Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

A number of different analytical and numerical methods have previously been proposed to estimate solar cell/module parameters. For a single diode model, the parameters include the photocurrent, the saturation current, the parasitic series and shunt resistances, and the ideality factor. Among the proposed optimization techniques, the differential evolution (DE) based method besides its computational advantage, provides better accuracy compared to other methods. However, it is not an easy task to determine the control parameters for the DE algorithm. This paper presents an improved adaptive DE (IADE) based optimization technique. New formulas for the scaling factor and crossover rate are proposed. The proposed IADE algorithm uses a simple structure based on the feedback of fitness value in the evolutionary process. It provides better performance for estimation of the solar cell and module parameter values than other popular optimization methods such as particle swarm optimization, genetic algorithm, conventional DE, simulated annealing (SA), and a recently proposed analytical method. Comparisons with existing methods for solar cell and module parameter estimation are presented using both synthetic and experimental data under various environmental conditions. The proposed scheme offers higher accuracy, and also eliminates the need for users to manually tune the control parameters of DE algorithm.