Neural network based solar cell model

This paper presents a neural network based approach for improving the accuracy of the electrical equivalent circuit of a photovoltaic module. The equivalent circuit parameters of a PV module mainly depend on solar irradiation and temperature. The dependence on environmental factors of the circuit parameters is investigated by using a set of current–voltage curves. It is shown that the relationship between them is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the neural network is utilized to overcome these difficulties. The neural network is trained once by using some measured current–voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly without solving any nonlinear implicit equations that is necessary in conventional methods. To verify the proposed model, an experimental set up is installed. The comparison between the measured values and the proposed model results shows higher accuracy than the conventional model for all operating conditions.