矩阵算法和优化算法相结合的配电网故障定位

XU Biao

您当前的位置：

首页 >

文章列表页 >

矩阵算法和优化算法相结合的配电网故障定位

更新时间：2025-08-13

- 矩阵算法和优化算法相结合的配电网故障定位
- Automation of Electric Power Systems Vol. 43, Issue 5, Pages: 152-158(2019)
- 作者机构：
  
  强电磁工程与新技术国家重点实验室, 华中科技大学,湖北省,武汉市,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
XU Biao. [J]. Automation of electric power systems, 2019, 43(5): 152-158.
DOI：

XU Biao. [J]. Automation of electric power systems, 2019, 43(5): 152-158. DOI：

摘要

区段定位是配电网故障处理和供电恢复的重要环节。针对现有矩阵算法容错性差，而优化算法定位速度慢的问题，将两者进行优势互补，提出一种矩阵算法和优化算法相结合的配电网故障定位方法。首先，从故障告警的因果关联关系出发，建立配电网的矩阵描述并构建故障区段定位的改进矩阵判据，在告警信息正常时能够快速准确定位故障区段。其次，考虑存在告警信息畸变时，根据矩阵判据结果筛选可疑区段，并在此基础上构建优化模型进行容错判断，有效实现高容错性故障定位。最后，通过不同配电网系统算例仿真验证了所提定位方法的有效性和容错性。

Abstract

Fault location is an important link for fault emergency treatment and power restoration of distribution network. The existing matrix algorithm based method has poor fault tolerance

and the optimization algorithm based method has slow speed. To solve the promblems above

a comprehensive approach for distribution network fault location

which combines the advantages of both methods

is proposed in this paper. Firstly

the matrix description of the distribution network is established through causal analysis

based on which the improved matrix criterion is established to identity the fault section rapidly under normal-alarm condition. Secondly

the matrix criterion is used to filter suspicious sections under distorted-alarm condition

based on which the optimization model is constructed to achieve fault location with strong fault tolerance. Finally

the effectiveness and fault tolerance of the proposed method are verified by many case simulations of different distribution networks.

关键词

Keywords

references

Views

1938

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Detection and Location Method of Single-phase-to-ground Fault in Distribution Network Based on Zero-sequence Transient Fault Component

Fast Fault Back-tracing Method for Large-scale Complex Distribution Networks Based on Dynamic Graph Projection

Fault Location for Active Distribution Network with High Penetration Rate Based on Improved Spatio-Temporal Graph Neural Network

Related Author

LI Lin

GAO Houlei

YUAN Tong

PENG Fang

ZHANG Yujia

YUAN Ye

ZHOU Suyang

ZHU Hong

Related Institution

School of Electrical Engineering, Shandong University

School of Electrical and Information Engineering, Jiangsu University

School of Electrical Engineering, Southeast University

Nanjing Power Supply Branch of State Grid Jiangsu Electric Power Co., Ltd.

State Grid Smart Grid Research Institute Co., Ltd.

⁰