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 language||English (US)|
|Title of host publication||2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - May 2019|
|Event||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom|
Duration: May 12 2019 → May 17 2019
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||5/12/19 → 5/17/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF grants 1508993, 1509040, and 1711471.
© 2019 IEEE.
- Power system state forecasting
- data validation.
- recurrent neural network
- recurrent prox-linear net