Automatic Defect Inspection for Monocrystalline Solar Cell Interior by Electroluminescence Image Self-Comparison Method

<p>This work studies the application of fuzzy set (FS) and fuzzy logic (FC) methods to determine the optimal operating point of solar cell. The physical parameters of the solar cell have been measured practically using silicon solar cell. The important parameters of the silicon cell are compared with each other using fuzzy set comparison method (FSCM) based on (I-V) characteristic curves of the voltage of photovoltaic cell and the maximum power resulting from the cell; which is a simple method for the measurement. The results of the simulation method show that, the fuzzy set comparison method (FSCM) is better measuring these parameters.</p>

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Texture defect inspection for silicon solar cell

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.02702 ◽

2010 ◽

Vol 30 (10) ◽

pp. 2702-2704 ◽

Cited By ~ 4

Author(s):

Wu-jie ZHANG ◽

Di LI ◽

Feng YE

Keyword(s):

Solar Cell ◽

Silicon Solar Cell ◽

Defect Inspection

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PCB Image Registration Method Based on Mark Hole Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1475 ◽

2013 ◽

Vol 427-429 ◽

pp. 1475-1480

Author(s):

Na Gao ◽

Yan Wei Wang

Keyword(s):

Feature Extraction ◽

Image Registration ◽

Printed Circuit Board ◽

Comparison Method ◽

Circuit Board ◽

Reference Standard ◽

Defect Inspection ◽

Critical Aspect ◽

Registration Method ◽

Printed Circuit

In the processing of PCB (Printed Circuit Board) defect inspection with reference comparison method, the very critical aspect is to make the image to be inspected and the reference standard PCB image accurate registration . Image registration has many ways, according to the characteristics of PCB, a new image registration method based on Mark hole center feature extraction was presented, also this method is carried out relevant experimental verification.

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Solar cell surface defect inspection based on multispectral convolutional neural network

Journal of Intelligent Manufacturing ◽

10.1007/s10845-018-1458-z ◽

2018 ◽

Vol 31 (2) ◽

pp. 453-468 ◽

Cited By ~ 11

Author(s):

Haiyong Chen ◽

Yue Pang ◽

Qidi Hu ◽

Kun Liu

Keyword(s):

Neural Network ◽

Solar Cell ◽

Cell Surface ◽

Convolutional Neural Network ◽

Surface Defect ◽

Defect Inspection

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Pseudo colorization of electroluminescence images of multi-crystalline silicon solar cells for defect inspection

Modern Physics Letters B ◽

10.1142/s0217984919400104 ◽

2019 ◽

Vol 33 (14n15) ◽

pp. 1940010

Author(s):

Keh-Moh Lin ◽

Horng-Horng Lin ◽

Harshad Kumar Dandage ◽

Yi-Chun Du

Keyword(s):

Solar Cells ◽

Solar Cell ◽

Crystalline Silicon ◽

Silicon Solar Cells ◽

Defect Inspection ◽

Feature Clustering ◽

Crystalline Silicon Solar Cells ◽

Si Solar Cells ◽

Pseudo Color ◽

Human Inspection

Computer imaging of electroluminescence (EL) has been successfully applied to solar cell inspection in recent years, as EL image intensities reflect the efficiency levels and/or defects in sc-Si and mc-Si solar cells. In this paper, we propose a novel computational scheme for pseudo colorization of EL images to highlight defect regions in solar cells for human inspection. Specifically, given a template EL image and pseudo color labels on its defect regions, we impose the pseudo colors to other grayscale EL images, with respect to different defect types and image structures, by template feature clustering and pseudo color transferring. Our experiments show that the proposed approach indeed improves the readability of EL images and provides better visualization of solar cell defects.

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