Efficient Database Generation for Data-driven Security Assessment of Power Systems

TL;DR

This paper introduces a scalable, efficient algorithm for generating large, realistic power system security datasets by combining convex relaxation and network theory, significantly reducing computation time.

Contribution

The paper presents a novel modular algorithm that leverages convex relaxation and parallel processing to efficiently generate security assessment datasets for power systems.

Findings

Outperforms existing methods with less than 10% of their computation time

Successfully applied to IEEE 14-bus and NESTA 162-bus systems

Effectively handles multiple security definitions including N-k and small-signal stability

Abstract

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.