基于中心差分滤波器的电力系统动态状态估计

Arindam Chowdhury, Sayantan Chatterjee, Aritro Dey
{"title":"基于中心差分滤波器的电力系统动态状态估计","authors":"Arindam Chowdhury, Sayantan Chatterjee, Aritro Dey","doi":"10.1109/ICPC2T53885.2022.9777093","DOIUrl":null,"url":null,"abstract":"A nonlinear Sigma point Kalman filter known as Central Difference Filter which considers only first order Taylor series approximation with the help of interpolation formula, have been employed here for the first time during dynamic state estimation of power systems states. This paper also exhibits a comparative performance analysis of two estimation techniques namely Central difference filter (CDF) and Cubature Kalman filter technique (CKF) during power systems dynamic state estimation. The estimation is performed employing measurements from Remote terminal units (RTU) and Phasor measurement units (PMU). The whole simulation process is carried out for IEEE 30 bus test system. Holts two parameter linear exponential technique which often utilized as a state forecasting technique has been used here to forecast the systems state at the prediction step. The superiority of CDF over CKF, has been illustrated here on context of computation time and estimation accuracy.","PeriodicalId":283298,"journal":{"name":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Power Systems Dynamic State Estimation using Central Difference Filter\",\"authors\":\"Arindam Chowdhury, Sayantan Chatterjee, Aritro Dey\",\"doi\":\"10.1109/ICPC2T53885.2022.9777093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A nonlinear Sigma point Kalman filter known as Central Difference Filter which considers only first order Taylor series approximation with the help of interpolation formula, have been employed here for the first time during dynamic state estimation of power systems states. This paper also exhibits a comparative performance analysis of two estimation techniques namely Central difference filter (CDF) and Cubature Kalman filter technique (CKF) during power systems dynamic state estimation. The estimation is performed employing measurements from Remote terminal units (RTU) and Phasor measurement units (PMU). The whole simulation process is carried out for IEEE 30 bus test system. Holts two parameter linear exponential technique which often utilized as a state forecasting technique has been used here to forecast the systems state at the prediction step. The superiority of CDF over CKF, has been illustrated here on context of computation time and estimation accuracy.\",\"PeriodicalId\":283298,\"journal\":{\"name\":\"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPC2T53885.2022.9777093\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPC2T53885.2022.9777093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文首次将一种仅考虑一阶泰勒级数近似的非线性西格玛点卡尔曼滤波器——中心差分滤波器应用于电力系统的动态估计。本文还对中心差分滤波(CDF)和库图卡尔曼滤波(CKF)两种估计技术在电力系统动态估计中的性能进行了比较分析。采用远程终端单元(RTU)和相量测量单元(PMU)进行估计。对ieee30总线测试系统进行了整个仿真过程。本文采用常被用作状态预测技术的霍尔特二参数线性指数技术,在预测阶段对系统状态进行预测。CDF算法在计算时间和估计精度方面优于CKF算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Systems Dynamic State Estimation using Central Difference Filter
A nonlinear Sigma point Kalman filter known as Central Difference Filter which considers only first order Taylor series approximation with the help of interpolation formula, have been employed here for the first time during dynamic state estimation of power systems states. This paper also exhibits a comparative performance analysis of two estimation techniques namely Central difference filter (CDF) and Cubature Kalman filter technique (CKF) during power systems dynamic state estimation. The estimation is performed employing measurements from Remote terminal units (RTU) and Phasor measurement units (PMU). The whole simulation process is carried out for IEEE 30 bus test system. Holts two parameter linear exponential technique which often utilized as a state forecasting technique has been used here to forecast the systems state at the prediction step. The superiority of CDF over CKF, has been illustrated here on context of computation time and estimation accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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