  Power system security assessment methods require large datasets of operating
points to train or test their performance. As historical data often contain
limited number of abnormal situations, simulation data are necessary to
accurately determine the security boundary. Generating such a database is an
extremely demanding task, which becomes intractable even for small system
sizes. This paper proposes a modular and highly scalable algorithm for
computationally efficient database generation. Using convex relaxation
techniques and complex network theory, we discard large infeasible regions and
drastically reduce the search space. We explore the remaining space by a highly
parallelizable algorithm and substantially decrease computation time. Our
method accommodates numerous definitions of power system security. Here we
focus on the combination of N-k security and small-signal stability.
Demonstrating our algorithm on IEEE 14-bus and NESTA 162-bus systems, we show
how it outperforms existing approaches requiring less than 10% of the time
other methods require.
