Powering reliable solutions for you Development of a ...

The 11th IERE GM/The IERE – IIE Latin American Forum, 1 – 3 November 2011, Cancún, Mexico

Development of a Combined,

Experimental and Analytical Tool

for Real-Time Estimation of

Emergency Loading Capability

of Power Transformers

Enrique Betancourt, Gustavo Tamez

Prolec GE email: [email protected]

Roberto Liñan, Francisco Lopez

Instituto de Investigaciones Electricas [email protected]

Powering reliable solutions for you

http://vmwl1.iie.org.mx/sitioIIE/sitio/indice.php


Contents

1. Summary

2. Background – Introduction to Prolec GE

3. Development of Models and Tools – Thermal behavior of transformers

– Bubble evolution model

– Moisture migration model

4. Conclusion and Next Steps

Contents 1. Summary 3. Models and Tools

2. Background 3-1. 3-2.

4. Conclusion

2


1. Summary

• Power transformers (PTs) are very critical system components, due

to high acquisition costs and long lead times

• PTs exhibit high resistance to thermal overloads, but other than

thermal concerns set conservative limits to emergency service

• As soon as moisture is present in significant amount, the reliability of

the equipment can be limited by the “bubble evolution” phenomena

• Many variables are involved… situation that prevents most users to

take significant advantage of emergency overload capabilities

• A multigenerational research project was started by Prolec GE, with

support of IIE, to develop Real-Time Overload Capability Models

• The Models and associated Tools can be interfaced with M&D

systems and protection schemes



4. Conclusion

3


Introduction to

• Manufacturer of power and distribution transformers: Five product lines

• A Joint Venture between GE and Xignux, started 1995

• Facilities in Monterrey, Mexico, with more than 30 years experience in industry, and

Chennai, India

• Full capabilities, including design, manufacturing, testing, installation, and service

2. Background Contents 1. Summary

3. Models and Tools 2. Background

3-1. 3-2. 4. Conclusion

Apodaca facilities (10 mi from Monterrey), NL, Mexico

4


Generator Step-Up

• 1,000 MVA 3ϕ ó 500 MVA 1ϕ

• 550 kV (1,675 kV BIL) 50 or 60 Hz

• LTC in HV or LV, DETC in HV

Autotransformers

• 1,200 MVA 3ϕ , 600 MVA 1ϕ

• 550 kV (1,675 kV BIL) 50 or 60 Hz


Auxiliary Unit and Station Transformers

• 230 kV (1,050 kV BIL) 50 or 60 Hz


Shunt Reactors

50 MVAr 1ϕ

• 550 kV (1,550 kV BIL)

• 50 or 60 Hz

Power Transformers

Full range of products for T&D

Lo

gi

sti

cs More than 35 countries around the world

5


Primary facilities – Americas

Distribution and

Industrial Transformers

• 5 kVA - 167 kVA

• 225 kVA – 12,000 kVA,

up to 69 kV, 200 kV BIL

• 45 kVA – 5,000 kVA,

Up to 34.5kV, 200kV BIL

Residential

Industrial

Commercial

Industrial and Distribution Products

6


Prolec GE India - Global presence

• Acquisition of major share of IndoTech

Transformers Limited

• Serving Industrial and Utility markets in India

• Two facilities located in Chennai, with

modern equipment and qualified

personnel to handle up to 400 kV

7


Prolec GE’s R&D Program -Transformers for Smart Grids

• High reliability of energy delivery

Performance models for load management in real time

On line monitoring of operation reliability

• Environmental friendly manufacturing and operation

Application of biodegradable fluids

Low noise and lower losses

High-efficiency wind generator transformers

• Flexible, efficient operation of distribution grids

Distribution transformer with electronic control

Integral Solutions to prevent electricity theft

Electronic transformers

8


3. Development of Models and Tools

• 3-1. Thermal Behavior of Transformers



4. Conclusion

9


Thermal behavior of transformers -Rated Load

Winding Hottest Spot Temperature:

It is the hottest temperature of current carrying components of a transformer in contact with paper or insulating fluid

Temperature

Height

Prolec GE

Oil Copper Min.

Avg.

Max.

gr Winding to oil Gradient

H Hottest Spot Multiplier

P Hottest Spot temperature

Q Winding Average Temperature by resistance

A Top Oil Temperature

Hottest Spot Temperature

Model for

Steady state:

P= A + H x gr

IEEE C57.12.80

Traditional WTI

10


Computer Solution for Temperature Calculation

Temperature Calculations (IEEE Section 7)

11


Overloads- IEEE Definitions

Operation above the maximum nameplate capacity or design ambient temperature

Results on higher temperatures than those specified in standards

Faster aging and degradation of materials and components.

• Planned Overload

• Long Term Emergency Overload

• Short Term Emergency Overload

Overloading a transformer implies some risk

Risks should be identified and assessed in advance

12


Is that accepted by the user as normal and repetitive. Maximum hottest spot temperature is in the range of 120 -130°C. Is not related to system outages. Higher loss of life than rated load. Medium risk.

IEEE C57.91

Suggested Temperature Limits: (IEEE C57.91, table 8)

Winding Hottest Spot 130 °C

Metallic Parts 150 °C

Top Oil 110 °C

Planned Overloads

13


Long Term Emergency Overloads

Results from the prolonged outage of some system element, this is not normal operation, but may persist for some time. The hottest spot temperature is in the range of 130 -140°C. Medium-High Risk.


Winding Hottest Spot 140 °C


Top Oil 110 °C

IEEE C57.91 14


Unusually heavy loading brought about by the occurrence of one or more unlikely events (second or third contingencies) that seriously disturb the system. Hottest spot temperature may exceed 140°C for a short time. Acceptance of this condition may be preferable to other alternatives. High risk.

IEEE C57.91


Winding Hottest spot 180 °C*


Top Oil 110 °C

*Possible Bubble Evolution

Short Term Emergency Overloads

15


IEEE C57.91

Comparative table of loading types

Temperature limits suggested in IEEE C57.91

16


Risk Assessment

Risk

of

Failure

Temp. Current

120°C

130°C

140°C

160°C+

1.0 pu

1.3 pu

1.5 pu

1.8 pu+

Pressure

Normal Loading

Planned Overload

Short Term Emergency Overload

Long Term Emergency Overload

17


Moisture Migration and Bubble Evolution Models

Assumption: Under any

significant load, oil’s

absolute moisture contents

is uniform.

Relative moisture contents

can be measured with the

moisture sensor.

When thermal equilibrium is

reached, known moisture in

oil vs. Moisture in paper

curves can be applied.

Moisture contents in

conductor insulation

Bubble evolution

threshold

Moisture condensation

threshold

INPUTS/SENSORS RULES OUTPUTS

Moisture in oil sensor

Oil temperature (top,

middle, bottom)

Oil temperature profile

Load current H (A,B,C)

Load current X (a,b,c)

Load current Y

(a1,b1,c1)

Win

din

g

tem

pe

ratu

re

pro

file

Moisture contents of paper

Overload capability

18



• 3-2. Bubble Evolution Model



4. Conclusion

19


Consequences of moisture in paper

• Accelerated aging

• Reduced dielectric withstand

• Risk of bubble evolution within the windings’ insulation

0

20

40

60

80

100

120

0 10 20 30 40 50 60

Water in oil, ppm

Ele

ctr

ic S

tren

gth

, %

0

20

40

60

80

100

120

0 2 4 6 8 10

Water in paper, %

Ele

ctr

ic s

tre

ng

th,

%

( D. J. Allan, 1990 )

20


40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10

WCP (%)

Te

mp

era

tura

(C

)

Kobayashi rapido

Kobayashi lento

Oommen sin gas

Scala baja presión

Scala alta presión

Oommen gas N2

Mc Nutt 50C

Mc Nutt 75C

Oommen tres discos

Oommen un disco

Davydov

Bubble evolution phenomena

Bubble evolution models. Test specimen.

21


Experimental setup for the Bubble Evolution Model

Duplicated test set up for redundant results

Visual detection of bubbles

22


Experimentation variables

Factors:

• Moisture contents in paper

• (Over-) Load profile

• Hydrostatic pressure

• Oil solubility

• Aging degree of paper

• Total gas contents in oil

• Geometrical data from windings and packages

• Thermo-hydraulic factors of windings

23


Experimental results

Comparison with models reported in literature

Those models include only a few variables each, not as

comprehensive as the model developed here

Water contents of paper (%)

Tem

pera

ture

(ºC

)

24


• 3-3. Moisture Migration Model



4. Conclusion


25


Sources of moisture in transformers

• Residual moisture

• Insulation structural components can release moisture

retained during factory and, or field processing, to reach

equilibrium with surrounding oil

• Moisture ingress

• Moisture penetration through seals and gaskets

• Ineffective free breathing dessicant

• Moisture brought in during internal inspection

• Molecular infiltration (small contribution)

• Thermal decomposition of solid insulating materials

26


Moisture migration between oil and paper

• In normal operation, moisture migrates with cyclic

dynamism between cellulosic (hygroscopic)

insulation components and oil, according to

changes in operating temperature

• A «pseudo-equillibrium» can be defined

• Paper insulation of windings is the material that

experiences the most intensive dynamics,

because of its proximitty to high temperature

components, and its low density.

• At higher temperatures, moisture goes from paper

to oil, at lower temperatures moisture comes back

to paper (and other insulating components).

27


Moisture migration between oil and paper

• Water solubiltiy in oil is a strong function of temperature.

• At this time, only moisture in oil can be practically monitored, at

locations far enough from core and coil energized parts.

• Oil flow through transformers radiators or coolers represents close

enough absolute moisture in oil conditions of oil passing through

the windings (average of HV and LV conditions).

• Complex thermal-geometrical models are requiered to estimate in

real time the moisture dynamics taking place within the windings,

starting from moisture in oil measurements at the radiators

headers.

Temperature (°C)

Wa

ter

Co

nte

nt

(pp

m)

0 10 20 30 40 50 60 70 0

50

100

150

200

250

300 100 % RH

80 % RH

60 % RH

40 % RH

20 % RH

Moisture in Oil (ppm)

Mo

istu

re in

Pa

pe

r (%

)

28


Distribution of moisture among transformers

components

Wooden

Blocks (55%)

Oil

(1%)

PB Barriers

(22%)

Paper in Wdgs.

(22%)

( Source: CIGRE )

29


Absorption of moisture in paper

• Paper can absorb 10% of its weight in water

• Water has a low solubility in transformers mineral oil; saturation

is reached at ambient temperature with as few as 50 ppm of

water

• In order of magnitude, paper can absorb as much as 2000

times more moisture than oil

30


Experimental Phase I: Feasibility

Experimental setup to investigate

feasibility of a dynamic moisture

migration model for primary

station transformers.

A reduced scale model was

designed to investigate dynamic

moisture relationships and

compare with state of art

publications

Moisture and temperature

sensors were incorporated into a

few section-model of a winding

package

A temperature profile was

created, axially and radially within

the windings

31


Experimental Phase I: Location of sensors

32


Experimental Phase I: Experimental results

Temperature profiles

Time (days)

HYDRAN readings

Time (days)

Time (days) Time (days)

Moisture and Temperature Moisture in oil and paper

33


Experimental Phase I: Validation of results

• The experimentation process

released formulas for estimation of

moisture in paper from moisture in

oil measurements, for a steady

state condition in the transformer.

• Three isothermal curves were

developed, for 60, 75 and 90°C

HST, with three points of dynamic

equilibrium on each curve.

• The steady state curves show

good similarity with T.V.

Oommen’s static equilibrium

curves. Moisture equilibrium curves (T.V.Oommen’s) compared

to experimental results. Hot spot is reference temperature.

34


Experimental Phase II : Moisture Distribution Model

35


Experimental Phase II: Formulas for local moisture

levels

Basic formula for the model:

Temperature distribution on windings, dielectric properties of paper as a

function of moisture and temperature, are the main parameters of the

experimental model.

36


Experimental Phase II : Model

The resultant mathematical model can estimate moisture in paper within an interval

of 0.7% to 3.0% moisture contents.

• The model allows for estimation of axial distribution of moisture on

transformers winding paper insulation.

• The model integrated to a computational tool can be used for on-line or off

line applications.

• Critical variables are moisture in oil passing through the windings, top oil

temperature and oil’s acidity level, to estimate moisture in insulation.

The variables used in the model are:

• Moisture in oil

• Transformers design parameters (termohydraulic models)

• Transformers operation parameters: load and corresponding temperature

distribution, steady dtate and transient

• Water solubility of insulating oil

• Aging conditions of paper

• Load profile (load current vs. time)

37


Experimental Phase III : Validation of Computational

Tools

38


Experimental Phase III: Full scale Validation

MOISTURE VARIATION , MEASUREMENTS VS. MODEL ESTIMATIONS

DIRECT MEASUREMENT (%) MODEL ESTIMATION (%) VARIATION (%)

CONDITION 1

Disk 40 0.85 0.71 -16.47

Disk 10 1.75 1.89 8.00

CONDITION 2

Disk 40 1.92 1.95 1.56

Disk 10 3.84 3.29 -14.32

CONDITION 3

Disk 40 2.51 2.56 1.99

Disk 10 4.03 4.14 2.72

• Paper strips were used as witness samples within each of the three stages of load

profiles. Methodology recommended in IEC 60814 was applied for evaluation of

samples.

• At each experimental heat-up stage the samples were extracted under

pseudoequilibrium conditions, and tested with the Karl Fischer method (KFT).

• Results were compared with our model predictions.

39


Numerical Models

• Each transformer design has a different temperature profile

• In general, performance parameters will change with time,

making necessary manual correction of models

• Using Adaptative Models, individual differences can be corrected

for each transformer; factory heat runs allow fine tuning of

thermal models

40


Moisture Migration and Bubble Evolution Models

Assumption: Under any

significant load, oil’s

absolute moisture contents

is uniform.

Relative moisture contents

can be measured with the

moisture sensor.

When thermal equilibrium is

reached, known moisture in

oil vs. Moisture in paper

curves can be applied.

Moisture contents in

conductor insulation

Bubble evolution

threshold

Moisture condensation

threshold

INPUTS/SENSORS RULES OUTPUTS

Moisture in oil sensor

Oil temperature (top,

middle, bottom)

Oil temperature profile

Load current H (A,B,C)

Load current X (a,b,c)

Load current Y

(a1,b1,c1)

Win

din

g

tem

pe

ratu

re

pro

file

Moisture contents of paper

Overload capability

41


This model is intended for estimation of moisture contents in paper insulation of

transformers windings.

Moisture Migration Tool

®®

42


Main output: Margin before bubble formation

MARGIN RISK


4. Conclusion

• A Real Time Loadability Margin (RTLM) is an important parameter

for equipment operating in future, smart grids

• For the particular case of Transformers, the parameters can be

derived from material properties and design data

• The challenge to develop an RTLM is to consider all relevant

variables for any particular design

• Extensive experimentation can yield practical models to estimate

bubble evolution margins under given overload profiles

• Without RTLM models, utilities either leave unused potential

emergency capacity… or run unknown risks

Contents 1. Summary 3. Subject


4. Conclusion

44


Next Steps

• Validation of accuracy and reliability of the new computational tools,

by monitoring aged transformers in the field, that fall within the

scope of the model: power transformers up to 100 MVA

• Development of an electronic device incorporating the Real Time

Loadability models

• Future incorporation of a Real Time Loadability Margin within

transformer’s parameters monitored for System Operation, and

within asset management control, for a reliable Network Health

Index

• Expand model´s scope and incorporate automatic verification and

correction of models parameters over time

45


Questions or Comments?

…

Thank you for your attention!

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