集中供热管网泄漏的分类识别
摘要：提出了一种集中供热管网泄漏位置的分类识别方法。该方法首先利用管网的非稳态水力仿真对集中供热管网某管道发生泄漏的工况进行数值模拟，并记录各换热站的供水压力变化；其次，基于压力信号的经验模态分解(EMD),得到本征模态函数(IMF),利用特征分解构建集中供热管网的泄漏工况数据集；最后，基于K-means算法进行管网分区，并利用K最邻近(KNN)算法进行管网泄漏的分类识别。结果表明，该方法具有较好的性能。
关键词：供热管网非稳态水力模型数值模拟泄漏诊断分类识别
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[2] ZHOU S,O'NEILL Z,O'NEILL C.A review of leakage detection methods for district heating networks[J].Applied thermal engineering,2018,137:567- 574.
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[5] WANG L,ZHANG H,JIA H.A leak detection method based on EPANET and genetic algorithm in aater distribution systems[J].Advances in intelligent and soft computing,2012,114:459- 465.
[6] POULAKIS Z,VALOUGEORGIS D,PAPADIMITRIOU C.Leakage detection in water pipe networks using a Bayesian probabilistic framework[J].Probabilistic engineering mechanics,2003,18(4):315- 327.
[7] 高扬，李彪，曹慧哲，等.环状热网泄漏检测模型研究[J].煤气与热力，2015,35(4):8- 12.
[8] 由世俊，米雷洋，王雅然，等.集中供热管网的非稳态水力建模与动态响应分析[J].天津大学学报(自然科学与工程技术版),2019,52(8):849- 856.
[9] 杜坤，龙天渝，刘佳，等.渗漏供水管网水力特性研究[J].给水排水，2014,50(1):152- 156.
[10] 杜坤.供水管网水力模型校核与漏损定位研究[D].重庆：重庆大学，2014:87- 111.
[11] SUN J,WANG R,DUAN H.Multiple-fault detection in water pipelines using transient-based time-frequency analysis[J].Journal of hydro informatics,2016,18(6):975- 989.
[12] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the royal society a-mathematical physical and engineering sciences,1998,454:903- 995.
[13] HUANG N E,SHEN S S P.希尔伯特-黄变换及其应用[M].张海勇，韩东，王芳，等译.北京：国防工业出版社，2017:10- 35.
[14] 贺伟.HHT理论算法及其在现代信号处理中的应用研究[M].长春：吉林大学出版社，2019:2- 15.
[15] COVER T,HART P.Nearest neighbor pattern classification[J].IEEE transactions on information theory,1967,13(1):21- 27.
[16] 米雷洋.基于粒子群算法的大型多热源环状管网优化调度技术研究[D].天津：天津大学，2018:25- 44.
[17] 王振武.数据挖掘算法原理与实现[M].北京：清华大学出版社，2017:159- 184.
Classification and identification of leakage in central heating networks
Abstract: In this paper, a classification and identification method for the leakage location of central heating networks is proposed. Firstly, the unsteady hydraulic simulation of the pipe network is used to simulate the leakage of a pipe in the central heating network, and the change of water supply pressure of each heat exchange station is recorded. Secondly, based on the empirical mode decomposition(EMD) of pressure signal, the intrinsic mode function(IMF) is obtained, and the leakage condition data set of central heating network is constructed by using the eigen decomposition. Finally, the K-means algorithm is used to partition the pipe network, and the K-nearest neighbor(KNN) algorithm is used to classify and identify the leakage of the pipe network. The results show that the method has good performance.
Keywords: heating network; unsteady hydraulic model; numerical simulation; leakage identification; classification and identification;
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