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

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

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Fill factor, Open circuit voltage, Power conversion efficiency, Short circuit current density
Divisions: Faculty of Electronics and Computer Engineering
Depositing User: Sabariah Ismail
Date Deposited: 06 Aug 2021 14:38
Last Modified: 06 Aug 2021 14:38
URI: http://eprints.utem.edu.my/id/eprint/25212
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