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

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  • 1. 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© IAEME: www.iaeme.com/ijeet.aspJournal Impact Factor (2012): 3.2031 (Calculated by GISI)www.jifactor.com ©IAEME 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]. 81
  • 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEThe magnetizing characteristic of a transformer core is non-linear and will produceharmonics as it is saturated. The source of the harmonics in the transformer magnetic fluxmay be due to transformer itself. Once the power system polluted with harmonics then thefunction of transformer will be affected due to different losses in transformer. The generatedharmonics can flow into the distributed power system causing many problems for the powernetwork operation. Consequently to avoid all these problems and to improve the quality ofthe delivered energy harmonics parameter such as magnitude and phase angle should beknown. Fast methods for the measuring and estimating harmonics signal are thus requiredvarious digital signal analysis techniques are being used for the measurement and estimationof power system harmonics. These include fast Fourier Transform, Least Square algorithmand others [2]. Recently the application of Artificial Neural Network for power systemproblems has gained considerable attention. This paper presented a novel technique, based onNeural Network for analysis of power system harmonics due to transformer load on powersystem.Power system Harmonic sources are Converters, Devices which includes semi-conductor elements, Generators, Motors,Transformers, Lightening equipments working by gas discharge principle, Photovoltaicsystems, Computers, Electronic ballasts, Uninterruptible power supplies, Switching powersupplies, Welding machines, Control circuits, Frequency converters, Static VARcompensators, Arc furnaces, HVDC transmission systems, Electrical Communicationsystems[1].Transformer Modern transformers operate at increasing levels of saturation in order to reduce theweight and cost of the core used in the same. For economic reasons transformers are normallydesigned to make good use of the magnetic properties of the core material. This means that atypical transformer using a good quality grain-oriented steel might be expected to run with apeak magnetic flux density in the steady state . If a transformer running with this peakoperating magnetic flux density is subjected to a magnetic flux density which will produceconsiderable saturation. Because of this and due to the hysteresis, the transformer corebehaves as a highly non-linear element and generates harmonic voltages and currents. Theequivalent circuit of a transformer is given below fig (1) Fig(1) Equivalent Circuit of a TransformerHere Rp and Xp show the primary circuit resistance and the leakage reactance, R’s and X’sshows the secondary resistance and leakage reactance that is transformed (reduced) to theprimary respectively. RFE is the resistance which symbolizes the iron losses and IFE is thecurrent related to this losses. In parallel to the resistance, Xm shows the magnetizationreactance and Im is the related current passes through. 82
  • 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEThe harmonic currents produce harmonic fields in the core and harmonic voltages in thewindings. Relatively small value of harmonic fields generates considerable magnitude ofharmonic voltages. These effects get even more pronounced for higher order harmonics. Asthese harmonic voltages get short circuited through the low impedance of the supply theyproduce harmonic currents. These currents produce effects according to Lenz’s law and tendto neutralize the harmonic flux and bring the flux wave to a sinusoid. Normally thirdharmonic is the largest in its magnitude. In a single phase transformer the harmonics areconfined mostly to the primary side as the source impedance is much smaller compared to theload impedance. The understanding of the phenomenon becomes clearer if the transformer issupplied with a sinusoidal current source. In this case current has to be sinusoidal and theharmonic currents cannot be supplied by the source and hence the induced EMF will bepeaky containing harmonic voltages. When the load is connected on the secondary side theharmonic currents flow through the load and voltage tends to become sinusoidal. Theharmonic voltages induce electric stress on dielectrics and increased electro staticinterference. The harmonic currents produce losses and electromagnetic interference [3]. Fig (2) Harmonics Generated By TransformerARTIFICIAL NEURAL NETWORK For many decades, it has been a good of science and engineering to developintelligent machines with a large number of simple elements. The interest in neural networkcomes from the networks ability to mimic human brain as well as its ability to learn andrespond. As a result, neural networks have been used a large number of applications and haveproven to be effective in performing complex functions in a variety of fields. There includepattern recognition, classification, vision, control systems, and prediction. Adaptation orlearning is a major focus of neural net research that provides a degree of robustness to the NNmodel. In predictive modeling the goal is to map a set of input patterns on to a set of outputpatterns. NN accomplishes this task by learning from a series of input / output data setspresented to the network. The trained network is then used to apply what it has learned toapproximate or predict the corresponding output. The human nervous system is a verycomplex 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 subnetworks. Each neuron in the brain is composed of a body, on axon and multitude ofdendrites [4]. A biological neuron is shown in fig (3). 83
  • 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME Fig (3) A Biological NeuronThe 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 endbulb and a dendrite is called a synapse. The axon of a single neuron forms synaptic connectionswith many other neurons. Depending upon the type of neuron, of synapse connections from otherneurons may range from a few hundred to 104.The cell body of a neuron sums the incomingsignal from dendrites as well as the signals from numerous synapses on its surface. A particularneuron will send an impulse to its axon if sufficient input signal are received to stimulate theneuron 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 isshown in below fig (4) Fig (4) An Artificial neuronIt consists of three basic components that include weights, threshold and a signal activationfunction. Each neurons receives inputs x1, x2, x3,…..xn, which are connected to the input side ofthe 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 thenode output; whereas for negative weight XTW tends to inhibit the node output. The node’sinternal threshold Ө is the magnitude offset that affect the activation of the node output as follow nY = ∑ (XiWi) - Өk………………….. i=1An 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 back- propagation (BP) learning algorithm. This network as its name indicatesis made up of multilayer’s. Thus architecture of this class besides processing an input and anoutput layer also has one or more intermediary layers called hidden layers. The computationalunits of the hidden layer are known as the hidden neurons or hidden units. The hidden layer aids 84
  • 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEin 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 arereferred to as input hidden layer weights. Again, the hidden layer neurons are linked to the outputlayer neurons and the corresponding weights are referred to as hidden output layer weights. Fig(5). Multilayer feed- forward NetworkBack propagation learning (training) Backpropagation is a systematic method of training multi layer Artificial NeuralNetworks. 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) Recallphase. In the training phase, first, the weights of the network are randomly initialized. Thenthe output of the network is calculated and compared to the desired value. In sequel, the errorof the network is calculated and used to adjust the weights of the output layer. In similarfashion, the network error is also propagate backward and used to update the weights of theprevious layers.ANN Designing Process [6] ANN designing process involves five steps: gathering input data, normalizing thedata, selecting the ANN architecture, and Training the Network, Validation-testing thenetwork.Gathering Input Data The configuration of the experimental system block diagram is shown in below fig (6) Fig (6) Experimental Set-Up 85
  • 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEIn the above block diagram a experimental set-up is shown, a transformer is connected withpower supply and linear or non-linear load is connected with this transformer. Due totransformer and other loads on this transformer, harmonics are generating in power system. Adata acquisition card is connected at input line of transformer to collect the distortedcurrent/voltage waveform or data. These collected waveform/data transmitted to PC throughRS-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 theaccuracy of the subsequent numeric computation and is an important stage for training of theANN. 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 importantdecision for selecting the ANN architecture. Choosing these parameters to a feed forwardbackpropagation topology is the art of the ANN designer. In this paper the a Feed ForwardBackpropagation topology is used and ANN configuration has 40 input neurons receiving 40sampled points of the distorted waveform and 7 output neurons producing the magnitude andangles of harmonic components up to the 13th odd harmonics. The hidden layer has 52neurons to bridge input layer with output layer. For a set of input there is a corresponding setup 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 updateneurons almost simultaneously .This model requires a large amount of RAM during operationand therefore only the odd harmonics which are known to have adverse effects on powersystem application, were used to train the Neural Network. Before the start of training, theinitial weight were randomized to value between -0.5 and +0.5. These input and targetoutputs were “shown” to the ANN in a sequential manner so that the weights were updatedstep by step according to the backpropagation learning algorithm. The error between theactual output and the target was evaluated after every upd Fig(7) Training of ANNThe back propagation learning algorithm employed works toward reduction of the RMSerror, and the training ceases as the total sum of square error reaches just below the error 86
  • 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMEcriteria initially set. The weights are then supposed to have converged enough that theyshould represent the non-linear transfer functions between inputs and outputs of the ANNmodel 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. Subsequentlytraining yielded a slower convergence rate.Testing To test the generalizing capabilities of the magnitude networks the distortedwaveforms that contained odd harmonics up to the 13th harmonic with no noise added wereconsidered for the training process.RESULT AND DISCUSSION In this research paper, Artificial Neural Network is used to efficiently measure themagnitude of harmonic component in power system generated due to transformer load. Theinput 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. 87
  • 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME 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 88
  • 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEMECONCLUSIONS An artificial neural network model was developed and implemented for power systemharmonics component measurement. It was tested off-line under different conditions. Theresult of the off-line test indicates that the ANN model has very high accuracy for powersystem harmonics component measurement. The developed ANN model was implemented ona PC with Matlab Software (with ANN Toolbox) using a data acquisition card. The ANNmodel 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 thecurrent and the other harmonics being generated are of order 7,11,13,17,19…..In undernormal excitation condition transformer core may have entered slightly the saturation regionand being to generate some harmonics in the excitation current. In overvoltage condition,harmonic amplitudes increase with respect to excitation voltage.REFERENCE1) Krishna Vasudevan, G.Sridhara Rao,P.Sasidhara Rao,”Electrical Machine-1” IIT Madras,Google.com,online.google search2) Ibrahim EI-Amin, Ihab Arafah,” Artificial Neural Networks for Power Systems Harmonic Estimation” IEEE Explore. 14-18 Oct 1998 Volume: 2 Page(s): 999 - 1009 vol.23) Nptel.iitm.ac.in/courses/IIT-NADRAS/Electrical-II/pdf/1-5.pdf4) C.Naga Bhaskar , G.Vijay Kunar,”Neural Network And Fuzzy Logic “ BS Publications,20115) S.Rajasekaran, G.A.Vijayalakshmi Pai “Neural Network, Fuzzy Logic And Genetic Algorithms “ PHI,20036) H.Selcuk , Yasar Birbir “Application Of Artificial Neural Network for Harmonic Estimation in Different Produced Induction Motor”, International Jornel Of Circuit, System And Signal Processing , Issue 4 Volume 1 2007.7) W.W.L Keerthipala ,Low Tah Chong,Tham Chong Leong,” Artificial Neural Network Model for Analysis of Power System Harmonics” IEEE Xplore, 15-18 Sept. 2008.8) Xihong Wu,Wei He,Zhanlong Zhang,Jun Deng,Bing Li,”The Harmonics Analaysis Of Power System Based On Artificial Network” IEEE ,Automation Congress, 2008. WAC 2008. World, Sept. 28 2008-Oct. 2 2008 Page(s): 1 - 4 J. Mazumdar, R. Harley, F. Lambert and G.K. Venayagamoorthy, “Using a neural network to distinguish between the contributions to harmonic pollution of non linear loads and the rest of the power system,” IEEE Transactions on Power Electronics, Power Electronics Specialists Conference, 2005. PESC 05. IEEE . Dharmendra Kumar Singh, A. S.9) Zadgaonkar, ”Power System harmonics Analysis Using Multi-Layer Feed Forward Artificial Neural Network Model” International Journal of Electronics And computers” vol 4 no1 2012 issn:0975379610) J. Arrillaga, B.C.Smith, N.R.Wood and A.R.Wood, “Power System Harmonic Analysis,”John Wiley edition,2ND 200311) J. Arrillaga, N.R. Watson, and S. Chen, “Power System Quality Assessment,”John Wiley & Sons, England,2000 89
  • 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 1, January- February (2013), © IAEME12) J.C.Liand Y.P. Wu, “FFT algorithm for the harmonic analysis of three-phase transformer banks with magnetic saturation,” IEEE Transactions on Power Delivery, Vol. 6, Issue 1, pp. 158 - 161, Jan 1991.13) Pallavi.H.Agarwal, Prof.Dr.P.M.George and Prof.Dr.L.M.Manocha, “Material Removal Rate Prediction Of C-Sic Composite: Comparative Analysis Of Neural Network And Fuzzy Logic” International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 128 - 137, Published by IAEME.14) K.Pounraj, Dr.V.Rajasekaran and S.Selvaperumal, “Fuzzy Co-Ordination of UPFC For Damping Power System Oscillation” International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 1, 2012, pp. 226 - 234, Published by IAEME.15) A.Padmaja, V.S.Vakula, T.Padmavathi and S.V.Padmavathi, “Small Signal Stability Analysis Using Fuzzy Controller And Artificial Neural Network Stabilizer” International Journal of Electrical Engineering & Technology (IJEET), Volume 1, Issue 1, 2010, pp. 47 - 70, Published by IAEME.16) G.Suresh babu,U.K.Choudhury and G.Tulasi ram das, “A Novel Approach In Designing A Filter For A Solid Rotor Alternator To Minimise Harmonics” International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 2, 2012, pp. 328 - 342, Published by IAEME.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.BIOGRAPHIESDharmendra kumar obtained M. Tech. Degree in Electronics Design and Technology fromTezpur University, Tezpur, Assam in the year 2003. Currently he is pursuing research workin 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. Heobtained 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. Thetesting technique for quality of wood developed by him was included in ISI in 1979. He wasawarded Ph. D. in 1985 by Indira Gandhi Kala & Sangeet University, Khairagah for his workon “Acoustical and Mechanical Properties of Wood for Contemporary Indian MusicalInstrument Making.” He obtained another Ph. D. in 1986 by Pt. Ravishankar ShuklaUniversity on “Investigation of Dynamic Properties of Non-Conducting Materials UsingElectrical Analogy.” He has 47 years of teaching experience. He is currently adding glory tothe post of Vice Chancellor of Dr. C. V. Raman University. He has published more than 500technical papers for journals, national and international conferences. He was the JointDirector, Technical Education, Govt. of Chhattisgarh in 2004 & the Principal of NIT, Raipurin 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. 90

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