Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks
External quantum efficiency (EQE) of a solar cell provides information on the internal operations of the solar cells which can be used in optimization of solar cell design. The EQE of solar cells for space applications is adversely affected by the influence of charged particles in space. Usually num...
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sg-ntu-dr.10356-844962020-05-28T07:18:14Z Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks Patra, Jagdish C. Maskell, Douglas L. School of Computer Engineering External quantum efficiency (EQE) of a solar cell provides information on the internal operations of the solar cells which can be used in optimization of solar cell design. The EQE of solar cells for space applications is adversely affected by the influence of charged particles in space. Usually numerical model based software, e.g., PC1D, are used to estimate the EQE and fitted with the measured EQE to obtain degradation performance of space solar cells. However, the accuracy of these models may be limited due to complex phenomena and interactions occurring between the junctions of the solar cells and the nonlinear influence of charged particles. In this paper we propose an artificial neural network (ANN)-based model to estimate the EQE performance of triple-junction InGaP/GaAs/Ge solar cells under the influence of a wide range of charged particles. Using the experimental data from Sato et al. [1], it is shown that the ANN-based models provide a better estimate of the EQE than the PC1D model [1] in terms of mean square error and correlation coefficient. 2013-07-25T06:37:58Z 2019-12-06T15:46:07Z 2013-07-25T06:37:58Z 2019-12-06T15:46:07Z 2012 2012 Journal Article Patra, J. C., & Maskell, D. L. (2012). Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks. Renewable Energy, 44, 7-16. 0960-1481 https://hdl.handle.net/10356/84496 http://hdl.handle.net/10220/12246 10.1016/j.renene.2011.11.044 en Renewable energy © 2011 Elsevier Ltd. |
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External quantum efficiency (EQE) of a solar cell provides information on the internal operations of the solar cells which can be used in optimization of solar cell design. The EQE of solar cells for space applications is adversely affected by the influence of charged particles in space. Usually numerical model based software, e.g., PC1D, are used to estimate the EQE and fitted with the measured EQE to obtain degradation performance of space solar cells. However, the accuracy of these models may be limited due to complex phenomena and interactions occurring between the junctions of the solar cells and the nonlinear influence of charged particles. In this paper we propose an artificial neural network (ANN)-based model to estimate the EQE performance of triple-junction InGaP/GaAs/Ge solar cells under the influence of a wide range of charged particles. Using the experimental data from Sato et al. [1], it is shown that the ANN-based models provide a better estimate of the EQE than the PC1D model [1] in terms of mean square error and correlation coefficient. |
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School of Computer Engineering |
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School of Computer Engineering Patra, Jagdish C. Maskell, Douglas L. |
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Article |
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Patra, Jagdish C. Maskell, Douglas L. |
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Patra, Jagdish C. Maskell, Douglas L. Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks |
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Patra, Jagdish C. |
title |
Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks |
title_short |
Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks |
title_full |
Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks |
title_fullStr |
Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks |
title_full_unstemmed |
Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks |
title_sort |
modeling of multi-junction solar cells for estimation of eqe under influence of charged particles using artificial neural networks |
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2013 |
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https://hdl.handle.net/10356/84496 http://hdl.handle.net/10220/12246 |
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1681056193019117568 |