The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynamics. They often require the use of small time step sizes and therefore become computationally expensive. To accelerate these simulations, we propose a simulator - PINNSim - that allows to take significantly larger time steps. It is based on Physics-Informed Neural Networks (PINNs) for the solution of the dynamics of single components in the power system. To resolve their interaction we employ a scalable root-finding algorithm. We demonstrate PINNSim on a 9-bus system and show the increased time step size compared to a trapezoidal integration rule. We discuss key characteristics of PINNSim and important steps for developing PINNSim into a fully fledged simulator. As such, it could offer the opportunity for significantly increasing time step sizes and thereby accelerating time-domain simulations.

翻译：电力系统的动态行为可由一组微分代数方程描述。时域仿真用于模拟这些动态的演化过程，通常需要采用较小的时间步长，因而计算成本高昂。为加速此类仿真，我们提出一种仿真器——PINNSim——该仿真器允许采用显著更大的时间步长。其核心基于物理信息神经网络（PINNs）求解电力系统中单一元件的动态特性，并采用可扩展的求根算法处理元件间的交互作用。我们在9节点系统上验证了PINNSim的性能，并展示了其相较于梯形积分法则所能实现的更大时间步长。本文讨论了PINNSim的关键特性，以及将其发展为完整仿真器所需的重要步骤。该仿真器有望显著增大时间步长，从而加速时域仿真过程。