Leakage detection in a real distribution network using a SOM

Abstract:A meter network is a set of meters installed throughout a water distribution system to measure system variables, such as the pipe flow rate and pressure. In the current hyper-connected world, meter networks are being exposed to meter failure conditions, such as malfunction of the meter's physical system and communication system failure. Therefore, a meter network's robustness should be secured for reliable provision of informative meter data. This paper introduces a multi-objective optimal meter placement model that maximizes the detection probability, minimizes false alarms, and maximizes the robustness of a meter network given a predefined number of meters. A meter network's robustness is defined as its ability to consistently provide quality data in the event of meter failure. Based on a single-meter failure simulation, a robustness indicator for the meter network is introduced and maximized as the third objective of the proposed model. The proposed model was applied to the Austin network to determine the independent placement of pipe flow and pressure meters with three or five available meters. The results showed that the proposed model is a useful tool for determining meter locations to secure high detectability and robustness.

Section: Pipe Burst Detection Method: Western Electric Company (Wec) mentioning

confidence: 99%

Robust Meter Network for Water Distribution Pipe Burst Detection

Jung

Kim

2017

“…These research efforts delve into the use of flow meters and pressure sensors stationed at specific locations on the pipe to detect leakages. For instance, Aksela et al [27] uses the knowledge of reported leak experience with the data collected from flow meter readings to model and train the system. Farley et al [28] presented a methodology for the detection of pipe burst, achieved by identifying the optimal locations of pressure sensors.…”

Section: Background and Related Workmentioning

confidence: 99%

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Adedeji

Hamam

Abe

et al. 2017

Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact leaking pipelines connected to this segment(s) where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.

“…Some of the existing methodologies developed for leak detection/location are based solely on the analysis of flow and/or pressure measurements: Artificial Neural Networks have been used for leak detection [13][14][15] and also for leak location [16]; Poulakis et al [17] and Qi [18] proposed a Bayesian system identification methodology for leak location; Fuzzy Inference Systems, which are computational techniques from the field of Artificial Intelligence, were used by Mounce et al [19] to detect leaks in WDN, and Fuzzy set theory was also used by Islam et al [20] for leak detection and location; Aksela et al [21] used self-organizing maps, combining flow data with a leak function to model leakages, to solve the leakage detection problem; Gertler et al [22] applied principal component analysis to locate leaks; Jung et al [23] used nonlinear Kalman filter; Okeya et al used a modified Kalman filter [24]; Kang et al [25] used control limit analysis to detect bursts; and Goulet et al [5] used a model-falsification methodology for leak-detection.…”

Section: Introductionmentioning

confidence: 99%

Locating Leaks with TrustRank Algorithm Support

Ribeiro

Sousa

Marques

et al. 2015

This paper presents a methodology to quantify and to locate leaks. The original contribution is the use of a tool based on the TrustRank algorithm for the selection of nodes for pressure monitoring. The results from these methodologies presented here are: (I) A sensitivity analysis of the number of pressure transducers on the quality of the final solution; (II) A reduction of the number of pipes to be inspected; and (III) A focus on the problematic pipes which allows a better office planning of the inspection works to perform atthe field. To obtain these results, a methodology for the identification of probable leaky pipes and an estimate of their leakage flows is also presented. The potential of the methodology is illustrated with several case studies, considering different levels of water losses and different sets of pressure monitoring nodes. The results are discussed and the solutions obtained show the benefits of the developed methodologies.