Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks

Ami Preis; Andrew J. Whittle, M.ASCE; Avi Ostfeld, M.ASCE; and Lina Perelman

Journal of Water Resources Planning and Management

Volume 137, Issue 4 (July 2011)

This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data.To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system–aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system.

基于简化城市供水管网模型的高效液压状态估计技术

作者：Ami Preis; Andrew J. Whittle, M.ASCE; Avi Ostfeld, M.ASCE; and Lina Perelman

期刊：水资源规划与管理杂志

发表时间：2011年7月

编目：137卷第4期

本文介绍并演示了城市供水网络在线液压状态评估的一种有效的方法。这种方法采用在线预测校正程序来预测未来需水量。统计数据驱动算法（M5模型树算法）被应用于预测未来需水量，一种改进的优化技术（遗传算法）和在线监测数据被用于校正这些预测量。为满足实时液压状态评估的计算效率要求通常包括成千上万的链接和节点，正在发展一种基于水系统聚合技术的简化模型。简化模型实现了完整网络的高精确度表示，但大大简化了计算的PC循环，有利于实现在线模拟。所提出的方法被应用在典型的市政供水系统上。