  Power system cascading failures become more time variant and complex because
of the increasing network interconnection and higher renewable energy
penetration. High computational cost is the main obstacle for a more frequent
online cascading failure search, which is essential to improve system security.
In this work, we show that the complex mechanism of cascading failures can be
well captured by training a graph convolutional network (GCN) offline.
Subsequently, the search of cascading failures can be significantly accelerated
with the aid of the trained GCN model. We link the power network topology with
the structure of the GCN, yielding a smaller parameter space to learn the
complex mechanism. We further enable the interpretability of the GCN model by a
layer-wise relevance propagation (LRP) algorithm. The proposed method is tested
on both the IEEE RTS-79 test system and China's Henan Province power system.
The results show that the GCN guided method can not only accelerate the search
of cascading failures, but also reveal the reasons for predicting the potential
cascading failures.
