用一个流量计找到它们所有的水网泄漏定位

Iyswarya Narayanan, Arunchandar Vasan, V. Sarangan, A. Sivasubramaniam
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引用次数: 17

摘要

泄漏定位是全球水务公司面临的主要问题。理想情况下,泄漏是由网络范围的计量基础设施检测和定位的。然而,在许多公用事业中,网内计量很少出现在称为区域计量区域(DMA)的子网的入口。我们考虑了泄漏定位的问题,使用数据从单个流量计放置在DMA的入口。我们使用标准的基于时间序列的建模来检测电流表读数是否泄漏,如果是,则估计过量流量。传统的方法使用先验的完全校准的水力模型来将多余的流量映射回一组候选泄漏位置。然而,获得一个精确的水力模型是昂贵的,因此,超出了许多水务公司的能力范围。我们提出了一种替代方法,以一种新颖的方式利用网络结构和静态特性。具体地说,我们将中心性度量的使用扩展到基础架构域,并使用这些度量从多余的泄漏流映射到候选泄漏位置。我们在基准水务公司网络拓扑以及从欧洲水务公司获得的真实数据上评估我们的方法。在基准拓扑上,我们的方法得到的局部化与一个完整的水力模型得到的局部化相当。在一个真实的网络中,我们能够定位我们可以访问的三个泄漏中的两个。在这两种情况下,我们发现实际泄漏位置在我们的方法确定的候选集中;此外,该方法修剪了多达78%的DMA位置,表明高度本地化。
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One meter to find them all-water network leak localization using a single flow meter
Leak localization is a major issue faced by water utilities worldwide. Leaks are ideally detected and localized by a network-wide metering infrastructure. However, in many utilities, in-network metering is minimally present at just the inlets of subnetworks called District Metering Area (DMA). We consider the problem of leak localization using data from a single flow meter placed at the inlet of a DMA. We use standard time-series based modeling to detect if a current meter reading is a leak or not, and if so, to estimate the excess flow. Conventional approaches use an a-priori fully calibrated hydraulic model to map the excess flow back to a set of candidate leak locations. However, obtaining an accurate hydraulic model is expensive and hence, beyond the reach of many water utilities. We present an alternate approach that exploits the network structure and static properties in a novel way. Specifically, we extend the use of centrality metrics to infrastructure domains and use these metrics to map from the excess leak flow to the candidate leak location(s). We evaluate our approach on benchmark water utility network topologies as well as on real data obtained from an European water utility. On benchmark topologies, the localization obtained by our method is comparable to that obtained from a complete hydraulic model. On a real-world network, we were able to localize two out of the three leaks whose data we had access to. Of these two cases, we find that the actual leak location was in the candidate set identified by our approach; further, the approach pruned as much as 78% of the DMA locations, indicating a high degree of localization.
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