对比两种滤波方法，提升供水管网状态估计与漏水定位精度
A comparison between joint and dual UKF implementations for state estimation and leak localization in water distribution networks
- 用联合或双估计算法融合压力、用水量和流量数据
- 双估计算法在漏点定位上更准，但计算复杂度更高
- 适用于城市供水系统实时监控与故障诊断
现代城市的可持续发展高度依赖于高效的供水管理，包括有效的压力控制以及泄漏检测与定位。因此，准确获取管网水力状态信息至关重要。本文比较了两种基于无迹卡尔曼滤波（UKF）的数据驱动状态估计算法，通过融合压力、需求和流量数据实现水头与流量的估计。一种方法采用单一估计算法的联合状态向量，另一种采用双估计算法结构。我们分析了它们的主要特性，讨论差异、优势与局限性，并从精度与复杂度角度进行理论对比。最后，在L-TOWN基准测试中展示了多个估计结果，验证了其在实际应用中的性能表现。
原文摘要 · Abstract (English)
The sustainability of modern cities highly depends on efficient water distribution management, including effective pressure control and leak detection and localization. Accurate information about the network hydraulic state is therefore essential. This article presents a comparison between two data-driven state estimation methods based on the Unscented Kalman Filter (UKF), fusing pressure, demand and flow data for head and flow estimation. One approach uses a joint state vector with a single estimator, while the other uses a dual-estimator scheme. We analyse their main characteristics, discussing differences, advantages and limitations, and compare them theoretically in terms of accuracy and complexity. Finally, we show several estimation results for the L-TOWN benchmark, allowing to discuss their properties in a real implementation.
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