Power system instability prediction from the solution pattern of differential Riccati equations

J. Khodaparast, O. B. Fosso, M. Molinas, J. A. Suul
{"title":"Power system instability prediction from the solution pattern of differential Riccati equations","authors":"J. Khodaparast, O. B. Fosso, M. Molinas, J. A. Suul","doi":"10.1049/tje2.12414","DOIUrl":null,"url":null,"abstract":"Power system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.","PeriodicalId":510109,"journal":{"name":"The Journal of Engineering","volume":"34 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/tje2.12414","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 system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying system analysis, the differential Riccati equation (DRE) plays a vital role when the power system is subjected to a severe disturbance. The Möbius transformation is proposed in this paper to solve the DRE with singularity issues. It is shown that the solution of the DREs follows a specific mathematical pattern when the power system is stable but does not follow this pattern when the system progresses toward instability. The proposed method can be used in large‐signal stability analysis to predict instability and make the stability analysis more efficient. Additionally, the vector‐DRE is proposed to generalize the index in a large‐scale power system. Results show that analyzing the corresponding Riccati equation's behaviour can help researchers predict a power system's performance and improve the control and management of the system.
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
从微分里卡提方程的解法预测电力系统的不稳定性
电力系统稳定性特征通常以小信号和大信号(暂态)稳定性进行评估。利用基于状态空间的电力系统模型在暂态条件下的时变 A 矩阵,可将线性时变系统概念用于大信号稳定性分析。在线性时变系统分析中,当电力系统受到严重扰动时,微分里卡提方程 (DRE) 起着至关重要的作用。本文提出了莫比乌斯变换来求解具有奇异性问题的 DRE。结果表明,当电力系统稳定时,DRE 的解遵循特定的数学模式,但当系统趋于不稳定时,则不遵循这一模式。所提出的方法可用于大信号稳定性分析,预测不稳定性,使稳定性分析更有效。此外,还提出了矢量-DRE,以在大规模电力系统中推广该指标。结果表明,分析相应的 Riccati 方程行为有助于研究人员预测电力系统的性能,并改善系统的控制和管理。
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
Ferrofluid‐based electrical machines: Conceptualization and experimental evaluation Anti‐leakage transmission method of high privacy information in electric power communication network based on digital watermarking technology A high‐accuracy and robust diagnostic tool for gearbox faults in wind turbines Optimal scheduling of the stand‐alone micro grids considering the reliability cost A domain adaptation‐based convolutional neural network incorporating data augmentation for power system dynamic security assessment
×
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