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Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics

arXiv 2410.07527 ↗First posted 10 Oct 2024cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We propose some novel enhancements to improve PINN training and accuracy and also implement several other recently proposed ideas from the literature. We successfully apply these to study the transient dynamics of synchronous generators. We also make progress towards applying PINNs to advanced inverter models. Such enhanced PINNs can allow us to accelerate high-fidelity simulations needed to ensure a stable and reliable renewables-rich future grid.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 10 Oct 2024.

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References · 19

  1. 2021Characterizing possible failure modes in physics-informed neural networkscited by 632
  2. 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 629
  3. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  4. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 329
  5. 2020Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanismcited by 231
  6. 2017Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equationscited by 158
  7. 2023An Expert's Guide to Training Physics-informed Neural Networkscited by 119
  8. 2018Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysiscited by 110
  9. 2024Challenges in Training PINNs: A Loss Landscape Perspectivecited by 109
  10. 2019Physics-Informed Neural Networks for Power Systemscited by 80
  11. 2023Tackling the Curse of Dimensionality with Physics-Informed Neural Networkscited by 78
  12. 2024Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sectorcited by 29
7 more
  1. 2023GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEscited by 20
  2. 2023Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibilitycited by 13
  3. 2023PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networkscited by 8
  4. 2021Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimizationcited by 6
  5. 2024Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systemscited by 4
  6. 2023Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessmentcited by 3
  7. 2015Power System Differential-Algebraic Equationscited by 2

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