TY - JOUR
T1 - Fast SVD Computations for Synchrophasor Algorithms
JF - IEEE Transactions on Power Systems
Y1 - 2015/03//
SP - 1
EP - 2
A1 - Wu, Tianying
A1 - S. Arash Nezam Sarmadi
A1 - Venkatasubramanian, Vaithianathan
A1 - Pothen, Alex
A1 - Kalyanaraman, Ananth
KW - AA13-004
AB - 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).
JO - IEEE Trans. Power Syst.
DO - 10.1109/TPWRS.2015.2412679
ER -