mirage

Chebyshev neural network-based model for dual-junction solar cells

DSpace/Manakin Repository

 

Search DR-NTU


Advanced Search Subject Search

Browse

My Account

Chebyshev neural network-based model for dual-junction solar cells

Show simple item record

dc.contributor.author Patra, Jagdish Chandra
dc.date.accessioned 2011-09-21T07:57:20Z
dc.date.available 2011-09-21T07:57:20Z
dc.date.copyright 2011
dc.date.issued 2011-09-21
dc.identifier.citation Patra, J. C. (2011). Chebyshev Neural Network-Based Model for Dual-Junction Solar Cells. IEEE Transactions on Energy Conversion, 26(1), 132-139.
dc.identifier.issn 0885-8969
dc.identifier.uri http://hdl.handle.net/10220/7096
dc.description.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.
dc.format.extent 8 p.
dc.language.iso en
dc.relation.ispartofseries IEEE transactions on energy conversion
dc.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: http://dx.doi.org/10.1109/TEC.2010.2079935].
dc.subject DRNTU::Engineering::Computer science and engineering::Computer applications::Physical sciences and engineering.
dc.title Chebyshev neural network-based model for dual-junction solar cells
dc.type Journal Article
dc.contributor.school School of Computer Engineering
dc.identifier.doi http://dx.doi.org/10.1109/TEC.2010.2079935
dc.description.version Accepted version
dc.identifier.rims 156797

Files in this item

Files Size Format View
61.Chebyshev Ne ... l-Junction Solar Cells.pdf 916.4Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record

Statistics

Total views

All Items Views
Chebyshev neural network-based model for dual-junction solar cells 567

Total downloads

All Bitstreams Views
61.Chebyshev Neural Network-Based Model for Dual-Junction Solar Cells.pdf 350
2011_IEEETEC_ChebyNN_DJSolarCell.pdf 11

Top country downloads

Country Code Views
United States of America 103
China 53
Singapore 41
Algeria 25
India 20

Top city downloads

city Views
Mountain View 70
Singapore 41
Beijing 21
Biskra 12
Tabriz 9

Downloads / month

  2014-09 2014-10 2014-11 total
61.Chebyshev Neural Network-Based Model for Dual-Junction Solar Cells.pdf 0 0 13 13