# 1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

arXiv: https://arxiv.org/abs/2211.02930 · HTML: https://mlsanity.com/paper/2211.02930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 5 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks](https://mlsanity.com/paper/1812.09464/) (2018)
- [Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks](https://mlsanity.com/paper/1810.05247/) (2018)
- [Graph-based Faulted Line Identification Using Micro-PMU Data in Distribution Systems](https://mlsanity.com/paper/2002.10966/) (2020)
- [Detecting and interpreting faults in vulnerable power grids with machine learning](https://mlsanity.com/paper/2108.07060/) (2021)
- [Towards Plug-and-Play Protection for Meshed Distribution Systems with DG](https://mlsanity.com/paper/1812.04430/) (2018)
- [Distributed Dynamic State-Input Estimation for Power Networks of Microgrids and Active Distribution Systems with Unknown Inputs](https://mlsanity.com/paper/2108.01306/) (2021)
- [Distributed Dynamic State Estimation for Microgrids](https://mlsanity.com/paper/2110.12133/) (2021)

## Built on by (most cited first, 1 of 1)

- [Generalization of Graph Neural Network Models for Distribution Grid Fault Detection](https://mlsanity.com/paper/2510.03571/) (2025)

## Abstract (arXiv)

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.