This paper addresses the challenge of transient stability in power systems
with missing parameters and uncertainty propagation in swing equations. We
introduce a novel application of Physics-Informed Neural Networks (PINNs),
specifically an Ensemble of PINNs (E-PINNs), to estimate critical parameters
like rotor angle and inertia coefficient with enhanced accuracy and reduced
computational load. E-PINNs capitalize on the underlying physical principles of
swing equations to provide a robust solution. Our approach not only facilitates
efficient parameter estimation but also quantifies uncertainties, delivering
probabilistic insights into the system behavior. The efficacy of E-PINNs is
demonstrated through the analysis of $1$-bus and $2$-bus systems, highlighting
the model's ability to handle parameter variability and data scarcity. The
study advances the application of machine learning in power system stability,
paving the way for reliable and computationally efficient transient stability
analysis.

本研究利用物理信息神经网络（PINNs）的集成模型（E-PINNs）来解决具有缺失参数和摆动方程中的不确定性传播的电力系统的暂态稳定性挑战，并在提高准确性和降低计算负载方面估计关键参数的角度和惯性系数。E-PINNs 利用摆动方程的基本物理原理提供稳健的解决方案，不仅促进了高效的参数估计，还量化了不确定性，为系统行为提供了概率洞察。通过对一台和两台电力系统的分析，证明了 E-PINNs 的有效性，突出了该模型处理参数变异性和数据稀缺性的能力。该研究推动了机器学习在电力系统稳定性方面的应用，为可靠且计算效率高的暂态稳定性分析铺平了道路。