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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –

6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 173-185, © IAEME

173

ANALYSIS OF GENERATED HARMONICS DUE TO SINGLE PHASE PWM

AC DRIVES LOAD ON POWER SYSTEM USING ARTIFICIAL NEURAL

NETWORK

Dharmendra Kumar singh, Ekta Singh Thakur, Smriti Kesharwani, Dr. A.S.Zadgaonkar

Dr. C.V. Raman University Kargi Road Kota Bilaspur (C.G), INDIA

ABSTRACT

Recently harmonic distortion in power systems is attracting significant attention. Traditional

methods for harmonic distortion analysis using either FFT or DFT are, however, susceptible to the

presence of noise or sub-harmonics in the distorted signals. Harmonic detection by using Fourier

transformation requires input data for one cycle of the current waveform and also requires time for

the analysis in next coming cycle. In this paper, an alternative method using neural network

algorithm has achieved satisfactory results for fast and precise harmonic detection in noisy

environments.

In this paper we are identifying the harmonics component in power system generated by a

control scheme of single-phase to three-phase PWM converters for low power three-phase induction

motor drives. Currently there are number of methods are available for identifying the harmonic

components in power system but we use the intelligence system (ANN) for this work.

Keyword: Power system, Harmonics, Artificial Neural Network, VFD.

1 INTRODUCTION

The increasing application of power electronic facilities in the industrial environment has led

to serious concerns about source line pollution and the resulting impacts on system equipment and

power distribution systems. Power systems, in the presence of electronic equipment, can produce

harmonics in the power signal waveforms. Power converters, specifically, are responsible for a

disproportionate amount of the harmonics troubling power systems today [1]. Converters are used in

variable-speed drives, power supplies, and UPS (uninterruptible power supply) systems; the term

converter can refer to rectifiers, inverters, and cycloconverters. Arc furnaces are another significant

source of harmonics. Harmonics in power systems can be the source of a variety of undesirable

effects.

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING

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ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 2, February (2014), pp. 173-185 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com

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In recent years, neural network has got special attention to the researchers because of its

simplicity, learning and generalization ability and it has been applied in the field of engineering such

as in harmonic detection [2-6]. In this paper we identify the harmonics component in power system

generated by a control scheme of single-phase to three-phase PWM converters for low power three-

phase induction motor drives. In low power residential and industrial applications, where only a

single-phase utility is available, a single-phase to three-phase power converter system is required to

feed the three-phase induction motor drives. Conventionally, a full–bridge diode rectifier plus three-

leg PWM inverter has been used. However, the diode rectifier produces harmonic currents to flow

into the supply [7]. Traditionally and currently there are different methods are available and in

use for identify the harmonic components in power system but we use the intelligence system

(ANN) for this work.

2 VARIABLE FREQUENCY DRIVES

INDUCTION motor for many years has been regarded as the workhorse in industrial

applications. In the last few decades, the induction motor has evolved from being a constant speed

motor to a variable speed, variable torque machine. Its evolution was challenged by the easiness of

controlling a DC motor at low power applications. When applications required large amounts of

power and torque, the induction motor became more efficient to use. With the invention of variable

voltage, variable frequency drives (VVVF), the use of an induction motor has increased. Variable

frequency Voltage Source Inverters (VSI's) are widely used to control the speed of 3-phase squirrel

cage Induction Motors (IM) over a wide range by varying the stator frequency. In particular the

VSI's are widely preferred in industries for individual medium to high power variable speed drive

systems, driving a group of motors connected in parallel at economic costs. Most modern variable

frequency drives operate by converting a three-phase voltage source to DC using rectifier. After the

power flows through the rectifiers it is stored on a dc bus. The dc bus contains capacitors to accept

power from the rectifier, stores it, and later deliver that power through the inverter section. The

inverter contains transistors that deliver power to the motor. The “Insulated Gate Bipolar Transistor”

(IGBT) is a common choice in modern VFDs. The IGBT can switch on and off several thousand

times per second and precisely control the power delivered to the motor. The IGBT uses “pulse

width modulation” (PWM) technique to simulate a sine wave current at the desired frequency to the

motor [8-10].

Fig(1): Block diagram of variable frequency drive with 3 phase induction motor load input and

output signal also show



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In this paper we use a single-phase to three-phase PWM converters for low power three-

phase induction motor drives which is shown in fig(1). In low power residential and industrial

applications, where only a single-phase utility is available, a single-phase to three-phase power

converter system is required to feed the three-phase induction motor drives. Conventionally, a full–

bridge diode rectifier plus three-leg PWM inverter has been used. However, the diode rectifier

produces harmonic currents to flow into the supply.

Fig(2): Circuit diagram of variable frequency drive

Fig(3): single-phase to three-phase PWM converters for low power three-phase induction motor

drives

3 ARTIFICIAL NEURAL NETWORK

The most popular Artificial Neural Network (ANN) architecture is multilayer Feedforward

Network with backpropagation (BP) learning algorithm.This network, as its name indicates is made

up of multilayer Thus architecture of this class besides processing on input, an output layer also have

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 in performing useful

intermediary computations before directing the input to the output layer. The input layer neurons are



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links are referred to as input hidden layer weights. Again the hidden layer neurons are liked to the

output layer neuron and the corresponding weights are referred to as hidden output layer weights.

A two layers network in which one is input layer and other is output layers called single layer

feed forward network. In this architecture the input layer receive the input signals and after

processing it forwarded to output layer for output the data. The synoptic links carrying the weights

connected every input neuron to the output neuron but not vice-versa. Such a network is said to be

feed forward in type or acyclic in nature. Despite the two layers, the network is termed single layer

since it is the output layer, alone which performs computation [11-13].

Fig (4): Multilayer Feed-forward Network

4. BACKPROPAGATION LEARNING RULE

There are several different training algorithms for feed-forward networks. All these

algorithms use the gradient of the performance function to determine how to adjust the weights to

minimize performance. The gradient is determined using a technique called back-propagation.

Back-propagation is a systematic method of training multilayer Artificial Neural Networks. It

is built on high mathematical foundation and has very good application potential. Even though it has

its own limitations, it is applied to a wide range of practical problems and has successfully

demonstrated its power.

The Back-propagation learning algorithm approach to be followed is basically a gradient

descent along the error surface to arrive at the optimum set of weights. The error is defined as the

squared difference between the desired output and the actual output obtained at the output layer of

the network due to application of an input pattern from the given input-output pattern pair. The

output is calculated using the current setting of the weights in all the layers. The optimum weight

may be obtained if the weight are adjusted in such a way that the gradient descent is made along the

total error surface [13].

5 ANN DESIGNING PROCESS

ANN designing process involves five steps: gathering input data, normalizing the data,

selecting the ANN architecture, and Training the Network, Validation-testing the network [14].



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5.1 Gathering Input Data

The configuration of the experimental system and experimental system block diagram is

shown below in

Experimental –setup

Fig(5): Image of experimental set-up

Fig(6): Block diagram of experimental set-up

In the above block diagram set-up, a transformer is connected with power supply. A linear or

non-linear load is connected with this transformer. Due to transformer and other loads are generated

harmonics in power system. Due to this power supply waveform is distorted. A data acquisition card

is connected at power common connection 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. Collected data is shown in waveform in fig (7).



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Fig(7): Supply current waveform when Variable frequency drive loaded with three phase induction

motor

5.2 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.

Fig(8): Normalised current waveform of collecting waveform/data



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Fig(9): Normalization and scalling of current waveform for ANN Input

5.3 Selecting the ANN Architecture

The numbers of layers and the number of processing element per layer are essential 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 ANN configuration has 32 iput neurons

receiving 32 sampled points of the distorted waveform and 32 output neurons producing the

magnitude of harmonic components up to t e 33th odd harmonics. The hidden layer has 65 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 . ANN Toolbox in MATLAB is used for this work. The

designed network is shown in fig(10)

Fig(10): Designed ANN for harmonics component identification



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5.4 Training of the ANN Model

The ANN model used is executed by a structured computer program that can update neurons

almost simultaneously .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 update. The

backpropagation learning algorithm employed [15] works toward reduction of the RMS error, and

the training ceases as the total sum of square error reaches just below the error critia 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

Fig(11): Training of designed ANN

It was observed that during the initial stage of training the rate of convergence in weights

update was fast at a learning rate of 0.05. This was seen in the rapid steady drop in total sum of

square. Subsequently training yielded a slower convergence rate. The learning rate ή was constantly

reduced whenever the total sum of square value changed too slowly. It was also reduced when the

total sum of square value oscillated for a prolonged period of training epochs due to entrapment in

local minima.

5.5 Testing To test the generalizing capabilities of the magnitude networks the distorted waveforms that

contained harmonics up to the 33rd odd harmonic with no noise added were considered for the

training process.

After the training and testing, the ANN used for unfamiliar input which is collected from

experimental set-up for the identification of the harmonic component.

6 RESULT AND DISCUSSION

Fig (12) and fig (13) shows the output of ANN for input voltage and current which is

collected from the experimental set-up. From the graph of ANN output we observe that odd

harmonics generated in power system due to the single-phase to three-phase PWM converters for

low power three-phase induction motor drives load.



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Fig(12): ANN Output for odd harmonicst

Fig(13): 1

st node ANN Output for dc component

Fig(14): 2

nd node ANN Output for fundamental frequency



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Fig(15): 3rd node ANN Output for 3

rd harmonics

Fig(16): 4

th node ANN output for 5

th harmonics

Fig(17): 5


th harmonics



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Fig(18): 7


th harmonics

Fig(19): 8


th harmonics

Fig(20): ANN output for phase angle for harmonics



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7 CONCLUSION

An artificial neural network model is developed and implemented for measuring harmonics

component in power system. This model is tested offline under different condition. the result

outcome from offline test indicate that the ANN model has providing very high accuracy in

harmonic component measurement , the proposed ANN model is implemented on pc with MATLAB

software using a data acquisition card. It was tested off-line under different conditions. The result of

the off-line test indicates that the ANN model has very high power system harmonics component

measurement accuracy. 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. The data is collected at Machine

lab in Dr.C.V.Raman University where the system is available. The output of the ANN show that

due to the single-phase to three-phase PWM converters for low power three-phase induction motor

drives odd current harmonics that is 3rd

,5th

,7th

,9th

,11th

,13th

,15th

,17th

etc are generated in power

system .So proper filter is required for elimination harmonics from load to supply.

8 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 Miss Pallavee Jaiswal for her suport in completing this Paper.

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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.

Ekta Singh Thakur has obtained B. E. degree in Electrical Engineering from Chhattisgarh Swami

Vivekananda Technical University.She pursuing M.Tech. from Dr C.V. Raman University.

Smriti Kesharwani has obtained B. E. degree in Electrical Engineering from Chhattisgarh Swami

Vivekananda Technical University. She pursuing M.Tech. from Dr C.V. Raman University.

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.