Copertina

Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Nadal, Ignasi Ventura, Nellikkath, Rahul, Chatzivasileiadis, Spyros
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
di: Karampinis, Ioannis, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
di: Nadal, Ignasi Ventura, et al.
Pubblicazione: (2024)

Scalable Physics-Informed Neural Networks for Accelerating Electromagnetic Transient Stability Assessment
di: Nadal, Ignasi Ventura, et al.
Pubblicazione: (2025)

PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
di: Stiasny, Jochen, et al.
Pubblicazione: (2023)

Correctness Verification of Neural Networks Approximating Differential Equations
di: Ellinas, Petros, et al.
Pubblicazione: (2024)

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets
di: Prat, Eléa, et al.
Pubblicazione: (2021)

Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
di: Karampinis, Ioannis, et al.
Pubblicazione: (2025)

Graph Neural Networks for Fast Contingency Analysis of Power Systems
di: Nakiganda, Agnes M., et al.
Pubblicazione: (2023)

Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
di: Ellinas, Petros, et al.
Pubblicazione: (2026)

Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems
di: Stock, Simon, et al.
Pubblicazione: (2024)

Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training
di: Giraud, Bastien, et al.
Pubblicazione: (2025)

Global Performance Guarantees for Neural Network Models of AC Power Flow
di: Chevalier, Samuel, et al.
Pubblicazione: (2022)

Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
di: Alcántara, Antonio, et al.
Pubblicazione: (2026)

Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection
di: Nadal, Ignasi Ventura, et al.
Pubblicazione: (2022)

Stochastic Quantum Power Flow for Risk Assessment in Power Systems
di: Sævarsson, Brynjar, et al.
Pubblicazione: (2023)

A hybrid Quantum-Classical Algorithm for Mixed-Integer Optimization in Power Systems
di: Ellinas, Petros, et al.
Pubblicazione: (2024)

Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets
di: Nadal, Ignasi Ventura, et al.
Pubblicazione: (2023)

Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
di: Stiasny, Jochen, et al.
Pubblicazione: (2024)

Digital Twin for Real-Time Security Assessment and Flexibility Activation in the Bornholm Distribution System
di: Sundhu, Anosh Arshad, et al.
Pubblicazione: (2026)

Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks
di: Tran, Huynh T. T., et al.
Pubblicazione: (2023)

Dual Pricing to Prioritize Renewable Energy and Consumer Preferences in Electricity Markets
di: Jong, Emilie, et al.
Pubblicazione: (2024)

A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary
di: Giraud, Bastien, et al.
Pubblicazione: (2025)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
di: Authier, Jules, et al.
Pubblicazione: (2023)

Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
di: Nellikkath, Rahul, et al.
Pubblicazione: (2024)

Improved Physics-Informed Neural Network based AC Power Flow for Distribution Networks
di: Eeckhout, Victor, et al.
Pubblicazione: (2024)

Physics-Informed Kolmogorov-Arnold Networks for Power System Dynamics
di: Shuai, Hang, et al.
Pubblicazione: (2024)

On Physics-Informed Neural Network Control for Power Electronics
di: Hui, Peifeng, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks for Accelerating Power System State Estimation
di: Falas, Solon, et al.
Pubblicazione: (2023)

Robust Power System State Estimation using Physics-Informed Neural Networks
di: Falas, Solon, et al.
Pubblicazione: (2025)

Extremum Seeking for PDE Systems using Physics-Informed Neural Networks
di: Guo, Haojin, et al.
Pubblicazione: (2025)

Co-Optimization of Network Topology and Variable Impedance Devices under Dynamic Line Ratings in Power Transmission Systems
di: Park, Junseon, et al.
Pubblicazione: (2026)

Neural Network Certification Informed Power System Transient Stability Preventive Control with Renewable Energy
di: Su, Tong, et al.
Pubblicazione: (2024)

Physics-informed Graphical Neural Network for Power System State Estimation
di: Ngo, Quang-Ha, et al.
Pubblicazione: (2023)

Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
di: Li, Sirui, et al.
Pubblicazione: (2025)

Improving Fairness in Photovoltaic Curtailments via Daily Topology Reconfiguration for Voltage Control in Power Distribution Networks
di: Gupta, Rahul K., et al.
Pubblicazione: (2024)

Scalable Neural Dynamic Equivalence for Power Systems
di: Shen, Qing, et al.
Pubblicazione: (2023)

On Simulation of Power Systems and Microgrid Components with SystemC-AMS
di: Bhadani, Rahul, et al.
Pubblicazione: (2024)

Acceleration of Power System Dynamic Simulations using a Deep Equilibrium Layer and Neural ODE Surrogate
di: Bossart, Matthew, et al.
Pubblicazione: (2024)

Improving the Accuracy of DC Optimal Power Flow Formulations via Parameter Optimization
di: Taheri, Babak, et al.
Pubblicazione: (2024)

Physics-Constrained Taylor Neural Networks for Learning and Control of Dynamical Systems
di: Nguyen, Nam T., et al.
Pubblicazione: (2024)