mlsanityData to 14 Oct 2026

Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants

arXiv 2405.06443 ↗First posted 10 May 2024cs.LG cs.SY eess.SYcited by 11

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Abstract (arXiv)

Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 16 of its 47 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

16 of 47 references.

References · 16

  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  2. 2020Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanismcited by 233
  3. 2022Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisonscited by 170
  4. 2018Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problemscited by 147
  5. 2021Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systemscited by 128
  6. 2023Tackling the Curse of Dimensionality with Physics-Informed Neural Networkscited by 79
  7. 2023Residual-based attention in physics-informed neural networkscited by 72
  8. 2020Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applicationscited by 64
  9. 2020Fusing Physics-based and Deep Learning Models for Prognosticscited by 40
  10. 2022Delta-PINNs: physics-informed neural networks on complex geometriescited by 28
  11. 2023Physics-informed neural networks for solving forward and inverse problems in complex beam systemscited by 24
  12. 2022Physics-aware Reduced-order Modeling of Transonic Flow via β-Variational Autoencodercited by 15
4 more
  1. 2020Learning Unknown Physics of non-Newtonian Fluidscited by 13
  2. 2024Learning in PINNs: Phase transition, diffusion equilibrium, and generalizationcited by 13
  3. 2023Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulationscited by 8
  4. 2022Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Supportcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 5.

11 papers.

  1. 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
  2. 2025A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learningcited by 6
  3. 2025Bayesian Physics Informed Neural Networks for Reliable Transformer Prognosticscited by 3
  4. 2025MILP initialization for solving parabolic PDEs with PINNscited by 1
  5. 2025Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networkscited by 0
  6. 2025Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantificationcited by 0
  7. 2026Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognosticscited by 0
  8. 2026UniPINN: A Unified PINN Framework for Multi-task Learning of Diverse Navier-Stokes Equationscited by 0
  9. 2026Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systemscited by 0
  10. 2026Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networkscited by 0
  11. 2026Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Reviewcited by 0