基于改进的UKF智能电网虚假数据攻击检测

doi:10.16182/j.issn1004731x.joss.22-0292

摘要/Abstract

摘要：

由于虚假数据注入攻击(false data injection attack，FDIA)对电力信息物理系统（grid cyber-physical systems，GCPS)的破坏性较强，且威胁性较大，针对其难以被有效检测难题，提出一种基于加权最小二乘法(weighted least squares，WLS)和改进的无迹卡尔曼(unscented Kalman filter，UKF)的电网虚假数据检测方法。对FDIA进行了数学建模，并通过对残差进行分析以说明FDIA的难以检测性，在有攻击向量的情况下，将改进的UKF用于系统的状态估计，同时利用WLS对系统迅速响应的优势，也对系统进行状态估计，采用一致性检验对2种方法的估计结果进行检测，最终判断是否存在FDIA。在IEEE14节点和IEEE57节点上进行实验分析并与支持向量机的检测方法进行检测成功率的对比，仿真结果表明，FDIA可被准确检测，从而验证了本文方法的可行性及有效性。

关键词: 智能电网, 虚假数据注入攻击, 改进的无迹卡尔曼, 状态估计, 攻击检测

Abstract:

Due to the disruption and threat of false data injection attack (FDIA) on grid cyber-physical systems (GCPS), and to address the problem that false data is difficult to be detected, a method for smart grid false datadetection based on weighted least squares (WLS) and improved unscented Kalman filter (UKF) is proposed. FDIA is modeled mathematically, and the residual analysis shows that the FDIA is difficult to be detected. In the case of the injection attack vector, the improved UKF is applied to state estimation. Meanwhile, the state estimation of the system is performed by the WLS, which is sensitive to the changes in the system. The results of the state estimation of the above two methods are used to execute a consistency test, and the situation of the FDIA is accurately determined based on the test results. Experimental analysis was conducted on the IEEE14 and IEEE57 systems and the detection rate was compared with the detection method of the support vector machine. The simulation results indicate that the FDIA can be detected accurately, thus the feasibility and effectiveness of the proposed method are demonstrated.

Key words: smart grid, FDIA, improved UKF, state estimation, attack detection

中图分类号:

TM73

魏利胜, 张倩. 基于改进的UKF智能电网虚假数据攻击检测[J]. 系统仿真学报, 2023, 35(7): 1508-1516.

Lisheng Wei, Qian Zhang. Detection of False Data Injection Attack in Smart Grid Based on Improved UKF[J]. Journal of System Simulation, 2023, 35(7): 1508-1516.

图/表 9

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参考文献 19

1	张晶, 陈垚, 孙俊, 等. 基于协同执行器的GCPS自适应调度模型[J]. 系统仿真学报, 2019, 31(10): 2112-2121.
	Zhang Jing, Chen Yao, Sun Jun, et al. GCPS Adaptive Scheduling Model Based on Cooperative Executor[J]. Journal of System Simulation, 2019, 31(10): 2112-2121.
2	蒋建波, 李鹏, 苗爱敏, 等. GCPS时滞稳定性分析的整体建模方法[J]. 系统仿真学报, 2018, 30(9): 3274-3282.
	Jiang Jianbo, Li Peng, Miao Aimin, et al. Integrated Modeling for Time Delay Stability Analysis in GCPS[J]. Journal of System Simulation, 2018, 30(9): 3274-3282.
3	彭大天, 董建敏, 蔡忠闽, 等. 假数据注入攻击下信息物理融合系统的稳定性研究[J]. 自动化学报, 2019, 45(1): 196-205.
	Peng Datian, Dong Jianmin, Cai Zhongmin, et al. On the Stability of Cyber-physical Systems Under False Data Injection Attacks[J]. Acta Automatica Sinica, 2019, 45(1): 196-205.
4	童晓阳, 王晓茹. 乌克兰停电事件引起的网络攻击与电网信息安全防范思考[J]. 电力系统自动化, 2016, 40(7): 144-148.
	Tong Xiaoyang, Wang Xiaoru. Inference and Countermeasure Presuppostion of Network Attack in Incident on Ukrainian Power Grid[J]. Automation of Electric Power Systems, 2016, 40(7): 144-148.
5	王琦, 邰伟, 汤奕, 等. 面向电力信息物理系统的虚假数据注入攻击研究综述[J]. 自动化学报, 2019, 45(1): 72-83.
	Wang Qi, Tai Wei, Tang Yi, et al. A Review on False Data Injection Attack Toward Cyber-physical Power System[J]. Acta Automatica Sinica, 2019, 45(1): 72-83.
6	Liu Xuan, Li Zuyi. False Data Attacks Against AC State Estimation With Incomplete Network Information[J]. IEEE Transactions on Smart Grid, 2017, 8(5): 2239-2248.
7	Mohammadi A, Plataniotis K N. Noncircular Attacks on Phasor Measurement Units for State Estimation in Smart Grid[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(4): 777-789.
8	Gu Yun, Liu Ting, Wang Dai, et al. Bad Data Detection Method for Smart Grids Based on Distributed State Estimation[C]//2013 IEEE International Conference on Communications (ICC). Piscataway, NJ, USA: IEEE, 2013: 4483-4487.
9	王勇, 武津园, 陈雪鸿, 等. 基于卡尔曼滤波的电力虚假数据注入攻击检测方法[J]. 上海电力大学学报, 2021, 37(2): 205-210.
	Wang Yong, Wu Jinyuan, Chen Xuehong, et al. Intrusion Detection Method of False Data Injection Attack in Power System Based on Kalman Filter[J]. Journal of Shanghai University of Electric Power, 2021, 37(2): 205-210.
10	罗小元, 朱鸣皋, 王新宇, 等. 基于自适应卡尔曼滤波器的智能电网隐蔽假数据攻击检测[J]. 信息与控制, 2018, 47(1): 16-21.
	Luo Xiaoyuan, Zhu Minggao, Wang Xinyu, et al. Adaptive Kalman Filter-based Detection of Covert False Data Injection Attacks in Smart Grids[J]. Information and Control, 2018, 47(1): 16-21.
11	何耀, 周聪, 郑凌月, 等. 基于扩展卡尔曼滤波的虚假数据攻击检测方法[J]. 中国电力, 2017, 50(10): 35-40.
	He Yao, Zhou Cong, Zheng Lingyue, et al. Detection Method Against False Data Injection Attack Based on Extended Kalman Filter[J]. Electric Power, 2017, 50(10): 35-40.
12	方航, 葛愿, 余诺, 等. 电力系统状态估计多算法融合[J]. 计算机工程与设计, 2015, 36(2): 502-506.
	Fang Hang, Ge Yuan, Yu Nuo, et al. Fusion Algorithm for State Estimation in Power Systems[J]. Computer Engineering and Design, 2015, 36(2): 502-506.
13	李江, 王义伟, 魏超, 等. 卡尔曼滤波理论在电力系统中的应用综述[J]. 电力系统保护与控制, 2014, 42(6): 135-144.
	Li Jiang, Wang Yiwei, Wei Chao, et al. A Survey on the Application of Kalman Filtering Method in Power System[J]. Power System Protection and Control, 2014, 42(6): 135-144.
14	孙怡, 何光宇, 翟少鹏. 基于无迹卡尔曼滤波的电力系统抗差动态估计[J]. 电测与仪表, 2020, 57(4): 1-6.
	Sun Yi, He Guangyu, Zhai Shaopeng. Robust Dynamic Estimation for Power System Based on Unscented Kalman Filter[J]. Electrical Measurement & Instrumentation, 2020, 57(4): 1-6.
15	Liu Yao, Ning Peng, Reiter M K. False Data Injection Attacks Against State Estimation in Electric Power Grids[J]. ACM Transactions on Information and System Security, 2011, 14(1): 13.
16	贺觅知. 基于卡尔曼滤波的电力系统动态状态估计算法研究[D]. 成都: 西南交通大学, 2006.
	He Mizhi. Power System Dynamic State Estimation Algorithm Based on Kalman Filter[D]. Chengdu: Southwest Jiaotong University, 2006.
17	曲正伟, 董一兵, 王云静, 等. 用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法[J]. 电力系统自动化, 2018, 42(10): 87-92.
	Qu Zhengwei, Dong Yibing, Wang Yunjing, et al. Improved Robust Unscented Kalman Filtering Algorithm for Dynamic State Estimation of Power Systems[J]. Automation of Electric Power Systems, 2018, 42(10): 87-92.
18	常盛. 考虑虚假数据攻击的电力信息物理系统检测方法研究[D]. 秦皇岛: 燕山大学, 2020.
	Chang Sheng. Research on Detection Method of Power Cyber-physical System considering False Data Attacks[D]. Qinhuangdao: Yanshan University, 2020.
19	杨杉, 谭博, 郭静波. 基于双马尔科夫链的新型能源互联网虚假数据注入攻击检测[J]. 电力自动化设备, 2021, 41(2): 131-137.
	Yang Shan, Tan Bo, Guo Jingbo. Detection of False Data Injection Attack for New-type Energy Internet Based on Double Markov Chains[J]. Electric Power Automation Equipment, 2021, 41(2): 131-137.

算法	平均电压误差	平均相角误差
EKF	0.003 7	0.007 5
改进的UKF	0.002 3	0.003 9

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