TY - GEN
T1 - Power System State Forecasting via Deep Recurrent Neural Networks
AU - Zhang, Liang
AU - Wang, Gang
AU - Giannakis, Georgios B
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - Power system state forecasting
KW - data validation.
KW - recurrent neural network
KW - recurrent prox-linear net
UR - http://www.scopus.com/inward/record.url?scp=85061226206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061226206&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8683139
DO - 10.1109/ICASSP.2019.8683139
M3 - Conference contribution
AN - SCOPUS:85061226206
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8092
EP - 8096
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -