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CHAPTER 2

INVESTIGATIONS OF TRANSFORMER OIL

CHARACTERISTICS

2.1 INTRODUCTION

Power transformers are the most significant components of power

system. Insulation used in transformer are of solid insulation system and

liquid dielectrics. It is important to investigate the cause of the insulation

degradation with respect to age.

The individual parameters of transformer oil have been studied

under different operating conditions. The power transformer has been

quantitatively modeled in order to estimate its reliability. The absence of

defined methodology to correlate the various parameters of transformer oil

reinforces the need to undertake this study.

In this work, changes in the characteristics of insulating oil (Break

down voltage, Flash point, Fire point, Furan compounds, Acidity, Dissolved

gases) were observed and their inter-relationships was determined using

Regression analysis. A new X method have been formed to over come the

problem of ratio codes method for determining fault using dissolved gases.

An expert system is also created using Fuzzy with basic software to

accurately determine the insulation efficiency of system and also the fault in

system due to degradation of oil.

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2.2 BREAKDOWN STRENGTH OF OIL

Dielectric strength of an insulation material depends on pressure,

temperature, humidity, electrode configuration and nature of applied voltage.

Breakdown strength analysis of transformer oil gives effective results through

which suitable dielectric material for the related high voltage applications can

be explored. The Breakdown Voltage (BDV) test kit consists of two

electrodes mounted on horizontal axis with 2.5 mm gap and enclosed in a

glass chamber as shown in Figure 2.1. Electrodes used in test apparatus is of

sphere-sphere electrode configuration since the sphere-sphere configurations

provides uniform field distribution. Diameter of sphere electrodes is of

19.8 mm.

The kit provided with standard oil test cup transparent cover, is

powered through a step up transformer capable of offering up to 60 kV. The

tests are carried as per IS6792 specifications and test results are shown in

Table 2.1. Nine samples with different BDV were taken as the reference for

conducting acidity, Flash, Fire point tests, DGA and Furan analysis. Oil in use

refers to the oil having satisfied with test standards and in working conditions.

Degraded oil refers to the oil contaminated in the moderate level. Highly

degraded oil refers to oil which is highly contaminated by various pollutants

like moisture, furan contents due to paper insulation deterioration and

dissolved gases. Highly burned oil refers to oil which is very highly

contaminated due to pollutants and various internal faults in winding.

Figure 2.1 Sphere-sphere electrode configurations

Dia =19.8 mm

2.5mm

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Table 2.1 Breakdown voltage of samples

Samples Conditions of sample BDV (kV)

[Mean ± SD]1 Oil in use (medium) 26 ±0.63

2 Oil in use (low) 30±0.773 Degraded oil (very low) 24±0.63

4 Degraded oil (moderate) 21±0.74

5 Degraded oil (very high) 18±0.63

6 Highly burned oil (low) 17±0.93

7 Degraded oil (medium) 19±0.74

8 Highly burned oil (high) 16±0.63

9 Degraded oil (low) 22±0.74

Each sample was subjected to five times of BDV test and their

average is taken as final value. The Standard Deviation (SD) of each test

samples is included along with final BDV value. Based on BDV value the

samples are further classified as very low, moderate and high with respect to

the gradient. From the test results it shall be identified that highly burned oil

shows very lower BDV.

2.3 ACIDITY

It is a measure of free organic and inorganic acid present in the oil

and expressed in terms of milligrams of Potassium Hydroxide (KOH)

required to neutralize the total free acids in one gram of oil. The acidity level

of transformer oil is measured as per British Standard BS2000 Part 1. From

the test results it shall be identified that acidity content is high for degraded

oil. Same nine samples were investigated for acidity value by means of

titration and also using PH meter. The observed results are listed in Table 2.2.

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The acidity of the samples for titration method is calculated using formula

Total Acidity = A N 56.1 / W mgKOH/g (1.1)

A - Quantity of KOH solution in ml

N - Normality of KOH solution used for titration

W - Weight of the sample in grams

Table 2.2 Acidity value of samples

Samples Titration Value

(mgKOH/g) PH meter

1 0.2243 5.70

2 0.2467 5.50

3 0.1802 6.10

4 0.3590 5.46

5 0.3300 5.67

6 0.3123 5.65

7 0.3256 5.70

8 0.2854 5.25

9 0.0900 6.53

2.4 FLASH POINT AND FIRE POINT

Flash point of a volatile liquid is the lowest temperature at which it

can vaporize to form an ignitable mixture in air. The flash point is an

empirical measurement rather than a fundamental physical parameter. The

Fire point is defined as the temperature at which the vapour continues to burn

after being ignited. It is the lowest temperature at which, on further heating

beyond the flash point, the sample support and combustion for five seconds.

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Flash and fire point test is carried out in Pensky-Martens closed cup

apparatus as per ASTM D3828. Total amount of transformer oil used for

analysis in the test apparatus is of 60 ml. The various combinations of

samples (Ideal sample along with natural dust and saw dust) are evaluated for

flash point and fire point values in the closed cup are shown in Table 2.3.

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Saw dust is composed of fine particles of wood and the material is

produced by cutting wood with a saw and they are of electrically insulating.

The factors such as moisture and density have little effect on the electric

resistance of saw dust. A low dielectric has low permittivity or low ability to

polarize and hold charge. Low dielectrics are very good insulators and the

high dielectric on the other hand has a high permittivity, because high

dielectrics are good at holding charge.

The Table 2.4 illustrates that while adding the normal dust with

humidity nature to samples; it acts as a conductive medium and hence

decreases the Flash and Fire point values.

While adding saw dust to samples it permits to act as insulating

medium and hence increases the Flash and Fire point values as evident from

results. It is inferred by means of proper combination of materials with

dielectric nature the flash and fire point values can be increased for

transformer oil.

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2.5 FURAN ANALYSIS

Cellulose is a natural polymer of glucose and it degrades slowly as

the polymer chains breakdown during service, releasing degradation products

in to the oil. Insulation paper eventually degrades to such an extent that it

loses all its mechanical strength and becomes susceptible to mechanical

damage, which is liable to expose the electrical integrity of the equipment.

Thermal degradation of cellulosic materials present in electrical

equipment with oil-paper insulation systems yields different amount of

furanic derivatives, the most common being 2-furfuraldehyde and these test

are conducted periodically after a period of 1-2 years.

Furanic compounds in electrical insulating liquids are extracted

from a known volume of test specimen by means of a liquid extraction. An

aliquant of the extract is introduced into a High Performance Liquid

Chromatography (HPLC) system which is equipped with suitable analytical

column and UV detector. Furanic compounds within the test specimen are

identified and quantified by comparison to standards of known concentration.

The tests are carried out as per International Electro technical

committee IEC-61198 specifications. Furan analysis of oil samples obviously

highlights the level of paper insulation. The test samples are investigated for

derivatives of Furanic compounds by means of HPLC and observed results

are listed in Table 2.5. The detail of instrument is provided in Appendix. The

test results reveal that eighth sample has highest value of deterioration of

insulation papers.

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Table 2.5 Furan analysis of test samples

Samples 2FFD 2AF 5MFFD 5HMFFD Total value

(ppb) 1 49 24 20 32 125

2 70 33 30 59 2683 100 30 65 82 283

4 121 42 57 90 320

5 123 40 57 100 330

6 500 10 150 265 1034

7 120 43 60 127 354

8 4900 35 120 287 5482

9 100 40 67 98 305

FFD - Furfural dehyde,

AF - Acetyl furan,

MFFD - Methyl Furfural dehyde

HMFFD - Hydroxy Methyl Furfural dehyde

2.6 REGRESSION ANALYSIS OF PARAMETERS

Regression analysis is a statistical tool for the investigation of

relationships between variables. It facilitates to recognize how the typical

value of the dependent variable changes when any one of the independent

variables is varied. Correlation coefficients measure the strength of

association between two variables. Some of the uniqueness of regression

coefficient is listed below.

Value of a correlation coefficient ranges between –1 and 1.

Greater the absolute value of a correlation coefficient, the

stronger the linear relationship.

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Weakest linear relationship is indicated by a correlation

coefficient equal to 0.

Strongest correlations (r = 1.0 and r = –1.0) occur when data

points fall exactly on a straight line.

Correlation becomes weaker as the data points become more

scattered.

Acidity (mgKOH/g)

Figure 2.2 Correlation of Acidity and Flash point

Acidity (mgKOH/g)

Figure 2.3 Correlation of Acidity and Fire point

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Acidity (mgKOH/g)

Figure 2.4 Correlation of Acidity and Break down voltage

Table 2.6 Regression analysis of critical parameters

Description Coefficient of determination

r2

Slope of the regression

line

Y intercept of the regression

line Acidity vs Flash point 0.2759 -133.24 176.510

Acidity vs Fire point 0.1158 -59.36 192.63

Acidity vs BDV 0.5670 38.94 -38.94

Acidity vs Furan 0.0222 2980.69 165.102

BDV vs Furan 0.643 -0.0023 23.418

Figures 2.2, 2.3, 2.4, 2.5 and 2.6 illustrates the correlation between

critical parameters Acidity, BDV, Flash point, Fire point and Furan contents.

Regression analysis of the Flash and Fire point implies that the Flash and Fire

point moderately linear with the acidity value as observed from Figures 2.2

and 2.3. Regression analysis of the Acidity and Breakdown voltage strongly

implies the linearity between them as observed from Figure 2.4. Results of

correlation coefficients from the Figures.2.2, 2.3, 2.4, 2.5 and 2.6 are listed in

Table 2.6.

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Regression analysis of Acidity and Furan content implies very high

non linearity as shown in Figure 2.5 Analysis of BDV and Furan content

implies strong linearity as shown in Figure 2.6.

Acidity (mgKOH/g)

Figure 2.5 Correlation of Acidity and Furan contents

Furan (ppb)

Figure 2.6 Correlation of Furan with Break down voltage

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2.7 DISSOLVED GAS ANALYSIS (DGA)

DGA is a diagnostic tool for detecting, evaluating incipient faults in

oil immersed transformers and test is carried for regular periodic intervals. It

mainly involves the following steps,

1. Sampling of transformer oil

2. Extraction of the gases from oil

3. Analysis of extracted gas mixture

4. Interpretation of gas data

Table 2.7 DGA analysis for test samples

Samples H2

ppm CH4

ppm C2H6

ppm C2H4

ppm C2H2

ppm CO2

ppm 1 650 116 4 43 528 528

2 0 19 12 0.3 0.3 910

3 17 1 4 39 0.7 1604 305 112 7 163 359 11998

5 1 8 3 2 0 2212

6 5 0.1 1 4 0.02 269

7 14 0.1 0.1 0.1 0.1 436

8 23 0.4 0.4 38 1 3986

9 21 26 14 0.5 0.5 857

The oil sample must be protected from all contaminations. The

removal of the gases from the oil can be accomplished by various methods.

After extraction, the extracted gas mixture is separated into individual

chemical compounds. Each compound is then identified and their

concentrations are determined using the Gas chromatograph. The detail of

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instrument is provided in Appendix. The test results reveal that transformer

containing sample 4 is highly affected by partial discharge fault.

Data from DGA provides,

Presence of fault

Development of fault

Suitably scheduling for maintenance

A fault is in this context defined as a process that causes abnormal

dissipation of energy within the transformer. When a fault occurs in the

transformer, the insulation system undergoes chemical degradation which

leads to a production of various gases that dissolves in the oil. These gases are

often referred to as key gases, based on their concentrations and interpreting

methodology various type of faults in the transformer can be recognized.

Interpretation is performed using Roger’s and Dorenburg’s

methods. Dissolved gas analysis of test samples are carried out as per IS

9434:1992 specifications and results are listed in Table 2.7.

2.8 PROPOSED ‘X’ METHOD

Traditional methods for determining the faults using dissolved

gases are of Rogers 3 ratio method as shown in Table 2.8 and Dorenburg 4

ratio method as shown in Table 2.9. The drawback of these methodologies is

some code of range of ratios does not harmonize with the Roger’s method and

others with the Doernenburg’s method.

The characteristics of the fault are not the same in both methods.

Besides the faults in the Roger’s table are not present in the Doernenburg’s

table and those in Doernenburg’s table are not in the Roger’s table.

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Table 2.8 Roger’s Ratio Method

Ratios of Characteristic Gases Code of Range of Ratios R1 R2 R3

Less than 0.1 0 1 0

0.1-1 1 0 0

1-3 1 2 1

Greater than 3 2 2 2

Sl. No. Characteristic Fault 0 No fault 0 0 0

1 PD of low energy density 0 1 0

2 PD of high energy density 1 1 0

3 Discharge of low energy 1-2 0 1-2

4 Discharge of high energy 1 0 2

5Thermal fault of low temperature<150oc

0 0 1

6Thermal fault of low temperature150oc-300oc

0 2 0

7Thermal fault of medium temperature300oc-700oc

0 2 1

8Thermal fault of high temperature>700oc

0 2 2

R1 = C2H2/ C2H4R2 = CH4/ H2R3 = C2H4/ C2H6, R4=C2H6/ CH4

In this Proposed X method, the two ratio methods are merged and

include all types of characteristics faults that are present in both the ratio

methods. The role of intertwined Fuzzy with basic software like visual basic,

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because of the versatility of its functions and general program capabilities is

chosen to interpret the performance.

Fuzzy logic is a form of many-valued logic or probabilistic logic; it

deals with reasoning that is approximate rather than fixed and exact.

Compared to traditional binary sets (where variables may take on true or false

values) fuzzy logic variables may have a truth value that ranges in degree

between 0 and 1. Fuzzy logic has been extended to handle the concept of

partial truth, where the truth value may range between completely true and

completely false.

Fuzzy logic has been applied to many fields, from control

theory to artificial intelligence. Classical logic only permits propositions

having a value of truth or falsity. The notion of whether 1+1=2 is absolute,

immutable, mathematical truth. However, there exist certain propositions with

variable answers, such as asking various people to identify a color. The notion

of truth doesn't fall by the wayside, but rather a means of representing and

reasoning over partial knowledge is afforded, by aggregating all possible

outcomes into a dimensional spectrum.

If a software program could manipulate the data from an oil sample

in various ways and return the results of several analyses there were two main

advantages. It would increase the system confidence if all methods were to

agree and in the case of varying diagnosis, each could be investigated quickly

and compared to one another.

Advantages of the application of fuzzy logic in fault gas analysis

were published by several authors. It was pointed out, that uncertainties of

diagnosis due to the values close to limit values of classification procedures

can be effectively handled by fuzzy sets. "Fuzzy version" of different

methods, like Key gas analysis were made. A typical solution is the

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application of a fuzzy inference system to approximate the relationship

between the measured values and the faults causing the different gas

combinations. Its application for fault gas analysis is presented through the

Ratio methods.

Table 2.9 Doerenburg’s Ratio Method

R2 R4 R3 R1 Evolution

0 0 0 0 If CH2 H2 ‘0’or less than 0.1 Partial discharge otherwise normal deterioration

1 0 0 0 Slight overheating below 150oc

1 1 0 0 Slight overheating below 150oc-200 oC

0 1 0 0 Slight overheating below 200oc-300 oC

0 0 1 0 General conductor overheating

1 0 1 0 Circulating current/overheated joints

0 0 0 1 Flashover without power follow current

0 1 0 1 Tap changer selector breaking current

0 0 1 1 Arc with power follow through or persistent sparking

On entering the gas concentration (ppm) and rating of transformer,

the system will calculate the R1, R2, R3 and assign code of range of ratio

values from Table 2.8 and corresponding fault will be displayed. If the code

of range of ratios is not in the Rogers table means it will move to Dorenburgs

method given in Table 2.9 to obtain R4 and the corresponding fault will

determined.

A typical solution is the application of a fuzzy inference system to

approximate the relationship between the measured values and the faults

causing the different gas combinations. The degree of uncertainty can be

taken into consideration and the upper and a lower limit is calculated

separately to specific faults.

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2.8.1 Analysis of Insulation Efficiency by Expert System

Graphical User Interface (GUI) is developed using Fuzzy logic and

visual basic in order to estimate the fault present in Proposed X method and

the conditions of transformer oil insulation by correlating all the parameters.

Based on recommendations suggested by the IS, IEC, ASTM, BS standards

and also on dexterity of engineers. The adequate limits are fixed for BDV,

acidity, flash, fire point and furan compounds for determining the insulation

condition of transformer.

The minimum and maximum limits to ensure the performance of

transformer oil insulation efficiency are given by Table 2.10. The results to be

exhibited based on conditions satisfaction are given in Table 2.11; it was

framed by means of Expert knowledge in the corresponding field.

Fuzzy strategy entwined with basic software is adopted to ensure

the new merged X conditions for Rogers and Dorenburgs methods. Analysis

of dissolved gas with their corresponding fault is carried out by means of

Fuzzy logic entwined with basic software (Visual Basic), since fuzzy strategy

is used to cover wide range of data’s shown in Rogers and Dorenburgs

method. A typical solution is the application of a fuzzy inference system to

approximate the relationship between the measured values and the faults

causing the different gas combinations. It is important to remark, that setting

the crossing of membership functions the degree of uncertainty can be taken

into consideration. The second difference, that by the use of these functions,

an upper and a lower limit are calculated for the specific faults.

An index model of display system used in the expert system (X

method) with their appropriate faults is given in the Table 2.12. In the model

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of expert system, on entering the data of gas concentrations in ppm and rating

of transformer it will display the fault respective to the ppm concentration of

gases. The details given in the Table 2.12 are of example datas given as input

(transformer rating and ppm concentration of gas) to the system and their

corresponding faults are displayed automatically by expert system. Screen

shots of transformer oil insulation efficiency system and panel view of input,

output panel formed are illustrated in the Figures 2.7, 2.8 and 2.9 respectively.

Table 2.10 Limits for analysis of transformer oil efficiency

S.NoTests conducted on

transformer oil Min. Limit

Max. Limit

1 Furan Analysis(ppb) 0 Above 2500

2 Acidity content(mgKOH/g) 0 0.35

3 Break down voltage(kV) 25 50

4 Flash point (oC) 120 180

5 Fire point (oC) 150 220

Table 2.11 Conditions for analysis of transformer oil efficiency

S. No Total number of conditions to be

satisfied Results to be exhibited

1 5" The Transformer oil is in Good condition"

2 4 " Satisfactory"

3 3" The Transformer Oil has to be monitored"

4 < 3 " The Transformer oil is not in Good condition"

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Figure 2.7 Screen shot of transformer oil efficiency software

Table 2.12 DGA investigations using Fuzzy logic strategy - X method

MVATransformer

rating H2

ppmCH4

ppmC2H6

ppmC2H4

ppmC2H2

ppmFault type

16 650 116 4 43 528Discharge of Low Energy

50 0 19 12 0.3 0.3Sight Over heating below150

C)

3.15 23 0.4 0.4 38 1Generalconductor over heating

10 1 8 3 2 0Thermal fault of low temperature 150 to 300 ( C)

25 19 2 1 4 0.1General over Heating

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Figure 2.8 Screen shot of input panel developed using Fuzzy logic

strategy - X method

Figure 2.9 Screen shot of output panel developed using Fuzzy logic

strategy - X method

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2.9 DISCUSSION

Tests were carried out on nine samples to investigate various

critical characteristics.

It is inferred on adding saw dust to samples it permits to act as

insulating medium and increases flash point.

It is inferred that on adding dust with humidity contents reduces

the flash and fire point.

Furan analysis of test samples reveals that highly burned oil

shows higher production of furanic compounds. More over the

time duration and cost wise approach for determining furanic

derivatives was very high by HPLC process.

Regression analysis implies that breakdown voltage shows

stronger linearity for furan and acidity.

DGA analysis of test samples reveals that sample four

containing transformer was subjected to very high partial

discharge problems.

The expert system developed for analyzing insulation efficiency

and DGA methods using fuzzy helps the operator to have an

easy solution about the dielectric strength of his transformer.

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2.10 CONCLUSION

The experimentation on various transformer oil samples has been

carried out and the results are interpreted. The methodology has been found to

formulate measures through which effective monitoring of the power

transformer can pave way to determine the life time of insulation. The

strategy has been coined to incorporate measures for evaluating unpredictable

failures and unnatural outages. It can be concluded that predictive monitoring

strategies will enable an extension in life and facilitate proper asset

management. The next chapter deals with investigations of thermal

degradation and spectral response of transformer oil.