Author Name:- Mohammad Aslam Ansari
Department of Electrical Engineering
Abstract:- Power transformer is one of the most
important and expensive equipment in electrical network. The transformer oil
is a very important component of power transformers. It has twin functions of
cooling as well as insulation. The oil properties like viscosity, specific
gravity, flash point, oxidation stability, total acid number, breakdown
voltage, dissipation factor, volume resistivity and dielectric constant suffer
a change with respect to time. Hence it is necessary that the oil condition be
monitored regularly to predict, if possible, the remaining lifetime of the
transformer oil, from time to time. Six properties such as moisture content,
resistivity, tan delta, interfacial tension and flash point have been
considered. The data for the six properties with respect to age, in days, has
been taken from literature, whereby samples of ten working power transformers
of 16 to 20 MVA installed at different substations in Punjab, India have been
considered. This paper aims at developing ANN and ANFIS models for predicting
the age of in-service transformer oil samples. Both the the models use the six
properties as inputs and age as target. ANN (Artificial Neural Network) model
uses a multi-layer feedforward network employing back propagation algorithm,
and ANFIS (Adaptive Neuro Fuzzy Inference System) model is based on Sugeno
model. The two models have been simulated for estimating the age of unknown
transformer oil samples taken from generator transformers of Anpara Thermal
Power Project in state of U.P. India. A comparative analysis of the two models
has been made whereby ANFIS model has been found to yield better results than
ANN model.
I. Introduction
Power transformer is
one of the most important constituent of electrical power system. The
transformer oil, a very important ingredient of power transformers, acts as a
heat transfer fluid and also serves the purpose of electrical insulation. Its
insulating property is subjected to the degradation because of the ageing, high
temperature, electrical stress and other chemical reactions. Hence it is
necessary that the oil condition be monitored regularly. This will help to
predict, if possible, the in-service period or remaining lifetime of the
transformer oil, from time to time.
There are several characteristics which can be measured to assess the
present condition of the oil. The main oil characteristics are broadly
classified as physical, chemical and electrical characteristics; some of these
are viscosity, specific gravity, flash point, oxidation stability, total acid
number, breakdown voltage, dissipation factor, volume resistivity and
dielectric constant. There exists a co-relation among some of the oil
properties and suffer a change in their values with respect to time [2]. This
variation of oil properties with respect to time has been utilised to develop
the two models as said earlier
The training
data for the proposed work have been obtained from literature, whereby ten
working transforms of 16 to 20 MVA, 66/11 KV installed at different substations
in the state of Punjab, India have been considered. The six properties of
transformer oil such as breakdown voltage (BDV), moisture, resistivity, tan
delta, interfacial tension and flash point have been considered as inputs and
age as target. Test data have been taken from generator transformers of 250
MVA, 15.75kV/400kV from Anpara Thermal Power Project in state of U. P., India.
II.
“Ann” and “Anfis” methods
It is known that
classical models need linear data for their processing, therefore models like
ANN and ANFIS that are based on soft computing techniques, play an important
role for solving these kinds of non-linear problems.
Neural networks exhibit characteristics such as mapping capabilities or
pattern association, generalization, robustness, fault tolerance, parallel and
high speed processing. Neural networks can be trained with known examples of a
problem to acquire knowledge about it. Once trained successfully, the network
can be put to effective use in solving unknown or untrained instances of the
problem. ANN model which uses multilayer feed forward
network is based on back propagation (BP) learning algorithm of neural network.
Backpropagation gives very good
answers when presented with inputs never seen before. This property of
generalization makes it possible to train a network on giving set of
input-target pairs and get good output.
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