01425nas a2200193 4500008003900000022001400039245005500053210005500108260001200163300001000175520083500185653001301020100001701033700002601050700003801076700001701114700002501131856007501156 2015 d a0885-895000aFast SVD Computations for Synchrophasor Algorithms0 aFast SVD Computations for Synchrophasor Algorithms c03/2015 a1 - 23 aMany 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).10aAA13-0041 aWu, Tianying1 aSarmadi, Arash, Nezam1 aVenkatasubramanian, Vaithianathan1 aPothen, Alex1 aKalyanaraman, Ananth uhttps://certs.lbl.gov/publications/fast-svd-computations-synchrophasor