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Title: 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 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.
Authors:
 [1] ; ORCiD logo [1] ;  [2]
  1. Washington State Univ., Pullman, WA (United States)
  2. 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 1949-3053
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 Large-Scale 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 Large-Scale 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 Large-Scale 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 Large-Scale Ambient Oscillation Monitoring},
author = {Wu, Tianying and Venkatasubramanian, Vaithianathan Mani and Pothen, Alex},
abstractNote = {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.},
doi = {10.1109/TSG.2016.2608965},
journal = {IEEE Transactions on Smart Grid},
number = 3,
volume = 8,
place = {United States},
year = {2016},
month = {9}
}