We propose in this paper a data driven state estimation scheme for generating nonlinear reduced models parametric families of PDEs, directly providing data-to-state maps, represented terms Deep Neural Networks. A major constituent is sensor-induced decomposition model-compliant Hilbert space warranting approximation problem relevant metrics. It plays similar role as Parametric Background Data W...

We propose a technique to assess the vulnerability of power system state estimation. aim at identifying measurements that have high potential being target false data injection attacks. From perspective adversary, such following characteristics: (1) influential on variable estimates; (2) corrupting their measured values is likely be undetected. Additionally, characteristics should not change sig...