Fariborz Maseeh Dept. of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, 301 E Dean Keeton St, Austin, 78712, TX, USA.
Fariborz Maseeh Dept. of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, 301 E Dean Keeton St, Austin, 78712, TX, USA.
Water Res. 2024 Oct 15;264:122201. doi: 10.1016/j.watres.2024.122201. Epub 2024 Aug 5.
Operators of water distribution systems (WDSs) need continuous and timely information on pressures and flows to ensure smooth operation and respond quickly to unexpected events. While hydraulic models provide reasonable estimates of pressures and flows in WDSs, updating model predictions with real-time sensor data provides clearer insights into true system behavior and enables more effective real-time response. Despite the growing prevalence of distributed sensing within WDSs, standard hydraulic modeling software like EPANET do not support synchronous data assimilation. This study presents a new method for state estimation in WDSs that combines a fully physically-based model of WDS hydraulics with an Extended Kalman Filter (EKF) to estimate system flows and heads based on sparse sensor measurements. To perform state estimation via EKF, a state-space model of the hydraulic system is first formulated based on the 1-D Saint-Venant equations of conservation of mass and momentum. Results demonstrate that the proposed model closely matches steady-state extended-period models simulated using EPANET. Next, through a holdout analysis it is found that fusing sensor data with EKF produces flow and head estimates that closely match ground truth flows and heads at unmonitored locations, indicating that state estimation successfully infers internal hydraulic states from sparse sensor measurements. These findings pave the way towards real-time operational models of WDSs that will enable online detection and mitigation of hazards like pipe leaks, main bursts, and hydraulic transients.
供水系统（WDS）的操作人员需要连续、及时地了解压力和流量信息，以确保系统平稳运行，并对突发事件做出快速响应。水力模型虽然可以对 WDS 中的压力和流量进行合理估计，但通过实时传感器数据更新模型预测，可以更清楚地了解系统的真实行为，并实现更有效的实时响应。尽管分布式传感在 WDS 中的应用越来越广泛，但像 EPANET 这样的标准水力建模软件并不支持同步数据同化。本研究提出了一种新的 WDS 状态估计方法，该方法将 WDS 水力的全物理模型与扩展卡尔曼滤波器（EKF）相结合，根据稀疏传感器测量值来估计系统流量和水头。为了通过 EKF 进行状态估计，首先根据质量和动量守恒的一维圣维南方程构建水力系统的状态空间模型。结果表明，所提出的模型与使用 EPANET 模拟的稳态扩展期模型非常吻合。接下来，通过留一法分析发现，将传感器数据与 EKF 融合，可以生成与未监测位置的实际流量和水头非常吻合的流量和水头估计值，这表明状态估计可以成功地从稀疏传感器测量值中推断出内部水力状态。这些发现为 WDS 的实时运行模型铺平了道路，从而可以实现在线检测和缓解管道泄漏、主爆管和水力瞬变等危害。
