  Due to the increasing share of renewables, the analysis of the dynamical
behavior of power grids gains importance. Effective risk assessments
necessitate the analysis of large number of fault scenarios. The computational
costs inherent in dynamic simulations impose constraints on the number of
configurations that can be analyzed. Machine Learning (ML) has proven to
efficiently predict complex power grid properties. Hence, we analyze the
potential of ML for predicting dynamic stability of future power grids with
large shares of inverters. For this purpose, we generate a new dataset
consisting of synthetic power grid models and perform dynamical simulations. As
targets for the ML training, we calculate the fault-ride-through probability,
which we define as the probability of staying within a ride-through curve after
a fault at a bus has been cleared. Importantly, we demonstrate that ML models
accurately predict the fault-ride-through probability of synthetic power grids.
Finally, we also show that the ML models generalize to an IEEE-96 Test System,
which emphasizes the potential of deploying ML methods to study probabilistic
stability of power grids.
