Pattern Mining and Anomaly Detection based on Power System Synchrophasor Measurements

Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability, and helps prevent electric grid blackouts due to early anomaly detection. The study presented in this paper is based on real PMU measurements of the U.S. Western Interconnection system. Given the nonlinear and non-stationary PMU data, we developed a robust anomaly detection framework that uses wavelet-based multi-resolution analysis with moving-window-based outlier detection and anomaly scoring to identify potential PMU events. Candidate events were evaluated via spatiotemporal correlation analysis and classified for a better understanding of event types, resulting in successful anomaly detection and classification of the recorded events.

[1]  Adrian E. Raftery,et al.  MCLUST: Software for Model-Based Cluster Analysis , 1999 .

[2]  Vahid Madani,et al.  Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks , 2011, Proceedings of the IEEE.

[3]  S. Bruno,et al.  "Taking the pulse" of Power Systems: Monitoring Oscillations by Wavelet Analysis and Wide Area Measurement System , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[4]  V. Alarcón-Aquino,et al.  Anomaly detection in communication networks using wavelets , 2001 .

[5]  T.J. Overbye,et al.  Line Outage Detection Using Phasor Angle Measurements , 2008, IEEE Transactions on Power Systems.

[6]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[7]  Adrian E. Raftery,et al.  Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering , 2007, J. Classif..

[8]  J. Benedetto,et al.  The Theory of Multiresolution Analysis Frames and Applications to Filter Banks , 1998 .

[9]  Y. Meyer.  Principe d'incertitude, bases hilbertiennes et algèbres d'opérateurs , 1986 .

[10]  Galina Antonova,et al.  The Use of Synchrophasors for Wide Area Monitoring of Electrical Power Grids , 2015 .

[11]  Hao Zhu,et al.  Phasor Measurement Unit Placement for Identifying Power Line Outages in Wide-Area Transmission System Monitoring , 2014, 2014 47th Hawaii International Conference on System Sciences.

[12]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[13]  James F. Davis,et al.  Clustering in wavelet domain: A multiresolution ART network for anomaly detection , 2004 .

[14]  Venkataramana Ajjarapu,et al.  Real-Time Monitoring of Short-Term Voltage Stability Using PMU Data , 2013, IEEE Transactions on Power Systems.

[15]  I Kamwa,et al.  Adaptive Phasor and Frequency-Tracking Schemes for Wide-Area Protection and Control , 2011, IEEE Transactions on Power Delivery.

[16]  Jiasong Mu,et al.  Statistical wavelet-based anomaly detection in big data with compressive sensing , 2013, EURASIP Journal on Wireless Communications and Networking.

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

[18]  P. V. Etingov,et al.  Frequency response analysis using automated tools and synchronized measurements , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[19]  Ali A. Ghorbani,et al.  Network Anomaly Detection Based on Wavelet Analysis , 2009, EURASIP J. Adv. Signal Process..

[20]  Le Xie,et al.  Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach , 2017, IEEE Transactions on Power Systems.

[21]  Samir Avdakovic,et al.  Wavelets as a tool for power system dynamic events analysis – State-of-the-art and future applications , 2015 .

[22]  A. Y. Chikhani,et al.  Power quality detection and classification using wavelet-multiresolution signal decomposition , 1999 .

[23]  Raymond A. de Callafon,et al.  Feature Based Grid Event Classification from Synchrophasor Data , 2017, ICCS.

[24]  Jugal K. Kalita,et al.  Network Anomaly Detection: Methods, Systems and Tools , 2014, IEEE Communications Surveys & Tutorials.

[25]  S. M. Brahma,et al.  Preliminary work to classify the disturbance events recorded by phasor measurement units , 2012, 2012 IEEE Power and Energy Society General Meeting.

[26]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[27]  Joe H. Chow,et al.  Detecting data integrity attacks on SCADA systems using limited PMUs , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[28]  Sambarta Dasgupta,et al.  Real-time monitoring of short-term voltage stability using PMU data , 2014 .

[29]  Mohammad Shahidehpour,et al.  Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art , 2014, IEEE Access.

[30]  Joe H. Chow,et al.  Classification and Detection of PMU Data Manipulation Attacks Using Transmission Line Parameters , 2018, IEEE Transactions on Smart Grid.

[31]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Arun G. Phadke,et al.  Synchronized Phasor Measurements and Their Applications , 2008 .

[33]  Debomita Ghosh,et al.  Performance simulation of phasor measurement unit for wide area measurement system , 2014, Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC).

[34]  Tony DeRose,et al.  Multiresolution analysis for surfaces of arbitrary topological type , 1997, TOGS.

[35]  Adrian E. Raftery,et al.  MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering , 2006 .

[36]  Raymond A. de Callafon,et al.  Optimal Filtering for Grid Event Detection from Real-time Synchrophasor Data , 2016, ICCS.