• Leak location: Once the leak is
identi?ed, the WPM is employed to locate the leak point..
• Leak Detection: RTTM method: The pressure-?ow pro?le of the
pipeline is calculated based on the measurements of the pipeline inlet and
outlet. Substituting the collected measurements into a mathematical model, the
predicted operating parameters can be evaluated by employing the Method of
Characteristics (MOC) . Preliminary leak detection is considered by comparing
the predicted modelled values to the measured values.
WSNi system is
Responsible for collecting monitored water pressure and ?ow rate parameters by
the use of autonomous sensors. Firstly, the segment i of pipeline is divided
into equal segments and sensor nodes are placed in each segment ends. Then,
hierarchical WSN architecture is implemented where sensors are grouped into
clusters. Each cluster head transmits the data to a Base Station (BSi) which
will be analysed by the RCC to recognize the presence of the leak and its
position. Hybrid method is implemented as following:
The global architecture is
shown in Fig. 1. It is divided into two sub-systems: WSN system and Remote
Control Centre (RCC). For each segment i of the pipeline,
III. PLDS ARCHITECTURE
the threshold values are adapted, high false alarm rates will be recorded
during transient periods of the pipeline. Moreover, unless a localization
technique is attached to the method, it cannot localize the actual location of
the leak on its own.
The mass balance method for leak
detection is straightforward (Burgmayer and Durham 2000; Martins and Seleghim
2010). It is based on the principle of mass conservation. The existence of leak
causes an unbalance between the output and input mass ?ow rate as well as the
line pack variable rate (Liou 1996; Parry et al. 1992). This is variable that
de?nes the actual amount of gas in a pipeline or distribution system. A leak
alarm is raised once the difference between the volume of ?uid entering a
section of the pipeline and the volume of the ?uid leaving the section exceeds
some pre-set threshold. (Liu 2008) presented a detailed theory and the
implementation issues that are encountered in this method. In their work, they
further pointed out that the volume or mass can be obtained by using readings
of commonly used process variables such as temperature, pressure and ?ow rate.
(Rougier 2005) presented a hybrid mass balance method, which incorporates
probabilistic method to the mass balance method. The main drawback of this
method is that the probabilistic method requires a substantial amount of
computational power. One of the advantages of the mass balance method however
is the ease with which it can be implemented on existing pipeline infrastructure.
It is also able to rely on existing instrumentation already available on the
pipeline; 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 overall
accuracy of measuring instruments. Another limitation of the mass balance
method is its inability to detect small leak in real-time. Thus, resulting in
loss of signi?cant amount of ?uid before an alarm is raised. A further
limitation is that the mass balance method easily affected by random
disturbances around the pipeline as well as the pipe dynamics.
Mass balance method
processing is one of the alternative methods for leak detection (USDT 2007). In
the set-up stage, the output obtained from the system due to a known alteration
in ?ow is obtained. Subsequently, digital signal processing is carried on the
obtained measurements in order to detect variations in system response. The
application of digital signal processing helps in isolation of original leak
responses from noisy data. Encouraging results have been obtained from the
application of this method for both gas and liquid pipelines (Golby and Woodward
1999; USDT 2007). The main advantage of this method is that the mathematical
model 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 size
grows substantially. An additional disadvantage of this method is its high cost
and complexity when it comes to installation and testing
Digital signal processing
As this negative pressure wave travels
towards the terminal ends of the pipeline section, pressure sensors stationed
at the terminal ends are able to measure the pressure reduction signal. This
can be achieved because when the wave reaches the terminal ends, it causes a
drop ?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, different
time difference of the negative pressure wave is obtained at the terminal ends.
From the knowledge of the different time difference that the pressure sensors
on both sides of the leak detect, the pipeline section length and negative
pressure wave velocity, the position of the leak can be obtained (Ge et al.
2008; Ma et al. 2010).
In the negative
pressure wave method, once a leak occurs the pressure of the ?uid drops. This
is due to the sudden decrease of liquid density at the position of the leak.
Subsequently, pressure wave source propagates outwards for the point of leakage
towards the opposite sides of the leak. Considering the pressure of the ?uid
before and after the leak as a reference, the wave produced by such leakage is
termed the negative pressure wave.
Negative pressure wave method
Verde and Visairo (2001) proposed a
method, which uses a linearized, discretized pipe ?ow model on an N-node grid
and a bank of observers. The observers are modeled in such a way that when
leakage occurs, all observers are reset except one. Localization of the leakage
is obtained by the location of the non-responsive observer. Meanwhile, the
quantity the leak can be obtained from the output of the other observers.
Moreover, a detection system that utilizes an adaptive Luenberger-Type
observer, based on a set of two-coupled one dimensional ?rst order non-linear
hyperbolic partial differential equation, is proposed by (Aamo et al. 2006;
Hauge et al. 2007). Although this method is able to detect tiny leaks less
than 1 % of ?ow (Scott and Barrufet 2003), it has the drawback of having high
cost, as it requires huge instrumentation for obtaining data in real time.
Moreover, another disadvantage of this method is the complexity of models
employed that can be handled only by an expert.
This method depends on pipe ?ow
models developed to employing equations such as: conservation of momentum, mass
and energy as well as the equation of state of the ?uid. The presence of
leakage is determined by the estimated value and measured value of the ?ow.
Continuous monitoring noise levels and transient events minimize false alarm
rate. Billmann and Isermann (1987) designed an observer with friction
adaptation that in the event of leakage it generates a different output from
one obtained from measurements. Thus, from this difference leakage can be
Real time transient modelling
II .leak detection
combining the RTTM (Real Time
Monitoring 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 previous
implemented hybrid pipeline leak detection methods. Section III details and
describes the water pipeline model. In section IV, we detail the PLDS
architecture. Section V illustrates the leak detection methodology. Finally,
section VII concludes this paper.
we focus on sensing the continuously
water parameters (pressure and ?ow rate) to detect the presence of the leak and
to locate its position. Thus, the originality of our contribution is to deploy
a hybrid method
distribution is generally installed through underground pipes. Monitoring the
underground water pipelines is more difficult than monitoring the water
pipelines located on the ground in open space. This situation will cause a
permanent loss if there is a disturbance in the pipeline such as leakage. Leaks
in pipes can be caused by several factors, such as the pipe’s age, improper
installation, and natural disasters. Therefore, a solution is required to
detect and to determine the location of the damage when there is a leak.
Wireless Sensor Network (WSN) is considered as a reliable solution for Pipeline
Leak Detection Systems (PLDS) to supervise pipeline and to detect and localize
Keywords—Wireless Sensor Network, Pipeline monitoring, Leak, Hybrid
technique, Detection, Localization,
monitoring of leaks in pipelines is an important issue to be addressed by
researchers and the public. This is due the fact that they can have a great
impact both economically and environmentally. In recent years, the effect of
leakages of pipelines carrying oil, gas and nuclear ?uids have posed a threat
on humans as well as marine life. This paper provides a survey of recent
methods of detecting pipeline leaks with special focus on Real Time Transient
Modeling and Wave Propagation Method is implemented to detect and locate the
position of the leak in a water pipeline. A mathematical model is carried out
to solve the transient based leak detection model and different scenarios are
developed to estimate the relationship between the pressure ?uctuation and leak
position. The obtained results approve the potentiality of the proposed