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Volume 46 Issue 4
Apr.  2024
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ZHAO Qiang, LIU Shengjie, HAN Dongcheng, LIU Changyu, YANG Shizhi. Improved K-means Clustering-based Defect Detection Method for Photovoltaic Panels[J]. Infrared Technology , 2024, 46(4): 475-482.
Citation: ZHAO Qiang, LIU Shengjie, HAN Dongcheng, LIU Changyu, YANG Shizhi. Improved K-means Clustering-based Defect Detection Method for Photovoltaic Panels[J]. Infrared Technology , 2024, 46(4): 475-482.
shu

Improved K-means Clustering-based Defect Detection Method for Photovoltaic Panels

  • 1.

    School of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China

  • 2.

    Anhui Research Center of Generic Technology in New Display Industry, Hefei 230601, China

  • 3.

    Anhui Easpeed Technology Co. Ltd., Hefei 230088, China

  • 4.

    Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China

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  • Received Date: September 16, 2022
  • Revised Date: December 11, 2022
    Graphical Abstract
    • Abstract
  • An image processing method based on the HSV space model with an improved K-means clustering algorithm is proposed to accurately identify and extract the hot spot part of photovoltaic modules. First, the infrared image is transformed into the HSV space and bilaterally filtered to remove noise and improve the image contrast. Second, the Gaussian kernel function is used to extract the image grayscale probability density function, and then the initial clustering center is obtained. Finally, K-means clustering is applied to the image using prior knowledge to extract and quantify the hot spot defects. The research results show that the method can quickly detect and locate the hotspot position and calculate the degree of damage to the photovoltaic panel, and has high accuracy, good sensitivity, and stability.
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    • References (27)
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