基于残差观测器的智能电网虚假数据攻击检测研究*

doi:10.11985/2023.01.012

电气工程学报 ›› 2023, Vol. 18 ›› Issue (1): 111-117.doi: 10.11985/2023.01.012

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基于残差观测器的智能电网虚假数据攻击检测研究^*

张明月¹(), 王新宇²^,³()

1.燕山大学建筑工程与力学学院秦皇岛 066004
2.燕山大学电气工程学院秦皇岛 066004
3.燕山大学电力电子节能与传动控制河北省重点实验室秦皇岛 066004

收稿日期:2022-01-17 修回日期:2022-03-02 出版日期:2023-03-25 发布日期:2023-04-19
通讯作者: 王新宇，男，博士，讲师。主要研究方向为电气控制、信息物理安全。E-mail：wxyzmya@ysu.edu.cn
作者简介:张明月，女，硕士，讲师。主要研究方向为电气控制、数字信号处理。E-mail：zhangmingyue91@ysu.edu.cn
基金资助:
*国家自然科学基金青年科学基金(62103357);河北省自然科学基金青年(F2021203043);河北省教育厅自然科学基金青年(QN2021139);江苏省配电网智能技术与装备协同创新中心开放基金(XTCX202203)

Detection of False Data Attack in Smart Grid Based on Residual Observer

ZHANG Mingyue¹(), WANG Xinyu²^,³()

1. School of Civil Engineering and Mechanics, Yanshan University, Qinhuangdao 066004
2. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004
3. Key Lab of Power Electronics for Energy Conservation & Motor Drive of Hebei Province,Yanshan University, Qinhuangdao 066004

Received:2022-01-17 Revised:2022-03-02 Online:2023-03-25 Published:2023-04-19

摘要/Abstract

摘要：

信息物理系统的深度融合实现了智能电网系统的高效运行，但也使其面临潜在的信息物理攻击安全威胁。攻击者通过注入虚假数据可以实现测量输出无变化，进而欺骗传统基于Kalman的卡方检测方法。考虑虚假数据攻击对系统内部状态变化的影响，提出了基于内部状态变化的神经网络观测器虚假数据攻击检测方法。基于建立的智能电网物理动态模型，分析了虚假数据攻击的隐蔽特性。进而考虑虚假数据攻击对系统内部状态变化的影响，提出基于神经网络观测器的状态残差检测方法。此外，考虑扰动对检测阈值的影响，设计自适应阈值替代传统的经验阈值从而缩短虚假数据攻击检测时间。最后，在IEEE三电机六总线验证了所提基于神经网络观测器的状态残差攻击检测方法的优越性。

关键词: 智能电网, 虚假数据攻击, 神经网络观测器, 自适应阈值, 攻击检测

Abstract:

The deep integration of cyber-physical systems enables efficient operation of smart grid systems while also exposing them to security threats posed by cyber-physical attacks. By injecting false data, attackers can achieve no change in measurement output and thus deceive the traditional detection methods based on chi-square. By considering the impact of false data attack on the internal state change of the system, a detection method against false data attack based on neural network observer is proposed. Based on the established smart grid physical dynamics model, the stealthy characteristics of the false data attack are analyzed. Considering the impact of the false data attack on the internal state change of the system, the state residual detection method based on the neural network observer is proposed. In addition, considering the impact of perturbation on the threshold, adaptive thresholds are designed to replace the traditional empirical thresholds for cutting the false data attack detection time. Finally, the superiority of the proposed state residual attack detection method based on neural network observer is verified in IEEE 3-generator 6-bus grid system.

Key words: Smart grid, false data attack, neural network observer, adaptive threshold, attack detection

中图分类号:

TM73

张明月, 王新宇. 基于残差观测器的智能电网虚假数据攻击检测研究^*[J]. 电气工程学报, 2023, 18(1): 111-117.

ZHANG Mingyue, WANG Xinyu. Detection of False Data Attack in Smart Grid Based on Residual Observer[J]. Journal of Electrical Engineering, 2023, 18(1): 111-117.

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基于残差观测器的智能电网虚假数据攻击检测研究^*

Detection of False Data Attack in Smart Grid Based on Residual Observer

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