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