The Research on Adaptive Topology Reconfiguration Algorithm for Medium Voltage Distribution Networks Based on Deep Reinforcement Learning

The efficient and flexible operation of mediumvoltage distribution networks is critical for ensuring reliable and high-quality power delivery. As the complexity of these networks increases, traditional topology reconstruction methods face challenges in optimizing performance while meeting multiple operational objectives. This paper proposes an adaptive topology reconstruction algorithm based on Deep Reinforcement Learning (DRL) to address these challenges. The topology reconstruction problem is formalized as a Markov Decision Process (MDP), with a multi-dimensional state representation that captures network topology, electrical parameters, and temporal dynamics. A novel deep Q-network structure is introduced, incorporating graph convolutional networks, attention mechanisms, and multi-task learning to effectively handle the complex characteristics of distribution networks. Extensive simulations demonstrate the proposed algorithm's superiority in optimizing network performance compared to existing methods.