Fast anomaly detection in SmartGrids via sparse approximation theory

The SmartGrid (SG) is a complex system connecting physical components (e.g., human, weather, power plants) and logical components (e.g., control algorithms, communication infrastructure, protocols). The large number of components and the interactions between the individual components induce an extremely intricate behavior of the overall system. Detecting anomalies in the behavior of the system requires a large number of observations and is unpractical. A novel learning and estimation framework to analyze stochastic processes over graphs associated with SG systems is proposed. The critical observation behind the proposed framework in that these systems induce an underlying sparse structure which enables dimension reduction via compressed sensing-like schemes. Numerical results show that the compression approach proposed herein reduces by orders of magnitude the number of observations required to detect an anomalous behavior of the SG.

[1]  Shen Jie.  Survey on Smart Grid Technology , 2009 .

[2]  Mark Crovella,et al.  Graph wavelets for spatial traffic analysis , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  Namrata Vaswani,et al.  LS-CS-Residual (LS-CS): Compressive Sensing on Least Squares Residual , 2009, IEEE Transactions on Signal Processing.

[4]  Pierluigi Mancarella,et al.  Distributed multi-generation: A comprehensive view , 2009 .

[5]  R. Tibshirani,et al.  A note on the group lasso and a sparse group lasso , 2010, 1001.0736.

[6]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[7]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[8]  Urbashi Mitra,et al.  Structure-based learning in wireless networks via sparse approximation , 2012, EURASIP J. Wirel. Commun. Netw..

[9]  Pierluigi Siano,et al.  Failure Identification in Smart Grids Based on Petri Net Modeling , 2011, IEEE Transactions on Industrial Electronics.

[10]  William H. Sanders,et al.  Intrusion Detection for Advanced Metering Infrastructures: Requirements and Architectural Directions , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[11]  R. Coifman,et al.  Diffusion Wavelets , 2004 .

[12]  Marco Levorato,et al.  Residential Demand Response Using Reinforcement Learning , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[13]  John N. Tsitsiklis,et al.  Analysis of temporal-difference learning with function approximation , 1996, NIPS 1996.