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Title: Chebyshev neural network-based model for dual-junction solar cells
Authors: Patra, Jagdish Chandra
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Physical sciences and engineering
Issue Date: 2011
Source: Patra, J. C. (2011). Chebyshev Neural Network-Based Model for Dual-Junction Solar Cells. IEEE Transactions on Energy Conversion, 26(1), 132-139.
Series/Report no.: IEEE transactions on energy conversion
Abstract: Design and development process of solar cells can be greatly enhanced by using accurate models that can predict their behavior accurately. Recently, there has been a surge in research efforts in multijunction (MJ) solar cells to improve the conversion efficiency. Modeling of MJ solar cells poses greater challenges because their characteristics depend on the complex photovoltaic phenomena and properties of the materials used. Currently, several commercial complex device modeling software packages, e.g., ATLAS, are available. But these software packages have limitations in predicting the behavior of MJ solar cells because of several assumptions made on the physical properties and complex interactions. Artificial neural networks have the ability to effectively model any nonlinear system with complex mapping between its input and output spaces. In this paper, we proposed a novel Chebyshev neural network (ChNN) to model a dual-junction (DJ) GaInP/GaAs solar cell. Using the ChNN, we have modeled the tunnel junction characteristics and developed models to predict the external quantum efficiency, and I-V characteristics both at one sun and at dark levels. We have shown that the ChNN-based models perform better than the commercial software, ATLAS, in predicting the DJ solar cell characteristics.
ISSN: 0885-8969
DOI: 10.1109/TEC.2010.2079935
Rights: © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [DOI:].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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