Fast Parallel Stochastic Subspace Algorithms for LargeScale Ambient Oscillation Monitoring
With the installation of synchrophasors widely across the power grid, measurementbased oscillation monitoring algorithms are becoming increasingly useful in identifying the realtime 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 largescale 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 largescale 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 realtime oscillation monitoring of the largescale system using hundreds of PMU measurements becomes feasible.
 Authors:

^{[1]};
^{[1]}
;
^{[2]}
 Washington State Univ., Pullman, WA (United States)
 Purdue Univ., West Lafayette, IN (United States)
 Publication Date:
 Grant/Contract Number:
 SC0010205; 1552323
 Type:
 Accepted Manuscript
 Journal Name:
 IEEE Transactions on Smart Grid
 Additional Journal Information:
 Journal Volume: 8; Journal Issue: 3; Journal ID: ISSN 19493053
 Publisher:
 IEEE
 Research Org:
 Purdue Univ., West Lafayette, IN (United States)
 Sponsoring Org:
 National Science Foundation (NSF); USDOE
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
 OSTI Identifier:
 1418629
Wu, Tianying, Venkatasubramanian, Vaithianathan Mani, and Pothen, Alex. Fast Parallel Stochastic Subspace Algorithms for LargeScale Ambient Oscillation Monitoring. United States: N. p.,
Web. doi:10.1109/TSG.2016.2608965.
Wu, Tianying, Venkatasubramanian, Vaithianathan Mani, & Pothen, Alex. Fast Parallel Stochastic Subspace Algorithms for LargeScale Ambient Oscillation Monitoring. United States. doi:10.1109/TSG.2016.2608965.
Wu, Tianying, Venkatasubramanian, Vaithianathan Mani, and Pothen, Alex. 2016.
"Fast Parallel Stochastic Subspace Algorithms for LargeScale Ambient Oscillation Monitoring". United States.
doi:10.1109/TSG.2016.2608965. https://www.osti.gov/servlets/purl/1418629.
@article{osti_1418629,
title = {Fast Parallel Stochastic Subspace Algorithms for LargeScale Ambient Oscillation Monitoring},
author = {Wu, Tianying and Venkatasubramanian, Vaithianathan Mani and Pothen, Alex},
abstractNote = {With the installation of synchrophasors widely across the power grid, measurementbased oscillation monitoring algorithms are becoming increasingly useful in identifying the realtime 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 largescale 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 largescale 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 realtime oscillation monitoring of the largescale system using hundreds of PMU measurements becomes feasible.},
doi = {10.1109/TSG.2016.2608965},
journal = {IEEE Transactions on Smart Grid},
number = 3,
volume = 8,
place = {United States},
year = {2016},
month = {9}
}