Abstract
In order to monitor the behavior of the solar cell, we tried to identify the intrinsic structure of the double diode model of the solar cell for different values of temperature and irradiance. In this context, we tried to predict the values of the seven electrical parameters of solar cell according to the two meteorological factors by using a feedforward artificial neural network. This tool allows a dynamic prediction of the seven parameters. To achieve our goal, we trained our network by the Levenberg–Marquardt algorithm using learning data and we tested its ability of prediction by a test data which are completely different. Therefore, our network determines the seven parameters in an optimal way, such as it gives the appropriate value of each electrical parameter for any value of temperature and irradiance. The obtained results show how each parameter varies according to the two meteorological factors.
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Dkhichi, F. (2022). Parameter Prediction of Solar Cell’s Double Diode Model Using Neural Network. In: Bendaoud, M., Wolfgang, B., El Fathi, A. (eds) The Proceedings of the International Conference on Electrical Systems & Automation. ICESA 2021. Springer, Singapore. https://doi.org/10.1007/978-981-19-0035-8_7
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DOI: https://doi.org/10.1007/978-981-19-0035-8_7
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