Power System State Forecasting via Deep Recurrent Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

State forecasting plays a critical role in power system monitoring, by offering system awareness even ahead of the time horizon, enhancing system observability, and providing efficient identification of the grid topology and link parameter changes. However, available approaches relying on linear estimators or single-hidden-layer feed-forward neural networks (FNNs), cannot capture long-term nonlinear dependencies in the voltage time series, and lead to suboptimal performance. To bypass these hurdles, this paper advocates deep recurrent neural networks (RNNs) for power system state forecasting. Deep RNNs capture long-term dependencies, and are easy to implement. By also leveraging the physics behind power systems, a novel architecture based on prox-linear nets (RPLN) is further developed for state forecasting based on past measurements. Simulated tests show improved performance of the proposed RNN and RPLN predictors when compared to FNN and vector autoregression based alternatives.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8092-8096
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period5/12/195/17/19

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Keywords

  • Power system state forecasting
  • data validation.
  • recurrent neural network
  • recurrent prox-linear net

Cite this

Zhang, L., Wang, G., & Giannakis, G. B. (2019). Power System State Forecasting via Deep Recurrent Neural Networks. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 8092-8096). [8683139] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683139