A method for detecting photovoltaic panel faults using a drone equipped with a multispectral camera

Ran Duan, Zhenling Ma
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Abstract

Abstract. Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults.
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使用配备多光谱相机的无人机检测光伏板故障的方法
摘要利用太阳能的光伏发电站规模不断扩大,导致运行维护需求增加。对这些电站内的组件进行高效检查至关重要。然而,由于光伏发电面积大、光伏板数量多、地理环境复杂,人工检测方法效率极低,无法满足现代光伏电站的要求。为解决这一问题,本文提出了一种利用配备多光谱相机的无人飞行器(UAV)对光伏板进行热点检测的方法和系统。无人飞行器捕捉光伏电站的可见光和红外图像,然后进行摄影测量处理,以确定成像位置和姿态。利用这些信息将红外图像拼接在一起,形成具有地理参考价值的整体图像。在红外图像上进行热点检测,可识别故障光电板,促进有效的检查和维护。实验在中国广东省清远市的一个光伏发电站进行。结果表明,所提出的方法能有效准确地检测出有热点故障的电池板。
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