CNN based Automatic Detection of Defective Photovoltaic Modules using Aerial Imagery

Pornthep Sarakon, Benya Lertpornsuksawat, Temsiri Sapsaman, Titan Janthori
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Abstract

The efficiency of photovoltaic modules decreases over time, and this decrease can be accelerated by environmental factors such as high temperatures. It is important to detect defective photovoltaic modules as soon as possible to prevent further degradation and loss of power output. Aerial thermographic inspections are a non-invasive method of detecting defective photovoltaic modules. This method uses thermal imaging to detect differences in the temperature of photovoltaic modules. By detecting defective photovoltaic modules early, the owner can save money on replacement costs and loss of power output.
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基于CNN的航空图像缺陷光伏组件自动检测
光伏组件的效率会随着时间的推移而下降,而这种下降可能会因高温等环境因素而加速。尽快发现有缺陷的光伏组件,以防止进一步退化和功率输出损失,这一点非常重要。航空热成像检测是一种检测有缺陷光伏组件的非侵入性方法。该方法利用热成像技术检测光伏组件的温度差异。通过及早发现有缺陷的光伏组件,业主可以节省更换成本和电力输出损失。
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