Unsupervised Clustering on PMU Data for Event Characterization on Smart Grid

Eric Klinginsmith, Richard Barella, Xinghui Zhao, Scott Wallace

2016

Abstract

In the past decade, with the world-wide initiative of upgrading the electrical grid to smart grid, a significant amount of data have been generated by the grid on a daily basis. Therefore, there has been an increasing need in handling and processing these data efficiently. In this paper, we present our experience in applying unsupervised clustering on PMU data for event characterization on the smart grid. We show that although the PMU data are time series in nature, it is more efficient and robust to apply clustering methods on carefully selected features from the data collected at certain instantaneous moments in time. These features are more representative at the moments when the events have the most impact on the grid. Experiments have been carried out on real PMU data collected by Bonneville Power Administration in their wide-area monitoring system in the pacific northwest, and the results show that our instantaneous clustering method achieves high homogeneity, which provides great potentials for identifying unknown events in the grid without substantial training data.

References

  1. Americans for a Clean Energy Grid (2014). Synchrophasors. http://cleanenergytransmission.org/wpcontent/uploads/2014/08/Synchrophasors.pdf. Accessed: 2015-09-08.
  2. Antoine, O. and Maun, J.-C. (2012). Inter-area oscillations: Identifying causes of poor damping using phasor measurement units. In Power and Energy Society General Meeting, 2012 IEEE, pages 1-6. IEEE.
  3. Bailey, K. (1994). Numerical taxonomy and cluster analysis. Typologies and Taxonomies.
  4. Chang, G., Chao, J.-P., Huang, H.-M., Chen, C.-I., and Chu, S.-Y. (2008). On tracking the source location of voltage sags and utility shunt capacitor switching transients. IEEE Transactions on Power Delivery, 23(4):2124-2131.
  5. Dahal, O. P., Brahma, S. M., and Cao, H. (2014). Comprehensive clustering of disturbance events recorded by phasor measurement units. IEEE Transactions on Power Delivery, 29(3):1390-1397.
  6. Diao, R., Sun, K., Vittal, V., O'Keefe, R., Richardson, M., Bhatt, N., Stradford, D., and Sarawgi, S. (2009). Decision tree-based online voltage security assessment using pmu measurements. IEEE Transactions on Power Systems, 24(2):832-839.
  7. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, volume 96, pages 226-231.
  8. Gomez, F. R., Rajapakse, A. D., Annakkage, U. D., and Fernando, I. T. (2011). Support vector machine-based algorithm for post-fault transient stability status prediction using synchronized measurements. IEEE Transactions on Power Systems, 26(3):1474-1483.
  9. Jiang, J.-A., Yang, J.-Z., Lin, Y.-H., Liu, C.-W., and Ma, J.-C. (2000). An adaptive pmu based fault detection/location technique for transmission lines. i. theory and algorithms. IEEE Transactions on Power Delivery, 15(2):486-493.
  10. Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 285-289. ACM.
  11. Liang, X., Wallace, S., and Zhao, X. (2014). A technique for detecting wide-area single-line-to-ground faults. In Proceedings of the 2nd IEEE Conference on Technologies for Sustainability (SusTech 2014), SusTech 7814, pages 1-4. IEEE.
  12. Liao, T. W. (2005). Clustering of time series dataa survey. Pattern recognition, 38(11):1857-1874.
  13. Liu, G. and Venkatasubramanian, V. (2008). Oscillation monitoring from ambient pmu measurements by frequency domain decomposition. In IEEE International Symposium on Circuits and Systems (ISCAS 2008), pages 2821-2824.
  14. Nguyen, D., Barella, R., Wallace, S., Zhao, X., and Liang, X. (2015). Smart grid line event classification using supervised learning over pmu data streams. In Proccedings of the 6th IEEE International Green and Sustainable Computing Conference. American Electric Reliability Corporation (2014). Real-Time Application of Synchro phasors for Improving Reliability. http://www.nerc.
  15. com/docs/oc/rapirtf/RAPIR%20final%20101710.pdf .
  16. Accessed: 2015-09-08.
  17. Ray, P. K., Mohanty, S. R., Kishor, N., and Catala˜o, J. P. (2014). Optimal feature and decision tree-based classification of power quality disturbances in distributed generation systems. IEEE Transactions on Sustainable Energy, 5(1):200-208.
  18. Rokach, L. and Maimon, O. (2005). Clustering methods. In Data mining and knowledge discovery handbook, pages 321-352. Springer.
  19. Rosenberg, A. and Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In EMNLP-CoNLL, volume 7, pages 410- 420.
  20. Salat, R. and Osowski, S. (2004). Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems, 19(2):979-986.
  21. Singh, B., Sharma, N., Tiwari, A., Verma, K., and Singh, S. (2011). Applications of phasor measurement units (pmus) in electric power system networks incorporated with facts controllers. International Journal of Engineering, Science and Technology, 3(3).
  22. Tate, J. E. and Overbye, T. J. (2008). Line outage detection using phasor angle measurements. IEEE Transactions on Power Systems, 23(4):1644-1652.
  23. Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., et al. (2001). Constrained k-means clustering with background knowledge. In ICML, volume 1, pages 577- 584.
  24. Zhang, Y.-G., Wang, Z.-P., Zhang, J.-F., and Ma, J. (2011). Fault localization in electrical power systems: A pattern recognition approach. International Journal of Electrical Power & Energy Systems, 33(3):791-798.
Download


Paper Citation


in Harvard Style

Klinginsmith E., Barella R., Zhao X. and Wallace S. (2016). Unsupervised Clustering on PMU Data for Event Characterization on Smart Grid . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 233-240. DOI: 10.5220/0005863702330240


in Bibtex Style

@conference{smartgreens16,
author={Eric Klinginsmith and Richard Barella and Xinghui Zhao and Scott Wallace},
title={Unsupervised Clustering on PMU Data for Event Characterization on Smart Grid},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005863702330240},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Unsupervised Clustering on PMU Data for Event Characterization on Smart Grid
SN - 978-989-758-184-7
AU - Klinginsmith E.
AU - Barella R.
AU - Zhao X.
AU - Wallace S.
PY - 2016
SP - 233
EP - 240
DO - 10.5220/0005863702330240