  We consider the problem of predicting power failure cascades due to branch
failures. We propose a flow-free model based on graph neural networks that
predicts grid states at every generation of a cascade process given an initial
contingency and power injection values. We train the proposed model using a
cascade sequence data pool generated from simulations. We then evaluate our
model at various levels of granularity. We present several error metrics that
gauge the model's ability to predict the failure size, the final grid state,
and the failure time steps of each branch within the cascade. We benchmark the
graph neural network model against influence models. We show that, in addition
to being generic over randomly scaled power injection values, the graph neural
network model outperforms multiple influence models that are built specifically
for their corresponding loading profiles. Finally, we show that the proposed
model reduces the computational time by almost two orders of magnitude.
