  Advances in data-driven methods have sparked renewed interest for
applications in power systems. Creating datasets for successful application of
these methods has proven to be very challenging, especially when considering
power system security. This paper proposes a computationally efficient method
to create datasets of secure and insecure operating points. We propose an
infeasibility certificate based on separating hyperplanes that can a-priori
characterize large parts of the input space as insecure, thus significantly
reducing both computation time and problem size. Our method can handle an order
of magnitude more control variables and creates balanced datasets of secure and
insecure operating points, which is essential for data-driven applications.
While we focus on N-1 security and uncertainty, our method can extend to
dynamic security. For PGLib-OPF networks up to 500 buses and up to 125 control
variables, we demonstrate drastic reductions in unclassified input space
volumes and computation time, create balanced datasets, and evaluate an
illustrative data-driven application.
