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

This paper develops an accurate and computationally efficient data-driven algorithm to detect voltage events using streaming PMU data. We find that real-world phasor measurement unit (PMU) data matrices exhibit a unique row-sparse structure when the low-rank component is stripped away during system events. An innovative Proximal Bilateral Random Projections (P-BRP) algorithm is developed to decompose the PMU data matrix into a low-rank matrix and a row-sparse event-pattern matrix. These matrices’ useful features are 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 a state-of-the-art benchmark.