Artificial neural network‐based modelling of compensated multi‐crystalline solar‐grade silicon under wide temperature variations-Reference-Cited by-同舟云学术

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Artificial neural network‐based modelling of compensated multi‐crystalline solar‐grade silicon under wide temperature variations

Published:2016-08 Issue:7 Volume:10 Page:1010-1016
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Generation

Author:

Patra Jagdish Chandra¹,Modanese Chiara²,Acciarri Maurizio³

Affiliation:

1. Faculty of Science, Engineering and TechnologySwinburne University of TechnologyHawthorn3122VictoriaAustralia

2. Department of Materials Science and EngineeringNorwegian University of Science and TechnologyAlfred Getz vei 2N‐7491TrondheimNorway

3. Department of Materials ScienceMilano‐Bicocca Solar Energy Research CenterUniversity of Milano BicoccaVia Cozzi 53I‐20125MilanoItaly

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2015.0375

Reference34 articles.

1. SEMI PV17‐0611– Specification for Virgin Silicon Feedstock Materials for Photovoltaic Applications 2012

2. Effect of compensation and of metallic impurities on the electrical properties of Cz‐grown solar grade silicon;Libal J.;J. Appl. Phys.,2008

3. Low temperature‐coefficient for solar cells processed from solar‐grade silicon purified by metallurgical route;Tanay F.;Prog. Photovolt. Res. Appl.,2011

4. Oxygen distribution on a multicrystalline silicon ingot grown from upgraded metallurgical silicon;Di‐Sabatino M.;Sol. Energy Mater. Sol. Cells,2011

5. Electron and hole mobility reduction and Hall factor in phosphorus compensated p‐type silicon;Rougieux F.E.;J. Appl. Phys.,2010

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