Data Integrity Attack Strategy against State Estimation Results of Distributed Power System

Abstract

Data integrity attacks fall under the category of cyber-physical attacks. These attacks manipulate the measurement values obtained from supervisory control and data acquisition (SCADA) systems. Data integrity attacks can lead to discrepancies in the power grid's state estimation outcomes, influencing critical decisions, and potentially posing a security threat to the power grid's operation. So, it is necessary to study how to conduct data integrity attacks from attackers’ perspectives to enhance the secure operation of smart grids. This paper examines the dynamic state estimation model that employs extended Kalman filter (EKF) technology and presents the distributed state estimation model based on the Kalman filter and weighted least squares method. Building upon the data integrity attack in centralized state estimation, we investigate the data integrity attack in distributed state estimation. We formulate a series of attack schemes from different angles. We conduct simulation experiments for both centralized and distributed state estimation using the IEEE-30 standard node system. We analyze and discuss the discrepancies in state estimation outcomes for the power system under various attack strategies. The results illustrate the efficacy of data integrity attack tactics against state estimation.