Dynamic State Estimation of Generators Under Cyber Attacks

TL;DR

This paper presents a robust Cubature Kalman Filter approach for real-time dynamic state estimation of power system generators under cyber attacks, specifically false data injection and denial of service, demonstrating improved accuracy and resilience.

Contribution

Introduces a novel application of RCKF for generator state estimation under cyber attacks, comparing it with CKF and validating effectiveness on standard power system models.

Findings

RCKF outperforms CKF in accuracy under cyber attacks

Simulation results confirm the robustness of the proposed method

Effective detection of cyber attack impacts on generator states

Abstract

Accurate and reliable estimation of generator's dynamic state vectors in real time are critical to the monitoring and control of power systems. A robust Cubature Kalman Filter (RCKF) based approach is proposed for dynamic state estimation (DSE) of generators under cyber attacks in this paper. First, two types of cyber attacks, namely false data injection and denial of service attacks, are modelled and thereby introduced into DSE of a generator by mixing the attack vectors with the measurement data; Second, under cyber attacks with different degrees of sophistication, the RCKF algorithm and the Cubature Kalman Filter (CKF) algorithm are adopted to the DSE, and then the two algorithms are compared and discussed. The novelty of this study lies primarily in our attempt to introduce cyber attacks into DSE of generators. The simulation results on the IEEE 9-bus system and the New England 16-machine 68-bus system verify the effectiveness and superiority of the RCKF.