mlsanityData to 14 Oct 2026

Neural Network-based Power Flow Model

arXiv 2112.08418 ↗First posted 15 Dec 2021eess.SY cs.LG cs.SYcited by 10

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Abstract (arXiv)

Power flow analysis is used to evaluate the flow of electricity in the power system network. Power flow calculation is used to determine the steady-state variables of the system, such as the voltage magnitude/phase angle of each bus and the active/reactive power flow on each branch. The DC power flow model is a popular linear power flow model that is widely used in the power industry. Although it is fast and robust, it may lead to inaccurate line flow results for some transmission lines. Since renewable energy sources such as solar farms or offshore wind farms are usually located far away from the main grid, accurate line flow results on these critical lines are essential for power flow analysis due to the unpredictable nature of renewable energy. Data-driven methods can be used to partially address these inaccuracies by taking advantage of historical grid profiles. In this paper, a neural network (NN) model is trained to predict power flow results using historical power system data. Although the training process may take time, once trained, it is very fast to estimate line flows. A comprehensive performance analysis between the proposed NN-based power flow model and the traditional DC power flow model is conducted. It can be concluded that the proposed NN-based power flow model can find solutions quickly and more accurately than DC power flow model.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

4 of 14 references.

References · 4

  1. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  2. 2021Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurementscited by 8
  3. 2021Machine Learning Assisted Approach for Security-Constrained Unit Commitmentcited by 5
  4. 2019Sensitivity Factors based Transmission Network Topology Control for Violation Reliefcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 0; 2026: 2.

10 papers.

  1. 2022Reduced Optimal Power Flow Using Graph Neural Networkcited by 11
  2. 2022Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operationscited by 6
  3. 2023Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitmentcited by 6
  4. 2023Graph Neural Network-based Power Flow Modelcited by 5
  5. 2024N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Networkcited by 3
  6. 2023Quantum Neural Networks for Power Flow Analysiscited by 1
  7. 2024Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flowcited by 1
  8. 2024Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splittingcited by 1
  9. 2026PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learningcited by 0
  10. 2026Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimationcited by 0