An Effective Method for Parameter Estimation of a Solar Cell

Abstract

Parameter extraction of the photovoltaic cell is a highly nonlinear complex optimization problem. This article proposes a new hybrid version of whale optimization and particle swarm optimization algorithm to optimize the photovoltaic cell parameters. The exploitation ability of particle swarm optimization with adaptive weight function is implemented in the pipeline mode with a whale optimization algorithm to improve its exploitation capability and convergence speed. The performance of the proposed hybrid algorithm is compared with six different optimization algorithms in terms of root mean square error and rate of convergence. The simulation result shows that the proposed hybrid algorithm produces not only optimized parameters at different irradiation levels (i.e., 1000 W/m2, 870 W/m2, 720 W/m2, and 630 W/m2) but also estimates minimum root mean square error even at a low level of irradiations. Furthermore, the statistical analysis validates that the average accuracy and robustness of the proposed algorithm are better than other algorithms. The best values of root mean square error generated by the proposed algorithm are 7.1700×10−4 and 9.8412×10−4 for single-diode and double-diode models. It is observed that the estimated parameters based on the optimization process are highly consistent with the experimental data.