Leak Localization in a Real Water Distribution Network Based on Search-Space Reduction
Publication: Journal of Water Resources Planning and Management
Volume 145, Issue 7
Abstract
This research article presents a model-based framework for detecting and localizing leaks in water distribution networks (WDNs). The framework uses optimization and systematic search space reduction. The method employs two stages: (1) the search space reduction (SSR) stage and (2) the leakage detection and localization stage (LDL). During SSR, the number of decision variables is reduced along with the range of possible values, while trying to preserve the optimum solution. Then, at the LDL stage, the size and area of a leak are found. The leak localization method is formulated as an optimization problem, which identifies leakage node locations and their associated emitter coefficients. This is achieved such that the differences between the simulated and field-observed values for pressure head and flow are minimized. The optimization problem is solved by using a genetic algorithm. A model that has already been calibrated at least according to threshold standards is necessary for this methodology. Two case studies are discussed in this paper including a real WDN example with artificially generated data, which investigated the limits of this method. The second case study is a real water system in the United Kingdom, where the method was implemented to detect a leak event that actually happened. The results suggest that leaks that cause a hydraulic impact larger than the sensor data error can be detected and localized with this method. The real case outcome shows that the presented method can reduce the search area for finding the leak to within 10% of the WDN (by length). The method can also contribute to more timely detection and localization of leakage hotspots, thus reducing economic and environmental impacts. The optimization model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and physical measurement limitations from the pressure and flow field tests.
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Acknowledgments
This work is part of the first author’s STREAM Engineering Doctorate project and is sponsored by the UK Engineering and Physical Science Research Council (Grant No. EP/L015412/1), Severn Trent Water Ltd., and WITS Consult Ltd. The industrial supervisors who supported this work were Chris Gilbert (Severn Trent Water) and Paul Sage (WITS Consult).
References
ADEC (Alaska Department of Environmental Conservation). 1999. Technical review of leak detection technologies. Juneau, AK: ADEC.
Casillas Ponce, M., L. Castañón, and V. Cayuela. 2012. “Extended-horizon analysis of pressure sensitivities for leak detection in water distribution networks.” IFAC Proc. Volumes 45 (20): 570–575. https://doi.org/10.3182/20120829-3-MX-2028.00091.
Casillas Ponce, M., L. Castañón, and V. Cayuela. 2014. “Model-based leak detection and location in water distribution networks considering an extended-horizon analysis of pressure sensitivities.” J. Hydroinf. 16 (3): 649–670. https://doi.org/10.2166/hydro.2013.019.
Cheng, W., and Z. He. 2011. “Calibration of nodal demand in water distribution systems.” J. Water Resour. Plann. Manage. 137 (1): 31–40. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000093.
Colombo, A., P. Lee, and B. Karney. 2009. “A selective literature review of transient-based leak detection methods.” J. Hydro-Environ. Res. 2 (4): 212–227. https://doi.org/10.1016/j.jher.2009.02.003.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
Do, N., A. Simpson, J. Deuerlein, and O. Piller. 2016. “Calibration of water demand multipliers in water distribution systems using genetic algorithms.” J. Water Resour. Plann. Manage. 139 (6): 604–613. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000691.
Farley, B., J. B. Boxall, and S. R. Mounce. 2008. “Optimal locations of pressure meter for burst detection.” In Proc., 10th Water Distribution System Analysis Symp., 1–11. Reston, VA: ASCE.
Farley, B., S. Mounce, and J. Boxall. 2011. “Field validation of optimal instrumentation methodology for burst/leak detection and location.” In Proc., Water Distribution Systems Analysis Conf. Reston, VA: ASCE.
Farley, B., S. Mounce, and J. Boxall. 2013. “Development and field validation of a burst localization methodology.” J. Water Resour. Plann. Manage. 139 (6): 604–613. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000290.
Ferrandez-Gamot, L., P. Busson, J. Blesa, S. Tornil-Sin, V. Puig, E. Duviella, and A. Soldevila. 2015. “Leak localization in water distribution networks using pressure residuals and classifiers.” IFAC-Papers Online 48 (21): 220–225. https://doi.org/10.1016/j.ifacol.2015.09.531.
Gao, Y., M. Brennan, P. Joseph, J. Muggleton, and O. Hunaidi. 2005. “On the selection of acoustic/vibration sensors for leak detection in plastic water pipes.” J. Sound Vibr. 283 (3): 927–941. https://doi.org/10.1016/j.jsv.2004.05.004.
Goulet, J., S. Coutu, and I. Smith. 2013. “Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks.” Adv. Eng. Inf. 27 (2): 261–269. https://doi.org/10.1016/j.aei.2013.01.001.
Guardian. 2018. “Thames water given maximum £8.5m fine for missing leak target.” Accessed May 24, 2018. https://www.theguardian.com/environment/2017/jun/14/thames-water-given-maximum-fine-for-missing-leak-target.
Insurance Times. 2018. “Insurers face £4m payout for south London flood.” Accessed May 24, 2018. https://www.insurancetimes.co.uk/insurers-face-4m-payout-for-south-london-flood/1403944.article.
Kapelan, Z. 2002. “Calibration of WDS hydraulic models.” Ph.D. thesis, Dept. of Engineering, Univ. of Exeter.
Kun, D., L. Tian-Yu, W. Jun-Hui, and G. Jin-Song. 2015. “Inversion model of water distribution systems for nodal demand calibration.” J. Water Resour. Plann. Manage. 141 (9): 04015002. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000506.
Lambert, A., M. Fantozzi, and M. Shepherd. 2018. “FAVAD and N1 update.” Accessed August 6, 2018. http://www.leakssuite.com/favad-and-n1update.
Li, R., H. Huang, K. Xin, and T. Tao. 2015. “A review of methods for burst/leakage detection and location in water distribution systems.” Water Sci. Technol.: Water Supply 15 (3): 429. https://doi.org/10.2166/ws.2014.131.
Liggett, J. A., and L. Chen. 1994. “Inverse transient analysis in pipe networks.” J. Hydraul. Eng. 120 (8): 934–955. https://doi.org/10.1061/(ASCE)0733-9429(1994)120:8(934).
Moors, J., L. Scholten, J. van der Hoek, and J. den Besten. 2018. “Automated leak localization performance without detailed demand distribution data.” Urban Water J. 15 (2): 116–123. https://doi.org/10.1080/1573062X.2017.1414272.
Nasirian, A., M. Maghrebi, and S. Yazdani. 2013. “Leakage detection in water distribution network based on a new heuristic genetic algorithm model.” J. Water Resour. Prot. 5 (3): 294–303. https://doi.org/10.4236/jwarp.2013.53030.
Ormsbee, L., and S. Lingireddy. 1997. “Calibrating hydraulic network models.” J. Am. Water Works Assoc. 89 (2): 42–50. https://doi.org/10.1002/j.1551-8833.1997.tb08177.x.
Pérez, R., V. Puig, J. Pascual, J. Quevedo, E. Landeros, and A. Peralta. 2011. “Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks.” Control Eng. Pract. 19 (10): 1157–1167. https://doi.org/10.1016/j.conengprac.2011.06.004.
Pudar, R. S., and J. A. Liggett. 1992. “Leaks in pipe networks.” J. Hydraul. Eng. 118 (7): 1031–1046. https://doi.org/10.1061/(ASCE)0733-9429(1992)118:7(1031).
Puust, R., Z. Kapelan, D. Savic, and T. Koppel. 2010. “A review of methods for leakage management in pipe networks.” Urban Water J. 7 (1): 25–45. https://doi.org/10.1080/15730621003610878.
Ribeiro, L., J. Sousa, A. Marques, and N. Simões. 2015. “Locating leaks with trustRank algorithm support.” Water 7 (12): 1378–1401. https://doi.org/10.3390/w7041378.
Rossman, L. A. 2000. EPANET 2: User’s manual. Cincinnati: USEPA.
Sanz, G., and R. Pérez. 2014. “Demand pattern calibration in water distribution networks.” Procedia Eng. 70: 1495–1504. https://doi.org/10.1016/j.proeng.2014.02.164.
Savic, D. A., Z. Kapelan, and P. Jonkergouw. 2009. “Quo vadis water distribution model calibration?” Urban Water J. 6 (1): 3–22. https://doi.org/10.1080/15730620802613380.
Savic, D. A., and G. A. Walters. 1995. Genetic algorithm techniques for calibrating network models. Devon, UK: Centre for Systems and Control Engineering, Univ. of Exeter.
Severn Trent Water Ltd. 2018. “Thank you to all of our customers.” Accessed May 24, 2018. https://www.stwater.co.uk/news/news-releases/thank-you-to-all-of-our-customers/.
Soldevila, A., R. Fernandez-Canti, J. Blesa, S. Tornil-Sin, and V. Puig. 2016. “Leak localization in water distribution networks using model-based Bayesian reasoning.” In Proc., European Control Conf. New York: IEEE.
Wu, Z. Y., and P. Sage. 2006. “Water loss detection via genetic algorithm optimization-based model calibration.” In Proc., 8th Annual Int. Symp. on Water Distribution System Analysis. Reston, VA: ASCE.
Wu, Z. Y., P. Sage, and D. Turtle. 2010. “Pressure-dependent leak detection model and its application to a district water system.” J. Water Resour. Plann. Manage. 136 (1): 116–128. https://doi.org/10.1061/(ASCE)0733-9496(2010)136:1(116).
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©2019 American Society of Civil Engineers.
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Received: May 25, 2018
Accepted: Nov 30, 2018
Published online: Apr 26, 2019
Published in print: Jul 1, 2019
Discussion open until: Sep 26, 2019
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