International Journal of Scientific & Engineering Research, Volume 3, Issue 11, November-2012 1

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Assessment of Transformer Oil Quality Using Fuzzy Logic

Technique

INTRODUCTION

The life of a transformer is dependant upon life of its insulation. The insulation of a transformer deteriorates over a span of time with temperature, moisture and oxygen. Oil quality assessment is the technique to decide the quality of transformer oil after a specific period of use. The quality of transformer oil decides the efficiency of the transformer while in service. This chapter presents an innovative methodology for assessment of transformer oil quality by using fuzzy logic based decision support system.
The quality of transformer oil can be termed as good if it fulfills the following functional requirements for at least 30 years of service.

It acts as an electrical insulator.

It acts as a coolant for transformer winding and core.

Transformer oil free from water particles and sludge acts as a good insulator. Gases are produced in a transformer when it is subjected to electrical, thermal and environmental stresses. These gases get dissolved in oil and provide information about the incipient faults developing in transformer. The oil quality assessment is necessary for deciding preventive maintenance schedules of transformers. So far during the course of study of literature, no work has been found on the assessment of oil quality for deciding future course of action, after a specific period of use. In this dissertation work, an attempt is being made to assess oil quality by using fuzzy logic to arrive at a
decision regarding the action to be taken.

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RECONDITIONING OF TRANSFORMER OIL

There is a tendency of transformer oil to absorb water vapour from atmosphere due to breathing during service and even during transportation and storage. Water is injurious to transformer insulation system due to the following reasons [1].

It reduces the electric strength of oil.

It reduces the resistivity of oil.

It accelerates deterioration of solid insulation.

Air dissolved in oil induces risk of bubble formation and accelerated oxidation process, leading to chemical deterioration of oil. The biggest contaminator, moisture enters transformer oil from following sources.

by leakage past gasket

by absorption within the transformer as a product of degradation of insulation at high temperatures

Transformer oil also gets impregnated with various types of impurities. These may be solid ones like hygroscopic fibers, suspended particles or liquid ones like dissolved water organic and inorganic gases. They bring about a considerable reduction in the dielectric strength of the oil. As an example, oil at 20oC saturated with water (44 PPM) attains only about 25% of the original electric strength with water content of 10 PPM.
Thus, transformer oil is reconditioned to eliminate the following elements.

free and dissolved water

solid impurities

dissolved gases

The possible reconditioning processes are:

Single Filtering and Degassing

Double Filtering and Degassing

Reclamation

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The oil quality assessment method developed in this dissertation is based on finding the Total Hydrocarbon Gas (TCG) content in the oil. Depending upon TCG and severity of different gases present, the Oil Degradation Index (ODI) is evaluated and used to assign one of the above possible courses of action, if it is suitable for reconditioning. The single stage and double stage filtering and degassing are done by vacuum plants. This procedure involves course and fine filtration followed by degassing and dehumidification. The transformer oil is spread over a large surface area under vacuum, after which oil is delivered for use. The single stage and double stage filtering decision depends upon degradation level of used oil as manifested by the concentration of dissolved gases. In addition there are two other classifications.

No Filtering

Do Not Use

“No Filtering” condition means oil is not degraded and can be reused without any requirement of reconditioning. “Do Not Use” condition means whole transformer oil is to be replaced with new one, as the oil is degraded to an extent that its reconditioning has become impossible.
Oil reclamation is a process to remove heterogeneous atoms and oxidized products from oil. This is a chemical treatment of oil followed by filtering and degassing. The aim is to remove acid and other harmful compounds by precipitation, sludge formation or by use of chain reaction of organic compounds.

FUZZY MEASURE FOR ASSESSMENT OF TRANSFORMER OIL QUALITY

It is easy to visualize that considerable uncertainty is involved in the process of defining transformer oil quality. Hence, it is imperative to use fuzzy
logic for this purpose. The fuzzy measure for the assessment of oil quality is

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based on a combination of knowledge about TCG along with particular key gases evolved due to fault condition. The decision making process is based on the degree of match between current and permissible limits of dissolved gases and the fuzzy rule based system. The method is designed to compute the fuzzy measure named as transformer Oil Degradation Index (ODI), by integration of the information of different hydrocarbon gases formed in transformer oil. This approach uses a knowledge base to derive Oil Degradation Index (ODI). The transformer oil quality data is transformed into a normalized fuzzy number with membership grade function adjusted for characterizing transformer oil quality based on the data available from both normal and faulty transformers.

Fuzzy Membership Functions

The input variables to the present classification have different ranges in various hydrocarbon gases viz. CH4, H2, C2H6, C2H4, and C2H2 developed during incipient fault detection on the basis of DGA and TCG (Total Gas Content). Trapezoidal membership functions (trapmf) are used for all input variables. The term set of membership functions for all input variables except
TCG are:

Low

Moderate

High

Severe

The input variable TCG uses another term set ‘V. High’ in addition to above term sets. The input membership functions along with different term sets are shown in Figure 4.1. This figure is generalized for different inputs. The ranges of membership functions for input variables are shown in Table
4.1.
There is only one output variable to assign transformer oil quality. This
is called Oil Degradation Index (ODI). ODI value is computed for each input

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variable defined above. This ODI value and severity of fault as defined by rule base, decide the process to be adopted for reconditioning. The input membership functions are designed to give a constant output over substantial range. The membership of each input variable varies over a definite range, uniformly on both sides (trapezoidal). The full membership (value 1) covers
80% area of total range. This is the reason why output gives a constant ODI in maximum cases thus, making this measure insensitive to small changes in gas concentrations. This is an extremely useful property to take care of normal changes due to operations and ageing. Another significant feature is that this ODI is further correlated to the specific reconditioning procedure of transformer oil. The output membership functions are shown in Figure 4.2 and corresponding range and type of membership functions are shown in Table
4.2.

input i

Low

1

Moderate

High V. High Severe

Fig. : Input membership function used in oil reconditioning

input i

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ODI

MF 1

1

MF 2 MF 3 MF 4 MF 5

1

ODI

Note:

Fig. : Output membership function used in oil reconditioning

MF 1 = No Filtering
MF 2 = Single Filtering & Degassing MF 3 = Double Filtering & Degassing MF 4 = Reclamation
MF 5 = Do Not Use

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Fuzzy Rules

The fuzzy rules are developed to match a specific output condition of oil depending upon various input values. 38 rules are developed in all.
Table 4.3 shows the input data for oil quality analysis. The corresponding outputs are shown in Table 4.4. The output oil quality or Oil Degradation Index (ODI) is shown for individual group of inputs.

Table 4.3: Input Data for Oil Quality Assessment

INPUT DATA

S. No.

CH4

H2

C2H6

C2H4

C2H2

TCG

1.

4

3

4

3

0

14

2.

2

1

1

0

0

4

3.

2

2

0

0

0

4

4.

3

2

3

1

0

9

5.

2

2

4

3

0

11

6.

4

2

4

3

0

13

7.

3

2

3

0

0

8

8.

20

9

5

10

0

44

9.

4

7

3

1

0

15

10.

10

14

8

5

0

37

11.

15

13

4

3

0

35

12.

10

13

2

1

0

26

13.

7

17

13

3

0

40

14.

9

12

3

7

0

31

15.

1

0

0

0

0

1

16.

125

95

16

281

0

517

17.

212

68

54

470

0

804

18.

107

32

17

265

0

421

19.

31

42

16

115

0

204

20.

144

60

67

449

9

729

21.

95

1076

4

71

231

1477

22.

20

240

5

28

96

389

23.

32

338

1

32

50

453

24.

117

531

27

132

848

1655

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25.

71

79

3

72

115

340

26.

38

41

3

45

58

185

27.

104

110

30

86

131

461

28.

248

125

10

147

179

709

29.

262

222

27

168

410

1089

30.

91

98

18

72

154

433

31.

43

48

3

75

81

250

32.

1107

201

110

2016

6350

9784

33.

47

442

117

67

69

742

34.

1417

114

296

2096

0

3923

35.

4

2

3

4

0

13

36.

99

747

13

97

1036

1992

37.

122

41

31

143

188

525

38.

339

59

42

392

1

833

39.

4

7

3

2

0

16

40.

34

21

5

47

62

169

41.

21

199

0

40

144

404

42.

5365

2973

427

5532

2124

16421

43.

61

65

16

143

3

288

44.

87

16

75

395

30

603

45.

186

813

15

249

1001

2264

46.

38

212

15

47

78

390

47.

16615

2754

3657

31476

613

55155

48.

1393

800

304

2817

3000

8314

49.

770

199

217

1508

72

2766

50.

8784

4906

1404

9924

9671

34689

51.

13

24

5

43

319

404

52.

584

266

328

862

1

2041

53.

10

160

3

1

1

175

54.

619

80

326

2480

0

3505

55.

3997

231

1726

5584

0

11538

56.

24

127

0

32

81

264

57.

4066

9474

353

6552

12997

33442

58.

1053

507

297

1440

17

3314

59.

695

416

74

867

0

2050

60.

207

441

43

224

261

1176

61.

38

212

47

15

78

390

62.

1393

800

304

2817

3000

7314

63.

770

199

217

1508

72

2766

64.

8784

4906

1404

9924

9671

34689

65.

1742

425

7299

37043

158

62349

66.

95

1076

4

71

231

1477

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67.

754

244

172

1281

27

2478

68.

167

117

48

481

7

820

69.

1324

858

208

2793

7672

12855

70.

369

137

144

1242

16

1908

71.

27

274

5

33

97

436

72.

370

1249

56

606

1371

3652

73.

20

240

5

28

96

389

74.

79

33

30

215

5

362

75.

22

307

2

33

109

473

76.

144

60

67

449

9

729

77.

9739

2004

2750

5113

0

19606

78.

107

127

11

157

224

623

Table 4.4: OUTPUT: OIL Degradation Index and Suggested

Reconditioning Method

S. No.

Oil

Degradation

Index (ODI)

Reconditioning Method

1.

0.0784

No Filtering

2.

0.0784

No Filtering

3.

0.0784

No Filtering

4.

0.0784

No Filtering

5.

0.0784

No Filtering

6.

0.0784

No Filtering

7.

0.0784

No Filtering

8.

0.0784

No Filtering

9.

0.0784

No Filtering

10.

0.0784

No Filtering

11.

0.0784

No Filtering

12.

0.0784

No Filtering

13.

0.0784

No Filtering

14.

0.0784

No Filtering

15.

0.0784

No Filtering

16.

0.2500

Single Filtering and Degassing

17.

0.2500

Single Filtering and Degassing

18.

0.2500

Single Filtering and Degassing

19.

0.2500

Single Filtering and Degassing

20.

0.2500

Single Filtering and Degassing

21.

0.4500

Double Filtering and Degassing

22.

0.2500

Single Filtering and Degassing

23.

0.2500

Single Filtering and Degassing

24.

0.4500

Double Filtering and Degassing

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25.

0.2500

Single Filtering and Degassing

26.

0.2500

Single Filtering and Degassing

27.

0.3500

Double Filtering and Degassing

28.

0.2500

Single Filtering and Degassing

29.

0.3500

Double Filtering and Degassing

30.

0.2500

Single Filtering and Degassing

31.

0.2500

Single Filtering and Degassing

32.

0.8610

Do Not Use

33.

0.4500

Double Filtering and Degassing

34.

0.6500

Reclamation

35.

0.0784

No Filtering

36.

0.4500

Double Filtering and Degassing

37.

0.4500

Double Filtering and Degassing

38.

0.2500

Single Filtering and Degassing

39.

0.0784

No Filtering

40.

0.2500

Single Filtering and Degassing

41.

0.2500

Single Filtering and Degassing

42.

0.7770

Do Not Use

43.

0.2500

Single Filtering and Degassing

44.

0.4500

Double Filtering and Degassing

45.

0.6500

Reclamation

46.

0.2500

Single Filtering and Degassing

47.

0.7770

Do Not Use

48.

0.7770

Do Not Use

49.

0.6500

Reclamation

50.

0.7770

Do Not Use

51.

0.4500

Double Filtering and Degassing

52.

0.4500

Double Filtering and Degassing

53.

0.2500

Single Filtering and Degassing

54.

0.6500

Reclamation

55.

0.7770

Do Not Use

56.

0.2500

Single Filtering and Degassing

57.

0.7770

Do Not Use

58.

0.6500

Reclamation

59.

0.4500

Double Filtering and Degassing

60.

0.3500

Double Filtering and Degassing

61.

0.2500

Single Filtering and Degassing

62.

0.7770

Do Not Use

63.

0.4500

Double Filtering and Degassing

64.

0.7770

Do Not Use

65.

0.8710

Do Not Use

66.

0.4500

Double Filtering and Degassing

67.

0.4500

Double Filtering and Degassing

68.

0.2500

Single Filtering and Degassing

69.

0.7770

Do Not Use

70.

0.4500

Double Filtering and Degassing

71.

0.2500

Single Filtering and Degassing

72.

0.6500

Reclamation

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73.

0.2500

Single Filtering and Degassing

74.

0.2500

Single Filtering and Degassing

75.

0.2500

Single Filtering and Degassing

76.

0.2500

Single Filtering and Degassing

77.

0.7770

Do Not Use

78.

0.2500

Single Filtering and Degassing

4.2 CONCLUSION

The method applied in this work is original and innovative in nature. During periodical incipient fault diagnosis of transformers, Oil Degradation Index (ODI) gives additional information for representing quality of oil. This helps in better monitoring of transformer condition. The output results of oil quality assessment can be used for making appropriate decisions regarding the procedure to be adopted for the reconditioning of transformer oil. This will improve life of transformer oil and life of transformer as a whole. Hence, preventive maintenance schedules can be programmed optimally with minimum breakdown.

REFERENCES

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International Journal of Scientific & Engineering Research, Volume 3, Issue 11, November-2012 12

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6. G.N.S. Kalyani D.V.S.S. Sivasarma, “AI Techniques for Condition Monitoring of Power Trasformers Using DGA”. National Power Systems Conference, NPSC 2004, 1110 – 1115.
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