Impact analysis of false data injection attacks on power system static security assessment

Static security assessment (SSA) is an important procedure to ensure the static security of the power system. Researches recently show that cyber-attacks might be a critical hazard to the secure and economic operations of the power system. In this paper, the influences of false data injection attack (FDIA) on the power system SSA are studied. FDIA is a major kind of cyber-attacks that can inject malicious data into meters, cause false state estimation results, and evade being detected by bad data detection. It is firstly shown that the SSA results could be manipulated by launching a successful FDIA, which can lead to incorrect or unnecessary corrective actions. Then, two kinds of targeted scenarios are proposed, i.e., fake secure signal attack and fake insecure signal attack. The former attack will deceive the system operator to believe that the system operates in a secure condition when it is actually not. The latter attack will deceive the system operator to make corrective actions, such as generator rescheduling, load shedding, etc. when it is unnecessary and costly. The implementation of the proposed analysis is validated with the IEEE-39 benchmark system.

[1]  Kan Chen,et al.  A Collaborative Intrusion Detection Mechanism Against False Data Injection Attack in Advanced Metering Infrastructure , 2015, IEEE Transactions on Smart Grid.

[2]  Anjan Bose,et al.  On-line power system security analysis , 1992, Proc. IEEE.

[3]  Rui Zhang,et al.  Real-time transient stability assessment model using extreme learning machine , 2011 .

[4]  Kit Po Wong,et al.  A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems , 2012, IEEE Transactions on Power Systems.

[5]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2011, TSEC.

[6]  K. Morison,et al.  Power system security assessment , 2004, IEEE Power and Energy Magazine.

[7]  Zuyi Li,et al.  Modeling Load Redistribution Attacks in Power Systems , 2011, IEEE Transactions on Smart Grid.

[8]  Zuyi Li,et al.  Quantitative Analysis of Load Redistribution Attacks in Power Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Oliver Kosut,et al.  Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation , 2015, IEEE Transactions on Power Systems.

[11]  Mehul Motani,et al.  Detecting False Data Injection Attacks in AC State Estimation , 2015, IEEE Transactions on Smart Grid.

[12]  Bruno Sinopoli,et al.  Integrity Data Attacks in Power Market Operations , 2011, IEEE Transactions on Smart Grid.

[13]  Gabriela Hug,et al.  Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks , 2012, IEEE Transactions on Smart Grid.

[14]  Zhao Yang Dong,et al.  A Review of False Data Injection Attacks Against Modern Power Systems , 2017, IEEE Transactions on Smart Grid.

[15]  Wei Yu,et al.  On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures , 2014, IEEE Transactions on Parallel and Distributed Systems.

[16]  Xinyu Yang,et al.  A Novel En-route Filtering Scheme against False Data Injection Attacks in Cyber-Physical Networked Systems , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[17]  Lang Tong,et al.  Impact of Data Quality on Real-Time Locational Marginal Price , 2012, IEEE Transactions on Power Systems.

[18]  I.S. Saeh,et al.  Static security assessment using artificial neural network , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[19]  Le Xie,et al.  Impact analysis of locational marginal price subject to power system topology errors , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[20]  Zuyi Li,et al.  Modeling of Local False Data Injection Attacks With Reduced Network Information , 2015, IEEE Transactions on Smart Grid.

[21]  Jinping Hao,et al.  Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids , 2015, IEEE Transactions on Industrial Informatics.

[22]  Kit Po Wong,et al.  Forecasting-Aided Imperfect False Data Injection Attacks Against Power System Nonlinear State Estimation , 2016, IEEE Transactions on Smart Grid.

[23]  Zhao Yang Dong,et al.  Real-time prediction of event-driven load shedding for frequency stability enhancement of power systems , 2012 .

[24]  Lei Wang,et al.  Implementation of online security assessment , 2006, IEEE Power and Energy Magazine.

[25]  Yan Xu Dynamic security assessment and control of modern power systems using intelligent system technologies , 2013 .

[26]  Junbo Zhao,et al.  Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks , 2017, IEEE Transactions on Smart Grid.

[27]  Wen-Long Chin,et al.  Blind False Data Injection Attack Using PCA Approximation Method in Smart Grid , 2015, IEEE Transactions on Smart Grid.

[28]  Lang Tong,et al.  Malicious Data Attacks on the Smart Grid , 2011, IEEE Transactions on Smart Grid.

[29]  Lang Tong,et al.  On Topology Attack of a Smart Grid: Undetectable Attacks and Countermeasures , 2013, IEEE Journal on Selected Areas in Communications.