Protection of Power System State Estimation against False Data Injection Attacks

Abstract

Power System State Estimation (PSSE) is the backbone of monitoring in modern power systems. Hence, any deficiency in the operation of the PSSE algorithm may result in wrong control and protection decisions. It is shown in the literature that PSSE can be targeted by cyberattackers who aim to manipulate the PSSE output by injecting false data into system measurements. This paper proposes a measurement classification-based method to protect the PSSE against false data injection attacks. Power flow measurements are classified based on their redundancy into critical and essential sets. The proposed method secures the critical measurements subset and uses different essential subsets for running the PSSE, which helps to identify the attacked and non-attacked measurements. A sensitivity analysis has been carried out to show the proposed method's reliability and probability of failure.