Online Voltage Event Detection Using Synchrophasor Data With Structured Sparsity-Inducing Norms
- Resource Type
- Periodical
- Authors
- Kong, X.; Foggo, B.; Yamashita, K.; Yu, N.
- Source
- IEEE Transactions on Power Systems IEEE Trans. Power Syst. Power Systems, IEEE Transactions on. 37(5):3506-3515 Sep, 2022
- Subject
- Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Sparse matrices
Matrix decomposition
Phasor measurement units
Event detection
Power systems
Feature extraction
Clustering algorithms
Phasor measurement unit (PMU)
event detection
low-rank and sparse matrix decomposition
bilateral random projection
- Language
- ISSN
- 0885-8950
1558-0679
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. 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.