assess and predict the PV cell's output current by realistically reflecting its actual performance.