Cyber attack detection in PMU measurements via the expectation-maximization algorithm

This paper presents the detection and identification of cyber attacks in phasor measurement unit (PMU) data using the expectation-maximization algorithm. Power systems today is prone to malicious cyber attack with its greater complexity and dependence on PMUs. While the conventional power system estimation is very advanced and robust, this paper will extend the power system estimation to inherently consider the possibility of malicious cyber attack. The detection is incorporated into the estimation problem in our approach, which will be solved by the EM algorithm. The proposed algorithm is applied on an IEEE 14-bus system to illustrate the performance of the algorithm.

[1]  Kai Sun,et al.  An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision Trees , 2007, IEEE Transactions on Power Systems.

[2]  Ying-Hong Lin,et al.  An adaptive PMU based fault detection/location technique for transmission lines. I. Theory and algorithms , 2000 .

[3]  J.E. Daggle.  Postmortem analysis of power grid blackouts - The role of measurement systems , 2006, IEEE Power and Energy Magazine.

[4]  Bikash Pal,et al.  Statistical Representation of Distribution System Loads Using Gaussian Mixture Model , 2010, IEEE Transactions on Power Systems.

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  Ami Wiesel,et al.  Distributed change detection in Gaussian graphical models , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[7]  L. Tong,et al.  Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[8]  J. S. Thorp,et al.  State Estimlatjon with Phasor Measurements , 1986, IEEE Transactions on Power Systems.

[9]  Asuman E. Ozdaglar,et al.  A distributed newton method for dynamic Network Utility Maximization with delivery contracts , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[10]  M. Hemmingsson,et al.  Wide area system monitoring and control - terminology, phenomena, and solution implementation strategies , 2004, IEEE Power and Energy Magazine.

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

[12]  Deepa Kundur,et al.  Towards a Framework for Cyber Attack Impact Analysis of the Electric Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[13]  Z. Chen,et al.  Reliability analysis of cyber security in an electrical power system associated WAN , 2012, 2012 IEEE Power and Energy Society General Meeting.

[14]  T. Zourntos,et al.  A flocking-based dynamical systems paradigm for smart power system analysis , 2012, 2012 IEEE Power and Energy Society General Meeting.

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

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