Unsupervised Clustering on PMU Data for Event Characterization on Smart Grid

Eric Klinginsmith, Richard Barella, Xinghui Zhao, Scott Wallace


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.


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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

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,},

in EndNote Style

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