Proceedings of the 8th WSEAS International Conference on ELECTRIC POWER SYSTEMS, HIGH VOLTAGES, ELECTRIC MACHINES (POWER '08)
Transformer oil’s service life identification using neural networks
L. EKONOMOU1
1
2
P.D. SKAFIDAS2
D.S. OIKONOMOU3
Hellenic American University, 12 Kaplanon Street, 106 80 Athens
National Technical University of Athens, 9 Iroon Politechniou St., 157 80 Athens
3
Agricultural University of Athens, 75 Iera Odos Street, 118 55 Athens
GREECE
Abstract: In this paper a neural network method is developed and presented for high and medium voltage
transformer oil’s service life identification. Mineral oils are used as insulating and cooling agents in
transformers due to their good aging behaviour and low viscosity. Their in-service characteristics are
continuous varied with time having as a result the degradation of the insulation and the decrease of the
residual operating time of transformer oil. In this paper an alternative to up today method is presented in
order to identify transformer oil’s service life based on neural networks (NN). In contrast to the traditional
time consuming and expensive measurements a NN model is presented, trained with actual recorded data,
capable to identify with accuracy the transformer oil voltage breakdown and consequently its service life.
The NN model is applied on operating high and medium voltage transformers presented very accurate
results. The proposed approach can be useful in the work of electrical maintenance engineers reducing
maintenance time and cost.
Key-Words: Dielectric characteristics; multilayer perceptron; neural networks; oil voltage breakdown;
service life; transformer oil.
on-line and off-line monitoring-diagnosis systems
[4].
1. Introduction
Power transformers are integral components of
power systems. One of the factors that can reduce
the efficiency of power systems is their failure,
creating significant cost implications, unscheduled
supply interruptions and in very extreme cases
even explosions. These undesirable consequences
are mainly due to the degradation of the insulation
of the transformer oil, which is an essential means
for the electrical insulation and heat dissipation of
transformers [1]. Therefore is obvious that the
accurate identification of transformer oil’s residual
operating time can help in the more effective
management of transformer’s maintenance
schedule and in the increase of system’s reliability.
The current work presents a new approach for the
transformer’s oil service life identification based
on artificial intelligence and more specifically
neural networks (NN). NN present to give
solutions with significant results in almost every
power systems problem varying from load
forecasting [5] and power systems transient
stability [6], to prediction of the magnetic
performance of strip-wound magnetic cores [7] and
high voltage transmission line problems [8],
exploiting NN computational speed, ability to
handle complex non-linear functions, robustness
and great efficiency even in cases where full
information for the studied problem is absent.
The safe and prolonged operation of power
transformers is generally provided through time
consuming and expensive field measurements
related mainly to the transformer oil’s breakdown
voltage, acidity and water content. Alternative
methods have been proposed based on polynomial
regression models [1], measurements on oil’s
dielectric properties (complex permittivity and
tanδ) [2], gas chromatography [3] and integrated
ISSN: 1790-5117
A NN model capable to identify with accuracy the
transmission oil’s breakdown voltage - and
consequently its service life - is developed based
on actual field data and is applied on operating
high and medium voltage transformers presented
very accurate results. The proposed approach can
be useful in the work of electrical maintenance
222
ISBN: 978-960-474-026-0
Proceedings of the 8th WSEAS International Conference on ELECTRIC POWER SYSTEMS, HIGH VOLTAGES, ELECTRIC MACHINES (POWER '08)
In order to train the network, a suitable number of
representative examples of the relevant
phenomenon must be selected so that the network
can learn the fundamental characteristics of the
problem. More than a hundred different learning
algorithms are available, but the most popular one
is the backpropagation. In a backpropagation NN
the learning algorithm has two phases. First a
training input pattern is presented to the network
input layer. The network then propagates the input
pattern from layer to layer until the output pattern
is generated by the output layer. If this pattern is
different from the desired output, an error is
calculated and then propagated backwards through
the network from the output layer to the input
layer. The weights are modified as the error is
propagated [9, 10].
engineers reducing the maintenance time and cost,
offering greater efficiency in power systems.
2. Neural networks (NN)
A neural network consists of a number of very
simple and highly interconnected processors,
called neurons, which are analogous to the
biological neurons in the brain. The neurons are
connected by weighted links that pass signals from
one neuron to another. Each link has a numerical
weight associated with it. Weights are the basic
means of long-term memory in NN. They express
the strength, or importance, of each neuron input.
A neural network “learns” through repeated
adjustments of these weights. The characteristic
feature of these networks are that they consider the
accumulated knowledge acquired during training
and respond to new events in the most appropriate
manner, giving the experience gained during the
training process. In this work a typical neural
network model known as multilayer perceptron
model (MLP) has been used (Fig. 1). The MLP is a
feed forward NN with an input layer of source
neurons, at least one middle or hidden layer of
computational neurons, and an output layer of
computational neurons. The input layer accepts
input signals from the outside world and
redistributes these signals to all neurons in the
hidden layer. The hidden layer detects the feature.
The weights of the neurons in the hidden layers
represent the features in the input patterns. The
output layer establishes the output pattern of the
entire network [9].
NN models are determined according to their
architecture, i.e., the network’s structure, transfer
function and learning algorithm. In the
backpropagation networks the MLP architecture is
generally decided by trying a) varied combinations
of number of hidden layers and number of nodes in
a hidden layer, b) different transfer functions and
c) learning algorithms in order to be selected the
most suitable NN model architecture, which has
the best generalizing ability amongst the all tried
combinations [10, 11], i.e., minimization of the
sum-squared error. Once the training process is
completed and the weights and bias of each neuron
in the neural network is set, the next step is to
check the results of training by seeing how the
network performs in situations encountered in
training and in others not previously encountered.
3. Transformer oil
Mineral oils are fabricated by refining a fraction of
the hydrocarbons collected during the distillation
of petroleum crude stock. Their good aging
behaviour and low viscosity make them good
insulating and cooling agents. Furthermore they
are wide available and have a relative low cost.
These characteristics have as a result the extensive
use of them as transformer oils [1-4].
However there are instances of transformers failing
whilst in service, creating significant cost
implications for power suppliers and in extreme
cases explosion with a consequent threat of
workers for severe injury or death and significant
environmental impacts. Therefore continuous
monitoring of residual operating time of
Fig.1 MLP feed forward neural network.
ISSN: 1790-5117
223
ISBN: 978-960-474-026-0
Proceedings of the 8th WSEAS International Conference on ELECTRIC POWER SYSTEMS, HIGH VOLTAGES, ELECTRIC MACHINES (POWER '08)
transformer oil is required something that is
conducted worldwide from all electric companies
based on real measurements. The most popular
measurements for this purpose are the transformer
oil breakdown voltage, the acidity and the water
content measurements, which either solely or in
combination with the others can conclude on the
degradation of the oil’s insulation [1, 3].
As it has mentioned earlier each NN model is
determined according to its structure, the transfer
function and the learning algorithm, which are
used in an effort to learn the network the
fundamental characteristics of the examined
problem. The learning algorithms and the transfer
functions are used to adjust the network’s weights
and biases in order to minimize the sum-squared
error. The structure of the networks i.e. the number
of hidden layers and the number of nodes in each
hidden layer, is generally decided by trying varied
combinations for selecting the structure with the
best generalizing ability amongst the tried
combinations. In general one hidden layer is
adequate to distinguish input data that are linearly
separable, whereas extra layers can accomplish
nonlinear separations [10]. This approach was
followed, since the selection of an optimal number
of hidden layers and nodes is still an open issue.
4. Design of the proposed NN
The goal is to develop a neural network
architecture that could identify the oil breakdown
voltage of high and medium voltage transformers.
Identifying the oil breakdown voltage conclusions
on the oil’s service life can be deduced.
Five parameters that play important role to the
transformer’s oil breakdown voltage were selected
as the inputs to the neural network, while as an
output the oil voltage breakdown value was
considered. These data constitute actual
collected/measured data from measurements
performed by Hellenic Public Power Corporation
S.A. [12] and National Technical University of
Athens [13].
In this work several different multilayer perceptron
models were designed and tested in order to be
identified the model with the best generalizing
ability. These were combinations of five different
learning algorithms, five different transfer
functions and several different structures consisted
of 1 to 3 hidden layers with 2 to 50 neurons in each
hidden layer (table 2).
Table 1 presents the proposed NN model
architecture for the assessment of transformer’s oil
voltage breakdown. The five used input parameters
are: the transformer type (medium or high voltage),
the transformer service period (in months), the
total acidity (in mg KOH/g oil), the relative density
value of the oil and the water content (in ppm).
The output parameter of the NN model is the
transformer’s oil voltage breakdown. It must be
mentioned that the authors have selected only
those five input parameters for the developed NN
model, although there are also others that affect the
transformer’s oil voltage breakdown. The reason
was that there were not available records of data
for any other parameters.
Table 2. Designed MLP NN Models
Structure
- 1 to 3 hidden layers
Learning
Algorithm
- Gradient
Descent
- 2 to 50 neurons in
each hidden layer
- LevenbergMarquardt
Transfer
Function
- Hyperbolic
Tangent Sigmoid
- Logarithmic
Sigmoid
- Quasi-Newton - Hard-Limit
- Conjugate
- Competitive
Gradient
- Random Order - Linear
Incremental
Table 1. ANN Architectures
Input Variables
- transformer type T
Output Variables
- oil voltage breakdown B
5. NN Training, Validation and
Testing
- service period S
- total acidity A
The MATLAB neural network toolbox [14] was
used to train the neural network models. Two
hundred and one values of each input and output
data, referring to two hundred and one different
- relative density D
- water content W
ISSN: 1790-5117
224
ISBN: 978-960-474-026-0
Proceedings of the 8th WSEAS International Conference on ELECTRIC POWER SYSTEMS, HIGH VOLTAGES, ELECTRIC MACHINES (POWER '08)
be mentioned that transformer oil breakdown
voltages for those ten samples has been also
determined according to the IEC testing method
[13] in order to compare them with the NN
assessed values. Fig. 2 presents the comparison of
the actual/measured transformer oil’s breakdown
values and these obtained using the designed NN
model. It is obvious that the results obtained
according to the proposed NN method are very
close to the actual ones, something which clearly
implies that the proposed NN model is well
working and has an acceptable accuracy. Fig. 3
presents the percentage error between actual
measured transformer oil’s breakdown voltage and
NN estimation values for each one of the ten
Hellenic operating transformers. The average
percentage error for these 10 transformers is 8.5 %.
measurements in 150 kV and 400 kV transformers,
were used to train and validate the neural network
models. In each training iteration 20% of random
samples were removed from the training set and
validation error was calculated for these data. The
training process was repeated until a root mean
square error between the actual output and the
desired output reaches the goal of 1% or a
maximum number of epochs (the presentation of
the set of training data to the network and the
calculation of new weights and biases), it was set
to 10,000, is accomplished. Finally, the
transformer oil’s breakdown voltage value was
checked with the number obtained from situations
encountered in the training and others which have
not been encountered.
Following the training, validation and testing
process of all possible combinations of structure,
transfer function and learning algorithm, it was
selected and used further to assess the transformer
oil’s breakdown voltage the model, that presented
the best generalizing ability, had a compact
structure, a fast training process and consumed low
memory. This NN model had the following
characteristics: two hidden layers, with sixteen,
and thirteen neurons in each hidden layer
respectively, gradient descent learning algorithm
and hyperbolic tangent sigmoid transfer function.
20,00%
Precentage error between actual
measurements and NN results
15,00%
10
10,00%
5,00%
0,00%
1
2
3
4
5
6
7
8
9
10
-5,00%
-10,00%
9
-15,00%
Transformer Samples
8
Transform er sam ples
7
Fig. 3: Percentage error between actual measured
transformer oil’s breakdown voltage and NN
estimation values for ten Hellenic operating
transformers.
6
5
4
3
2
1
6. Conclusions
0
5
10
15
20
25
30
35
40
45
Transformer oil's breakdown voltage (kV)
Actual measurements
NN estimation
Fig. 2: Comparison of the actual transformer oil’s
breakdown voltage and the NN estimation values
for ten Hellenic operating transformers.
The selected NN model has been used in order to
identify the transformer oil’s breakdown of ten
different operating high and medium voltage
transformers of the Hellenic power system. It must
ISSN: 1790-5117
225
The paper describes a neural network method for
transformer oil’s identification of high and medium
voltage transformers. Actual input and output data
measured from over 200 Hellenic operating
transformers were used in the training, validation
and testing process. The developed NN model is
applied on ten different operating transformers of
known transformer oil’s breakdown voltage,
presenting great accuracy. The proposed NN
approach can be useful in the studies of electrical
maintenance
engineers
since
from
the
transformer’s oil breakdown voltage easily can be
deduced safe conclusions on oil’s service life.
ISBN: 978-960-474-026-0
Proceedings of the 8th WSEAS International Conference on ELECTRIC POWER SYSTEMS, HIGH VOLTAGES, ELECTRIC MACHINES (POWER '08)
References:
[1]
M.A.A. Wahab, M.M. Hamada, A.G. Zeitoun,
G. Ismail, Novel modeling for the prediction of
aged transformer oil characteristics, Electric
Power Systems Research, Vol. 51, 1999, pp.
61-70.
[2]
C.T. Dervos, P. Vassiliou, P. Skafidas, C.
Paraskevas, Service life estimation of
transformer oil, Int. Conf. on Protection and
Restoration of the Enviroment VI, Skiathos,
Greece, 2002, pp. 1239-1246.
[3]
V.G. Arakelian, Effective diagnostics for oilfilled equipment, IEEE Electrical Insulation
Magazine, Vol. 18, 2002, pp. 26-38.
[4]
D. Nedelcut, D. Sacerdotianu, G.Tanasescu,
S.Nicolae, L. Voinescu. On-line and off-line
monitoring-diagnosis system (MDS) for power
transformers, International Conference on
Condition Monitoring and Diagnosis, Beijing,
China, 2008, pp. 949-955.
[5]
B. Kermanshahi, H. Iwamiya H, Up to the year
2020 load forecasting using neural nets,
Electric Power Energy Systems, Vol. 17, 2002,
pp. 789-797.
[6]
A.D.P. Lotufo, M.L.M. Lopes, C.R. Minussi,
Sensitivity analysis by neural networks applied
to power systems transient stability, Electric
Power Systems Research, Vol. 77, No. 7, 2007,
pp. 730-738.
[7]
G.K. Miti, A.J. Moses, Neural network-based
software tool for predicting magnetic
performance of strip-wound magnetic cores at
medium to high frequency, IEE Proc-Sci.
Meas. Technol., Vol. 151, No. 3, 2004, pp.
181-187.
[8]
L. Ekonomou, V.T. Kontargyri, St. Kourtesi,
T.I. Maris, I.A. Stathopulos, Artificial neural
networks in high voltage transmission line
problems, Institute of Physics Publishing,
Measurement, Science and Technology,
Institute of Physics Publishing, Measurement,
Science and Technology, Vol. 18, No.7, 2007,
pp. 2239-2244.
[9]
[11] R. Lippmann, An introduction to computing
with neural nets. IEEE ASSP Magazine, Vol. 4,
No. 2, 1987, pp. 4-22.
[12] Public
Power
http://www.dei.gr .
S.A.,
[13] C.T. Dervos, C.D. Paraskevas, P. Skafidas, P.
Vassiliou, Dielectric characterization of power
tranformer oils as a diagnostic life prediction
method, IEEE Electrical Insulation Magazine,
Vol. 21, 2005, pp. 11-19.
[14] H. Demuth, M. Beale, Neural Network
Toolbox: For use with MATLAB, The Math
Works, 1994.
Lambros Ekonomou was born on
January 9, 1976 in Athens, Greece. He
received a Bachelor of Engineering
(Hons) in Electrical Engineering and
Electronics in 1997 and a Master of
Science in Advanced Control in 1998
from University of Manchester
Institute of Science and Technology
(U.M.I.S.T.) in United Kingdom. In 2006 he graduated
with a Ph.D. in High Voltage Engineering from the
National Technical University of Athens (N.T.U.A.) in
Greece. His research interests concern high voltage
engineering, transmission and distribution lines,
lightning performance and protection, stability analysis,
control design, electrical drives and neural networks.
Panagiotis D. Skafidas was born in
Kalamata, Greece in 1964. He
received his B.Sc. degree in electrical
engineering from the National
Technical University of Athens
(NTUA), 1989. He also obtained the
Ph.D. degree in electrical engineering
from NTUA, 1994. He is currently as
a part time teaching assistant lecture in the Department
of Information Transmission Systems and Material
Technology of NTUA. His research activities cover
surface physics, high voltage insulators, dielectrics and
neural networks.
Dimitrios S. Oikonomou was born
on September 28, 1982 in Athens,
Greece. He received his degree in
Natural Resources Management and
Agricultural Engineering from the
Agricultural University of Athens in
Greece. He is currently a research
assistant
in
the
Agricultural
University of Athens. His research interests concern
neural networks, modelling, simulation, electrical
motors and electrical conversion.
M. Negnevitsky, Artificial intelligence: a
guide to intelligent systems, 2nd Edition,
Addison-Wesley, 2005.
[10] K. Hornik, Some new results on neural
network approximation. Neural Networks, Vol.
6, 1993, pp. 1069-1072.
ISSN: 1790-5117
Corporation
226
ISBN: 978-960-474-026-0