Artificial Neural Network Based Model of Photovoltaic Cell

Messaouda Azzouzi, Lakhdar Bessissa, Mona Fouad Moussa, Dumitru Popescu, Catalin Petrescu

Abstract


This work concerns the modeling of a photovoltaic system and the prediction of the sensitivity of electrical parameters (current, power) of the six types of photovoltaic cells based on voltage applied between terminals using one of the best known artificial intelligence technique which is the Artificial Neural Networks. The results of the modeling and prediction have been well shown as a function of number of iterations and using different learning algorithms to obtain the best results. 


Keywords


neural network; solar cell; renewable energy; prediction; modelling

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References


Initial. Zekai.«Sen. solar energy fundamentals and mode ling techniques.» Springer, 2008, pp. 15-25.

V. Badescu. «Modeling solar radiation at the earth’s surface.» Springer, 2008, pp. 10-21.

M. Chegaar, A. Lamir and A. Chibani. «Estimation global solar radiation using sunshine hours.» 4éme séminaire International Physique Energétique, Bechar ,2000.

International Physique Energétique, Bechar, 2000.

W. Omran. Performance analysis of grid connected photovoltaic systems. These de Doctorat, Université de Waterloo, 2010.

D. U. Sauer. «Electrochemical storage for photovoltaics» Fraunhofer Institute for Solar Energy Systems ISE, Freiburg, Germany, 2004.

D. Thévenard and M. Ross. «Validation and verification of component models and system models for the PV.» CETC-internal report 2003-035, Canada, 2003.

G. Notton et al. «Influence d’un ombrage partiel sur la production électrique d’un module photovoltaïque es silicium monocristallin.» Revue des énergies renouvelables, vol.13, no.1, pp. 49-62, 2010.

T. Townsend. A Method for estimating the long term performance of direct coupled photovoltaic systems. MS Thesis, solar energy laboratory, university of Wisconsin, Madison, 1989.

R. Roshanak. Réalisation et caractérisation des cellules solaires organiques à couche composites polymères incluant des nanotubes de carbones. Thèse de Doctorat de l'Université de Limoges, 2008.

R. Khezzar et al. «Comparaison entre les différents modèles électriques et détermination des paramètres de la caractéristique I-V d’un module photovoltaïque.» Revue des énergies renouvelables, vol.13, no.3, pp. 379-388, 2010.

C. Bishop. Neural networks for pattern recognition. Oxford University Press, 1995.

D. Dreyfus, J-M. Martinez, M. Samuelides, M. B. Gordon, F. Badran, S. Thiria and L. Hérault. «Réseaux de neurones.» Méthodologie et applications, Eyrolles, 2ème édition, 2004, pp. 12-35.

J. Hopfield. «Neural networks and physical systems with emergent collective computational abilities.» Proceedings of the National Academy of Sciences. vol.79, pp. 2554-2558,1982.




DOI: http://dx.doi.org/10.21622/resd.2017.03.2.218

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Copyright (c) 2017 Messaouda Azzouzi, Lakhdar BESSISSA, Mona Fouad MOUSSA, Dumitru POPESCU, Catalin PETRESCU

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Renewable Energy and Sustainable Development

E-ISSN: 2356-8569

P-ISSN: 2356-8518

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

resd@aast.edu