1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
56
ANALYSIS OF GENERATED HARMONICS DUE TO CFL LOAD ON
POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK
Dharmendra Kumar singh [1]
, Pragya Patel [2]
, Anjali Karsh [3]
, Dr.A.S.Zadgaonkar [4]
Dr. C.V.Raman University Kargi Road Kota Bilaspur (C.G), INDIA
ABSTRACT
The mass usage of CFLs is the problem with the network voltage distortion that arises due to
their distorted current which contains a high level of harmonic components even at pure sine wave
supply voltage. The combined effect of the widespread adaptation of the CFL can be just as
detrimental as one large harmonic source. Moreover mitigation of harmonic distortion caused by
CFLs is very difficult once they are widely distributed over the large power system network .In this
paper we identified the harmonic component generated in power system due to current harmonics
using artificial neural network. The ANN based methods has the advantages of parallel information
processing learning distribution pattern and memory which can be used in the measurement of the
harmonic to construct an appropriate network. Harmonic on-line detection can be achieved through
the study of the sampling.
Keyword: Power system, Harmonics, Artificial Neural Network, CFL.
I. INTRODUCTION
The idea of replacing inefficacious and short-aged incandescent lamps with efficacious and
long aged fluorescent lamps has resulted in the development of compact fluorescent lamps (CFLs).
Compact fluorescent lamps were primarily intended for residential and commercial customers.
Lasting much longer and consuming much less energy than incandescent lamps with comparable
luminous output, they represented promising new lamp types. As a part of their energy saving
strategy, nations across the world were promoting the use of CFLs. These countries were even
offering CFLs at a highly subsidized price to make them popular. The basic problem arising in the
mass usage of CFLs is the problem with the network voltage distortion that arises due to their
distorted current which contains a high level of harmonic components even at pure sine wave supply
voltage. Various references discussing the behavior of CFLs under various operating conditions like
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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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different system voltage, voltage distortions in the power system etc. Literature referred suggests
harmonics introduced into the networks by CFLs has been ignored earlier as individual CFL’s
injection is very small. The combined effect of the widespread adaptation of the CFL can be just as
detrimental as one large harmonic source. Moreover mitigation of harmonic distortion caused by
CFLs is very difficult once they are widely distributed over the large power system network. [1] [2]
[3] [4].
Harmonics Analysis Methods
Harmonics analysis is done using five methods:
1. Analog filter based method
2. Instantaneous reactive power theory based method
3. Fast Fourier transform (FFT) based method
4. Wavelets transform based method
5. ANN based method
Analog filter method is simple and cheap but has big error and bad performance in real
situation. Instantaneous reactive power theory has a simple circuit and good at real time situation but
yields big error during variation of voltage. FFT based method has problem that had to leakage effect
picket fence effect and aliasing effect. Wavelets transform is more complex than FFT. The ANN
based methods has the advantages of parallel information processing learning distribution pattern and
memory which can be used in the measurement of the harmonic to construct an appropriate network.
Harmonic on-line detection can be achieved through the study of the sampling.
2. CFL
Figure 3.1 displays a typical CFL ballast circuit, and is divided into four blocks for analysis
purpose. The 1st block usually has the protection, filtering and current peak limiting components. It
attenuates the electromagnetic interference generated by the high frequency stages of the ballast, and
also protects the ballast against possible transient phenomenon. The 2nd block is the ac/dc
conversion using full-bridge diode rectifier. This is followed by a capacitor in block 3 to provide a
smooth dc voltage for the resonant inverter in block 4. The lamp is supplied by a resonant inverter
started by the DIAC, and self-oscillating between 10 and 40 kHz. It also provides a high voltage to
strike across the tube. Generally the lamp appears as a constant resistive load as far as the dc bus bar
is concerned. The first three blocks have enormous impact on the CFL harmonic performance. CFL
can be divided into the following three main categories in terms of ballast circuitry and their attempts
on power-factor correction injection are very small. The combined effect however, of the widespread
adoption of CFLs can be just as detrimental as one large harmonic source. Moreover mitigation of
the harmonic distortion caused by CFLs is very difficult once in the network due to the dispersed
nature. Having one large harmonic source, such as a converter is easier to deal with than a multitude
for small dispersed harmonic sources, as harmonic filters can be designed to meet the system
requirements and installed at the devices terminals [5].

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (1): Typical CFL ballast circuit
3. ARTIFICIAL NEURAL NETWORK
Neural network which are simplified models of the biological neuron system, is a massively
parallel distributed processing system made up of highly interconnected neural computing elements
that have the ability to learn and thereby acquire knowledge and make it available for use. 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. [6] [7] [8].
4. EXPERIMENTAL SET-UP
we have used Dr.C.V.Raman University machine lab for experiment . 20W ,9 CFL are used
whose electrical specification given in table(1) . Thses 9 cfl are connected to the different switches
so that from 20W to 180W load will be provided to the power system. The 220V supply is provided
in this work the physical experimental set-up have shown in the figure(2). The circuit block
diagram of this set-up have shown in fig(3).
Table 1: Electrical Specification of CFL used in experiment
Operating Voltage 22OV-240 Volts
Frequency 50Hz
Power Consuption 20W
Current 85mA
Standard ISI(IS:-15111)
Made in INDIA
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 [9].
5.1 Gathering Input Data: The configuration of the experimental system and experimental system
block diagram is shown in below fig(2) and fig(3).

4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (2): Image of experimental set-up
Figure (3): Block diagram of experimental set-up
Figure (4): Hardware circuit for CFL ballast
In the above block diagram set-up, CFL with ballast circuit are connected to supply. 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.

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
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Figure (5): Supply current waveform when 180W CFL Load on supply
Figure (6): Supply current waveform when 160W CFL Load on supply
Figure (7): Supply current waveform when 140W CFL Load on supply
Figure (8): Supply current waveform when 120W CFL Load on s
5.2 Normalization of input data for ANN
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.

6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (9): Normalised current waveform for ANN Input (180 W CFL)
Figure(10): Normalised current waveform for ANN Input (160 W CFL)
Figure (11): Normalised current waveform for ANN Input (140 W CFL)
Figure (12): Normalised current waveform for ANN Input (120 W CFL)

7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (13): Normalised current waveform for ANN Input (100 W CFL)
5.3 Selecting the ANN Architecture
The selection of ANN Architecture are described by [10].
Figure (14): Designed ANN for harmonics component identification
5.4 Training of the ANN Model
The training of the ANN model is described by [10].
Figure (15): Training of designed ANN
5.5 Testing
To test the generalizing capabilities of the magnitude networks the distorted waveforms that
contained odd harmonics up to the 23rd
harmonic with no noise added were considered for the
training process.

8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (16): Waveform of ANN Output for magnitude (180W CFL)
Figure (17): Waveform of ANN Output for magnitude (160W CFL)
Figure (18): Waveform of ANN Output for magnitude (140W CFL)
Figure (19): Waveform of ANN Output for magnitude (120W CFL)

9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (20): Waveform of ANN Output for magnitude (100W CFL)
Figure (21): Bar graph of ANN output for 180W magnitude
Figure (22): Bar graph of ANN output for 160W magnitude
Figure (23): Bar graph of ANN output for 120W magnitude

10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure ( 24): Bar graph of ANN output for 100W magnitude
Figure (25): Phase angle of harmonics when 180W CFL load
Figure (26): Phase angle of harmonics when 140W CFL load
Figure (27): Waveform of ANN Output for Phase angle 120W

11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME
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Figure (28): Waveform of ANN Output for Phase angle 100W
6. RESULT AND DISCUSSION
The output of the ANN is shown in fig (16), fig (17), fig (18), fig (19), fig (20) for different
wattage CFL.CFL generate odd harmonics in power system. The magnitude of harmonics for low
wattage CFL is larger than the high wattage CFL .The dominant harmonics are non-triplen. The
magnitude of triplen harmonics is less then non-triplen harmonics.
7. CONCLUSION
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 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 CFL load for different wattage
are tested by this ANN and find that CFL generate odd harmonics ion power system.
ACKNOWLEDGEMENTS
We would like to express our 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 support 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
Pragya patel:- has obtained B.E. degree in Electrical Engineering from Govt. Engineering
College,Bilaspur in 2011,currently she is pursuing M.Tech in power system engineering.
Anjali Karsh:- has obtained B.E. degree in Electrical and Electronic Engineering from Chhatrapati
shivaji Institute of Technology, Durg in 2009, currently she is pursuing M.Tech in power system
engineering.
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 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.