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Data-driven Coordinated Voltage Control Method of Distribution Networks with High DG Penetration
School of Electrical Engineering & Automation, Tianjin University, Tianjin, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin, Tianjin, China.
School of Electrical Engineering&Automation, Tianjin University, Tianjin, China.
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
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2023 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 38, no 2, p. 1543-1557Article in journal (Refereed) Published
Abstract [en]

The highly penetrated distributed generators (DGs) aggravate the voltage violations in active distribution networks (ADNs). The coordination of various regulation devices such as on-load tap changers (OLTCs) and DG inverters can effectively address the voltage issues. Considering the problems of inaccurate network parameters and rapid DG fluctuation in practical operation, multi-source data can be utilized to establish the data-driven control model. In this paper, a data-driven coordinated voltage control method with the coordination of OLTC and DG inverters on multiple time-scales is proposed without relying on the accurate physical model. First, based on the multi-source data, a data-driven voltage control model is established. Multiple regulation devices such as OLTC and DG are coordinated on multiple time-scales to maintain voltages within the desired range. Then, a critical measurement selection method is proposed to guarantee the voltage control performance under the partial measurements in practical ADNs. Finally, the proposed method is validated on the modified IEEE 33-node and IEEE 123-node test cases. Case studies illustrate the effectiveness of the proposed method, as well as the adaptability to DG uncertainties.

Place, publisher, year, edition, pages
2023. Vol. 38, no 2, p. 1543-1557
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-61144DOI: 10.1109/tpwrs.2022.3172667ISI: 000965715100001Scopus ID: 2-s2.0-85132519095OAI: oai:DiVA.org:mdh-61144DiVA, id: diva2:1717001
Funder
Swedish Research Council, 2018-06007Available from: 2022-12-07 Created: 2022-12-07 Last updated: 2023-06-14Bibliographically approved

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Yan, Jinyue

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