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

Authors : Abdelmoumene BENAYAD, Smail BERRAH

Pages : 1896-1907

View : 14 | Download : 10

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In this paper, two hybrid estimation approaches, hybrid genetic algorithm insert ignore into journalissuearticles values(TR-GA); and hybrid particle swarm optimization insert ignore into journalissuearticles values(TR-PSO);, are used to estimate single-diode model InGaN/GaN solar cell parameters from J?V experimental data under AM0 illumination. These parameters are photocurrent density insert ignore into journalissuearticles values($J_{ph}$);, reverse saturation current density insert ignore into journalissuearticles values($J_{s}$);, ideality factor insert ignore into journalissuearticles values($A$);, series resistance insert ignore into journalissuearticles values($R_{s}$);, and shunt resistance insert ignore into journalissuearticles values($R_{sh}$);. The trust region insert ignore into journalissuearticles values(TR); method used in both approaches provides the initial conditions and helps to avoid the problem of premature convergence insert ignore into journalissuearticles values(due to local minimum);. Simulation results based on the minimization of the mean square error between experimental and theoretical J-V characteristics show that both applied methods have a similar degree of efficiency in terms of precision, whereas the TR-PSO method is more efficient in terms of convergence speed. The effect of different extracted parameters on the characteristics J-V and P-V is evaluated in a simulation study of an identified model.
Keywords : Photovoltaic cells, parameter extraction, single diode solar cell model, genetic algorithms, trust region, particle swarm optimization