Power System State Forecasting via Deep Recurrent Neural Networks

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...