Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics
Revisiting the Water Quality Sensor Placement Problem: Optimizing Network Observability and State Estimation Metrics
复制标题
DOI：
10.1061/(asce)wr.1943-5452.0001374
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
A. Taha;Shen Wang;Yi Guo;T. Summers;Nikolaos Gatsis;M. Giacomoni;Ahmed A. Abokifa
中科院分区：
文献类型：
--
作者：
A. Taha;Shen Wang;Yi Guo;T. Summers;Nikolaos Gatsis;M. Giacomoni;Ahmed A. Abokifa
配水管网中的实时水质传感器具有在全网范围内观测水质指标、检测污染事件以及对水质动态进行闭环反馈控制的潜力。为此，先前的研究已经调查了广泛的方法来指导WQ传感器的地理位置。这些方法为固定的传感器布局(SP)分配一个度量，然后进行度量优化以获得最优SP。这些指标包括最大限度地减少入侵检测时间、最大限度地减少受入侵事件影响的预期人口和受污染的水量。与文献不同，本文的目的是提供一种计算方法，该方法考虑了被忽略的状态估计度量和WQ动态的全网可观测性。该指标通过针对有噪声的WQ动态的卡尔曼滤波来找到最优的WQ传感器位置，从而使状态估计误差最小化--这是一种量化WDN可观测性的指标。为此，给出了整个无线传感器网络中WQ状态的状态空间动态特性，并给出了可观测性驱动的传感器布局算法。该算法考虑了由于水力剖面的变化而引起的WQ动态的时变特性--水力状态的集合，包括节点处的水头(压力)和由需求剖面在特定时间段内引起的链路中的流量。文中还提供了全面的案例研究，重点介绍了WDN运营商的主要发现、观察结果和建议。为了重现性，还包括GitHub代码。
Real-time water quality (WQ) sensors in water distribution networks (WDN) have the potential to enable network-wide observability of water quality indicators, contamination event detection, and closed-loop feedback control of WQ dynamics. To that end, prior research has investigated a wide range of methods that guide the geographic placement of WQ sensors. These methods assign a metric for fixed sensor placement (SP) followed by \textit{metric-optimization} to obtain optimal SP. These metrics include minimizing intrusion detection time, minimizing the expected population and amount of contaminated water affected by an intrusion event. In contrast to the literature, the objective of this paper is to provide a computational method that considers the overlooked metric of state estimation and network-wide observability of the WQ dynamics. This metric finds the optimal WQ sensor placement that minimizes the state estimation error via the Kalman filter for noisy WQ dynamics -- a metric that quantifies WDN observability. To that end, the state-space dynamics of WQ states for an entire WDN are given and the observability-driven sensor placement algorithm is presented. The algorithm takes into account the time-varying nature of WQ dynamics due to changes in the hydraulic profile -- a collection of hydraulic states including heads (pressures) at nodes and flow rates in links which are caused by a demand profile over a certain period of time. Thorough case studies are given, highlighting key findings, observations, and recommendations for WDN operators. Github codes are included for reproducibility.