GenAI-FDIA benchmarks physics-informed generative models for stealthy FDIA synthesis on IEEE testbeds, reports high evasion rates, and introduces an inference-time harmoniser plus warm-up schedules to fix projection displacement and covariance collapse.
False data injection on state estimation in power systems—attacks, impacts, and defense: A survey
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An admittance-guided optimization using harmonic perturbations and a physics-informed neural network identifies the worst-case dispatch command that drives a microgrid into sub-synchronous oscillations.
PINN with dynamic loss weighting for secure PSSE under FDIAs outperforms fixed-weight variants on IEEE 118-bus without adversarial training.
citing papers explorer
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GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
GenAI-FDIA benchmarks physics-informed generative models for stealthy FDIA synthesis on IEEE testbeds, reports high evasion rates, and introduces an inference-time harmoniser plus warm-up schedules to fix projection displacement and covariance collapse.
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Admittance-Guided Inverter Dispatch Command Manipulation Attack: A Grid Stability-Oriented Approach
An admittance-guided optimization using harmonic perturbations and a physics-informed neural network identifies the worst-case dispatch command that drives a microgrid into sub-synchronous oscillations.
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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.