基于非负定自适应卡尔曼滤波的电力系统虚假数据攻击检测

虚假数据攻击(false data attack, FDA)是通过对电网中远程终端单元(remote terminal unit, RTU)、同步相量测量单元(phasor measurement unit, PMU)等通信环节的攻击,误导电力系统的状态估计,给电力系统的安全可靠运行带来巨大威胁。构建了电网虚假数据攻击检测架构、电压信号状态空间模型和虚假数据攻击模型,提出了非负定自适应卡尔曼滤波算法来估计模型中的状态量,旨在准确检测电力系统中的虚假数据。通过对3节点电力系统仿真,结果验证文中所提的算法在保证滤波稳定性的同时,提高了攻击检测的运算速度。

Nonnegative-definite adaptive Kalman filter-based detection

False Data Attack (FDA) is an attack on communication links such as Remote Terminal Unit (RTU) and Phasor Measurement Unit (PMU) in power system, which misleads the state estimation of power system and poses a great threat to the safe and reliable operation of power system. The power network false data attack detection framework, voltage signal state space model and false data attack model are constructed. Nonnegative-definite adaptive Kalman filter algorithm is proposed to estimate the state in the model, in order to accurately detect the false data in the power system. The simulation results of 3 bus power system show that the proposed algorithm can improve the speed of attack detection while guaranteeing the stability of filtering.