Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures

The problem of constructing malicious data attack of smart grid state estimation is considered together with countermeasures that detect the presence of such attacks. For the adversary, using a graph theoretic approach, an efficient algorithm with polynomial-time complexity is obtained to find the minimum size unobservable malicious data attacks. When the unobservable attack does not exist due to restrictions of meter access, attacks are constructed to minimize the residue energy of attack while guaranteeing a certain level of increase of mean square error. For the control center, a computationally efficient algorithm is derived to detect and localize attacks using the generalized likelihood ratio test regularized by an L_1 norm penalty on the strength of attack.

[1]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

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

[3]  G. Krumpholz,et al.  Power System Observability: A Practical Algorithm Using Network Topology , 1980, IEEE Transactions on Power Apparatus and Systems.

[4]  Martin Grötschel,et al.  The ellipsoid method and its consequences in combinatorial optimization , 1981, Comb..

[5]  Gene H. Golub,et al.  Matrix computations , 1983 .

[6]  S. Kourouklis,et al.  A Large Deviation Result for the Likelihood Ratio Statistic in Exponential Families , 1984 .

[7]  M. Ribbens-Pavella,et al.  Bad Data Identification Methods In Power System State Estimation-A Comparative Study , 1985, IEEE Transactions on Power Apparatus and Systems.

[8]  William H. Cunningham On submodular function minimization , 1985, Comb..

[9]  Network Observability: Theory , 1985, IEEE Transactions on Power Apparatus and Systems.

[10]  Felix F. Wu,et al.  Detection of topology errors by state estimation (power systems) , 1989 .

[11]  Felix F. Wu,et al.  Detection of Topology Errors by State Estimation , 1989, IEEE Power Engineering Review.

[12]  Neri Merhav,et al.  When is the generalized likelihood ratio test optimal? , 1992, IEEE Trans. Inf. Theory.

[13]  Lamine Mili,et al.  Identification of multiple interacting bad data via power system decomposition , 1996 .

[14]  Amir Dembo,et al.  Large Deviations Techniques and Applications , 1998 .

[15]  David Hutchison,et al.  The Magic WAND-functional overview , 1998, IEEE J. Sel. Areas Commun..

[16]  Alexander Schrijver,et al.  A Combinatorial Algorithm Minimizing Submodular Functions in Strongly Polynomial Time , 2000, J. Comb. Theory B.

[17]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[18]  Bernard C. Levy,et al.  Principles of Signal Detection and Parameter Estimation , 2008 .

[19]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2009, CCS.

[20]  Stephen Boyd,et al.  Estimation of faults in DC electrical power system , 2009, 2009 American Control Conference.

[21]  Lang Tong,et al.  On malicious data attacks on power system state estimation , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[22]  Oliver Kosut Adversaries in networks , 2010 .

[23]  Lang Tong,et al.  Limiting false data attacks on power system state estimation , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).