13 efficiecy optimization of a vector controlled induction motor drive using an artificial neural...

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Efficiency Optimization of a Vector Controlled Induction MotorDrive Using an Artificial Neural Network

E. S. Abdin, G. A. Ghoneem, H. M. M. Diab, and S. A. Deraz.Depar tment of Electrical Engineering, Faculty of Engineering, Menofiya University, Egypt.E-mail :ezeldin2000@,vahoo.com

AbstractThis paper presents an approach for efficiencyoptimization of a vector controlled induction motordrive. The optimum flux-producing current is obtainedusing an artificial neural network. The artificial neuralnetwork model is established using Matlab/Simulinkand based on the load torque and speed data of anindirect vector-controlled induction motor drive. Thechange of iron core loss resistance due to flux andfrequency variation is taken into consideration.Simulation results of the proposed approach show asignificant improvement in energy saving andefficiency optimization.

I. INTRODUCTIONEfficiency improvement in adjustable speed drives hasbeen getting a lot of attention in recent years. Higherefficiency is important not only from the view pointsof energy saving and cooling system operation , butalso from the broad perspective of environmentalpollution control. From the energy saving view points,it is well known that more than 50 of the totalelectric energy is consumed by motors. Inductionmotors, especially, squirrel-cage types, are widelyused in electrical devices and are responsible for mostof the energy consumed by electric motors.For a given motor, operation under rated condition(with rated load torque and rated speed) is highlyefficient. However, in many applications, a motordrive operates far from the rated operating point.Under these circumstances, the motor efficiencybecomes low. This is due to the imbalance betweeniron and copper losses. It is well known that, for agiven operating point, the losses in an inductionmachine can he minimized by adjusting appropriatelythe level of magnetic flux [ I ] . This is due to the factthat the electromagnetic losses in a machine are adirect function of the magnetic flux and by a properadjustmen t of the flux, an appropriate balance betweeniron and copper losses can be achieved.In general, there are two different approaches toimprove the induction motor efficiency especiallyunder light-load conditions [Z-61. The first approach,named loss model controller, depends on a motor lossmodel to compute the optimum flux analytically [2,3].The main advantage of this approach is the simplicityand does not require extra hardware. However, the

main problem of this approach is in the requirement ofthe exact values of machine parameters which areunknown to the users and may vary due to changes inoperating conditions. The second approach, named online efficiency optimization control, depends onsearching for flux levels which minimizes 'th emachine's measured input power for a given torqueand speed [4,5,6]. This approach does not require apriori know ledge of machin e parameters. It is notonly insensitive to parameter Variations but alsoapplicable to partially unknown machine. However, itis slow in locating the energy efficient point and maynot work well due to measurement noise.In this pap er, an artificial neural network based as apredictor for optimum flux is presented for an indirectvector-controlled induction motor drive. The loadtorque and speed are used as inputs for the neuralnetwork. The optimum flux-producing current is takenas the neural network output. The neural networkmodel is established using Matlab/Simulink and basedon the data of an indirect vector-controlled inductionmotor drive. Two models are presented with the effectof iron core loss. The magnetizing resistance is takenas a constant in the first model. The change in themagnetizing resistance due to the flux and frequencyvariation is taken into consideration in the secondmodel. Simulation results of the proposed approachshow a significant improvement in energy saving andefficiency optimization.11. SYSTEM CONFIGURATIONFigure 1shows an indirect vector-controlled inductionmachine drive with an artificial neural network (ANN)based as a predictor for optimum magnetizing currentLJ.ccording to the principle of vector co ntrol, thesynchronously rotating vector components of statorcurrent IT5 and lh are controlled independently tocontrol the torque and rotor flux, respectively. Theelectromagnetic torque of the induction motor underfield orientation can be expressed as :

1 )3 P L ,2 L,Te = - ) - ) -hdJqr,,._..........,..... ... ....Where P is the num ber of poles, L is themagnetizing inductance and L , is the rotor inductance.This is similar to that for a separately exited DCmachine. The rotor flux-linkage qdr is exited by I ,

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through the dynamic delay due lo the rotor circuit andgiven by :

Fig. 1 System configuration

The symbol p in Eq. (2) is the time derivative operator(d/dt). At steady state, Eq. (2) becomes

p d , = L,I, and hence we hav eTe = - ) - ) - ) I d s I q s ..............................As shown in Fig. 2, for a given o perating point (torqueand speed), the rotor flux is decreased by reducing themagnetizing current (Ib), which ultimately results in acorresponding increase in the torque current (I,)(normally by action of the speed controller), such thatthe developed torque remains constant. As the flux isdecreased, the iron loss decreases with the attendantincrease of copper loss. However, the total system(converter and machine) loss decreases, resulting in adecrease of dc link power. The flux is continueddecreasing until the system settles down at theminimum input power point A as shown in Fig. 2,hence, getting the optimum magnetizing current IdsoPat given operating point. At any operating point, theANN will be used to give lhop.

(3)3 P L;2 L ,

111. MODELING OF THE DRlVE SYSTEMTo study the effects of parameter variations in aninduction motor drive running under rotor fluxorientation, with an artificial neural network basedpredictor, and to develop a suitable controller,extensive simulations are necessary in thatenvironment. Matlab Neural Network toolboxsoftware was found to have adequate capacity todevelop these types and models. The modelsdeveloped with Simulink posses very good interfaceand debugging options with the above toolbox. Ironcore loss is neglected in standard theory of vectorcontrol of AC drive. However, recent researchesindicate that iron loss may play an important role inestablishing accurate rotor flux oriented control of

induction machine[7-91. In this paper, two models arepresented with the effect of core loss. The first modeltakes R,, constant while the second one takes R,variable.

I DC ink

IdmpI,(rated)Id. + Decreasing

Fig. 2 Changes in cnre and copperlosses with changing flux producing c urrent IThe value of the magnetizing resistance R, used tomodel iron core loss is determined from standard no-load test. Since, the vector controlled inductionmachine o perates at variable levels of comm and flux ,then iron loss resistance w ould need to be representedas a function of both fwquency and flux. Theappropriate values of resistance for various flux levelsand frequencies are also obtained from the standard noload test. The iron loss resiritance can he expressedexperimentally as :R , = Rmb(-) '(-)' p ...................... ...( )

f k d (Prd rdR,, : the value of resistance at rated frequency, j L e dand rated flux, , prated.The dynamic model of an induction motor in astationary reference frame, as shown in Fig. 3 , can bewritten as:-V; = R, I: -vis ..................................... 5 )df

( 6 )A = RsI& +-(P* .....................................sdt( 7 )s9 r 97 df8)ddf

d

V s = R I s +-(P,, -o, PP; ..........................V;, = RF12r+- p i r +w, P;, ..........................

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(b)Fig, 3 . d-q axis equivalent circuits of an inductionmotor in stationary reference frame (a) q-axis cir cuit,(b) d-axis circuit.

For squirrel cage motor, V: = V: = 0Symbols I 'p denote voltage, current and fluxlinkage, respectively. Electrical rotor speed is corLeakage inductances are identified with index I indexm denotes parameters and variables associated withmain (magnetizing) flux. Index a is used forparameters and variables associated with iron coreloss. Superscript s denotes for stationary referenceframe.IV. PRINCIPLES OF THE NEURAL NETWORKMODELI NGArtificial neural network consists of a number ofinterconnected processing elements called neurons. Aneuron can be modeled to perform a mathematicalfunction such as a pure linear function, tan-sigmoidfunction, log-sigmoid, etc. The artificial neuralnetwork can be trained to solve complex nonlinearfunctions with variable parameters, which may not beattainable by conventional mathematical tools. Thisfeature is particularly suitable to the present paper ,where the optimal rotor flux value is a non linearfunction of both rotor speed and load torque.A. eural etwork StructureThere is no specific method to select the best NN

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configuration for a particular application. Several NNstmctu res have been test.ed and evaluated. The designcriteria for the present study are: a short training time,a small error and a small number of hidden layers. TheNN structure considered most appropriate is shown inFig. 4. The model has one input layer , one hiddenlayer and one output layer. The input layer includestwo neurons tn which the desired rotor speed or andload torque T, are connected a s inputs to network. Theoutput layer has only one neuron for the magnetizingcurrent Idr The neurons in both input and output layershave linear transfer functions. The hidden layer hasnine neurons where each of these neurons has a tan-sigmoid transfer function. Fig. 5shows in details theview of one neuron. The weights are used to adjust therelation between inputs and outputs. Biases have aconstant input of 1 which is applied to all neurons inthe neural network except the input layer.

n

input layer hidden layer ~ output layer(linear) ; tan-sigmoid) j b e a r )Fig.4 NN structure

i \ PI .., i ...,Po inputs

Fig. 5 . A detailed view of one neuron

B. eural etwork TrainingOnce the network weights and biases have beeninitialized to random values, the network is ready fortraining . During training, the weights and biases ofthe network are iteratively adjusted to minimize thenetwork performance function and to establish theprescribed input-output pattern . The performancefunction used is mean square error (mse) which is the


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average squared error between the network outputsand the target outputs. The training algorithm uses thegradient of th e performance function to determine howto adjust the weights to minimize performance. Thegradient is determined using a technique called back-propagation, which involves performing computationsbackwards through the network. In this pa pe r, the NNwill be applied to an indirect vector-controlledinduction motor drive. The steady-state speed wr andtorque TI f the machine are used to predict theoptimal magnetizing current lb. The speed and torquevalues are accessible from the indirect vectorcontrolled induction motor drive models (with R,constant and with R, variable). T he current Id to eachw, and T, ombination is obtained by adjusting b inthe vector control loop until the input power to thedrive reaches the minimum, as sh ow n in Fig. 2.In this study, 25 sets of input-output are sufficient totrain the network to predict correctly the optimal valueof I . In the input training data, the speed varies asfollows:lO , 30 , SO , 70 and 100% of its ratedvalues. Corresponding to each speed, the load torquehas also five different values ( I O , 30%, 50%, 70%and 100%of its rated values). In the back-propagationtraining,_the sma ll. changes to neuron's weights andbiases are made in the direction that minimize theperformance. This direction is found by taking onlythe sign o f the derivative to determine the direction ofthe weight update. The magnitude of the derivativehas no effect on the weight update The size of theweight update is determined by a separate value. Theper fo rmance h c t i o n i s se t to lo4 as stoppingcondition for training. Two 3-D plots for each outputof the trained NN are shown in Fig. 6, which aresufficiently accurate in representing the originaltraining data.V. SIMULATION RESULTS AND DISCUSSIONSA 0.75 HP induction motor was used as a case study.The parameters of the motor were determinedexperimentally an d given a s follows:.3-phase, 0.75 HP, 380V poles,R , = 1 6 . 6 8 R , R , = 8 . 8 4 4 R , x ~ l 2 n , x , = 2 0 . 5 R ,

~

X = 90R,R,b= 1700RThe proposed models have been implemented usingMatlab/Simulink. The gains of the speed controllersare selected after many tries as : kp = 10 , ki = 30.Figures 7 and 8 show the drive efficiency, the powerreduction and the percentage of power reduction forboth R, constant and R, variable, respectively. Theresults for the model with R, constant are notacceptable because the magnetizing resistance R,should be variable due to frequency and flux changes.With optimal flux operation, a significant efficiencyimprovement is obtained compared with rated fluxoperation as shown in Fig. 8.a. The efficiency gainsare increased, especially at light loads. From Figures

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8.b and 8.c, a reasonable amount of power can besaved by implementing the pi-oposed control scheme.Fig. 9shows the dynamic response of the model (withR, variable ) under variable load torque. The speedreference is set to its rated value while the load torqueis varied in steps. From Fig. 9, it is shown tha t a goodperformance in speed control is achieved with aminimum overshoot.

1

1

Fig. 6 Response of the trained neural network for themode l with (a) R,constant, (b) R, variable


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8

?-.622 -v.4 OBEVJ 0.2

0.0 .

c / ,U, = p.u

-w, = 1 p.ua,= .7 p.u_ - -I,:J- _ _ - - - wr 0.5 p.u- e _

,:a,-- w, = 0.3 p.uI a _ _ - -, ; ,I I2 I- r ~

____ated fluxptimum flux- .,,-' I I I I , I

0 0 0.2 0 1 0 6 0.8

- 6 1Ew, = 0.7 p.uor 0.5 p.uw, =0.3 p.u

Load Torq ue @.U)

Load Torque (p .u)

r O r = p.0.4 -?Pe -

Bo. -

ll 0.2 ------ rated fluxptimum flux

0.00.0 0.2 O I 0 0 0 .8Load Torque @.U

la)

w, = 1 p.u

w, = 0.5 p.ua

0 2 1 0 6 0 8Load Torque @.U

(b)

Load Torque pu)(C) (C)

Fig. I Model performance with R, constant Fig. 8 Model performance with R , variablea) system efficiency.b) power reduction.c) percentage of power reduction.

a) system efficiency.b) power reduction.c) percentage of power reduction.

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1 0.5 .............................../n

m0.995

~

Time (sec)Fig. 9 Dynamic response of the closed-loop model(wi th R, variable ) under variable load torque.

VI. CONCLUSIONAn artificial neural network based efficiencyoptimization control for a voltage source inverter fedinduction motor drive has been presented. Thetechnique trains a neural network model utilizing thespeed and load torque data of the motor and estimatesthe optimum rotor flux excitation current for thevector controller. The change of iron core lossresistance due to flux and frequency variation plays animportant part and should be taken into consideration.Simulation results show a significant improvement inenergy saving and efficiency optimization.REFERENCES

G. 0 Garcia, J C. Mendes Luis, R. M .Stephan, and E. H. Watanabe, An efficientcontroller for an adjustable speed inductionmotor drive, IEEE Trans. Ind. Electron., Vol.41, No. 5 , October 1994.Iordanis Kioskeridis, and Nikos Margaris, Loss minimization in scalar-controlledinduction m otor drives with search controllers,IEEE Trans. Electron., Vol. 11 , No. 2, March1996.lordanis Kioskeridis, and Nikos Margaris, Loss minimization in induction motoradjustable speed drives, IEEE Trans.Electron., Vol. 43, No. 1, February 1996.Gilbert0 C . D. Sousa, Bimal K. Bose, and JohnG. Cleland, Fuzzy logic based on-lineefficiency optimization of an indirect vector-

controlled induction motor drive, IEEE Trans.Ind. Electron., Vol. 42, No. 2, April 1995.D. S Krischien, D. W . Novotny, and W .Suwanwisoot, Minimizing induction motorlosses by excitation control in variablefrequency drives, IEEE Trans. lnd. Appl., Vol.1A-20, No. 5 , Sep/Oct.l984.Alexander Kusko, and Donald Caller, Controlmeans for minimization of losses in AC andD C motor drives, IEEB Trans. Ind. Appl., Vol.1A-19, No. 4, JulyiAug.1983.E. Levi, M. Sokola, A. Boglietti. and M.Pastorelli, Iron loss in rotor-flux-orientedinduction machines : identification, assessmentof detuning , and compensation, IEEE Trans.Pow er Electron., Vol. 11, No. 5 , Sept. 1996.E. Levi, Impact of i ion loss on behavior ofvector controlled induction machines, IEEETrans, Ind. Appl., Vol. 31, No. 6, pp. 1287-1296, 1995.

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