Multilayer Deep Deterministic Policy Gradient for Static Safety and Stability Analysis of Novel Power Systems - Yun Long; Youfei Lu; Hongwei Zhao; Renbo Wu; Tao Bao; Jun Liu (International Transactions on Electrical Energy Systems)
More and more renewable energy sources are integrated into novel power systems. The randomness and fluctuation of such renewable energy sources bring challenges to the static stability and safety analysis of novel power systems. In this work, a multilayer deep deterministic policy gradient is proposed to address the fluctuation of renewable energy sources. The proposed method is stacked with multilayer deep reinforcement learning methods that can be continuously updated online. The proposed multilayer deep deterministic policy gradient is compared with other deep learning algorithms. The feasibility, effectiveness, and superiority of the proposed method are verified by numerical simulations.