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基于深度检测网络的配网工程动态缺陷检测进展

Progress of Dynamic Defect Detection in Distribution Network Engineering Based on Deep Detection Network
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摘要 配网工程中大量的安全质量潜在缺陷给配网运行带来了极大的安全隐患。将深度检测网络应用于配网工程动态缺陷检测可加强配网工程管理、减少人工运维工作量,达到提质增效的目的。文章首先对配网工程缺陷种类进行梳理分类;然后,针对典型深度检测网络及其在配网工程动态缺陷检测领域的应用现状开展深入研究,并对现有研究成果进行详细综述;再次,总结深度检测网络应用于配网工程动态缺陷检测的关键问题及其解决方案。最后,对该领域的研究方向进行展望,期望为后续研究工作提供有益参考。 A large number of potential safety and quality defects have brought great potential safety hazards to the operation of distribution network. Applying the deep detection network to the dynamic defect detection of distribution network engineering could strengthen the management, reduce the workload of manual operation, and achieve the purpose of improving quality and efficiency. Firstly, the types of defects of distribution network engineering are classified. Secondly, the advanced deep detection network and its application in the field of dynamic defect detection in distribution network engineering are researched and analyzed in-dept, and the existing research achievements are reviewed in detail. Then, the key issues and its solutions of deep detection network applied to dynamic defect detection of distribution network engineering are summarized. Finally, the research direction is prospected in order to provide a valuable reference for the follow-up research.
作者 李运硕 段祥骏 李佳 林奕夫 任敬飞 杨婷 LI Yunshuo;DUAN Xiangjun;LI Jia;LIN Yifu;REN Jingfei;YANG Ting(China Electric Power Research Institute,Haidian District,Beijing 100192,China;Economic and Technological Research Institute of State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350001,Fujian Province,China;State Grid Shandong Electric Power Research Institute,Jinan 250002,Shandong Province,China;Nanjing Institute of Technology,School of Electric Power Engineering,Nanjing 211167,Jiangsu Province,China)
出处 《电力信息与通信技术》 2023年第2期40-52,共13页 Electric Power Information and Communication Technology
基金 国家电网有限公司总部科技项目资助“基于机器视觉深度学习的配网工程强化管控技术研究”(5400-202116141A)。
关键词 深度检测网络 配网工程动态缺陷 目标检测 机器视觉 模型部署 deep detection network dynamic defect detection of distribution network engineering target detection machine vision model deployment
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