Optimal Configuration of Traction Power Supply Systems Based on Deep Reinforcement Learning

Abstract

To address the coupling among power supply topology, module selection, capacity allocation, and N-2 contingency assessment in flexible traction power supply systems, a deep-reinforcement-learning-guided branch-and-bound method is proposed for equipment siting and sizing. The Actor network selects preferred branching actions, whereas the Critic network ranks search nodes; exact bound-based pruning and N-2 verification remain governed by the underlying branch-and-bound procedure. A feasibility-preservation result shows that any reported solution satisfies the prescribed N-2 capacity-support condition. Case study results show that the proposed method reduces the power deficit to zero under typical N-2 contingencies by establishing a medium-voltage DC through-feeding section and flexible interconnection channels. Compared with the reference case, the main configuration cost increases by 12.5%, indicating that the proposed method can enhance the system’s support capability under severe contingencies at the expense of a moderate increase in configuration cost.