Multi-objective particle swarm optimization on ultra-thin silicon solar cells

被引:2
作者
Atalay, Ipek Anil [1 ]
Gunes, Hasan Alper [1 ]
Alpkilic, Ahmet Mesut [1 ]
Kurt, Hamza [1 ]
机构
[1] TOBB Univ Econ & Technol, Dept Elect & Elect Engn, TR-06560 Ankara, Turkey
来源
JOURNAL OF OPTICS-INDIA | 2020年 / 49卷 / 04期
关键词
Solar cells; Anti-reflection; Absorption enhancement; Surface texturing; Light trapping; Multi-objective particle swarm optimization; ABSORPTION ENHANCEMENT; ANTIREFLECTION; FABRICATION; LITHOGRAPHY;
D O I
10.1007/s12596-020-00653-z
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Finding optimized parameters for any photonic device is a challenging problem, because as the search space enlarges the computation time and design complexity increase. For higher performance solar cells, various studies have been carried out to procure optimized parameters, to attain better performance and low cost as well. In this study, we used a multi-objective particle swarm optimization approach to search design space effectively and obtain fixed parameters for enhanced solar spectrum absorption. Numerical investigations are conducted for pyramid surface pattern, to find proper solar cell parameters for minimum reflection and maximum light trapping which give rise to enhanced absorption of photons. For the ultra-thin-film silicon solar cell having a thickness of 1 mu m, a designed double-sided pyramid structure provides an ideal short-circuit photocurrent of 34.23 mA/cm(2). In this regard, the proposed approach can be applied to different film thicknesses of semiconductors for different photonic applications by manipulating the reflection/transmission coefficient and light trapping mechanism.
引用
收藏
页码:446 / 454
页数:9
相关论文
共 50 条
[31]   Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing [J].
Alkayal, Entisar S. ;
Jennings, Nicholas R. ;
Abulkhair, Maysoon F. .
PROCEEDINGS OF THE 2016 IEEE 41ST CONFERENCE ON LOCAL COMPUTER NETWORKS - LCN WORKSHOPS 2016, 2016, :17-24
[32]   Handling Smart-Grid with a Multi-objective Particle Swarm Optimization algorithm [J].
Sellam, Mourad ;
Akherraz, Mohamed ;
Sayouti, Yassine .
PROCEEDINGS OF 2015 3RD IEEE INTERNATIONAL RENEWABLE AND SUSTAINABLE ENERGY CONFERENCE (IRSEC'15), 2015, :778-782
[33]   Multi-objective particle swarm optimization of industrial natural gas dehydration process [J].
Al-Jammali, Ali Sameer Ismail ;
Amooey, Ali Akbar ;
Nabavi, Seyed Reza .
CHEMICAL PAPERS, 2023, 77 (02) :1067-1080
[34]   A particle swarm approach for multi-objective optimization of electrical discharge machining process [J].
Chinmaya P. Mohanty ;
Siba Sankar Mahapatra ;
Manas Ranjan Singh .
Journal of Intelligent Manufacturing, 2016, 27 :1171-1190
[35]   Improved magnet system by multi-objective particle swarm optimization for Faraday rotators [J].
Qiao, Rui ;
Hu, Zhaohui ;
Sun, Jinji .
JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, 2025, 624
[36]   Improving wind turbine blade based on multi-objective particle swarm optimization [J].
Li, Yingjue ;
Wei, Kexiang ;
Yang, Wenxian ;
Wang, Qiong .
RENEWABLE ENERGY, 2020, 161 :525-542
[37]   An innovative hybrid multi-objective particle swarm optimization with or without constraints handling [J].
Cheng, Shixin ;
Zhan, Hao ;
Shu, Zhaoxin .
APPLIED SOFT COMPUTING, 2016, 47 :370-388
[38]   Microwave absorber optimal design using multi-objective particle swarm optimization [J].
Goudos, S. K. ;
Sahalos, J. N. .
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, 2006, 48 (08) :1553-1558
[39]   Adaptive candidate estimation-assisted multi-objective particle swarm optimization [J].
HongGui Han ;
LinLin Zhang ;
Ying Hou ;
JunFei Qiao .
Science China Technological Sciences, 2022, 65 :1685-1699
[40]   LCL Filter Parameter Optimization Design Based on Multi-Objective Particle Swarm [J].
Cao, Hannan ;
Zheng, Xuemei ;
Liu, Zhuang .
PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019), 2019, :2467-2472