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https://hdl.handle.net/10356/94184
Title: | Neural network-based model for dual-junction solar cells | Authors: | Patra, Jagdish Chandra | Keywords: | DRNTU::Engineering::Electrical and electronic engineering::Power electronics | Issue Date: | 2010 | Source: | Patra, J. C. (2010). Neural network-based model for dual-junction solar cells. Progress in Photovoltaics: Research and Applications, 19, 33-44. | Series/Report no.: | Progress in photovoltaics: research and applications | Abstract: | Design and development of solar cells can be substantially improved by using models which can provide accurate estimation of complex device characteristics. The artificial neural network (NN)-based models which learn from examples is an effective modeling technique that overcomes the deficiencies of conventional analytical techniques. In this paper, we propose NN-based modeling techniques for estimation of behavior of dual-junction (DJ) GaInP/GaAs solar cells involving complex phenomena, e.g., tunneling effect and complex interactions between the junctions. With extensive computer simulations we have compared performance of NN-based models with that of a sophisticated device simulator, ATLAS form Silvaco. We have shown that the NN-based models are able to estimate the solar cell characteristics close to that of the experimentally measured response. Compared with the response from ATLAS-based models, the NN-based models provide better results in estimation of tunneling phenomenon, determination of external quantum efficiency and I–V characteristics of DJ solar cells. | URI: | https://hdl.handle.net/10356/94184 http://hdl.handle.net/10220/7097 |
ISSN: | 1062-7995 | DOI: | 10.1002/pip.985 | Schools: | School of Computer Engineering | Rights: | © 2010 John Wiley & Sons, Ltd. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Journal Articles |
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