A Data-Driven Detection strategy of False Data in Cooperative DC Microgrids

被引：1

作者：

Yang, Yixian ^{[1
]}

Guo, Li ^{[1
]}

Li, Xialin ^{[1
]}

Li, Jiaxin ^{[1
]}

Liu, Wei ^{[2
]}

He, Huihui ^{[2
]}

机构：

[1] Tianjin Univ, Minist Educ, Key Lab Smart Grid, Tianjin, Peoples R China

[2] Natl Univ Def Technol, Coll Syst Engn, Hunan Key Lab Multienergy Syst Intelligent Interc, Changsha, Peoples R China

来源：

IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2021年

基金：

国家重点研发计划;

关键词：

DC microgrids; distributed control; cyber physical system; false data injection attack; data-driven; CYBER-ATTACK DETECTION; NETWORKS;

D O I：

10.1109/IECON48115.2021.9589318

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Distributed cooperative control strategies of DC microgrids (DCMG) reduce the detriment of communication delays, packet loss and link failure compared with centralized control. However, they are vulnerary to cyber-attacks. The operating objectives can be deviated by false data. Firstly, the adverse effects of false data are explained and modeled. A data-driven strategy based on linear regression is then proposed to remove the false data by offline learning and online judging of the transient process in DCMG without affecting the dynamic response. It successfully solves the problem of parameter selection of resilient control. Finally, the detection strategy is verified by detailed time-domain simulation.

引用

页数：6

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