Shuffled Complex Evolution on Photovoltaic Parameter Extraction: A Comparative Analysis

This paper proposes a method for extracting the intrinsic parameters of a photovoltaic (PV) generator by using shuffled complex evolution (SCE) technique for a double-diode PV model. The characteristic equation of a double-diode PV presents a nonlinear behavior and the determination of the intrinsic parameters from a I × V experimental curve requires the use of nonlinear optimization methods. To evaluate the accuracy of the SCE technique for extracting the intrinsic PV parameters, a comparison with other well-known methods is presented; in particular, analytic method, Levenberg-Marquardt, genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) are considered. This comparison is performed by using statistical analysis and by estimating the relative error of parameter values; it has been applied to an unknown PV module and to a known PV cell. The obtained results showed that, compared with other evolutionary methods (GA, DE and PSO), the SCE presents the lowest computational time and requires less iterations/generations to converge. All the results prove that the proposed method is feasible, faster, and presents better results than the conventional ones.