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Regular Paper | Open Access

Data-driven Static Equivalence with Physics-informed Koopman Operators

Wei Lin1, Changhong Zhao2( ), Maosheng Gao3, C. Y. Chung1
Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
School of Electrical Engineering, Chongqing University, Chongqing 400044, China
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Abstract

With deployment of measurement units, fitting static equivalent models of distribution networks (DNs) by linear regression has been recognized as an effective method in power flow analysis of a transmission network. 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 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 mapping can be established through 1) Koopman operator theory, and 2) physical features of power flow equations at the root node of a DN. 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.

Keywords

Data-drivendistribution networkKoopman operator theorystatic equivalent model

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 1,
January 2024
Pages 432-438
DOI: 10.17775/CSEEJPES.2022.08750
Cite this article:
Lin W, Zhao C, Gao M, et al. Data-driven Static Equivalence with Physics-informed Koopman Operators. CSEE Journal of Power and Energy Systems, 2024, 10(1): 432-438. https://doi.org/10.17775/CSEEJPES.2022.08750

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Received: 13 December 2022
Revised: 26 February 2023
Accepted: 15 March 2023
Published: 20 April 2023
© 2022 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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