  Machine learning techniques have been used in the past using Monte Carlo
samples to construct predictors of the dynamic stability of power systems. In
this paper we move beyond the task of prediction and propose a comprehensive
approach to use predictors, such as Decision Trees (DT), within a standard
optimization framework for pre- and post-fault control purposes. In particular,
we present a generalizable method for embedding rules derived from DTs in an
operation decision-making model. We begin by pointing out the specific
challenges entailed when moving from a prediction to a control framework. We
proceed with introducing the solution strategy based on generalized disjunctive
programming (GDP) as well as a two-step search method for identifying optimal
hyper-parameters for balancing cost and control accuracy. We showcase how the
proposed approach constructs security proxies that cover multiple contingencies
while facing high-dimensional uncertainty with respect to operating conditions
with the use of a case study on the IEEE 39-bus system. The method is shown to
achieve efficient system control at a marginal increase in system price
compared to an oracle model.
