scholarly journals Hybrid design of spectral splitters and concentrators of light for solar cells using iterative search and neural networks

2021 ◽  
pp. 100987
Author(s):  
Alim Yolalmaz ◽  
Emre Yüce
Keyword(s):  
Neural Networks ◽  
Solar Cells ◽  
Hybrid Design ◽  
Iterative Search

Characterisation and Modeling of Organic Solar Cells by Using Radial Basis Neural Networks

2016 ◽  
pp. 91-103 ◽  
Author(s):  
Dor Gotleyb ◽  
Grazia Lo Sciuto ◽  
Christian Napoli ◽  
Rafi Shikler ◽  
Emiliano Tramontana ◽  
...  
Keyword(s):  
Neural Networks ◽  
Solar Cells ◽  
Organic Solar Cells ◽  
Radial Basis Neural Networks ◽  
Radial Basis

Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks

2020 ◽  
Vol 108 ◽  
pp. 103334
Author(s):  
Xiong Zhang ◽  
Yawen Hao ◽  
Hong Shangguan ◽  
Pengcheng Zhang ◽  
Anhong Wang
Keyword(s):  
Neural Networks ◽  
Solar Cells ◽  
Surface Defects ◽  
Convolution Neural Networks

scholarly journals Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Grazia Lo Sciuto
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Solar Cells ◽  
Computer Science ◽  
Organic Solar Cells ◽  
Full Potential ◽  
Production Chain ◽  
Lecture Notes ◽  
Occurrence Matrix ◽  
Conversion Efficiencies

The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradation and stability of the technology. In order to extract their full potential, OSCs have to be optimized. On the other hand the production chain of the organic solar cells (OSC) can take advantage of the use of artificial intelligence (AI). In fact the integration into the production workflow makes solar cells more competitive and efficient. This paper presents some applications of the AI for optimization of OSCs production processes Full Text: PDF ReferencesLo Sciuto, G., Capizzi, G., Coco, S., Shikler, R., "Geometric shape optimization of organic solar cells for efficiency enhancement by neural networks." (2017) Lecture Notes in Mechanical Engineering, pp. 789-796. CrossRef Barnea, S.N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., Polap, D., "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network." (2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, pp. 378-389. CrossRef Ye, L.; Hu, H.; Ghasemi, M.; Wang, T.; Collins, B.A.; Kim, J.H.; Jiang, K.; Carpenter, J.H.; Li, H.; Li, Z.; et al. "Quantitative relations between interaction parameter, miscibility and function in organic solar cells." Nat. Mater. 2018, 17, 253-260. CrossRef Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610-621 (1973) CrossRef Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E., Wozniak, M.: Automatic classification of fruit defects based on co-occurrence matrix and neural networks. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 861-867, September 2015. CrossRef

scholarly journals Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

2015 ◽  
Vol 60 (3) ◽  
pp. 1673-1678 ◽  
Author(s):  
M. Musztyfaga-Staszuk ◽  
R. Honysz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Cells ◽  
Contact Resistance ◽  
Screen Printing ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Silicon Solar Cells ◽  
Front Electrode ◽  
Artificial Neural

Abstract This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP) method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace) and unconventional (2. Selective Laser Sintering). Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM). Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

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