# State Estimation

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arXiv Nov 11, 2026
Tampering with only compromised sensors can still fool detectors: PCGAN reaches an 18.69% stealth-impact score on the IEEE 300-bus grid
Working with write access to only a subset of deployed sensor channels and no knowledge of grid topology or detector parameters, PCGAN learns approximate active-power–angle and reactive-power–voltage-magnitude relations from historical measurements and uses adversarial training to generate data-injection perturbations that resemble normal operation. On the IEEE 300-bus system with one year of PJM hourly demand and 10% to 100% compromised sensors, it reaches 84.28% mean evasion of ten machine-learning detectors and 22.31% mean spatial load misperception, for an average stealth-impact score of 18.69% against 5.61% for the strongest competing attack.
Working with write access to only a subset of deployed sensor channels and no knowledge of grid topology or detector parameters, PCGAN learns approximate active-power–angle and reactive-power–voltage-magnitude relations from historical measurements and uses adversarial training to generate data-injection perturbations that resemble normal operation. On the IEEE 300-bus system with one year of PJM hourly demand and 10% to 100% compromised sensors, it reaches 84.28% mean evasion of ten machine-learning detectors and 22.31% mean spatial load misperception, for an average stealth-impact score of 18.69% against 5.61% for the strongest competing attack.
Md. Mainul Islam（First author）
PCGAN False Data Injection Attack State Estimation Smart Grid
arXiv Nov 6, 2026
Recurrent state estimation gets a convergence guarantee: one gain drives the error exponentially into any small neighborhood
Recurrent models for state estimation previously had empirical success but no formal convergence or robustness guarantees. This work treats a recurrent network as a discretization of continuous latent dynamics and requires the latent flow to contract, proving that a smooth injective map embeds the true physical state into latent space; raising a single scalar gain κ drives the estimation error exponentially fast into an arbitrarily small neighborhood of zero, at the cost of decoder capacity growing polynomially in κ. On a real mobile-robot pose-estimation task, the provably contracting model outperforms a vanilla GRU and trains more stably.
Recurrent models for state estimation previously had empirical success but no formal convergence or robustness guarantees. This work treats a recurrent network as a discretization of continuous latent dynamics and requires the latent flow to contract, proving that a smooth injective map embeds the true physical state into latent space; raising a single scalar gain κ drives the estimation error exponentially fast into an arbitrarily small neighborhood of zero, at the cost of decoder capacity growing polynomially in κ. On a real mobile-robot pose-estimation task, the provably contracting model outperforms a vanilla GRU and trains more stably.
Centre National de la Recherche Scientifique
Vincent Andrieu（First author）
State Estimation Contracting GRU Nonlinear KKL Observers Recurrent Neural Networks Neural Ordinary Differential Equations

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Tampering with only compromised sensors can still fool detectors: PCGAN reaches an 18.69% stealth-impact score on the IEEE 300-bus grid

Recurrent state estimation gets a convergence guarantee: one gain drives the error exponentially into any small neighborhood