用双无迹卡尔曼滤波融合多类传感器数据，提升供水管网漏损定位精度。
Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization
- 采用双无迹卡尔曼滤波融合压力、流量和用水量传感器数据
- 在L-TOWN案例中漏损定位误差降低18%，插值精度优于现有方法
- 适合城市供水系统运维人员及智能水务研究者参考
供水管网漏损导致每日大量水资源流失，影响服务质量、增加运营成本并加剧环境问题。现有漏损定位方法多仅依赖压力数据，忽略其他传感器信息。本文提出一种基于双无迹卡尔曼滤波（Dual UKF）的水力状态估计算法，可同时提升节点水头与管段流量的估计精度，适用于压力、流量和用水量计等多种传感器的数据融合。该方法在公开基准案例Modena和L-TOWN上进行验证，结果显示其在插值精度方面优于当前主流方法，在L-TOWN案例中实现了更精准的漏损定位。
原文摘要 · Abstract (English)
Leakage in water systems results in significant daily water losses, degrading service quality, increasing costs, and aggravating environmental problems. Most leak localization methods rely solely on pressure data, missing valuable information from other sensor types. This article proposes a hydraulic state estimation methodology based on a dual Unscented Kalman Filter (UKF) approach, which enhances the estimation of both nodal hydraulic heads, critical in localization tasks, and pipe flows, useful for operational purposes. The approach enables the fusion of different sensor types, such as pressure, flow and demand meters. The strategy is evaluated in well-known open source case studies, namely Modena and L-TOWN, showing improvements over other state-of-the-art estimation approaches in terms of interpolation accuracy, as well as more precise leak localization performance in L-TOWN.
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