research-article

Mining PMU Data Streams to Improve Electric Power System Resilience

Authors:

Eduardo Cotilla-Sanchez,

Robert BassAuthors Info & Claims

BDCAT '17: Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

Pages 95 - 102

https://doi.org/10.1145/3148055.3148082

Published: 05 December 2017 Publication History

Abstract

Phasor measurement units (PMUs) provide high-fidelity situational awareness of electric power grid operations. PMU data are used in real-time to inform wide area state estimation, monitor area control error, and event detection. As PMU data becomes more reliable, these devices are finding roles within control systems such as demand response programs and early fault detection systems. As with other cyber physical systems, maintaining data integrity and security are significant challenges for power system operators. In this paper, we present a comprehensive study of multiple machine learning techniques for detecting malicious data injection within PMU data streams. The two datasets used in this study are from the Bonneville Power Administration's PMU network and an inter-university PMU network among three universities, located in the U.S. Pacific Northwest. These datasets contain data from both the transmission level and the distribution level. Our results show that both SVM and ANN are generally effective in detecting spoofed data, and TensorFlow, the newly released tool, demonstrates potential for distributing the training workload and achieving higher performance. We expect these results to shed light on future work of adopting machine learning and data analytics techniques in the electric power industry.

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Cited By

Roy PBhattacharjee SAbedzadeh SDas S(2024)Noise Resilient Learning for Attack Detection in Smart Grid PMU InfrastructureIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322328821:2(618-635)Online publication date: Mar-2024
https://doi.org/10.1109/TDSC.2022.3223288
Farooq UAdham MAlsaid MBass R(2024)A Configurable Real-Time Event Detection Framework for Power Systems Using Swarm Intelligence OptimizationIEEE Access10.1109/ACCESS.2024.344531212(115687-115696)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3445312
Mahrukh MThomas M(2023)Load Altering Attacks- a Review of Impact and Mitigation Strategies2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)10.1109/REEDCON57544.2023.10150456(397-402)Online publication date: 1-May-2023
https://doi.org/10.1109/REEDCON57544.2023.10150456
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Index Terms

Mining PMU Data Streams to Improve Electric Power System Resilience

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cover image ACM Conferences

BDCAT '17: Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

December 2017

288 pages

ISBN:9781450355490

DOI:10.1145/3148055

General Chairs:
Ashiq Anjum
University of Derby, UK
,
Alan Sill
Texas Tech University, USA
,
Program Chairs:
Xinghui Zhao
Washington State University Vancouver, USA
,
Mohsen Farid
University of Derby, UK
,
Shrideep Pallickara
Colorado State University, USA
,
Jiannong Cao
The HongKong Polytechnic University, Hong Kong

Copyright © 2017 ACM.

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IEEE TCSC: IEEE Technical Committee on Scalable Computing

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New York, NY, United States

Publication History

Published: 05 December 2017

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Funding Sources

U.S. Department of Energy/Bonneville Power Administration

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UCC '17

Sponsor:

SIGARCH
IEEE TCSC

UCC '17: 10th International Conference on Utility and Cloud Computing

December 5 - 8, 2017

Texas, Austin, USA

Acceptance Rates

BDCAT '17 Paper Acceptance Rate 27 of 93 submissions, 29%;

Overall Acceptance Rate 27 of 93 submissions, 29%

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Cited By

Roy PBhattacharjee SAbedzadeh SDas S(2024)Noise Resilient Learning for Attack Detection in Smart Grid PMU InfrastructureIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322328821:2(618-635)Online publication date: Mar-2024
https://doi.org/10.1109/TDSC.2022.3223288
Farooq UAdham MAlsaid MBass R(2024)A Configurable Real-Time Event Detection Framework for Power Systems Using Swarm Intelligence OptimizationIEEE Access10.1109/ACCESS.2024.344531212(115687-115696)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3445312
Mahrukh MThomas M(2023)Load Altering Attacks- a Review of Impact and Mitigation Strategies2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)10.1109/REEDCON57544.2023.10150456(397-402)Online publication date: 1-May-2023
https://doi.org/10.1109/REEDCON57544.2023.10150456
Gaspar JCruz TLam CSimões P(2023)Smart Substation Communications and Cybersecurity: A Comprehensive SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.330546825:4(2456-2493)Online publication date: Dec-2024
https://doi.org/10.1109/COMST.2023.3305468
Dairi AHarrou FBouyeddou BSenouci SSun Y(2023)Semi-supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart GridsPower Systems Cybersecurity10.1007/978-3-031-20360-2_11(265-295)Online publication date: 9-Feb-2023
https://doi.org/10.1007/978-3-031-20360-2_11
Cavitt JWallace SZhao X(2022)Detecting cyber attacks with packet loss resilience for power systemsSustainable Computing: Informatics and Systems10.1016/j.suscom.2021.10062934(100629)Online publication date: Apr-2022
https://doi.org/10.1016/j.suscom.2021.100629
Kosen IHuang CChen ZZhang XMin LZhou DZhu LLiu Y(2020)UPS: Unified PMU-Data Storage System to Enhance T+D PMU Data UsabilityIEEE Transactions on Smart Grid10.1109/TSG.2019.291657011:1(739-748)Online publication date: Jan-2020
https://doi.org/10.1109/TSG.2019.2916570
Ma RBasumallik SEftekharnejad S(2020)A PMU-Based Data-Driven Approach for Classifying Power System Events Considering CyberattacksIEEE Systems Journal10.1109/JSYST.2019.296354614:3(3558-3569)Online publication date: Sep-2020
https://doi.org/10.1109/JSYST.2019.2963546
Roy PBhattacharjee SDas S(2020)Real Time Stream Mining based Attack Detection in Distribution Level PMUs for Smart GridsGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9322072(1-6)Online publication date: Dec-2020
https://doi.org/10.1109/GLOBECOM42002.2020.9322072
Liu XWallace SZhao XCotilla-Sanchez EBass R(2019)Episodic Detection of Spoofed Data In Synchrophasor Measurement Streams2019 Tenth International Green and Sustainable Computing Conference (IGSC)10.1109/IGSC48788.2019.8957211(1-8)Online publication date: Oct-2019
https://doi.org/10.1109/IGSC48788.2019.8957211

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