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دسته بندی:
بینایی ماشین - Machine vision
سال انتشار:
2021
عنوان انگلیسی مقاله:
Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation
ترجمه فارسی عنوان مقاله:
تشخیص و تعیین کمبود در تصاویر الکترولومینسانس ماژول های PV خورشیدی با استفاده از تقسیم بندی معنایی U-net
منبع:
Sciencedirect - Elsevier - Renewable Energy, 178 (2021) 1211-1222: doi:10:1016/j:renene:2021:06:086
نویسنده:
Lawrence Pratt
چکیده انگلیسی:
Electroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules that are otherwise invisible to the naked eye, much the same way an x-ray enables a doctor to detect cracks and fractures in bones. The prevalence of multiple defects, e.g. micro cracks, inactive regions, gridline defects, and material defects, in PV module can be quantified with an EL image. Modern, deep learning tech- niques for computer vision can be applied to extract the useful information contained in the images on entire batches of PV modules. Defect detection and quantification in EL images can improve the efficiency and the reliability of PV modules both at the factory by identifying potential process issues and at the PV plant by identifying and reducing the number of faulty modules installed. In this work, we train and test a semantic segmentation model based on the u-net architecture for EL image analysis of PV modules made from mono-crystalline and multi-crystalline silicon wafer-based solar cells. This work is focused on developing and testing a deep learning method for computer vision that is independent of the equipment used to generate the EL images, independent of the wafer-based module design, and independent of the image quality.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Electroluminescence | EL | PV | U-net | Semantic segmentation | Machine learning
قیمت: رایگان
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