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CN 34-1054/N

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Research on leakage location of secondary water distribution networks

  • Anhui Provincial Key Laboratory of Intelligent Building, Anhui Jianzhu University, Hefei 230022, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.04.008
  • Received Date: 08 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 30 April 2017
    Abstract

  • Abstract

    Solving the leakage problem of the secondary water distribution networks often requires the combination of detection instruments and worker experience. However, this kind of method has several disadvantages, such time consumption, low efficiency and strong subjectivity. A new leakage-location method based on data analysis was proposed. The method gathered data from networks’ pressure monitoring points at a high frequency and then built a data set under a no-leakage condition. K-means clustering algorithm was used to classify the data set, thus obtaining the pressure data features in different times. Comparing the new nodal pressure vector with the data set, one can find whether there is leakage and where it is. Experimental results show that the method can help locate leakage in secondary water distribution networks. Compared with the existing methods, the proposed approach is faster and more objective and of higher practical value.

    Abstract

    Solving the leakage problem of the secondary water distribution networks often requires the combination of detection instruments and worker experience. However, this kind of method has several disadvantages, such time consumption, low efficiency and strong subjectivity. A new leakage-location method based on data analysis was proposed. The method gathered data from networks’ pressure monitoring points at a high frequency and then built a data set under a no-leakage condition. K-means clustering algorithm was used to classify the data set, thus obtaining the pressure data features in different times. Comparing the new nodal pressure vector with the data set, one can find whether there is leakage and where it is. Experimental results show that the method can help locate leakage in secondary water distribution networks. Compared with the existing methods, the proposed approach is faster and more objective and of higher practical value.
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    • References(12)
  • [1]
    陆韬，刘燕，李佳，等. 我国供水管网漏损现状及控制措施研究[J]. 复旦学报(自然科学版), 2013, 52(6): 807-810, 816.
    LU Tao, LIU Yan, LI Jia, et al. Leakage situation and control solution of china water supply pipeline [J]. Journal of Fudan University (Natural Science), 2013, 52(6): 807-810, 816.
    [2]
    董深,吕谋,盛泽斌,等. 基于遗传算法的供水管网反问题漏失定位[J]. 哈尔滨工业大学学报, 2013, 45(2): 106-110.
    DONG Shen, LU Mou, SHENG Zebin, et al. Inverse transient leakage location of water supply network based on genetic algorithm[J]. Journal of Harbin Institute of Technology, 2013, 45(2): 106-110.
    [3]
    PALLETI V R, NARASIMHAN S, RENGASWAMY R, et al. Sensor network design for contaminant detection and identification in water distribution networks[J]. Computers & Chemical Engineering, 2016, 87(6): 246-256.
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    [5]
    豆海涛,黄宏伟,薛亚东. 隧道衬砌渗漏水红外辐射特征影响因素试验研究[J]. 岩石力学与工程学报, 2011, 30(12): 2426-2434.
    DOU Haitao, HUANG Hongwei, XUE Yadong.Experimental study of factors affecting thermal infrared radiation characteristics of tunnel lining water leakage[J]. Chinese Journal of Rock Mechanics and Engineering, 2011, 30(12): 2426-2434.
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    LAUMONIER M, GAILLARD F, SIFIRE D. The effect of pressure and water concentration on the electrical conductivity of dacitic melts: Implication for magnetotelluric imaging in subduction areas[J]. Chemical Geology, 2015, 418(15): 66-76.
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    SUSANTO V, SASAKI K, SUGAI Y, et al. Field test study on leakage monitoring at a geological CO2 storage site using hydrogen as a tracer [J]. International Journal of Greenhouse Gas Control, 2016, 50: 37-48.
    [8]
    LIU Cuiwei, LI Yuxing, FU Juntao, et al. Experimental study on acoustic propagation-characteristics-based leak location method for natural gas pipelines[J]. Process Safety and Environmental Protection, 2015, 96: 43-60.
    [9]
    CUI Xiwang, YAN Yong, MA Yifan, et al. Localization of CO2 leakage from transportation pipelines through low frequency acoustic emission detection[J]. Sensors and Actuators A: Physical, 2016, 237(1): 107-118.
    [10]
    李国杰，程学旗. 大数据研究：未来科技及经济社会发展的重大领域——大数据的研究现状与科学思考[J].中国科学院院刊, 2012, 27(6): 647-657.
    LI Guojie, CHENG Xueqi. Research status and scientific thinking of big data [J]. Bulletin of the Chinese Academy of Sciences, 2012, 27(6): 647-657.
    [11]
    ZHONG C M, MALINEN M, MIAO D Q, et al. A fast minimum spanning tree algorithm based on K-means [J]. Information Sciences, 2015, 295(C): 1-17.
    [12]
    SHAHRIVARI S, JALILI S. Single-pass and linear-time K-means clustering based on MapReduce [J]. Information Systems, 2016, 60(C): 1-12.
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    [1]
    陆韬，刘燕，李佳，等. 我国供水管网漏损现状及控制措施研究[J]. 复旦学报(自然科学版), 2013, 52(6): 807-810, 816.
    LU Tao, LIU Yan, LI Jia, et al. Leakage situation and control solution of china water supply pipeline [J]. Journal of Fudan University (Natural Science), 2013, 52(6): 807-810, 816.
    [2]
    董深,吕谋,盛泽斌,等. 基于遗传算法的供水管网反问题漏失定位[J]. 哈尔滨工业大学学报, 2013, 45(2): 106-110.
    DONG Shen, LU Mou, SHENG Zebin, et al. Inverse transient leakage location of water supply network based on genetic algorithm[J]. Journal of Harbin Institute of Technology, 2013, 45(2): 106-110.
    [3]
    PALLETI V R, NARASIMHAN S, RENGASWAMY R, et al. Sensor network design for contaminant detection and identification in water distribution networks[J]. Computers & Chemical Engineering, 2016, 87(6): 246-256.
    [4]
    KYLILI A, FOKAIDES P A, CHRISTOU P, et al. Infrared thermography (IRT) applications for building diagnostics: A review[J]. Applied Energy, 2014, 134(1): 531-549.
    [5]
    豆海涛,黄宏伟,薛亚东. 隧道衬砌渗漏水红外辐射特征影响因素试验研究[J]. 岩石力学与工程学报, 2011, 30(12): 2426-2434.
    DOU Haitao, HUANG Hongwei, XUE Yadong.Experimental study of factors affecting thermal infrared radiation characteristics of tunnel lining water leakage[J]. Chinese Journal of Rock Mechanics and Engineering, 2011, 30(12): 2426-2434.
    [6]
    LAUMONIER M, GAILLARD F, SIFIRE D. The effect of pressure and water concentration on the electrical conductivity of dacitic melts: Implication for magnetotelluric imaging in subduction areas[J]. Chemical Geology, 2015, 418(15): 66-76.
    [7]
    SUSANTO V, SASAKI K, SUGAI Y, et al. Field test study on leakage monitoring at a geological CO2 storage site using hydrogen as a tracer [J]. International Journal of Greenhouse Gas Control, 2016, 50: 37-48.
    [8]
    LIU Cuiwei, LI Yuxing, FU Juntao, et al. Experimental study on acoustic propagation-characteristics-based leak location method for natural gas pipelines[J]. Process Safety and Environmental Protection, 2015, 96: 43-60.
    [9]
    CUI Xiwang, YAN Yong, MA Yifan, et al. Localization of CO2 leakage from transportation pipelines through low frequency acoustic emission detection[J]. Sensors and Actuators A: Physical, 2016, 237(1): 107-118.
    [10]
    李国杰，程学旗. 大数据研究：未来科技及经济社会发展的重大领域——大数据的研究现状与科学思考[J].中国科学院院刊, 2012, 27(6): 647-657.
    LI Guojie, CHENG Xueqi. Research status and scientific thinking of big data [J]. Bulletin of the Chinese Academy of Sciences, 2012, 27(6): 647-657.
    [11]
    ZHONG C M, MALINEN M, MIAO D Q, et al. A fast minimum spanning tree algorithm based on K-means [J]. Information Sciences, 2015, 295(C): 1-17.
    [12]
    SHAHRIVARI S, JALILI S. Single-pass and linear-time K-means clustering based on MapReduce [J]. Information Systems, 2016, 60(C): 1-12.
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