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

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。

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.