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Award ID contains: 1932196

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  1. ; ; ; ; (, Proc. of 2023 International Conference on Machine Learning (ICML))
  2. ; ; ; (, Proc. the Eleventh International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; (, Proc. the Eleventh International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  4. ; ; ; (, IEEE Transactions on Power Systems)
    Full Text Available 
  5. ; ; ; (, ACM SIGEnergy Energy Informatics Review)
    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. 
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    Full Text Available 
  6. ; (, IEEE Transactions on Power Systems)
    Full Text Available 
  7. ; ; ; ; (, 2022 International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  8. ; ; (, Proc. 2022 56th Annual Conference on Information Sciences and Systems (CISS))
    Full Text Available 
  9. ; (, the 11th Bulk Power Systems Dynamics and Control Symposium – IREP’2022)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, the Tenth International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available