Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 34, Issue 5, Pages 3960-3963Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2019.2917586
Keywords
Anomaly detection; distribution PMU (microPMU); graph Laplacian matrix; spatiotemporal analysis
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Funding
- Alliance for Sustainable Energy, LLC [DE-AC36-08GO28308]
- U.S. Department of Energy Grid Modernization Lab Consortium
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This letter develops a novel anomaly detection method using the generalized graph Laplacian (GGL) matrix to visualize the spatiotemporal relationship of distribution-level phasor measurement unit (mu PMU) data. The mu PMU data in a specific time horizon are segregated into multiple segments. An optimization problem formulated as a Lagrangian function is utilized to estimate the GGL matrix. During the iterative process, an optimal update is constituted as a quadratic program problem. To perform the mu PMU-based spatiotemporal analysis, normalized diagonal elements of GGL matrix are proposed as a quantitative metric. The effectiveness of the developed method is demonstrated through real-world mu PMU measurements gathered from test feeders in Riverside, CA, USA.
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