Cover Image

Robust Power System State Estimation using Physics-Informed Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Falas, Solon, Asprou, Markos, Konstantinou, Charalambos, Michael, Maria K.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Systems and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks
by: Falas, Solon, et al.
Published: (2026)

Physics-Informed Neural Networks for Securing Water Distribution Systems
by: Falas, Solon, et al.
Published: (2020)

A Dynamic Recurrent Adjacency Memory Network for Mixed-Generation Power System Stability Forecasting
by: Ooi, Guang An, et al.
Published: (2025)

A Modular End-to-End Framework for Secure Firmware Updates on Embedded Systems
by: Falas, Solon, et al.
Published: (2020)

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

Design and Evaluation of a DC Microgrid Testbed for DER Integration and Power Management
by: S, Gokul Krishnan, et al.
Published: (2024)

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

Stochastic Power-Water Coordination: Unlocking Flexibility in Hybrid RO Desalination Plants via Variable-Speed Pumps and Tank Mixing
by: Hu, Rongxing, et al.
Published: (2026)

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

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
by: Ashraf, Inaam, et al.
Published: (2025)

Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
by: Kundacina, Ognjen, et al.
Published: (2023)

VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets
by: Mukherjee, Biswarup, et al.
Published: (2025)

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
by: Ramirez, Ibai, et al.
Published: (2025)

Resilient Distributed Integral Control for Multimachine Power Systems with Inherent Input Constraint Satisfaction
by: Kavvathas, Theodoros E., et al.
Published: (2023)

Safe and Robust Domains of Attraction for Discrete-Time Systems: A Set-Based Characterization and Certifiable Neural Network Estimation
by: Serry, Mohamed, et al.
Published: (2026)

Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation
by: Azimian, Behrouz, et al.
Published: (2023)

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
by: Jin, Hongwei, et al.
Published: (2024)

Power Failure Cascade Prediction using Graph Neural Networks
by: Chadaga, Sathwik, et al.
Published: (2024)

Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
by: Liu, Jun, et al.
Published: (2023)

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

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks
by: Ito, Junsei, et al.
Published: (2025)

An MDP-Based Approach for Distribution System Control with PV Generation and Battery Storage
by: Sosnowski, Robert, et al.
Published: (2024)

Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks
by: Esenov, Emir, et al.
Published: (2025)

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
by: Liu, Jun
Published: (2026)

Graph Neural Networks in Wind Power Forecasting
by: Castellano, Javier, et al.
Published: (2025)

Event-Triggered Islanding in Inverter-Based Grids
by: Zografopoulos, Ioannis, et al.
Published: (2023)

PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systems
by: Ghamizi, Salah, et al.
Published: (2024)

Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
by: Liu, Zheng
Published: (2026)

Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection and Mitigation
by: Maleki, Sajjad, et al.
Published: (2024)

Formally Verified Physics-Informed Neural Control Lyapunov Functions
by: Liu, Jun, et al.
Published: (2024)

Physics-Informed Deep B-Spline Networks
by: Wang, Zhuoyuan, et al.
Published: (2025)

Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow
by: Pham, Thuan, et al.
Published: (2024)

Verifiable Error Bounds for Physics-Informed Neural KKL Observers
by: Berin-Costain, Hannah, et al.
Published: (2026)

Learning Robust Satellite Attitude Dynamics with Physics-Informed Normalising Flow
by: Cena, Carlo, et al.
Published: (2025)

Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
by: Dogoulis, Pantelis, et al.
Published: (2026)

Robustness Certificates for Neural Networks against Adversarial Attacks
by: Taheri, Sara, et al.
Published: (2025)

Physics-Embedded Neural ODEs for Sim2Real Edge Digital Twins of Hybrid Power Electronics Systems
by: Zheng, Jialin, et al.
Published: (2025)

Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
by: Wu, Tong, et al.
Published: (2022)

Reduced Optimal Power Flow Using Graph Neural Network
by: Pham, Thuan, et al.
Published: (2022)