  In order to drastically reduce the heavy computational burden associated with
time-domain simulations, this paper introduces a Physics-Informed Neural
Network (PINN) to directly learn the solutions of power system dynamics. In
contrast to the limitations of classical model order reduction approaches,
commonly used to accelerate time-domain simulations, PINNs can universally
approximate any continuous function with an arbitrary degree of accuracy. One
of the novelties of this paper is that we avoid the need for any training data.
We achieve this by incorporating the governing differential equations and an
implicit Runge-Kutta (RK) integration scheme directly into the training process
of the PINN; through this approach, PINNs can predict the trajectory of a
dynamical power system at any discrete time step. The resulting
Runge-Kutta-based physics-informed neural networks (RK-PINNs) can yield up to
100 times faster evaluations of the dynamics compared to standard time-domain
simulations. We demonstrate the methodology on a single-machine infinite bus
system governed by the swing equation. We show that RK-PINNs can accurately and
quickly predict the solution trajectories.

Systems and Control;Systems and Control