Automated leak localization performance without detailed demand distribution data

Moors, J.J.A.; Scholten, Lisa; Hoek, Jan Peter van der; Besten, J. den

doi:10.1080/1573062x.2017.1414272

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…In practice, consumer demands are the greatest source of uncertainty when using modelbased methodologies for leakage diagnosis [12]. This is because demand variations can cause significant pressure variations, which may either hide the effect of a leakage (unless the leak size is large relative to the system inflow), or cause false positives [16].…”

Section: Demand and Parameter Uncertaintymentioning

confidence: 99%

“…The benchmark network 'Hanoi' has been used to perform a sensitivity analysis on the performance of the proposed methodology as a function of the pressure sensors installed in the network. The pressure sensors in the network were varied between one (1) to sixteen (16), which correspond to a coverage of 3.1% to 50% of the network nodes. A sensor placement procedure which optimizes sensitivity of all sensors to all possible leakages was used [9].…”

Section: Sensitivity Analysis Of the Number Of Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Leakage detection and localization in water distribution systems: A model invalidation approach

Vrachimis

Timotheou

Ηλιάδης

et al. 2021

Control Engineering Practice

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Model-based methodologies can assist in addressing the challenging problem of leakage detection and localization in water distribution systems. However, this is not trivial due to inherent non-linearities and parametric uncertainties. Most importantly, due to the small number of available sensor measurements compared to the number of system states, the inverse problem for estimating leakages is highly under-determined. In this work, the utilization of a priori available information about the system is proposed to formulate a hydraulic model of the system in its non-linear form in which uncertainties are modeled by intervals defined by a lower and upper bound. A novel optimization-based methodology then utilizes pressure and flow measurements to perform leakage detection through model-invalidation. A modification of the optimization algorithm is activated in the case of a detection to refine possible leak locations and retain only the ones that can be explained by the interval model and available measurements from multiple time-steps. The proposed methodology is demonstrated on a benchmark network and evaluated using a leakage diagnosis benchmark dataset.

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Section: Demand and Parameter Uncertaintymentioning

confidence: 99%

Section: Sensitivity Analysis Of the Number Of Sensorsmentioning

confidence: 99%

Leakage detection and localization in water distribution systems: A model invalidation approach

Vrachimis

Timotheou

Ηλιάδης

et al. 2021

Control Engineering Practice

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“…This network was chosen because it has been used as a case study in previous research (e.g. Moors et al, 2018), as a result detailed, validated models are available. The third model is the WDN of Tuindorp in Utrecht (the Netherlands).…”

Section: Studied Water Distribution Networkmentioning

confidence: 99%

Identifying critical elements in drinking water distribution networks using graph theory

Meijer

Post²,

Hoek

et al. 2020

Structure and Infrastructure Engineering

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Drinking water distribution networks (WDNs) are a crucial infrastructure for life in cities. Deterioration of this ageing, and partly hidden from view, infrastructure can result in losses due to leakage and an increased contamination risk. To counteract this, maintenance strategies are required to maintain the service level. Information on the most critical elements of a WDN, with respect to the functioning of the system as a whole, is essential for prioritising maintenance or rehabilitation activities. In this study a Graph theory based method is developed and applied for efficiently identifying the most critical elements. The main advantage of this method is that it avoids the need to perform elaborate hydrodynamic model calculations. Instead, the structure of the network is the main starting point. The results show that the structure of the network is more decisive than the hydraulics with respect to the criticality of the system's performance as a whole. Results depict that the suggested approach is applicable not only to the main (primary) network, but also to the capillaries which are normally beyond the scope of the traditional methods applied so-far because of the complexity of the networks and the required calculation time.

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“…The false positive rate was not lessened using leak detection method. A factorized distribution assigned the inflow demand across the consumption nodes with individual billing data and transmitted the demand across consumption nodes in (Moors et al, 2018) . The automatic leak localization method with demand distribution models for computing the leak localization performance.…”

Section: Related Workmentioning

confidence: 99%

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

Rayaroth

Gopalakrishnan

2018

Preprint

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<p><strong>Abstract.</strong> Water Distribution System distributes the water to customer with the better quality and pressure. Distribution system supplies the water from their source to usage point. Due to the leakage, the sufficient amount of water is not delivered to the consumer. Many researchers introduced the techniques for detecting the water leakage in distribution system. But, the water leakage detection accuracy was not improved and time consumption was also not reduced. To improve the water leakage detection performance, Enhanced BrownBoost Classifier based Glowworm Swarm Optimization (EBBC-GWO) Method is introduced. EBBC-GWO method introduces two models namely, Enhanced BrownBoost Classifier model and Glowworm Swarm Optimization model. Enhanced BrownBoost Classifier model considers k-Nearest Neighbor (k-NN) classifier as weak classifier. It classifies the training samples with neighbor's majority vote for allocating the object to the class. Brownboost classifier combines all k-NN classifier to construct strong classifier. By this way, data are classified as the normal data or abnormal data with higher accuracy. After classification, optimization process is executed where every solution corresponds to the glowworm (i.e., abnormal pressure data node) in search space. Every glowworm has objective function for addressing the optimization problem. Every glowworm operates in probabilistic means to choose the neighbor with higher luciferin value and transmit to it. Glowworm updates its location to the glowworm in dynamic decision space and optimal one is selected for water leakage detection. By this way, water leakage detection accuracy is improved with lesser false positive rate. Experimental evaluation of proposed EBBC-GWO method is carried out with respect to number of pressure data and sensor placement nodes. The results demonstrated that EBBC-GWO method is higher in case of classification accuracy, false positive rate, classification time and water leakage detection accuracy. The simulation results show that EBBC-GWO method increases the performance of water leakage detection accuracy and reduces classification time when compared to state-of-the-art works.</p>

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Automated leak localization performance without detailed demand distribution data

Cited by 14 publications

References 16 publications

Leakage detection and localization in water distribution systems: A model invalidation approach

Leakage detection and localization in water distribution systems: A model invalidation approach

Identifying critical elements in drinking water distribution networks using graph theory

Hybridisation of brownboost classifier and glowworm swarm based optimal sensor placement for water leakage detection

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