Solar Cells Defect Detection in Electroluminescence Images

LI Guan-nan; TAN Qing-chang; ZHANG Kuo; ZHANG Yu-peng

doi:10.3788/fgxb20133410.1400

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Solar Cells Defect Detection in Electroluminescence Images

更新时间：2020-08-12

- Solar Cells Defect Detection in Electroluminescence Images
- Chinese Journal of Luminescence Vol. 34, Issue 10, Pages: 1400-1407(2013)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2. 吉林大学机械科学与工程学院,吉林长春,130025
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20133410.1400
  CLC： TN248.2
- Received：17 May 2013，
  
  Revised：01 August 2013，
  
  Published：10 October 2013
- 稿件说明：
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李冠楠, 谭庆昌, 张阔, 张宇鹏. 基于电致发光影像的太阳能电池瑕疵检测[J]. 发光学报, 2013,34(10): 1400-1407

LI Guan-nan, TAN Qing-chang, ZHANG Kuo, ZHANG Yu-peng. Solar Cells Defect Detection in Electroluminescence Images[J]. Chinese Journal of Luminescence, 2013,34(10): 1400-1407
李冠楠, 谭庆昌, 张阔, 张宇鹏. 基于电致发光影像的太阳能电池瑕疵检测[J]. 发光学报, 2013,34(10): 1400-1407 DOI： 10.3788/fgxb20133410.1400.

LI Guan-nan, TAN Qing-chang, ZHANG Kuo, ZHANG Yu-peng. Solar Cells Defect Detection in Electroluminescence Images[J]. Chinese Journal of Luminescence, 2013,34(10): 1400-1407 DOI： 10.3788/fgxb20133410.1400.

摘要

太阳能电池制造的复杂性决定其在制造过程中会有很多瑕疵产生

瑕疵的存在会大大影响太阳能电池的发电效率和使用寿命。本文运用电致发光影像技术来凸显瑕疵

针对影像中的瑕疵人工检测率低且缺乏客观性的问题

选用了基于统计的瑕疵检测算法。检测时

选取扩展Haar特征作为样本像素点的特征值

应用改进的模糊C均值聚类法对正常样本进行分群训练

通过判断测试样本是否在正常样本群组之中的方法实现了样本的瑕疵检测

并近似地给出了瑕疵位置。实验结果表明

该方法对太阳能芯片电致发光影像中瑕疵的总辨识率可以达到96%。

Abstract

The defect will be introduced inevitably during the complexity manufacturing process of the solar cell. The existence of the defect significantly affects generating efficiency and service life. In this paper

the electroluminescence imaging technology is applied to highlight the defect. Aiming at the low rate of artificial detection and deficiency of objectivity

the algorithm of detecting defect which is based on statistics is proposed. In detection

the extensional Haar features are selected as the feature values of the pixel points. The improved fuzzy C-means clustering method is used to cluster the normal samples. By judging whether the testing sample is in the cluster of normal samples

the defect detection is carried out

and the location of the defect is provide at the same time. Experimental result shows that the total recognition rate of the defect in the electroluminescence image of solar cell is 96%.

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Keywords

references

Zhang M J, Li J D, Chen J S. Solar cell and production of polysilicon [J]. J. Mater. Metall., 2007, 6(1):33-38.[2] Fu Z, Zhao Y Z, Liu Y, et al. Solar cell crack inspection by image processing//2004 International Conference on Business of Electronic Product Reliability and Liability, New York: Cambridge University Press, 2004:77-80.[3] Tsai H J. The Scale Measurement and Defect Inspection for Printed Solar Cell. Chungli: National Central University, 2004.[4] Chen S Y. Surface Defect Detection for Solar Cells. Chungli: National Central University, 2004.[5] Fuyuki T, Kondo H, Yamazaki T, et al. Photographic surveying of minority carrier diffusion length in polycrystalline silicon solar cells by electroluminescence [J]. Appl. Phys. Lett., 2005, 86(26):262108.[6] Fuyuki T, Kitiyanan A. Photographic diagnosis of crystalline silicon solar cells utilizing electroluminescence [J]. Appl. Phys. A, 2009, 96(1):189-196.[7] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithm [M]. New York: Plenum Press, 1981.[8] Lienhart R, Maydt J. An extended set of Haar-like features for rapid object detection//2002 IEEE ICIP, New York: CRC Press, 2002:900-903.

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