Anomaly Detection and Localization via Graph Learning

Olabode Amusan () and Di Wu ()
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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

Energies, 2025, vol. 18, issue 6, 1-18

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.

Keywords: phasor measurement unit; graph learning; spatiotemporal relationship; anomaly detection; anomaly localization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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