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首页> 外文期刊>Journal of testing and evaluation >Diagnosis of Photovoltaic (PV) Panel Defects Based on Testing and Evaluation of Thermal Image
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Diagnosis of Photovoltaic (PV) Panel Defects Based on Testing and Evaluation of Thermal Image

机译：基于热像测试和评估的光伏面板缺陷诊断

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摘要

Photovoltaic (PV) solar energy can only be economical if the PV module operates reliably for 25-30 years under field conditions. The PV module and it overall reliability can be radically affected by faults during the manufacturing process, in real field conditions, transportation, and installation. So, there is a need for diagnosing defects in PV modules to improve their reliability. Operating temperature plays the key role for improving the efficiency of PV panels. The temperature within the PV cell unevenly increases because of such defects in the cell. As such, it is very important to monitor the temperature and temperature distribution in PV panels in order to locate such defects. Infrared thermography (IRT) plays a major role in predictive and preventive maintenance of PV panels and can determine the severity of the problem. This article investigates the delamination, snail trails, and bubbled faults of PV panels using digital thermal image analysis and their feature extraction. Real time experiments were conducted, and the test results are presented in this article. Thermal images of panels are captured using a (FLIR) T420bx (R) thermal imager. The thermal images of panels are analyzed by segmenting the image using the k-means clustering algorithm. Histogram statistical features such as mean, standard deviation, variance, entropy, skew, and kurtosis are extracted from the segmented thermal image. Based on these features, the defects in PV panels are identified with reasonable accuracy.
机译：光伏（PV）太阳能只有在现场条件下可靠地运行25-30年后才能经济。在制造过程中，在实际条件下，运输和安装过程中，故障会从根本上影响PV模块及其整体可靠性。因此，需要诊断PV模块中的缺陷以提高其可靠性。工作温度对于提高光伏面板的效率起着关键作用。由于电池中的此类缺陷，PV电池内的温度会不均匀地升高。因此，监视光伏面板中的温度和温度分布以找出此类缺陷非常重要。红外热成像（IRT）在光伏面板的预测性和预防性维护中起主要作用，并且可以确定问题的严重性。本文使用数字热图像分析及其特征提取来研究光伏面板的分层，钉痕和气泡缺陷。进行了实时实验，并在本文中介绍了测试结果。使用（FLIR）T420bx（R）热像仪捕获面板的热像。通过使用k均值聚类算法对图像进行分割来分析面板的热图像。从分割的热图像中提取直方图统计特征，例如均值，标准差，方差，熵，偏斜和峰度。基于这些特征，可以合理地确定光伏面板中的缺陷。

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