Plant, RebeccaRebeccaPlantStock, SimonSimonStockBabazadeh, DavoodDavoodBabazadehBecker, ChristianChristianBecker2022-06-092022-06-092022-06CIRED Porto Workshop 2022 (3): 940-944978-1-83953-705-9http://hdl.handle.net/11420/12852To 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.enMLE@TUHHAllgemeines, WissenschaftConference Paper10.1049/icp.2022.0852Conference Paper