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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases pp 71–86Cite as

GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11051)

Abstract

Given sensor readings over time from a power grid consisting of nodes (e.g. generators) and edges (e.g. power lines), how can we most accurately detect when an electrical component has failed? More challengingly, given a limited budget of sensors to place, how can we determine where to place them to have the highest chance of detecting such a failure? Maintaining the reliability of the electrical grid is a major challenge. An important part of achieving this is to place sensors in the grid, and use them to detect anomalies, in order to quickly respond to a problem. Our contributions are: (1) Online anomaly detection: we propose a novel, online anomaly detection algorithm that outperforms existing approaches. (2) Sensor placement: we construct an optimization objective for sensor placement, with the goal of maximizing the probability of detecting an anomaly. We show that this objective has the property of submodularity, which we exploit in our sensor placement algorithm. (3) Effectiveness: Our sensor placement algorithm is provably near-optimal, and both our algorithms outperform existing approaches in accuracy by \(59\%\) or more (F-measure) in experiments. (4) Scalability: our algorithms scale linearly, and our detection algorithm is online, requiring bounded space and constant time per update. Code related to this paper is available at: https://github.com/bhooi/gridwatch.

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Notes

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    IQR is a robust measure of spread, equal to the difference between the \(75\%\) and \(25\%\) quantiles.

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Acknowledgment

This material is based upon work supported by the National Science Foundation under Grant No. CNS-1314632, IIS-1408924, and by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, and in part by the Defense Advanced Research Projects Agency (DARPA) under award no. FA8750-17-1-0059 for the RADICS program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding parties. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Bryan Hooi, Dhivya Eswaran, Hyun Ah Song & Christos Faloutsos

  2. Department of Statistics, Carnegie Mellon University, Pittsburgh, USA

    Bryan Hooi

  3. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA

    Amritanshu Pandey, Marko Jereminov & Larry Pileggi

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  1. Bryan Hooi
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  2. Dhivya Eswaran
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  3. Hyun Ah Song
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  4. Amritanshu Pandey
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  5. Marko Jereminov
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  7. Christos Faloutsos
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Correspondence to Bryan Hooi .

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Editors and Affiliations

  1. IBM Research - Ireland, Dublin, Ireland

    Michele Berlingerio

  2. Institute for Scientific Interchange, Turin, Italy

    Francesco Bonchi

  3. University of Nottingham, Nottingham, UK

    Thomas Gärtner

  4. University College Dublin, Dublin, Ireland

    Neil Hurley

  5. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

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Hooi, B. et al. (2019). GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science(), vol 11051. Springer, Cham. https://doi.org/10.1007/978-3-030-10925-7_5

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