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When applied to a real-world safety critical system like the power grid,general machine learning methods suffer from expensive training, non-physicalsolutions, and limited interpretability. To address these challenges for powergrids, many recent works have explored the inclusion of grid physics (i.e.,domain expertise) into their method design, primarily through including systemconstraints and technical limits, reducing search space and defining meaningfulfeatures in latent space. Yet, there is no general methodology to evaluate thepracticality of these approaches in power grid tasks, and limitations existregarding scalability, generalization, interpretability, etc. This workformalizes a new concept of physical interpretability which assesses how a MLmodel makes predictions in a physically meaningful way and introduces anevaluation methodology that identifies a set of attributes that a practicalmethod should satisfy. Inspired by the evaluation attributes, the paper furtherdevelops a novel contingency analysis warm starter for MadIoT cyberattack,based on a conditional Gaussian random field. This method serves as an instanceof an ML model that can incorporate diverse domain knowledge and improve onthese identified attributes. Experiments validate that the warm startersignificantly boosts the efficiency of contingency analysis for MadIoT attackeven with shallow NN architectures.

Towards Practical Physics-Informed ML Design and Evaluation for Power Grid