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Authors: Jordan Landford 1 ; Rich Meier 2 ; Richard Barella 3 ; Scott Wallace 3 ; Xinghui Zhao 3 ; Eduardo Cotilla-Sanchez 2 and Robert B. Bass 1

Affiliations: 1 Portland State University, United States ; 2 Oregon State University, United States ; 3 Washington State University Vancouver, United States

ISBN: 978-989-758-184-7

Keyword(s): Spoofing, Phasor Measurement Unit (PMU), Synchrophasors, Correlation, Event Detection, Machine Learning, Support Vector Machine (SVM).

Related Ontology Subjects/Areas/Topics: Architectures for Smart Grids ; Energy and Economy ; Energy Monitoring ; Energy-Aware Systems and Technologies ; Evolutionary Algorithms in Energy Applications ; Smart Grids

Abstract: Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of “if” but a matter of “when” in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.

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Paper citation in several formats:
Landford, J.; Meier, R.; Barella, R.; Wallace, S.; Zhao, X.; Cotilla-Sanchez, E. and Bass, R. (2016). Fast Sequence Component Analysis for Attack Detection in 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 225-232. DOI: 10.5220/0005860302250232

@conference{smartgreens16,
author={Jordan Landford. and Rich Meier. and Richard Barella. and Scott Wallace. and Xinghui Zhao. and Eduardo Cotilla{-}Sanchez. and Robert B. Bass.},
title={Fast Sequence Component Analysis for Attack Detection in Smart Grid},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={225-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005860302250232},
isbn={978-989-758-184-7},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Fast Sequence Component Analysis for Attack Detection in Smart Grid
SN - 978-989-758-184-7
AU - Landford, J.
AU - Meier, R.
AU - Barella, R.
AU - Wallace, S.
AU - Zhao, X.
AU - Cotilla-Sanchez, E.
AU - Bass, R.
PY - 2016
SP - 225
EP - 232
DO - 10.5220/0005860302250232

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