基于人工智能图像处理的无人机光伏组件裂纹检测系统研究  

Research on Unmanned Aerial Vehicle Photovoltaic Module Crack Detection System Based on Artificial Intelligence Image Processing

作　　者：李华喜 陈金鑫 刘文阳 LI Huaxi;CHEN Jinxin;LIU Wenyang(Wuling Electric Power Co.,Ltd.,Changsha 410004,China)

机构地区：[1]五凌电力有限公司,湖南长沙410004

出　　处：《电工技术》2025年第5期128-130,共3页Electric Engineering

摘　　要：随着光伏产业的快速发展,光伏组件的健康状况对发电效率至关重要,因此提出了一种基于人工智能图像处理和无人机技术的光伏组件裂纹检测系统,该系统采用卷积神经网络进行裂纹自动检测和分类。通过实验验证,系统平均裂纹检出率达92.8%,满足大规模光伏电站的高效运维需求。研究结果表明,该系统在提高光伏组件故障检测效率和准确性方面具有显著优势,为光伏产业的智能化发展提供了可靠支持。With the rapid development of the photovoltaic industry,the health status of photovoltaic modules is crucial for power generation efficiency.This article proposes a photovoltaic module crack detection system based on artificial intelligence image processing and drone technology.The system adopts convolutional neural network for automatic crack detection and classification.Through experimental verification,its average crack detection rate reaches 92.8%,which meets the efficient operation and maintenance needs of large-scale photovoltaic power plants.The research results show that the system has significant advantages in improving the efficiency and accuracy of photovoltaic module fault detection,providing reliable support for the intelligent development of the photovoltaic industry.

关 键 词：人工智能 无人机 光伏组件 裂纹检测 

分 类 号：TM615[电气工程—电力系统及自动化]