Abstract
Available grid-forming (GFM) inverter models do not sufficiently match their frequency and voltage dynamics with field-operated industry grade inverters. To overcome this, a two-fold approach is proposed that leverages field data to develop a digital twin (DT) of Grey-box inverters. Firstly, a data-driven modeling approach for the rate of change of frequency (RoCoF) is introduced to model the frequency dynamics with virtual inertia (VI). Secondly, an automated parameter update process for voltage response alignment is proposed. The DT model is developed using field data from an industry grade GFM inverter operational in a microgrid (MG). The proposed approach is validated using multiple cases by comparing the simulation response with experimental data. It was confirmed from the analysis that practical GFM inverter dynamics with unknown VI information are captured effectively by the DT model.