基于安全强化学习的电网稳控策略智能生成方法

新型电力系统的“双高”趋势改变了电力系统经典稳定特性，导致稳定机理更复杂，系统稳定模式更多样，因此基于典型运行方式的在线稳定控制策略面临挑战。为解决新型电力系统的功角稳定问题，提出了基于安全强化学习的稳控策略智能生成方法。首先，建立了电力系统稳控问题的含约束马尔可夫模型，归纳并提出了紧急控制切机动作涉及的安全约束。其次，为了提高对于电网暂态响应的时空特征提取能力，构建了基于图卷积层和长短期记忆单元的特征感知网络。然后，为了提高稳控策略智能体的训练效率，提出了基于内嵌领域知识约束的近端策略优化算法稳控策略训练框架。最后，在IEEE 39节点系统和某实际电网中进行测试验证。结果表明，所提方法能够根据系统运行状态和故障响应自适应生成切机稳控策略，其决策效果和效率均优于现有的稳控策略。

Intelligent generation method of power system stability control strategy based on safe reinforcement learning

The trend of a “higher proportion of renewable energy and power electronics” in the new power system has changed the classical stability characteristics of the system. The stability mechanism is more complex, and the system stability modes are more diverse. Online stability control strategies based on typical operating modes face a challenge. Considering the rotor angle stability problem of the new power system, an intelligent generation stability control strategy based on safe reinforcement learning is proposed. First, a constrained Markov model for power system stability control problems is established, and the safety constraints involved in rotor angle stability control are summarized and proposed. Secondly, to improve the ability to extract spatial and temporal features of the power grid’s transient response, a feature perception network based on graph convolutional layers and long short-term memory units is constructed. Then, to improve the training efficiency of the stability control agent, a training framework of stability control strategies using proximal policy optimization algorithm based on embedded domain knowledge constraints is proposed. Finally, a case study is performed on the IEEE 39-bus system and a practical power grid. The results show that the proposed method can adaptively generate unit tripping strategies based on the system operating state and fault response, and its decision-making effectiveness and efficiency are superior to existing stability control strategies.