Research on sensor layout optimization and leakage localization of water distribution network based on model

LI Jiayu; WANG Yuqi; WANG Yaoli; CHANG Qing

Chinese

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Research on sensor layout optimization and leakage localization of water distribution network based on model

Research & Approach | 更新时间：2026-06-24

- Research on sensor layout optimization and leakage localization of water distribution network based on model
- Vol. 45, Issue 6, Pages: 47-52(2026)
- 作者机构：
  
  1. 太原理工大学电子信息工程学院,山西,晋中,030600
  2. 山西小浪底引黄水务集团有限公司,山西,太原,030012
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP212;TU991
- Published：2026
- 稿件说明：
移动端阅览
LI Jiayu, WANG Yuqi, WANG Yaoli, et al. Research on sensor layout optimization and leakage localization of water distribution network based on model[J]. 2026, 45(6): 47-52.
DOI：

LI Jiayu, WANG Yuqi, WANG Yaoli, et al. Research on sensor layout optimization and leakage localization of water distribution network based on model[J]. 2026, 45(6): 47-52. DOI：

摘要

高效准确的供水管网泄漏检测与定位技术是近年来研究热点。技术涉及常规压力、流量等传感器优化布局，以及在此基础上的供水管网泄露定位方法。本文首先研究了基于敏感性矩阵的Gram-Schmidt正交化(SMGSO)方法，进行压力传感器的优化布置。然后，研究对比了字典学习和基于模型两种定位方法的定位精度。结果显示:基于模型的定位方法具有较高的定位精度。以太原市某区的供水管网为例，结果表明：本文提出的传感器优化布置，定位精度最高可以提升12.5%。新的定位方法相对内点法的定位精度能提升2.5%。

Abstract

Efficient and accurate leakage detection and localization technology for water distribution networks is a research hotspot in recent years.The technology involves the optimal layout of conventional pressure and flow sensors

as well as the leakage localization method of water distribution network based on this layout.In this paper

the sensitivity matrix-based Gram-Schmidt orthogonalization(SMGSO)method is studied to optimize the layout of pressure sensors.Then

the localization precision of two localization methods including dictionary learning and model-based is studied and compared.The results show that the modelbased localization method has higher localization precision.Taking the water distribution network in a district of Taiyuan as an example

the results show that the proposed optimal sensor layout can improve the localization precision by up to 12.5%.The new localization method can improve the localization precision by 2.5% compared with the interior point method.

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