Maintaining good quality of transient stability models for power system planning and operational analysis is of great importance. Identification and calibration of bad parameters using PMU measurements that work well for multiple events remains a challenging problem. In this letter, we present a novel parameter calibration method based on off-policy deep reinforcement learning (DRL) algorithm with maximum entropy, soft actor critic (SAC), to automatically tune incorrect parameter sets considering multiple events simultaneously, which can save tremendous labor efforts for maintaining model accuracy and complying with industry standards. The effectiveness of the proposed approach is verified through numerical experiments conducted on a realistic power plant model.

中文翻译：

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基于Soft Actor-Critic的动态模型参数多事件协同标定

为电力系统规划和运行分析保持良好的暂态稳定性模型非常重要。使用适用于多个事件的 PMU 测量来识别和校准不良参数仍然是一个具有挑战性的问题。在这封信中，我们提出了一种基于离策略深度强化学习（DRL）算法和最大熵软演员评论家（SAC）的新参数校准方法，可以同时考虑多个事件自动调整不正确的参数集，可以节省大量人力努力保持模型准确性并遵守行业标准。通过在现实电厂模型上进行的数值实验验证了所提出方法的有效性。