[1]周建明,张海龙,赵 阳,等.基于天牛群-反馈神经网络的绞线串扰预估[J].南京师范大学学报(工程技术版),2022,22(02):023-28.[doi:10.3969/j.issn.1672-1292.2022.02.004]
　Zhou Jianming,Zhang Hailong,Zhao Yang,et al.Prediction of Twisted Wire Crosstalk Based on Beetle Swarm Optimization-Back Propagation Neural Network[J].Journal of Nanjing Normal University(Engineering and Technology),2022,22(02):023-28.[doi:10.3969/j.issn.1672-1292.2022.02.004]
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基于天牛群-反馈神经网络的绞线串扰预估
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
22卷
期数:
2022年02期
页码:
023-28
栏目:
电气工程
出版日期:
2022-06-30

文章信息/Info

Title:
Prediction of Twisted Wire Crosstalk Based on Beetle Swarm Optimization-Back Propagation Neural Network
文章编号:
1672-1292(2022)02-0023-06
作者:
周建明1; 张海龙1; 赵 阳1; 颜伟1; 刘兴发2
(1.南京师范大学南瑞电气与自动化学院,江苏 南京 210023)(2.中国电力科学研究院武汉分院电网环境保护国家重点实验室,湖北 武汉 430074)
Author(s):
Zhou Jianming1; Zhang Hailong1; Zhao Yang1; Yan Wei1; Liu Xingfa2
(1.School of NARI Electrical and Automation,Nanjing Normal University,Nanjing 210023,China)(2.State Key Laboratory of Power Grid Environmental Protection,Wuhan Branch of China Electric Power Research Institute,Wuhan 430074,China)
关键词:
多导体传输线; 串扰; 链参数; 天牛群算法
Keywords:
multiconductor transmission lines; crosstalk; chain parameter; beetle swarm optimization algorithm
分类号:
TM72
DOI:
10.3969/j.issn.1672-1292.2022.02.004
文献标志码:
A
摘要:
在三绞线间的串扰问题中,绞线不同扭转角度会带来单位长度(per unit length,PUL)RLCG寄生参数的变化,已不能通过常规方法直接求解传输线方程得到串扰. 需借助于频域链参数的理论,将三绞线进行若干分段,通过对每一段级联得到串扰. 提出天牛群(beetle swarm optimization,BSO)算法优化反馈神经网络(back propagation neural network,BPNN)的权值,使其误差更小. 预测绞线不同分段截面处的寄生参数,并将BSO算法与天牛须(beetle antennae search,BAS)算法的寻优能力进行比较. 最后,基于BSO-BP、BAS-BP和BP共3种方法所预测的寄生参数进行串扰求解,并与CST cable studio仿真值进行比较验证. 结果表明,BSO-BP算法与仿真值相比具有最好的吻合度,而初始的BP算法则效果最差.
Abstract:
With the development of current power equipment toward miniaturization,high frequency,and high power,the crosstalk caused by electromagnetic coupling between adjacent cables has become a problem that cannot be ignored. The research object of this paper is crosstalk between triple twisted wires. Different twist angles of twisted wires will bring about the change of per unit length(per unit length,PUL)RLCG parasitic parameters. It is no longer possible to directly solve the transmission line equation and obtain the crosstalk with conventional methods. With the help of the theory of frequency domain chain parameters,triple twisted wires are divided into several sections,and crosstalk is finally obtained by cascading each section. This paper proposes the Beetle Swarm Optimization(BSO)algorithm to optimize the weights of Back Propagation Neural Network(BPNN)to make the error smaller. Parasitic parameters at different sections of the strands are predicted,and optimization capabilities of BSO algorithm with Beetle Antennae Search(BAS)algorithm are compared. Finally,crosstalk is obtained on the basis of the parasitic parameters predicted by three methods of BSO-BP,BAS-BP and BP. Furthermore,crosstalk are compared with the simulation value of CST cable studio. The results show that BSO-BP algorithm has best agreement with simulated value,while the initial BP algorithm has the worst effect.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-01-15.
基金项目:江苏省社会发展重点项目(BE2019716)、电网环境保护国家重点实验室开发基金项目(GYW51202001558).
通讯作者:赵阳,博士,教授,博士生导师,研究方向:电磁兼容. E-mail:zhaoyang2@njnu.edu.cn
更新日期/Last Update: 1900-01-01