Maskable PPO-Based Topology Control for Reverse Power Flow Mitigation in PV-Rich Distribution Networks

The rapid proliferation of photovoltaic (PV) generation has transformed conventional distribution systems, resulting in frequent reverse power flow (RPF) and associated overvoltage issues. This paper presents a deep reinforcement learning (DRL)-based topology control method to autonomously mitigate RPF and voltage violations. A novel multi-discrete Maskable Proximal Policy Optimization (MPPO) algorithm is proposed, combining topology-aware action masking with a multi-discrete action representation to ensure constraint satisfaction and enhance training stability. The approach efficiently explores the feasible switching space while maintaining network radiality, load connectivity, and power flow solvability. Extensive case studies based on one year of operational data from a practical distribution system show that the proposed agent achieves an average RPF reduction of 24.3% across the test cases and restores normal voltage conditions in about 65% of scenarios, while satisfying other operational constraints. The results confirm that the proposed method provides a scalable, data-driven solution for topology reconfiguration in PV-rich distribution networks.