A false data injection attack method for generator dynamic state estimation

TL;DR

This paper introduces a novel false data injection attack model targeting generator dynamic state estimation, demonstrating its effectiveness through simulations on standard power system models.

Contribution

It develops the first FDI attack model based on generator dynamic state estimation using linearization and residual analysis, bypassing traditional detection methods.

Findings

FDI attacks successfully implemented on test systems

Attack effectiveness verified with CKF and RCKF algorithms

Simulation results confirm attack feasibility and impact

Abstract

Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators. Especially, false data injection (FDI) attacks deteriorate the accuracy of state estimation by injecting the false data into the measurement device. In this regard, this paper proposes for the first time an FDI attack model based on the dynamic state estimation of generators. Firstly, Taylor's formula was used to linearize the generator's measurement equation. Secondly, according to the principle that the measurement residuals before and after the FDI attack are equal, the expressions of the attack vectors were established, and they were applied to the measurement quantities to avoid the conventional bad data detection. Thereby, the FDI attacks were successfully implemented. Then, three attack scenarios were set according to the degree of the FDI attacks, and they were tested by the cubature Kalman filter (CKF) and the robust cubature Kalman filter (RCKF). Finally, the simulation results of the IEEE 9-bus system and the New England 16-machine 68-bus system verify the effectiveness of the proposed FDI attacks.