A Novel Application of Gene Expression

Programming in Transformer Diagnostics

A.Abu-Siada, Lai Sin Pin and Syed Islam

Curtin University of Technology, Australia

Abstract-Furans are the major degradation of insulating

paper in transformer oil. Hence the concentration of furans in oil

can be used as a good indicator of paper deterioration. Furan

concentration in transformer oil is currently measured using

High-Performance Liquid Chromatography (HPLC) or Gas

Chromatography-Mass Spectrometry (GC/MS). Both methods

provide accurate and reliable results in detecting furan

concentration. However, the two methods need very expensive

equipments and take long time to get the result for one sample.

Moreover it requires a trained expert to perform and interpret

the results. This paper introduces a novel approach for detecting

furan concentration in transformer oil through measuring its

spectral response. The Ultraviolet-to-Visible (UV-Vis) spectral

response of transformer oil can be measured instantly with

relatively cheap equipment and does not need an expert person to

conduct the test. Results show that there is a good correlation

between oil spectral response and its furan contents. Also, the

paper introduces a novel application for Gene Expression

Programming (GEP) to estimate the relationship between furan

concentration and spectral response of transformer oil.

I. INTRODUCTION

Power transformers are a vital link in a power system.

Monitoring and diagnostic techniques are essential to decrease

maintenance and improve reliability of the equipment.

Currently there are several of chemical and electrical

diagnostic techniques applied for power transformers[1]. As

the entire energized and high temperature transformer

components are immersed in the transformer oil, the

transformer oil is a key source to detect incipient faults, fast

developing faults, insulation trending and generally reflects the

health condition of the transformer. The electrical windings in

a power transformer consist of paper insulation immersed in

insulating oil. Paper insulation is composed of approximately

90% of cellulose, 6-7% hemi-cellulose and 3-4% of lignin.

Due to electrical and thermal stresses, oil and cellulose

decomposition occurs evolving gases that will decrease the

heat dissipation capability and the dielectric strength of the oil.

When degradation of paper insulation occurs, the cellulose

molecular chains get shorter and chemical products such as

furanic derivatives are produced and dissolve in the oil. Furans

are the major degradation of paper in oil and the 2-furaldehyde

in oil is the most prominent component of paper decomposition.

The increase in furans concentration in the oil corresponds to

the decrease in the tensile strength and the degree of

polymerization (DP) of the paper. De Pablo reported the

following relation between furfural and Degree of

polymerization based on viscosity DP

v

[2] :

FAL

DP

V

2

88

.

8

7100

+

= (1)

where 2FAL is the furfural concentration in mg/kg of oil.

Then the concentration of 2-furfuraldehyde in the oil can be

used as a good indicator of paper deterioration. It has been

estimated that new paper, under normal running conditions will

generate furfural at the rate of 1.7 ng/g of paper/h. The rate of

production increases with increasing degree of degradation and

a total yield of 0.5 mg of furfural/g of paper is expected in

100000 h of running (15-20 years)[3-5]. Furan level in a

transformer can be correlated with paper DP, and therefore an

in-service assessment of the mechanical strength of the paper

insulation can be made. Furans concentration is measured by

High-Performance Liquid Chromatography (HPLC) or Gas

Chromatography-Mass Spectrometry (GC/MS) based on

American Society for Testing and Materials (ASTM D5837,

Standard Specifications for Mineral Insulating Oil in Electrical

Apparatus)[6]. Both analysis methods provide accurate and

reliable results in detecting the concentration of furan

derivatives; 2- furfural (2-FAL), 2-Furfurol (2-FOL), 5-Hyroxy

methyl-2-furfural (5-HMF), 5-Methyl-2-furfural (5-MEF), and

2-Acetylfuran (2-ACF). However, these two methods need

very expensive equipments and take long time to get the results

in addition it requires for an expert person to perform and

interpret the results. The technique for furan testing is

described in IEC, but there is no guideline for interpretations.

This paper presents a novel approach for determination of

furan concentration in transformer oil through measuring its

spectral response. The Ultraviolet-to-Visible (UV-Vis) spectral

response of transformer oil can be measured instantly with

relatively cheap equipment and does not need an expert person

to conduct the test. Results show that there is a good

correlation between oil spectral response and its furan contents.

II. LABORATORY AGED OIL

The study has been performed on in-service as well as

laboratory aged transformer oil. Laboratory aged insulating oil

is prepared by utilizing the heating process available in IEC

61125[7]. Section of new craft paper (20mmx280mm) was cut

and wrapped around copper strips (3mmx10mm) then it was

impregnated in 25ml of new transformer oil (shell Diala B).

All samples were heated up to 100°C in a thermostatically-

controlled aluminum alloy block heater for 7 days. Oxygen

2008 Australasian Universities Power Engineering Conference (AUPEC'08)

Paper P-054 page 1

flow at a rate of 1 l/hr was supplied into each dry tube to

further accelerate the aging process.

III. FURAN ANALYSIS

All samples were prepared in accordance to standard ASTM

D 5837 and tested using GC/MS system for furan derivatives

identification and quantification. Table I shows furan

derivative concentration in particle per million (ppm) for

different oil samples using GC/MS. Results show that the 2-

furaldehyde (2-FAL) is the most prominent component of

paper decomposition. Therefore, the level of 2-furaldehyde in

transformer oil can be used as an indicator for paper

deterioration.

Table I

Furan concentration result by GC/MS

Test Sample 2-FAL 2-FOL 2-ACF 5-MEF 5-HMF

New Oil <0.01 <0.01 <0.01 <0.01 <0.01

Sample 1 3.1 <0.01 0.01 0.01 <0.01

Sample 2 5.1 <0.01 0.02 0.01 <0.01

Sample 3 10.0 <0.01 0.03 0.03 <0.01

Sample 4 15.0 0.01 0.05 0.05 <0.01

The correlation between 2-furaldehyde and DP with respect

to the solid insulation extent of damage is given in table II

depicting insulation paper dielectric and mechanical properties.

When DP test reveals a value of 250 or less, the paper is

considered to have lost all its mechanical strength, and the

transformer has reached its end of life [8].

Table II

DP and 2-Furfuraldehyde (2-FAL) Correlation

2-FAL (ppm) DP Value Significance

0-0.1 1200-700 Healthy Insulation

0.1-1.0 700-450 Moderate Deterioration

1-10 450-250 Extensive Deterioration

> 10 < 250 End of Life Criteria

IV. UV-VIS SPECTRAL RESPONSE

UV-Spectrophotometry is a non-intrusive test used to

determine the transformers integrity. UV-Spectrophotometry

is an accurate and sensitive method to analyze impurities in the

transformer oil using light absorbing properties of a sample.

Light transmitted through the oil sample containing various

contaminations is decreased by that fraction being absorbed

and is detected as a function of wavelength[9]. A

spectrophotometer measures the transmission, absorption or

reflection of the light spectrum for a given wavelength.

Absorption spectroscopy provides a measure of how much

light is absorbed by the oil sample which can be calculated

as[2]:

=

DR

DS

A log

(2)

Where

A is the absorbance, S is the sample intensity at

wavelength

, D is the dark intensity at wavelength

, R is

the reference intensity at wavelength

.

Same oil samples used for furan concentration measurement

using GC/MS were tested using a laboratory grade

spectrophotometer for absorbance spectrophotometry. The

experiment procedure was set up in reference to ASTM

E275[10]. Figure 1 shows the lab set up for measuring spectral

response for one oil sample. Figure 2 shows the spectral

response (absorbance) for different oil samples with different

furan concentration.

Figure 1. Lab set up for measuring the spectral response of transformer oil

Figure 2. UV/Vis Spectrum (Absorbance) for different oil samples with

different furan concentration

It can be shown from Fig. 2 that the new oil exhibits its

characteristics between 200 and 350nm uv-spectrum with

maximum absorbance at 250nm wavelength. However, in-

service and laboratory aged oil samples exhibit their respective

characteristics in the range of 200 and 470nm wavelength uv-

spectrum. Results show that absorbance as well as bandwidth

for maximum absorbance increases by a significant and easily

observable margin with oil deterioration and contaminations

which are reflected by the furan concentration level in the oil.

UV spectrum shows considerable noise for contaminated oil

which can be attributed to the variety of contaminations

including very high carbon and water content in the oil. Figure

2 shows a good correlation between the furan concentration in

transformer oil and its spectral response. To prove this

correlation in more details, Figure 3 shows the relationship

between spectral response parameters (maximum absorbance

2008 Australasian Universities Power Engineering Conference (AUPEC'08)

Paper P-054 page 2

and bandwidth wavelength) of transformer oil and its furan

concentration level; the more furan concentration the more

absorbance and more wavelength. Then the spectral response

parameters can be used as an alternative method to GC/MS to

determine the furan concentration in transformer oil. Spectral

response of transformer oil can be measured instantly with

relatively cheap equipment and does not need an expert person

to conduct the test. In the next section, Gene Expression

Programming (GEP) software tool is used to find a

mathematical correlation between furan concentration in

transformer oil and its spectral response.

Figure 3. Correlation between furan concentration in transformer oil and its

spectrum response parameters (wavelength and peak absorbance)

V. GENE EXPRESSION PROGRAMMING

Gene Expression Programming (GEP) is a new technique for

data analysis was first invented by Candida Ferreira in 1999[11,

12]. GEP is a learning algorithm that can find relationships

between variables in sets of data and builds models to explain

these relationships. GEP is similar to genetic algorithm (GA)

and genetic programming (GP) as it uses populations of

individuals, selects them according to fitness, and introduces

genetic variation using one or more genetic operators. The

nature of individuals is the fundamental difference between the

three algorithms. In GA the individuals are linear strings of

fixed length (chromosomes); in GP the individuals are

nonlinear entities of different sizes and shapes (parse tree); and

in GEP the individuals are encoded as linear strings of fixed

length expressed as nonlinear entities of different sizes and

shapes so that GEP combines the advantage of both GA and

GP while overcomes their some shortcomings[13]. One

important application of GEP is symbolic regression or

function finding, where the goal is to find expression that

performs well for all fitness cases within a certain error of the

correct value. In GEP individuals are selected according to its

fitness by roulette-wheel sampling with elitism and the best

individual is preserved. Mathematically, the fitness

i

f

of an

individual program i is expressed by the following

equation[14]:

∑

=

=

t

C

j

jjii

TCMf

1

,

)(

(3)

Where M is the range of selection, C

i,j

is the value returned by

the individual chromosome i for fitness case j (out of C

t

fitness

cases), and T

j

is the target value for fitness case j. Figure 4

illustrates the flowchart of the typical GEP algorithm[12].

Figure 4. The flowchart of GEP algorithm

GEP genes are composed of head and tail. The head contains

symbols that represent both functions (elements from the set

function F) and terminals (elements from the terminal set T),

whereas the tail contains only terminals. Therefore two

different alphabets occur at different regions within a gene. For

each problem, the length of the head h is chosen, whereas the

length of the tail t is a function of h and the number of

arguments of the function with the most arguments n, and is

evaluated by the equation:

T=h(n-1)+1 (4)

TABLE III

GEP Model results

Target Model Residual

0.5 0.539963 4.00E-02

1 0.886286 0.113714

2 1.963771 3.62E-02

3 3.139409 0.139409

4 4.014475 1.45E-02

5 4.912101 8.79E-02

7 7.12213 0.12213

11 11.13847 0.138472

12 11.85625 0.143752

13 12.98926 1.07E-02

15 14.94548 5.45E-02

2008 Australasian Universities Power Engineering Conference (AUPEC'08)

Paper P-054 page 3

To find the correlation between furan concentration in

transformer oil and its spectral response parameters, the

bandwidth and the maximum absorbance for each oil sample

are provided as the input (independent) parameters to the GEP

while the corresponding furan concentration is provided as the

target. Some other parameters should be specified to GEP such

as number of Genes, number of chromosomes, length of head

and the set functions. Table III shows the GEP model results

compared to the actual experimental measurements. The

absolute error between GEP model and the actual

measurements is given in the third column of table III. The

maximum error between the GEP model and the actual

measurements is 0.143752. .

Figure 5. GEP model and actual correlation between Furan and spectral

response parameters

Figure 6. Expression tree for the GEP model

Figure 5 shows the relationship between furan concentration

and spectral response parameters for experimental

measurements and GEP model. In Fig. 5, the vertical axis

represents the output (furan concentration in ppm), However, it

is difficult to represent the horizontal axis by real values as it

represents the two independent input variables (maximum

absorbance and bandwidth). From table III and Fig. 5, it can be

concluded that the GEP model is very accurate and efficient in

modeling the correlation between furan concentration in

transformer oil and its spectral response. The mean square

error between the model and target is only 0.065 and the

correlation coefficient is 0.9987. The model comes with an

Expression Tree (ET) (shown in Figure 6) that representing the

input/output relationship. The ET is translated to a

mathematical expression using the same program which

provides the model in many computer languages including

Matlab, FORTRAN, C++, Visual basic etc.

To check the capability of the model to estimate the furan

concentration in the transformer oil using its spectrum response

parameters; different random spectrum parameters have been

provided to the model as shown in table IV. The spectrum

parameters have been chosen to be inside and outside the

spectrum response range shown in Figure 2. The estimated

furan concentration corresponding to each parameter coincides

with the practical results shown in Figure 2. This proves the

capability of the model to interpolate/extrapolate the given

parameters to estimate a precise furan concentration in the

transformer oil.

Table IV

Capability of GEP model to interpolate/extrapolate results

BW (nm) Max. Absorbance Furan (ppm)

400 1.6 1.98714443

417 1.75 3.75018108

440 1.82 9.20123901

450 1.85 11.0897414

460 1.9 13.8267452

475 1.95 17.4311133

VI. CONCLUSIONS

Results show that there is a good correlation between oil

spectral response and its furan contents. Gene Expression

Programming has been used to find this correlation

mathematically. The great insight of GEP consisted in the

invention of chromosomes capable of representing any

expression tree. The structural and functional organisation of

GEP genes and their interplay with expression trees always

guarantee the production of valid programs. The paper has

introduced a novel application for GEP in transformer

diagnostics. Results show that GEP model can estimate

precisely the furan concentration of transformer oil using the

spectral response parameters (bandwidth and maximum

absorbance). The UV-Vis spectral response of transformer oil

can be measured instantly with relatively cheap equipment and

does not need an expert person to conduct the test or to

interpret the results or to interpret the results. Results show that

the model is very efficient and can replace the current

expensive and time consuming techniques to detect furan

concentration in transformer oil.

2008 Australasian Universities Power Engineering Conference (AUPEC'08)

Paper P-054 page 4

REFERENCES

[1] T. K. Saha, "Review of Modern Diagnostic techniques for

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[2] M. Arshad, "Remnant Life Estimation Model Using Fuzzy Logic

for Power Transformer Asset Management," PhD thesis, Curtin

University of Technology, 2005.

[3] Y. Shang, L. Yang, Z. J. Guo, and Z. Yan, "Assessing aging of

large transformers by furfural investigation," in Solid Dielectrics,

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[4] C. H. Zhang and J. M. K. Macalpine, "Furfural Concentration in

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Review," in Power Systems Conference and Exposition, 2006.

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Operation and Management, 2000. APSCOM-00. 2000

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[7] ITC, "Unused Hydrocarbon-Based Insulating Liquids-Test Methods

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[9] J. A. Palmer, W. Xianghui, R. A. Shoureshi, A. Mander, D.

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[10] ASTM, "Standard Practice for Describing and Measuring

Performance of Ultraviolet, Visible, and Near-Infrared

Spectrophotometers,," ASTM E275-01, vol. 03.06, pp. 72-81, 2001.

[11] W. Chuan-Sheng, H. Li, and K. Li-Shan, "The automatic modeling

of complex functions based on gene expression programming," in

Machine Learning and Cybernetics, 2005. Proceedings of 2005

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[13] C. Ferreira, "Gene Expression Programming: Mathematical

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2008 Australasian Universities Power Engineering Conference (AUPEC'08)

Paper P-054 page 5

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