Joint identification method of distribution network topology and line parameters based on sparse regression and clustering correction

Authors

• Ruifeng Zhang, School of Electrical Engineering ， Southwest Jiaotong University ， Chengdu 611756 ， China
• Bo Li, School of Electrical Engineering ， Southwest Jiaotong University ， Chengdu 611756 ， ChinaFollow
• Kai Liao, School of Electrical Engineering ， Southwest Jiaotong University ， Chengdu 611756 ， China
• Xueshun Ye, China Electric Power Research Institute ， Beijing 100192 ， China
• Jun Zhou, China Electric Power Research Institute ， Beijing 100192 ， China
• Lei Wu, Electric Power Research Institute ， State Grid Tianjin Electric Power Company ， Tianjin 300450 ， China

Keywords

distribution network, joint identification of topology and line parameters, advanced metering, sparseregression, clustering correction

Abstract

The inconsistency between equipment records and actual topology/line parameters during the long-term operation of distribution networks seriously limits the safe and efficient operation of distribution networks.To this end，a joint identification method for distribution network topology and line parameters based on sparse regression and clustering correction is proposed.Firstly，the coupling relationship between measurement data and distribution network topology/line parameters is analyzed to build the joint identification model.Subsequently，a preliminary topology identification-line parameter calculation-joint precision identification method is introduced to pre-identify topology by employing voltage similarity.Then，line parameters are solved via sequential backward selection and sparse regression，and accurate topology is extracted through an improved spatial clustering algorithm to achieve joint identification of distribution topology and line parameters.Finally，simulation experiments are carried out to verify the feasibility and effectiveness of the proposed method.Simulation experiments demonstrate that the proposed method effectively identifies distribution network topology and line parameters，exhibits strong robustness，and adapts well to various complex operating conditions.

DOI

10.19781/j.issn.1673-9140.2026.04.004

First Page

38

Last Page

50

Recommended Citation

Zhang, Ruifeng; Li, Bo; Liao, Kai; Ye, Xueshun; Zhou, Jun; and Wu, Lei (2026) "Joint identification method of distribution network topology and line parameters based on sparse regression and clustering correction," Journal of Electric Power Science and Technology: Vol. 41: Iss. 4, Article 4.
DOI: 10.19781/j.issn.1673-9140.2026.04.004
Available at: https://jepst.researchcommons.org/journal/vol41/iss4/4