mlsanityData to 7 Oct 2026

A Review of Graph Neural Networks and Their Applications in Power Systems

arXiv 2101.10025 ↗First posted 25 Jan 2021cs.LG cs.AI cs.SYcited by 62

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Abstract (arXiv)

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). Builds on 34 of its 112 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

34 of 112 references.

References · 34

  1. 2017Attention Is All You Needcited by 59,932
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22 more
  1. 2019Heterogeneous Graph Attention Networkcited by 732
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  13. 2019Optimal Power Flow Using Graph Neural Networkscited by 60
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  17. 2018Fault Location in Power Distribution Systems via Deep Graph Convolutional Networkscited by 29
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 10; 2023: 12; 2024: 16; 2025: 10; 2026: 11.

62 papers.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2021Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systemscited by 21
  3. 2022Graph Neural Networks Designed for Different Graph Types: A Surveycited by 21
  4. 2023PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networkscited by 19
  5. 2024PowerGraph: A power grid benchmark dataset for graph neural networkscited by 19
  6. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  7. 2022Solving AC Power Flow with Graph Neural Networks under Realistic Constraintscited by 11
  8. 2022Reduced Optimal Power Flow Using Graph Neural Networkcited by 11
  9. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  10. 2022Forecasting Unobserved Node States with spatio-temporal Graph Neural Networkscited by 9
  11. 2023Deep Statistical Solver for Distribution System State Estimationcited by 9
  12. 2021Embedding Power Flow into Machine Learning for Parameter and State Estimationcited by 6
38 more
  1. 2023Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitmentcited by 6
  2. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  3. 2024SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Gridscited by 5
  4. 2025Residual connections provably mitigate oversmoothing in graph neural networkscited by 5
  5. 2022Attention-based Deep Neural Networks for Battery Discharge Capacity Forecastingcited by 4
  6. 2022Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AIcited by 4
  7. 2022Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flowcited by 4
  8. 2024PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systemscited by 4
  9. 2026GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysiscited by 4
  10. 2022Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNNcited by 3
  11. 2023Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Gridcited by 3
  12. 2024N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Networkcited by 3
  13. 2024xAI-Drop: Don't Use What You Cannot Explaincited by 3
  14. 2024Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricitycited by 2
  15. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  16. 2024Optimal Power Grid Operations with Foundation Modelscited by 2
  17. 2025Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Studycited by 2
  18. 2026Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Modelscited by 2
  19. 2021Graph Attention Network For Microwave Imaging of Brain Anomalycited by 1
  20. 2022Large-Scale Sequential Learning for Recommender and Engineering Systemscited by 1
  21. 2023Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Schedulingcited by 1
  22. 2023Power grid operational risk assessment using graph neural network surrogatescited by 1
  23. 2023Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learningcited by 1
  24. 2024A Dual Power Grid Cascading Failure Model for the Vulnerability Analysiscited by 1
  25. 2024Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learningcited by 1
  26. 2024Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flowcited by 1
  27. 2024Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splittingcited by 1
  28. 2025SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimationcited by 1
  29. 2025Flow-Attentional Graph Neural Networkscited by 1
  30. 2025Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Trainingcited by 1
  31. 2025Graph Attention Network for Predicting Duration of Large-Scale Power Outages Induced by Natural Disasterscited by 1
  32. 2026GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networkscited by 1
  33. 2026MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Modelcited by 1
  34. 2026Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levelscited by 1
  35. 2022Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Networkcited by 0
  36. 2023Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systemscited by 0
  37. 2023Spectral Convergence of Complexon Shift Operatorscited by 0
  38. 2024Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learningcited by 0

50 most cited of 62 All 62 (JSON)