Efficient Database Generation for Data-driven Security Assessment of Power Systems
It addresses the bottleneck of computationally expensive database generation for data-driven power system security assessment.
The paper proposes a modular and scalable algorithm for generating large datasets of power system operating points for security assessment, reducing computation time to less than 10% of existing methods on IEEE 14-bus and NESTA 162-bus systems.
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.