摘要
供水管网爆管具有定位难、影响范围广的特点,长期困扰着供水企业。针对供水管网爆管区域识别问题,综合考虑多种影响因素下的爆管工况,利用爆管特征值矩阵构建爆管样本数据集,采用极限学习机算法(ELM)建立爆管区域识别模型;应用K-means聚类算法分析节点水力变化特征的相似性,并在此基础上对管网进行监测区域划分与监测点布设,形成多种监测方案;综合爆管识别率等参数,分析ELM在不同监测方案以及在噪声影响下的识别性能。采用实际管网算例进行了爆管区域识别分析,结果表明:该模型可以进行有效的爆管区域识别,同时结合不同分区方案可以提高爆管识别率;监测点的增加可以减小压力监测数据的噪声影响。
Water supply network pipe burst has the characteristics of difficult location and wide range of influence,which has troubled water supply enterprises for a long time.To solve the problem of identifying the pipe burst area of water supply networks,the burst conditions were comprehensively considered under various influencing factors,the pipe burst sample data set was constructed by using eigenvalue matrix,and the model for identifying the pipe burst area was established by extreme learning machine(ELM)algorithm.The similarity of node hydraulic change characteristics was analyzed by using K-means clustering algorithm.On this basis,the monitoring area of the pipe network was divided and the monitoring points were arranged to form a variety of monitoring schemes.The identification performance of ELM under different monitoring schemes and noise impact was analyzed by combining the identification rate of pipe burst and other parameters.The burst area was identified and analyzed in a practical pipe network.It was found that the model could effectively identify the pipe burst area.At the same time,it could effectively improve the identification rate of pipe burst by combining different zoning schemes.The addition of monitoring points could reduce the noise impact from pressure monitoring data.
作者
彭森
程蕊
吴卿
程景
孟涛
PENG Sen;CHENG Rui;WU Qing;CHENG Jing;MENG Tao(School of Environmental Science and Engineering,Tianjin University,Tianjin 300350,China;North China Municipal Engineering Design&Research Institute Co.Ltd.,Tianjin 300381,China)
出处
《中国给水排水》
CAS
CSCD
北大核心
2022年第7期56-62,共7页
China Water & Wastewater
基金
国家重点研发计划项目(2016YFC0802400)。
关键词
供水管网
爆管识别
极限学习机算法
K-MEANS聚类算法
water supply network
identification of pipe burst area
ELM algorithm
K-means clustering algorithm
