Accurate Smart-Grid Stability Forecasting Based on Deep Learning: Point and Interval Estimation Method

Abstract

The power grid stability is highly impacted by the fluctuating nature of renewable energy sources. This paper proposes a deep learning method-based bidirectional gated recurrent unit for smart grid stability prediction. For automatic tuning, this study employs Simulated Annealing algorithm to optimize the selected hyperparameters and enhance the model forecastability. The proposed forecasting model's performance is evaluated using electrical grid stability simulated data set. The proposed method provides an accurate point and interval grid stability prediction. Simulation results are conducted to prove the high performance of the proposed method. Furthermore, comparative analysis is performed to demonstrate the superiority of the proposed strategy over some state-of-the-art available solutions.