Photovoltaic Models Parameters Estimation Based on Elephant Herding Optimization Algorithm

Recently, renewable energy resources have been widely penetrated because of their technical advantages and environmental benefits. So, modeling and simulation of this resource are very important. Obtaining accurate photovoltaic (PV) solar cell's parameters is very significant to monitor maximum power point, performance assessment, regulation, efficiency measurements, and diagnose problems. In this paper, three photovoltaic parameters estimation models are considered. These three models, single-diode, two-diode, and triple-diode models, are presented in this work to investigate the effect of the model on precision. In this, work the Elephant Herding Optimization (EHO) algorithm is employed for the PV three models' parameters estimation. Real experimental data of a 55 mm diameter commercial Radiotechnique-Coprim (RTC France) solar cell was used for the EHO implementation. The suggested algorithm was assessed compared with several recent optimizers like the crow search algorithm, spotted hyena optimization, moth flame optimization, grasshopper optimization algorithm, and Harris hawk's optimization. The results revealed that the estimated I-V and P-V curves are very close to the experimental data at very low error. Also, the results demonstrate different models' effects in fitting the curves and proved the efficiency of the suggested EHO algorithm over other algorithms.