|Title||Fast SVD Computations for Synchrophasor Algorithms|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Tianying Wu, S. Arash Nezam Sarmadi, Vaithianathan Venkatasubramanian, Alex Pothen, Ananth Kalyanaraman|
|Journal||IEEE Transactions on Power Systems|
|Pagination||1 - 2|
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 Title||IEEE Trans. Power Syst.|