论文摘要
The problems of recovering the state of power systems and detecting the instances of bad data have been widely studied in literature. Nevertheless, these two operations have been designed and optimized for the most part in isolation. Specifically, state estimators are optimized based on the minimum mean-square error criteria, which is only optimal when the source of distortions in the data is Gaussian random noise. Hence, the state estimators fail to perform optimality when the data is further contaminated by bad data, which cannot necessarily be modeled by additive Gaussian terms. The problem of power state estimation has been studied extensively. But the fundamental performance limits and the attendant decision rules are unknown when the data is potentially compromised by random bad data（due to sensor failures） or structured bad data（due to cyber attacks, which are also referred to false data injection attacks）. This paper provides a general framework that formalizes the underlying connection between state estimation and bad data detection routines.We aim to carry out the combined tasks of detecting the presence of random and structured bad data, and form accurate estimations for the state of power grid. This paper characterizes the optimal detectors and estimators. Furthermore, the gains with respect to the existing state estimators and bad data detectors are established through numerical evaluations.
论文目录
文章来源
类型: 期刊论文
作者: Ali TAJER,Saurabh SIHAG,Khawla ALNAJJAR
来源: Journal of Modern Power Systems and Clean Energy 2019年05期
年度: 2019
分类: 工程科技Ⅱ辑,信息科技
专业: 电力工业,互联网技术
单位: Rensselaer Polytechnic Institute,University of Sharjah, United Arab Emirates
基金: supported by the US NationalScience Foundation (No. ECCS-1554482)
分类号: TM73;TP393.08
页码: 1071-1080
总页数: 10
文件大小: 605K
下载量: 3
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