电站中光伏组件的人工智能热红外检测技术进展

Progress in Artificial Intelligence Thermal Infrared Detection Technology for Photovoltaic Modules in Power Plant

摘要:

基于温度红外图像的光伏组件缺陷检测是实现光伏电站规模化组件质量检测的重要技术。文章简要介绍了光伏组件热斑产生的原因和危害,重点从热斑检测、热斑定位和提取三个方面总结和对比了光伏组件红外图像及视频的人工神经网络模型及其性能。其中改进的YOLOv5模型对光伏组件的热斑检出精度达到了98.8%,Lucas-Kanade稀疏光流跟踪算法的热斑定位精度达到97.5%。简单讨论了适应大规模光伏电站运维需求的热斑检测技术的发展趋势。

Abstract:

The defect detection of photovoltaic modules based on temperature infrared image is an important technology to realize the large-scale modules quality detection of photovoltaic power plant. In this paper, the causes and hazards of hot spot of photovoltaic modules were briefly introduced. The artificial neural network model and its performance of infrared image and video of photovoltaic modules were summarized and compared from three aspects:hot spot detection, hot spot location and extraction. The hot spot detection accuracy of the improved YOLOv5 model for photovoltaic modules reached 98.8%, and the hot spot positioning accuracy of the Lucas-Kanade sparse optical flow algorithm reached 97.5%. At the end of this paper, the development trend of hot spot detection technology adapted to meet the operation and maintenance needs of large-scale photovoltaic power plant was briefly discussed.