01200nas a2200205 4500008003900000022001400039245007500053210006900128260001200197300001000209520058400219653001300803653000900816653001000825653001000835100002200845700001400867700003800881856007500919 2015 d a0885-897700aFast Frequency-Domain Decomposition for Ambient Oscillation Monitoring0 aFast FrequencyDomain Decomposition for Ambient Oscillation Monit c02/2015 a1 - 13 aThis paper proposes a multidimensional ambient oscillation monitoring algorithm denoted Fast Frequency Domain Decomposition (FFDD). Based on a new theoretical result, the algorithm is offered as an improvement over previously proposed Frequency Domain Decomposition (FDD) in that FFDD does not require time-consuming Singular Value Decomposition (SVD) and it does not require cross spectrum estimates. FFDD is useful for fast real-time ambient modal estimation of large number of synchrophasor measurements. Algorithm is tested on an archived event data from a real power system.10aAA13-00410aAARD10aCERTS10aRTGRM1 aKhalilinia, Hamed1 aZhang, Lu1 aVenkatasubramanian, Vaithianathan uhttps://certs.lbl.gov/publications/fast-frequency-domain-decomposition01425nas 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