基于BD3系统的电力基准站精确时间同步

IF 1.9 Q4 ENERGY & FUELS
Ting Zou, Yuchen Huang, Zhanqiang Cheng, Jinshen Liu, Hongwei Guo
{"title":"基于BD3系统的电力基准站精确时间同步","authors":"Ting Zou,&nbsp;Yuchen Huang,&nbsp;Zhanqiang Cheng,&nbsp;Jinshen Liu,&nbsp;Hongwei Guo","doi":"10.1016/j.gloei.2023.06.007","DOIUrl":null,"url":null,"abstract":"<div><p>A Beidou 3 (BD3) system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion. However, optimizing route selection to reduce both time synchronization error and delay is a challenging problem. In this paper, we establish a software-defined network-enabled power reference station time synchronization framework based on BD3. Then, we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization. A back propagation (BP) neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection, which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence. Simulation results show the superior performance of BP-RS in synchronization delay, synchronization error, and adaptability with changing routing topologies.</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"6 3","pages":"Pages 334-342"},"PeriodicalIF":1.9000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accurate time synchronization of power reference station based on BD3 system\",\"authors\":\"Ting Zou,&nbsp;Yuchen Huang,&nbsp;Zhanqiang Cheng,&nbsp;Jinshen Liu,&nbsp;Hongwei Guo\",\"doi\":\"10.1016/j.gloei.2023.06.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A Beidou 3 (BD3) system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion. However, optimizing route selection to reduce both time synchronization error and delay is a challenging problem. In this paper, we establish a software-defined network-enabled power reference station time synchronization framework based on BD3. Then, we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization. A back propagation (BP) neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection, which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence. Simulation results show the superior performance of BP-RS in synchronization delay, synchronization error, and adaptability with changing routing topologies.</p></div>\",\"PeriodicalId\":36174,\"journal\":{\"name\":\"Global Energy Interconnection\",\"volume\":\"6 3\",\"pages\":\"Pages 334-342\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Energy Interconnection\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2096511723000506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Energy Interconnection","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096511723000506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

基于北斗3 (BD3)系统的功率参考站可以通过多跳路由方式向设备发送同步数据包,为配电系统提供高精度的时间同步。然而,优化路由选择以减少时间同步误差和延迟是一个具有挑战性的问题。在本文中,我们建立了一个基于BD3的软件定义网络供电基准站时间同步框架。然后,通过多跳选路优化,提出了最小化累积同步误差和延迟的联合问题。提出了一种BP神经网络改进的智能时间同步选路算法BP- rs,该算法学习最优选路,利用BP神经网络动态调整勘探因子,实现快速收敛。仿真结果表明,BP-RS在同步延迟、同步误差和对路由拓扑变化的适应能力等方面具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accurate time synchronization of power reference station based on BD3 system

A Beidou 3 (BD3) system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion. However, optimizing route selection to reduce both time synchronization error and delay is a challenging problem. In this paper, we establish a software-defined network-enabled power reference station time synchronization framework based on BD3. Then, we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization. A back propagation (BP) neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection, which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence. Simulation results show the superior performance of BP-RS in synchronization delay, synchronization error, and adaptability with changing routing topologies.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Global Energy Interconnection
Global Energy Interconnection Engineering-Automotive Engineering
CiteScore
5.70
自引率
0.00%
发文量
985
审稿时长
15 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信