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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

[email protected]

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).

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