  Deep decarbonization of the energy sector will require massive penetration of
stochastic renewable energy resources and an enormous amount of grid asset
coordination; this represents a challenging paradigm for the power system
operators who are tasked with maintaining grid stability and security in the
face of such changes. With its ability to learn from complex datasets and
provide predictive solutions on fast timescales, machine learning (ML) is
well-posed to help overcome these challenges as power systems transform in the
coming decades. In this work, we outline five key challenges (dataset
generation, data pre-processing, model training, model assessment, and model
embedding) associated with building trustworthy ML models which learn from
physics-based simulation data. We then demonstrate how linking together
individual modules, each of which overcomes a respective challenge, at
sequential stages in the machine learning pipeline can help enhance the overall
performance of the training process. In particular, we implement methods that
connect different elements of the learning pipeline through feedback, thus
"closing the loop" between model training, performance assessments, and
re-training. We demonstrate the effectiveness of this framework, its
constituent modules, and its feedback connections by learning the N-1
small-signal stability margin associated with a detailed model of a proposed
North Sea Wind Power Hub system.
