Load Margin Constrained Moving Target Defense against False Data Injection Attacks

Conference · Wed Mar 30 04:00:00 EDT 2022 · 2022 IEEE Green Technologies Conference (GreenTech)

DOI:https://doi.org/10.1109/greentech52845.2022.9772024· OSTI ID:1907345

Zhang, Hang ^[1]; Fulk, Noah ^[2]; Liu, Bo ^[2]; Edmonds, Lawryn ^[2]; Liu, Xuebo ^[2]; Wu, Hongyu ^[2]

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1. Kansas State University,Mike Wiegers Department of Electrical and Computer Engineering,Manhattan,Kansas,USA,66506; Kansas State University
2. Kansas State University,Mike Wiegers Department of Electrical and Computer Engineering,Manhattan,Kansas,USA,66506

Cyber physical security of power systems with high penetration of renewable generation has attracted attention from researchers. One critical issue is that cyber-physical attacks, disguised as uncertain renewable generation, can target conventional power system state estimation (SE). Moving target defense (MTD) is a promising defense strategy to detect stealthy false data injection (FDI) attacks against SE. However, all existing studies myopically perturb the reactance of transmission lines equipped with distributed flexible AC transmission system (D-FACTS) devices without adequately considering the system voltage stability. Exacerbated by the renewable generation uncertainty, existing MTD may cause voltage instability when the power grid is under stress. To address this issue, we propose a novel MTD framework that explicitly considers system voltage stability by using continuation power flow. We utilize the sensitivity matrix of power injection to line impedance, on which an optimization problem for maximizing load margin is formulated. This framework is validated on the IEEE 14-bus system and the IEEE 118-bus system, in which net load redistribution attacks are launched by sophisticated attackers. Steady-state simulations and dynamic simulations on PSS/E show the effectiveness of the proposed framework in circumventing the voltage instability while maintaining the detection effectiveness of MTD. The impact of the proposed method on attack detection effectiveness is also revealed.

• Research Organization: Kansas State University
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• DOE Contract Number: EE0008767
• OSTI ID: 1907345
• Report Number(s): DOE-KSU-8767
• Conference Information: Journal Name: 2022 IEEE Green Technologies Conference (GreenTech)
• Country of Publication: United States
• Language: English