Photovoltaic parameter extraction using Shuffled Complex Evolution

This paper proposes a method of extracting the intrinsic parameters of a Photovoltaic (PV) generator by using Shuffled Complex Evolution (SCE) technique for a single-diode PV model. The characteristic equation of a single-diode PV generator presents a nonlinear behavior, which its solution to obtain the intrinsic parameters from an I × V experimental curve requires to use nonlinear optimization methods. To evaluate the effectiveness of the usage of SCE in extracting the intrinsic parameters of a PV generator, it is presented a comparison with Genetic Algorithms (GA) nonlinear optimization method. This evaluation uses statistic analysis as comparison criteria for an unknown PV module and relative error for each parameter in a known PV cell. The proposed SCE and AG are consider evolutionary optimization methods, so this paper shows that SCE needs less iterations/generations to converge than the other. Results show that the proposed method is feasible, faster and presents better results than the conventional technique.