Application of artificial neural network in fault location technique

K. Li, L. Lai, A. K. David
{"title":"Application of artificial neural network in fault location technique","authors":"K. Li, L. Lai, A. K. David","doi":"10.1109/DRPT.2000.855668","DOIUrl":null,"url":null,"abstract":"Recent restructuring of the power industries such as open access and deregulation have an impact on the reliability and security of power systems. New technologies for protection and control schemes are therefore necessary to be introduced in order to maintain the system reliability and security to an acceptable level. Artificial intelligence (AI) techniques naturally become the best choice to improve the performance of the present system used. Most faults which have infeed sources from both ends of the line, especially earth faults with fault resistance, are very difficult to identify. This paper presents a novel approach that can overcome the above difficulties. The artificial neural network (ANN) is used to identify the fault location, as well as the fault resistance in a wide range of system conditions. The training of the ANN is relatively simple and fast. The predicated results from the ANN are proved to be accurate for a wide range of system conditions.","PeriodicalId":127287,"journal":{"name":"DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DRPT.2000.855668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

Recent restructuring of the power industries such as open access and deregulation have an impact on the reliability and security of power systems. New technologies for protection and control schemes are therefore necessary to be introduced in order to maintain the system reliability and security to an acceptable level. Artificial intelligence (AI) techniques naturally become the best choice to improve the performance of the present system used. Most faults which have infeed sources from both ends of the line, especially earth faults with fault resistance, are very difficult to identify. This paper presents a novel approach that can overcome the above difficulties. The artificial neural network (ANN) is used to identify the fault location, as well as the fault resistance in a wide range of system conditions. The training of the ANN is relatively simple and fast. The predicated results from the ANN are proved to be accurate for a wide range of system conditions.
人工神经网络在故障定位技术中的应用
最近电力行业的重组,如开放接入和放松管制,对电力系统的可靠性和安全性产生了影响。因此,为了将系统的可靠性和安全性维持在可接受的水平,必须采用新的保护和控制方案技术。人工智能(AI)技术自然成为提高现有系统性能的最佳选择。大多数线路两端都有馈电源的故障,特别是带故障电阻的接地故障,很难识别。本文提出了一种克服上述困难的新方法。人工神经网络(ANN)被用于在广泛的系统条件下识别故障位置,以及故障电阻。人工神经网络的训练相对简单和快速。在广泛的系统条件下,人工神经网络的预测结果是准确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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