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

Authors: Morteza Talebi, Jianan Wang, Zhihua Qu

Abstract:

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063232

References:

[1] E. Handschin, F. Schweppe, J. Kohlas, and A. Fiechter, Bad data analysis for power system state estimation, IEEE Transactions on Power Apparatus and Systems, vol. 94, no. 2, pp. 329-337, 1975.
[2] Y. Liu, P. Ning, and M. K. Reiter, False data injection attacks against state estimation in electric power grids, in Proc. ACM Conf. Comput. Commun. Security, Chicago, IL, Nov. 2009.
[3] K. Clements, G. Krumpholz, and P. Davis, Power system state estimation residual analysis: an algorithm using network topology, IEEE Transactions on Power Apparatus and Systems, no. 4, pp. 1779-1787, 1981.
[4] A. Monticelli, Electric power system state estimation, Proceedings of the IEEE, vol. 88, no. 2, pp. 262282, 2000.
[5] L. Xie, Y. Mo, and B. Sinopoli, False data injection attacks in electricity markets, in 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm)., pp. 226-231,IEEE 2010.
[6] T. T. Kim and H. V. Poor, Strategic protection against data injection attacks on power grids, IEEE Transactions on Smart Grid, vol. 2, pp.326- 333, 2011.
[7] R. Bobba, K. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, and T. Overbye, Detecting false data injection attacks on dc state estimation, in CPSWEEK 2010, the First Workshop on Secure Control Systems, 2010.
[8] O. Kosut, L. Jia, R. Thomas, and L. Tong, Malicious data attacks on the smart grid, IEEE Transactions on Smart Grid, no. 4, pp. 645-658,2011.
[9] G. Dan and H. Sandberg, Stealth attacks and protection schemes for state estimators in power systems, in First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 214-219, 2010.
[10] A. Giani, E. Bitar, M. McQueen, P. Khargonekar, K. Poolla, and M. Garcia. Smart grid data integrity attacks: Characterizations and countermeasures. In Proceedings of the IEEE SmartGridComm, October 2011.
[11] H. Sandberg, A. Teixeira, and K. H. Johansson. On security indices for state estimators in power networks. In Preprints of the FirstWorkshop on Secure Control Systems, CPSWEEK 2010, Stockholm, Sweden, April 2010.
[12] K. C. Sou, H. Sandberg, and K. H. Johansson. Electric power net- work security analysis via minimum cut relaxation. In Proceedings of the 50th IEEE Conference on Decision and Control, December 2011.
[13] Y. Yuan, Z. Li, and K. Ren, Modeling load redistribution attacks in power systems, IEEE Transactions on Smart Grid, vol. 2. no. 2, pp. 382-390, Jun. 2011.
[14] A. Gomez-Exposito, A. Abur, A. de la Villa Jaen, and C. Gomez-Quiles, Amultilevel state estimation paradigm for smart grids, Proceedings of the IEEE, vol. 99, pp. 952-976, 2011.
[15] Ben-Israel Adi and Greville Thomas N.E., Generalized Inverses, 2nd Edition, Wiley-Interscience, Chpt. 2, Sec.4, 2003.
[16] G. Marsaglia and G. P. H. Styan. Equalities and inequalities for ranks of matrices. Linear and Multi-linear Algebra, 269-292, 2 1974.
[17] R. W. Farebrother, Linear least squares computations, Marcel Dekker INC, pp. 160, 1988.
[18] Muni S. Srivastava and Hirokazu Yanagihara, Testing the equality of several covariance matrices with fewer observations than the dimension, Elsevier, Journal of Multivariate Analysis 101,pp.1323, 2010.
[19] R.D. Zimmerman and C.E. Murillo-Sanchez. MATPOWER, A MATLAB Power System Simulation Package. http://www.pserc.cornell.edu/matpower/manual.pdf, September 2007.