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Physics–Data fusion–based traceable frequency trajectory prediction method for power systems
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DOI：10.1016/j.ijepes.2026.111822.png)
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Abstract
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• Propose a physics-data fusion dual-network collaborative method for power system frequency prediction. • Embedding the SFR model as a physics-based loss term within the neural network reduces the model’s dependence on training data. • Using model-output physically meaningful parameters to reconstruct the frequency response enhances the model's physical traceability. • Combining PINN with LSTM enhances the model’s ability to capture temporal features.
Keywords:
Frequency stability
Frequency trajectory prediction
Physics-informed neural networks
Long short-term memory networks
Physical traceability
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