Generalized Graph Laplacian Based Anomaly Detection for Spatiotemporal MicroPMU Data

Anthony Florita, Yingchen Zhang, Mingjian Cui, Jianhui Wang

Research output: Contribution to journalArticlepeer-review

20 Scopus Citations


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 (uPMU) data. The uPMU data in a specific time horizon is 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 (QP) problem. To perform the uPMU-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 uPMU measurements gathered from test feeders in Riverside, CA.
Original languageAmerican English
Pages (from-to)3960-3963
Number of pages4
JournalIEEE Transactions on Power Systems
Issue number5
StatePublished - 2019

NREL Publication Number

  • NREL/JA-5D00-72781


  • anomaly detection
  • distribution PMU
  • graph Laplacian matrix
  • spatiotemporal analysis


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