Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning

Zhiwei Liao (), Ye Liu, Bowen Wang and Wenjuan Tao
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Zhiwei Liao: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Ye Liu: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Bowen Wang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Wenjuan Tao: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2024, vol. 17, issue 7, 1-17

Abstract: Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example.

Keywords: active low-voltage distribution network; Elastic-Net regression; AMIE algorithm; knowledge reasoning; segment location; hierarchical topology identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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