Fast voltage collapse evaluation via fuzzy decision tree method

H. Abidin, K. Lo, Z.F. Hussein
{"title":"Fast voltage collapse evaluation via fuzzy decision tree method","authors":"H. Abidin, K. Lo, Z.F. Hussein","doi":"10.1109/PECON.2003.1437406","DOIUrl":null,"url":null,"abstract":"Voltage stability is considered to be a complex field of study since it has a number of contributing factors. Due to this, numerous studies or research has been made to look into various methods of analysis, detection and mitigation. In general, these methods would involve either complex computation for accurate results but suffers from high computation time. Some methods may also be simple and fast but then has the disadvantage of inaccuracy. This paper presents an alternative method of analysing the voltage stability problem by incorporating machine learning techniques, i.e. fuzzy decision tree method. The author proposed a general overview on how the algorithm is created. The algorithm is then tested using an IEEE 300 bus test system to test the algorithm's capability. Results presented show that the proposed FDT has a lot of future potential as an online tool for voltage stability analysis.","PeriodicalId":136640,"journal":{"name":"Proceedings. National Power Engineering Conference, 2003. PECon 2003.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2003-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. National Power Engineering Conference, 2003. PECon 2003.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PECON.2003.1437406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Voltage stability is considered to be a complex field of study since it has a number of contributing factors. Due to this, numerous studies or research has been made to look into various methods of analysis, detection and mitigation. In general, these methods would involve either complex computation for accurate results but suffers from high computation time. Some methods may also be simple and fast but then has the disadvantage of inaccuracy. This paper presents an alternative method of analysing the voltage stability problem by incorporating machine learning techniques, i.e. fuzzy decision tree method. The author proposed a general overview on how the algorithm is created. The algorithm is then tested using an IEEE 300 bus test system to test the algorithm's capability. Results presented show that the proposed FDT has a lot of future potential as an online tool for voltage stability analysis.
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
基于模糊决策树的电压崩溃快速评价
电压稳定性被认为是一个复杂的研究领域,因为它有许多影响因素。因此,人们进行了大量的研究,以探讨各种分析、检测和缓解方法。一般来说,这些方法要么需要复杂的计算才能得到准确的结果,要么需要耗费大量的计算时间。有些方法也可能是简单和快速的,但随后有不准确的缺点。本文提出了一种结合机器学习技术分析电压稳定问题的替代方法,即模糊决策树方法。作者对如何创建算法提出了一个总体概述。然后使用IEEE 300总线测试系统对算法进行测试,以测试算法的性能。结果表明,所提出的FDT作为电压稳定性在线分析工具具有很大的潜力。
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
Fabrication and testing of linear electric generator for use with a free-piston engine A hybrid approach for security evaluation and preventive control of power systems Study the effective angle of photovoltaic modules in generating an optimum energy Object oriented sparse linear solver component for power system analysis Current efforts in the management of power frequency electric and magnetic fields in Malaysia
×
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