Improved Sheep Flock Heredity Algorithm based fault detection and localization in power transmission lines

C. Prasath, N. Subramaniam
{"title":"Improved Sheep Flock Heredity Algorithm based fault detection and localization in power transmission lines","authors":"C. Prasath, N. Subramaniam","doi":"10.1109/ICCPCT.2016.7530361","DOIUrl":null,"url":null,"abstract":"Power transmission is one of the fields drastically growing in the world presently. In this paper, it is aimed to provide a solution for detecting the fault and its location accurately by utilizing ISFHA-[Improved Sheep Flock Heredity Algorithm]. It is necessary to satisfy the customer in terms of power quality transmission. Power quality damages occur due to short circuit, natural disasters and other problems. The estimation of fault and location of the fault can be detected using the local and global searching techniques of ISFH algorithm. Since the ISFHA utilizes the optimization methodology the accuracy of fault detection and localization is high. Also, the effects of ISFHA parameters such as population, crossover and chromosome generation including fitness function to achieve the optimized result. The simulation results are compared with the existing results obtained using GA is compared to evaluating the performance of the ISFHA.","PeriodicalId":431894,"journal":{"name":"2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCPCT.2016.7530361","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Power transmission is one of the fields drastically growing in the world presently. In this paper, it is aimed to provide a solution for detecting the fault and its location accurately by utilizing ISFHA-[Improved Sheep Flock Heredity Algorithm]. It is necessary to satisfy the customer in terms of power quality transmission. Power quality damages occur due to short circuit, natural disasters and other problems. The estimation of fault and location of the fault can be detected using the local and global searching techniques of ISFH algorithm. Since the ISFHA utilizes the optimization methodology the accuracy of fault detection and localization is high. Also, the effects of ISFHA parameters such as population, crossover and chromosome generation including fitness function to achieve the optimized result. The simulation results are compared with the existing results obtained using GA is compared to evaluating the performance of the ISFHA.
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
基于改进羊群遗传算法的输电线路故障检测与定位
电力传输是当今世界发展迅速的领域之一。本文旨在利用改进羊群遗传算法(ISFHA- Improved Sheep Flock genetic Algorithm)为故障的准确检测和定位提供一种解决方案。有必要在电能质量传输方面满足客户。由于短路、自然灾害等问题,电能质量会受到损害。利用ISFH算法的局部搜索和全局搜索技术,可以实现故障估计和故障定位。由于ISFHA采用了优化方法,因此故障检测和定位精度高。此外,ISFHA参数如种群、交叉和染色体生成(包括适应度函数)对优化结果的影响。将仿真结果与已有的遗传算法结果进行了比较,对ISFHA的性能进行了评价。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A study on the increasing in the performance of a solar photovoltaic cell during shading condition Design and analysis of hybrid DC-DC boost converter in continuous conduction mode Optimal control of islanded microgrid with adaptive fuzzy logic & PI controller using HBCC under various voltage & load variation Mouse behaviour based multi-factor authentication using neural networks A novel approach to maximize network life time by reducing power consumption level using CGNT model
×
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