  This paper proposes a data-driven graphical framework for the real-time
search of risky cascading fault chains (FCs). While identifying risky FCs is
pivotal to alleviating cascading failures, the complex spatio-temporal
dependencies among the components of the power system render challenges to
modeling and analyzing FCs. Furthermore, the real-time search of risky FCs
faces an inherent combinatorial complexity that grows exponentially with the
size of the system. The proposed framework leverages the recent advances in
graph recurrent neural networks to circumvent the computational complexities of
the real-time search of FCs. The search process is formalized as a partially
observable Markov decision process (POMDP), which is subsequently solved via a
time-varying graph recurrent neural network (GRNN) that judiciously accounts
for the inherent temporal and spatial structures of the data generated by the
system. The key features of this structure include (i) leveraging the spatial
structure of the data induced by the system topology, (ii) leveraging the
temporal structure of data induced by system dynamics, and (iii) efficiently
summarizing the system's history in the latent space of the GRNN. The proposed
framework's efficiency is compared to the relevant literature on the IEEE
39-bus New England system and the IEEE 118-bus system.

Systems and Control;Systems and Control;Signal Processing