  This paper introduces for the first time, to our knowledge, a framework for
physics-informed neural networks in power system applications. Exploiting the
underlying physical laws governing power systems, and inspired by recent
developments in the field of machine learning, this paper proposes a neural
network training procedure that can make use of the wide range of mathematical
models describing power system behavior, both in steady-state and in dynamics.
Physics-informed neural networks require substantially less training data and
can result in simpler neural network structures, while achieving high accuracy.
This work unlocks a range of opportunities in power systems, being able to
determine dynamic states, such as rotor angles and frequency, and uncertain
parameters such as inertia and damping at a fraction of the computational time
required by conventional methods. This paper focuses on introducing the
framework and showcases its potential using a single-machine infinite bus
system as a guiding example. Physics-informed neural networks are shown to
accurately determine rotor angle and frequency up to 87 times faster than
conventional methods.
