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Enabling State Estimation for Fault Identification in Water Distribution Systems Under Large Disasters

机译：大灾难下供水系统故障识别的使能状态估计

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We present a graphical model based approach for on-line state estimation of water distribution system failures during large-scale disasters. Water distribution systems often exhibit extreme fragilities during large-scale disasters (e.g., earthquakes) resulting in massive pipe breaks, water contamination, and disruption of service. To monitor and identify potential problems, hidden state information must be extracted from limited and noisy data environments. This requires estimating the operating states of the water system quickly and accurately. We model the water system as a factor graph, characterizing the non-linearity of fluid flow in a network that is dynamically altered by leaks, breaks and operations designed to minimize water loss. The approach considers a structured probabilistic framework which models complex interdependencies within a high-level network topology. The proposed two-phase approach, which begins with a network decomposition using articulation points followed by the distributed Gauss-Newton Belief Propagation (GN-BP) based inference, can deliver optimal estimates of the system state in near real-time. The approach is evaluated in canonical and real-world water systems under different levels of physical and cyber disruptions, using the Water Network Tool for Resilience (WNTR) recently developed by Sandia National Lab and Environmental Protection Agency (EPA). Our results demonstrate that the proposed GN-BP approach can yield an accurate estimation of system states (mean square error 0.02) in a relatively fast manner (within 1s). The two-phase mechanism enables the scalability of state estimation and provides a robust assessment of performance of large-scale water systems in terms of computational complexity and accuracy. A case study on the identification of 'faulty zones' shows that 80% broken pipelines and 99% loss-of-service to end-users can be localized.
机译：我们提出了一种基于图形模型的方法，用于大规模灾难期间供水系统故障的在线状态估计。在大规模灾难（例如地震）期间，配水系统通常表现出极度脆弱，导致大量管道破裂，水污染和服务中断。为了监视和识别潜在问题，必须从有限且嘈杂的数据环境中提取隐藏状态信息。这就需要快速，准确地估计水系统的运行状态。我们将水系统建模为一个因子图，以表征网络中流体流动的非线性，该非线性因泄漏，中断和旨在最大程度减少水损失的操作而动态变化。该方法考虑了一个结构化的概率框架，该框架对高级网络拓扑中的复杂相互依赖性进行建模。提议的两阶段方法以使用关节的网络分解开始，然后进行基于分布式高斯-牛顿信念传播（GN-BP）的推理，可以近乎实时地提供系统状态的最佳估计。使用桑迪亚国家实验室和环境保护局（EPA）最近开发的水网络抵御能力工具（WNTR），在规范和现实世界的水系统中，在不同程度的物理和网络破坏下对该方法进行了评估。我们的结果表明，所提出的GN-BP方法可以以相对较快的方式（在1秒以内）产生对系统状态的精确估计（均方误差0.02）。两阶段机制可实现状态估计的可伸缩性，并在计算复杂性和准确性方面提供对大型水系统性能的可靠评估。一项关于“故障区域”识别的案例研究表明，可以确定80％破裂的管道和99％的最终用户服务中断。

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