Fast SVD Computations for Synchrophasor Algorithms

TitleFast SVD Computations for Synchrophasor Algorithms
Publication TypeJournal Article
Year of Publication2015
AuthorsTianying Wu, S. Arash Nezam Sarmadi, Vaithianathan Venkatasubramanian, Alex Pothen, Ananth Kalyanaraman
JournalIEEE Transactions on Power Systems
Pagination1 - 2
Date Published03/2015

Many singular value decomposition (SVD) problems in power system computations require only a few largest singular values of a large-scale matrix for the analysis. This letter introduces two fast SVD approaches recently developed in other domains to power systems for speeding up phasor measurement unit (PMU) based online applications. The first method is a randomized SVD algorithm that accelerates computation by introducing a low-rank approximation of a given matrix through randomness. The second method is the augmented Lanczos bidiagonalization, an iterative Krylov subspace technique that computes sequences of projections of a given matrix onto low-dimensional subspaces. Both approaches are illustrated on SVD evaluation within an ambient oscillation monitoring algorithm, namely stochastic subspace identification (SSI).

Short TitleIEEE Trans. Power Syst.