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Leakage localization in water distribution using data-driven models and sensitivity analysis

机译:利用数据驱动的模型和敏感性分析泄漏定位在水分布中

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Water scarcity is becoming an increasing problem worldwide, and an issue compounding the problem is water leakage in the piping networks delivering potable/consumable water to end-users (Sensus, 2012). In this paper, we consider the problem of isolating leakages in water supply networks using reduced network models. Using a reduced order model of the network, the expected behaviour of the network can be estimated and then compared with actual measurements obtained from the network. The result of this comparison is a set of residuals which are used to isolate a leakage to a network node. The localization is based on a sensitivity matrix which captures the residuals’ sensitivities to leakages. As the reduced order model is adaptive based on measurements from the network, the reduced order model is plug-and-play commissionable. The calculation of the sensitivity matrix is based on an EPANET model of the network and is performed off-line.
机译:水资源稀缺正在成为全球范围内的越来越多的问题,并将问题复杂的问题是管道网络中的漏水,将饮用/消费水送到最终用户(Sensus,2012)。在本文中,我们考虑使用减少的网络模型来分离供水网络中泄漏的问题。使用网络的减少阶模型,可以估计网络的预期行为,然后与从网络获得的实际测量进行比较。该比较的结果是一组残差,用于将泄漏与网络节点隔离。本地化基于灵敏度矩阵,其捕获残留对泄漏的敏感性。随着降低的订单模型是基于来自网络的测量值的自适应,降低的订单模型是即插即用的诱导。灵敏度矩阵的计算基于网络的EPANET模型,并执行离线。

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