Anomaly Detection and Localization via Graph Learning
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/6/1475 |
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| author | Olabode Amusan Di Wu |
| author_facet | Olabode Amusan Di Wu |
| author_sort | Olabode Amusan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a95e160e0f524c48aa2d8097e236c35a |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-a95e160e0f524c48aa2d8097e236c35a2025-08-20T02:11:09ZengMDPI AGEnergies1996-10732025-03-01186147510.3390/en18061475Anomaly Detection and Localization via Graph LearningOlabode Amusan0Di Wu1Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USADepartment of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USAPhasor 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.https://www.mdpi.com/1996-1073/18/6/1475phasor measurement unitgraph learningspatiotemporal relationshipanomaly detectionanomaly localization |
| spellingShingle | Olabode Amusan Di Wu Anomaly Detection and Localization via Graph Learning Energies phasor measurement unit graph learning spatiotemporal relationship anomaly detection anomaly localization |
| title | Anomaly Detection and Localization via Graph Learning |
| title_full | Anomaly Detection and Localization via Graph Learning |
| title_fullStr | Anomaly Detection and Localization via Graph Learning |
| title_full_unstemmed | Anomaly Detection and Localization via Graph Learning |
| title_short | Anomaly Detection and Localization via Graph Learning |
| title_sort | anomaly detection and localization via graph learning |
| topic | phasor measurement unit graph learning spatiotemporal relationship anomaly detection anomaly localization |
| url | https://www.mdpi.com/1996-1073/18/6/1475 |
| work_keys_str_mv | AT olabodeamusan anomalydetectionandlocalizationviagraphlearning AT diwu anomalydetectionandlocalizationviagraphlearning |