Article(id=1223210594233340365, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221586, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1656691200000, receivedDateStr=2022-07-02, revisedDate=1661356800000, revisedDateStr=2022-08-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1769565731819, onlineDateStr=2026-01-28, pubDate=1687622400000, pubDateStr=2023-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769565731819, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769565731819, creator=13701087609, updateTime=1769565731819, updator=13701087609, issue=Issue{id=1223210584024400210, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='6', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769565729385, creator=13701087609, updateTime=1769593153259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223325608164348105, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223325608164348106, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223210584024400210, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=98, endPage=101, ext={EN=ArticleExt(id=1223210594585661929, articleId=1223210594233340365, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Calculation of Water Supply Network Leakage Based on Kalman Filter Algorithm, columnId=1222925283779400191, journalTitle=Water Resources and Power, columnName=WATER CONSERVANCY AND HYDROPOWER ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

With the acceleration of urbanization in China, the demand for water resources is increasing, and the leakage of the water supply pipe network has caused waste of water resources. Therefore, leakage control is imminent. The first is to accurately calculate the leakage of the pipe network, but the current methods have certain deficiencies. Based on the Kalman filter, this paper establishes an analysis model of pipeline network leakage, and uses the empirical formula of node leakage, standard Kalman filter (SKF) and innovation adaptive Kalman filter (IAKF) to calculate the leakage of the pipeline network, respectively. Taking the actual pipe network project as an example, the EPANET simulation was carried out, and the calculation results were compared. The results show that using both filtering algorithms is more accurate than the nodal leakage formula, and the result of the IAKF algorithm is better than that of the SKF algorithm. This provides a theoretical basis for accelerating the research on leakage control technology and implementing scientific and effective leakage control methods, which has certain significance for urban water saving and sustainable development.

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随着我国城市化进程的加快，对水资源的需求与日俱增，而供水管网的漏损造成了水资源的大量浪费，因此供水管网漏损控制研究已迫在眉睫。其中需要精确计算管网漏失量，但目前方法均存在一定不足。基于Kalman滤波，建立管网漏失分析模型，分别运用节点漏失量经验公式、标准卡尔曼滤波算法（SKF）与新息自适应卡尔曼滤波算法（IAKF）计算管网的漏失量。以实际管网工程为例进行EPANET模拟，并将所得计算结果进行比较。结果表明，两种滤波算法计算结果均比节点漏失公式更加精确，且IAKF算法的计算结果优于SKF算法。这也为加快漏损控制技术研究，实施科学有效的漏损控制手段提供理论依据，对城市节水和可持续发展具有一定的意义。

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王一霖（1998-），男，硕士研究生，研究方向为给排水系统分析与优化，E-mail：jwwsc995@outlook.com

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王一霖（1998-），男，硕士研究生，研究方向为给排水系统分析与优化，E-mail：jwwsc995@outlook.com

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