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... [ view full abstract ]

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 IxV 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.