An angle-based leak detection method using pressure sensors in water distribution networks

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Huimin Yu, Hua Zhou, Xiaodan Weng, Zhihong Long, Yu Shao, Tingchao Yu
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引用次数: 0

Abstract

Abstract Leak detection has significant implications for the long-term stable operation of water distribution networks (WDNs). This study presented a novel leak detection method by calculating the angular variance between a pressure vector and other vectors in the database, to evaluate the presence of an anomaly in a network. The top priority for this method was to establish a reliable dataset collected from the pressure sensors, which is generated by EPANET 2.2. Numerous node water demand data in normal conditions were generated by the Monte Carlo method, and leak conditions with various leak flows were simulated by creating leak holes in the pipes. Through learning the composite normal and abnormal data in a certain proportion, the angle-based outlier detection model was employed to identify abnormal events. This angle-based method was applied in an actual WDN and the identification performance for anomalies was compared with that of previous detection methods. The results indicated that the novel method proposed in this study could significantly improve the accuracy and efficiency of leak detection compared to the threshold-based and distance-based detection methods.
基于角度的配水管网压力传感器泄漏检测方法
泄漏检测对供水管网的长期稳定运行具有重要意义。该研究提出了一种新的泄漏检测方法,通过计算数据库中压力矢量与其他矢量之间的角方差来评估网络中异常的存在。该方法的首要任务是建立由EPANET 2.2生成的压力传感器收集的可靠数据集。采用蒙特卡罗方法生成大量节点正常情况下的需水量数据,并通过在管道上设置漏孔模拟不同泄漏流量下的泄漏情况。通过学习一定比例的正异常复合数据,采用基于角度的离群点检测模型对异常事件进行识别。将这种基于角度的方法应用于实际WDN中,并与以往检测方法的异常识别性能进行了比较。结果表明,与基于阈值和距离的检测方法相比,本文提出的新方法可以显著提高泄漏检测的准确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
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