Sequence-to-Sequence Forecasting-aided State Estimation for Power Systems
This work addresses the need for accurate state forecasting in power systems, particularly with increasing renewable energy integration, but it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of real-time multi-step power system state estimation by proposing an end-to-end deep learning framework using sequence-to-sequence models with BiGRUs, achieving superior predictive accuracy compared to existing methods on real datasets.
Power system state forecasting has gained more attention in real-time operations recently. Unique challenges to energy systems are emerging with the massive deployment of renewable energy resources. As a result, power system state forecasting are becoming more crucial for monitoring, operating and securing modern power systems. This paper proposes an end-to-end deep learning framework to accurately predict multi-step power system state estimations in real-time. In our model, we employ a sequence-to-sequence framework to allow for multi-step forecasting. Bidirectional gated recurrent units (BiGRUs) are incorporated into the model to achieve high prediction accuracy. The dominant performance of our model is validated using real dataset. Experimental results show the superiority of our model in predictive power compared to existing alternatives.