基于AUKF的分布式电源系统虚假数据攻击检测方法

Detection method of false data attack in distributed generation system based on AUKF

虚假数据注入攻击(FDIA)是一种典型的网络攻击方式,其通过破坏数据完整性进而误导电力系统状态估计结果,严重危害电网运行安全。随着国家大力发展新能源产业,越来越多的分布式电源注入电力系统,使得电网中大量测量数据具有随机、多变的特性,分布式电源系统中的虚假数据检测难度大大增加。针对这一问题,本文构建了分布式电源系统状态估计模型和FDIA模型,采用了一种基于自适应无迹卡尔曼滤波(AUKF)的检测算法,通过AUKF算法对电网中的状态量进行估计,在此过程中经一致性检验、虚假数据检测并计算相应的阈值,判断系统是否受到攻击。结果表明,当系统中注入攻击强度为[-10,20]dB的虚假数据时,采用该方法均能准确识别虚假数据;当系统中测量值发生突变时,不会被该算法误判为虚假数据注入,避免造成错误的调度选择。

False data injection attack(FDIA)is a typical network attack.It can mislead the result of power system state estimation by destroying data integrity,which seriously endangers the security of power network operation.With the vigorous development of the new energy industry,more and more distributed power sources are injected into the power system,which makes a large number of measurement data in the power network have random and variable characteristics,and the detection of false data in the distributed power system is more difficult.To solve this problem,this paper builds a distributed power system state estimation model and FDIA model,uses a detection algorithm based on adaptive unscented Kalman filter(AUKF),estimates the state amount in the power network through the AUKF algorithm,and determines whether the system is under attack through consistency check,false data detection and calculation of corresponding thresholds.The results show that this method can accurately identify false data when injection attack intensity is[-10,20]dB in the system;when measurement values in the system change,it will not be misjudged as false data injection by the algorithm to avoid wrong scheduling selection.