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Camera enhanced compressive light beam induced current sensing for efficient defect detection in photovoltaic cells

机译:相机增强压缩光束感应电流检测光伏电池中有效缺陷检测

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摘要

Recently, use of the compressive sensing-based light beam induced current (CS-LBIC) method has led to significant improvements in the scanning speed of the standard LBIC method that made the method suitable for practical use in fast and detailed characterization of photovoltaic cells. However, the sparsity of the defect signal obtained using the compressive sensing model is greatly increased by presence of bus bars and finger structures in the testing area, meaning that more measurements are required for reasonable signal recovery. In this paper, a camera-enhanced CS-LBIC method is proposed that eliminates the effects of both bus bars and finger structures and thus further improves the scanning efficiency of the CS-LBIC method. An image of the testing area was captured using an optical camera and the areas without the presence of bus bars and fingers were extracted as photosensitive areas. Then, an effective sensing and reconstruction algorithm was proposed that allowed direct recovery of the defect signal from the photosensitive area. The experimental results indicate that in a 10,000 point scan, defects can be located with a sampling ratio of 2%-5% using the proposed method with a scanning duration on the scale of tens of seconds, which is approximately five times faster than the reported state-of-art results. The proposed method shows significant potential for improvement of the efficiency and speed of meticulous inspection and classification of photovoltaic cells.
机译:最近,使用压缩感测的光束感应电流(CS-LBIC)方法导致了标准LBIC方法的扫描速度的显着改善,该方法使得该方法适用于光伏电池的快速和详细表征的实际应用。然而,使用压缩感测模型获得的缺陷信号的稀疏性通过测试区域中的汇流条和手指结构的存在大大增加,这意味着合理的信号恢复需要更多的测量。在本文中,提出了一种相机增强的CS-LBIC方法,其消除了汇流条和手指结构的效果,从而进一步提高了CS-LBIC方法的扫描效率。使用光学摄像机捕获测试区域的图像,并且在没有汇流条的情况下的情况下的区域和指状物被提取为光敏区域。然后,提出了一种有效的感测和重建算法,其允许直接恢复来自光敏区域的缺陷信号。实验结果表明,在10,000点扫描中,使用扫描持续时间的扫描持续时间,缺陷在10,000点扫描中,缺陷可以在数十秒的尺度上扫描持续时间,这比报告的速度快约五倍。最先进的结果。所提出的方法显示出改善细致检查的效率和速度的显着潜力和光伏电池的分类。

著录项

  • 来源
    《Solar Energy》 |2019年第5期|212-217|共6页
  • 作者单位

    Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Shaanxi Peoples R China;

    Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Shaanxi Peoples R China;

    Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Shaanxi Peoples R China;

    Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Shaanxi Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Defect detection; Compressive sensing; Light beam induced current; Photovoltaic cell;

    机译:缺陷检测;压缩感测;光束感应电流;光伏电池;

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