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TOWARDS COST-EFFECTIVE MAINTENANCE
OF POWER TRANSFORMER BYACCURATELY
PREDICTING ITS INSULATION CONDITION
Refat Atef Ghunem1, Khaled Bashir Shaban2, Ayman Hassan El-Hag3, and Khaled Assaleh3
1 Electrical and Computer Engineering Department, University of Waterloo
2 Computer Science and Engineering Department, Qatar University
3 Electrical Engineering Department, University of University of Sharjah
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INTRODUCTION
Transformer asset management has been a priority
to power utilities: Improving condition monitoring and diagnosis
methods
Preparing solid asset maintenance plans
Maintenance costs are found to be heavy burden
on utilities
Developing a well constructed determining process
for transformer health index
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MEGGER TEST
A classical time-based preventive maintenance test
Easy, fast, and inexpensive test to be conducted
Indicates insulation deterioration
Efficient tool for developing trend analysis
A diagnostic tool in unplanned outages
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MEGGER TEST DRAWBACKS
Temperature effect
Rule in practice: factor of two is attributed tomegger test readings for every 10 degrees
change
Moisture Content effects
Accelerating deterioration
Reducing the dielectric strength
Faults are not detected by the insulation
resistance parameter
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TRADITIONAL MAINTENANCE PRACTICES
Reducing the unplanned outage cost and period for
tripped transformer insulation inspectionRule-of-thumb: if the transformer trips due to the
operation of differential, restricted earth fault and
Buchholz relays , insulation inspection must be
conducted before reconnecting to the network
Megger test clearance of abnormal situation is not
reliable for reconnecting the transformer
Supportive tests should be conducted
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SUPPORTIVE TESTS
Other tests are conducted to enhance the diagnosis reliabilityof the megger test:
Oil Breakdown Voltage (BDV)
Oil water content Temperature correction should be considered
Due to equilibrium process in the oil-paper system
Dissolved-gas analysis (DGA):
CO2/CO ratio
Total Dissolved Combustible Gases (TDCG)
BUT, costly ($200/sample) and time consuming(few days)
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TOWARDS COST-EFFECTIVE MAINTENANCE
Optimizing the number of time- based maintenance
tests
Predicting oil quality and dissolved gases
parameters informs about the critical tests that
should be taken
Developing well constructed asset maintenance
plans with optimized number of tests and periodsbetween them
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THE LITERATURE
8
Authors Model Predictors Output Accuracy Comments
2009&
2008
Khaled Shaban, A. H.
El-Hag, and Andrei
Matveev
&
Khaled Assaleh &
Ayman El- Hag
Artificial
Neural Network
(ANN)
&
Polynomial
Network
Megger
Breakdown
voltage(BDV),oil
water content,
interfacial tension(IF)
and acidity
&BDV
Water content
IF
BDV:84%
Water content:60%
IF:95%
Acidity:75%
&
Water content: could not be
estimated
IF:93%
BDV:84%
BDV and IF are indication for
oil contamination only
2004
&
1999
Mohamed Ahmed Abdel
Wahab
&
Mohamed A.A. Wahab,
M.M. Hamada, andA.G. Zeitoun, G. Ismail
ANN
&
Polynomial
Regression models
Oil acidity , oil water
content and transformer
age
&
Service period
BDV
&
BDV, oil water
content and acidity
More than 90% for both
BDV is not comprehensive and
&
No. of data is very small
2009
&
2008
Sheng-wei Fei, Cheng
Liang liu, and Yu bin
Miao
&
Sheng-Wei Fei, and Yu
Sun
Support vector
machine with
genetic algorithm
DGA DGA trend
Less than 90%
&
More than 90%
It is not practical
2000
&
2003
I N da Silva, A N de
Souza, R M C Hossri,and J H C Hossri
&
J.S.Navamany, and P.S.
Ghosh
ANN
furan, volume of carbon
monoxide (CO),Interfacial tension and
acidity
&
CO ,CO2 and furfural
Aging Degree
N/A
&
Around 90%
It is not cost-effective
2009
Ali Naderian Jahromi,
Ray Piercy, Stephen
Cress, Jim R.R. Service
andWang Fan
Weighted averageAll standard transformer
tests
Transformer health
indexN/A
The weights are not assigned
based on quantities correlations
&
Relatively hard to be practiced
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ANN FOR PREDICTION
Artificial neural network (ANN) is used as a
modeling technique Multi-layer feed forward ANN with Back
propagation algorithm
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THE EXPERIMENT
Data from transformer maintenance records of 19 power transformers in Abu DhabiTransmission and Despatch Company (TRANSCO) are used
Voltage rating of 220/33/11KV
Rating is in the range of 50-140MVA
Range of 7-15 years into service
54 samples are used to predict oil BDV
31 samples are used to predict oil water content
32 samples are used to predict TCG
33 samples are used to predict CO2/CO ratio
All the samples are used for training and testing to predict all the available data
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SAMPLE OF THE DATASET
Insulation Resistance
(M) Parameters
HV/E LV/E TV/E
BDV
(kV)
Water
Content
(ppm)
TDCG
(ppm) CO2/CO
1 300 240 400 71.4 26.8 497 8.25
2 820 880 1100 75 30.57 737 7.98
3 280 280 380 74 16.46 672 6.46
4 560 580 620 67 22.62 665 6.23
5 270 220 390 75 17.63 894 4.85
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RESULTS
4 ANNs:
3 neurons in the input layer (HV/E, LV/E and TV/E) 2 hidden layers with three neurons at the first
hidden layer and 10 neurons at the second hiddenlayer
1 neuron at the output layer (BDV, water content,
TDCG and CO2/CO)
Prediction rate:
Oil BDV - 96% Oil water content - 84%
CO2/CO ratio - 91%
TDCG - 88%
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RESULTS
Sample NO.
0 5 10 15 20 25 30 35 40 45 50 55 60
BDVvalue(KV)
62
64
66
68
70
72
74
76
78
80
Actual oil BDV
Predicted oil BDV
Size of testing set
(samples)
Prediction
Accuracy
1 96.66
2 92.79
3 92.534 93.61
5 92.75
6 92.93
7 93.61
8 93.52
9 94.27
10 93.99
11 92.29
12 95.25
Average
Prediction
Accuracy
93.68
This result agrees with the results of the models proposed in [1] and [2]. Transformer oil
breakdown voltage gives an indication about the dielectric capability of transformer oil to
withstand high electrical stresses. Also, BDV can be an indication about the deterioration state
of the insulation system in the transformer. This is because the contamination particles
resulted from the insulation deterioration in the transformer oil decrease the BDV value. Thisexplains the high correlation presented between IR and oil BDV.
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RESULTS
Sample NO.
0 5 10 15 20 25 30 35
Watercontentvalue(ppm)
16
18
20
22
24
26
28
30
32
34
Actual oil water content
Predicted oil water content
Size of testing
set (samples)
Prediction
Accuracy
1 84.73
2 80.613 80.52
4 80.13
5 78.88
6 80.16
7 79.36
8 74.39
9 81.3310 74.74
11 76.57
12 82.91
Average
Prediction
Accuracy 79.53
compared to BDV the prediction accuracy has been reduced between 10-15%. This can be attributed to
the relatively wider scatter of the data of water content (17-32 ppm) compared to BDV (63.6-78.8 kV)
and the relatively small size of the training set (31 vs. 54). Moreover, the oil temperature effect in
altering the value of the measured oil water content was not completely considered as the oil sample
was taken between 60-65 C. Such narrow band of temperature variation may still influence the
accuracy of the water content measurement. The proposed model to predict oil water content in
this study can be considered superior to other proposed models [1-2] where the effect oftemperature variation on measured samples was normalized.
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RESULTS
Sample NO.
0 5 10 15 20 25 30 35
CO2
/COratio
3
4
5
6
7
8
9
10
11
Actual CO2/CO ratio
Predicted CO2/CO ratio
Size of testing set
(samples)
Prediction
Accuracy
1 91.02
2 82.313 75.60
4 73.84
5 77.27
6 83.76
7 75.84
8 67.29
9 80.4610 82.03
11 79.79
12 74.68
Average
Prediction
Accuracy 78.66
Although CO2/CO ratio is more effective in diagnosing cellulose involvement in incipient
faults rather than indicating the deterioration of cellulose, the proposed model shows an
acceptable reliability. This agrees with the practice in utilities to use CO2/CO ratio
with other insulation quality parameters as indicators for cellulose thermaldegradation.
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RESULTS
Sample NO.
0 5 10 15 20 25 30 35
TDCGvalue(ppm)
200
400
600
800
1000
1200
1400
Actual TDCG
Predicted TDCG
Size of testing
set (samples)
Prediction
Accuracy
1 88.37
2 73.693 75.29
4 74.25
5 68.59
6 69.22
7 75.89
8 75.70
9 74.1710 70.59
11 73.82
12 70.11
Average
Prediction
Accuracy
74.14
Insulation resistance is affected by the deterioration and the abnormal excess of
degradation, thus, IR can be an indicative parameter to the TDCG.Although TDCG as a
parameter is a reflection of incipient faults rather than a parameter for aging
index, an acceptable and reliable correlation is verified in the proposed model.
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CONCLUSIONS
Prediction techniques can improve transformerinsulation diagnosis and reduce the predictive and
corrective maintenance costs.
The proposed models suggests a decent potential forpredicting oil BDV, water content, TDCG and CO2/COratio.
It can be more efficient to test the correlations of thetransformer insulation parameters in differentconditions (wide temperature ranges, service ages and
voltage levels, etc.)
Increasing data size can enhance the generalizationcapability of the model
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