基于深度强化学习组合优化的配电网拓扑控制研究

Research on Distribution Network Topology Control Based on Deep Reinforcement Learning Combinatorial Optimization

基于深度强化学习的配电网故障恢复拓扑控制策略,文章首先设计配电网拓扑状态表征和决策动作规则支撑组合优化求解;其次,利用改进指针网络结构配合深度强化学习算法实现适用于多类故障恢复策略的模型自学习和端到端计算;最后,通过改进掩码机制降低探索求解复杂度进而提升训练学习效率。通过在预设条线路上随机设置故障组合,在单一和混合初始状态样本集上验证文章提出的改进机制和模型计算有效性,以期为深度学习技术在配电网运行方式优化研究提供有效参考。

This paper studies the topology control strategy of distribution network fault recovery based on deep reinforcement learning.First,design the distribution network topology state representation and decision-making action rules to support the combined optimization solution.Secondly,use the improved pointer network structure and deep reinforcement learning algorithm to achieve model self-learning and end-to-end calculation suitable for multiple types of failure recovery strategies.Finally,by improving the mask mechanism to reduce the complexity of exploration and solving,and effectively improve the efficiency of training and learning.By randomly setting fault combinations on the preset lines,the effectiveness of the improved mechanism and model proposed in this paper is verified on the single and mixed initial state sample sets,which provides an effective reference for the application of deep learning technology in the optimization of distribution network operation mode.