mlsanityData to 7 Oct 2026

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

arXiv 1812.09464 ↗First posted 22 Dec 2018cs.LG stat.AP stat.MLcited by 29

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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.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2018.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 5; 2024: 2; 2025: 3; 2026: 6.

29 papers.

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  1. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  2. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  3. 2021Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systemscited by 21
  4. 2021Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Gridscited by 18
  5. 2021Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Networkcited by 7
  6. 2021Embedding Power Flow into Machine Learning for Parameter and State Estimationcited by 6
  7. 2021Graph neural network-based fault diagnosis: a reviewcited by 5
  8. 2022Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AIcited by 4
  9. 2021PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labelscited by 3
  10. 2023A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Gridcited by 3
  11. 2025PowerGNN: A Topology-Aware Graph Neural Network for Electricity Gridscited by 3
  12. 2021Detecting and interpreting faults in vulnerable power grids with machine learningcited by 2
17 more
  1. 2022Spatial-temporal associations representation and application for process monitoring using graph convolution neural networkcited by 1
  2. 20221-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systemscited by 1
  3. 2022Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Networkcited by 1
  4. 2023Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithmscited by 1
  5. 2023Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUscited by 1
  6. 2024Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networkscited by 1
  7. 2026Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Gridscited by 1
  8. 2026Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levelscited by 1
  9. 2023Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimationcited by 0
  10. 2023Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTMcited by 0
  11. 2024Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learningcited by 0
  12. 2025A Multi-Scale Attention-Based Attack Diagnosis Mechanism for Parallel Cyber-Physical Attacks in Power Gridscited by 0
  13. 2025Generalization of Graph Neural Network Models for Distribution Grid Fault Detectioncited by 0
  14. 2026FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution Systemcited by 0
  15. 2026Graph Machine Learning: An Opportunity for Power Systemscited by 0
  16. 2026A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPNcited by 0
  17. 2026EvEMTBench: An Open Benchmark for Machine Learning in Power System Protectioncited by 0