Online Voltage Event Detection Using Synchrophasor Data with Structured Sparsity-Inducing Norms

Kong, Xianghao; Foggo, Brandon; Yamashita, Koji; Yu, Nanpeng

doi:10.1109/tpwrs.2021.3134945

Online Voltage Event Detection Using Synchrophasor Data with Structured Sparsity-Inducing Norms

Journal Article · Mon Dec 13 00:00:00 EST 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2021.3134945· OSTI ID:1867797

Kong, Xianghao ^[1]; Foggo, Brandon ^[2]; Yamashita, Koji ^[2]; Yu, Nanpeng ^[2]

Univ. of California, Riverside, CA (United States); University of California, Riverside
Univ. of California, Riverside, CA (United States)

This paper develops an accurate and computationally efficient data-driven framework to detect voltage events from PMU data streams. It develops an innovative Proximal Bilateral Random Projection (PBRP) algorithm to quickly decompose the PMU data matrix into a low-rank matrix, a row-sparse event-pattern matrix and a noise matrix. Here, the row-sparse pattern matrix significantly distinguishes events from normal behavior. These matrices are then fed into a clustering algorithm to separate voltage events from normal operating conditions. Large-scale numerical study results on real-world PMU data show that the proposed algorithm is computationally more efficient and achieves higher F scores than state-of-the-art benchmarks.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Riverside, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: OE0000916

OSTI ID:: 1867797

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 5 Vol. 37; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Online Voltage Event Detection Using Synchrophasor Data with Structured Sparsity-Inducing Norms

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