Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components

2025/11/07 by Karampinis, Ioannis, Ellinas, Petros, Vorwerk, Johanna +1
Engineering · Physics and Astronomy · #FOS: Electrical engineering #Model Reduction and Neural Networks #Power System Optimization and Stability #Real-time simulation and control systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2511.05216

openalex publication_date 2025/11/07 · openalex created_date 2025/11/11 · openalex updated_date 2026/07/28

Abstract

Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for large-scale or online applications. We propose a neural-operator framework for surrogate modeling of power system components, using Deep Operator Networks (DeepONets) to learn mappings from system states and time-varying inputs to full trajectories without step-by-step integration. To enhance generalization and data efficiency, we introduce Physics-Informed DeepONets (PI-DeepONets), which embed the residuals of governing equations into the training loss. Our results show that DeepONets, and especially PI-DeepONets, achieve accurate predictions under diverse scenarios, providing over 30 times speedup compared to high-order ODE solvers. Benchmarking against Physics-Informed Neural Networks (PINNs) highlights superior stability and scalability. Our results demonstrate neural operators as a promising path toward real-time, physics-aware simulation of power system dynamics.

Citations

• Deep Operator Neural Network Model Predictive Control
• Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
• SOAP: Improving and Stabilizing Shampoo using Adam
• Physics-Informed Deep Neural Operator Networks
• Long-time integration of parametric evolution equations with physics-informed DeepONets
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A Time-Power Series Based Semi-Analytical Approach for Power System Simulation
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code

Related

• Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
• Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
• Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy
• Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation
• Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics