Bad Data Detection Method for Smart Grids based on Distributed State Estimation

Bad Data Injection (BDI) in Smart Grid is considered to be the most dangerous cyber attack, as it might lead to energy theft on the end users, false dispatch on the distribution process, and device breakdown on the power generation. State Estimation and Bad Data Detection, which are applied to reduce the observation errors and detect false data in the traditional power grid, could not detect the bad data in smart grid. In this paper, three BDI attack cases in IEEE 14-bus system are designed to bypass the traditional bad data detection. The potential risks on economy and security are analyzed exploiting the MATPOWER. A new method based on Distributed State Estimation (DSE) is proposed to detect BDI, named as DSE-based bad data detection. The power system is divided into several subsystems, and a Chi-squares test is applied to detect the bad data respectively in each subsystem. Simulation results demonstrate that the DSE-based bad data detection can detect all bad data in three attack cases. Moreover, it can locate the bad data in specific subsystem which is helpful for the further identification.