Undergraduate Research on Adding Relay Models and Generator Capability Curves to Synthetic Electric Grids

Stephen E. Hurt, Jonathan Snodgrass, T. Overbye
{"title":"Undergraduate Research on Adding Relay Models and Generator Capability Curves to Synthetic Electric Grids","authors":"Stephen E. Hurt, Jonathan Snodgrass, T. Overbye","doi":"10.1109/TPEC56611.2023.10078672","DOIUrl":null,"url":null,"abstract":"Synthetic electric power systems are important models that allow researchers to conduct and publish their work without using nonpublic data about the real grid. These synthetic grids are often missing models that are important to certain studies, such as, fault analysis, cascading failure, or geomagnetically induced currents (GICs). Furthermore, these cases often lack the data to build these models because the data is nonpublic, or the data is synthetic. Because the data is synthetic, it is generally within an acceptable range, but it might not necessarily be precise enough for certain models such as generator capability curves. Using the synthetic data to build the generator capability curves, will often lead to unrealistic results. The generator capability curves can instead be estimated using data from the existing data set. Line distance relay and time-overcurrent relay models can also be added to the case, using known data. With these models, a synthetic case can be made much more realistic without the need to obtain or protect nonpublic, real grid data.","PeriodicalId":183284,"journal":{"name":"2023 IEEE Texas Power and Energy Conference (TPEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Texas Power and Energy Conference (TPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TPEC56611.2023.10078672","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

Synthetic electric power systems are important models that allow researchers to conduct and publish their work without using nonpublic data about the real grid. These synthetic grids are often missing models that are important to certain studies, such as, fault analysis, cascading failure, or geomagnetically induced currents (GICs). Furthermore, these cases often lack the data to build these models because the data is nonpublic, or the data is synthetic. Because the data is synthetic, it is generally within an acceptable range, but it might not necessarily be precise enough for certain models such as generator capability curves. Using the synthetic data to build the generator capability curves, will often lead to unrealistic results. The generator capability curves can instead be estimated using data from the existing data set. Line distance relay and time-overcurrent relay models can also be added to the case, using known data. With these models, a synthetic case can be made much more realistic without the need to obtain or protect nonpublic, real grid data.
在综合电网中加入继电器模型和发电机能力曲线的本科生研究
合成电力系统是重要的模型,它允许研究人员在不使用真实电网的非公开数据的情况下进行和发表他们的工作。这些合成网格通常缺少对某些研究很重要的模型,例如故障分析、级联故障或地磁感应电流(gic)。此外,这些案例通常缺乏构建这些模型的数据,因为数据是非公开的,或者数据是合成的。由于数据是合成的,因此通常在可接受的范围内,但对于某些模型(如发电机能力曲线)可能不够精确。利用综合数据来构建发电机性能曲线,往往会导致不切实际的结果。发电机的能力曲线可以用现有数据集的数据来估计。线路距离继电器和时间过流继电器模型也可以添加到情况下,使用已知的数据。有了这些模型,一个综合案例可以变得更加真实,而不需要获取或保护非公开的、真实的网格数据。
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:604180095
Book学术官方微信