The simulation of power system dynamics poses a computationally expensive task. Considering the growing uncertainty of generation and demand patterns, thousands of scenarios need to be continuously assessed to ensure the safety of power systems. Physics-Informed Neural Networks (PINNs) have recently emerged as a promising solution for drastically accelerating computations of non-linear dynamical systems. This work investigates the applicability of these methods for power system dynamics, focusing on the dynamic response to load disturbances. Comparing the prediction of PINNs to the solution of conventional solvers, we find that PINNs can be 10 to 1000 times faster than conventional solvers. At the same time, we find them to be sufficiently accurate and numerically stable even for large time steps. To facilitate a deeper understanding, this paper also present a new regularisation of Neural Network (NN) training by introducing a gradient-based term in the loss function. The resulting NNs, which we call dtNNs, help us deliver a comprehensive analysis about the strengths and weaknesses of the NN based approaches, how incorporating knowledge of the underlying physics affects NN performance, and how this compares with conventional solvers for power system dynamics.

翻译：电力系统动态仿真是一个计算成本高昂的任务。考虑到发电与负荷模式日益增长的不确定性，需要持续评估数千个场景来确保电力系统的安全性。物理信息神经网络（PINNs）近期作为显著加速非线性动力系统计算的前景解决方案而兴起。本文研究了这些方法在电力系统动态中的适用性，重点关注对负荷扰动的动态响应。通过将PINN预测与传统求解器的解进行比较，我们发现PINN的计算速度可比传统求解器快10至1000倍。同时，即使采用大步长，这些方法仍具有足够的精度和数值稳定性。为促进更深层次理解，本文还提出了一种新的神经网络（NN）训练正则化方法，在损失函数中引入基于梯度的项。我们将由此得到的神经网络称为dtNN，其有助于全面分析基于NN方法的优缺点、底层物理知识融入如何影响NN性能，以及这些方法与电力系统动态传统求解器的对比情况。