Observer-based cyber attack detection and isolation in smart grids

In this paper, we investigate the cyber security problem for large scale smart grid systems under false data injection attack. An observer-based algorithm is proposed to detect and isolate the cyber attack by using real-time synchrophasor measurements. Combining the smart grid system with graph theory, the system is divided into several different coupled areas via sparsity of the connection topology. According to the partition, the system is decomposed equivalently into several areas. Then, the output of the smart grid system can be gotten through wide area measurement system based on PMUs, and the asymmetric weighted Laplacian can be obtained by running a nonlinear least-square estimation algorithm. By designing a modified observer, the residual is computed, and the adaptive residual threshold, used to detect and isolate the attack in the areas of the system, is also shown with considering the system model uncertainties. Then, an iterative algorithm detecting and isolating the cyber attack is proposed based on the calculated residual threshold. Finally, some simulations are provided to illustrate the effectiveness of the proposed algorithm.