基于改进的UKF智能电网虚假数据攻击检测

Detection of False Data Injection Attack in Smart Grid Based on Improved UKF

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 cyberphysical 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。

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 data detection 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.