Identification of most vulnerable line for divergent trend of power flow using Lyapunov Exponent

S. Nangrani, S. Bhat
{"title":"Identification of most vulnerable line for divergent trend of power flow using Lyapunov Exponent","authors":"S. Nangrani, S. Bhat","doi":"10.1109/PICC.2015.7455773","DOIUrl":null,"url":null,"abstract":"Complexity of power system is increasing due to expansion of interconnections. Blackouts and cascaded failures are associated with divergent situations in power system. Dynamics of power system pushes the behavior differently under different values of parameters such as increase in load demand at very sensitive bus in power system which may trigger exponential rise in power flows through lines leading to cascaded outages one after other. To study impact of power demand at sensitive bus causing divergent flow over lines, paper proposes chaotic performance index as a watchdog for relative divergent trend of power flow assessment. This will help power system planners to formulate artificial intelligent ways to identify weak chaotic bus and most vulnerable line showing divergent growth of power flow in a given power system network. This paper proposes discrete Largest Lyapunov Exponent based computation for dynamic security assessment. For a particular outage one can identify the most vulnerable transmission line based on algorithm presented in paper. Results and discussions will further support viewpoint of such studies.","PeriodicalId":373395,"journal":{"name":"2015 International Conference on Power, Instrumentation, Control and Computing (PICC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Power, Instrumentation, Control and Computing (PICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICC.2015.7455773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Complexity of power system is increasing due to expansion of interconnections. Blackouts and cascaded failures are associated with divergent situations in power system. Dynamics of power system pushes the behavior differently under different values of parameters such as increase in load demand at very sensitive bus in power system which may trigger exponential rise in power flows through lines leading to cascaded outages one after other. To study impact of power demand at sensitive bus causing divergent flow over lines, paper proposes chaotic performance index as a watchdog for relative divergent trend of power flow assessment. This will help power system planners to formulate artificial intelligent ways to identify weak chaotic bus and most vulnerable line showing divergent growth of power flow in a given power system network. This paper proposes discrete Largest Lyapunov Exponent based computation for dynamic security assessment. For a particular outage one can identify the most vulnerable transmission line based on algorithm presented in paper. Results and discussions will further support viewpoint of such studies.
利用李雅普诺夫指数识别潮流发散趋势的最脆弱线路
随着互联网络的不断扩大,电力系统的复杂性日益增加。停电和级联故障与电力系统的不同情况有关。电力系统的动力学特性在不同的参数值下推动着不同的行为,如电力系统中非常敏感的母线上负荷需求的增加,可能引发线路上的潮流呈指数级上升,导致相继的级联停电。为了研究敏感母线的电力需求对线路分流的影响,本文提出了混沌性能指标作为潮流评估相对发散趋势的看门狗。这将有助于电力系统规划者制定人工智能方法来识别给定电网中表现出潮流发散增长的弱混沌母线和最脆弱线路。提出了基于离散最大李雅普诺夫指数的动态安全评估方法。针对特定的停电情况,本文提出的算法可以识别出最脆弱的输电线路。结果和讨论将进一步支持这些研究的观点。
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