Square Root Kalman Filter-based Cyber Attack Detection And Isolation In Smart Grid

With the improvement of technological level,the traditional power grid system is deeply integrated with advanced control,communication,and computer technologies,which forms the smart grid system.However,due to the openness and complexity of smart grid systems,which makes them face more security threats from network attacks.In particular,malicious fake data injection attacks with hidden characteristics can evade the bad data detection mechanism and affect the normal operation of the power grid system by tampering with the internal state of the system.This paper study the detection and localization of fake data attacks in the grid system by Kalman filtering algorithm to address the security threats faced by the smart grid system.The main works is as follows:Firstly,for the problem of detecting fake data injection attacks suffered by smart grid systems,an attack detection algorithm based on the square root extended Kalman filter is designed.Considering the input nonlinear state-space model and the instability of the ordinary extended Kalman filter,the square-root extended Kalman filter is proposed to estimate the state of the system.Based on obtained state estimates,a detector based on the state residuals is proposed to detect the fake data attacks in the system.Finally,the effectiveness of the detection algorithm is verified by simulation.Secondly,for the problem of false data attack detection in smart grid systems considering the influence of unknown input noise,an attack detection algorithm based on adaptive square-root central difference Kalman filter is proposed.Considering the influence of input-output nonlinearity,the grid state-space model is optimized,and the stealthiness of the fake data injection attack is analyzed on the basis of the model.Considering the influence of unknown input noise and the complexity of nonlinear extended Kalman filter calculation,an adaptive square-root central difference Kalman filter is designed to accurately estimate the system state.Finally,based on the designed filtering algorithm,the attack is detected by a detector based on state residuals.Finally,for the localization problem of false data injection attacks in smart grid systems,a localization algorithm based on an adaptive square-root central differential Kalman filter bank is proposed.Firstly,the smart grid state space model is dimensioned so that it has N measurement output nodes.Secondly,adaptive threshold is proposed for faster detection and localization of attacks.Finally,a localization algorithm based on an adaptive square-root central difference Kalman filter bank is designed to realize the localization of attacks within the smart grid system.