• the collected measurements into a mathematical model, the

 • Leak location: Once the leak isidenti?ed, the WPM is employed to locate the leak point.. • Leak Detection: RTTM  method: The pressure-?ow pro?le of thepipeline is calculated based on the measurements of the pipeline inlet andoutlet. Substituting the collected measurements into a mathematical model, thepredicted operating parameters can be evaluated by employing the Method ofCharacteristics (MOC) .

Preliminary leak detection is considered by comparingthe predicted modelled values to the measured values. WSNi system isResponsible for collecting monitored water pressure and ?ow rate parameters bythe use of autonomous sensors. Firstly, the segment i of pipeline is dividedinto equal segments and sensor nodes are placed in each segment ends. Then,hierarchical WSN architecture is implemented where sensors are grouped intoclusters.

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Each cluster head transmits the data to a Base Station (BSi) whichwill be analysed by the RCC to recognize the presence of the leak and itsposition. Hybrid method is implemented as following:The global architecture isshown in Fig. 1. It is divided into two sub-systems: WSN system and RemoteControl Centre (RCC). For each segment i of the pipeline, A.      global architecture III.

PLDS ARCHITECTURE.Thus, unlessthe threshold values are adapted, high false alarm rates will be recordedduring transient periods of the pipeline. Moreover, unless a localizationtechnique is attached to the method, it cannot localize the actual location ofthe leak on its own.The mass balance method for leakdetection is straightforward (Burgmayer and Durham 2000; Martins and Seleghim2010). It is based on the principle of mass conservation. The existence of leakcauses an unbalance between the output and input mass ?ow rate as well as theline pack variable rate (Liou 1996; Parry et al.

1992). This is variable thatde?nes the actual amount of gas in a pipeline or distribution system. A leakalarm is raised once the difference between the volume of ?uid entering asection of the pipeline and the volume of the ?uid leaving the section exceedssome pre-set threshold.

(Liu 2008) presented a detailed theory and theimplementation issues that are encountered in this method. In their work, theyfurther pointed out that the volume or mass can be obtained by using readingsof commonly used process variables such as temperature, pressure and ?ow rate.(Rougier 2005) presented a hybrid mass balance method, which incorporatesprobabilistic method to the mass balance method. The main drawback of thismethod is that the probabilistic method requires a substantial amount ofcomputational power. One of the advantages of the mass balance method howeveris the ease with which it can be implemented on existing pipeline infrastructure.It is also able to rely on existing instrumentation already available on thepipeline; thus, resulting in low cost implementation (Murvay and Silea 2012;Wan et al. 2011). However, its performance relies on the size of the leak,frequency at which balance measurements are obtained as well as on the overallaccuracy of measuring instruments.

Another limitation of the mass balancemethod is its inability to detect small leak in real-time. Thus, resulting inloss of signi?cant amount of ?uid before an alarm is raised. A furtherlimitation is that the mass balance method easily affected by randomdisturbances around the pipeline as well as the pipe dynamics. D.

   Mass balance method Digital signalprocessing is one of the alternative methods for leak detection (USDT 2007). Inthe set-up stage, the output obtained from the system due to a known alterationin ?ow is obtained. Subsequently, digital signal processing is carried on theobtained measurements in order to detect variations in system response.

Theapplication of digital signal processing helps in isolation of original leakresponses from noisy data. Encouraging results have been obtained from theapplication of this method for both gas and liquid pipelines (Golby and Woodward1999; USDT 2007). The main advantage of this method is that the mathematicalmodel of the pipeline is not needed. However, just like the statistical method,if there is a leak in the set-up phase, it will not be detected until its sizegrows substantially.

An additional disadvantage of this method is its high costand complexity when it comes to installation and testing C.   Digital signal processing        As this negative pressure wave travelstowards the terminal ends of the pipeline section, pressure sensors stationedat the terminal ends are able to measure the pressure reduction signal. Thiscan be achieved because when the wave reaches the terminal ends, it causes adrop ?rst at the station inlet pressure and then the station outlet pressure.Since the leakage can be at any random point on the pipeline section, differenttime difference of the negative pressure wave is obtained at the terminal ends.From the knowledge of the different time difference that the pressure sensorson both sides of the leak detect, the pipeline section length and negativepressure wave velocity, the position of the leak can be obtained (Ge et al.2008; Ma et al. 2010).

In the negativepressure wave method, once a leak occurs the pressure of the ?uid drops. Thisis due to the sudden decrease of liquid density at the position of the leak.Subsequently, pressure wave source propagates outwards for the point of leakagetowards the opposite sides of the leak. Considering the pressure of the ?uidbefore and after the leak as a reference, the wave produced by such leakage istermed the negative pressure wave.  B.   Negative pressure wave method            Verde and Visairo (2001) proposed amethod, which uses a linearized, discretized pipe ?ow model on an N-node gridand a bank of observers. The observers are modeled in such a way that whenleakage occurs, all observers are reset except one. Localization of the leakageis obtained by the location of the non-responsive observer.

Meanwhile, thequantity the leak can be obtained from the output of the other observers.Moreover, a detection system that utilizes an adaptive Luenberger-Typeobserver, based on a set of two-coupled one dimensional ?rst order non-linearhyperbolic partial differential equation, is proposed by (Aamo et al. 2006;Hauge et al. 2007). Although this method is able to detect tiny leaks lessthan 1 % of ?ow (Scott and Barrufet 2003), it has the drawback of having highcost, as it requires huge instrumentation for obtaining data in real time.

Moreover, another disadvantage of this method is the complexity of modelsemployed that can be handled only by an expert.              This method depends on pipe ?owmodels developed to employing equations such as: conservation of momentum, massand energy as well as the equation of state of the ?uid. The presence ofleakage is determined by the estimated value and measured value of the ?ow.Continuous monitoring noise levels and transient events minimize false alarmrate. Billmann and Isermann (1987) designed an observer with frictionadaptation that in the event of leakage it generates a different output fromone obtained from measurements.

Thus, from this difference leakage can bedetected.  A.   Real time transient modelling II .leak detectiontechnologiescombining the RTTM (Real TimeMonitoring System Method) 4 and the Wave Propagation Method (WPM)  for water leak monitoring and pipe modeling.

The rest of paper is organized as follows: section II reviews the previousimplemented hybrid pipeline leak detection methods. Section III details anddescribes the water pipeline model. In section IV, we detail the PLDSarchitecture. Section V illustrates the leak detection methodology. Finally,section VII concludes this paper.we focus on sensing the continuouslywater parameters (pressure and ?ow rate) to detect the presence of the leak andto locate its position. Thus, the originality of our contribution is to deploya hybrid methodWaterdistribution is generally installed through underground pipes.

Monitoring theunderground water pipelines is more difficult than monitoring the waterpipelines located on the ground in open space. This situation will cause apermanent loss if there is a disturbance in the pipeline such as leakage. Leaksin pipes can be caused by several factors, such as the pipe’s age, improperinstallation, and natural disasters. Therefore, a solution is required todetect and to determine the location of the damage when there is a leak.

Wireless Sensor Network (WSN) is considered as a reliable solution for PipelineLeak Detection Systems (PLDS) to supervise pipeline and to detect and localizeleaks. I.   INTRODUCTION Keywords—Wireless Sensor Network, Pipeline monitoring, Leak, Hybridtechnique, Detection, Localization,  Abstract— Themonitoring of leaks in pipelines is an important issue to be addressed byresearchers and the public. This is due the fact that they can have a greatimpact both economically and environmentally.

In recent years, the effect ofleakages of pipelines carrying oil, gas and nuclear ?uids have posed a threaton humans as well as marine life. This paper provides a survey of recentmethods of detecting pipeline leaks with special focus on Real Time TransientModeling and Wave Propagation Method is implemented to detect and locate theposition of the leak in a water pipeline. A mathematical model is carried outto solve the transient based leak detection model and different scenarios aredeveloped to estimate the relationship between the pressure ?uctuation and leakposition. The obtained results approve the potentiality of the proposedtechnique