摘要
随着智能电网业务数据量的快速增长,对控制信息传输的低时延和高可靠性的要求不断提高。时间感知整形器(time-aware shaper,TAS)机制是时间敏感网络(time-sensitive networking,TSN)标准中的关键技术之一。传统TAS机制不能充分利用带宽资源,难以满足变电站内多种流量的不同时延需求。为此,提出一种对保护窗口进行动态细粒度划分的方法。首先,提出一种基于国际电工委员会(International Electrotechnical Commission,IEC)61850通信与TSN技术融合的流量映射方案;然后,设计了时间窗口划分的效用分析模型;最后,提出一种基于效用的动态时间窗口划分算法,求解划分效用最大的划分方案。仿真结果表明,该算法通过优化TAS机制的时间窗口划分策略,在多种场景下显著降低了网络的整体传输时延,尤其将关键性面向对象变电站事件(generic object-oriented substation event,GOOSE)1A流量的传输时延控制在3 ms以内,同时保持采样测量值(sampled measured value,SMV)流量的丢包率不超过3%。
With the rapid growth of the business data volume of smart grids,the requirements for low-latency and high reliability in control information transmission keep continuously increasing.The time-aware shaper(TAS)mechanism is one of the key technologies in time-sensitive networking(TSN)standard.However,the traditional TAS mechanisms can not fully utilize the communication bandwidth resources,which makes it difficult to simultaneously meet the diverse latency requirements of various traffic flows within substations.To address this issue,this paper proposes the idea of dynamic and fine grained protection time window division.We first propose a traffic mapping scheme based on the integration of International Electrotechnical Commission(IEC)61850 communication and TSN technology.Then,a utility analysis model for time window division is designed.Finally,a utility-based dynamic time window division algorithm is proposed to obtain the maximum benefits overall.The simulation results show that,by optimizing the time window division strategy of the TAS mechanism,the proposed algorithm significantly reduces overall network transmission latency under various scenarios.In particular,the transmission delay of critical generic object-oriented substation event(GOOSE)1A traffic is kept within 3 ms,while ensuring that the packet loss rate of sampled measured value(SMV)traffic does not exceed 3%.
作者
李伟
白杨
祁步仁
徐建
LI Wei;BAI Yang;QI Buren;XU Jian(Information and Telecommunication Branch,State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210024,China;School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《山东电力技术》
2025年第6期75-85,94,共12页
Shandong Electric Power
基金
国网江苏省电力有限公司科技项目(J2023107)。
