基于无人机巡检的光伏缺陷检测与定位

Photovoltaic Defect Detection and Positioning Based on UAV Patrol Inspection

光伏发电是一种清洁和可再生的能源形式,然而,光伏组件在实际运行过程中可能出现各种缺陷,如裂纹、漏电等问题,这些缺陷会影响光伏系统的能量损失和安全性。随着太阳能光伏发电的广泛应用,光伏组件的可靠性和性能监测变得尤为重要。为了提高光伏组件的检测效率和准确性,该文针对无人机巡检中缺陷光伏组件缺陷检测及定位技术展开研究,提出一种深度学习分割技术和传统图像处理方法相结合的技术,可以快速、准确地检测光伏的缺陷,并确定该缺陷所在其组件位置的归属。

Photovoltaic power generation is a clean and renewable form of energy,however,photovoltaic modules in the actual operation process may have a variety of defects,such as cracks,leakage and other problems,these defects will affect the energy loss and safety of the photovoltaic system.With the wide application of solar photovoltaic power generation,the reliability and performance monitoring of photovoltaic modules become particularly important.In order to improve the detection efficiency and accuracy of photovoltaic module,this paper studies the defect detection and location technology of photovoltaic module in UAV patrol inspection,and puts forward a technology which combines deep learning segmentation technology with traditional image processing method.The defect of photovoltaic can be detected quickly and accurately,and the location of the defect can be determined.