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Water 2015, 7(11), 6496-6515; doi:10.3390/w7116496

Optimal Sensor Placement for Leak Location in Water Distribution Networks using Evolutionary Algorithms

1
Supervision and Advanced Control Chair, Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501, 64849 Monterrey, Mexico
2
Advanced Control Systems Research Group, Technical University of Catalonia, Rambla Sant Nebridi 10, 08222 Terrassa, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Received: 20 May 2015 / Revised: 21 October 2015 / Accepted: 26 October 2015 / Published: 16 November 2015
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Abstract

In this paper, a sensor placement approach to improve the leak location in waterdistribution networks is proposed when the leak signature space (LSS) method is used.The sensor placement problem is formulated as an integer optimization problem where thecriterion to be minimized is the number of overlapping signature domains computed fromthe original LSS representation. First, a semi-exhaustive search approach based on a lazyevaluation mechanism ensures optimal placement in the case of low complexity scenarios.For more complex cases, a stochastic optimization process is proposed, based on eitherthe genetic algorithms (GAs) or particle swarm optimization (PSO). Experiments on twodifferent networks are used to evaluate the performance of the resolution methods, as well asthe efficiency achieved in the leak location when using the sensor placement results. View Full-Text
Keywords: water distribution networks; leak isolation; sensor placement; leak signature space water distribution networks; leak isolation; sensor placement; leak signature space
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Casillas, M.V.; Garza-Castañón, L.E.; Puig, V. Optimal Sensor Placement for Leak Location in Water Distribution Networks using Evolutionary Algorithms. Water 2015, 7, 6496-6515.

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