Application of artificial neuronal networks in extracting parameters of solar cells
Mohammed Khalis, R. Masrour
Abstract
This paper presents a new neural network-based approach that aims to use the back propagation multilayer perceptual (MLP) propagation algorithm to improve the extraction of parameters from a solar cell based on the single-diode model from the experimentally measured characteristic I ( V ). The I ( V ) current function as a model function for the neural network, is used the Lambert function W and I can be expressed as an explicit function. The main five parameters of interest of the function I ( V ) are the photocurrent, I ph , the saturation current in inverse diode, I 0 , the ideality factor of the diode, n , the resistance in series, R S and shunt resistance, R Sh . We have used the Matlab to find the five parameters of the cell. To verify the proposed approach, we chose two different solar cells made of mono- and polycrystalline silicon. The comparison between the measured values and the results of the proposed model shows great precision. Finally, the values found of the five parameters by our approach are compared with those found by the method of least squares (MLS).