A new open-source toolbox trains physics-informed neural networks for power system components and demonstrates a 9th-order synchronous machine with AVR and governor, reaching 2.26e-3 mean absolute error with fast inference.
A critical review of the integration of renewable energy sources with various technologies,
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
support 1representative citing papers
citing papers explorer
-
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
A new open-source toolbox trains physics-informed neural networks for power system components and demonstrates a 9th-order synchronous machine with AVR and governor, reaching 2.26e-3 mean absolute error with fast inference.