Dynamic load altering attack detection in smart grid based on multiple fading factor adaptive Kalman Filter

This paper focuses on the detection method of dynamic load altering attack (D-LAA) in smart grid. Firstly, the discrete system model of Cyber Physical Power System (CPPS) under D-LAA is introduced, and considering the practical situation to add Gaussian white noise with mean value of 0. Secondly, when the noise model is inaccurate, the estimation accuracy of the standard Kalman Filter (KF) is reduced, the single fading factor adaptive Kalman Filter (SFAKF) is widely used, improving the SFAKF by introducing multiple fading factors, the multiple fading factors adaptive Kalman Filter (MFAKF) can estimate the smart grid state in real time, and the estimation performance is superior to that of the standard KF and the SFAKF. Finally, based on this state estimation method combined with the Residual Function Detection Method (RFDM), the effectiveness and feasibility of the method is verified by simulation with the IEEE 6-buses and 30-buses smart grid system as an example.