termografia - inspeção infravermelho-subestação
TRANSCRIPT
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FACTORS OF INFLUENCE OVER INFRARED THERMAL INSPECTION IN
OUTDOOR INDUSTRIAL SUBSTATIONS
Edson C Bortoni, Senior Member, IEEE, Laerte dos Santos, Guilherme S Bastos, Luiz E SouzaElectrical and Energy Systems Institute, Systems Engineering and Information Technology Institute
Itajub Federal University
Av. BPS, 1303 37501-903 Itajub-MG, Brazil
Abstract This paper presents some difficulties
encountered when evaluating information from infrared
thermal inspections conducted in uncovered industrial
substations. Procedural, technical and environmental arethe main factors of influence identified. Based on field
data and in laboratory tests, preliminary mathematical
models are derived, which are suitable either to forecast
the system behavior under specified conditions or to
remove the influence such components.
Keywords Industrial substations, Infrared thermal
inspections.
I.INTRODUCTION
Infrared (IR) thermal inspection is a valuable tool to
determine the operating conditions of substation components.
Problems such as high resistance contacts, short- and open-
circuits, inductive heating, harmonics, load imbalance and
overloads can often be detected through IR thermal
inspections. Applications of such technology to power andindustrial systems are presented since the sixties [1-2].
Despite thermal inspection seems to be a simple task,
there are a number of limitations and exogenous influences
that conduct to erroneous diagnosis and eventually impede
the failure detection [3]. Low emissivity of the components
under inspection, load current variation and small dimensionsof the inspected object located at large distances are
examples of drawbacks that must be overcame in an IRthermal inspection. Environmental quantities such as the
solar radiation, atmospheric attenuation, wind speed,
precipitation, and environment temperature and humidity
variation are uncertainty factors that must be added when
inspecting uncovered substations.
The works of Madding and Lyon [4] and Snell [5]
consider the loading conditions and environmental
components influence of IR thermal inspections. Lyon Jr. et
al. [6] evaluate the relationship between the current loading
and the temperature rise in a faulty connector. In addition,
the papers discusses about the limitations of techniques of
condition evaluation based only in the absolute temperature
or in the temperature rise, which can conduct to wrong
diagnosis.
This work tries to contribute with the understanding of the
influence of environmental and technical quantities over the
IR thermal inspection, by presenting actual data obtained in
field and developing mathematical models that allows not
only to consider the environmental influences over the results
of a thermal inspection, but also to remove the effect of some
of these quantities to forecast the system behavior when
operating in specific conditions of interest. Therefore, the
determination of the expected component temperature under
extreme load conditions, environment temperature and other
factors of influence becomes possible.
II.INFLUENCE FACTORS IN IR OUTDOORINSPECTIONS
Infrared (IR) thermal inspection is a valuable tool to
determine the operating conditions of substation components.
Problems such as high resistance contacts, short- and open-
circuits, inductive heating, harmonics, load imbalance andoverloads can often be detected through IR thermal
inspections. Applications of such technology to power and
industrial systems are presented since the sixties [1-2].
Such factors of influence can be characterized as
procedural, technical or environmental factors. The influence
of Procedural factor is minimized when certified personnel isemployed [7]. This work is concerned with the technical and
environmental influence factors. Figure 1 shows a typical
scene of an IR inspection in a high-voltage substation. The
main elements are the inspector, the thermal-camera, the
equipment under analysis and the environment.
Low emissivity of the component under analysis, load
current variation, small dimensions components at large
distances and employed equipment are examples of technical
factors of influence. In addition, for outdoor environments,
there are other factors of influence such as solar radiation,
atmospheric attenuation; wind velocity, ambient temperature
changes, rain and humidity are some of the environmental
factors that can turn the evaluation of an IR inspection in a
difficult task.
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Fig. 1. Procedural, technical and environmental sources of
influence.
In order to evaluate the extent of the influence of such
factors, an acclimatized chamber was developed to conduct
tests in laboratory under controlled conditions. The chamber
was designed to accommodate the component under test and
to carry out the same current loading conditions observed in
actual operation. Temperature is measured both throughcontact sensor and thermal-camera. All the information is
recorded using a data acquisition system and ready for use in
the analysis. Figure 2 shows the main components of the
developed chamber.
Fig. 2. Sketch of the developed acclimatized chamber.
Fig. 3. Developed acclimatized chamber.
This work presents some initial results with the application
of the acclimatized chamber. Two models for
characterization of loading current influence was developed
and applied in laboratory and in field, which are presented inthe following section. Figure 3 presents the installation of a
connector to be tested at the acclimatized chamber.
III.INFLUENCE OF LOADING CURRENT
It is well-known that a component operating temperature
is proportional to the square of the operating current. On the
other hand, in order to evaluate a component behavior, it is
desirable to know its temperature when working at the worst
condition, i.e., with the maximum current. Nevertheless, to
find this condition when carrying out inspections in field is
not guaranteed. The common procedure is to apply a
correction factor over the temperature rise that is the square
of the maximum current to operating current ratio, in order to
estimate the temperature at the worst condition.A test was conducted employing the developed chamber
where a load current was applied to a connector under test.
The temperature was recorded and presented at figure 4.
Notice in that picture that there are three stages at 600 (A),
with three different temperatures (!), 30C, 54C and 39C.
If one applies the criterion to correct the temperature rise
values with the square of the maximum current to operating
current ratio, to obtain the temperature for 800 (A), would
find 17.7C, 60.4C or 33.7C, none of them equal to 51C,
which is the right value obtained by test (fig. 5). It is well
known that this technique must be applied for constant
currents; nevertheless varying current is what happens in
power systems, mainly in industrial substations.In order to overcome the limitations of this method, two
temperature models suitable to estimate the temperature for
the heaviest current are presented as follows.
Fig.4. Load current and temperature of the connector under test.
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A.Thermal model parameters estimationAccurate thermal modeling is a complex task.
Nevertheless, a simple thermal model can be constructed
under the assumption that the temperature rise is a functionof the square of the operating current and that the
temperature rise over the environment temperature is the
main variable concerned to the heat exchange.Therefore, considering the object under analysis as a
homogeneous body, the temperature rise over the
environmental temperature () in a time period is the result
of the summation of two components: An increasing
component due to the present loading period and adecreasing component of the final temperature of the last
period:
(1)AA T/t0T/t
F e)e1(
+=
Where F is the final temperature rise that, at the
operating loading condition of the present period, the
component would reach in the steady state (C), 0 is the
final temperature rise of the last period (C), t is the duration
of the studied period (s), TA is the heating time constant (s).This model was applied to identify the component thermal
characteristics, i.e., its time constant and the temperature rise
dependence with the square of the operating current. A test
was carried out in the lab using the acclimatized chamber.
In this case, only the operating current was object of
variation, remaining constant all of the other variables.
Figure 5 presents graphical results of the applied current and
obtained temperature.
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
Temperature (C)
Current / 10 (A)
Fig.5. Applied current test at laboratory.
By inspection and employing a least square algorithm it
was possible to obtain the time constant of the connector
under analysis. The final temperature rise for each applied
current is showed in table 1.
TABLE I
Connector thermal characteristicsI (A) F (C) TA (s)200 4.70 39.1
400 15.2 36.7
600 30.6 33.5
800 51.9 33.9
Such results allow obtaining the equation that describes
the dependence of the final temperature rise with the square
of the applied current.
(2)24F I10781.0152.2 +=
This expression is of great value since it presents the
maximum temperature elevation for any applied current at
this component.
B.Auto-regressive modelDespite the former method allows the knowledge of the
final temperature rise for any applied current flowing in the
component under test, it is not useful for practical purposes,
as long as the current varies according to the system load.
For such situations another model is proposed, that is an
auto-regressive model. The idea behind this model is that,
neglecting environmental influence, the present temperaturerise is not only a function of the present current, but it is also
influenced by currents that of the past, as presented in
equation (3).
(3)2 tntn2
t1t12
t0t0t Ia...IaIa +++=
In a general form:
(4)=
=
n
0i
2titit Ia
Where t (C) is the temperature rise at instant t (s), ai
(C/A) are constant coefficients obtained by a least square
algorithm, I (A) is the operating current in a time period t-
it.The model is suitable to determine the temperature rise for
any current. If one considers that the loading current is
constant and equal to the current of interest, the finaltemperature rise for a given current,I, is given by:
(5)=
=
n
0i
i2
F aI
The correct definition of the time interval (t) and the
number of backward intervals (n) deserves someconsiderations. It depends on the available data and on the
time constant of the device under analysis. The time constant
depends not only on the body mass and material, but also on
where the heating is generated. As long as thermal-cameras
detects the temperature based on the surface emissivity, if the
heat is internally generated the necessary time to get the
thermal information becomes higher. Otherwise, a heat
generated at the surface will result in a smaller time constant.
After some testing it was noticed that three intervals within
the period of a time constant is sufficient to obtain reliable
results.
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Figure 6 presents the behavior of current and temperature
during the tests in laboratory. Data was collected at each
minute during approximately five hours.
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
Temperature (C)
Current / 10 (A)
Fig. 6. Current and temperature during the tests in laboratory.
As long as the average time constant of the studied deviceis 35.8 (min), two auto-regressive models were tested
considering 6 and 3 backward intervals of 10 minutes. Thefirst will consider previous currents of about 60 minutes
(about twice the time constant) and the former will take into
account currents that occurred in 30 minutes. The resulting
coefficients are presented in table 2.
TABLE II
Determined coefficients for the auto-regressive modelsModel a0 a1 a2 a3 a4 a5 a6 a6x10 0.044 0.185 0.185 0.099 0.146 0.046 0.144 0.848
3x10 0.236 0.212 0.104 0.237 - - - 0,789
Table 3 presents a comparison of the final temperature rise
calculated according to the studied models, while figure 7
presents the observed and estimated temperatures.
TABLE III
Final temperature rise comparisonI (A) Thermal
model
AR Model
6x10
AR Model
3x10
200 4.70 3.39 3.15
400 15.2 13.5 12.6
600 30.6 30.5 28.4
800 51.9 54.2 50.5
Fig. 7. Observed and estimated temperatures.
This same methodology was applied to data obtained in
field, as an actual situation. A survey was carried out in a
high-voltage uncovered substation. Information about load
current, target and ambient temperature, solar radiation andwind speed was simultaneously acquired during
approximately 36 hours at every 5 minutes. The results are
presented in figure 7.
Fig. 8. Obtained field data record.
Auto-regressive models 1x30, 2x30 and 4x30 was applied
to the information of temperature and current in the interval
between the last 8PM and 5AM, where solar radiation and
wind speed presented few influence to the target temperature
rise. Table 4 presents the determined coefficients of such
models.
TABLE IV
Determined coefficients for the auto-regressive models
Model a0 a1 a2 a3 a4 a1x30 0.381 1.093 - - - 1.475
2x30 0.187 0.173 1.119 - - 1.479
4x30 0.096 -0.093 0.142 -0.039 1.373 1.478
With great agreement, the summation of the auto-
regressive models coefficients is around an average value of
1.477, i.e., the final temperature rise for any current can be
obtained by multiplying this constant to the square of the
given current. The comparison of the estimated temperature
rise from de models with the measured one is presented in
figure 9.
Fig. 9. Observed and estimated temperature rise from 7PM to 7AM.
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IV.INFLUENCE OF ENVIRONMENTAL FACTORS
The main environmental factors considered in this work
are the temperature, humidity, rain, wind velocity and solar
radiation. A simple multivariable linear model to estimate the
temperature rise over the environmental temperature is
proposed:
+=
Jj
jjI xw (6)
Where (C) is the temperature rise over the
environmental temperature, I (C) is the temperature rise
due to the load current, obtained from the previous presented
models,Jis the set of considered environmental factors, wj is
the weight of influence of the xj environmental factor. Thisequation is rewritten for each instant of time allowing for the
obtaining of the weight coefficients by using the least
squares method.
This methodology was applied to a different set of dataobtained in field in order to estimate the influence of the rain,
wind velocity and solar radiation. A setup was prepared with
a thermal-camera and a meteorological data acquisition
system as presented in figure 10.
(a)
(b)Fig. 10. Connector under analysis.
The field data recorded is presented in figure 11.
Humidity information was not used because it was observed
a strong inverse correlation with the environmental
temperature.
Fig. 11. Obtained field data record.
A 1x45 autoregressive model was used in this case as long
as it was observed a delay of 45 minutes between the current
and the temperature rise. Employing the presented
formulation, a model was adjusted to obtain the temperature
rise as a function of load current (I), wind speed (xWS), solarradiation (xSR) and rain (xR), that is:
(7)079.13x428.1x10626.6x241.1I10906.4 RSR3
WS25
++=
As long as this equation relates the temperature rise as a
function of several quantities, it can also be employed toobtain the temperature rise for the worst conditions, i.e.,
maximum load current and solar radiation, and minimum
wind speed and rain, as well. Figure 12 presents the
estimated absolute temperatures for regular operation, which
showed excellent agreement with the field obtained in field,
and that calculated for extreme conditions.
Fig. 12. Calculated temperatures for regular and extreme
conditions.
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CONCLUSIONS
The work presented the first results of a study under
development which aims at obtaining more consistent
procedures for IR inspections in outdoor environments, under
the influence of environmental factors of influence. The
work makes use of an acclimatized chamber that allows
simulating several environmental and loading conditions.
Two methodologies were presented to estimate the
temperature rise of a component working in any conditions;
they are the thermal model and the auto-regressive model.Both models showed very good agreement when applied
both in laboratory and in field. It was presented a proposal of
technique to include environmental factors of influence in the
analysis. The method was capable to catch the influence of
such factors, allowing for replicate the tested conditions and
also for determining the absolute temperature for extreme
conditions. Field tests were carried out with a system capable
to acquire meteorological quantities in order to validate dedeveloped methodologies.
REFERENCES
[1] G. Ferreti, A. Giorgi, A New Type of Pyrometer Employed for
Preventive Maintenance in Electric Utilities, L`Energia
Elettrica, N 12, 1969.
[2] C.W. Brice, Infrared detection of hot spots in energized
transmission and distribution equipment, Electric Power
Systems Research, Volume 1, Issue 2, April 1978, pp 127-130.
[3] J. Snell, R.W. Spring, Developing Operational Protocol for
Thermographic Inspection Programs, SPIE Vol. 1682, 1992.
[4] R. Madding, B.R. Lyon Jr., Environmental Influences on IR
Thermography Surveys, Maintenance Technology 1999.[5] J. Snell, A Different Way to Determine Repair Priorities Using
a Weighted Matrix Methodology, Snell Infrared 2001.
[6] B.R. Lyon Jr, G.L. Orlove, L.P. Donna, The Relationship
between Current Load and Temperature for Quasi-Steady State
and Transient Conditions, Infrared Training Center 2002.
[7] L. Santos, E.C. Bortoni, L.C. Barbosa, R.A. Arajo,
Centralized vs. decentralized thermal IR inspection policy:
Experience from a major Brazilian electric power company,
Conference 5782 Thermosense XXVII Proceedings of SPIE,
vol. 5782, 2005.
BIOGRAPHIES
Edson da Costa Bortoni (S1994, M1996, SM2005) was born in Maring,
Brazil, on December 1, 1966. He graduated from Itajub Federal University
(UNIFEI), Itajub, Brazil, in 1990, received the M.Sc. degree in energy
systems planning from University of Campinas (UNICAMP), Brazil, in
1993, and the D.Sc. degree in energy and electrical automation from the
University of So Paulo (USP), So Paulo, Brazil, in 1998. Presently he is a
Professor at UNIFEI. His areas of interest include instrumentation, power
generation, and energy systems. He was a professor at So Paulo State
University (FEG-UNESP) and University of Amazonas, Brazil. Dr. Bortoni
is also Senior Member of ISA and SPIE.
Laerte dos Santos was born in Passos, Brazil, on March 18, 1964. He
graduated in Computer Technology from the State University of Minas
Gerais (UEMG) and received the M.Sc. degree in Energy Engineering from
UNIFEI in 2006. He is with FURNAS electric company since 1982 and is
working with infrared thermography since 1996. Currently he is working
towards his D.Sc. degree in Power Systems at UNIFEI. Mr. Santos is a
Level III Infrared thermographer.
Guilherme Sousa Bastos was born in Volta Redonda, Brazil, on December
22, 1977. He graduated in Electrical Engineering from Itajub Federal
University (UNIFEI), Itajub, Brazil, in 2001, and has the M.Sc. degree in
Industrial Systems Automation from UNIFEI, 2004. Currently he is working
towards his D.Sc. degree in Mobile Cooperative Robotics at Technological
Institute of Aeronautics (ITA), So Jos dos Campos, Brazil. Presently he is
a Professor at UNIFEI. His areas of interest include robotics, automation,
instrumentation, optimization and artificial intelligence.
Luiz Edival de Souza was born in Itanhandu, Brazil, on April 1, 1957. He
graduated from UNIFEI (1978), received the M.Sc. degree in mechanical
engineering from UNIFEI (1981) and the D.Sc. degree in automation from
UNICAMP (1987).