基于自适应Kalman滤波的智能电网假数据注入攻击检测

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

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.

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.