  The application of deep learning methods to speed up the resolution of
challenging power flow problems has recently shown very encouraging results.
However, power system dynamics are not snap-shot, steady-state operations.
These dynamics must be considered to ensure that the optimal solutions provided
by these models adhere to practical dynamical constraints, avoiding frequency
fluctuations and grid instabilities. Unfortunately, dynamic system models based
on ordinary or partial differential equations are frequently unsuitable for
direct application in control or state estimates due to their high
computational costs. To address these challenges, this paper introduces a
machine learning method to approximate the behavior of power systems dynamics
in near real time. The proposed framework is based on gradient-enhanced
physics-informed neural networks (gPINNs) and encodes the underlying physical
laws governing power systems. A key characteristic of the proposed gPINN is its
ability to train without the need of generating expensive training data. The
paper illustrates the potential of the proposed approach in both forward and
inverse problems in a single-machine infinite bus system for predicting rotor
angles and frequency, and uncertain parameters such as inertia and damping to
showcase its potential for a range of power systems applications.
