State Estimation in the Presence of Cyber Attacks Using Distributed Partition Technique

The security of smart grid (SG) is an open problem. False data injection attacks (FDIAs) could pose serious risks to automated smart grid and can cause power system outages which eventually could lead to huge economical losses. Cyber-attacks on critical infrastructure are big concerns to the nation's energy reliability; and attackers come up with new attack strategies that couldn't be detected by the traditional bad data detection methods. Although bad data detection (BDD) schemes based on traditional state estimation and chi-square tests within power systems have been used and considered very reliable in detecting false measurements, these BDD schemes and state estimators have been found vulnerable and failed to combat engineered cyber-attacks. In this paper, a novel chi-square detector has been used with a combination of two state estimators in Distributed Partitioning State Estimation (DPSE), results show it is very effective to combat FDIAs when compared with traditional state estimation techniques. The main idea of DPSE is to increase the sensitivity of the chi-square tests by partitioning the large grids into small blocks and applying the tests on each partition individually. State estimator modelled on a novel chi-square detector which is based on particle swarm optimization (PSO) algorithm significantly improved the results. Numerical simulations conducted in MATPOWER confirm the feasibility and effectiveness of the proposed method.