| 电站中光伏组件的人工智能热红外检测技术进展 |
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| 引用本文： | 宁静,许盛之,龚友康,王丽朝,江茜.电站中光伏组件的人工智能热红外检测技术进展[J].半导体光电,2023,44(2):161-167. |
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| 作者姓名： | 宁静 许盛之 龚友康 王丽朝 江茜 |
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| 作者单位： | 南开大学 电子信息与光学工程学院 光电子薄膜器件与技术研究所, 天津 300350;薄膜光电子技术教育部工程研究中心, 天津 300350;天津市光电子薄膜器件与技术重点实验室, 天津 300350 |
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| 基金项目： | 天津市研究生科研创新项目(2022SKYZ188).＊通信作者:许盛之 E-mail:185859840@qq.com |
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| 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。
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| 关 键 词： | 光伏组件 红外图像 热斑 人工智能 无人机 |
| 收稿时间： | 2023/1/16 0:00:00 |
Progress in Artificial Intelligence Thermal Infrared Detection Technology for Photovoltaic Modules in Power Plant |
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| NING Jing,XU Shengzhi,GONG Youkang,WANG Lichao,JIANG Qian.Progress in Artificial Intelligence Thermal Infrared Detection Technology for Photovoltaic Modules in Power Plant[J].Semiconductor Optoelectronics,2023,44(2):161-167. |
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| Authors: | NING Jing XU Shengzhi GONG Youkang WANG Lichao JIANG Qian |
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| Affiliation: | Institute of Photo-Electronics Thin Film Devices and Technology of College of Electronic Information and Optical Engin.of Nankai University, Tianjin 300350, CHN;Engineering Research Center of Thin Film Optoelectronics Technology, Ministry of Education, Tianjin 300350, CHN;Key Lab.of Photoelectronics Thin Film Devices and Technology, Tianjin 300350, CHN |
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| 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. |
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| Keywords: | photovoltaic module infrared image hot spot artificial intelligence unmanned aerial vehicle |
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