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OA

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.