Distributed Attack-Resilient Grid State Estimation Algorithm using Optimal Filter and Graph Theory

Smart grid is built by the combination of electric and information technologies and achieves the two-way interaction between power utilization and power generation. Unfortunately, new security threats appears together with the cyber-physical communication systems. In order to properly monitoring the power network, the cyber attack detection and state estimation is required to identify attack and states. This paper considers the problem of robust state estimation in smart grid and suggests a technique for the distributed state estimation in power networks. Firstly, the distribution power system incorporating multiple synchronous generators are modelled as a state-space framework where attack occurs in measurements. Basically, the false data injection attacks can interfere with state estimation by tampering with sensor measurements. Using mean squared error principle, the distributed dynamic state estimation algorithm is designed where local and neighbouring gains are obtained using optimal filter and graph theory. Extensive simulation results show that the proposed approach can able to estimate the system state within a short period of time