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Future tools

for assessment of transformers

Ernst Gockenbach

Gottfried Wilhelm Leibniz Universität Hannover

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➢ Introduction➢ why assessment for power transformers

➢ actual situation

➢ ageing parameters

➢ Basis/Goal of assessment

➢Actual tools➢examples

➢Future tools➢examples

➢Health Index➢principle

➢examples

➢Conclusions

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➢examples

➢Conclusions

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Introduction

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0

10

20

30

40

50

60

70

80

90

100

0

10

20

30

40

50

60

70

80

90

100

1-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55

Service years of power transformers (basis 2002)

Nu

mb

er

of

un

its

Source: M. Stach: „Betriebswirtschaftliche Gesichtspunkte im Asset-

Management im Zeitalter der Fusion“, Micafil Symposium 2002, Stuttgart

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Cumulative distribution of service years

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Source: S. Tenbohlen, F. Vahidi: „Zuverlässigkeitsbewertung von

Leistungstransformatoren“, HS- Symposium 2012, Stuttgart

220 kV and 380 kV

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Failure hazard rate

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Source: S. Tenbohlen, F. Vahidi: „Zuverlässigkeitsbewertung von

Leistungstransformatoren“, HS- Symposium 2012, Stuttgart

220 kV and 380 kV

--- failure hazard rate

moving average

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Ageing parameters

Ageing of transformer insulation isbased on the following parameters

➢ thermal stress

➢ electrical stress

➢ mechanical stress

➢ chemical stress

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➢examples

➢Conclusions

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Basis of assessment

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➢ knowledge of apparatus

➢ knowledge of electrical insulating material

➢ knowledge of measuring and monitoring technique including noise suppression

➢ knowledge of reference values

➢ knowledge on the influence of the recorded parameter on the electrical insulating material

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Goal of assessment

➢ extension of the remaining useful life of the

transformer

➢ improvement of loading possibility of the

transformer

➢ higher availability and service reliability

➢ condition-based maintenance and repair

➢ prevention of loss and destruction

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➢examples

➢Conclusions

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Actual tools

➢ breakdown voltage measurement of liquid

part of the insulation system

➢measurement of moisture content in the liquid

part and calculation of moisture content in the

solid part of the insulation

➢ gas-in-oil (DGA) analysis with evaluation of

the reasons for the different kind of gases

➢ surface tension measurement

➢ acid content determination

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Actual tools

➢ furan measurement

➢methanol measurement

➢ if possible evaluation of the degree of

polymerisation (DP)

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Overview of monitoring and diagnosis devices

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PD, FRA, Bushing

Voltages, Currents

TemperaturesDissolved Gas in Oil

Data Acquisition Data Mining

Clustering

Discrimination

InformationFusion

Diagnosis

ANN

Fuzzy

CBR

MBR

Online

&

Offline

Sensors

Pre-p

roce

ssing

Featu

re E

xtra

ction

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Thermal Aging of Paper

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0

5

10

15

20

25

30

35

40

45

80 90 100 110 120 °C 140

temperature q

ag

ein

g f

acto

rC6

C98

1 2V

C8

C98

1 2V

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Furan as function of DP

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0

2

4

6

8

ppm

12

20040060080010001200

degree of polymerisation

fura

n c

on

ten

t

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DGA

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PD

T1

T2

T3D2

D+T

20

20

40

4060

60

80

80

80

60

20

40

%CH 4

2 2%C H

42%C H

D1

Source: IEC 60589

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Equilibrium curves

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Wa

ter

Co

nte

nt

in P

ap

er

Water Content in Oil

0

1

2

3

4

5

6

7

8

9

11

0 10 20 30 40 50 60 70 90

20 °C 30 °C 40 °C

50 °C

60 °C

80 °C

100°C

ppm

%

Source: T.V. Oommen, „Moisture Equilibrium Charts for Insulation Drying

Practice“, IEEE Trans. on Power Apparatus and Systems,

vol. PAS-103, No.10, pp. 3063-3067,

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Breakdown behaviour of mineral oil

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wrel (T) = ratio between absolute humidity and solubility

mineral oil

Source: M. Beyer, W. Boeck, K. Möller, W. Zaengl

„Hochspannungstechnik, Springer Verlag

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Breakdown behaviour of liquids

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0

10

20

40

50

60

70

80

0 20 40 60 80 100 120 140 160

Mineral oilEster liquid

Limit for unaged liquid

Limit for aged liquid

2.5 mm

Relative water content wrel in %

Bre

ak

do

wn

vo

lta

ge

in

kV

20

30

70

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Source: Omicron brochure

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Schematic diagram for PD localization

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windingPD ???

bushing

xD (t)

section PD ???

PD

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Test arrangement for PD localization

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Dig iti ze r

50O

50O

1234567

Low

pass

Low

pass

100kO

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Results of PD localization

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0

-5

5

0

-0.05

0.05

at the bushing

at the neutral

calculated from bushing

calculated from neutral

0

-5

5

Origin 3

0

-5

5

Origin 5

0 50

Measured data Origin 1

10 20 30 40 0 5010 20 30 40

Time in s

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Frequency Response Analysis (FRA)

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Source: University Stuttgart, Institute of Power

Transmission and High Voltage Technology (IEH)

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DGA with MSS criteria

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Source: Ivanka Atanasova-Höhlein, Siemens Lecture,

DGA – Method in the Past and for the Future

MSS = R. Müller, K. Soldner, H. Schliesing, (1977)

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MSS criteria = Discharge of high energy

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Source: Ivanka Atanasova-Höhlein,

Siemens Lecture, DGA – Method in

the Past and for the Future

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➢examples

➢Conclusions

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Frequency Response Analysis (FRA)

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AKVOptical

transmitter

DSO

AKVOptical

transmitterdB

Capacitive

sensor

Optical

transmitter

Impulse

generator

Rogowski

coil

Neutralpoint

Optical

receiver

Bushing/sensor

Battery power

Battery power

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DGA with Intelligent Agent-Based System

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Source: A. Akbari, A. Setayeshmehr, H. Borsi, E. Gockenbach,

„Intelligent Agent-Based System Using Dissolved Gas Analysis to

Detect Incipient Faults in Power Transformers” IEEE Electrical

Insulation Magazine Vol. 26, No. 6, 2010

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Modified Debye model

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Hari Charan Verma, Arijit Baral, Arpan K. Pradhan, Sivaji Chakravorti, „Condition assessment of

various regions within non-uniformly aged cellulosic insulation of power transformer using modified

Debye model” IET Sci. Meas. Technol., 2017, Vol. 11 Iss. 7, pp. 939-947

Simulation of temperature gradient which creates non-uniform

ageing of cellulosic insulation in transformers

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PDC measurement

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Hari Charan Verma, Arijit Baral, Arpan K. Pradhan, Sivaji Chakravorti, „Condition assessment of

various regions within non-uniformly aged cellulosic insulation of power transformer using modified

Debye model” IET Sci. Meas. Technol., 2017, Vol. 11 Iss. 7, pp. 939-947

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➢examples

➢Conclusions

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Health Index HI

➢ Health Index HI should give an information

about the actual status of the transformer

insulation system

➢ Health Index depends on different para-

meters and the figures could be different

depending on the used parameters

➢ Health Index gives a relative information in

terms of categories like good, moderate, bad

➢ Health Index can help asset management

➢ Health Index requires confirmation by

measurement and research

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Determination of Health Index HI

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Dun Lin, Yao-Yu Xu, Yu Liang, Yuan Li, Ning Liu, Guan-Jun Zhang, A Risk Assessment Method of

Transformer Considering the Economy and Reliability of Power Network“, 1st International

Conference on Electrical Materials and Power Equipment - Xi'an – China, pp. 584 - 587, 2017

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Example of Health Index HI1(1)

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Félix Ortiz, Inmaculada Fernández, Alfredo Ortiz, Carlos J. Renedo, Fernando Delgado, Cristina Fernández,

Health Indexes for Power Transformers - A Case Study, IEEE Electrical Insulation Magazine,

September/October — Vol. 32, No. 5, pp. 7 – 17, 2016

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Parameter of Health Index HI1(1)

Health Index part (1) is based on the following oil

parameter

➢ dielectric strength

➢ dissipation factor

➢ acidity

➢ moisture

➢ color

➢ interfacial tension

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The part (3) can take five different values

corresponding to the furan concentration

in the oil as shown below

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Health Index I1

Calculation of Health Index I1

➢ k1 (physical and dielectric properties) = 8

➢ k2 (dissolved gases content) = 10

➢ k3 (furan content) = 5

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Example of Health Index I2(1)

Health Index I2 part (1) is based on the

following oil parameter

➢ state of the insulating paperconcentrations of CO and CO2concentrations of furans

➢ concentrations of five gases dissolved

in the oil, H2, CH4, C2H6, C2H4, C2H2

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Calculation of Health Index I2(1)

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HI2(C,O) is one-third of

the sum F1 + F2 + F3

Cfur is the furan concentration

in the oil expressed in ppm

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Health Index part (3) is based on the

following oil parameter

➢ acid content of the oil (expressed as the

mass of KOH required to neutralize 1 g of oil)

➢ dielectric strength

➢ moisture content

➢ dielectric losses




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This part is given by the age and loading

of the transformer, and will be calculated

according

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where HI2(0) is an initial factor.

B is an aging coefficient, t1 is the year in which HI2(0) was evaluated, and t2is the year in which the state of the transformer is now being evaluated.

HI2(0) is related to the condition of the transformer when it entered service,

and its value is usually 0.5, whereas it is about 6.5 when the

transformer reaches the end of its service lifetime.

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➢ k1 (state of the insulating paper) = 0,266

➢ k2 (concentrations of five dissolved gases in the oil) = 0,095

➢ k3 ((acid content of the oil) = 0,07

➢ k4 (age and loading of the transformer) = 0,569




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Conclusions

➢ Traditional assessment tools are

useful

➢ More data allows a better assessment

➢ Data mining will improve the results

➢ Neuronal networks combine more parameters

➢ Results should be verified by measurements and

research activities

➢ More data does not mean better results

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Thank you for your attention

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