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Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis

Received: 3 August 2021     Accepted: 21 August 2021     Published: 30 August 2021
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Abstract

Real-time anomaly detection is a critical monitoring task for power systems. Most studies of power network detection fail to identify small fault signals or disturbances that might lead to damages or system-wide blackout. This work presents a methodology for analyzing high-dimensional PMU data and detecting early events for large-scale power systems in a non-Gaussian noise environment. Also, spatio-temporal correlations of PMU data are explored and determined by the factor model for anomaly detection. Based on random matrix theory, the factor model monitors the variation of spatio-temporal correlations in PMU data and estimates the number of dynamic factors. Kullback-Leibler Divergence is employed to measure the deviation between two spectral distributions: the empirical spectral distribution of the covariance matrix of residuals from online monitoring data and its theoretical spectral distribution determined by the factor model. Using IEEE 57-bus, IEEE 118-bus, and Polish 2383-bus systems, three different case studies demonstrate that the proposed method is more effective in identifying early-stage anomalies in high-dimensional PMU data collected from large-scale power networks. Performance evaluations validate that this method is sensitive and robust to small fault signals compared with other statistical approaches. The proposed method is a data-driven approach that doesn’t require any prior knowledge of the topology of power networks.

Published in American Journal of Electrical Power and Energy Systems (Volume 10, Issue 4)
DOI 10.11648/j.epes.20211004.12
Page(s) 60-73
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Anomaly Detection, Spatio-Temporal Correlation, Kullback-Leibler Divergence (KLD), Factor Model

References
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  • APA Style

    Qing Feng, Ghadir Radman, Xuebin Li. (2021). Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis. American Journal of Electrical Power and Energy Systems, 10(4), 60-73. https://doi.org/10.11648/j.epes.20211004.12

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    ACS Style

    Qing Feng; Ghadir Radman; Xuebin Li. Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis. Am. J. Electr. Power Energy Syst. 2021, 10(4), 60-73. doi: 10.11648/j.epes.20211004.12

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    AMA Style

    Qing Feng, Ghadir Radman, Xuebin Li. Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis. Am J Electr Power Energy Syst. 2021;10(4):60-73. doi: 10.11648/j.epes.20211004.12

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  • @article{10.11648/j.epes.20211004.12,
      author = {Qing Feng and Ghadir Radman and Xuebin Li},
      title = {Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {10},
      number = {4},
      pages = {60-73},
      doi = {10.11648/j.epes.20211004.12},
      url = {https://doi.org/10.11648/j.epes.20211004.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20211004.12},
      abstract = {Real-time anomaly detection is a critical monitoring task for power systems. Most studies of power network detection fail to identify small fault signals or disturbances that might lead to damages or system-wide blackout. This work presents a methodology for analyzing high-dimensional PMU data and detecting early events for large-scale power systems in a non-Gaussian noise environment. Also, spatio-temporal correlations of PMU data are explored and determined by the factor model for anomaly detection. Based on random matrix theory, the factor model monitors the variation of spatio-temporal correlations in PMU data and estimates the number of dynamic factors. Kullback-Leibler Divergence is employed to measure the deviation between two spectral distributions: the empirical spectral distribution of the covariance matrix of residuals from online monitoring data and its theoretical spectral distribution determined by the factor model. Using IEEE 57-bus, IEEE 118-bus, and Polish 2383-bus systems, three different case studies demonstrate that the proposed method is more effective in identifying early-stage anomalies in high-dimensional PMU data collected from large-scale power networks. Performance evaluations validate that this method is sensitive and robust to small fault signals compared with other statistical approaches. The proposed method is a data-driven approach that doesn’t require any prior knowledge of the topology of power networks.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis
    AU  - Qing Feng
    AU  - Ghadir Radman
    AU  - Xuebin Li
    Y1  - 2021/08/30
    PY  - 2021
    N1  - https://doi.org/10.11648/j.epes.20211004.12
    DO  - 10.11648/j.epes.20211004.12
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 60
    EP  - 73
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20211004.12
    AB  - Real-time anomaly detection is a critical monitoring task for power systems. Most studies of power network detection fail to identify small fault signals or disturbances that might lead to damages or system-wide blackout. This work presents a methodology for analyzing high-dimensional PMU data and detecting early events for large-scale power systems in a non-Gaussian noise environment. Also, spatio-temporal correlations of PMU data are explored and determined by the factor model for anomaly detection. Based on random matrix theory, the factor model monitors the variation of spatio-temporal correlations in PMU data and estimates the number of dynamic factors. Kullback-Leibler Divergence is employed to measure the deviation between two spectral distributions: the empirical spectral distribution of the covariance matrix of residuals from online monitoring data and its theoretical spectral distribution determined by the factor model. Using IEEE 57-bus, IEEE 118-bus, and Polish 2383-bus systems, three different case studies demonstrate that the proposed method is more effective in identifying early-stage anomalies in high-dimensional PMU data collected from large-scale power networks. Performance evaluations validate that this method is sensitive and robust to small fault signals compared with other statistical approaches. The proposed method is a data-driven approach that doesn’t require any prior knowledge of the topology of power networks.
    VL  - 10
    IS  - 4
    ER  - 

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Author Information
  • Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, USA

  • Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, USA

  • Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, USA

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