Design and development process of solar cells can be greatly enhanced by using accurate models in order to predict accurately their behaviour. The main aim of this paper is to investigate the application of neural network based PV equivalent circuit model to improve the model accuracy and to show the necessity of including the variation of all parameters according the change of the operating conditions. The radial basis function neural network is utilized to predict the electric current, power and equivalent circuit parameters by only using data of irradiation and temperature. A lot of available experimental data were used for training the radial basis function neural network, which employs a backpropagation algorithm. Simulation and experimental validation is reported.

A radial basis function neural network based approach for the model parameters estimation of a photovoltaic module

CAPIZZI, GIACOMO;NAPOLI, CHRISTIAN;TINA, Giuseppe Marco
2011

Abstract

Design and development process of solar cells can be greatly enhanced by using accurate models in order to predict accurately their behaviour. The main aim of this paper is to investigate the application of neural network based PV equivalent circuit model to improve the model accuracy and to show the necessity of including the variation of all parameters according the change of the operating conditions. The radial basis function neural network is utilized to predict the electric current, power and equivalent circuit parameters by only using data of irradiation and temperature. A lot of available experimental data were used for training the radial basis function neural network, which employs a backpropagation algorithm. Simulation and experimental validation is reported.
9788890584305
Solar cell; Neural model; Circuital model
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/90765
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