International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) ...
Upcoming SlideShare
Loading in …5
×

40220140503007

220 views
154 views

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
220
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

40220140503007

  1. 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 INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 3, March (2014), pp. 56-68 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2014): 6.8310 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
  2. 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 57 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. 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 58 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. 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 59 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. 5. 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 60 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. 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 61 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. 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 62 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. 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 63 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. 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 64 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. 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 65 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. 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 66 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. REFERENCES [1] Zhiliang Wei “Compact Fluorescent Lamps phase dependency modelling and harmonic assessment of their widespread use in distribution systems” A thesis in Electrical and Computer Engineering at the University of Canterbury, Christchurch, New Zealand. September 2009 [2] Angula Nashandi and Prof. Gary Atkinson-Hope “Impact of large numbers of CFLs on distribution systems “ lighting and application October 2007 - Vector - Page 24 [3] Alberto Dolara * and Sonia Leva, “Power Quality and Harmonic Analysis of End User Devices” Energies ISSN 1996-1073 2012, 5, 5453-5466; [4] Charles Ndungu1* John Nderu1 Livingstone Ngoo1,2 “Effects of Compact Fluorescence Light (Cfl) Bulbs on Power Quality” Journal of Energy Technologies and Policy, ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online) Vol.2, No.3, 2012.
  12. 12. 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 67 [5] Dwyer, R, Khan, A K, McGranaghan, M, Tang L, McCluskey, R K, Sung, R, Houy, T: “Evaluation of Harmonic Impact from Compact Fluorescent Lights on Distribution Systems”, IEEE Transactions on Power Delivery, Vol. 10, No. 4, November 1995. [6] Bhasker C.Naga, Kumar G. Vijay (2011) “Neural Network and Fuzzy Logic” B.S. Publication, ISBN:-978-81-81075-40-1. [7] Jaker.M. ZArada “Introduction to Artificial Neural Systems” Jaico Publishing houre ISBN: -81-7224-650-1. [8] Bhasker C.Naga, Kumar G. Vijay (2011) “Neural Network and Fuzzy Logic” B.S. Publication, ISBN:-978-81-81075-40-1. [9] Singh Dharmendra kumar, Kar Moushmi , Zadgaonkar A.S. (2013) “Analysis of Generated Harmonics Due To Transformer Load On Power System Using Artificial Neuarl Network” IJEET,ISSN-0976-6545(Print),ISSN-0976-6553(Online),Volume4 , issue 1, pp.81-90. [10] Dharmendra Kumar singh, Ekta Singh Thakur, Smriti Kesharwani, Dr. A.S.Zadgaonkar,” Analysis Of Generated Harmonics Due To Single Phase Pwm Ac Drives Load On Power System Using Artificial Neural Network “IJARET, ISSN 0976 - 6480 (Print),ISSN 0976 - 6499 (Online),Volume 5, Issue 2, February (2014), pp. 173-185 [11] NATAN GOTHELF “Power Quality Effects of CFLs– A Field Study” HARMONIZER, Power Quality Consulting AB, Sweden [12] Antonio Nassara and Max Mednik “Introductory physics of harmonic distortion in fluorescent lamps” Am. J. Phys. 71 ~6!, June 2003 [13] J. Arrillaga and N.R. Watson, Power System Harmonics, second edition. Chichester, England:Wiley, 2003. [14] R.R. Verderber, O.C. Morse, and W.R. Alling, Harmonics from compact fluorescent lamps, IEEE Transactions on Industry Applications, Vol.29, No.3 (1993), pp.670-674. [15] J. Cunill-Sola, and M. Salichs, Study and Characterization of Waveforms From Low-Watt Compact Fluorescent Lamps With Electronic Ballasts, IEEE Transactions on Power Delivery,Vol.22, no.4 (2007), pp.2305-2311. [16] N.R. Watson, T.L. Scott and S. Hirsch, Implications for distribution networks of high penetration of compact fluorescent lamps, IEEE Transactions on Power Delivery, Vol.24, no.3 (2009), pp.1521-1528. [17] Emmanuel E, Gentile T J, Pileggi D J, Gulachenski E M, Root C E: “The Effect of Modern Compact Fluorescent Lights on Voltage Distortion”, Presented at the IEEE/PES 1992 Summer Power Meeting, Seattle, WA, July, 1992. [18] Henderson, R: “Harmonics of Compact Fluorescent Lamps in the Home”, Domestic Use of Electrical Energy Conference, 1999 [19] IEEE Std. 519-1992, “IEEE recommended practices and requirements for harmonic control in electrical power systems”, IEEE std.519-1992, IEEE, Institute of Electrical and Electronic Engineers, USA, April 12, 1993. [20] Sawiki, J and Galewsi, M, “Economical definition of distortion power”, Proceeding of the spring seminar on Nonsinusoidal Systems, Zielona Gora University, Poland, 1999, pp. 31. [21] Porges, F, “The Design of Electrical Services for Buildings”, 3rd Edition, Chapman and Hall Ltd, New York, 1989, pp. 84-86. [22] SuperHarm Electrotek Concepts, User’s Guide, Version 4.3.0, USA, October, 2004, pp. 1.0-4.35. [23] Singh Dharmendra kumar, Zadgaonkar A.S (2012) “Power System Harmonics Analysis Using Multi-layer Feed-Forward Artificial Neural Network Model” IJEC, ISSN: 0975- 3796(Print), Volume 4 , issue 1, 2012, pp.11-24. [24] Singh Dharmendra kumar, (2013) “Impact of power thefts and Power system quality standards in Indian scenario-Survey”. IJSER, ISSN-2229-5518, Volume 4, Issue 12, December.
  13. 13. 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 68 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.

×