Secure Power Systems Against Malicious Cyber-Physical Data Attacks: Protection and Identification

The security of power systems against malicious cyberphysical data attacks becomes an important issue. The adversary always attempts to manipulate the information structure of the power system and inject malicious data to deviate state variables while evading the existing detection techniques based on residual test. The solutions proposed in the literature are capable of immunizing the power system against false data injection but they might be too costly and physically not practical in the expansive distribution network. To this end, we define an algebraic condition for trustworthy power system to evade malicious data injection. The proposed protection scheme secures the power system by deterministically reconfiguring the information structure and corresponding residual test. More importantly, it does not require any physical effort in either microgrid or network level. The identification scheme of finding meters being attacked is proposed as well. Eventually, a well-known IEEE 30-bus system is adopted to demonstrate the effectiveness of the proposed schemes. Keywords—Algebraic Criterion, Malicious Cyber-Physical Data Injection, Protection and Identification, Trustworthy Power System.

[1]  Hirokazu Yanagihara,et al.  Testing the equality of several covariance matrices with fewer observations than the dimension , 2010, J. Multivar. Anal..

[2]  R. Farebrother Linear least squares computations , 1988 .

[3]  G. Styan,et al.  Equalities and Inequalities for Ranks of Matrices , 1974 .

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

[5]  G. Krumpholz,et al.  Power System State Estimation Residual Analysis: An Algorithm Using Network Topology , 1981, IEEE Transactions on Power Apparatus and Systems.

[6]  Henrik Sandberg,et al.  Stealth Attacks and Protection Schemes for State Estimators in Power Systems , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

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

[8]  Karl Henrik Johansson,et al.  Electric power network security analysis via minimum cut relaxation , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  H. Vincent Poor,et al.  Strategic Protection Against Data Injection Attacks on Power Grids , 2011, IEEE Transactions on Smart Grid.

[10]  Klara Nahrstedt,et al.  Detecting False Data Injection Attacks on DC State Estimation , 2010 .

[11]  A. Monticelli,et al.  Electric power system state estimation , 2000, Proceedings of the IEEE.

[12]  Kameshwar Poolla,et al.  Smart grid data integrity attacks: characterizations and countermeasuresπ , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[13]  Bruno Sinopoli,et al.  False Data Injection Attacks in Electricity Markets , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[14]  E. Handschin,et al.  Bad data analysis for power system state estimation , 1975, IEEE Transactions on Power Apparatus and Systems.

[15]  Karl Henrik Johansson,et al.  On Security Indices for State Estimators in Power Networks , 2010 .