10 ISH 2007 - Online Monitoring of a Transformer by Means of FRA

8/12/2019 10 ISH 2007 - Online Monitoring of a Transformer by Means of FRA

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Online Monitoring of a transformer by means of FRA

R. Wimmer*, S. Tenbohlen, K. Feser

University of Stuttgart, IEH, Pfaffenwaldring 47, 70569 Stuttgart, Germany

*Email: [email protected]

Abstract: Mechanical deformation in transformerwindings can be detected with the transfer function(TF). Normally the transformer will be disconnectedfrom the power supply for recording of the TF (offlinemeasuring). Another possibility is to calculate the TFfrom the transient overvoltages generated by switchingoperations or lightning strikes. These stochastic

incoming transient overvoltages can be recorded duringoperation and used for calculation of the TF (online

measuring). On the basis of these online-data diverse

problems like separation of excitation- and responsesignal, too low signal-to-noise ratio, etc. and theirsolution will be shown in this paper. An increase ofsensitivity of online measured TF can be obtained byusing only transient overvoltages produced from the

circuit breaker of the transformer for TF calculation andnot the stochastic incoming transient overvoltages.

1 INTRODUCTION

Determination of the transfer function (TF) ontransformers is a sensitive diagnostic method to get

information about the mechanical condition of thewindings. This diagnostic method is mainly used beforeand after transportation and in case of an error, if thetransformer was exposed to a too high current, by meansof an offline measuring. To do such TF measurementsthe transformer has to be disconnected, the electric lineshave to be dismounted from the transformer and themeasuring setup has to be assembled. It was shown that

a worst measuring setup leads to non reproduciblemeasurement results [2]. However, the TF as a sensitive

diagnostic method needs a high reproducibility inmeasurement results, because it is a comparativemethod.

An online measurement procedure was developed todetermine the TF. The advantage of this method is, thatthe transformer does not have to go out of operation and

the measuring setup is fixing. With an onlinemeasurement procedure it is possible to record everytransient overvoltage produced by switching operationsor lightning strikes during operation of the transformer.These overvoltages can be used for the calculation ofTFs. To investigate this adequate monitoring systemwas installed on a 350-MVA-system-interconnecting-

transformer.

2 THEORETICAL BACKGROUND

2.1 The transformer as a two-port network

Concerning the external terminals, the transformercan be considered as a passive, time-invariant, complexand linear network. The linearity is based on the fact,

that most kinds of lamination do not have a noteworthymagnetization for frequencies higher than 10 kHz [4],

[5]. For that reason the transformer can be regarded as atwo-port network (Fig. 1).

Fig. 1: The transformer as a two-port network

The excitation of the transformer results from a transientsignal. In principle all measurable electrical parameterson the transformer terminal are suitable for the responsesignal. According fig. 1 for each response signal one

TF can be defined:

TF of input current:

( ) ( )( )

( )( )( )( )fUfI

tUFFT

tIFFTfTF

in

in

in

inin == (1)

TF of output currents:

( ) ( )( )( )( ) ( )( )fUfI

tUFFTtIFFTfTF

in

nout

in

noutnIout

,,,, == (2)

TF of output voltages:

( ) ( ))

( )( )( )

( )fUfU

tUFFT

tUFFTfTF

in

nout

in

nout

nUout

,,

,, == (3)

2.2 Accuracy of the measurements

The measurement signal is influenced by a series ofdisturbing signals. Even with ideal measuringconditions the quantization noise affects themeasurement signal, because of the limited number of

amplitude steps. The effect is, that measurement signals

Transformer

complex RLCM-network

Iout,n

Iin

Iout2

Iout1

Uin

Uout,n

Uout2

Uout1

...


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are faulty and the following assessment is valid ((4),(5)):

{|X()| - |XS()|} < |XN()| < {|X()| + |XS()|}

{|Y(

)| - |YS(

)|} < |YN(

)| < {|Y(

)| + |YS(

)|}

(4)

(5)

Thereby |X()| and |Y()| are the magnitudes of the

measurement signals, |XS()| and |YS()| the noise levels

and |XN()| and |YN()| the wanted in- and output

signals. As a result of this it can be observed that thewanted signal lives within a tolerance band which can

be calculated as follows ((6), (7)):

( ) ( )

( )( ) ( )( ) ( )

S

S

N

N

XX

YY

X

YTF

+==

min

max

max (6)

( )

( )

( )

( ) ( )

( ) ( )

S

S

N

N

XX

YY

X

Y

TF +

==max

min

min (7)

3 SIMULATION OF AN 110 KV

SUBSTATION BY MEANS OF

EMTP-ATP

The transfer function of a transformer shall bedetermined form transient overvoltages reaching the

transformer. Apart from the transfer behavior of thetransformer, the transient behavior of the substation,

which is primarily dominated by reflection sequences, ismeasured with the transfer function, too. Differentcircuit states cause different reflection behavior and thisdirectly influences the result of TF. The influence of thesubstation on the transfer function is investigated withEMTP-ATP simulation program.

3.1 Realization of the model

Fig. 2 graphically shows the implemented substationof the low voltage side. Abbreviations: Dx disconnectorx, CBx circuit breaker x and BBx bus bar x. The bus barand the line sections for the connection of the respectivecomponents were regarded as overhead lines. Outgoing

lines were modeled as pair wise overhead lines with

several kilometers length. A 50 Hz voltage source wasattached to the end of these overhead lines.Disconnector and circuit breakers were simulated asideal switches without frequency behavior. To replicatethe transformer as simply as possible, a TACS-model

was used, which allows the usage of a rational functionup to 7. order. Such a rational function is not able to

imitate real transformer behavior. Therefore a rationalfunction of at least order 30 would be required.Nevertheless, a transfer function with three resonancefrequencies is enough for principle inspection. At onejunction the EMTP-ATP-model is stressed by a signallike a 1.2/50 impulse. The comparison betweensimulated und real signals, measured at one terminal of

the transformer, is shown in fig. 3a and fig 3b.

-0 .6

-0 .4

-0 .2

0. 0

0. 2

0. 4

0. 6

0 2 0 40 60 8 0

t ime t

|TFUout/Uin(f)|

s

kV

transient signal injected on D9transient signal injected on transformer input

Fig. 3 a): Transient signal of the simulation, injected once on

D9, once on the input of the transformer

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

0 20 40 60 80

time t

|TFUout/Uin(f)|

s

kVmeasured real signal of

transformer input

Fig. 3 b): Measured transient signal on the input of the

transformer

Fig. 2: The low voltage side of the substation as EMTP-ATP model


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In reality the exciting signal will not have the pulseform of a standard lightning impulse; it rather looks likea VFTO (Very Fast Transient Overvoltage). This is onereason for the differences of the signal waveforms. Theother reasons are: unknown place of the source for the

real transient signal and simplification of the model.

3.2 Results of the EMTP-ATP simulation

The simulations are done with followingconfigurations:

Tab. 1: Configurations of the EMTP-ATP model for the

simulation

Point size Closed circuit breaker Closed disconnector

Config. 1 CB1, CB3, CB5, CB6,

CB7

D1, D3, D7, D9, D12, D13,

D15, D17, D18, D19, D14

Config. 2 CB1, CB2, CB3, CB5,

CB6, CB7

D1, D3, D4, D6, D7, D9,

D12, D13, D15, D17, D18,

D19, D24Config. 3 CB1, CB2, CB3, CB5,

CB6, CB7

D1, D3, D4, D5, D7, D9,

D12, D14, D15, D16, D18,

D20, D24

Fig. 4 a) fig. 4 b) show, that the transfer functiondepends both on the circuit state of the substation and ofthe location of the excitation. Especial in the lowerfrequency range substantial changes of the transferfunction are visible. This can be explained with the fact,that a distance of more than 60 m is existent betweenthe transformer and disconnector 1 (D1) and voltage

divider 1 (VD1) respectively.The comparison with curve progressions of real

transfer functions reveals similarities (fig. 4 c)). As aresult of this it is clear that all transfer function have tobe classified according to the difference of circuit stateand location of excitation.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.2 0.4 0.6 0.8 1.0frequency f

|TFUout/Uin(f)|

MHz

V/V

TF of the emulated transformerTF of Config.1 and excitation on D9TF of Config.1 and excitation on D3

Fig. 4 a): Transfer function with fixed circuit state of thesubstation and different location of the injected signal

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.2 0.4 0.6 0.8 1.0frequency f

|TFUout/Uin(f)|

MHz

V/V

TF of the emulated transformerTF of Config.1 and excitation on D9TF of Config.2 and excitation on D9TF of Config.3 and excitation on D9

Fig. 4 b): Transfer function with a fixed location of the

injected signal and different circuit states of the substation

0

1

2

3

4

5

6

7

8

9

0 0.2 0.4 0.6 0.8 1frequency f

|TFU-2U/U-1U(f)|

MHz

V/V

TF of U-U2/U-U1 on 01.08.2005TF of U-U2/U-U1 on 16.12.2005TF of U-U2/U-U1 on 20.12.2005TF of U-U2/U-U1 on 16.05.2006

Fig. 4 c): Online measured transfer functions of the real

transformer

4 TF CALCULATION USING

TRANSIENT OVERVOLTAGES

FROM THE POWER GRID

4.1 Detection of dominant excitation signal

Switching operations or lightning strikes generatetransient overvoltages traveling along the lines. Thesesignals are completely different from those measured

offline. Capacitive coupling between the lines,reflection and reignitions induce a sequence of partialevents with oscillating characteristics (see fig. 5). Onereason of the oscillation is the resonance circuit of thelines, composed of inductances and earth capacitances.This is also shown in fig. 3 a). It cannot be assumed thatall partial events of a phase are excitation signals. They

can be answer signals of the transformer, too.Consequently these parts of the signal must be cut out

from the recorded signal.

The Signal-to-Noise-Ratio of the respective partialevent should not be too small, for a meaningful


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calculation of the TF. The current signal is searched forabsolute values, which are higher than an adjustabletrigger level. The algorithm cuts out each belongingpartial event from the other recorded signals with anadequate time period [9].

Due to the oscillation it is difficult to distinguishbetween the excitation and the answer signal on the

basis of the curve progression. Another possibility couldbe the time delay between excitation and answer signalbecause of propagation delay of the transformer.However, such a distinction is not possible because ofthe low sample rate of 10 MS/s.

Fig. 5: Recorded transient event with zoomed peak. 50 Hzcomponent stronger attenuated as the peaks because of the

transfer characteristics of the sensors

A method that worked satisfactorily includes

considerations about the cut off frequencies of thepartial events. It is assumed, that the excitation signalhas the steepest rising edge and therewith the highestcut-off frequency. The problem here is, to reliablydetect the frequency where the signal turns into noise.Due to the fact that the spectrum of the signal fallsseveral times under the noise level the detection

becomes difficult (see fig. 6). This is a statisticalproblem and was already investigated and solved inother research studies [1], [6], [10]. The Hinkleycriterion used as a jump detector is offered as a solutionof the problem. Therein the Hinkley sum S is computedrecursively like (1). The Hinkley function increases as

long as a signal exists. At that point where only noisedominates, the function decreases. The absolute

maximum of the Hinkley curve indicates the position ofthe cut-off frequency, as shown in fig. 6. The Hinkleyfunction is calculated with the Fourier transformed

signal fta(f) as follows:

2|)(|)1()( 21

++= fftafSfS (1)

Abbreviations:

1is the initial value2is the jump value.

The determination of 1 and 2 is done as fallows:First of all the frequency range from 3 MHz up to5 MHz is divided into 10 regions. The maximum andminimum values are searched within each region. After

that, 1can be calculated from the averaged minimumvalue and 2 from the averaged maximum value, asdescribed in (2), (3). Thereby it is assumed that in the

frequency range beyond 3 MHz only noise is present.

=

+

++=

9

01

2.03

)1(2.03|))(min(|

10

1

i iMHzMHz

iMHzMHzffta (2)

=

+++=

9

02

2.03

)1(2.03|))(max(|10

1

i iMHzMHziMHzMHzffta (3)

-50

-40

-30

-20

-10

0

10

0 1 2 3 4 5

2= -26.3 dB (noise level)

1= -45 dB

absolut maxima at fcut-off= 1.89 MHz

0.0

0.2

0.4

0.6

0.8

1.0

1.2

V

1.6

spectrum of the signalHinkley function

frequency fMHz

dBVs

|FFT(U-1U(t))|

valueoftheHinkley

function

Fig. 6: Spectrum of a signal with the corresponding Hinkley

function and the thresholds 1and 2.

Tests with offline measured similar excitation andanswer signals have shown a high reliability of thisalgorithm.

4.2 Classification of transfer functions

The FRA is a comparative diagnostic method, i.e. acomparison and its assessment of results of an actualand an older measurement. If the insulation- and

winding-conditions did not change the curvecharacteristic should not change, too. To achieve high

reproducibility, the basic condition must be the same[7]. It was already mentioned that different circuit statesof the substation causes different reflection behavior.Another influencing factor is the tap changer position[8]. One possibility is to sort the transfer functionsaccording to different circuit states and tap changer

positions. This could be done with an adequate database. Additionally it is necessary to store the location ofthe transient cause, too. This was shown in the EMTP-ADP simulation. It is obvious that such a data basewould increases with the size of the substation. Hence

this method to categorize transfer functions is not


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practical for a larger substation. Another possibility is tocategorize the transfer function with respect of a certaintolerance according to different minima and maxima,like fig. 7. Thereby, the knowledge concerning thecircuit state of the substation and the tap changer

position is no longer necessary. The idea of thisapproach is that a change of the mechanical condition of

transformer coils creates new categories of transferfunction.

TF of U-2U/U-1U on 01.08.2005TF of U-2U/U-1U on 04.08.2005TF of U-2U/U-1U on 16.12.2005TF of U-2U/U-1U on 20.12.2005TF of U-2U/U-1U on 21.12.2005TF of U-2U/U-1U on 13.04.2006

TF of U-2U/U-1U on 16.05.2006

MHz

frequency f

1.00.60.40.20.00

2

4

V/V

8

|TFU-2U/U-1U(f)

MHz

frequency f

1.00.60.40.20.00

1

2

V/V

5

|TFU-2U/U-1U(f)

TF of U-2U/U-1U on 01.08.2005TF of U-2U/U-1U on 27.10.2005TF of U-2U/U-1U on 19.12.2005TF of U-2U/U-1U on 13.01.2006TF of U-2U/U-1U on 16.02.2006TF of U-2U/U-1U on 15.03.2006TF of U-2U/U-1U on 18.04.2006

3

MHz

frequency f

1.00.60.40.20.00

1

V/V

5

|TFU-2U/U-1U(f)

TF averaged in time domain

TFs of category 1

averaging

TFs ofcategory x

TFs ofcategory xy

TFs ofcategory xyz

Fig. 7: Categorizing and averaging of the respective TF

The difficulty of the method which sorts the transferfunction according to different minima and maxima is to

filter out the fundamental resonance frequencies fromthe unimportant. The curve progression and co-domain

of the transfer function should be irrelevant for the filterrules. The following criteria worked satisfactorily andwere consulted [3]. the area of the resonance, bounded by near-by

minima have to exceed a threshold

the frequency gap between a maxima and a minimamust have a minimum value

the distance between a maxima and a minima haveto exceed a threshold

the maxima and minima have to withstand a

smoothing algorithmAfter categorizing the sensitivity of the reference TF

can be increased by using averaging. This will be donein time domain. The advantage of averaging is not onlythe increasing sensitivity of TF, but also the reducing ofthe size of the data set. It is not necessary to store everyrecorded data set. The storage of the time domainsignals of the averaged TFs is sufficient.

The reason for damping differences between TFs

can be explained with the virtual not-continuesspectrum of the signals. That means that the signals fallsbelow the noise level within a narrow frequency regionas it is shown in fig. 7 at approximately 70 kHz. It is

obvious that a division by such function values leads tobig failure in TF results. As a result of this, the

categorizing of the transfer function is done only bymeans of resonance frequencies. Data sets with cut-off

frequencies of the exciting signal lower than 1.5 MHzare rejected, because of the high uncertainty in upper the

frequency range.

5 TF CALCULATIONS USING

OVERVOLTAGES OF THE SWITCH-

ON EVENT OF THE TRANSFORMER

Using overvoltages of the switch-on event of thetransformer is advantageous because there are less

influence factors in comparison to the TF calculationusing transient overvoltages from the power grid. Thelocation of the excitation is always the same and onevoltage level is completely disconnected from thetransformer. Additionally the switch-on eventguarantees good reproducibility of the transient signals

and the signals do not have strong oscillations, becauseof the nearby circuit breaker and disconnectorrespectively. For that reason it is easier to distinguishbetween excitation and answer signal (see fig.8). Due tothe good reproducibility of the transient impulses by

switch-on events, the signals can be well adjusted for agood modulation of the analogue-digital-converter(ADC).

Fig. 8 also shows that a significant multiphase

excitation can be excluded, because the transient eventof one phase has finished when the next transient eventof the other phase starts. Considering the tap changerposition, the transfer function can be calculated withthese data sets. Fig. 9 a) shows a good correlationbetween regarded transfer functions. The difference in

damping at approximately 870 kHz can be explainedwith a higher uncertainty of the time signals in that

frequency range. This is confirmed by fig. 9 b) where

the tolerance bands of the transfer functions are shown.


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0.0 0.5 1.0 1.5 2.0 ms 3.0-400

-200

0

kV

200

600

phase Uphase Vphase W

firing pulseat phase U

firing pulseat phase V

firing pulse

of phase W

new firing pulseat phase U

time t

voltageU

Fig. 8: Firing order of the switch-on event

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 MHz 1.00

3

6

15

k-1

9

frequency f

|TFI-1N/U-1V(f)|

TF of I-1N/U-1V measured on 04.09.2006

TF of I-1N/U-1V measured on 07.09.2006

Fig. 9 a): Comparison of two transfer function using data sets ofthe switch-on event

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 MHz 1.00

3

6

15

k -1

9

frequency f

|TFI-1N/U-1V(f)|

tolerance band of TF measured on 04.09.2006

tolerance band of TF measured on 07.09.2006

overlap area of the tolerance bands

Fig. 9 b): Tolerance band of the transfer functions

6 CONCLUSION

As opposed to the offline-measurements online-measurements allows a permanent monitoring of theinsulation- and winding-condition. Another aspect of

online-monitoring is that exceeding voltage- and currentstresses can be monitored, too.

Transfer functions, calculated from online measuredtransient overvoltages of the power grid, are subjectedto uncertainty. In an EMTP-ATP model simulation itcould be shown that the circuit state of the substationhas significant influence on the transfer function.

Nevertheless, it was succeeded to categorize the TF

depending on the circuit state, location of the transientsignal excitation and tap changer position withoutadditional monitoring of these devices. However, this isonly possible with many calculation procedures and itleads to a reduction of TF sensitivity. Therewith a

sensitive diagnostic of the winding condition is mademore difficult.

More sensitive transfer functions can be obtained byusing transient signals occurring during the switch-onevent of a transformer. Therewith, nearly the samesensitivity as with an offline TF measurement can beachieved. A new calculation of such online measuredTFs is only possible with a new switch-on event though.

A monitoring system must be run and maintained all

the time for online monitoring of the transfer function.

7 REFERNCES

Books:[1] M. Basseville, I. V. Nikiforov: Detection of abrupt changes, A

Simon & Schuster Company, Englewood Cliffs, New Jersey

07632, ISBN 0-13-126780-9

Papers Presented at Conferences (Unpublished):[2] R. Wimmer, S. Tenbohlen, K. Feser, A. Kraetge, M. Krger, J.

Christian, The influence of connection and grounding techniqueon the repeatability of FRA-results, presented at the 15 th. Int.

Symposium on High Voltage Engineering, Ljubljana, Slovenia,

2007[3] R. Wimmer, S. Tenbohlen, K. Feser, A. Kraetge, M. Krger, J.

Christian, Development of Algorithms to Assess the FRA,

presented at the 15th. Int. Symposium on High VoltageEngineering, Ljubljana, Slovenia, 2007

Papers from Conference Proceedings (Published):[4] J. Bak-Jensen, B. Bak-Jensen, S. D. Mikkelsen: Detection of

Faults and Aging Phenomena in Transformers by Transfer

Functions IEEE Transactions on Power Delivery, Vol. 10, No.

1, Jan. 1995, pp. 308-314[5] S. M. Islam, G. Ledwich: Locating Transformer Faults through

Sensitivity Analysis of High Frequency Modeling Using

Transfer Function Approach, IEEE Int. Symp. on ElectricalInsulation, Montral, 1996, Conference Record pp. 38-41

[6] D. Perriot-Mathonna: Improvements in the application of

stochastic estimation algorithms-Parameter jump detection,

Automatic Control, IEEE Transaction on Volume 29, Issue 11,

Nov 1984, pp. 962 969[7] R. Wimmer, M. Krger, Erhhung der Reproduzierbarkeit von

FRA-Messungen durch Standardisierung, StuttgarterHochspannungssymposium 2006, pp.45-66, Stuttgart 2006,

ISBN 3-00-018361-2[8] R. Wimmer, K. Feser, J. Christian: Reproducibility of Transfer

Function Results, 13th International Symposium on High

Voltage Engineering, Delft, 25.-29. August, 2003-Millpress,

Rotterdam ISBN 90 77017 79 8, p.532

[9] R. Wimmer, K. Feser, Calculation of the Transfer Function of a

Power Transformer with Online Measuring Data, APTADM inWroclaw, Poland, 15. - 17.09.2004

Dissertations:[10] T. Hayder: Schutz von Regeltransformatoren", dissertation,

University of Stuttgart, Sierke Verlag, 2007, ISBN 13 978-3-933893-79-6

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10 ISH 2007 - Online Monitoring of a Transformer by Means of FRA