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基于改进蚁群算法和深度学习的无人机配电网绝缘子检测

Distribution network insulator detection based on improved ant colony algorithm and deep learning for UAV.

作者信息

Zhou Yimin, Zhang Dong, Ma Xingming

机构信息

State Grid Heilongjiang Electric Power Co., Ltd., Daqing Power Supply Company Substation Maintenance Center, Daqing 163000, China.

State Grid Heilongjiang Electric Power Co., Ltd., Daqing Power Supply Company Substation Operation and Maintenance Center, Daqing 163000, China.

出版信息

iScience. 2024 May 27;27(6):110119. doi: 10.1016/j.isci.2024.110119. eCollection 2024 Jun 21.

DOI:10.1016/j.isci.2024.110119
PMID:38974473
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11225854/
Abstract

Under the background of the accelerating speed of urban and rural construction, the geographical environment of overhead transmission lines has also changed greatly. Using unmanned aerial vehicle (UAV) to realize intelligent line inspection can significantly shorten inspection time and improve inspection efficiency. In this paper, the intelligent power inspection of UAVs is studied from two levels: path planning and UAV control, and the insulator is identified through actual image recognition. At the path planning level, the improved swarm intelligence algorithm is used to conduct simulation experiments on the UAV flight path to find a safe and effective route. Insulator identification and defect location of overhead transmission lines are trained on the insulator dataset collected by deep learning technology to achieve accurate insulator identification and improve the efficiency of UAV inspection, which has great application prospects in engineering.

摘要

在城乡建设速度加快的背景下，架空输电线路的地理环境也发生了很大变化。利用无人机实现智能线路巡检可显著缩短巡检时间，提高巡检效率。本文从路径规划和无人机控制两个层面研究无人机的智能电力巡检，并通过实际图像识别来识别绝缘子。在路径规划层面，采用改进的群体智能算法对无人机飞行路径进行仿真实验，以找到安全有效的路线。利用深度学习技术采集的绝缘子数据集对架空输电线路的绝缘子识别和缺陷定位进行训练，实现绝缘子的准确识别，提高无人机巡检效率，在工程中具有很大的应用前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e6e8/11225854/9517d53fae24/gr16.jpg
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本文引用的文献

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Task Offloading Strategy Based on Mobile Edge Computing in UAV Network.基于无人机网络中移动边缘计算的任务卸载策略
Entropy (Basel). 2022 May 22;24(5):736. doi: 10.3390/e24050736.