基于视觉的硅太阳能电池检测方法的研究
发布时间：2019-06-10 20:55
【摘要】：随着能源危机的出现，太阳能在新能源开发和利用中扮演着重要角色，光伏发电作为近期发展起来具有很大潜力的新能源技术，其核心部分为太阳能电池。太阳能电池生产过程中产生的瑕疵，会导致光电转换效率的下降和产品生产成本的提高，因此在生产过程中需对其进行检测。传统的太阳能电池检测依赖于人工判别，存在着人工检测易出现偏差误判且成本偏高等弊端。本文将基于视觉的检测算法应用在硅太阳能电池生产的各阶段，依次为对太阳能硅片瑕疵检测算法的研究、对硅太阳能电池片瑕疵检测算法的研究以及对硅太阳能电池组件瑕疵检测算法和分类算法的研究。 针对太阳能硅片瑕疵检测，本文采用近红外LED作为光源设备和近红外CCD相机作为图像采集设备，得到太阳能硅片表面和内部的隐裂图像。在隐裂瑕疵检测算法设计上，根据隐裂瑕疵在硅片上呈现出低灰度级和高梯度级的特点，传统边缘检测法或二值化法对于低对比度图像并不适用。因此本文采用各向异性扩散算法进行隐裂检测，算法根据图像中不同的梯度分布，对高梯度值—瑕疵区域进行锐化处理，对低梯度值—无瑕疵区域进行平滑处理，即实现在锐化瑕疵的同时抑制噪声的目的。但在算法中，扩散函数、锐化参数等数值的改变直接影响到瑕疵检测的结果，目前也缺乏统一的扩散函数表达方式。因此本文提出将改进的异性扩散算法作为图像锐化算子进行图像边缘提取后，根据确定的种子像素利用区域生长算法将隐裂瑕疵从背景中分割出来，经试验算法运算精度高，可满足在线太阳能硅片瑕疵检测的要求。 针对太阳能电池片瑕疵检测，本文在分析了各种图像分割算法后，提出了一种改进的Otsu算法作为瑕疵特征提取方法。在线瑕疵检测分为两种情况：第一种为判断电池片是否出现瑕疵，如果出现瑕疵则立即丢弃，不影响生产；第二种为检测出瑕疵电池片后需要对瑕疵进行定位回溯，以使用户发现造成瑕疵的原因，进行产品质量追踪。针对第一种情况，本文在研究了典型瑕疵特征后，采用空间域的方法进行瑕疵检测。针对第二种情况，本文采用“空间域-频率域-空间域”的图像重建方法得到了无瑕疵图像，再将原始图像和重建图像进行差分运算，进而实现图像瑕疵定位。根据单晶硅和多晶硅表面纹理呈现出的差异性，对具有规律纹理特征的单晶硅太阳能电池采用多解析度、多分辨率的小波变换进行图像重建；对具有随机纹理特征的多晶硅太阳能电池采用代表全局频域信息的傅里叶图像重建算法。 针对太阳能电池组件瑕疵检测，，由于太阳能电池组件是由一系列太阳能电池片经过串联或并联形式得到的，但瑕疵并不会出现在太阳能电池组件中每个子部分。因此如果以单一电池片作为模板应用前面介绍的图像重建算法进行逐一搜索，数据冗余大、并且效率低。针对这一问题，本文采用通过训练得到的独立成份分析（ICA）分离矩阵重构待检图像，以增强瑕疵信息并滤除组件图像的规律性纹理。ICA方法之一的FastICA具有了收敛速度快等优点,但也存在当初始点远离极值点而无法收敛等缺点，弱化了ICA算法的瑕疵检测能力。本文提出将粒子群优化算法（PSO）引入到FastICA算法中，由PSO算法得到的全局最佳位置作为最佳分类矩阵，并求出独立分量IC，最后重建检测图像以判断其是否存在瑕疵。针对太阳能电池瑕疵分类算法的研究，本文提出了基于AdaBoost分类器的支持向量机（SVM）算法进行样本训练，之后对输入的待检测图像应用SVM分类器进行瑕疵分类，输出分类结果。针对太阳能电池组件瑕疵检测和分类都需要事先进行样本训练的弊端，本文提出了一种无需参考样本的自适应阈值的瑕疵检测和分类方法，速度得到明显优化，并且处理效果令人满意，对于目前依赖人工检测的太阳能电池生产行业有着非常大的应用前景。
[Abstract]:With the emergence of the energy crisis, the solar energy plays an important role in the development and utilization of the new energy, and the photovoltaic power generation, as a new energy technology with great potential for recent development, is the core part of the solar cell. The defects generated in the production process of the solar cell can lead to the reduction of the photoelectric conversion efficiency and the improvement of the production cost of the product, so that the production process needs to be detected. The traditional detection of the solar cell depends on the artificial discrimination, and the defects that the manual detection is easy to be misjudged and the cost is high is high. In this paper, a visual detection algorithm is applied to each stage of the production of silicon solar cell, in order to study the defect detection algorithm of the solar silicon wafer, the research on the defect detection algorithm of the silicon solar cell, and the research on the defect detection algorithm and the classification algorithm of the silicon solar cell. In the light of the defect detection of the solar silicon wafer, the near-infrared LED is used as the light source device and the near-infrared CCD camera as the image acquisition equipment to obtain the hidden crack of the surface and the interior of the solar silicon chip. The traditional edge detection method or the binary method does not apply to the low contrast image according to the characteristics of low gray scale and high gradient level on the silicon wafer according to the hidden flaw detection algorithm. In this paper, the anisotropic diffusion algorithm is used to detect the crack, and the algorithm is used to sharpen the defect area of the high gradient value according to the different gradient distribution in the image, and the non-defective area of the low gradient value is smoothed, that is, the purpose of suppressing the noise while sharpening the defect is realized. However, in the algorithm, the change of the diffusion function and the sharpening parameter directly affects the result of the flaw detection, and there is also a lack of uniform diffusion function expression party. In this paper, the improved anisotropic diffusion algorithm is used as the image sharpening operator to carry out image edge extraction, and the hidden flaws are separated from the background by the regional growth algorithm according to the determined seed pixels, and the accuracy of the algorithm is calculated by the test algorithm. high, can meet that defect detection of the on-line solar silicon chip, In order to detect the defect of solar cell, an improved Otsu algorithm is put forward as a feature of the flaw detection after various image segmentation algorithms are analyzed. The method comprises the following steps of: first, judging whether the battery piece is defective or not, and immediately discarding the defect if the defect is present, and does not affect the production; and the second is to carry out positioning and backtracking on the defect after the defective battery piece is detected, so that the user can find the defect For the reason, make the product quality In the first case, after the typical flaw feature is studied, the method of space domain is used to make the flaw. In the second case, the image reconstruction method of the "space domain-frequency domain-space domain" is used to obtain the defect-free image, then the original image and the reconstructed image are subjected to differential operation, and the image-vanishing point is realized. The multi-resolution and multi-resolution wavelet transform is applied to the single-crystal silicon solar cell with regular texture characteristics according to the difference between the single-crystal silicon and the surface texture of the polysilicon. image reconstruction; a polycrystalline silicon solar cell with a random texture feature using a Fourier image representative of global frequency domain information The invention relates to a building algorithm, aiming at the defect detection of a solar cell module, because the solar cell module is obtained by series or parallel connection of a series of solar cell pieces, the defects do not appear in the solar cell module, therefore, the data redundancy is large if the image reconstruction algorithm described above is applied as a template by a single battery slice, And the efficiency is low. Aiming at the problem, an independent component analysis (ICA) separation matrix obtained by training is adopted to reconstruct the image to be detected, so that the defect information is enhanced and the image of the component is filtered out. The FastICA of one of the ICA methods has the advantages of fast convergence speed and the like, but also has the disadvantage that the initial point is far from the extreme point and cannot be converged, and the defect of the ICA algorithm is weakened. In this paper, a particle swarm optimization algorithm (PSO) is introduced into the FastICA algorithm, the global optimal position obtained by the PSO algorithm is used as the best classification matrix, and the independent component IC is obtained. In this paper, a support vector machine (SVM) algorithm based on AdaBoost classifier is proposed for sample training. In order to detect and classify the defect of the solar cell module, the defect of the sample training is required in advance. In this paper, the defect detection and classification method of the self-adaptive threshold without reference sample is proposed, the speed is obviously optimized, and the processing effect The fruit is satisfactory, and is very large for the solar cell production industry which is currently relying on manual detection
【学位授予单位】：河北农业大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TP391.41;TM914.4
本文编号：2496724
[Abstract]:With the emergence of the energy crisis, the solar energy plays an important role in the development and utilization of the new energy, and the photovoltaic power generation, as a new energy technology with great potential for recent development, is the core part of the solar cell. The defects generated in the production process of the solar cell can lead to the reduction of the photoelectric conversion efficiency and the improvement of the production cost of the product, so that the production process needs to be detected. The traditional detection of the solar cell depends on the artificial discrimination, and the defects that the manual detection is easy to be misjudged and the cost is high is high. In this paper, a visual detection algorithm is applied to each stage of the production of silicon solar cell, in order to study the defect detection algorithm of the solar silicon wafer, the research on the defect detection algorithm of the silicon solar cell, and the research on the defect detection algorithm and the classification algorithm of the silicon solar cell. In the light of the defect detection of the solar silicon wafer, the near-infrared LED is used as the light source device and the near-infrared CCD camera as the image acquisition equipment to obtain the hidden crack of the surface and the interior of the solar silicon chip. The traditional edge detection method or the binary method does not apply to the low contrast image according to the characteristics of low gray scale and high gradient level on the silicon wafer according to the hidden flaw detection algorithm. In this paper, the anisotropic diffusion algorithm is used to detect the crack, and the algorithm is used to sharpen the defect area of the high gradient value according to the different gradient distribution in the image, and the non-defective area of the low gradient value is smoothed, that is, the purpose of suppressing the noise while sharpening the defect is realized. However, in the algorithm, the change of the diffusion function and the sharpening parameter directly affects the result of the flaw detection, and there is also a lack of uniform diffusion function expression party. In this paper, the improved anisotropic diffusion algorithm is used as the image sharpening operator to carry out image edge extraction, and the hidden flaws are separated from the background by the regional growth algorithm according to the determined seed pixels, and the accuracy of the algorithm is calculated by the test algorithm. high, can meet that defect detection of the on-line solar silicon chip, In order to detect the defect of solar cell, an improved Otsu algorithm is put forward as a feature of the flaw detection after various image segmentation algorithms are analyzed. The method comprises the following steps of: first, judging whether the battery piece is defective or not, and immediately discarding the defect if the defect is present, and does not affect the production; and the second is to carry out positioning and backtracking on the defect after the defective battery piece is detected, so that the user can find the defect For the reason, make the product quality In the first case, after the typical flaw feature is studied, the method of space domain is used to make the flaw. In the second case, the image reconstruction method of the "space domain-frequency domain-space domain" is used to obtain the defect-free image, then the original image and the reconstructed image are subjected to differential operation, and the image-vanishing point is realized. The multi-resolution and multi-resolution wavelet transform is applied to the single-crystal silicon solar cell with regular texture characteristics according to the difference between the single-crystal silicon and the surface texture of the polysilicon. image reconstruction; a polycrystalline silicon solar cell with a random texture feature using a Fourier image representative of global frequency domain information The invention relates to a building algorithm, aiming at the defect detection of a solar cell module, because the solar cell module is obtained by series or parallel connection of a series of solar cell pieces, the defects do not appear in the solar cell module, therefore, the data redundancy is large if the image reconstruction algorithm described above is applied as a template by a single battery slice, And the efficiency is low. Aiming at the problem, an independent component analysis (ICA) separation matrix obtained by training is adopted to reconstruct the image to be detected, so that the defect information is enhanced and the image of the component is filtered out. The FastICA of one of the ICA methods has the advantages of fast convergence speed and the like, but also has the disadvantage that the initial point is far from the extreme point and cannot be converged, and the defect of the ICA algorithm is weakened. In this paper, a particle swarm optimization algorithm (PSO) is introduced into the FastICA algorithm, the global optimal position obtained by the PSO algorithm is used as the best classification matrix, and the independent component IC is obtained. In this paper, a support vector machine (SVM) algorithm based on AdaBoost classifier is proposed for sample training. In order to detect and classify the defect of the solar cell module, the defect of the sample training is required in advance. In this paper, the defect detection and classification method of the self-adaptive threshold without reference sample is proposed, the speed is obviously optimized, and the processing effect The fruit is satisfactory, and is very large for the solar cell production industry which is currently relying on manual detection
【学位授予单位】：河北农业大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TP391.41;TM914.4
【参考文献】
相关期刊论文 前1条
1 张舞杰;李迪;叶峰;;硅太阳能电池纹理缺陷检测[J];计算机应用;2010年10期
本文编号：2496724
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