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This paper proposes a Cycle-Space Detector (CSD) for detecting blind false data injection attacks on power systems, where an autoencoder generates stealthy perturbations aligned with the measurement Jacobian null space. The CSD uses topology-derived cycle constraints to improve detection without requiring precise line parameters.
The article introduces Newton's Lantern, a reinforcement learning framework for finetuning warm start models to solve the AC power flow problem more efficiently, particularly near voltage collapse.