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Title: Bayesian robust hankel matrix completion with uncertainty modeling for synchrophasor data recovery
Synchrophasor data suffer from quality issues like missing and bad data. Exploiting the low-rankness of the Hankel matrix of the synchrophasor data, this paper formulates the data recovery problem as a robust low-rank Hankel matrix completion problem and proposes a Bayesian data recovery method that estimates the posterior distribution of synchrophasor data from partial observations. In contrast to the deterministic approaches, our proposed Bayesian method provides an uncertainty index to evaluate the confidence of each estimation. To the best of our knowledge, this is the first method that provides confidence measure for synchrophasor data recovery. Numerical experiments on synthetic data and recorded synchrophasor data demonstrate that our method outperforms existing low-rank matrix completion methods.  more » « less
Award ID(s):
1932196
PAR ID:
10349430
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ACM SIGEnergy Energy Informatics Review
Volume:
2
Issue:
1
ISSN:
2770-5331
Page Range / eLocation ID:
1 to 19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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