Online Nodal Demand Estimation in Branched Water Distribution Systems Using an Array of Extended Kalman Filters

Area of Science:

• Hydraulic Engineering
• Control Systems Engineering
• Water Resource Management

Background:

• Accurate estimation of nodal demands is crucial for efficient water distribution system management.
• Traditional methods often require extensive sensor networks or complex hydraulic models.
• Limited measurements at the system inlet pose a challenge for demand estimation.

Purpose of the Study:

• To develop a model-based methodology for estimating multiple nodal demands in water distribution systems.
• To utilize only inlet pressure and flow rate measurements for demand estimation.
• To implement a cascade configuration of extended Kalman filters (EKFs) for this purpose.

Main Methods:

• A model-based approach employing an array of multiple extended Kalman filters (EKFs) in a cascade configuration.
• Each EKF acts as an unknown input observer for a specific pipeline section.
• The filters are designed using an adapted rigid water column model for the pipeline.

Main Results:

• The proposed methodology effectively estimates multiple nodal demands using limited inlet measurements.
• Simulation and experimental tests on standardized pipeline systems validate the method's accuracy.
• The approach demonstrates robustness under both steady-state and variable input conditions.

Conclusions:

• The cascaded EKF approach provides an effective solution for nodal demand estimation in water distribution systems.
• This method offers a practical way to infer demands using readily available inlet measurements.
• The findings support improved operational efficiency and management of water networks.