# Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1812.09464 · HTML: https://mlsanity.com/paper/1812.09464/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 29 of 29)

- [A Review of Graph Neural Networks and Their Applications in Power Systems](https://mlsanity.com/paper/2101.10025/) (2021)
- [Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions](https://mlsanity.com/paper/2205.10129/) (2022)
- [Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems](https://mlsanity.com/paper/2102.06349/) (2021)
- [Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids](https://mlsanity.com/paper/2109.03604/) (2021)
- [Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2103.03729/) (2021)
- [Embedding Power Flow into Machine Learning for Parameter and State Estimation](https://mlsanity.com/paper/2103.14251/) (2021)
- [Graph neural network-based fault diagnosis: a review](https://mlsanity.com/paper/2111.08185/) (2021)
- [Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI](https://mlsanity.com/paper/2208.08485/) (2022)
- [PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels](https://mlsanity.com/paper/2107.02275/) (2021)
- [A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid](https://mlsanity.com/paper/2309.09921/) (2023)
- [PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids](https://mlsanity.com/paper/2503.22721/) (2025)
- [Detecting and interpreting faults in vulnerable power grids with machine learning](https://mlsanity.com/paper/2108.07060/) (2021)
- [Spatial-temporal associations representation and application for process monitoring using graph convolution neural network](https://mlsanity.com/paper/2205.05250/) (2022)
- [1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems](https://mlsanity.com/paper/2211.02930/) (2022)
- [Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network](https://mlsanity.com/paper/2212.10454/) (2022)
- [Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms](https://mlsanity.com/paper/2303.00428/) (2023)
- [Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs](https://mlsanity.com/paper/2304.14680/) (2023)
- [Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks](https://mlsanity.com/paper/2406.17073/) (2024)
- [Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids](https://mlsanity.com/paper/2604.20403/) (2026)
- [Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels](https://mlsanity.com/paper/2607.29293/) (2026)
- [Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation](https://mlsanity.com/paper/2303.00105/) (2023)
- [Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM](https://mlsanity.com/paper/2312.11898/) (2023)
- [Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning](https://mlsanity.com/paper/2401.12235/) (2024)
- [A Multi-Scale Attention-Based Attack Diagnosis Mechanism for Parallel Cyber-Physical Attacks in Power Grids](https://mlsanity.com/paper/2503.05797/) (2025)
- [Generalization of Graph Neural Network Models for Distribution Grid Fault Detection](https://mlsanity.com/paper/2510.03571/) (2025)
- [FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System](https://mlsanity.com/paper/2602.24254/) (2026)
- [Graph Machine Learning: An Opportunity for Power Systems](https://mlsanity.com/paper/2608.16494/) (2026)
- [A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN](https://mlsanity.com/paper/2609.02538/) (2026)
- [EvEMTBench: An Open Benchmark for Machine Learning in Power System Protection](https://mlsanity.com/paper/2609.28149/) (2026)

## Abstract (arXiv)

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.