  This paper presents a 1-D convolutional graph neural network for fault
detection in microgrids. The combination of 1-D convolutional neural networks
(1D-CNN) and graph convolutional networks (GCN) helps extract both
spatial-temporal correlations from the voltage measurements in microgrids. The
fault detection scheme includes fault event detection, fault type and phase
classification, and fault location. There are five neural network model
training to handle these tasks. Transfer learning and fine-tuning are applied
to reduce training efforts. The combined recurrent graph convolutional neural
networks (1D-CGCN) is compared with the traditional ANN structure on the
Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%,
98.75%, and 95.6% for fault detection, fault type classification, fault phase
identification, and fault location respectively.
