电力系统及其自动化学报2024，Vol.36Issue(4)：50-58,9.DOI:10.19635/j.cnki.csu-epsa.001307
基于混合强化学习的主动配电网故障恢复方法
Fault Recovery Method for Active Distribution Network Based on Hybrid Reinforcement Learning
摘要
Abstract
In response to the fault recovery problem of distribution network with high-proportion new energy connec-tion,a fault recovery method for active distribution network based on hybrid reinforcement learning is proposed.First,a fault recovery model of active distribution network is constructed with minimizing the fault losses as its recovery objec-tive and the safe operation requirements of distribution network as constraints.Second,a reinforcement learning envi-ronment for fault recovery is established,and a hybrid reinforcement learning method is put forward based on the char-acteristics of state and action spaces.This method uses a dueling double deep Q network(D3QN)algorithm to handle the discrete action space and perform switch actions.In addition,it uses a deep deterministic policy gradient(DDPG)algorithm to handle the continuous action space and adjust the power output.Finally,a simulation experiment is carried out on an IEEE33-node system,and the results verify the feasibility and superiority of the proposed method.关键词
主动配电网/故障恢复/混合强化学习/状态空间/动作空间Key words
active distribution network/fault recovery/hybrid reinforcement learning/state space/action space分类
信息技术与安全科学引用本文复制引用
徐岩,陈嘉岳,马天祥..基于混合强化学习的主动配电网故障恢复方法[J].电力系统及其自动化学报,2024,36(4):50-58,9.基金项目
国家电网有限公司科技项目(kj2021-003) （kj2021-003）
