Extended-horizon analysis of pressure sensitivities for leak detection in water distribution networks: Application to the Barcelona network

Casillas, Myrna V.; Garza-Castañón, Luis E.; Puig, Vicenç

doi:10.23919/ecc.2013.6669568

Cited by 31 publications

(69 citation statements)

References 9 publications

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“…A previous optimal sensor placement [8] based on the Angle method [13] is used to compare how the proposed method improves. The criteria used for this placement is not optimum for the k-NN classifier method but still it is a good criteria, since the aim of this method is to maximize the angle (cosine) value between residuals of different classes, which objective is to separate the classes in the residual space to obtain a better accuracy (same final objective).…”

Section: Case Study and Resultsmentioning

confidence: 99%

Optimal sensor placement for classifier-based leak localization in drinking water networks

Soldevila¹,

Tornil-Sin

Fernandez-Canti³

et al. 2016

2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol)

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Abstract-This paper presents a sensor placement method for classifier-based leak localization in Water Distribution Networks. The proposed approach consists in applying a Genetic Algorithm to decide the sensors to be used by a classifier (based on the k-Nearest Neighbor approach). The sensors are placed in an optimal way maximizing the accuracy of the leak localization. The results are illustrated by means of the application to the Hanoi District Metered Area and they are compared to the ones obtained by the Exhaustive Search Algorithm. A comparison with the results of a previous optimal sensor placement method is provided as well.

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Section: Case Study and Resultsmentioning

confidence: 99%

Optimal sensor placement for classifier-based leak localization in drinking water networks

Soldevila¹,

Tornil-Sin

Fernandez-Canti³

et al. 2016

2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol)

Self Cite

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“…Then, leak location is achieved by looking for the smallest Hamming distance between FSM columns and the binarized actual residual vector. This and several other alternative leak location methods are compared in [11].…”

Section: B Leak Detection and Locationmentioning

confidence: 99%

“…Therefore, the result obtained in a single run of the ECM algorithm could be already considered meaningful. With the plausibility matrix (11), obtained from the clustering algorithm, a hard partition has been obtained by assigning each element to its highest plausibility cluster applying Eq. (9).…”

Section: Sensor Placement Analysismentioning

confidence: 99%

Clustering techniques applied to sensor placement for leak detection and location in water distribution networks

Sarrate

Blesa

Nejjari

2014

22nd Mediterranean Conference on Control and Automation

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Abstract-This work presents an optimization strategy that maximizes the leak locatability performance of water distribution networks (WDN). The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of locatability while the sensor configuration cost satisfies a budgetary constraint. The method is based on pressure sensitivity matrix analysis and an exhaustive search strategy. In order to reduce the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed in a district metered area (DMA) in the Barcelona WDN.

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“…On the other hand, in [21], a new approach for sensor placement for leak location in WDN is proposed. This approach is used in combination with the projection-based location scheme proposed in [10,11]. Here, the sensor placement problem is formulated as an integer optimization problem where the optimization criterion concerns the minimization of the number of non-isolable leaks according to the isolability criteria introduced.…”

Section: Introductionmentioning

confidence: 99%

“…Although this approach presents satisfactory results under ideal conditions, its performance decreases in the presence of nodal demand uncertainty and noise in the measurements. This method has recently been applied to a real fault scenario [9], and an improvement of this approach has been developed [10,11], where an extended time horizon analysis of pressure measurements is considered and a comparison between different performance metrics is achieved.…”

Section: Introductionmentioning

confidence: 99%

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

Casillas

Garza-Castañón

Puig

2015

Water

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In this paper, a sensor placement approach to improve the leak location in water distribution networks is proposed when the leak signature space (LSS) method is used. The sensor placement problem is formulated as an integer optimization problem where the criterion to be minimized is the number of overlapping signature domains computed from the original LSS representation. First, a semi-exhaustive search approach based on a lazy evaluation mechanism ensures optimal placement in the case of low complexity scenarios. For more complex cases, a stochastic optimization process is proposed, based on either the genetic algorithms (GAs) or particle swarm optimization (PSO). Experiments on two different networks are used to evaluate the performance of the resolution methods, as well as the efficiency achieved in the leak location when using the sensor placement results.

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Extended-horizon analysis of pressure sensitivities for leak detection in water distribution networks: Application to the Barcelona network

Cited by 31 publications

References 9 publications

Optimal sensor placement for classifier-based leak localization in drinking water networks

Optimal sensor placement for classifier-based leak localization in drinking water networks

Clustering techniques applied to sensor placement for leak detection and location in water distribution networks

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

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