Generalized Graph Laplacian Based Anomaly Detection for Spatiotemporal MicroPMU Data

Journal Article · Fri May 17 00:00:00 EDT 2019 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/TPWRS.2019.2917586· OSTI ID:1544537

Cui, Mingjian ^[1]; Wang, Jianhui ^[1]; Florita, Anthony R. ^[2]; Zhang, Yingchen ^[2]

---

1. Southern Methodist Univ., Dallas, TX (United States)
2. National Renewable Energy Lab. (NREL), Golden, CO (United States)

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Grid Modernization Laboratory Consortium; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1544537
• Report Number(s): NREL/JA--5D00-72781
• Journal Information: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 5 Vol. 34; ISSN 0885-8950
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
anomaly detection
distribution PMU
graph Laplacian matrix
spatiotemporal analysis