Analysis of generated harmonics due to transformer load on power system using

INTERNATIONAL JOURNAL OF Issue 1, January- February (2013), © IAEME–
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976
6545(Print), ISSN 0976 – 6553(Online) Volume 4,
ELECTRICAL ENGINEERING
& TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 1, January- February (2013), pp. 81-90 IJEET
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ANALYSIS OF GENERATED HARMONICS DUE TO
TRANSFORMER LOAD ON POWER SYSTEM USING ARTIFICIAL
NEURAL NETWORK

Dharmendra Kumar singh Dr.Moushmi Kar Dr.A.S.Zadgaonkar
Dr. C.V. Raman University Kargi Road Kota Bilaspur (C.G), INDIA

ABSTRACT

In power system transformer are major component and widely used in different
sector. Modern transformers operate at increasing levels of saturation in order to reduce the
weight and cost of the core used. Because of this and due to the hysteresis, the
transformer core behaves as a highly non-linear element and generates harmonic voltages and
currents in power system. Once the power system polluted with harmonics then the function
of transformer will be affected due to different losses in transformer. The generated
harmonics can flow into the distributed power system causing many problems for the power
network operation. Consequently to avoid all these problems and to improve the quality of
the delivered energy harmonics parameter such as magnitude and phase angle should be
known. Fast methods for the measuring and estimating harmonics signal are thus required
various digital signal analysis techniques are being used for the measurement and estimation
of power system harmonics. Recently the application of Artificial Neural Network for power
system problems has gained considerable attention. This paper presented a novel technique,
based on Neural Network for analysis of power system harmonics due to transformer load on
power system.

Keyword: Power system, Harmonics, Artificial Neural Network, and Transformer.

INTRODUCTION

In power system transformer are major component and it uses in large number in
industries, commercial place, domestic, institutions, transportation, communication,
entertainment etc. Bobbins that have iron core will cause harmonics in electrical power
systems. Transformers are the most common between those. As being one of the most
important elements in power system transformer are the oldest non-linear element known [1].

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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME

The magnetizing characteristic of a transformer core is non-linear and will produce
harmonics as it is saturated. The source of the harmonics in the transformer magnetic flux
may be due to transformer itself. Once the power system polluted with harmonics then the
function of transformer will be affected due to different losses in transformer. The generated
harmonics can flow into the distributed power system causing many problems for the power
network operation. Consequently to avoid all these problems and to improve the quality of
the delivered energy harmonics parameter such as magnitude and phase angle should be
known. Fast methods for the measuring and estimating harmonics signal are thus required
various digital signal analysis techniques are being used for the measurement and estimation
of power system harmonics. These include fast Fourier Transform, Least Square algorithm
and others [2]. Recently the application of Artificial Neural Network for power system
problems has gained considerable attention. This paper presented a novel technique, based on
Neural Network for analysis of power system harmonics due to transformer load on power
system.

Power system Harmonic sources are
Converters, Devices which includes semi-conductor elements, Generators, Motors,
Transformers, Lightening equipments working by gas discharge principle, Photovoltaic
systems, Computers, Electronic ballasts, Uninterruptible power supplies, Switching power
supplies, Welding machines, Control circuits, Frequency converters, Static VAR
compensators, Arc furnaces, HVDC transmission systems, Electrical Communication
systems[1].

Transformer
Modern transformers operate at increasing levels of saturation in order to reduce the
weight and cost of the core used in the same. For economic reasons transformers are normally
designed to make good use of the magnetic properties of the core material. This means that a
typical transformer using a good quality grain-oriented steel might be expected to run with a
peak magnetic flux density in the steady state . If a transformer running with this peak
operating magnetic flux density is subjected to a magnetic flux density which will produce
considerable saturation. Because of this and due to the hysteresis, the transformer core
behaves as a highly non-linear element and generates harmonic voltages and currents. The
equivalent circuit of a transformer is given below fig (1)

Fig(1) Equivalent Circuit of a Transformer

Here Rp and Xp show the primary circuit resistance and the leakage reactance, R’s and X’s
shows the secondary resistance and leakage reactance that is transformed (reduced) to the
primary respectively. RFE is the resistance which symbolizes the iron losses and IFE is the
current related to this losses. In parallel to the resistance, Xm shows the magnetization
reactance and Im is the related current passes through.

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The harmonic currents produce harmonic fields in the core and harmonic voltages in the
windings. Relatively small value of harmonic fields generates considerable magnitude of
harmonic voltages. These effects get even more pronounced for higher order harmonics. As
these harmonic voltages get short circuited through the low impedance of the supply they
produce harmonic currents. These currents produce effects according to Lenz’s law and tend
to neutralize the harmonic flux and bring the flux wave to a sinusoid. Normally third
harmonic is the largest in its magnitude. In a single phase transformer the harmonics are
confined mostly to the primary side as the source impedance is much smaller compared to the
load impedance. The understanding of the phenomenon becomes clearer if the transformer is
supplied with a sinusoidal current source. In this case current has to be sinusoidal and the
harmonic currents cannot be supplied by the source and hence the induced EMF will be
peaky containing harmonic voltages. When the load is connected on the secondary side the
harmonic currents flow through the load and voltage tends to become sinusoidal. The
harmonic voltages induce electric stress on dielectrics and increased electro static
interference. The harmonic currents produce losses and electromagnetic interference [3].

Fig (2) Harmonics Generated By Transformer

ARTIFICIAL NEURAL NETWORK

For many decades, it has been a good of science and engineering to develop
intelligent machines with a large number of simple elements. The interest in neural network
comes from the networks ability to mimic human brain as well as its ability to learn and
respond. As a result, neural networks have been used a large number of applications and have
proven to be effective in performing complex functions in a variety of fields. There include
pattern recognition, classification, vision, control systems, and prediction. Adaptation or
learning is a major focus of neural net research that provides a degree of robustness to the NN
model. In predictive modeling the goal is to map a set of input patterns on to a set of output
patterns. NN accomplishes this task by learning from a series of input / output data sets
presented to the network. The trained network is then used to apply what it has learned to
approximate or predict the corresponding output. The human nervous system is a very
complex neural network. The brain is the central element of the human nervous system,
consisting of near 1x10 biological neurons that are connected to each other through sub
networks. Each neuron in the brain is composed of a body, on axon and multitude of
dendrites [4]. A biological neuron is shown in fig (3).

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Fig (3) A Biological Neuron

The dendrites receive signals from other neurons. The axon can be considered as a long tube,
which divides into branches terminating in little end bulls. The small gap between an end
bulb and a dendrite is called a synapse. The axon of a single neuron forms synaptic connections
with many other neurons. Depending upon the type of neuron, of synapse connections from other
neurons may range from a few hundred to 104.The cell body of a neuron sums the incoming
signal from dendrites as well as the signals from numerous synapses on its surface. A particular
neuron will send an impulse to its axon if sufficient input signal are received to stimulate the
neuron to its threshold the input will quickly decay and will not generate any action.
The biological neuron model is the foundation of an artificial neuron [4]. An artificial neuron is
shown in below fig (4)

Fig (4) An Artificial neuron

It consists of three basic components that include weights, threshold and a signal activation
function. Each neurons receives inputs x1, x2, x3,…..xn, which are connected to the input side of
the neuron. Each input is multiplied by the associated weight of the neuron connection XTW.
Depending upon the activation function if the weight is positive, XTW commonly excites the
node output; whereas for negative weight XTW tends to inhibit the node output. The node’s
internal threshold Ө is the magnitude offset that affect the activation of the node output as follow
n
Y = ∑ (XiWi) - Өk…………………..
i=1

An activation function performs a mathematical operation on the signal output.

Multilayer Feed forward Network
The most popular Artificial Neural Network (ANN) architecture is multilayer feed-
forward Network backpropagation (BP) learning algorithm. This network as its name indicates
is made up of multilayer’s. Thus architecture of this class besides processing an input and an
output layer also has one or more intermediary layers called hidden layers. The computational
units of the hidden layer are known as the hidden neurons or hidden units. The hidden layer aids
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in performing useful intermediary computations before directing the input to the output layer.
The input layer neurons are linked to the hidden layer neurons and the weights on these links are
referred to as input hidden layer weights. Again, the hidden layer neurons are linked to the output
layer neurons and the corresponding weights are referred to as hidden output layer weights.

Fig(5). Multilayer feed- forward Network

Back propagation learning (training)
Backpropagation is a systematic method of training multi layer Artificial Neural
Networks. It is built on high mathematical foundation and has very good application potential
[5]. The Backpropagation algorithms consist of two phases: (1) Training phase and (2) Recall
phase. In the training phase, first, the weights of the network are randomly initialized. Then
the output of the network is calculated and compared to the desired value. In sequel, the error
of the network is calculated and used to adjust the weights of the output layer. In similar
fashion, the network error is also propagate backward and used to update the weights of the
previous layers.

ANN Designing Process [6]
ANN designing process involves five steps: gathering input data, normalizing the
data, selecting the ANN architecture, and Training the Network, Validation-testing the
network.

Gathering Input Data
The configuration of the experimental system block diagram is shown in below fig (6)

Fig (6) Experimental Set-Up

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In the above block diagram a experimental set-up is shown, a transformer is connected with
power supply and linear or non-linear load is connected with this transformer. Due to
transformer and other loads on this transformer, harmonics are generating in power system. A
data acquisition card is connected at input line of transformer to collect the distorted
current/voltage waveform or data. These collected waveform/data transmitted to PC through
RS-485 for ANN input which is designed in MATLAB.

Normalization of input and output data sets
Normalization of data is a process of scaling the numbers in a data set to improve the
accuracy of the subsequent numeric computation and is an important stage for training of the
ANN. Normalization also helps in shaping the activation function. For this reason [-1, 1]
normalization function has been used.

Selecting the ANN Architecture
The numbers of layers and the number of processing element per layer are important
decision for selecting the ANN architecture. Choosing these parameters to a feed forward
backpropagation topology is the art of the ANN designer. In this paper the a Feed Forward
Backpropagation topology is used and ANN configuration has 40 input neurons receiving 40
sampled points of the distorted waveform and 7 output neurons producing the magnitude and
angles of harmonic components up to the 13th odd harmonics. The hidden layer has 52
neurons to bridge input layer with output layer. For a set of input there is a corresponding set
up of output “target” values already stored in a data array.

Training Of the ANN Model
The ANN model used, is executed by a structured computer program that can update
neurons almost simultaneously .This model requires a large amount of RAM during operation
and therefore only the odd harmonics which are known to have adverse effects on power
system application, were used to train the Neural Network. Before the start of training, the
initial weight were randomized to value between -0.5 and +0.5. These input and target
outputs were “shown” to the ANN in a sequential manner so that the weights were updated
step by step according to the backpropagation learning algorithm. The error between the
actual output and the target was evaluated after every upd

Fig(7) Training of ANN

The back propagation learning algorithm employed works toward reduction of the RMS
error, and the training ceases as the total sum of square error reaches just below the error

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criteria initially set. The weights are then supposed to have converged enough that they
should represent the non-linear transfer functions between inputs and outputs of the ANN
model accurately.
It was observed that during the initial stage of training (within the first 500 training epochs)
the rate of convergence in weights update was fast at a learning rate of 0.05. Subsequently
training yielded a slower convergence rate.

Testing
To test the generalizing capabilities of the magnitude networks the distorted
waveforms that contained odd harmonics up to the 13th harmonic with no noise added were
considered for the training process.

RESULT AND DISCUSSION

In this research paper, Artificial Neural Network is used to efficiently measure the
magnitude of harmonic component in power system generated due to transformer load. The
input and output waveform are shown.

Fig(9) Power system supply ,voltage and current waveform when resitive load
connected

Fig(10) Distorted voltage and current waveform of power supply when transformer
and motor load connected.

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Fig(8) Output waveform of Trained ANN with Target

Fig(11) Distorted Current waveform for ANN input

Fig(12) ANN Output with diffent harmonics component in distorted current
waveform

Fig(13) ANN output of different harmonics component in distorted voltage waveform

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CONCLUSIONS

An artificial neural network model was developed and implemented for power system
harmonics component measurement. It was tested off-line under different conditions. The
result of the off-line test indicates that the ANN model has very high accuracy for power
system harmonics component measurement. The developed ANN model was implemented on
a PC with Matlab Software (with ANN Toolbox) using a data acquisition card. The ANN
model was able to measure the harmonic components of voltage and current at various levels.
It can be seen that except for fundamental component, 3rd and 5th harmonics dominate the
current and the other harmonics being generated are of order 7,11,13,17,19…..In under
normal excitation condition transformer core may have entered slightly the saturation region
and being to generate some harmonics in the excitation current. In overvoltage condition,
harmonic amplitudes increase with respect to excitation voltage.

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ACKNOWLEDGEMENTS

I would like to express my sincerest gratitude to all staff of EEE Department Dr C.V.
Raman University who has contributed, directly or indirectly, in accomplishing this paper.
Special thanks to extend Mr Judas Pratap Singh for his suport in completing this Paper.

BIOGRAPHIES

Dharmendra kumar obtained M. Tech. Degree in Electronics Design and Technology from
Tezpur University, Tezpur, Assam in the year 2003. Currently he is pursuing research work
in the area of Power Quality under the guidance of Prof A. S. Zadgaonkar.

Dr. A. S. Zadgaonkar has obtained B. E. degree in Electrical Engineering from Pt.
Ravishankar Shukla University, studying at Govt. Engineering College, Raipur in 1965. He
obtained M. E. in 1978 from Nagpur University. His research paper for M. E. was awarded
“best paper” by the Institution of Engineers (India) in the year 1976 & 1977 respectively. The
testing technique for quality of wood developed by him was included in ISI in 1979. He was
awarded Ph. D. in 1985 by Indira Gandhi Kala & Sangeet University, Khairagah for his work
on “Acoustical and Mechanical Properties of Wood for Contemporary Indian Musical
Instrument Making.” He obtained another Ph. D. in 1986 by Pt. Ravishankar Shukla
University on “Investigation of Dynamic Properties of Non-Conducting Materials Using
Electrical Analogy.” He has 47 years of teaching experience. He is currently adding glory to
the post of Vice Chancellor of Dr. C. V. Raman University. He has published more than 500
technical papers for journals, national and international conferences. He was the Joint
Director, Technical Education, Govt. of Chhattisgarh in 2004 & the Principal of NIT, Raipur
in 2005. He is life member of Acoustical Society of India, Biomedical Society of India,
Linguistic Society of India, Indian Society for Technical Education and many social bodies.

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