Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Data-driven coordinated voltage control method of distribution networks with high DG penetration

Huo, Yanda, Li, Peng, Ji, Haoran, Yu, Hao, Yan, Jinyue, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Wang, Chengshan 2023. Data-driven coordinated voltage control method of distribution networks with high DG penetration. IEEE Transactions on Power Systems 38 (2) , pp. 1543-1557. 10.1109/TPWRS.2022.3172667

[thumbnail of Source File_TPWRS-01725-2021.pdf]
Preview
PDF - Accepted Post-Print Version
Download (1MB) | Preview

Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0885-8950
Date of First Compliant Deposit: 20 July 2022
Date of Acceptance: 30 April 2022
Last Modified: 06 Nov 2023 19:42
URI: https://orca.cardiff.ac.uk/id/eprint/151225

Citation Data

Cited 8 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics