Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems-Reference-Cited by-同舟云学术

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Bayesian Physics-informed Neural Networks for system identification of inverter-dominated power systems

Published:2024-10 Issue: Volume:235 Page:110860
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Stock Simon^ORCID,Babazadeh Davood^ORCID,Becker Christian^ORCID,Chatzivasileiadis Spyros^ORCID

Funder

Hamburg University of Technology

Publisher

Elsevier BV

Reference25 articles.

1. Power system dynamic state estimation: Motivations, definitions, methodologies, and future work;Zhao;IEEE Trans. Power Syst.,2019

2. Estimation of power system inertia using nonlinear koopman modes;Susuki,2018

3. Discovering governing equations from data by sparse identification of nonlinear dynamical systems;Brunton;Appl. Math.,2016

4. Physics-informed neural networks for non-linear system identification for power system dynamics;Stiasny,2021

5. Physics-informed neural networks for power systems;Misyris,2020

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