【摘要】 随着电网运行方式日趋复杂，潮流收敛性调整的难度也随之增加，传统依赖人工经验的调整方式存在响应滞后、效率低下等问题，难以应对控制变量类型多、维数高的复杂场景。为此，文中提出一种基于混合动作空间深度强化学习的潮流调整方法。首先，结合物理先验与数据驱动方法，构建潮流收敛性鉴别器，用于实时判别潮流收敛状态，并将其输出的收敛概率作为深度强化学习的奖励引导信号。随后，定义收敛性调整的强化学习环境，其状态空间融合系统级整体特征与节点级个体特征，动作空间覆盖连续与离散控制变量，奖励函数结合收敛判别结果与潮流调整反馈，引导策略向可行解区域优化。接着，构建混合动作空间的Actor-Critic网络，对设备选择、连续调节与离散控制等子任务分层解耦建模。最后，基于改进的IEEE 39和118节点系统开展仿真分析与对比实验。结果表明，相较于纯数据驱动模型，引入物理先验的潮流收敛性鉴别器可显著提升判别精度与泛化能力；采用连续-离散动作的协调优化策略，较现有方法在潮流调整效率与收敛成功率方面均实现明显提升。更多还原

【Abstract】 As the operation modes of power systems become increasingly complex, the difficulty of power flow convergence adjustment also increases. Traditional methods that rely on human expertise suffer from delayed response and low efficiency,making them ill-suited for complex scenarios involving diverse and high-dimensional control variables. To address this challenge, a power flow adjustment method based on deep reinforcement learning with a hybrid action space is proposed.Firstly, a power flow convergence discriminator is developed by integrating physical priors with data-driven techniques to enable real-time identification of whether the power flow converges. The output convergence probability is used as a reward guidance signal in deep reinforcement learning. Then, a reinforcement learning environment for convergence adjustment is defined. The state space integrates system-level statistical features with node-level individual features. The action space encompasses both continuous and discrete control variables. And the reward function combines convergence identification results with feedback from the adjustment process to guide policy optimization toward the feasible region. Next, an Actor-Critic network with a hybrid action space is constructed, which hierarchically decouples and models subtasks including device selection, continuous regulation, and discrete control. Finally, simulation analysis and comparative experiments are conducted on the improved IEEE 39-bus and 118-bus systems. The results demonstrate that the power flow convergence discriminator enhanced with physical priors significantly improves both the accuracy and generalization capability of convergence identification compared to traditional models. Furthermore, the proposed coordinated optimization strategy, which integrates continuous-discrete actions, achieves notable improvements in power flow adjustment efficiency and convergence success rate over existing methods.更多还原