mlsanityData to 7 Oct 2026

State Estimation in Electric Power Systems Leveraging Graph Neural Networks

arXiv 2201.04056 ↗First posted 11 Jan 2022cs.LG cs.IT cs.SYcited by 11

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Abstract (arXiv)

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 11 Jan 2022.

Builds on

8 of 14 references.

References · 8

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  4. 2018Real-time Power System State Estimation and Forecasting via Deep Neural Networkscited by 32
  5. 2021Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systemscited by 21
  6. 2021IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systemscited by 6
  7. 2017Distributed Gauss-Newton Method for State Estimation Using Belief Propagationcited by 4
  8. 2020Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustnesscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 1; 2025: 1; 2026: 1.

11 papers.

  1. 2023Deep Statistical Solver for Distribution System State Estimationcited by 9
  2. 2022Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networkscited by 4
  3. 2022Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networkscited by 2
  4. 2024Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricitycited by 2
  5. 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
  6. 2022Towards Practical Physics-Informed ML Design and Evaluation for Power Gridcited by 1
  7. 2022Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networkscited by 1
  8. 2023Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithmscited by 1
  9. 2023Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUscited by 1
  10. 2023Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimationcited by 0
  11. 2026Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimationcited by 0