1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems
Abstract
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.
Cite
@article{arxiv.2211.02930,
title = {1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems},
author = {Bang L. H. Nguyen and Tuyen Vu and Thai-Thanh Nguyen and Mayank Panwar and Rob Hovsapian},
journal= {arXiv preprint arXiv:2211.02930},
year = {2022}
}
Comments
arXiv admin note: text overlap with arXiv:2210.15177