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

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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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.
Original languageEnglish
JournalIEEE Transactions on Power Systems
VolumePP
Issue number99
Number of pages12
ISSN0885-8950
DOIs
Publication statusAccepted/In press - 2019
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Convex relaxation, Data-driven, Power system analysis, Small-signal stability

ID: 164553529