A PMU-based Multivariate Model for Classifying Power System Events


  • Rui Ma
  • Sagnik Basumallik
  • Sara Eftekharnejad


Bag of Pattern (BOP), classification, Phasor Measurement Units (PMUs), Symbolic Aggregation approXimation (SAX)


Real-time transient event identification is essential for power system situational awareness and protection. The increased penetration of Phasor Measurement Units (PMUs) enhance power system visualization and real time monitoring and control. However, a malicious false data injection attack on PMUs can provide wrong data that might prompt the operator to take incorrect actions which can eventually jeopardize system reliability. In this paper, a multivariate method based on text mining is applied to detect false data and identify transient events by analyzing the attributes of each individual PMU time series and their relationship. It is shown that the proposed approach is efficient in detecting false data and identifying each transient event regardless of the system topology and loading condition as well as the coverage rate and placement of PMUs. The proposed method is tested on IEEE 30-bus system and the classification results are provided.


M. Biswal, Yifan Hao, P. Chen, S. Brahma, H. Cao, and P. De Leon, “Signal features for classification of power system disturbances using PMU data,” in 2016 Power Systems Computation Conference (PSCC), 2016, pp. 1–7.

S. Bruno, M. Benedictis, and M. Scala, “Taking the pulse of Power Systems: Monitoring Oscillations by Wavelet Analysis and Wide Area Measurement System,” in 2006 IEEE PES Power Systems Conference and Exposition, 2006, pp. 436–443.

S.-W. Sohn, A. J. Allen, S. Kulkarni, W. M. Grady, and S. Santoso, “Event detection method for the PMUs synchrophasor data,” in 2012 IEEE Power Electronics and Machines in Wind Applications, 2012, pp. 1–7.

A. R. Messina, V. Vittal, D. Ruiz-Vega, and G. Enriquez-Harper, “Interpretation and Visualization of Wide-Area PMU Measurements Using Hilbert Analysis,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1763–1771, Nov. 2006.

L. Fan, R. Kavasseri, Z. Miao, D. Osborn, and T. Bilke, “Identification of system wide disturbances using synchronized phasor data and ellipsoid method,” in 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008, pp. 1–10.

J. Ma, Y. V. Makarov, C. H. Miller, and T. B. Nguyen, “Use multidimensional ellipsoid to monitor dynamic behavior of power systems based on PMU measurement,” in IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, 2008.

O. P. Dahal, H. Cao, S. Brahma, and R. Kavasseri, “Evaluating performance of classifiers for supervisory protection using disturbance data from phasor measurement units,” in IEEE PES Innovative Smart Grid Technologies, Europe, 2014, pp. 1–6.

S. Brahma, R. Kavasseri, H. Cao, N. R. Chaudhuri, T. Alexopoulos, and Y. Cui, “Real-Time Identification of Dynamic Events in Power Systems Using PMU Data, and Potential Applications—Models, Promises, and Challenges,” IEEE Trans. Power Deliv., vol. 32, no. 1, pp. 294–301, Feb. 2017.

S. S. Negi, N. Kishor, K. Uhlen, and R. Negi, “Event Detection and Its Signal Characterization in PMU Data Stream,” IEEE Trans. Ind. Informatics, vol. 13, no. 6, pp. 3108–3118, Dec. 2017.

L. Ye and E. Keogh, “Time series shapelets: a novel technique that allows accurate, interpretable and fast classification,” Data Min. Knowl. Discov., vol. 22, no. 1–2, pp. 149–182, Jan. 2011.

V. Kekatos, G. B. Giannakis, and R. Baldick, “Grid topology identification using electricity prices,” in 2014 IEEE PES General Meeting | Conference & Exposition, 2014, pp. 1–5.

M. A. Rahman and H. Mohsenian-Rad, “False data injection attacks with incomplete information against smart power grids,” in 2012 IEEE Global Communications Conference (GLOBECOM), 2012, pp. 3153–3158.

M. Esmalifalak, H. Nguyen, R. Zheng, and Zhu Han, “Stealth false data injection using independent component analysis in smart grid,” in 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2011, pp. 244–248.

S. Basumallik, S. Eftekharnejad, N. Davis, and B. K. Johnson, “Impact of false data injection attacks on PMU-based state estimation,” in 2017 North American Power Symposium (NAPS), 2017, pp. 1–6.

J. Jiang, X. Zhao, S. Wallace, E. Cotilla-Sanchez, and R. Bass, “Mining PMU Data Streams to Improve Electric Power System Resilience,” in Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT ’17, 2017, pp. 95–102.

J. Landford et al., “Fast Sequence Component Analysis for Attack Detection in Smart Grid,” 2016 5th Int. Conf. Smart Cities Green ICT Syst., pp. 225–232, 2016.

P. Senin and S. Malinchik, “SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model,” in 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1175–1180.

E. Keogh, L. Wei, X. Xi, M. Vlachos, S.-H. Lee, and P. Protopapas, “Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures,” VLDB J., vol. 18, no. 3, pp. 611–630, Jun. 2009.

E. Keogh, “Data mining and machine learning in time series databases,” Tutor. SIGKDD 2004, 2004.

J. Lin, E. Keogh, L. Wei, and S. Lonardi, “Experiencing SAX: a novel symbolic representation of time series,” Data Min. Knowl. Discov., vol. 15, no. 2, pp. 107–144, Aug. 2007.

C. HUANG et al., “Data quality issues for synchrophasor applications Part I: a review,” J. Mod. Power Syst. Clean Energy, vol. 4, no. 3, pp. 342–352, Jul. 2016.

E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, “Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases,” Knowl. Inf. Syst., vol. 3, no. 3, pp. 263–286, Aug. 2001.

J. Lin, R. Khade, and Y. Li, “Rotation-invariant similarity in time series using bag-of-patterns representation,” J. Intell. Inf. Syst., vol. 39, no. 2, pp. 287–315, Oct. 2012.

D. B. Patil and Y. V Dongre, “A Fuzzy Approach for Text Mining,” I.J. Math. Sci. Comput., vol. 4, pp. 34–43, 2015.

Z. Wang and T. Oates, “Time Warping Symbolic Aggregation Approximation with Bag-of-Patterns Representation for Time Series Classification,” in 2014 13th International Conference on Machine Learning and Applications, 2014, pp. 270–275.

M. G. Baydogan, G. Runger, and E. Tuv, “A Bag-of-Features Framework to Classify Time Series,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2796–2802, Nov. 2013.

P. Ordonez, T. Armstrong, T. Oates, and J. Fackler, “Using Modified Multivariate Bag-of-Words Models to Classify Physiological Data,” in 2011 IEEE 11th International Conference on Data Mining Workshops, 2011, pp. 534–539.

J. Han, M. Kamber, and J. Pei, Data mining : concepts and techniques. Elsevier Science, 2011.

S. Ekici, “Classification of power system disturbances using support vector machines,” Expert Syst. Appl., vol. 36, no. 6, pp. 9859–9868, Aug. 2009.

V. Murugesan, Y. Chakhchoukh, V. Vittal, G. T. Heydt, N. Logic, and S. Sturgill, “PMU Data Buffering for Power System State Estimators,” IEEE Power Energy Technol. Syst. J., vol. 2, no. 3, pp. 94–102, Sep. 2015.

L. Xie, Y. Chen, and P. R. Kumar, “Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 2784–2794, Nov. 2014.

P. Chaovalit, A. Gangopadhyay, G. Karabatis, and Z. Chen, “Discrete wavelet transform-based time series analysis and mining,” ACM Comput. Surv., vol. 43, no. 2, pp. 1–37, 2011.

C. Bishop and N. M. Nasrabadi, “Pattern Recognition and Machine Learning,” J. Electron. Imaging, vol. 16, no. 4, p. 049901, 2007.

Y. Li, G. Li, Z. Wang, Z. Han, and X. Bai, “A Multifeature Fusion Approach for Power System Transient Stability Assessment Using PMU Data,” Math. Probl. Eng., vol. 2015, pp. 1–10, Dec. 2015.

R. Gore and M. Kande, “Analysis of Wide Area Monitoring System architectures,” in 2015 IEEE International Conference on Industrial Technology (ICIT), 2015, pp. 1269–1274.