Detection method of false data injection attacks on power grids based on vector auto‑regression model

Authors

Jianghong CHEN, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;Hubei Provincial Engineering Technology Research Center for Power Transmission Line, Yichang 443002, ChinaFollow
Jiali RAO, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;Hubei Provincial Engineering Technology Research Center for Power Transmission Line, Yichang 443002, China
Weiliang LI, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;Hubei Provincial Engineering Technology Research Center for Power Transmission Line, Yichang 443002, China
Yang HU, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China;Hubei Provincial Engineering Technology Research Center for Power Transmission Line, Yichang 443002, China

Keywords

false data injection attack;vector auto-regression;weighted least squares;state estimation;attack detection

Abstract

False data injection attack (FDIA) is one of the major factors threatening the operational security of power grids. It primarily targets communication links within power grids, misleading the state estimation results of the power system and posing significant risks to grid security. Addressing the challenges of effectively detecting FDIA and the non-positive definite covariance matrix of process noise and measurement noise in power system state estimation, this paper introduces the vector auto-regression (VAR) model into power system state estimation and proposes an FDIA detection method based on VAR and weighted least squares (WLS). Firstly, a VAR state estimation model is established, treating measurement noise as a stable quantity and estimating only process noise, thereby resolving the non-positive definite issue of the covariance matrix. Secondly, both VAR and WLS are used for power system state estimation, and the results of the two methods are detected using consistency checks and measurement residual tests to determine the presence of FDIA. Finally, simulation results from IEEE 14-bus and IEEE 30-bus systems demonstrate that the proposed detection method can successfully detect FDIA with a high success rate, verifying the feasibility and effectiveness of the method.

DOI

10.19781/j.issn.1673-9140.2024.03.001

First Page

1

Last Page

9

Recommended Citation

CHEN, Jianghong; RAO, Jiali; LI, Weiliang; and HU, Yang (2024) "Detection method of false data injection attacks on power grids based on vector auto‑regression model," Journal of Electric Power Science and Technology: Vol. 39: Iss. 3, Article 1.
DOI: 10.19781/j.issn.1673-9140.2024.03.001
Available at: https://jepst.researchcommons.org/journal/vol39/iss3/1