A physics-informed latent neural ODE, which blends a nominal droop-control inverter model with learned neural dynamics, reproduces proprietary grid-forming inverter behavior better than an RNN in a load-step test.
Generative adversarial nets,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies
A physics-informed latent neural ODE, which blends a nominal droop-control inverter model with learned neural dynamics, reproduces proprietary grid-forming inverter behavior better than an RNN in a load-step test.