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基于无线传感器网络的特高压直流输电线路合成电场智能监测系统研究 被引量:8

Intelligent Monitoring System of Total Electric Field Under UHVDC Transmission Line Based on Wireless Sensor Network
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摘要 特高压直流输电线路的地面合成电场是评价电磁环境的重要参数,因其受环境气候等因素影响而随机变化,在对其研究和评价时需进行长时间测量。为此利用无线传感器网络灵活度高、移动性强的优势,结合多智能体技术研制了合成电场智能监测系统。系统中各节点智能体根据监测电场强弱和剩余能量控制自身状态的转换,同时与系统中其他节点智能体交互信息来动态成簇,自组织地通过协作共同完成监测任务。针对系统中基于多智能体的自组织机制进行了能耗分析。最后将合成电场智能监测系统应用于工程实测,验证了该智能系统的可行性和自组织机制在节能方面的有效性。 Ground total electric field under ultra high voltage direct current (UHVDC) transmission lines is an important parameter for the evaluation of the electromagnetic environment. In view of its random change because of the changes of environmental and climate factors, it is necessary to perform long-time measurement for the research and evaluation. Utilizing the advantages of wireless sensor network (WSN) in respect of high flexibility and strong mobility and combining with multi-agent technology, an intelligent monitoring system of ground total electric field was developed. The state transformation of each node agent in the monitoring system could be controlled according to the electric field intensity and its residual energy, meanwhile through exchanging information each node agent could be dynamically clustered with other node agents in the monitoring system, thus the monitoring task could be accomplished by self-organized cooperation. Then energy consumption analysis of the self-organization mechanism based on multi-agents was performed. Finally, the proposed intelligent monitoring system of ground total electric field was applied to actual engineering, and the feasibility of the proposed intelligent monitoring system as well as the effectiveness of the self-organization mechanism in energy-saving were verified by measured results.
出处 《电网技术》 EI CSCD 北大核心 2012年第4期10-15,共6页 Power System Technology
基金 国家自然科学基金项目(51037001) 高等学校博士学科点专项科研基金资助项目(20111102110007)~~
关键词 特高压直流输电 合成电场 电场监测 无线传感器网络 多智能体 自组织机制 能耗 UHVDC electric field monitoring transmission total electric field wireless sensor network(WSN) multi-agent self-organization mechanism energy consumption
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