基于神经网络系统的高压架空输电线路故障定位研究

Xiangning Lin, P. Mao, H. Weng, Bin Wang, Z. Bo
{"title":"基于神经网络系统的高压架空输电线路故障定位研究","authors":"Xiangning Lin, P. Mao, H. Weng, Bin Wang, Z. Bo","doi":"10.1109/ISAP.2007.4441662","DOIUrl":null,"url":null,"abstract":"A distributed & hierarchical NN (DHNN) system based on the integrated module architecture and hierarchy architecture is proposed in this paper. The DHNN system adequately uses the powerful function of artificial neural networks at aspects of pattern identification, nonlinear-approaching, associative memory et al. Its information processing mechanism coincides with the processing law of classification-sketchiness-accuracy in human biologic NN system. This system not only can deal with the advanced information required by fault location for HV overhead transmission lines but accurately locate the fault sites. Thus this method for fault location presented in this paper can thoroughly eliminate the disadvantages in other extant fault location methods, such as convergence to the false root or divergence in the procedure of iteration, which result in the great location error in practice. This paper pioneers a new direction to the study and application on fault location. Results from theoretical analyses and simulations by EMTP show that fault location precision of this method can completely satisfy practical requirements.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Study on Fault Location for High Voltage Overhead Transmission Lines Based on Neural Network System\",\"authors\":\"Xiangning Lin, P. Mao, H. Weng, Bin Wang, Z. Bo\",\"doi\":\"10.1109/ISAP.2007.4441662\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A distributed & hierarchical NN (DHNN) system based on the integrated module architecture and hierarchy architecture is proposed in this paper. The DHNN system adequately uses the powerful function of artificial neural networks at aspects of pattern identification, nonlinear-approaching, associative memory et al. Its information processing mechanism coincides with the processing law of classification-sketchiness-accuracy in human biologic NN system. This system not only can deal with the advanced information required by fault location for HV overhead transmission lines but accurately locate the fault sites. Thus this method for fault location presented in this paper can thoroughly eliminate the disadvantages in other extant fault location methods, such as convergence to the false root or divergence in the procedure of iteration, which result in the great location error in practice. This paper pioneers a new direction to the study and application on fault location. Results from theoretical analyses and simulations by EMTP show that fault location precision of this method can completely satisfy practical requirements.\",\"PeriodicalId\":320068,\"journal\":{\"name\":\"2007 International Conference on Intelligent Systems Applications to Power Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Conference on Intelligent Systems Applications to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISAP.2007.4441662\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Intelligent Systems Applications to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAP.2007.4441662","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

提出了一种基于集成模块结构和层次结构的分布式分层神经网络(DHNN)系统。DHNN系统充分利用了人工神经网络在模式识别、非线性逼近、联想记忆等方面的强大功能。其信息处理机制与人体生物神经网络中分类-粗略-准确的处理规律一致。该系统既能处理高压架空线路故障定位所需的高级信息,又能准确定位故障点。因此,本文提出的故障定位方法可以彻底消除现有故障定位方法收敛于假根或迭代过程发散导致实际定位误差较大的缺点。本文为断层定位的研究和应用开辟了新的方向。理论分析和EMTP仿真结果表明,该方法的故障定位精度完全满足实际要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Fault Location for High Voltage Overhead Transmission Lines Based on Neural Network System
A distributed & hierarchical NN (DHNN) system based on the integrated module architecture and hierarchy architecture is proposed in this paper. The DHNN system adequately uses the powerful function of artificial neural networks at aspects of pattern identification, nonlinear-approaching, associative memory et al. Its information processing mechanism coincides with the processing law of classification-sketchiness-accuracy in human biologic NN system. This system not only can deal with the advanced information required by fault location for HV overhead transmission lines but accurately locate the fault sites. Thus this method for fault location presented in this paper can thoroughly eliminate the disadvantages in other extant fault location methods, such as convergence to the false root or divergence in the procedure of iteration, which result in the great location error in practice. This paper pioneers a new direction to the study and application on fault location. Results from theoretical analyses and simulations by EMTP show that fault location precision of this method can completely satisfy practical requirements.
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
自引率
0.00%
发文量
0
×
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