一种智能电网状态估计初始猜测的搜索方法

Yang Weng, R. Negi, M. Ilić
{"title":"一种智能电网状态估计初始猜测的搜索方法","authors":"Yang Weng, R. Negi, M. Ilić","doi":"10.1109/SmartGridComm.2012.6486051","DOIUrl":null,"url":null,"abstract":"AC power system state estimation process aims to produce a real-time “snapshot” model for the network. Therefore, a grand challenge to the newly built smart grid is how to “optimally” estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.","PeriodicalId":143915,"journal":{"name":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A search method for obtaining initial guesses for smart grid state estimation\",\"authors\":\"Yang Weng, R. Negi, M. Ilić\",\"doi\":\"10.1109/SmartGridComm.2012.6486051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AC power system state estimation process aims to produce a real-time “snapshot” model for the network. Therefore, a grand challenge to the newly built smart grid is how to “optimally” estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.\",\"PeriodicalId\":143915,\"journal\":{\"name\":\"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SmartGridComm.2012.6486051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SmartGridComm.2012.6486051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

摘要

交流电力系统状态估计过程旨在为网络生成实时的“快照”模型。因此,新建的智能电网面临的一个重大挑战是如何“最优”估计不确定性增加的状态,例如间歇性风力发电或不连续的车辆充电。在数学上,这种估计问题在文献中通常被表述为加权最小二乘问题。由于问题是非凸的,目前的求解器,例如那些实现牛顿方法的,对于这些问题通常实现局部最优,而不是期望的全局最优。由于这种局部最优问题,电流估计器可能导致不正确的用户断电,甚至在不稳定的智能电网中造成代价高昂的停电。为了初始化迭代求解器,本文提出利用历史数据和能源管理系统快速增长的计算能力,有效地获得良好的初始状态。具体而言,在基于最近邻搜索的贝叶斯框架中提出了核脊回归。仿真结果表明,该方法的初始估计优于现有的工业方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A search method for obtaining initial guesses for smart grid state estimation
AC power system state estimation process aims to produce a real-time “snapshot” model for the network. Therefore, a grand challenge to the newly built smart grid is how to “optimally” estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.
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