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Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems

机译:大型光伏系统中基于无人机检查的可见缺陷检测

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摘要

The asset assessment and condition monitoring of large-scale photovoltaic (PV) systems spanning over a large geographical area has imposed urgent challenges and demands for novel and efficient inspection paradigm. In this study, an automatic UAV-based inspection system is presented and implemented for asset assessment and defect detection for large-scale PV systems. Two typical visible defects of PV modules, snail trails and dust shading, are characterised and the defect detection through image processing algorithms based on first order derivative of Gaussian function and feature matching is carried out for the aerial PV module images captured by visible light cameras. The functionality of the developed unmanned aerial vehicle (UAV)-based inspection system can be easily extended with more advanced fault detection algorithms and different forms of sensing devices (e.g. infrared thermal camera) for specialised inspection tasks. Such UAV-based imaging can carry out a variety of inspection and condition monitoring tasks in PV systems spanning over a large geographical area in an autonomous or supervised fashion with significantly promoted efficiency in comparison with conventional methods.
机译:跨越大地理区域的大型光伏(PV)系统的资产评估和状态监视已提出了新的,有效的检查范式的紧迫挑战和要求。在这项研究中,提出并实现了一种基于无人机的自动检查系统,用于大型光伏系统的资产评估和缺陷检测。表征了光伏组件的两个典型的可见缺陷,即蜗牛迹和灰尘阴影,并基于可见光相机捕获的航空光伏组件图像,通过基于高斯函数的一阶导数和特征匹配的图像处理算法进行了缺陷检测。已开发的基于无人机(UAV)的检查系统的功能可以通过更高级的故障检测算法和不同形式的传感设备(例如红外热像仪)轻松扩展,以进行专门的检查任务。与传统方法相比,这种基于UAV的成像可以以自主或有监督的方式在跨越较大地理区域的PV系统中执行各种检查和状态监视任务,并且效率得到显着提高。

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