=Paper=
{{Paper
|id=Vol-1507/dx15paper19
|storemode=property
|title=A SCADA Expansion for Leak Detection in a Pipeline
|pdfUrl=https://ceur-ws.org/Vol-1507/dx15paper19.pdf
|volume=Vol-1507
|dblpUrl=https://dblp.org/rec/conf/safeprocess/CarreraVC15
}}
==A SCADA Expansion for Leak Detection in a Pipeline==
https://ceur-ws.org/Vol-1507/dx15paper19.pdf
Proceedings of the 26th International Workshop on Principles of Diagnosis
A SCADA Expansion for Leak Detection in a Pipeline∗
Rolando Carrera∗ Cristina Verde∗ and Raúl Cayetano∗∗
∗
Universidad Nacional Autónoma de México, Instituto de Ingeniería
e-mail: rcarrera@unam.mx, verde@unam.mx
∗∗
Universidad Nacional Autónoma de México, Posgrado de Ingeniería
email: rcayetanos@ii.unam.mx
Abstract pattern recognition and analytical models for failure diagno-
sis.
A solution for expanding an already existing
pipeline SCADA for real time leak detection is But all the later is pure academical, our aim here is
presented. The work consisted in attaching a FDI to share some of our practical experiences acquired dur-
scheme to an industrial SCADA that regulates liq- ing a re-engineering project that consisted on adding a real
uid distribution from its source to end user. For time leak detection and location layer to an already exist-
isolation of the leak a lateral extraction is pro- ing SCADA. The original objectives of that SCADA were
posed instead of the traditional pressure profile of the administration and delivery of some products, through
the pipeline. Friction value is a function of pipe pipelines, from the source to the end user. As it was our first
physical parameters, but on line friction estima- approach to integrating a FDI to an existing SCADA and
tion achieved better results. Aspects that were im- that we didn’t have experience on this subject, we proposed
portant in the integration of the FDI scheme into a solution that involves simple algorithms for detecting and
the SCADA were the non synchrony of pipeline locating a leak. In future work we’ll use more elaborate
variables (flow, pressure) and their accessibility, algorithms as dedicated observers or detecting two simulta-
that leaded to data extrapolation and the use of neous leaks.
data base techniques. Vulnerability of the loca- In order to show how we solved the targets of the project
tion algorithm due to sensors bandwidth and sen- we divided the solution in five major parts (each one in-
sitivity is showed, so the importance of selecting cluded in sections 2 to 6 down here). Some of them are
them. The FDI scheme was programmed in Lab- extracted from available theory, as the dynamical model for
VIEW and executed in a personal computer. a flow in a pipe and the expression for leak location, and
others are consequence of the experience achieved in our lab
facilities, as the calculus of pipe friction and and the choice
1 Introduction of sensors, and finally the data acquisition imposed by the
nature of the available SCADA.
Leak detection and isolation in pipelines is an old problem
that has attracted the attention of the scientific community Delivering a fluid to clients means steady operation, then
since decades. A paradigmatic example is the oil leakage in our solution required a suitable model for that condition,
the Siberian region [1], where the effects on the surrounding section two describes how to achieve a simple steady state
nature have been disastrous. In Mexico, a semi desert coun- model for a pipeline. Once the model is at hand an appro-
try, there is the need to transport water to the population on priate expression for leak location is needed, for that pur-
long distances via aqueducts; this requires complex supervi- pose in section three a simple method for locating a leak is
sion systems that detect leakages in early ways. Also, there presented. From our experience, pipe friction plays a fun-
exist a complex net of pipelines that transport oil and its damental role in the exact location of the leak and that real
by-products; in this net, besides the leakage problem, there time estimated friction is better than a beforehand constant
exist also the illegal extraction of the product transported in one; an on-line expression for calculating the pipeline fric-
the pipeline; this forces that the distribution system should tion is showed in section four. In this project we didn’t have
have a leak detection and location monitoring system. the option to choose sensors, but we consider appropriate to
Since the 1970’s years have been issued several works share here our experience in this matter, a comparative study
that have been fundamental for the detection and location on how different type of sensors affect the leak location is
of leakages as the one of Siebert [2], where on the basis presented in section five. The data acquisition system of
of the steady state pressure profile along the pipeline sim- the SCADA is based on a MODBUS system and a database
ple expressions are derived, based on correlations,that detect with the information of the pipe variables, we didn’t have
and locate a leakage. Later Isermann [3] published a survey the right to get into the MODBUS but in the database, sec-
showing the state of the art on fault detection by using the tion six shows how the indirect measurement of pipe vari-
plant model and parameter identification. Recently, Verde ables issue was solved by using ethernet and data bases,
published a book [4] making emphasis on signal processing, also, the extrapolation of data of non existing data during
sample times is presented. Finally, the concluding remarks
∗
Supported by II-UNAM and IT100414-DGAPA-UNAM. of this work are presented in section seven.
145
Proceedings of the 26th International Workshop on Principles of Diagnosis
2 Pipeline steady state model with
In most applications a dynamical model of the system is re- M i (Qi ) = µi Qi |Qi | + sin(αi ) = mi (Qi ) + sin(αi ) (5)
quired but not here because of the steady operation of the
pipeline, then a steady state model is more suitable. Be- that is independent of the spacial coordinate z i , and µi :=
sides, the pipeline lies buried in the field and has an irregular f i /2Di (Ai )2 g. Then the solution of (4) reduces to
topography, but it is possible to derive a model that handles
it like a horizontal one. This model is simpler as will be H i (z i ) = −M i (Qi )z i + H i (0) for 0 ≤ z i ≤ Li (6)
showed. with H i (0) the pressure head at the beginning of section
In the following we modify the model of a pipeline with i. Defining boundary conditions for section i in terms of
topographical profile as showed in Figure 1 into one with a pressure at the ends:
right profile piezometric head, where the pressure variable
depends on a reference value h, as is the hight over sea level H i (z i = 0) := Hin
i
H i (z i = Li ) := Hout
i
. (7)
along the pipeline. Consider the one dimension simplified
with (7) in (6), we obtain
flow model in a pipeline with n sections [5],
i i
1 ∂Qi (z i , t) ∂H i (z i , t) Hin − Hout = M i (Qi )Li = mi (Qi )Li + ∆Hi , (8)
+g
A i ∂t ∂z i
(1) where ∆Hi = L sin(α ) is the height difference between
i i
i i i i
f Q (z , t)|Q (z , t)| section ends.
+ + g sin αi = 0 It is reported in [7] and [8] that the pressure head
2Di (Ai )2
∂H i (z i , t) b2 ∂Qi (z i , t) P i (z i )
+ =0 (2) H i (z i ) = (9)
∂t gAi ∂z i ρg
which assumes that fluid is slightly compressible, pipe walls
are slightly deformable and negligible convective changes can be written in terms of the piezometric head H̃ i (z i ), wich
in velocity. Q is volumetric flow, H is pressure head, A depends on a heigth h that can be related to sea level, i.e.
is pipe cross-sectional area, g is gravity, f 1 is the D’Arcy- H̃ i (z i ) = H i (z i ) + h(z i ), (10)
Weissbach friction [6], b is the velocity of pressure wave,D
is pipe diameter, z is distance variable and t the time. Super h(z i ) in m over reference datum or sea level, ρ is fluid den-
index i = 1, 2, ..., n indicates pipeline section characterized sity. Then the profile pressure (8) is equivalent to
by its slop with angle αi , n is the total number of sections. i i
H̃in − H̃out = mi (Qi )Li (11)
2340
for section i and sea level h(z i ) along the section. Finally,
2320
Sensors locations considering that boundary conditions are related by
Heigth over sea level [m]
i i+1
2300 H̃out = H̃in , (12)
2280 from this equation and (11) one gets
n
∑
2260 1 n
H̃in − H̃out = Li mi (Qi ) (13)
2240 i=1
2220 which is function of the piezometric head for a pipeline with
n sections without branches.
2200
0 1 2 3 4 5 6 The profile of Figure 1 corresponds to the topography of
Length [m] 4
x 10 the pipeline under study. The pressure head H(z) and the
resulting piezometric head H̃(z) are shown in Figures 2 and
Figure 1: 60 km Pipeline topographical layout 3, respectively. Take into account the uniformity of H̃(z)
similar to the one of a horizontal pipeline. The reference
We start with the following hypothesis: the system works datum was the height of the first sensors location.
in steady state and that the pipeline lay on an horizontal sur- As a consequence, if H̃in1
= H̃in and H̃out
n
= H̃out , be-
face. Therefore we need a steady state model that takes into sides if m (Q ) = m(Q) = M (Q) for all i, then Equation
i i
account these conditions. (13) becomes
In order to describe the behaviour of the pressure head
H i (z i , t) along a section without branches it is assumed H̃in − H̃(z) = LM (Q) (14)
steady state flow, so from (2) one gets ∑n
where L = i=1 Li the total length of the pipeline. Equa-
∂Q (z , t) i i tion (14) is the steady state piezometric model for the
=0 ⇒ Qi constant (3) pipeline viewed as a horizontal one.
∂z i
Combining (1) and (2)
3 Leak location
dH i (z i )
+ M i (Qi ) = 0, (4) We consider a leakage as an outlet pipe at the leak location
dz i as is shown in Figure 4. A branch or lateral pipe in sec-
1
This friction characterizes the shear stress exerted by the con- tion i breaks the continuity of variables Q(z, t) and H(z, t),
duit walls on the flowing fluid. therefore new boundary conditions must be satisfied [9]. In
146
Proceedings of the 26th International Workshop on Principles of Diagnosis
1000 H1 = H2 = H3 . Thereafter in the study was included only
the balance
950 Q1 − Q2 − Q3 = 0, (17)
as consequence,
Pressure head [m]
900
Q1 = Qin , Q3 = Qout (18)
850
with Qin y Qout flows at the ends of the pipeline. So the
differential equation (4) transforms in two equations
800 dH 1 (z)
− M (Q1 ) = 0; for 0 ≤ z ≤ zb
dz
750
(19)
0 1 2 3 4 5 6 dH 3 (z)
Length [m]
x 10
4
− M (Q3 ) = 0; for zb < z ≤ L,
dz
Figure 2: Pipeline pressure head profile H(z) describing the pressure head along the section with a branch
in point zb . As the equations (19) have the same form as (4),
their solutions also have the same as (6). Therefore, with
980
boundary conditions:
960
1. H 1 (z = 0) = Hin ,
940
2. H 3 (z = L) = Hout ,
Piezometric head
920 3. Qin = Qout + Qzb and
900 4. Hzb − ϵ = Hzb + ϵ with ϵ → 0
880 Assuming that all pipes have same diameters, solutions of
(19) evaluated at the ends are reduced to
860
Hin − Hzb
840 − M (Qin ) = 0
zb
820
0 1 2 3 4 5 6 (20)
Length [m] 4
x 10 Hzb − Hout
− M (Qout ) = 0.
L − zb
Figure 3: Profile of the piezometric head H̃(z)
Obtaining the variable zb associated to the position of the
branch
particular, the union of three pipes is associated to a geome- M (Qout )Li + Hout − Hin
try shown in Figure 4 and the corresponding conditions that zb =
M (Qout ) − M (Qin )
describe the action of separating flow are reduced to
L sin α + m(Qout )L + Hout − Hin
H2 = H1 + κ12 (H2 , H1 ) (15) = , (21)
m(Qout ) − m(Qin )
H3 = H1 + κ13 (H3 , H1 ) (16)
in terms of the piezometric head
where H2 and H3 are pressures at the beginning of pipes
2 and 3 and the functions κ1η (·, ·) with η = 2, 3 repre- m(Qout )L + H̃out − H̃in
zb = . (22)
sent losses caused by friction and change of flow direc- m(Qout ) − m(Qin )
tion. For adjusting the order of magnitude of these func-
tions flow simulations were held with Pipelinestudio [10] Equation (22) is the key for leak isolation. In order to see
with the topology of the study case shown in Figure 1. Sim- the performance of this leak location method some experi-
ulation reported that terms κ12 and κ13 were negligible, then ments were held in our pipe prototype [11], which is an iron
pipe of 200 m long, 4 inches diameter and six valves at-
tached to it for leak simulations. Table 1 shows the percent
deviations of locating the leak position. In each experiment
a valve was fully open. Coriolis sensors were used.
ࡽ
4 Pipeline friction
ͳ ࡴ ͵
The D’Arcy-Weissbach friction is a function of the pipe
parameters, [6] and [12], and operation conditions, as the
ࡽ ࡴ Reynolds number. For practical purposes the friction f is
ࡴ ࡽ
obtained from tables provided by the pipe manufacturers.
But we observed that that value differs from the real one of a
ࢠ࢈ ࡸ െ ࢠ࢈ working pipeline where, no matter that is working in steady
ࢠൌ ࢠൌࡸ
state, the value is influenced by noise -caused by pipe in-
ner surface imperfections and attachments (nipples, elbows,
Figure 4: Union of three branches in point zb of pipeline etc.)-, therefore using a previous fixed value of f is of no
with transversal section areas A1 , A2 and A3 use in Equation (1).
147
Proceedings of the 26th International Workshop on Principles of Diagnosis
ods are based on processing a residual that is a flow differ-
Table 1: Location error in percentage of total pipe length ence. Due to our lack of experience, and by suggestion of
Experiment ∆zb [%] Valve position [m] a supplier, we start our flow measurements with a paddle
1 1.66 11.54 wheel flow sensor [13]. Later on, as ultrasonic sensors are
2 2.93 49.83 widely used in the field, we decide to change to them [14],
3 0.135 80.36 thinking that our measurements would be better. Finally,
4 0.54 118.37 we reached the conclusion that success on leak detection
5 0.375 148.93 and location depends strongly on the sensors quality (make
6 3.42 186.95 and sensing principle), so we acquired sensors based on the
Mean 1.0 Coriolis effect [15].
An experiment that we made in our pipe prototype was
to cause a leakage (outflow in a extraction point) and esti-
To overcome the problem of not having the friction right mate the location with the measurements of the three sen-
value, we proposed a solution that was an on line friction sors. Figure 6 shows the deviation of the calculated location
estimation. In the following we show how to calculate this depending on the type of sensor. Oscillations are observed
friction. For that, we part from the steady state momentum around the operating point, which leads to the necessity of
equation, Equation (4). Turning back the original parame- signal filtering in the diagnosis process. Table 2 shows the
ters we get error leak location, Paddle Wheel and Coriolis sensors have
similar error, but standard deviation is bigger with the Pad-
dH f dle Wheel. In order to compare performance in the fourth
+ g Q |Q| + gsinα = 0 (23)
dz 2DA2 column the accuracy of the instruments are presented; re-
solving the integral, considering that H0 and HL are pres- mark that Coriolis error standard deviation is about seventy
sures at he beginning and at the end of the pipeline and L times bigger than sensor accuracy. The observation here is
the length, results that the quality of the results depends more on the behavior
of the flow than on the accuracy of the instrument used.
f
g(HL − H0 ) = −( Q2 + gsinα)L (24) Leak location (Real position= 49.8 m)
2DA2 ∞ 60
where Q∞ is volumetric flow in steady state, the abso-
lute term disappears when flow goes in one direction only. 50
Friction has the following expression 40
Coriolis
Location [m]
2 Paddle wheel
2DA g (H0 − HL − Lsinα) 30
f= (25) Ultrasonic
L Q2∞
20
Equation (25) is used to calculate on line the friction
value, as is shown in Figure 5, experiment realized in our 10
pipeline prototype. The calculated friction has a consider-
0
able amount of noise, but this noise can be attenuated via
weighted mean value with forgetting factor (MVFF, contin- −10
0 1000 2000 3000 4000 5000 6000 7000
uous line in figure). Actually, we are working on the use of Samples
recursive identification procedures for a better friction esti-
mated. Figure 6: Leak location with the three sensors
0.026
Unfiltered
MVFF
Table 2: Leak location errors
0.025
Sensor Error Error STD Accuracy
[%] [%] [% FS]
Paddle wheel -0.28 3.36 0.50
Friction
0.024
Ultrasonic 2.12 1.39 2.00
Coriolis 0.28 0.84 0.05
0.023
One of our goals in the SCADA expansion project was to
0.022
deliver results in real time. For this, sensors experiments
0 100 200 300 400 were performed to determine which one would have the
Samples faster response. An index to take into account is the time
response, it can be appreciated in Figure 6 but is practically
Figure 5: Friction estimated, raw and filtered the same, therefore we measured the settling time from the
moment when the leakage valve is opened. In Figures 7,
8 and 9 the flow development is observed, dotted line indi-
cates the time when the leakage valve is opened to 100%. In
5 Influence of sensors on location Table 3 are the measured times, being the ultrasonic sensor
Flow measurement in a pipeline is fundamental for leak lo- which requires more time (this by the number of points used
cation, in view that most of the pipeline leak detection meth- to calculate a mean value).
148
Proceedings of the 26th International Workshop on Principles of Diagnosis
Considering the settling time and noise in measurements
Table 3: Sensors settling time (taking the STD as the measure for that), Coriolis sensor has
Sensor ts [s] the best performance. Experiments showed in this section
Paddle wheel 3 were made with 1 s sampling period.
Ultrasonic 35
Coriolis 4 6 Asynchronous data and data bases
In the academy, we are used to work with benchmark sys-
tems or laboratory facilities with ad hoc data acquisition
systems, sufficient sensors, controlled environments, etc.
21
Qe But these conditions are not necessarily in the practice, as
Qs was the case of the SCADA expansion, where the access
20
to flow and pressure sensors of the pipeline were not avail-
able, but through a database. So the solution adopted was as
19
follows:
Flow [L/s]
18 1. The leak locator is on a dedicated computer, indepen-
dent of the system that regulates de distribution of the
17 fluid, it connects to the database server, see Figure 10,
via intranet or VPN (Virtual Private Network) connec-
16 tion in a LAN (Local Area Network) system.
2. With proper permission a program, task performed
leak start
15
20 40 60 80 100 120 140 160 180 with Visual Studio 2010 tool that runs every minute
Time [min] (it is a program without GUI -Graphic User Interfacer-
that runs silently), brings system data and creates a
Figure 7: Flow measurement at the pipe ends, paddle wheel database with pipeline flow and pressure information,
sensors data required by the locator for proper operation.
3. The locator program (made in the LabVIEW plat-
form, [16]) periodically takes data (through SQL data
19
Qe server of Microsoft), applies the detection algorithm
Qs and when detects a leak proceeds to locate it, displays
18.5 on the screen the location of the leak (Figures 12 and
13), generates a visual warning and creates a file with
18 data leakage.
Flow [l/s]
17.5
17
leak start
16.5
20 40 60 80 100 120 140 160 180
Time [min]
Figure 8: Flow measurement at the pipe ends, ultrasonic
sensors
Figure 10: Communication scheme between leak locator
and database
19.5
Qe But the data acquisition system of SCADA do not meet
19
Qs the condition of sampling the system variables with con-
stant sampling period. The nominal sampling period was
18.5 3 min, but in reality this varies from one to several tens of
minutes. On the other hand, the locator was assigned a sam-
Flow [l/s]
18
pling period of 3 min, determined by the condition that nom-
17.5 inally SCADA performs a polling of all measuring stations
in that time span. To solve the problem of having a value
17 of flow and pressure of each station at all sampling time, it
was added to the localizer an algorithm that extrapolates the
16.5
leak start
missing data when it is not available. Two algorithms were
16 tested, one that retains the last data in the following sam-
20 40 60 80 100 120 140 160 180
Time [min] pling periods and one that generates straight line with the
last two values available, that when the value of the variable
Figure 9: Flow measurement at the pipe ends, Coriolis sen- that is brought from the database is not a new one, then the
sors one determined by straight line is used. In order to compare
results with both proposals a simulation with real data with
149
Proceedings of the 26th International Workshop on Principles of Diagnosis
three leaks was carried on, in Figure 11 the real and extrapo- It connects to the database in the SCADA through TCP
lated input flow data are shown. It can be seen that at certain sockets and VPN.
intervals the extrapolation by a straight line delivers values
that may be beyond the normal range of measurements, this
situation is exacerbated in large intervals with empty data as
the line grows monotonically delivering data outside the re-
gion of validity. In Figure 12 the location of a leak is shown
when extrapolated data are used and in Figure 13 when re-
tained data are used. The pipe length is about 20 km, so
that retention has outperformed extrapolation, since the lat-
ter yields higher values than the length of the pipe. Original
leak location was about 10 km.
17.5 Extrapolation
Retained
Real
17
Flow [m3/min]
16.5
16 Figure 14: Communications between client and database
15.5
For data handling JSON format is used, which is broadly
0 50 100
Time [min]
150 200 used for information interchange trough internet. JSON
(Java Script Object Notation) is a data interchange text for-
mat, easy for humans to read and write [17]. JSON is a
Figure 11: Graphics with original, extrapolated and retained collection of pairs {variable name : value}, realized as an
data of input flow with three leaks object, record, structure, dictionary, hash table, keyed list,
or associated array, see in Figure 15 an object example.
Figure 12: Leaks location with extrapolated data Figure 15: JSON data format for an object
An example of a JSON string for reporting a leak is the
following:
{"service":"event",
"options": {
"action":"new",
"vector": {
"Module":XXX,
Figure 13: Leaks location with retained data "EventID":XXX,
"Quantity":XXX,
6.1 Alternate database communication "PipeID":XXX,
"Location":XXX,
As part of the project requirements, an alternate way of com- "TimeEvent":"yyyymmddhhmmss"}
munication with the SCADA database was experimented. In }}
previous section the communication between leak locator
and database was direct trough a LAN system, the alternate Communications broker attends clients requests (leaks lo-
way was through a third party via internet and VPN connec- cator is not the only one) and also SCADA requests. The
tion. Figure 14 shows the principal elements of this scheme. database attached to the broker contains not only pipeline
The client is the computer with the locator program build data but also data generated by the other clients. At the end,
in LabVIEW platform that performs basically two activities: the SCADA has an interface in which information of leak-
leak detection and location, and request and sending data age events is displayed.
to communications broker using JSON strings. The remote Figure 16 shows a test ran with real data but off line. That
client interface is a Java process that runs locally and han- experience showed that locator not always received answers
dles communication, authentication, data formatting, en- from the broker. But this communications scheme is still in
cryption and security of the communication with data server. development.
150
Proceedings of the 26th International Workshop on Principles of Diagnosis
References
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7 Conclusions
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To the moment this paper was written our FDI system
is in the proof stage at the SCADA facilities and we are
waiting for in the field results.
8 Acknowledgments
Authors are very thankful to Jonathán Velázquez who
helped us by solving the database issues emerged in this
project.
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Proceedings of the 26th International Workshop on Principles of Diagnosis
152