Fast Parallel Stochastic Subspace Algorithms for Large-Scale Ambient Oscillation Monitoring

With the installation of synchrophasors widely across the power grid, measurement-based oscillation monitoring algorithms are becoming increasingly useful in identifying the real-time oscillatory modal properties in power systems. When the number of phasor measurement unit (PMU) channels grows, the...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 8; no. 3; pp. 1494 - 1503
Main Authors: Tianying Wu, Venkatasubramanian, Vaithianathan Mani, Pothen, Alex
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computation
Computer memory
Covariance matrices
Formulations
large-scale dense matrix computations
MATHEMATICS AND COMPUTING
Matrix decomposition
Monitoring
Oscillators
parallel computing
Phasor measurement units
Power system oscillations
Real time
Real-time systems
Singular value decomposition
Sparse matrices
stochastic subspace identification
synchrophasors
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:With the installation of synchrophasors widely across the power grid, measurement-based oscillation monitoring algorithms are becoming increasingly useful in identifying the real-time oscillatory modal properties in power systems. When the number of phasor measurement unit (PMU) channels grows, the computational time of many PMU data based algorithms is dominated by the computational burden in processing large-scale dense matrices. In order to overcome this limitation, this paper presents new formulations and computational strategies for speeding up an ambient oscillation monitoring algorithm, namely, stochastic subspace identification (SSI). Based on previous work, two fast singular value decomposition (SVD) approaches are first applied to the SVD evaluation within the SSI algorithm. Next, block structures are exploited so that the large-scale dense matrix computations can be processed in parallel. This helps in memory savings as well as in overall computational time. Experimental results from three sets of archived data of the western interconnection demonstrate that the new approaches can provide significant speedups while retaining modal estimation accuracy. With proposed fast parallel algorithms, the real-time oscillation monitoring of the large-scale system using hundreds of PMU measurements becomes feasible.
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USDOE
SC0010205; 1552323
National Science Foundation (NSF)
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2608965