A robust parameter estimation approach based on stochastic fractal search optimization algorithm applied to solar PV parameters

Modeling of solar photovoltaic (PV) cell/modules to estimate its parameters with the measured current–voltage (I–V ) values is a very important issue for the control, optimization, and effectiveness of the PV systems. Therefore, in this research work, a robust approach based on Stochastic Fractal Search (SFS) optimization algorithm is introduced to estimate accurate and reliable values of solar PV parameters for its precise modeling. To assess the excellence of the proposed SFS algorithm, different solar PV equivalent circuit models, i.e. single-diode model (SDM), double-diode model (DDM), and PV module model are taken into consideration. The introduced algorithm is examined under three different case studies; (i) first case study: an experimental standard dataset of a commercial R.T.C. France silicon solar cell working at 33°C, and solar radiance of 1000 W/m²; (ii) second case study: using a polycrystalline solar panel STP6 120/36 with 36 cells in series working at 22°C, and (iii) third case study: an experimental dataset of ESP-160 PPW PV module working at 45°C, this experimentation were carried out in the Laboratory of Renewable Energy at Assiut University, Egypt. The results obtained using the proposed method are compared with other recently published works, and hence, the achieved results show the superiority, perfectness, and effective modeling concerning various performance parameters. Thereby, the proposed SFS approach can be used for effective PV modeling to improve the efficiency of the PV system.