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Anomaly Detection and Localization via Graph Learning

Author

Listed:
  • Olabode Amusan

    (Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA)

  • Di Wu

    (Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA)

Abstract

Phasor measurement units (PMUs) are being installed at an unprecedented rate on power systems, offering unique situation awareness capability. This paper presents a graph learning-based method for detecting and locating anomalies using PMU data. In this method, the graph learning technique is used to characterize the spatiotemporal relationship of distributed PMU data by constructing a spatiotemporal graph. Then, graph analysis is used to detect and locate anomalies by evaluating the global connectivity of spatiotemporal graphs at different times and the local connectivity of nodes in the relevant spatiotemporal graphs. The proposed method was verified using the IEEE-39 bus system and realistic PMU data. The method accurately identifies anomalies with an accuracy of 97% with a precision and recall of 80% and 100%, respectively. The results show the superiority and robustness of the proposed method as a powerful tool for detecting and locating anomalies using PMU data.

Suggested Citation

  • Olabode Amusan & Di Wu, 2025. "Anomaly Detection and Localization via Graph Learning," Energies, MDPI, vol. 18(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1475-:d:1614212
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