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基于图论法和贝叶斯理论的供水管网漏损定位方法 被引量:3

Leakage Location Method of Water Supply Network Based on Graph Theory and Bayesian Theorem
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摘要 目前大多数漏损定位方法都依赖于管网水力模型,但当水力模型精度无法达到要求或由于基础资料缺失无法建立模型时,基于模型的方法就会失效。为此,以压力监测数据为基础进行供水管网漏损定位研究,即基于图论法建模理论得出插值估计管网节点压力,通过分析各节点压力监测值和漏损发生后实测值的残差估计漏损位置,并借助贝叶斯理论对定位结果进行时序推理,将一定时段内概率最大的节点视为发生漏损的位置。借助L镇案例对管网发生单点漏损时的状态进行模拟,验证了漏损定位方法的可行性和定位性能。 At present,most leakage location methods rely on the hydraulic model of the pipe network.However,when the accuracy of the hydraulic model cannot meet certain requirements,or when the model cannot be established due to the lack of basic data,the model-based method will fail.For this purpose,the study of the leakage location of the water supply network was carried out on the basis of pressure monitoring data.Based on the modeling theory of graph theory,the interpolation method for estimating the node pressure of pipe network was obtained.The leakage position was estimated by analyzing the residual of the pressure monitoring value of each node and the measured value after the leakage occurs.With the help of Bayesian theorem,time-series reasoning was carried out on the positioning results,and the node with the largest probability in a certain period of time was regarded as the location where leakage occurs.By using the case of L Town,the state of pipe network was simulated when single point leakage occured,and the feasibility and location performance of the leakage location method were verified.
作者 王彤 康炳卿 李钟毓 朱多林 王晴怡 赵红斌 许德伦 洪磊 WANG Tong;KANG Bing-qing;LI Zhong-yu;ZHU Duo-lin;WANG Qing-yi;ZHAO Hong-bin;XU De-lun;HONG Lei(School of Civil Engineering and Architecture,Ministry of Housing and Urban-Rural Development,Chang’an University,Xi’an 710061,China;Key Laboratory of Water Supply and Sewerage,Ministry of Housing and Urban-Rural Development,Chang’an University,Xi’an 710061,China)
出处 《水电能源科学》 北大核心 2023年第8期135-138,125,共5页 Water Resources and Power
基金 水资源高效开发利用重点专项(2018YFC0406200)。
关键词 供水管网 漏损定位 图论法 贝叶斯理论 压力监测数据 water supply network leakage location graph theory Bayes theorem pressure monitoring data
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