A Heterogeneous Graph-Based Multi-Task Learning for Fault Event
  Diagnosis in Smart Grid
Dibaloke Chanda Nasim Yahya Soltani
Precise and timely fault diagnosis is a prerequisite for a distribution
system to ensure minimum downtime and maintain reliable operation. This
necessitates access to a comprehensive procedure that can provide the grid
operators with insightful information in the case of a fault event. In this
paper, we propose a heterogeneous multi-task learning graph neural network
(MTL-GNN) capable of detecting, locating and classifying faults in addition to
providing an estimate of the fault resistance and current. Using a graph neural
network (GNN) allows for learning the topological representation of the
distribution system as well as feature learning through a message-passing
scheme. We investigate the robustness of our proposed model using the IEEE-123
test feeder system. This work also proposes a novel GNN-based explainability
method to identify key nodes in the distribution system which then facilitates
informed sparse measurements. Numerical tests validate the performance of the
model across all tasks.