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Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model

Thin-film Copper Indium Gallium Selenide (CIGS) solar cell is identified to be one of the promising structures to replace conventional silicon-based solar cell due to its lower cost and reduced thickness. Nevertheless, the impact of layer thickness and doping concentration of a window layer - Zinc o...

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Main Authors: Salehuddin, Fauziyah, Kaharudin, Khairil Ezwan, Mohd Zain, Anis Suhaila, Roslan, Ameer Farhan
Format: Article
Language:English
Published: Elsevier B.V. 2020
Online Access:http://eprints.utem.edu.my/id/eprint/25212/2/KEKAHARUDIN_JESTEC_NO4VOL15.PDF
http://eprints.utem.edu.my/id/eprint/25212/
https://jestec.taylors.edu.my/Vol%2015%20issue%204%20August%202020/15_4_47.pdf
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spelling my.utem.eprints.252122021-08-06T14:38:45Z http://eprints.utem.edu.my/id/eprint/25212/ Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model Salehuddin, Fauziyah Kaharudin, Khairil Ezwan Mohd Zain, Anis Suhaila Roslan, Ameer Farhan Thin-film Copper Indium Gallium Selenide (CIGS) solar cell is identified to be one of the promising structures to replace conventional silicon-based solar cell due to its lower cost and reduced thickness. Nevertheless, the impact of layer thickness and doping concentration of a window layer - Zinc oxide (ZnO), a buffer layer - Cadmium sulfide (Cds) and an absorber layer (CIGS) needs to be intelligently controlled for more balanced CIGS solar cell performances. Thus, this paper proposes a newly predictive analytics using a combination of Grey relational analysis (GRA), multiple linear regressions (MLR) and genetic algorithm (GA) to optimize the CIGS solar cell parameters for better device performances. The CIGS solar cell model is developed and simulated using solar cell capacitance simulator (SCAPS). The final results prove that the proposed combinational GRA-MLR-GA model has successfully optimized the CIGS solar cell parameters in which ZnO thickness (TZnO), Cds thickness (TCds), CIGS thickness (TCIGS) and CIGS doping concentration (NaCIGS) are predictively optimized to be 0.03 μm, 0.03μm, 2.86 μm and 9.937x1017 cm-3 respectively. The most optimum magnitudes for open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), and power conversion efficiency (η) after the predictive analytics are measured at 0.8206 V, 32.419 mA/cm2, 83.23% and 22.14% reciprocally. Elsevier B.V. 2020-08 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25212/2/KEKAHARUDIN_JESTEC_NO4VOL15.PDF Salehuddin, Fauziyah and Kaharudin, Khairil Ezwan and Mohd Zain, Anis Suhaila and Roslan, Ameer Farhan (2020) Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model. Journal of Engineering Science and Technology, 15 (4). pp. 2823-2840. ISSN 1823-4690 https://jestec.taylors.edu.my/Vol%2015%20issue%204%20August%202020/15_4_47.pdf
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Thin-film Copper Indium Gallium Selenide (CIGS) solar cell is identified to be one of the promising structures to replace conventional silicon-based solar cell due to its lower cost and reduced thickness. Nevertheless, the impact of layer thickness and doping concentration of a window layer - Zinc oxide (ZnO), a buffer layer - Cadmium sulfide (Cds) and an absorber layer (CIGS) needs to be intelligently controlled for more balanced CIGS solar cell performances. Thus, this paper proposes a newly predictive analytics using a combination of Grey relational analysis (GRA), multiple linear regressions (MLR) and genetic algorithm (GA) to optimize the CIGS solar cell parameters for better device performances. The CIGS solar cell model is developed and simulated using solar cell capacitance simulator (SCAPS). The final results prove that the proposed combinational GRA-MLR-GA model has successfully optimized the CIGS solar cell parameters in which ZnO thickness (TZnO), Cds thickness (TCds), CIGS thickness (TCIGS) and CIGS doping concentration (NaCIGS) are predictively optimized to be 0.03 μm, 0.03μm, 2.86 μm and 9.937x1017 cm-3 respectively. The most optimum magnitudes for open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), and power conversion efficiency (η) after the predictive analytics are measured at 0.8206 V, 32.419 mA/cm2, 83.23% and 22.14% reciprocally.
format Article
author Salehuddin, Fauziyah
Kaharudin, Khairil Ezwan
Mohd Zain, Anis Suhaila
Roslan, Ameer Farhan
spellingShingle Salehuddin, Fauziyah
Kaharudin, Khairil Ezwan
Mohd Zain, Anis Suhaila
Roslan, Ameer Farhan
Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model
author_facet Salehuddin, Fauziyah
Kaharudin, Khairil Ezwan
Mohd Zain, Anis Suhaila
Roslan, Ameer Farhan
author_sort Salehuddin, Fauziyah
title Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model
title_short Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model
title_full Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model
title_fullStr Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model
title_full_unstemmed Predictive Analytics Of Cigs Solar Cell Using A Combinational Gra-Mlr-Ga Model
title_sort predictive analytics of cigs solar cell using a combinational gra-mlr-ga model
publisher Elsevier B.V.
publishDate 2020
url http://eprints.utem.edu.my/id/eprint/25212/2/KEKAHARUDIN_JESTEC_NO4VOL15.PDF
http://eprints.utem.edu.my/id/eprint/25212/
https://jestec.taylors.edu.my/Vol%2015%20issue%204%20August%202020/15_4_47.pdf
_version_ 1707769093630722048
score 13.211869

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