基于自适应<b>Kalman</b>滤波的智能电网假数据注入攻击检测

罗小元; 潘雪扬; 王新宇; 关新平

doi:10.16383/j.aas.c190636

基于自适应Kalman滤波的智能电网假数据注入攻击检测

doi: 10.16383/j.aas.c190636

罗小元^1,,
潘雪扬^1,,
王新宇^{1, 2,},
关新平^3,

1.
燕山大学电气工程学院秦皇岛 066004
2.
西华大学汽车测控与安全四川省重点实验室成都 610039
3.
上海交通大学电信学院上海 200240

基金项目: 国家自然科学基金 (61873228, 62103357), 河北省教育厅青年基金 (QN2021139), 河北省自然基金 (F2021203043), 汽车测控与安全四川省重点实验室开放基金 (QCCK2022-006)资助

详细信息

作者简介:
罗小元：燕山大学自动化系教授. 2005年获得燕山大学控制科学与工程学科博士学位. 主要研究方向为网络控制系统, CPS网络攻击检测. E-mail: xyluo@ysu.edu.cn

潘雪扬：燕山大学控制科学与工程专业硕士研究生. 主要研究方向为卡尔曼滤波和智能电网攻击检测. E-mail: onty123@126.com

王新宇：燕山大学电气工程系讲师. 2020年获得燕山大学控制科学与工程学科博士学位. 主要研究方向为智能电网攻击检测与防御. 本文通信作者. E-mail: wangxinyuphd@163.com

关新平：上海交通大学电子信息与电气工程学院教授. 1999年获得哈尔滨工业大学控制科学与工程学科博士学位.主要研究方向为无线网络系统, CPS网络攻击检测. E-mail: xpguan@sjtu.edu.cn

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出版历程
- 收稿日期: 2019-09-06
- 网络出版日期: 2022-10-25
- 刊出日期: 2022-12-23

Detection of False Data Injection Attack in Smart Grid via Adaptive Kalman Filtering

1.
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004
2.
Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu 610039
3.
School of of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240

Funds: Supported by National Natural Science Foundation of China (61873228, 62103357), Science and Technology Youth Foundation of Hebei Education Department (QN2021139), Natural Science Foundation of Hebei Province (F2021203043), and Open Research Fund of Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province (QCCK2022-006)

More Information

Author Bio:
LUO Xiao-Yuan　Professor at the School of Electrical Engineering, Yanshan University. He received his Ph.D. degree in control science and engineering from Yanshan University in 2005. His research interest covers detection of cyber attack of CPS and networked control systems

PAN Xue-Yang　 Master student in the Department of Control Science and Engineering, Yanshan University. His research interest covers Kalman filter and smart grid attack detection

WANG Xin-Yu　 Lecturer in the Department of Electrical Engineering, Yanshan University. He received his Ph.D. degree in control science and engineering from Yanshan University in 2020. His research interest covers attack detection and defense in smart grid. Corresponding author of this paper

GUAN Xin-Ping　Professor at the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. He received his Ph.D. degree in control science and engineering from Harbin Institute of Technology in 1999. His research interest covers wireless networked systems and detection of cyber attack in CPS

摘要

摘要: 研究了一种针对智能电网中假数据注入攻击的有效检测方法. 假数据注入攻击可以保持攻击前后残差基本不变, 绕过传统的不良数据检测技术. 首先基于电网模型, 分析了假数据注入攻击的攻击特性, 针对噪声统计特性未知且无迹Kalman滤波 (Unscented Kalman filter, UKF) 不稳定的现象, 提出了自适应平方根无迹Kalman滤波改进算法. 基于状态估计值, 结合中心极限定理提出检测算法, 并与欧几里得检测方法、巴氏系数检测方法进行比较. 最后, 仿真表明本文所提检测算法的优越性.
- 智能电网 /
- 虚假数据注入攻击 /
- 攻击检测 /
- 自适应平方根无迹卡尔曼滤波
Abstract: In this paper, an effective detection method for false data injection attack in smart grid is studied. False data injection attack can keep the residual unchanged before and after the attack and bypass the traditional bad data detection technology. Firstly, based on the grid model, the attack characteristics of false data injection attack are analyzed. Aiming at the phenomenon that the noise statistical characteristics are unknown and the unscented Kalman filter (UKF) is unstable, an effective detection method for false data injection attack is proposed. An improved algorithm of adaptive square root unscented Kalman filter is proposed. Based on the state estimation and the central limit theorem, the algorithm is compared with Euclidean method and bayonet coefficient method. Finally, the simulation shows the superiority of the algorithm.
- Smart grid /
- false data injection /
- attack detection /
- adaptive square-root unscented Kalman filter

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图 1 3总线电网模型

Fig. 1 3-bus grid model

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图 2 系统遭受攻击框图

Fig. 2 block diagram of system under attack

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图 3 ASRUKF下的状态估计

Fig. 3 State estimation in ASRUKF

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图 4 两种检测方法针对隐蔽假数据攻击

Fig. 4 Two detection methods for covert false data attack

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图 5 巴氏相似性系数

Fig. 5 Bhattacharyya coefficient

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图 6 $S\tilde x$的Q-Q图

Fig. 6 Quantile-quantile plot of $S\tilde x$

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图 7 本文提出的攻击检测方法

Fig. 7 Attack detection proposed in this paper

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图 8 误检率${P_F}$

Fig. 8 False alarm rate ${P_F}$

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