Dynamic load altering attack detection based on adaptive fading Kalman filter in smart grid

Abstract

Abstract This paper mainly studies a detection method of dynamic load altering attacks (D‐LAAs) in smart grids. First, communication factors are considered, and a smart grid discrete system model under D‐LAA attack is established. Second, for closed‐loop D‐LAAs, an adaptive fading Kalman filter (AFKF) is designed to estimate the states of smart grids with Gaussian noise in real time, and a Euclidean distance ratio detection algorithm based on AFKF is proposed to detect D‐LAAs. Moreover, the proposed detection algorithm can identify D‐LAAs even in the presence of noise in the measurement data, significantly enhancing the speed of attack detection. Finally, take a smart grid with three generators and six buses as an example. Its feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified by simulations. The simulations are carried out through the real‐time hardware‐in‐the‐loop simulation platform, which is mainly composed of StarSim and multi‐tasking devices.

Keywords

• cyber‐physical systems
• smart power grids