  Cyberthreats are an increasingly common risk to the power grid and can thwart
secure grid operations. We propose to extend contingency analysis to include
cyberthreat evaluations. However, unlike the traditional N-1 or N-2
contingencies, cyberthreats (e.g., MadIoT) require simulating hard-to-solve N-k
(with k >> 2) contingencies in a practical amount of time. Purely physics-based
power flow solvers, while being accurate, are slow and may not solve N-k
contingencies in a timely manner, whereas the emerging data-driven alternatives
are fast but not sufficiently generalizable, interpretable, and scalable. To
address these challenges, we propose a novel conditional Gaussian Random
Field-based data-driven method that performs fast and accurate evaluation of
cyberthreats. It achieves speedup of contingency analysis by warm-starting
simulations, i.e., improving starting points, for the physical solvers. To
improve the physical interpretability and generalizability, the proposed method
incorporates domain knowledge by considering the graphical nature of the grid
topology. To improve scalability, the method applies physics-informed
regularization that reduces model complexity. Experiments validate that
simulating MadIoT-induced attacks with our warm starter becomes approximately
5x faster on a realistic 2000-bus system.
