用于变压器局部放电监测的柔性天线和分布式深度学习模式识别研究

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, APPLIED Journal of Physics D: Applied Physics Pub Date : 2024-09-10 DOI:10.1088/1361-6463/ad759f
Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li
{"title":"用于变压器局部放电监测的柔性天线和分布式深度学习模式识别研究","authors":"Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li","doi":"10.1088/1361-6463/ad759f","DOIUrl":null,"url":null,"abstract":"Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.","PeriodicalId":16789,"journal":{"name":"Journal of Physics D: Applied Physics","volume":"11 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on flexible antenna and distributed deep learning pattern recognition for partial discharge monitoring of transformer\",\"authors\":\"Yuexuan Sun, Chang-Heng Li, Yunfeng Long, Zhengyong Huang and Jian Li\",\"doi\":\"10.1088/1361-6463/ad759f\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.\",\"PeriodicalId\":16789,\"journal\":{\"name\":\"Journal of Physics D: Applied Physics\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Physics D: Applied Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6463/ad759f\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics D: Applied Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1361-6463/ad759f","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
引用次数: 0

摘要

电力变压器是电力系统的重要组成部分,对局部放电的连续监测可以为变压器的故障诊断和运行维护提供更合理的方案。然而,刚性局部放电超高频天线无法与被监测设备贴合安装,且局部放电超高频信号衰减严重,导致检测能效低,增益性能无法满足需求。集中式深度学习局部放电模式识别方法训练效率低,分布式深度学习可以提高训练效率,但多源异构数据会降低模型精度。基于此,本文设计了一种小型化、宽频带、高增益、高弯曲变形稳定性的超高频柔性复合螺旋天线,并研究了一种基于残差收缩网络的联合学习模式识别方法,在保证精度的同时大幅提高了训练效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Research on flexible antenna and distributed deep learning pattern recognition for partial discharge monitoring of transformer
Power transformer is an important part of the power system, and continuous monitoring of partial discharges can provide a more reasonable program for fault diagnosis and operational maintenance of the transformer. However, the rigid partial discharge UHF antenna can not be installed in a conformal fit with the monitored equipment, and the partial discharge UHF signal attenuation is serious, resulting in low detection energy efficiency and gain performance can not meet the demand. The centralized deep learning local discharge pattern recognition method has low training efficiency, and distributed deep learning can improve the training efficiency, but the heterogeneous data from multiple sources will reduce the model accuracy. Due to this, this paper designs a UHF flexible composite helical antenna with miniaturization, wide bandwidth, high gain and high bending deformation stability, and investigates a federated learning pattern recognition method based on residual contraction network, which substantially improves the training efficiency while ensuring the accuracy.
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
Journal of Physics D: Applied Physics
Journal of Physics D: Applied Physics 物理-物理:应用
CiteScore
6.80
自引率
8.80%
发文量
835
审稿时长
2.1 months
期刊介绍: This journal is concerned with all aspects of applied physics research, from biophysics, magnetism, plasmas and semiconductors to the structure and properties of matter.
期刊最新文献
Recent progresses and applications on chiroptical metamaterials: a review Oxygen vacancies kinetics in TaO 2 − ... Numerical simulations of a low-pressure electrodeless ion source intended for air-breathing electric propulsion Electrical surface breakdown characteristics of micro- and nano-Al2O3 particle co-doped epoxy composites Wide-angle reflection control with a reflective digital coding metasurface for 5G communication systems
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1