电力系统保护与控制2026，Vol.54Issue(12)：32-43,12.DOI:10.19783/j.cnki.pspc.251397
基于状态探索感知深度强化学习的变电站电压无功控制策略
Substation voltage and reactive power control strategy based on state exploration-aware deep reinforcement learning
摘要
Abstract
The large-scale integration of renewable energy sources and intermittent loads has intensified fluctuations in substation voltage and reactive power,posing higher requirements for voltage and reactive power control(VQC).Deep reinforcement learning(DRL)offers a promising solution;however,existing DRL-based methods generally suffer from exploration bias during training,limiting their online control performance.To address this issue,this paper proposes a substation VQC strategy based on state-exploration-aware DRL(SEA-DRL).First,a SEA-DRL framework is developed,in which the intrinsic structure of the state space is captured via unsupervised representation learning.Based on state visitation frequency,a dynamic exploration incentive mechanism is then introduced to balance low-frequency state exploration and policy optimization,thereby enhancing overall learning performance.Then,considering the discrete operating characteristics of substation regulation equipment,the proposed framework is integrated with the soft actor-critic algorithm for discrete action spaces(SAC-D)to construct a SEA-SAC-based VQC strategy for substations.Finally,simulation results on a 220 kV substation in City C demonstrate that the proposed method achieves effective control performance under complex operating conditions.关键词
变电站/电压无功控制/自动电压控制/深度强化学习/探索偏置Key words
substation/voltage and reactive power control/automatic voltage control/deep reinforcement learning/exploration bias引用本文复制引用
闫何贵枝,颜伟,高倩,张昊栋..基于状态探索感知深度强化学习的变电站电压无功控制策略[J].电力系统保护与控制,2026,54(12):32-43,12.基金项目
This work is supported by the Young Scientists Fund of the National Natural Science Foundation of China(No.52507079). 国家自然科学基金青年科学基金项目资助(52507079) （No.52507079）
国网重庆市电力公司科学技术项目资助(SGCQ0000DKJS2310217) （SGCQ0000DKJS2310217）
