Field testing of an optimal sensor placement methodology for event detection in an urban water distribution network

This paper presents a method to identify ‘optimal’ locations of pressure sensor instruments for the detection of leak/burst events and the results of a set of field trials conducted to evaluate the approach. The identification method is based on complete enumeration studies using hydraulic model simulations of a wide range of burst events and evaluating the response to each event at all possible monitoring points. The field trials simulated leak/burst events through the opening of fire hydrants within a selected District Metered Area (DMA), five different hydrants were opened systematically in the DMA to simulate different leak/burst events. By installing pressure instrumentation at different locations in the DMA, an understanding of how accurately the model methodology can determine sensitivity of instrument location can be obtained. Prior to and during the hydrant openings pressure data was collected at eight different instrument locations within the DMA. These pressure instruments were installed to cover different model predicted sensitivities and to provide good spatial coverage. The results show that pressure instrumentation location is crucial to sensitivity and that the modelling methodology is able to predict instrument location sensitivity to leak/burst events and thus offer an improvement over current industry practice for instrument deployment. It should be noted that this field application made use of current UK standard models, with no additional calibration or updating.

[1]  Andrew J. Day,et al.  Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system , 2003, Inf. Fusion.

[2]  R. Fenner,et al.  Kalman Filtering of Hydraulic Measurements for Burst Detection in Water Distribution Systems , 2011 .

[3]  Joby Boxall,et al.  Estimation of burst rates in water distribution mains , 2007 .

[4]  James G. Uber,et al.  Comparison of Physical Sampling and Real-Time Monitoring Strategies for Designing a Contamination Warning System in a Drinking Water Distribution System , 2006 .

[5]  Stephen R. Mounce,et al.  Optimal Locations of Pressure Meters for Burst Detection , 2009 .

[6]  G Olsson,et al.  Failure monitoring in water distribution networks. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[7]  Avi Ostfeld Optimal Design and Operation of Multiquality Networks under Unsteady Conditions , 2005 .

[8]  Enrique Cabrera,et al.  Performance Indicators for Water Supply Services: Third Edition , 2006 .

[9]  T Waldron Where are the Advancements in Leak Detection , 2005 .

[10]  Zoran Kapelan,et al.  Optimal Sampling Design Methodologies for Water Distribution Model Calibration , 2005 .

[11]  Stephen R. Mounce,et al.  Burst detection using hydraulic data from water distribution systems with artificial neural networks , 2006 .

[12]  James G. Uber,et al.  Sampling Design Methods for Water Distribution Model Calibration , 1998 .

[13]  Zheng Yi Wu,et al.  WATER LOSS DETECTION VIA GENETIC ALGORITHM OPTIMIZATION-BASED MODEL CALIBRATION , 2008 .

[14]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[15]  Joby Boxall,et al.  Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows , 2010 .