Fuzzy logic approach to control the magnetization level in the magnetic 2

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Fuzzy logic approach to control the magnetization level in the magnetic 2

  1. 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME19FUZZY LOGIC APPROACH TO CONTROL THE MAGNETIZATIONLEVEL IN THE MAGNETIC CORE OF A WELDING TRANSFORMERRama Subbanna. S.1, Kamalakar. K.S.K.1, Dr. Suryakalavarthi.M21Department of Electrical and Electronics Engineering, St. Martins Engineering College,Hyderabad, India2Department of Electrical and Electronics Engineering, Jawaharlal Nehru TechnologicalUniversity, Hyderabad, IndiaABSTRACTThis paper aims to develop a Fuzzy Logic based controller to control the saturationlevel in the magnetic core of a welding transformer. The magnetization level controller is asubstantial component of a middle-frequency direct current (MFDC) resistance spot weldingsystem (RSWS). The basic circuit of a resistance spot welding system consists of an inputrectifier, an inverter, a welding transformer, and a full-wave rectifier which is mounted on theoutput of the welding transformer. The presence of unbalanced resistances of the transformersecondary windings and the difference in characteristics of output rectifier diodes can causethe transformers magnetic core to become saturated. This produces spikes in the primarycurrent and finally leads to the over-current protection switch-off of the entire system. Toprevent the occurrence of such a phenomena, the welding system control must detect andmaintain the magnetic core saturation within certain limits. The welding current at the full-wave rectifier is normally controlled by the pulse width modulated primary voltage of thetransformer supplied by the input converter. Previously, an Artificial Neural Network baseddetector was proposed to detect the saturation level. In this paper, a fuzzy logic basedcontroller is proposed to maintain the saturation within optimal limits. This is achieved by acombined closed-loop control of the welding current and closed-loop control of the iron coresaturation level.Keywords: Controllers, Fuzzy logic applications, Hysteresis, Transformers, Welding.INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING& TECHNOLOGY (IJEET)ISSN 0976 – 6545(Print)ISSN 0976 – 6553(Online)Volume 4, Issue 3, May - June (2013), pp. 19-28© IAEME: www.iaeme.com/ijeet.aspJournal Impact Factor (2013): 5.5028 (Calculated by GISI)www.jifactor.comIJEET© I A E M E
  2. 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME20I. INTRODUCTIONThe aim of this work is to develop and evaluate a novel method to control themagnetization level in the magnetic core of a welding transformer. It is based on fuzzy logicand requires the optimum operating values of the flux and welding current across the weldingsystem. The magnetization level controller is a substantial component of a middle-frequencydirect current (MFDC) resistance spot welding system (RSWS), where the welding currentand the flux density in the welding transformer’s magnetic core are controlled by twohysteresis controllers [1].The resistance spot welding systems described in different realizations [2]-[5], arewidely used in the automotive industry. Although the alternating or direct currents (dc) canbe used for welding, this work focuses on the RSWS (Fig.1) with dc welding current. Theresistances of the two secondary windings R2, R3 and characteristics of the rectifier diodes,connected to these windings, can slightly differ. References [6]-[9] show that combination ofthese small differences can result in increased dc component in welding transformer’smagnetic core flux density. It causes increasing magnetic core saturation with high impact onthe transformer’s primary current i1, where current spikes eventually appear, leading to theover-current protection switch-off of the entire system. However, the problematic currentspikes can be prevented either passively [6] or actively [7]-[9].When the current spikes are prevented actively, closed-loop control of the weldingcurrent and magnetic core flux density is required. Thus, the welding current and themagnetic core flux density must be measured. While the welding current is normallymeasured by the Rogowski coil [10], the magnetic core flux density can be measured by theHall sensor or by a probe coil wound around the magnetic core. In the latter, the flux densityvalue is obtained by analogue integration of the voltage induce in the probe coil [7].Integration of the induced voltage can be unreliable due to the unknown integration constantin the form of remnant flux and drift in analogue electronic components. The drift can be keptunder control by the use of closed-loop compensated analogue integrator [9].An advanced, two hysteresis controllers based control of the RSWS, where currentspikes are prevented actively by the closed-loop control of the welding current and fluxdensity in the welding transformer’s magnetic core, is presented in [9]. This solution requiresmeasuring of the welding current, while instead of measured flux density only informationabout magnetization level in the magnetic core is required. Some methods tested on weldingtransformer’s magnetic core, that can be applied for magnetization level detection arepresented in [7], [8]. All these methods require Hall sensor or probe coils which make themless interesting for applications in industrial RSWS, due to the relatively high sensitivity onvibrations, mechanical stresses and high temperatures. In order to overcome these problems,an ANN based magnetic core magnetization level detector was presented previously. Its only(single) input is the measured transformer’s primary current. The ANN, based on themagnetic core magnetization level detector, is trained to recognize the waveform of thecurrent spikes, which appear in the primary current when the magnetic core is approachingthe saturated region. Upon detection of a spike, the ANN target signal makes it possible forthe transformer supply voltage to change direction which also changes the magnetic fluxdensity accordingly. This way, the system is controlled using the ANN detector and over-current protection switch-off is prevented.In this paper, a fuzzy logic based controller is introduced which can be used in placeof the other hysteresis controller. The fuzzy controller supplements the ANN based detector,
  3. 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME21in that it helps to limit the flux and the welding current within optimal operating values. Theinputs and their corresponding outputs are given to the fuzzy controller in the form ofmembership functions. While the inputs include flux and welding current, the only output isthe transformer primary voltage. The necessary rules are defined within the fuzzyinterference system so that whenever the flux or welding current exceed the optimal values,the transformer voltage must reverse its direction. This way, the flux starts to reduce andfinally leading to a suppression of the primary current. The popular and advantageousMamdani algorithm was used to employ the proposed controller.II. DYNAMIC MODEL OF THE RSWSThe RSWS, shown in Fig.1, consists of an input rectifier, an H-bridge inverter, asingle phase transformer and a full-wave output rectifier [9]. The three-phase alternatingcurrent (ac) voltages u1, u2, u3, supplied from the electric grid, are rectified in the inputrectifier in order to produce the direct current (dc) bus voltage. This voltage is used in the H-bridge inverter, where different switching patterns and modulation techniques can be applied,to generate ac voltage uH, required for supply of the welding transformer. The weldingtransformer has one primary and two secondary windings. They are marked with indices 1, 2and 3, respectively. The currents, the number of turns, the resistance and the leakageinductances of the primary and two secondary welding transformer’s windings are denoted byi1, i2, i3, N1, N2, N3, R1, R2, R3, and Lσ1, Lσ2, Lσ3. The effects of the eddy current losses areaccounted by the resistor RFe.R1 and L1 are the resistance and inductance of the load. The output rectifier diodes D1and D2 are connected to both transformer’s secondary coils. They generate the dc weldingcurrent i1 which has a dc value a few times higher than the amplitudes of ac currents i2 and i3that appear in the transformer’s secondary coils without the rectifier diodes.Fig.1: Schematic presentation of the RSWS [1]The dynamic model of the RSWS was built based on the schematic presentation,shown in Fig.1. In this work the model is built with the programme package Matlab/Simulinkbased on the following set of equations (1) – (9).uH = R1i1+Lσ1(di1/dt)+ N1(dφ /dt)…………………………………………..……...........(1)0 = R2i2+Lσ2(di2/dt)+ N2(dφ /dt)+dip1+ RLiL+LL(d(i2+ i3)/dt)…….............…….............(2)0 = R3i3+Lσ3(di3/dt)- N3(dφ /dt)+dip2+ RLiL+LL(d(i2+ i3)/dt)…….............………...........(3)N1ip+N2i2- N3i3 = H(B)lic+2δB/µ0…………………………………………..……............(4)
  4. 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME22iL = i2+ i3………………………………………………….………….........................(5)i1 = iFe+ iP………………………………………………………………….......................(6)iFe = N1(dφ /dt)/RFe………………………………………………………...…...................(7)φ = BAFe……………………………………………………….…………........................(8)θ = N1i1+ N2i2-N3i3.............................................................................................................(9)The results of simulations, obtained by the dynamic model of the RSWS, show thatsmall difference in resistances R2, R3 and in characteristics of the rectifier diodes D1 and D2can cause unbalanced time behaviour of the magnetic core flux density B and the currentspikes in the primary current i1, shown in Fig.2. The a) and b) graphs in Fig. 2 show the samevariables in different time scales. The current spikes appear approximately after 0.06s(Fig.3(c)). After 0.07s the current spikes become high enough to cause the over-currentprotection switch off of the RSWS.2(a) 2(b)2(c) 2(d)Fig.2(a),2(b),2(c): Time behaviour of primary current i1 Fig.2(d): Time behaviour of Flux BII. FUZZY LOGIC CONTROLLER DEVELOPMENTThis paper proposes a fuzzy logic based controller of magnetization level in themagnetic core. The inputs for the proposed controller are the flux and the welding current.The output is the welding transformers primary voltage. The input and output ranges are setin the corresponding membership functions depending on the data obtained from tests
  5. 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME23performed and from the manufacturer. In this case, the entire dynamic model of the RSWS aswell as the fuzzy logic controller developed have been done so using the MATLAB/SIMULINK package. Triangular membership functions are chosen in developing this model.Fig.3: Fuzzy Logic Controller Model for saturation controlThe membership function ranges for each variable are set according to the dataobtained from the dynamic model as well as the data obtained from the previously designedANN based detector.(a) (b)(c)Fig.4: (a) and (b) represent the Input Membership Function plots of Flux and WeldingCurrent respectively. (c) represents the Output Membership Function plot of the Transformerprimary voltage.
  6. 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME24The Rule-base for the proposed operation of the Fuzzy-Logic Saturation Controller is asshown in Fig.5.RULE NUMBER CONDITIONRule 1 IF Flux is “High” THEN Voltage is “Low”Rule 2 IF Flux is “Low” THEN Voltage is “High”Rule 3 IF Welding Current is “Moderate” THEN Voltage is “Moderate”Rule 4IF Flux is “High” AND Welding Current is “Low” THEN Voltageis“Low”Rule 5IF Flux is “Low” AND Welding Current is “Low” THENVoltage is“High”Rule 6 IF Flux is “Moderate” THEN Voltage is “Moderate”Rule 7 IF Welding Current is “High” THEN Voltage is “Moderate”Fig.5: Rule-base of the proposed fuzzy-logic Hysteresis ControllerThe Rule viewer and surface plots presented in the figures below show that thecontroller works well with the presented rules and provides a reliable method to control andmaintain the magnetization levels of the welding transformer under acceptable limits. Thus,the proposed fuzzy logic based controller can be utilized as an alternative for typically usedhysteresis controllers which possess several drawbacks such as production of noise andmechanical vibrations.
  7. 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME25Fig.6(a): Rule viewer of the proposed fuzzy logic saturation controllerFig.6(b): Surface plot of the fuzzy logic saturation controllerIII. OPERATION OF THE PROPOSED CONTROLLERThe proposed controller operates in such a way that whenever the flux or weldingcurrent value exceeds a preset limit, the supply transformer’s primary voltage is eitherreversed or made to zero depending on the condition. The welding system with the proposedfuzzy-logic controller supplemented by an artificial neural network based detector works as aclosed-loop system where the inputs to the welding system are the flux and the weldingcurrent and its output is the supply transformer’s primary voltage. The limiting values aretaken as Bm=1T, Im=14kA, IM=15kA and Udc=560V. The operational circuit is as shown inFig.7(a). The flux, welding current and voltage limits are set according to the table inFig.7(b). The various mapping values obtained from the proposed controller are as shown inthe table in Fig.7(c).
  8. 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME26Fig.7(a): The closed loop control of transformer primary current i1 and welding current iLusing the fuzzy logic hysteresis controller and the ANN based saturation detector.LOW MODERATE HIGHFLUX <-1 -1 - 1 1<WELDING <14000 14000-15000 15000<CURRNTVOLTAGE -650 - -300 -300 – 300 300 - 650Fig.7(b): Ranges of each of the Input-Output Membership FunctionsFLUX WELDING CURRENT VOLTAGE-1.43 6680 4581.8 10200 -4740 14600 0-1.72 15100 4151.21 11600 -4460 3950 0-1.91 1230 4761.54 10500 -4641.76 29300 -357Fig.7(c): Input-Output Mapping Values obtained from Fuzzy Controller
  9. 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME27IV. CONCLUSIONA novel fuzzy logic based controller is developed to control the magnetization levels inthe core of a welding transformer. The controller developed works in conjunction with an ANNbased saturation detector presented in [11]. By employing the proposed ANN based saturationdetector and the fuzzy logic based magnetization controller, the welding system can operateefficiently and properly. The proposed Fuzzy Controller works well and can be used to obtainclosed-loop control. The rules used work well and the input-output mapping obtained is just asrequired. From the surface and from the above table, the values obtained are appropriate for thecontroller to be used as a closed loop saturation controller. The system works such that wheneverany specified limit of flux or welding current is exceeded, the primary voltage is reversed in itsdirection or made to zero.REFERENCES[1] K. Deželak, J. Pihler, G. Štumberger, B. Klopčič and D. Dolinar, “Artificial Neural NetworkApplied for Detection of Magnetization Level in the Magnetic Core of a Welding Transformer,”IEEE Transactions on Magnetics, vol. 46, no. 2, pp. 634–637, February 2010.[2] W. Li, E. Feng, D. Cerjanec, and G. A. Grzazinski, “A comparison of AC and DC resistancewelding of automotive steels,” in Sheet Metal Welding Conf. XI, Sterling Heights, MI, May2004.[3] A.Canova, G. Gruosso, and B. Vusini, “Electromagnetic modelling of resistance spotwelding system,” in ISEF 2007, XIII Int. Symp. Electromagnetic Fields in Mechatronics, CzechRepublic, 2007.[4] B. M. Brown, “A comparison of AC and DC resistance welding of automotive steels,”Welding J., vol. 66, no. 1, pp. 18-23, Jan. 1987.[5] W. Li, D. Cerjanec, and G. A. Grzadzinski, “A comparative study of single AC andmultiphase DC resistance spot welding,” ASME Trans. J. Manuf. Sci. Eng., vol. 127, no. 3,pp.583-889, 2005.[6] B. Klopčič, G. Štumberger, and D. Dolinar, “Iron core saturation of a welding transformerin a medium frequency resistance spot welding system caused by the asymmetric output rectifiercharacteristics,” in Conf. Rec. IAS 2007 Annu. Meeting, New Orleans, LA, Sep. 2007, pp. 2319–2326.[7] K. Deželak, B. Klopčič, G. Štumberger, and D. Dolinar, “Detecting saturation level in theiron core of a welding transformer in a resistance spot-welding system,” J. Magn. Magn. Mater.vol. 320, no. 20, pp. 878–883, Oct. 2008.[8] K. Deželak, G. Štumberger, B. Klopčič, D. Dolinar, and J. Pihler, “Iron core saturationdetector supplemented by an artificial neural network,” Prz. Elektrotech., vol. 84, no. 12, pp.157–159, 2008.[9] B. Klopčič, D. Dolinar, and G. Štumberger, “Advanced control of a resistance spot weldingsystem,” IEEE Trans. Power Electron., vol. 23, no. 1, pp. 144–152, Jan. 2008.[10] D. J. Ramboz, “Machinable Rogowski coil, design, and calibration,” IEEE Trans. Instrum.Meas., vol. 45, no. 2, Apr. 1996.[11] Rama Subbanna,S., Kamalakar,K.S.K., Dr.Suryakalavathi,M., “Artificial Neural network forthe detection of Magnetization level in the magnetic core of a welding Transformer”,IJAER,Vol8,no.6, pp.56-60, 2013.[12] J. M. Mendel, “Fuzzy Logic Systems for Engineering: A Tutorial”, Proc. IEEE, Vol. 83,pp. 345-377, 1995.
  10. 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 3, May - June (2013), © IAEME28AUTHOR’S BIBLIOGRAPHYRAMA SUBBANNA,S. graduated from Jawahalal Nehru TechnologicalUniversity in the year 2003. M.Tech from Jawahalal Nehru TechnologicalUniversity, Hyderabad, in the year 2006. Currently, He is pursuing Ph.D fromJawahalal Nehru Technological University, Anatapur, India. Presently, he isworking as Assistant Professor in St.Martin’s Engineering College. Hisresearch includes Transformer, Hysteresis, Controllers, ANN Techniques andFuzzy Logic Methods.KAMALAKAR,K.S.K is a final year student of B.Tech in Electrical andElectronics Engineering at St. Martins Engineering College, J.N.T.U-Hyderabad. His research interests are mainly concerned with applications ofartificial intelligence and software aspects of Computer Engineering.Dr.SURYAKALAVATHI,M. graduated from S.V.University, Tirupati in theyear 1988 . M.Tech from S.V.University, Tirupati in the year 1992. Ph.D fromJ.N.T.University, Hyderabad in the year 2006 and Post Doctoral from CMU,USA. She is presently professor of Electrical and Electronics Engineeringdepartment, J.N.T.U.College of Engineering, Hyderabad. She presented morethan 50 research papers in various national and international conferences andJournals. Her research area includes High Voltage Engineering, PowerSystems, Artificial Intelligence Methods.

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