Data-driven Based Identification Method of Feeder-Consumer Connectivity in Low-voltage Distribution Network

Jie TANG; Yongzhi CAI; Lai ZHOU; Yingqi YI; Guopei CHEN; Shangda LIANG; Yongjun ZHANG

doi:10.7500/AEPS20191021007

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Data-driven Based Identification Method of Feeder-Consumer Connectivity in Low-voltage Distribution Network

Application Research | 更新时间：2025-06-16

- Data-driven Based Identification Method of Feeder-Consumer Connectivity in Low-voltage Distribution Network
- Automation of Electric Power Systems Vol. 44, Issue 11, Pages: 127-134(2020)
- 作者机构：
  
  1.广东电网有限责任公司，广东省广州市 510700
  2.智慧能源工程技术研究中心，华南理工大学电力学院，广东省广州市 510640
  3.广州市奔流电力科技有限公司，广东省广州市 510700
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20191021007
  CLC：
- Received：21 October 2019，
  
  Revised：2020-02-02，
  
  Published Online：03 April 2020，
  
  Published：10 June 2020
- 稿件说明：
移动端阅览
唐捷,蔡永智,周来等.基于数据驱动的低压配电网线户关系识别方法[J].电力系统自动化,2020,44(11):127-134.

TANG Jie,CAI Yongzhi,ZHOU Lai,et al.Data-driven Based Identification Method of Feeder-Consumer Connectivity in Low-voltage Distribution Network[J].Automation of Electric Power Systems,2020,44(11):127-134.
唐捷,蔡永智,周来等.基于数据驱动的低压配电网线户关系识别方法[J].电力系统自动化,2020,44(11):127-134. DOI： 10.7500/AEPS20191021007.

TANG Jie,CAI Yongzhi,ZHOU Lai,et al.Data-driven Based Identification Method of Feeder-Consumer Connectivity in Low-voltage Distribution Network[J].Automation of Electric Power Systems,2020,44(11):127-134. DOI： 10.7500/AEPS20191021007.

摘要

低压配电网的线户关系自动识别是智能低压配电网实现的基础。通过分析低压配电网中负荷节点电压所呈现的空间、时间以及时空特性，依托“互联网+”智慧能源发展带来的海量电网运行数据，提出了基于数据驱动的低压配电网线户关系识别方法。通过建立基于基尔霍夫电流定律的优化模型，并将其转化为二次规划问题求解，实现低压配电网线户关系识别。同时为了提高求解效率并解决空房用户的识别问题，提出了一种基于电压时空特性的电能表聚类方法。73用户算例系统和多个试点台区应用效果验证了所提方法的有效性，并分析了电能表计量误差、数据长度对识别结果的影响。

Abstract

The automatic identification of feeder-consumer connectivity in low-voltage distribution network (LVDN) is the basis of intelligent LVDN. By analyzing the space

time and spatiotemporal characteristics of the voltage for load nodes in LVDN

a data-driven based identification method of feeder-consumer connectivity in LVDN is proposed

relying on the massive operation data of power grid produced by the development of the “Internet +” smart energy. An optimization model based on Kirchhoff’s current law is established to identify feeder-consumer connectivity

which is transformed into a quadratic programming problem. At the same time

in order to improve the solution efficiency and solve the identification problem for vacant users

an electricity meter clustering method based on voltage spatiotemporal characteristics is proposed. The effectiveness of the proposed method is verified on a case system with 73 users and multiple pilot LVDNs. Furthermore

the influence of meter measurement error and data length on the recognition results is investigated.

关键词

Keywords

references

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