Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Jacobs, Nicholas; Hossain‐McKenzie, Shamina; Sun, Shining; Payne, Emily; Summers, Adam; Al‐Homoud, Leen; Layton, Astrid; Davis, Kate; Goes, Chris

doi:10.1049/cps2.12087

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Journal Article · Fri Feb 16 23:00:00 EST 2024 · IET Cyber-Physical Systems: Theory & Applications

DOI:https://doi.org/10.1049/cps2.12087· OSTI ID:2308887

Jacobs, Nicholas ^[1]; ^[1]; Sun, Shining ^[2]; Payne, Emily ^[3]; Summers, Adam ^[4]; Al‐Homoud, Leen ^[2]; Layton, Astrid ^[3]; Davis, Kate ^[2]; Goes, Chris ^[1]

Cyber Resilience R&,D Sandia National Laboratories Albuquerque New Mexico USA
Electrical and Computer Engineering Texas A&,M University College Station Texas USA
J. Mike Walker ’66 Department of Mechanical Engineering Texas A&,M University College Station Texas USA
Electric Power Research Sandia National Laboratories Albuquerque New Mexico USA

Abstract

Cyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

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Sponsoring Organization:: USDOE

OSTI ID:: 2308887

Journal Information:: IET Cyber-Physical Systems: Theory & Applications, Journal Name: IET Cyber-Physical Systems: Theory & Applications Journal Issue: 4 Vol. 9; ISSN 2398-3396

Publisher:: Institution of Engineering and Technology (IET)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

References (28)

Growth and Development Ulanowicz, Robert E. https://doi.org/10.1007/978-1-4612-4916-0	book	January 1986
Machine learning driven smart electric power systems: Current trends and new perspectives Ibrahim, Muhammad Sohail; Dong, Wei; Yang, Qiang Applied Energy, Vol. 272 https://doi.org/10.1016/j.apenergy.2020.115237	journal	August 2020
Clustering and supervisory voltage control in power systems Baranwal, Mayank; Salapaka, Srinivasa International Journal of Electrical Power & Energy Systems, Vol. 109 https://doi.org/10.1016/j.ijepes.2019.02.025	journal	July 2019
Data clustering: 50 years beyond K-means Jain, Anil K. Pattern Recognition Letters, Vol. 31, Issue 8 https://doi.org/10.1016/j.patrec.2009.09.011	journal	June 2010
Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis Schütz, Thomas; Schraven, Markus Hans; Fuchs, Marcus Renewable Energy, Vol. 129 https://doi.org/10.1016/j.renene.2018.06.028	journal	December 2018
Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics Rocchetta, Roberto Renewable and Sustainable Energy Reviews, Vol. 159 https://doi.org/10.1016/j.rser.2022.112185	journal	May 2022
A Comparative Analysis of Community Detection Algorithms on Artificial Networks Yang, Zhao; Algesheimer, René; Tessone, Claudio J. Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep30750	journal	August 2016
Modularity and community structure in networks Newman, M. E. J. Proceedings of the National Academy of Sciences, Vol. 103, Issue 23 https://doi.org/10.1073/pnas.0601602103	journal	May 2006
The modularity of pollination networks Olesen, J. M.; Bascompte, J.; Dupont, Y. L. Proceedings of the National Academy of Sciences, Vol. 104, Issue 50 https://doi.org/10.1073/pnas.0706375104	journal	December 2007
Community Structure in Directed Networks Leicht, E. A.; Newman, M. E. J. Physical Review Letters, Vol. 100, Issue 11 https://doi.org/10.1103/PhysRevLett.100.118703	journal	March 2008
Module identification in bipartite and directed networks Guimerà, Roger; Sales-Pardo, Marta; Amaral, Luís A. Nunes Physical Review E, Vol. 76, Issue 3 https://doi.org/10.1103/physreve.76.036102	journal	September 2007
Node Importance Evaluation Method for Cyberspace Security Risk Control Yao, Jiaxin; Lin, Bihai; Huang, Ruiqi 2021 11th International Conference on Information Technology in Medicine and Education (ITME) https://doi.org/10.1109/ITME53901.2021.00036	conference	November 2021
Spatio-Temporal Failure Propagation in Cyber-Physical Power Systems Boyaci, Osman; Narimani, M. Rasoul; Davis, Katherine 2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE) https://doi.org/10.1109/SGRE53517.2022.9774040	conference	March 2022
pyPMU — Open source python package for synchrophasor data transfer Sandi, Stevan; Krstajic, Bozo; Popovic, Tomo 2016 24th Telecommunications Forum (TELFOR) https://doi.org/10.1109/TELFOR.2016.7818916	conference	November 2016
Cyber-Physical Observability for the Electric Grid Jacobs, Nicholas; Hossain-McKenzie, Shamina; Summers, Adam 2020 IEEE Texas Power and Energy Conference (TPEC) https://doi.org/10.1109/TPEC48276.2020.9042494	conference	February 2020
A Cyber-Physical Modeling and Assessment Framework for Power Grid Infrastructures Davis, Katherine R.; Davis, Charles M.; Zonouz, Saman A. IEEE Transactions on Smart Grid, Vol. 6, Issue 5 https://doi.org/10.1109/TSG.2015.2424155	journal	September 2015
Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems Wu, Jiaxin; Chen, Xin; Badakhshan, Sobhan IEEE Transactions on Industrial Informatics https://doi.org/10.1109/tii.2022.3199240	journal	January 2022
Hierarchical Spectral Clustering of Power Grids Sanchez-Garcia, Ruben J.; Fennelly, Max; Norris, Sean IEEE Transactions on Power Systems, Vol. 29, Issue 5 https://doi.org/10.1109/tpwrs.2014.2306756	journal	September 2014
CPIndex: Cyber-Physical Vulnerability Assessment for Power-Grid Infrastructures Vellaithurai, Ceeman; Srivastava, Anurag; Zonouz, Saman IEEE Transactions on Smart Grid, Vol. 6, Issue 2 https://doi.org/10.1109/tsg.2014.2372315	journal	March 2015
Cyber-Physical Resilience: Definition and Assessment Metric Clark, Andrew; Zonouz, Saman IEEE Transactions on Smart Grid, Vol. 10, Issue 2 https://doi.org/10.1109/tsg.2017.2776279	journal	March 2019
CP-TRAM: Cyber-Physical Transmission Resiliency Assessment Metric No authors listed IEEE Transactions on Smart Grid, Vol. 11, Issue 6, p. 5114-5123 https://doi.org/10.1109/tsg.2020.2996137	journal	May 2020
Potential oscillators and keystone modules in food webs Kadoya, Taku; Gellner, Gabriel; McCann, Kevin S. Ecology Letters, Vol. 21, Issue 9 https://doi.org/10.1111/ele.13099	journal	June 2018
Towards effective clustering techniques for the analysis of electric power grids Hogan, Emilie; Cotilla-Sanchez, Eduardo; Halappanavar, Mahantesh Proceedings of the 3rd International Workshop on High Performance Computing, Networking and Analytics for the Power Grid - HiPCNA-PG '13 https://doi.org/10.1145/2536780.2536785	conference	January 2013
Metrics for graph comparison: A practitioner’s guide Wills, Peter; Meyer, François G. PLOS ONE, Vol. 15, Issue 2 https://doi.org/10.1371/journal.pone.0228728	journal	February 2020
Harmonized Automatic Relay Mitigation of Nefarious Intentional Events (HARMONIE) - Special Protection Scheme (SPS) Hossain-McKenzie, Shamina; Jacobs, Nicholas; Summers, Adam https://doi.org/10.2172/1890265	report	September 2022
Towards the Characterization of Cyber-Physical System Interdependencies in the Electric Grid Hossain-McKenzie, Shamina; Jacobs, Nicholas; Summers, Adam Power and Energy Conference at Illinois (PECI) 2023 - Urbana, Illinois, United States of America - March - 2023 https://doi.org/10.2172/2431849	conference	March 2023
Voltage collapse prediction for interconnected power systems Al-Hinai, Amer S. West Virginia University Graduate Theses, Dissertations, and Problem Reports https://doi.org/10.33915/etd.1065	dissertation	January 2000
Risk management guide for information technology systems : Stoneburner, Gary; Goguen, Alice; Feringa, Alexis National Institute of Standards and Technology https://doi.org/10.6028/NIST.SP.800-30	report	July 2002

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