InGaN/GaN tandem solar cell parameter estimation: a comparative study

Abstract

In this paper, two hybrid estimation approaches, hybrid genetic algorithm (TR-GA) and hybrid particleswarm optimization (TR-PSO), are used to estimate single-diode model InGaN/GaN solar cell parameters from J–Vexperimental data under AM0 illumination. These parameters are photocurrent density (Jph ), reverse saturation currentdensity (Js ), ideality factor (A), series resistance (Rs ), and shunt resistance (Rsh ). The trust region (TR) method usedin both approaches provides the initial conditions and helps to avoid the problem of premature convergence (due to localminimum). Simulation results based on the minimization of the mean square error between experimental and theoreticalJ–V characteristics show that both applied methods have a similar degree of efficiency in terms of precision, whereasthe TR-PSO method is more efficient in terms of convergence speed. The effect of different extracted parameters on thecharacteristics J–V and P–V is evaluated in a simulation study of an identified model.