This letter proposes a physics-informed action network (PIAN) for power system transient stability preventive control (TSPC). The network firstly renders deep learning to reduce the TSPC complexity. Unlike common data-driven methods that superficially imitate control experience, TSPC is then analytically embedded into the proposed PIAN network, so that to enforce the network to learn in-depth physical patterns. The well-learned PIAN enables highly generalized real-time decisions. Comparisons with one model-based and two data-driven baselines on the IEEE 39-bus system and the IEEE 145-bus system highlight that, the proposed method enables highly reliable control decisions, and beats the others in terms of decision efficiency and generalizability.

中文翻译：

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用于暂态稳定预防控制的物理信息行动网络


这封信提出了一种用于电力系统暂态稳定预防控制（TSPC）的物理信息行动网络（PIAN）。该网络首先渲染深度学习以降低TSPC复杂度。与表面上模仿控制经验的常见数据驱动方法不同，TSPC 然后被分析性地嵌入到所提出的 PIAN 网络中，以便强制网络学习深入的物理模式。经过充分学习的 PIAN 可实现高度通用的实时决策。与 IEEE 39 总线系统和 IEEE 145 总线系统上的一种基于模型和两种数据驱动基线的比较表明，所提出的方法可实现高度可靠的控制决策，并在决策效率和通用性方面优于其他方法。