Estimation of smart grid topology using SCADA measurements

Power grid topology is essential for various aspects of smart grid monitoring and operations. Recent studies show that by using the grid topology, an adversary can construct stealthy attacks that can cause significant disruption to power delivery and the critical infrastructure. This paper shows that the power grid topology can be approximately estimated simply by observing multiple power injection measurement data. We formulate the topology estimation problem as a blind convex optimization problem and solve using an efficient alternating direction method of multipliers (ADMM). To evaluate the performance, eigenvalue analysis and centrality measures from the complex network theory are used. Experimental results using IEEE test system demonstrate that the estimated grid topology is very close to the original topology and exhibits almost similar patterns and characteristics.

[1]  Lang Tong,et al.  Subspace Methods for Data Attack on State Estimation: A Data Driven Approach , 2014, IEEE Transactions on Signal Processing.

[2]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[3]  Adnan Anwar,et al.  Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements , 2017, J. Comput. Syst. Sci..

[4]  Georgios B. Giannakis,et al.  Online Energy Price Matrix Factorization for Power Grid Topology Tracking , 2014, IEEE Transactions on Smart Grid.

[5]  Rushikesh K. Joshi,et al.  CIM-Based Connectivity Model for Bus-Branch Topology Extraction and Exchange , 2011, IEEE Transactions on Smart Grid.

[6]  Yin Zhang,et al.  An alternating direction algorithm for matrix completion with nonnegative factors , 2011, Frontiers of Mathematics in China.

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

[8]  Lang Tong,et al.  Data Framing Attack on State Estimation , 2013, IEEE Journal on Selected Areas in Communications.

[9]  Luigi Vanfretti,et al.  An efficient automated topology processor for state estimation of power transmission networks , 2014 .

[10]  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.

[11]  Saverio Bolognani,et al.  Identification of power distribution network topology via voltage correlation analysis , 2013, 52nd IEEE Conference on Decision and Control.

[12]  A. B. M. Nasiruzzaman,et al.  Comparative study of power grid centrality measures using complex network framework , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[13]  Tomaso Erseghe,et al.  Topology Estimation for Smart Micro Grids via Powerline Communications , 2013, IEEE Transactions on Signal Processing.

[14]  O. de Weck,et al.  Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[16]  Rong Zheng,et al.  Stealth false data injection using independent component analysis in smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[17]  H. Vincent Poor,et al.  Blind topology identification for power systems , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[18]  Euhanna Ghadimi,et al.  Optimal Parameter Selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic Problems , 2013, IEEE Transactions on Automatic Control.

[19]  Thomas J. Overbye,et al.  Identification of power system topology from synchrophasor data , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[20]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

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

[22]  Backhaus Scott,et al.  Learning topology of the power distribution grid with and without missing data , 2016 .

[23]  Zahir Tari,et al.  Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid , 2015, Inf. Syst..

[24]  Raheem A. Beyah,et al.  A physical overlay framework for insider threat mitigation of power system devices , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[25]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[26]  Georgios B. Giannakis,et al.  Grid topology identification using electricity prices , 2013, 2014 IEEE PES General Meeting | Conference & Exposition.

[27]  Di Wu,et al.  Extended Topological Metrics for the Analysis of Power Grid Vulnerability , 2012, IEEE Systems Journal.