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多机器人网络物理系统在输电线路环境变量监测中的应用。

Multi-Robot Cyber Physical System for Sensing Environmental Variables of Transmission Line.

机构信息

School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China.

State Grid Jilin Electric Power Co., Ltd., Baishan Power Company, Baishan 134300, China.

出版信息

Sensors (Basel). 2018 Sep 18;18(9):3146. doi: 10.3390/s18093146.


DOI:10.3390/s18093146
PMID:30231509
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6165351/
Abstract

The normal operation of a power grid largely depends on the effective monitoring and maintenance of transmission lines, which is a process that has many challenges. The traditional method of the manual or remote inspection of transmission lines is time-consuming, laborious, and inefficient. To address this problem, a novel method has been proposed for the Multi-Robot Cyber Physical System (MRCPS) of a power grid based on inspection robots, a wireless sensor network (WSN), and multi-agent theory to achieve a low-cost, efficient, fault-tolerant, and remote monitoring of power grids. For the sake of an effective monitoring system for smart grids, the very research is conducted focusing on designing a methodology that will realize the efficient, fault-tolerant, and financial balance of a multi-robot team for monitoring transmission lines. Multiple testing scenarios are performed, in which various aspects are explored so as to determine the optimal parameters balancing team performance and financial cost. Furthermore, multi-robot team communication and navigation control in smart grid environments are introduced.

摘要

电网的正常运行在很大程度上取决于对输电线的有效监测和维护,这是一个具有诸多挑战的过程。传统的输电线人工或远程检测方法既耗时、费力又效率低下。针对这一问题,提出了一种基于巡检机器人、无线传感器网络(WSN)和多代理理论的电网多机器人网络物理系统(MRCPS)的新型方法,以实现电网的低成本、高效、容错和远程监控。为了实现智能电网的有效监控系统,非常有必要研究设计一种方法,实现用于监测输电线的多机器人团队的高效、容错和财务平衡。进行了多个测试场景,以探索各种方面,从而确定平衡团队性能和财务成本的最佳参数。此外,还介绍了智能电网环境中多机器人团队的通信和导航控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/0323fa8f6e15/sensors-18-03146-g020.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/0323fa8f6e15/sensors-18-03146-g020.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/a3655e9e8cfe/sensors-18-03146-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/121497c5babe/sensors-18-03146-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/9b8ea85c9ca5/sensors-18-03146-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/3d1e4e596a65/sensors-18-03146-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/708a54f85192/sensors-18-03146-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/f47898319f31/sensors-18-03146-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/fba0784da7f5/sensors-18-03146-g018a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/545d3fb54c99/sensors-18-03146-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3eaa/6165351/0323fa8f6e15/sensors-18-03146-g020.jpg

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[2]
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本文引用的文献

[1]
Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data.

Sensors (Basel). 2018-4-22

[2]
A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data.

Sensors (Basel). 2018-2-15

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