Data-driven static equivalence with physics-informed Koopman operators
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AbstractWith the deployment of measurement units, fitting the static equivalent models of distribution networks (DNs) by linear regression has been recognized as an effective method in the power flow analysis of a transmission network. The increasing volatility of measurements caused by variable distributed renewable energy sources makes it more difficult to accurately fit such equivalent models. To tackle this challenge, this letter proposes a novel data-driven method to improve the equivalency accuracy of DNs with distributed energy resources. This letter provides a new perspective that an equivalent model can be regarded as a mapping from internal conditions and border voltages to border power injections. Such a mapping can be established through 1) Koopman operator theory, and 2) physical features of power flow equations at the root node of a DN. The performance of the proposed method is demonstrated on the IEEE 33-bus and IEEE 136-bus test systems connected to a 661-bus utility system.
Acceptance Date20/04/2023
All Author(s) ListWei Lin, Changhong Zhao, Maosheng Gao, C. Y. Chung
Journal nameCSEE Journal of Power and Energy Systems
Year2024
Month1
Volume Number10
Issue Number1
PublisherCSEE
Pages432 - 438
eISSN2096-0042
LanguagesEnglish-United States

Last updated on 2024-16-10 at 14:11