A1 Journal article (refereed)
Graph Laplace Regularization-based pressure sensor placement strategy for leak localization in the water distribution networks under joint hydraulic and topological feature spaces (2024)

Cheng, M., Li, J., Wang, C., Ye, C., & Chang, Z. (2024). Graph Laplace Regularization-based pressure sensor placement strategy for leak localization in the water distribution networks under joint hydraulic and topological feature spaces. Water Research, In Press. https://doi.org/10.1016/j.watres.2024.121666

JYU authors or editors

Publication details

All authors or editors: Cheng, Menglong; Li, Juan; Wang, Chunyue; Ye, Chaoxiong; Chang, Zheng

Journal or series: Water Research

ISSN: 0043-1354

eISSN: 1879-2448

Publication year: 2024

Volume: In Press

Publisher: Elsevier

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1016/j.watres.2024.121666

Publication open access: Not open

Publication channel open access: 

Abstract

Urban water distribution networks (WDNs) have wide range and intricate topology, which include leakage, pipe burst and other abnormal states during production and operation. With the continuous development of the Internet of Things (IoT) technology in recent years, the means of monitoring the WDNs by using wireless sensor network technology has gradually received attention and extensive research. Most of the existing researches select the deployment location of sensors according to the hydraulic state of the WDNs, but the connectivity and topology between the nodes of the WDNs are not fully considered and analyzed. In this study, a new method that can integrate the topological features and hydraulic model information of the WDN is proposed to solve the problem of optimal sensor placement. First, the method preprocesses the covariance matrix of the pressure sensitivity matrix of the water distribution network by a diffusion kernel-based data prefiltering method and obtains the new network topology weights and its Laplacian matrix under the constraints of the network topology through a data-based graphical Laplacian learning method. Then, the sensor placement problem is transformed into a matrix minimum eigenvalue constraint problem by the graph Laplace regularization (GLR)-based method, and finally the selection of sensor nodes is accomplished by the method based on Gershgorin Disc Alignment (GDA). The proposed strategy is tested on a passive Hanoi network, an active Net 3 network, and a larger network, PA2, and is compared with some existing methods. The results show that the proposed solution achieves good performance in three different leak localization methods.

Keywords: water distribution; water pipes; leakages; leakage water

Free keywords: Graph Laplace Regularization (GLR); optimal sensor placement; leak localization; water distribution network; graph learning

Contributing organizations

Ministry reporting: Yes

Preliminary JUFO rating: 3