电网电压控制的安全强化学习
Safe Reinforcement Learning for Grid Voltage Control
摘要：
低压减载被认为是在紧急情况下恢复电网电压稳定的标准方法，然而该方案通常无效地切除大量负荷。强化学习（RL）被认为是一种很有前途的方法来解决这些问题，但RL方法通常不能保证系统的安全性。在本文中，我们讨论了一对夫妇的新的安全RL方法，即约束优化方法和基于障碍函数的方法，可以安全地恢复紧急事件下的电压。该方法具有通用性，可应用于其它安全关键控制问题。在39节点IEEE基准上进行了数值仿真，以验证所提出的安全RL紧急控制的有效性。
英文摘要：
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently. Reinforcement learning (RL) has been adopted as a promising approach to circumvent the issues; however, RL approach usually cannot guarantee the safety of the systems under control. In this paper, we discuss a couple of novel safe RL approaches, namely constrained optimization approach and Barrier function-based approach, that can safely recover voltage under emergency events. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark are performed to demonstrate the effectiveness of the proposed safe RL emergency control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
