Contingency Analyses with Warm Starter using Probabilistic Graphical Model
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Cyberthreats are an increasingly common risk to the power grid and can thwart secure grid operation. We propose to extend contingency analysis (CA) that is currently used to secure the grid against natural threats to protect against cyberthreats. However, unlike traditional N-1 or N-2 contingencies, cyberthreats (e.g., MadIoT) require CA to solve harder N-k (with k >> 2) contingencies in a practical amount of time. Purely physics-based solvers, while robust, are slow and may not solve N-k contingencies in a timely manner, whereas the emerging data-driven alternatives to power grid analytics are fast but not sufficiently generalizable, interpretable, or scalable. To address these challenges, we propose a novel conditional Gaussian Random Field-based data-driven method that is bothfast and robust. It achieves speedup by 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 the model size and complexity. Experiments validate that simulating MadIoT-induced N-k contingencies with our warm starter requires 5x fewer iterations for a realistic 2000-bus system.
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