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Differentiable programming for transient power system simulations
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DOI：10.1016/j.ijepes.2026.111825.png)
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Abstract
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• Proposes end-to-end differentiable power system simulations. • Composable software differentiates numerical solutions of differential equations. • A case study demonstrates online training of the models in a simulation environment.
Keywords:
Differentiable programming
Scientific machine learning
Power system dynamics
Transient stability
Gradient
Physics-informed machine learning
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