【摘要】：太陽能電池硅片的質(zhì)量是影響電池片轉(zhuǎn)換效率以及電池組件發(fā)電效率的一個關(guān)鍵因素,因此對太陽能電池硅片的質(zhì)量檢測在生產(chǎn)和實驗中顯得尤為重要。常用的太陽能電池硅片有單晶硅片和多晶硅片,硅片在生產(chǎn)過程中受諸多因素的影響,或多或少地存在一些缺陷。多晶硅片常見的缺陷有邊緣不純、高不純度、位錯缺陷,單晶硅片常見的缺陷有漩渦缺陷。硅片缺陷的存在會極大地降低電池片的發(fā)電效率,減少電池組件的使用壽命,甚至影響光伏發(fā)電系統(tǒng)的穩(wěn)定性。 目前在實際生產(chǎn)實驗中,大都是采用太陽能電池片電致發(fā)光缺陷檢測,以人眼觀察或者自動檢測的方法進行檢測。由于人眼觀察的方法具有很強的主觀性,并且人眼容易疲勞,大大降低了檢測的可靠性和效率。另外,由于電致發(fā)光缺陷檢測是針對電池片進行的檢測,不能夠檢測生產(chǎn)過程中硅片、擴散片等過程片的缺陷,這樣就提高了生產(chǎn)成本,降低了生產(chǎn)效率；并且電致發(fā)光檢測技術(shù)是接觸式檢測,會給電池片帶來不同程度的損傷。因此,一種能在生產(chǎn)過程中可以針對太陽能電池硅片缺陷的非接觸式高效準(zhǔn)確的自動檢測方法是非常有價值的。本文以數(shù)字圖像處理技術(shù)作為基礎(chǔ),對太陽能電池硅片光致發(fā)光缺陷檢測分類方法進行了相關(guān)研究,并且提出了硅片缺陷的自動檢測分類方法。 本文的工作主要包括以下部分： 1.首先對光致發(fā)光圖像預(yù)處理,包括圖像去噪、增強、邊緣檢測、直線檢測、圖像旋轉(zhuǎn),目標(biāo)硅片自動分割。 2.然后利用高斯曲線擬合多晶硅片圖像灰度曲線方法計算分割閾值并分割缺陷,提取缺陷的面積比例與分布特征；對于單晶硅片,利用高斯曲線擬合圖像中抽樣像素的灰度和值曲線,提取擬合標(biāo)準(zhǔn)差；通過頻域濾波結(jié)合二值化方法提取高頻圖像中高強度部分面積比；在高頻二值化圖像細(xì)化后,提取霍夫變換檢測圓結(jié)果；得到漩渦缺陷的三個特征。 3.最后構(gòu)造出缺陷檢測分類樹模型,實現(xiàn)缺陷的檢測分類,對多晶硅片的三種缺陷采用排除法依次檢測。并且基于C#完成系統(tǒng)軟件各個功能模塊的設(shè)計編寫與整合。在實際應(yīng)用中完成系統(tǒng)軟件的測試,結(jié)果顯示缺陷的檢測分類準(zhǔn)確率可以達到95%以上,證明本文方法的正確性與系統(tǒng)軟件設(shè)計的合理性。 本文提出一種在太陽能電池片生產(chǎn)中,對多晶硅片和單晶硅片進行非接觸式自動化缺陷檢測分類的方法,并且實現(xiàn)了軟件的設(shè)計編寫。實驗證明本文的方法高效準(zhǔn)確,有著很大的應(yīng)用前景。
[Abstract]:The quality of solar cell silicon wafer is a key factor that affects the conversion efficiency of solar cell and the generation efficiency of battery module. Therefore, the quality detection of solar cell silicon wafer is particularly important in production and experiment. There are single crystal silicon wafers and polycrystalline silicon wafers in common use in solar cells. The silicon wafers are affected by many factors in the process of production, and there are some defects more or less. The common defects of polysilicon wafer are edge impurity, high impurity, dislocation defect and swirl defect of single crystal silicon wafer. The existence of wafer defects will greatly reduce the generation efficiency of the battery chip, reduce the service life of the battery components, and even affect the stability of photovoltaic power generation system. At present, in the actual production experiments, most of the solar cell electroluminescent defect detection, using human eye observation or automatic detection method to detect. The method of human eye observation is very subjective and easy to fatigue, which greatly reduces the reliability and efficiency of detection. In addition, because the detection of electroluminescent defects is aimed at the battery chip, it can not detect the defects of silicon wafer and diffusion wafer in the production process, which increases the production cost and reduces the production efficiency. And electroluminescent detection technology is contact detection, which will bring different damage to the battery chip. Therefore, a non-contact, efficient and accurate automatic detection method for silicon wafer defects in solar cells is very valuable. Based on the digital image processing technology, this paper studies the photoluminescence defect detection and classification method of solar cell silicon wafer, and puts forward the automatic detection and classification method of silicon wafer defect. The work of this paper mainly includes the following parts: 1. First, the photoluminescence image preprocessing, including image denoising, enhancement, edge detection, line detection, image rotation, target wafer segmentation. 2. Then using Gao Si curve fitting polysilicon chip image gray-scale curve method to calculate the segmentation threshold and segment defects, extract the defect area ratio and distribution characteristics; For monocrystalline silicon wafer, Gao Si curve is used to fit the gray and value curves of sampling pixels in the image, and the fitting standard deviation is extracted, and the high intensity partial area ratio in high frequency image is extracted by frequency domain filtering combined with binarization method. After the high-frequency binary image thinning, the Hough transform is extracted to detect the circle, and three features of the vortex defect are obtained. 3. Finally, a defect detection tree model is constructed to realize defect detection and classification. The three defects of polysilicon wafer are detected by eliminating method in turn. And based on C # to complete the design and integration of each functional module of the system software. The system software is tested in practical application. The result shows that the accuracy of defect detection and classification can reach more than 95%, which proves the correctness of the method and the rationality of the system software design. In this paper, a non-contact automatic defect detection and classification method for polysilicon wafer and single crystal silicon wafer in solar cell production is proposed, and the software is designed and compiled. Experiments show that this method is effective and accurate, and has a great prospect of application.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM914.4;TP391.41

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