| 时亚涛,戴 芳,杨畅民.太阳能光伏电池缺陷检测[J].电子测量与仪器学报,2020,34(4):157-164 |
| 太阳能光伏电池缺陷检测 |
| Defect detection of solar photovoltaic cell |
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| DOI： |
| 中文关键词: 块数据删除模型 非线性回归模型 回归诊断 缺陷检测 Cook 距离 |
| 英文关键词:block case deletion model non-linear regression model regression diagnosis defect detection cook distance |
| 基金项目:西安市科技计划(201805037YD15CG21(7))资助项目 |
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| 摘要点击次数: 489 |
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| 中文摘要: |
| 太阳能是一种极具吸引力的替代电力能源,太阳能光伏电池是太阳能发电系统的基础。 太阳能光伏电池中的各类缺陷
严重影响光伏电池的光电转化效率和使用寿命。 为有效地检测出这些缺陷,提出了一种基于块数据删除模型的缺陷检测方法。
首先,对太阳能光伏电池图像进行傅里叶变换去除母线并调节亮度和对比度,然后将图像分块,通过块数据删除模型找出去除
母线后的图像中所有的异常块,并将这些异常块全部剔除,利用余下的图像块通过非线性回归模型重建图像的背景。 最后,用
待检图像与得到的背景图像作差以突出缺陷区域,达到缺陷检测的目的。 实验结果表明,所提出的方法能够有效地检测出太阳
能光伏电池中多种类型的缺陷,如隐裂、断栅和碎片等。 用该方法对 313 幅太阳能光伏电池图像进行实验,其中 158 幅无缺陷
图像均未检测出缺陷,而另外 155 幅含有隐裂、断栅等缺陷的图像,仅有 5 幅出现误检,缺陷检测率达 96. 77%。 |
| 英文摘要: |
| Solar energy is an attractive source of electricity. Solar photovoltaic cells are the basis of solar power generation systems.
However, various types of defects in solar photovoltaic cells seriously affect the photoelectric conversion efficiency and service life of
photovoltaic cells. To effectively detect these defects, a defect detection method based on a block case deletion model is proposed. First,
the solar photovoltaic cell image using Fourier transform is preprocessed, it removes the bus bar and adjusts the brightness and contrast,
and divides the image into blocks. Then, in the processed image, all abnormal blocks are found and all of them are removed by using the
case deletion model. The background of the image is reconstructed from the remaining image patches by a non-linear regression model.
Finally, the defect area is highlighted by the difference between the image waiting for checking and the resulting background image. The
experimental results show that the proposed method can effectively detect many kinds of defects in Solar cells, such as micro-cracks,
breaks and fragment, etc. the method is used to experiment with 313 solar photovoltaic cell images. For 158 non-defective images, the
test results are normal. The remaining 155 images containing defects such as cracks and broken gates have only 5 images mis-detected,
and the detection rate of defective images is 96. 77%. |
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