Modelling of DC Power Equations Applied to State Estimation in High Renewable Penetration Power Systems

Stefany Yánez Rojas, Carlos Barrera-Singaña, Paul Muñoz Pilco, Darío Jaramillo Monje, Wilson Pavón
{"title":"Modelling of DC Power Equations Applied to State Estimation in High Renewable Penetration Power Systems","authors":"Stefany Yánez Rojas, Carlos Barrera-Singaña, Paul Muñoz Pilco, Darío Jaramillo Monje, Wilson Pavón","doi":"10.1109/GlobConHT56829.2023.10087445","DOIUrl":null,"url":null,"abstract":"Growth in power systems has led to an increase in operational complexity, highlighting the importance of maintaining them in optimal conditions. State estimation in electric power systems is a crucial tool for determining the state of transmission networks through the use of sensors and topology information. This information is then utilized for contingency analysis and error detection/identification to ensure system reliability. To maintain optimal power system operations, state estimation and its associated techniques are critical components. This work focuses on analyzing DC state estimation using a statistical method and weighted least squares methodology. Anomalous measurements are filtered and corrected using Chi-Square. The algorithm was developed in MATLAB and verified in DIgSILENT PowerFactory. The IEEE 14-bus system, with added wind turbines, was used as a test system to determine security in the power grid through estimator confidence parameters. The results provide valuable insights into the efficacy of the DC state estimation process.","PeriodicalId":355921,"journal":{"name":"2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobConHT56829.2023.10087445","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

Growth in power systems has led to an increase in operational complexity, highlighting the importance of maintaining them in optimal conditions. State estimation in electric power systems is a crucial tool for determining the state of transmission networks through the use of sensors and topology information. This information is then utilized for contingency analysis and error detection/identification to ensure system reliability. To maintain optimal power system operations, state estimation and its associated techniques are critical components. This work focuses on analyzing DC state estimation using a statistical method and weighted least squares methodology. Anomalous measurements are filtered and corrected using Chi-Square. The algorithm was developed in MATLAB and verified in DIgSILENT PowerFactory. The IEEE 14-bus system, with added wind turbines, was used as a test system to determine security in the power grid through estimator confidence parameters. The results provide valuable insights into the efficacy of the DC state estimation process.
高可再生渗透电力系统状态估计中的直流功率方程建模
电力系统的增长导致运行复杂性的增加,突出了将其保持在最佳状态的重要性。电力系统状态估计是利用传感器和拓扑信息确定输电网络状态的重要工具。然后利用这些信息进行应急分析和错误检测/识别,以确保系统的可靠性。为了保持电力系统的最佳运行,状态估计及其相关技术是关键的组成部分。本文重点研究了用统计方法和加权最小二乘方法分析直流状态估计。使用卡方对异常测量进行过滤和校正。该算法在MATLAB中开发,并在DIgSILENT PowerFactory中进行了验证。采用IEEE 14总线系统作为测试系统,通过估计器置信度参数确定电网的安全性。研究结果对直流状态估计过程的有效性提供了有价值的见解。
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