基于图强化学习的配电网故障恢复决策

Fault Recovery Decision of Distribution Network Based on Graph Reinforcement Learning

针对配电网拓扑变化时启发式等算法在配电网故障恢复决策中求解效果与适应性变差的问题,提出了一种基于图强化学习的故障恢复决策方法。首先,利用图数据表征故障恢复中的决策信息,包括配电网拓扑结构与电气特征信息。然后,在图强化学习模型中设置前置图神经网络接收图数据输入,应对故障恢复过程中配电网的拓扑变化。最后,由内嵌图神经网络的强化学习智能体输出最终故障恢复策略以提高决策速度。采用改进的PG&E 69节点配电网算例进行验证,结果表明所提算法求解速度达到毫秒级,较启发式和遗传算法在求解效率上提高了6%~7%,故障恢复策略的负荷恢复率也更高。

To solve the problem that the effectiveness and adaptability of heuristic algorithms in distribution network fault recovery decision become poor when the topology of distribution network changes,a fault recovery decision method based on graph reinforcement learning is proposed.First,the graph data is used to characterize the decision information in fault recovery,including distribution network topology and electrical characteristics.Then,the pre-graph neural network is set up in the graph reinforcement learning model to receive the graph data input and cope with the topological changes of the distribution network in the process of fault recovery.Finally,the reinforcement learning agent of the embedded graph neural network outputs the final fault recovery strategy to improve the decision speed.A modified PG&E 69-bus distribution network case is used for validation.The results show that the proposed algorithm can reach millisecond level in solving speed,and improve the solving efficiency by 6%~7% compared with heuristic and genetic algorithms,Also,the load recovery rate of the fault recovery strategy is higher.