Real-time inertia estimation in an inverter-dominated distribution grid using a physics-informed recurrent neural network

Publikationstyp
Conference Paper
Date Issued
2022-06
Sprache
English
Author(s)
Plant, Rebecca
Stock, Simon
Babazadeh, Davood
Becker, Christian
Institut
Elektrische Energietechnik E-6
TORE-URI
http://hdl.handle.net/11420/12852
Volume
2022
Issue
3
Start Page
940
End Page
944
Citation
CIRED Porto Workshop 2022 (3): 940-944
Contribution to Conference
CIRED workshop on E-mobility and power distribution systems 2022
Publisher DOI
10.1049/icp.2022.0852
Scopus ID
2-s2.0-85166329622
Publisher
Institution of Engineering and Technology (IET)
ISBN
978-1-83953-705-9
Peer Reviewed
true
ISBN of container
978-1-83953-705-9
To ensure frequency stability, estimation of system inertia becomes essential in modern power grids. Growing shares of virtual inertia from inverter-coupled resources (ICRs) shift this task to the distribution grids (DGs) and introduce new challenges. Using a physics-informed neural network (PINN) to combine data-driven modelling with knowledge of system dynamics, this study presents an approach to real-time system inertia estimation in inverter-dominated DGs. Based on the PINN literature framework, a modified loss function (LF) with adaptive weighting is proposed for a recurrent PINN. The approach is evaluated on a 14-bus medium voltage (MV) DG model, featuring virtual inertia from distributed ICRs with characteristic nonlinearities.
Subjects
MLE@TUHH
DDC Class
000: Allgemeines, Wissenschaft