Dynamic State Estimation of Generators Under Cyber Attacks

被引：32

作者：

Li, Yang ^{[1
]}

Li, Zhi ^{[1
]}

Chen, Liang ^{[2
]}

机构：

[1] Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Jiangsu, Peoples R China

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

中国国家自然科学基金;

关键词：

Dynamic state estimation; cyber attacks; false data injection; denial of service; generator; robust cubature Kalman filter; PMU data; KALMAN FILTERS; PMU DATA;

D O I：

10.1109/ACCESS.2019.2939055

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate and reliable estimation of generator's dynamic state vectors in real time are critical to the monitoring and control of power systems. A robust Cubature Kalman Filter (RCKF) based approach is proposed for dynamic state estimation (DSE) of generators under cyber attacks in this paper. First, two types of cyber attacks, namely false data injection and denial of service attacks, are modelled and thereby introduced into DSE of a generator by mixing the attack vectors with the measurement data; Second, under cyber attacks with different degrees of sophistication, the RCKF algorithm and the Cubature Kalman Filter (CKF) algorithm are adopted to the DSE, and then the two algorithms are compared and discussed. The novelty of this study lies primarily in our attempt to introduce cyber attacks into DSE of generators. The simulation results on the IEEE 9-bus system and the New England 16-machine 68-bus system verify the effectiveness and superiority of the RCKF.

引用

页码：125253 / 125267

页数：15

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