PINN with dynamic loss weighting for secure PSSE under FDIAs outperforms fixed-weight variants on IEEE 118-bus without adversarial training.
Enhancement of distribution system state estimation using pruned physics-aware neural networks
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Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks
PINN with dynamic loss weighting for secure PSSE under FDIAs outperforms fixed-weight variants on IEEE 118-bus without adversarial training.