  We explore the possibility to use physics-informed neural networks to
drastically accelerate the solution of ordinary differential-algebraic
equations that govern the power system dynamics. When it comes to transient
stability assessment, the traditionally applied methods either carry a
significant computational burden, require model simplifications, or use overly
conservative surrogate models. Conventional neural networks can circumvent
these limitations but are faced with high demand of high-quality training
datasets, while they ignore the underlying governing equations.
Physics-informed neural networks are different: they incorporate the power
system differential algebraic equations directly into the neural network
training and drastically reduce the need for training data. This paper takes a
deep dive into the performance of physics-informed neural networks for power
system transient stability assessment. Introducing a new neural network
training procedure to facilitate a thorough comparison, we explore how
physics-informed neural networks compare with conventional
differential-algebraic solvers and classical neural networks in terms of
computation time, requirements in data, and prediction accuracy. We illustrate
the findings on the Kundur two-area system, and assess the opportunities and
challenges of physics-informed neural networks to serve as a transient
stability analysis tool, highlighting possible pathways to further develop this
method.
