Extraction of uncertain parameters of single-diode photovoltaic module using hybrid particle swarm optimization and grey wolf optimization algorithm

Accurate modeling of the photovoltaic (PV) module is essential because of the comprehensive system installation in electrical power stations. The scientists have therefore suggested a photovoltaic single-diode model (SDM) for effective PV modelling. The SDM is a simple and non-linear model comprising five unknown parameters. This paper, therefore, presents a novel hybrid approach called particle swarm optimization (PSO) and grey wolf optimization (GWO), in order to extract unknown parameters, such as I_sd, I_p, a, R_se, and R_shfrom the SDM model. This paper also shows a new cost function based on the values of the datasheet instead of using extensive experiments. This paper, therefore, used standard test condition (STC) data to estimate two parameters by optimizing three remaining parameters by using PSOGWO algorithm. This proposed algorithm is applied to two commercial PV panels, namely KC200GT and SQ85, to find its parameters. Following this, the I-V curves of these PV modules were plotted under STC for five individual runs of the simulation. To prove the performance of the proposed PSOGWO algorithm, it is compared based on the statistical results with other algorithms, such as GWO and hybrid GWO-cuckoo search (GWOCS).