AI 中文总结
针对电力系统暂态稳定评估中临界清除时间估计的难题，提出事件结构物理信息神经网络ES-PINN，实现可微临界清除边界，在IEEE节点系统实验中提升了精度与计算效率。
AI 中文摘要
暂态稳定评估用于判断电力系统在扰动后能否恢复，对防止发电机跳闸和级联停运至关重要，关键指标是临界清除时间（CCT），即故障清除前维持同步的最长时间。可靠的CCT估计颇具挑战，因为复杂的故障清除动态需要对多种故障严重程度和清除时间重复仿真。本文提出事件结构物理信息神经网络（ES-PINN），其表示与故障前、故障中及清除后的摇摆动态对齐，并在事件界面强制执行精确的状态链。光滑轨迹诱导稳定裕度定义了CCT边界的可微近似，支持精确边界提取、局部敏感性分析，以及通过提炼读出实现的可选直接CCT预测。本文进一步证明了局部残差到轨迹再到CCT的误差估计，其中精确事件链消除了单独的状态界面缺陷项。在IEEE 9、14和30节点系统上的实验表明，ES-PINN在机械和电气故障及多种清除配置下，始终优于匹配的神经代理基线，提升了保留轨迹和稳定边界的精度。额外的全网络DAE验证、多故障实验和运行时间分析进一步证明了所提框架的有效性和计算效率。
英文摘要
Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trips and cascading outages. A key metric is the critical clearing time (CCT), which specifies the maximum time available to clear a fault before synchronism is lost. Reliable CCT estimation is challenging because complicated fault-clearing dynamics require repeated simulations over many fault severities and clearing times. We propose an event-structured physics-informed neural network (ES-PINN) that aligns its representation with the pre-fault, fault-on, and post-clearing swing dynamics and enforces exact state chaining across event interfaces. A smooth trajectory-induced stability margin defines a differentiable approximation of the CCT boundary, enabling accurate boundary extraction, local sensitivity analysis, and optional direct CCT prediction through a distilled readout. We further prove a local residual-to-trajectory-to-CCT error estimate, in which exact event chaining eliminates separate state-interface defect terms. Experiments on IEEE 9-, 14-, and 30-bus systems show that ES-PINN consistently improves held-out trajectory and stability-boundary accuracy over matched neural-surrogate baselines across mechanical and electrical contingencies with multiple clearing configurations. Additional full-network DAE validation, multi-fault experiments, and runtime analyses further demonstrate the effectiveness and computational efficiency of the proposed framework.
