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

Qing Feng; Ghadir Radman; Xuebin Li

doi:doi:10.11648/j.epes.20211004.12

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

Qing Feng, Ghadir Radman, Xuebin Li

Published in American Journal of Electrical Power and Energy Systems (Volume 10, Issue 4)

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), 2024. 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

Qing Feng

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

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

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

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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  -

Copy | Download