Carrier-based unbalanced phase voltage space vector PWM strategy for asymmetrical parameter type two-phase induction motor drives - ScienceDirect
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page., July 2009, Pages Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10250, ThailandShow moreAbstractThis paper proposes a space vector pulse width modulation (SVPWM) method based on a carrier providing unbalanced phase voltages for asymmetrical parameter type two-phase induction motor drives using a three-leg voltage source inverter (VSI). The principle is fully described. The proposed space vector equivalent phase leg reference voltages are derived from conventional ones. With the proposed SVPWM method, the output voltages are displaced at 90° with controllable amplitudes. The validity of the proposed method is verified by both calculated and experimental results.KeywordsCarrier-based space vector PWMAsymmetrical parameter type two-phase induction motorThree-leg voltage source inverterCheck if you have access through your login credentials or your institution.ororRecommended articlesCiting articles (0)Tài li?u A neural-network-based space-vector PWM controller for a three-level - Tài li?u text
Tài li?u A neural-network-based space-vector PWM controller for a three-level voltage-fed inverter induction motor drive doc
660 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 38, NO. 3, MAY/JUNE 2002A Neural-Network-Based Space-Vector PWMController for a Three-Level Voltage-FedInverter Induction Motor DriveSubrata K. Mondal, Member, IEEE, Jo?o O. P. Pinto, Student Member, IEEE, and Bimal K. Bose, Life Fellow, IEEEAbstract—A neural-network-based implementation ofspace-vector modulation (SVM) of a three-level voltage-fedinverter is proposed in this paper that fully covers the linearundermodulation region. A neural network has the advantageof very fast implementation of an SVM algorithm, particularlywhen a dedicated application-specific IC chip is used insteadof a digital signal processor (DSP). A three-level inverter hasa large number of switching states compared to a two-levelinverter and, therefore, the SVM algorithm to be implemented ina neural network is considerably more complex. In the proposedscheme, a three-layer feedforward neural network receives thecommand voltage and angle information at the input and gen-erates symmetrical pulsewidth modulation waves for the threephases with the help of a single timer and simple logic circuits.The artificial-neural-network (ANN)-based modulator distributesswitching states such that neutral-point voltage is balanced inan open-loop manner. The frequency and voltage can be variedfrom zero to full value in the whole undermodulation range. Asimulated DSP-based modulator generates the data which areused to train the network by a backpropagation algorithm inthe MATLAB Neural Network Toolbox. The performance of anopen-loop volts/Hz speed-controlled induction motor drive hasbeen evaluated with the ANN-based modulator and comparedwith that of a conventional DSP-based modulator, and showsexcellent performance. The modulator can be easily applied to avector-controlled drive, and its performance can be extended tothe overmodulation region.Index Terms—Induction motor drive, neural network,space-vector pulsewidth modulation, three-level inverter.I. INTRODUCTIONTHREE-LEVEL insulated-gate-bipolar-transistor (IGBT)-or gate-turn-off-thyristor (GTO)-based voltage-fedconverters have recently become popular for multimegawattdrive applications because of easy voltage sharing of devicesand superior harmonic quality at the output compared toPaper IPCSD 02–005, presented at the 2001 Industry Applications SocietyAnnual Meeting, Chicago, IL, September30–October5,andapprovedfor publi-cation in the IEEE TRANSACTIONSONINDUSTRYAPPLICATIONSby the IndustrialDrives Committee of the IEEE Industry Applications Society. Manuscript sub-mitted for review October 15, 2001 and released for publication March 9, 2002.This work was supported in part by General Motors Advanced Technology Ve-hicles (GMATV) and Capes of Brazil.S. K. Mondal and B. K. Bose are with the Department of Electrical Engi-neering, The University of Tennessee, Knoxville, TN
USA (e-mail:; bbose@utk.edu).J. O. P. Pinto waswith the Department of Electrical Engineering, The Univer-sity of Tennessee, Knoxville, TN
USA. He is now with the Univer-sidade Federal do Mato Grosso do Sul, Campo Grande, MS
Brazil(e-mail: jpinto@utk.edu).Publisher Item Identifier S )05012-0.the conventional two-level converter at the same switchingfrequency. Space-vector pulsewidth modulation (PWM) hasrecently grown as a very popular PWM method for voltage-fedconverter ac drives because it offers the advantages of improvedPWM quality and extended voltage range in the undermodu-lation region. A difficulty of space-vector modulation (SVM)is that it requires complex and time-consuming online com-putation by a digital signal processor (DSP) [1]. The onlinecomputational burden of a DSP can be reduced by using lookuptables. However, the lookup table method tends to give reducedpulsewidth resolution unless it is very large.The application of artificial neural networks (ANNs) isrecently growing in the power electronics and drives areas. Afeedforward ANN basically implements nonlinear input–outputmapping. The computational delay of this mapping becomesnegligible if parallel architecture of the network is imple-mented by application-specific IC (ASIC) chip. A feedforwardcarrier-based PWM technique, such as SVM, can be lookedupon as a nonlinear mapping phenomenon where the commandphase voltages are sampled at the input and the correspondingpulsewidth patterns are established at the output. Therefore,it appears logical that a feedforward backpropagation-typeANN which has high computational capability can implementan SVM algorithm. Note that the ANN has inherent learningcapability that can give improved precision by interpolationunlike the standard lookup table method.This paper describes feedforward ANN-based SVM imple-mentation of a three-level voltage-fed inverter. In the begin-ning, SVM theory for a three-level inverter is reviewed briefly.The general expressions of time segments of inverter voltagevectors for all the regions have been derived and the corre-sponding time intervals are distributed so as to get symmet-rical pulse widths and neutral-point voltage balancing. Basedon these results, turn-on time expressions for switches of thethree phases have been derived and plotted in different modes.A complete modulator is then simulated, and the simulation re-sults help to train the neural network.The performanceof acom-plete volts/Hz-controlled drive system is then evaluated with theANN-based SVM and compared with the equivalent DSP-baseddrive control system. Both static and dynamic performance ap-pear to be excellent.II. SVM STRATEGY FORNEURALNETWORKNeural-network-based SVM for a two-level inverter has beendescribed in the literature [2], [3]. It will now be extended to a$17.00 (C) 2002 IEEEMONDAL et al.: A NEURAL-NETWORK-BASED SPACE VECTOR PWM CONTROLLER 661Fig. 1. Schematic diagram of three-level inverter with induction motor load.Fig. 2. Open-loop volts/Hz speed control using the proposedneural-network-based PWM controller.three-level inverter. Of course, the SVM implementation for athree-level inverter is considerably more complex than that of atwo-level inverter [1], [4]–[7]. Fig. 1 shows the schematic dia-gram of a three-level IGBT inverter with induction motor load.For ac–dc–ac power conversion, a similar unit is connectedat the input in an inverse manner. The phase, for example,gets the state(positive bus voltage) when the switchesand are closed, whereas it gets the state (negativebus voltage) whenand are closed. At neutral-pointclamping, the phase gets thestate when either orconducts depending on positive or negative phase currentpolarity, respectively. For neutral-point voltage balancing, theaverage current injected atshould be zero. Fig. 2 shows thevolts/Hz-controlled induction motor drive with the proposedANN-based space-vector PWM which will be described later.The neural network receives the voltageandanglesignals at the input as shown, and generates thePWM pulses for the inverter. For a vector-controlled drive withsynchronous current control, the ANN will have an additionalvoltage component, which is shown to be zero in thiscase. The switching states of the inverter are summarized inTable I, where, and are the phases and , andare dc-bus points, as indicated before. Fig. 3(a) shows therepresentation of the space voltage vectors for the inverter, andFig. 3(b) shows the same figure withswitching statesindicating that each phase can have,or state. Thereare 24 active states and the remaining are zero states,, and that lie at the origin. Evidently, neutralcurrent will flow through the pointin all the states exceptthe zero states and outer hexagon corner states. As shown inFig. 3(a), the hexagon has six sectors– as shown and eachsector has four regions (1–4), giving altogether 24 regions ofTABLE ISWITCHINGSTATES OF THEINVERTER(X = U; V; W)operation. The inner hexagon covering region 1 of each sectoris highlighted. The command voltage vectortrajectory,shown by a circle, can expand from zero to that inscribed in thelarger hexagon in the undermodulation region. The maximumlimit of the undermodulation region is reached when the modu-lation factorwhere ( commandor reference voltage magnitude andpeak value ofphase fundamental voltage at square-wave condition). Notethat a three-level inverter must operate below the square-wavecondition.A. Operation Modes and Derivation of Turn-On TimesIn this paper, as indicated in Fig. 3(a), mode 1 is defined if thetrajectory is within the inner hexagon, whereas mode 2 is de-fined for operation outside the inner hexagon. In a hybrid mode(covering modes 1 and 2), thetrajectory will pass throughregions 1 and 3 of all the sectors. In space-vector PWM, the in-vertervoltage vectors correspondingto the apexesof the trianglewhich includes the reference voltage vector are generally se-lected to minimize harmonics at the output. Fig. 3(c) shows thesectortriangle formed by the voltage vectors , and .If the command vectoris in region 3 as shown, the followingtwo equations should be satisfied for space-vector PWM:(1)(2)where, , and are the respective vector time intervalsandsampling time. Table II shows the analytical timeexpressions for, , and for all the regions in the six sec-tors wherecommand voltage vector angle [see Fig. 3(c)]and( command voltage and dc-linkvoltage). These time intervalsare distributed appropriately so asto generate symmetrical PWM pulses with neutral-point voltagebalancing. Table III shows the summary of selected switchingsequences of phase voltages for all the regions in the six sec-tors [4]. Note that the sequence in opposite sectors (– , – ,and– ) is selected to be of a complimentary nature for neu-tral-point voltage balancing. Fig. 4 shows the correspondingPWM waves of the three phases in all the four regions of sector. Each switching pattern during is repeated inverselyin the nextinterval with appropriate segmentation of ,, and intervals in order to generate symmetrical PWMwaves. The figure also indicates, for example, turn-on time of-and-states of phase voltage in mode1. These wave patterns are, respectively, defined as pulsed andnotched waves. It can be shown that similar wave patterns arealso valid for the sectorsand (odd sector). If PWM wavesare plotted in the even sector (or ), it can be shown thatstates appear as notched waves whereas states appear as662 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 38, NO. 3, MAY/JUNE 2002Fig. 3. Space voltage vectors of a three-level inverter. (a) Space-vectordiagram showing different sectors and regions. (b) Space-vector diagram showing switchingstates. (c) SectorAspace vectors indicating switching times.pulsed waves. The turn-on times for different phases can be de-rived with the help of Table II and Fig. 4 for all the regions in thesix sectors. For example, the phase-turn-on time expressionsin mode 1 can be derived as-forforforforforfor(3)-forforforforforfor(4)whereand denotes the sector name.Similarly, the corresponding expressions for mode 2 can bederived as shown in (5) and (6), shown at the bottom of the nextpage, whereindicates the region number. Similar equationscan also be derived forand phases. Because of waveformsymmetry, the turn-off times (see Fig. 4) can be given as- -(7)- -(8)and the correspondingand state pulsewidths are evidentfrom the figure. The remaining time interval in a phase corre-sponds to zero state as indicated. Equations (3) and (4) can beexpressed in the general form-(9)whereis the bias time and turn-on signalat unit voltage. Fig. 5 shows the plot of (9) for bothandstates at several magnitudesof . Mode1 ends when the curvesreach the saturation level. Both the functions aresymmetrical but are opposite in phase. Fig. 6 shows the sim-ilar plots of (5) and (6) in mode 2 which are at higher voltages.Note that the curves are not symmetrical because of saturationat. The saturation of-in sector mode 2 is evi-dent from the waveforms of Fig. 4(b)–(d). Mode 2 ends in theupper limit when the turn-on time curves touch the zero line.For phasesand , the curves in Figs. 5 and 6 are similar butmutually phase shifted byangle. Note that both-and-vary linearly with magnitude in the whole un-dermodulation range except the saturation regions. It is possibleto superimpose both Figs. 5 and 6 with the common bias timeand variable .The digital word corresponding toas afunction of angle for both and states in all the phasesand in all the modes can be generated by simulation for traininga neural network. Then,-and-values can besolved from the equations corresponding to the superimposedFigs. 5 and 6.MONDAL et al.: A NEURAL-NETWORK-BASED SPACE VECTOR PWM CONTROLLER 663III. NEURAL-NETWORK-BASEDSPACE-VECTORPWMThe derivation of turn-on times and the correspondingfunctions, as discussed above, permits neural-network-basedSVM implementation using two separate sections: one is theneural net section that generates thefunction from theangleand the other is linear multiplication with the voltagesignal. Fig. 7 shows the neural network topology with theperipheral circuits to generate the PWM waves. It consists of a1–24–12 network with sigmoidal activation function for middleand output layers. The network receives theangle at theinput and generates 12 turn-on time signals as shown with fouroutputs for each phase (i.e., two forand two for states)which are correspondingly defined as, ,, and for phase . This segmentationcomplexity is introduced for avoiding sector identification anduse of only one timer at the output which will be explainedlater. These outputs are multiplied by the signal, scaled bythe factor, and digital words-are generated for eachchannel as indicated in the figure. These signals are comparedwith the output of a singleUP/DOWNcounter and processedthrough a logic block to generate the PWM outputs.-forforforforforforforforforfor(5)-forforforforforforforforforfor(6)664 IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 38, NO. 3, MAY/JUNE 2002TABLE IIANALYTICALTIMEEXPRESSIONS OFVOLTAGEVECTORS INDIFFERENTREGIONS ANDSECTORSTABLE IIISEQUENCING OFSWITCHINGSTATES INDIFFERENTSECTORS ANDREGIONSA. ANN Output Signal Segmentation and ProcessingIt was mentioned before that, in the PWM waves of the oddsector,or , states appear as pulsed waves andstates appearas notched waves (see Fig. 4). On the other hand, inthe even sector,or states appear as notched wavesandstates appear as pulsed waves. This can be easily veri-fied by drawing waveforms in any of these sectors. In order toavoid a sector identification (odd or even) problem and use onlyone timer, the ANN output signals are segmented and processedthrough logic circuits to generate the PWM waves. As men-Fig. 4. Waveforms showing sequence of switching states for the four regionsin sectorA. (a) Region 1(? =30 ). (b) Region 2(? =15 ). (c) Region 3(? =30 ). (d) Region 4(? =45 ).tioned above, each phase output signal is resolved into andpairs of component signals. The segmentation and processing
Tài li?u liên quanI am not quite sure why I get this error message:
[Error!!!] has continuous states, but doesn't have a mdlDerivatives routine.
While I have both &mdlDerivatives routine& and continuous states S-fucntion!
Help please
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On Sat, 12 Feb :06 +0100, Christoph ROLZ wrote:
&Using MatLab's 'movie' function you can play a movie recorded by the
&'getframe' command. It is possible to specify the number of times the
&movie is played and the frames per second.
&Unfortunately, I've not found a way to stop the playback of a movie.
&Is it possible to, let's say I've specified the movie should be
&20 times, to disrupt the playback after an user interrupt?!
&Thanks a lot,
if you want to play the movie, tried thi command
index is the number of frame in video file.
index = 1:100
mov = aviread('filename',index)
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&Bulent Bayraktar& &bayrakta@purdue.edu& writes:
& I have one question: Is Matlab's 'graycomatrix' function actually in
& accordance with original Haralick's 1973 paper?
& I mean, are they supposed to be the same algorithm? If yes, I don't
& think Matlab did a good job on this.
& For example, try it on this matrix (i.e., 'image'):
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& compared.
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Warning: Running low on memory.
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Warning: Running low on memory.
Simulation will terminate in approximately
Warning: Running low on memory.
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&Valcler& && wrote in message
news:292.1.JavaMail.root@gallium.mathforum.org...
& I use Matlab 6 which is used with some worksheets.
& Currently the spreadsheet uses a macro that calls some files &.m& as the
& command:
& Shell ( &C:\ matlab\bin\win32\MATLAB.EXE /r CALCULATE&)
& The problem is if it running in this manner, it doesn't gives an error
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Wrap your code in a TRY/CATCH and make sure you call EXIT after the
TRY/CATCH block completes.
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Version 8.3
MATLAB Compiler
Version 5.1
On 08/22/...Hi!
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tak for din sko !!
I think you have to make one yourself.
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i tried that
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any other !!
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&fcrdan$kmf$1@&...
& i tried that
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Univ. of Wyoming
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-----------------------------------------------
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dialog is generated by wxGlade 0.2.3.
I have a custom validator derived from wxGenericValidator that I
connect to the wxTextCtrl like this (unfortunately, I have to write
this from memory):
textVal = new URLValidator(url);
textVal-&SetWindow(text_ctrl_1);
text_ctrl_1-&SetValidator(textVal);
URLValidator is nothing more than wxGenericValidator with it's own
Validate() method.
It says in the documentation somewhere that if I cal...Im trying to connect client side applet with remote server using RMI.
At first all things going ok and the remote object returns the output
correctly. But when i try to modify the client side applet to call
another method in the remote object, its still returning the output of
the old method.
I tried to restart the server, restart the rmiegistry
and compile all the code again but still the problem exist. But when i
restart my computer and run the application the result would be the
output of the new method.
it doesn't make sense but that's how it's.
Sorry for long message&
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