OPTIMAL OPERATION OF INDUCTION MOTORS USING ARTIFICIAL NEURAL NETWORKBASED ON PARTICLE SWARM OPTIMIZATION (PSO)

Radwan H. A. Hamid Amr M. A. Amin Refaat S. Ahmed Adel A. A. El-GammalFaculty of Engineering, Helwan University, Helwan, Cairo, Egypt

E-mail: AmrmaAnminyahoo.com, ade1_e1gamma12000 yahoo.com

Abstract: This paper presents the application of neural network yield sub-optimal efficiency operation especially when the load isbased on PSO for losses and operating cost minimization control in light [2]. Then to improve the motor efficiency, the flux must bethe induction motor drives. In this paper, four strategies for reduced when it operates under light load conditions [2], obtaining ainduction motor speed control are proposed. Those four strategies balance between copper and iron losses.are based on PSO and called Maximum Efficiency Strategy, The challenge to engineers, however, is to be able to predict theMinimum Stator Current Strategy, Maximum Power Factor appropriate flux values at any operating points over the completeStrategy, and Maximum Weighted Cost Strategy. The proposed torque and speed range which will minimize the machines losses,technique is based on the principle that the flux level in a machine hence maximizing the efficiency.can be adjusted to give the minimum amount of losses and In general, there are three different approaches to improve theminimum operating cost for a given value of speed and load torque. induction motor efficiency especially under light-load conditionsThe main advantages of the proposed technique are; its simple [4].structure and its straightforward maximization of induction motor A. LOSSES MODEL CONTROLLER (LMC)efficiency and its operating cost for a given load torque. As will be This controller depends on a motor losses model to compute thedemonstrated, PSO is so efficient in finding the optimum operating optimum flux analytically [8]. The main advantage of this approachmachine's flux level. The optimum flux level is a function of the is its simplicity and it does not require extra hardware. In addition,machine load and speed requirements. Simulation results show that it provides smooth and fast adaptation of the flux, and may offera considerable energy and cost savings is achieved in comparison optimal performance during transient operation [5]. However, thewith the conventional method of operation under the condition of main problem of this approach is that it requires the exact values ofconstant voltage to frequency ratio and field oriented control. machine parameters. These parameters include the core losses and

the main inductance flux saturation, which are unknown to the usersKeywords: Induction Motor, Maximum Efficiency, operating cost, and change considerably with temperature, saturation, and skinParticle Swarm Optimization (PSO) effect. In addition these parameters may vary due to changes in the

operating conditions. But with continuing improvement ofNOMENCLATURE evolutionary parameter determination algorithms as presented in

rs stator resistance; r, rotor resistance ; Xls stator leakage [11] the disadvantages of motor parameter dependency is slowlyreactance; XI, rotor leakage reactance; a, 3ez Supply frequency; S disappearing.slip; (o, rotor speed; (Ob base speed; 03s slip speed; (m air gap flux; B. SEARCH CONTROLLER (SC)is stator current I rotor current; Te electromagnetic torque ; TL This controller measures the input power of the machine driveload torque; Ploss total power losses; PC, copper losses; Pfe iron regularly at fixed time intervals and searches for the flux valuelosses; Ps stray losses; Pf,, mechanical losses; ke, kh eddy current which results in minimum power input for given values of speedand hysteresis coefficients; cst, stray losses coefficient; Cf,, and load torque [2], [4], [7]. This particular method does notmechanical losses coefficient; SI,S2,S3 magnetizing curve demand knowledge of the machine parameters and the searchcoefficients. procedure is simple to implement. However, some disadvantages

1. INTRODUCTION appear in practice, such as continuous disturbances in the torque,It is estimated that more than 50% of the world electric energy slow adaptation (7 sec.) [4], difficulties in tuning the algorithm for a

generated is consumed by electric machines [1]. Improving given application, and the need for precise load information. Inefficiency in electric drives is important, mainly, for two reasons: addition, the precision of the measurements may be poor due toeconomic saving and reduction of environmental pollution. signal noise and disturbances. This in turn may cause the SCInduction motors have a high efficiency at rated speed and torque. method to give undesirable control performance. Moreover,However, at light loads, the iron losses increase dramatically, nominal flux is applied in transient state and is tuned after thereducing considerably the efficiency. The main induction motor system reaches steady state to an optimal value by numerouslosses are usually split into 5 components: stator copper losses, increments, thus lengthening the optimization process [3], [9-10].rotor copper losses, iron losses, mechanical losses and stray losses. So the SC technique may be slow in obtaining the optimal point.The efficiency which decreases with increasing losses can be Also, in real systems, it may not reach a steady-state and so causeimproved by minimizing the losses. Copper losses decrease with oscillations in the air gap flux that result in undesirable torquedecreasing the stator and the rotor currents while the core losses disturbances. For these reasons, this is not a good method inessentially increase with increasing air-gap flux density. A study of industrial drives.the copper and core losses components reveals that their trends C. LOOK UP TABLE SCHEMEconflict. When the core losses increase, the copper losses tends to It gives the optimal flux level at different operating points. Thisdecrease. However, for a given load torque, there is an air-gap flux table, however, requires costly and time-consuming priordensity at which the total losses is minimized. Hence, electrical measurements for each motor [4].losses minimization process ultimately comes down to the selection In this paper, a new control strategy uses the loss model controllerof the appropriate air-gap flux density of operation. Since the air- based on PSO is proposed. This strategy is simple in structure andgap flux density must be variable when the load is changing, control has the straightforward goal of maximizing the efficiency for aschemes in which the (rotor, air-gap) flux linkage is constant will given load torque. The resulting induction motor efficiency is

1-4244-0726-5/06/$20.OO '2006 IEEE 2408

reasonably close to optimal. It is well known that the presence of The slip is defined by:uncertainties (the rotor resistance, for instance) makes the result no s -___ ___° (4)more optimal. Digital computer simulation results are obtained to °e ° s + °demonstrate the effectiveness of the proposed method.

The rotor current is given by2. PARTICLE SWARM OPTIMIZATION (5)

Particle swarm optimization (PSO) is an evolutionary computation I '2technique (a search method based on a natural system) developed '¾ + x /2by Kennedy and Eberhart [3], [5]. The system initially has a Xpopulation of random solutions. Each potential solution, called a The electromagnetic torque is given byparticle. Each particle is given a random velocity and is flown ( r' >through the problem space. The particles have memory and each K s 9 (6)particle keeps track of its previous best position (called the pbest) T r 2 0and its corresponding fitness. There exist a number of pbest for the r + X 2respective particles in the swarm and the particle with greatest sa

fitness is called the global best (gbest) of the swarm. The basic The stator current is related to the air gap flux and theconcept of the PSO technique lies in accelerating each particle electromagnetic torque as:towards its pbest and gbest locations, with a random weighted 2 T 2acceleration at each time step. is + 3 + + CL (7)The main steps in the particle swarm optimization process are in

described as follows: x(a) Initialize a population of particles with random positions and Where C L = 1+ 2 x ir (8)xmvelocities in d dimensions of the problem space and fly them. The air gap flux is related to the electromagnetic torque as:(b) Evaluate the fitness of each particle in the swarm.(c) For every iteration, compare each particle's fitness wit its + X 22 (9)

previous best fitness (pbest) obtained. If the current value is ±r I VT e

better than pbest, then set pbest equal to the current value and The efficiency is defined as the output power divided by thethe pbest location equal to the current location in the d- electric power supplied to the stator (inverter losses are included):dimensional space. p

(d) Compare pbest of particles with each other and update the t, out (10)swarm global best location with the greatest fitness (gbest). P in

(e) Change the velocity and position of the particle According to The conventional power factor is defined as the input power dividedequations (1) and (2) respectively. by the apparent power.

Vid W*Vid+c1 *rand 1*(Pid - Xid)+ C2 *rand2* (Pgd - Xid) (1) = Pe (11)

d - X id + Vid (2) +

Where: Vid and Xid represent the velocity and position of the The induction motor losses are the following:i_th particle with d dimensions, respectively. randl and rand2 1. Copper losses: these are due to flow of the electric currentare two uniform random functions, and W is the inertia through the stator and rotor windings and are given by:weight, which is chosen beforehand. p = r I + (12)

(f) Repeat steps (a) to (e) until convergence is reached based on 2. Iron losses: these are the losses due to eddy current andsome desired single or multiple criteria.

PSO has many parameters and these are described as follows: W is hysteresis, given bcalled the inertia weight that controls the exploration and Pcore = ke(i + a ( + kh (I + s) a ( (13)exploitation of the search space because it dynamically adjustsvelocity. Vmax is the maximum allowable velocity for the particles 3. Stray losses: these arise on the copper and iron of the motor(i.e. in the case where the velocity of the particle exceeds Vmax, and are given by:then it is limited to Vmax). Thus, resolution and fitness of search p = C co

2 i 2 (14)depends on Vmax. If Vmax is too high, then particles will move s str r r

beyond a good solution. If Vmax is too low, particles will be 4. Mechanical losses: these are due to the friction of the

trapped in local minima. The constants cl and c2 in (1) and (2), 2termed as cognition and social components, respectively. Theses are P = C c) r (15)the acceleration constants which changes the velocity of a particle 5. inverter lossestowards pbest and gbest (generally, somewhere between pbest and The approximate inverter loss as a function of stator current isgbest).gieby

3. BACKGROUNDgiebyThe following definitions are useful in subsequent analyses. Pim =KK, jili is + Ki is(6Referring to the analysis of the induction motor presented in [6], the Where K1nv K2n are coefficients determined by the electricalper-unit frequency is characteristics of a switching element where: K1nv 3. 1307e-

a O=0±)e(0)s+ (3) 005, K2jnV=0.0250d) b d) b ~~~~~~~~~~~~Thetotal power losses are expressed as:

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P~oss -Pu+P~oe +P +P +P_ -[r I 2 +r'I2 ]+ This objective function gives an operating point which trades off the.ilsses .P ,e Ps+i,Pi, =inverse of stator current, PF, and efficiency of the motor depending

+[ke(k+S2) 2 _,+kh(1+s) a QbJ+[Cstr 11]+( 7) onthe values of weights cUl, c2, and U3.

+[C1a i +K21 is] Weighted cost function=ac, (77) + ca2 + ca3(PF) (25)The output power is given by:

P out = T l x CO r (18) Where(aX + C2 + a3 (26)The input power is given by: Where Uc1, U2, and U3 are weighting functions of motor efficiency,

inverse of stator current, and the motor power factor, respectively.Pin =PoI + Iosses [r5< + ]+ [ke (i +S )a2 0t4n+ These weighting functions vary in different cases according to the

.2 .C.2 importance of cost function components. For example, if stator

+[kh(+s) a OM]+[Cstr 2 ]+[C (O + ( current minimization is given a higher priority, U2 is increased. Thei2 . weighting factors are dependent of the operating condition (TL, (0r).

+ [Kin s +K2inv is ]+T,X Cr At certain operating point, there are unique values of U,, U2, U3 DThe efficiency is expressed as: and ez, that maximize the weighted cost function. PSO adjusts these

factors at certain load condition to maximize the weighted cost

(Te x O) (20) function. The optimization in should observe the fact that the77

e ramplitude of the stator current and flux cannot exceed their

s s + rr r e + s a m +sspecified maximum.(k(I + s) a /2)+ C r2 i 2)+ Four objective functions are generated from the weighted cost

overLskhif s)astrrirl2ll ; function. These functions are+ Crfc r )+ (Klinvi2 K 2invis )+ (TlxC r) 1- Ul= 1, 2= 0 and C3 = 0, Maximize i

The operating cost of the induction machine should be calculated 2. ( = u,2 = 1, and (X3 = 0, Minimize I,over the whole life cycle of the machine. That calculation can be 3. (xI = 0, u2 = 0, and (X3 = 1, Maximize PFmade to evaluate the cost of the consumed electrical energy. The 4. (x , cX2, and (X3, are adjusted using PSO to Maximize thevalue of average energy cost considering the power factor penalties weighted cost functioncan be determined by the following stages: 4.1 MAXIMUM EFFICIENCY STRATEGY1. If 0 < PF < 0.7 In MES (Maximum Efficiency Strategy), the slip frequency is

L0.9 - PF 1 (21) adjusted so that the efficiency of the induction motor drive systemC = Co L C l J o is maximized. In this strategy the weighting factors take the0.01 100 following values: U, = 1, U2 = 0, and U3 = 0. Figure I shows the2. If 0.7 < PF< 0.92, If PF . 0.9 PF = 0.9 efficiency variation with respect to the rotor and slip speed at

c 1 + 0. 9 - PF 0.51 (22) various levels of load torque. From the figure, it is obvious that at0LY 0.01 ) 100 j certain load torque and rotor speed, there is a certain value of slip

frequency at which the maximum efficiency occurs. The task of3. If 0.9 < PF . 1 If 0.95 . PF .PF PF=0.95 PSO controller is to find that value of flux or slip frequency atc = C 0 1 + r 0.9 -PF ) 0.5 1 (23) which the maximum efficiency occurs. At certain load torque and

L V 0.01 ) 100l rotor speed, the PSO controller determines the slip frequency (0, at

If the average energy cost C is calculated, it can be used to establish which the maximum efficiency occur.

the present value of losses. The total cost of the machine is the sum 4.2 MINIMUM STATOR CURRENT STRATEGY,In MSCS (Minimum Stator Current Strategy), the slip frequency

is adjusted so that the stator current of the induction motor ismaintenance costs. minimized. In this strategy the weighting factors take the following

PW =CxTxNxP 1 (F24i values: U =0, U2 =1, and U3 =0. Figure 2 shows the stator currentL cXTXNXPoutxK (24) variation with respect to the rotor and slip speed at various levels of

L'/1_ load torque. From the figure, it is obvious that at certain load torqueWhere: and rotor speed, there is a certain value of slip frequency at whichPWL - present worth value of losses the minimum stator current occurs. The task of PSO controller is toC0 energy cost (L.E/KwH), L.E is the Egyptian Pound find that value of flux or slip frequency at which the minimumC modified energy cost (L.E/KwH) stator current occurs. At certain load torque and rotor speed, theT running time per year (Hrs / year) PSO controller determines the slip frequency oz, at which theN evaluation life (years) minimum stator current occur.

Pout the output power (Kwatt) 4.3 MAXIMUM POWER FACTOR STRATEGYIn MPFS (Maximum Power Factor Strategy), the slip frequency isadjusted so that the power factor of the induction motor is

4. MOTOR DRIVE PERFORMANCE OPTIMIZATION maximized. In this strategy the weighting factors take the followingIn this work, the proposed objective function is given by (25) and values: U1l 0, UC2 =0, and UC3 =1. After extensive manipulationcalled the weighted cost function. This function is a combination [12],between important three terms in induction motor drive. The first{K xK2+K (7term is the efficiency, the second is the inverse of the stator current, PF l (K12 K 2±{K ) (27)and the third is the power factor. j(Kv ± K 3 )( 2 ±+1)

Where

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K COs x X rr (28)0 b X r OR1PX

K = x K t ' X" (9)ECO - Xrr)A

K =r e(X~~xKe (30) 1'Where: cbi 11

rr Ir + M X ss XIs + XM (31) Ws (iU 0 wr flAWhere X" is the subtransient reactance. Figure 1 Efficiency versus rotor speed and slip speed at rated load

x x=X X I (32) torque.X m+ X Ir

The power factor of the selected machine versus the rotor speedand the slip frequency is shown in fig. (3).4.4 MAXIMUM WEIGHTED COST STRATEGY 2

In MWCS (Maximum Weighted Cost Strategy), the weightingfactors and the slip frequency are adjusted for each operating point fi

so that the weighted cost function is maximized. At certain loadtorque and rotor speed, the PSO controller determines the weightingfactors and the slip frequency os at which the maximum weightedcost function occur. Table (1) lists the output of the PSO controller Iover a wide range of operating points. The range of each load WtIpVl WaPUtorque and rotor speed is 0.2 PU to 1.2 PU with a step of 0.2 PU Figure 2 stator current versus rotor speed and slip speed atmaking 36 operating points. different levels of load torque.

5. PRINCIPLES OF THE NEURAL NETWORK MODELINGSince the relation between the rotor flux level and shaft speed

and torque is a complex function of machine parameters, a trainedback-propagation network is considered most appropriate for thisapplication. In this study, 36 sets of input-output are sufficient to _,4

train the network to predict correctly the optimal value of the slipspeed (es and weighting factors. In the input training data, the speedvaries as follows: 20%, 40%, 60%, 80% and 100% 120% of its oMrated values. Corresponding to each speed, the load torque has alsosix different values (20%, 40%, 60%, 80% and 100% 120% of its

W'S 0-5rated values). In each case ezs is tuned continuously until the input Fig 3 Power factor versus slip frequency and rotor speed.power to the motor reaches its minimum. Subsequently thecommand speed and load torque values are recorded as a pair ofinputs for the NN and the final eos value is taken as thecorresponding output.

3 - Phase supply380V- 50Hz d-Ln

controlle generIao 7 iiaibiTLIIII1 1 1 1 111

Neural (O* We l

BasedonPSO| | tO)r |Reference CHysteresis llllll ~ ~ ~ ~ ~~T*e| Current ||Current| | l

Figure 4 the proposed drive system using PSO based on the loss model controller

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Table (1) training model for MMWCS S1=1.07, S2=-0.69, S3=0.77. For cost analysis, the following(a) or = 0.2 PU (b) or = 0.4 PU (c) (or = 0.6 PU values were assumed: CO=0.05, N=15, T=8000.(d) or = 0.8 PU (e) or = 1 PU (f) cor = 1.2 PU The block diagram of the optimization process based on PSO is

(a) shown in fig. 4. In the proposed controller, the trained neuralcor - 0.2 PU, MMWCS network based on PSO algorithm receives the rotor speed and

T (PU) al a2 a3 (os (PU) load torque. The neural network then determines the slip0.2 0.4556 0.4556 0.0888 0.0343 frequency at which the optimal fitness function occurs at that0.4 0.4690 0.4690 0.0620 0.0355 rotor speed and load torque.0.6 0.4942 0.4942 0.0117 0.0399 Figures (5) shows the comparison between the constant voltage to0.8 00.50 000.50 0.0000 0.0447 frequency ratio strategy, the field oriented control strategy, the1 0.4976 0.4976 0.0049 0.0506 maximum efficiency strategy MES, the minimum stator current

1.2 0.4986 0.4986 0.0028 0.0552 strategy MSCS, and the maximum power factor strategy at rated(b)

0.4986 0.49860.0028 0.0552rotor speed. It is obvious from fig. (5-a) that: The stator current is(b) - minimized using MES and MSCS, on the other hand, the statorT_______ or (U0.4 PU, MNIWCS current exceeds its limit using MPFS if the load torque increasesT (PU) at a2 a3 Cos (PU) to 0.5 PU. Figure (5-b) show a great improvement in efficiency

0.2 0.4024 0.4024 0.1953 0.0386 using MES and MSCS when compared with other strategies0.4 0.4280 0.4280 0.1440 0.0385 especially at light loads. A high PF is reached using MPFS over0.6 0.4651 0.4651 0.0697 0.0415 all range of load torque. A very poor PF is obtained using0.8 0.4651 0.4651 0.0347 0.0461 conventional methods (field oriented control strategy and1 0.4927 0.4927 0.0147 0.0510 constant voltage to frequency ratio) especially at light loads. This

1.2 0.4952 0.4952 0.0096 0.0557 is clear through fig. (5-c). On the other hand, fig. (5-d) shows the(c) percentage of operating cost saving when comparing MES with

cor= 0.6 PU, MMNWCS other strategies for ez, 1 PU. It is obvious from fig (6-d) that theT (PU) al a2 a3 cos (PU) saving has a noticeable value especially at light loads and rated

0.2 0.3713 0.3713 0.2574 0.0428 speed that can as high as 450 % using CVFRS. This difference0.4 0.3876 0.3876 0.2248 0.0428 decreases to 65% using MPFS and decay to 1% using MSCS. It is0.6 0.4345 0.4345 0.1310 0.0435 clear that the PWL using MSCS is approximately the same of the0.8 0.4646 0.4646 0.0708 0.0474 PWL using the maximum efficiency strategy MES.1 0.4761 0.4761 0.0479 0.0521 Figure (6) compares the stator current, the efficiency, the PF, and

1.2 0.4833 0.4833 0.0333 0.0567 the operating cost saving of the induction motor drive system(d) under the maximum efficiency strategy with the maximum

_(or=d0.8 PU,MMWCSweighted cost strategy at ez, 1 PU. It is obvious from this figureT__ _c(PU PU, MNIWCS that the stator current and the efficiency is almost the same forT (PU) al a2 a3 (OS (PU) both strategies for all operating points.0.2 0.3483 0.3483 0.3033 0.0462 It is obvious from this figure that the saving has a noticeable0.4 0.3758 0.3758 0.2483 0.0452 value especially at light loads and rated speed that can as high as0.6 0.4076 0.4076 0.1848 0.0465 17 00. It is clear that the PWL using the maximum weighted cost0.8 0.4333 0.4333 0.1333 0.0498 strategy is less than the PWL using the maximum efficiency1 0.4608 0.4608 0.0783 0.0535 strategy. The reason for that difference is due to the difference in

1.2 0.4758 0.4758 0.0483 0.0575 their power factor values. The difference in power factor values is(e) shown in fig. (6-c).

|_ _ wor =1 PU, MMWCST (PU) al a2 a3 Cos (PU) Wu= 1 PU

0.2 0.3452 0.3452 0.3096 0.0474 2 -------- -- ----------- _.--..0.4 0.3702 0.3702 0.2596 0.0469 __M0.6 0.4002 0.4002 0.1996 0.04790.8 0.4202 0.4202 0.1596 0.05121 0.4452 0.4452 0.1096 0.0547 -

1.2 0.4702 0.4702 0.0596 0.0582 ......

(I) -w|_____ Ocor 1.2 PU, MMWCST (PU) al a2 a3 Cos (PU) 02 0T3 04 P05 } 0

T~~~~~~~~~~-

0.2 0.3277 0.3277 0.3446 0.0505 (a)0.4 0.3552 0.3552 0.2896 0.0494 Wr I PU0.6 0.3852 0.3852 0.2296 0.0501

0v4102 0v4102 0v1796 .... 0.0527 ~~~~~~~~~~~~.7......... ............. ........0.8 0.4102 0.4102 0.1796 0.05271 0.4302 0.4302 0.1396 0.0560

1.2 0.4552 0.4552 0.0896 1-0.0595

6. SIMUL ATIONRESULTS -.;W-

The simulation is carried out on a three-phase, 380 V, 1-HP, 50 0.Ei--f--------------.-----------.-------'- PS1Hz, and 4-pole, squirrel cage induction motor. The motor rparameters are RS=0.0598, X1S=0.0364, Xm=0.8564, Xir=0.0546, -5 ' .4 '. 'l

Rr=0.0403, Ke=0.0380, Kh=O.O38O, Cstr=0.0 150, CfW-O.OO93, T 4PU)(b)

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-- ----1- - - adaptively adjusts the slip frequency such that the drive system isoperateda h iiu osadmnmmoeaigcs.I

___------___was found that the optimal system slip changes with variations inspeed and load torque. When comparing the proposed strategy

_ _ __-- F;with the conventional methods field oriented control strategy and

-------- constant voltage to frequency ratio), It was found thata----------------------- _------- _--------------_------ significant efficiency improvement is obtained at light loads for

all speeds. On the other hand, small efficiency improvement is(c) achieved at near rated loads. Finally, when comparing the

MWCS with MES, it was found that, the saving in PWL using the---------- AAE-it VC> ---MWCS is greater than that of the MES, especially at light loads

and rated speed.------8.-----

[1] Ojo, 0.; Dong, G.; "Efficiency optimizing control ofc3c)-------- ----------- -- ---------------- --induction-----motor----using---naturalcionvariables"nturlApplied"App Powerwe

Electronics Conference and Exposition, 2004. APEC '04.r (1-") ~~~~~~Nineteenth Anmual IEEE Volume 3, 2004 Page(s): 1622 -

(d) 1627 Vol.3Figure 5 the comparison between CVFRS, FOGS, MES, MSCS, [2] Abdin, E.S.; Ghoneem, G.A.; Diab, H.M.M.; Deraz, S.A.;and MPFS of the induction motor at rated rotor speed. a- Stator "Efficiency optimization of a vector controlled inductioncurrent b-Efficiency c-power factor d-operating cost saving motor drive using an artificial neural network" Industrial

____________________________________Electronics Society, 2003. IECON '03. The 29th Annual1 _-4 ~~~~~~~~~~~Conference of the IEEE, Volume 3, 2-6 Nov. 2003

Page(s):2543 - 2548 Vol.3------[3] Poirier, E.; Ghribi, M.; Kaddouri, A.; "Losses minimization

------control of induction motor drives based on geneticalgorithms" Electric Machines and Drives Conference, 2001.

___________------------------_------------------_-------IEMDC---2001.----IEEE- International001IEE2001rnPage(s)01:475(s)4754787r (F.") ~~~~~~~[4]Abrahamsen, F.; Blaabjerg, F.; Pedersen, J.K.; Thoegersen,

(a) P.B.; "Efficiency-optimized control of medium-size induction0.-7.5 ~~~~~~~~~~~~~~motordrives" Industry Applications, IEEE Transactions on,

Volume: 37, Issue: 6, Nov.-Dec. 2001 Pages: 1761 - 17670o7 1111111-1[5] Zhao, B.; Guo, C.X.; Gao, Y.J.; "A multiagent-based particle

_Y~~----- ------------swarm----optimization-----approach-----forimoptimalprachreactivelrac powerweo i I ~~~~~~~~~~~~~~dispatch"Power Systems, IEEE Transactions on Volume 20,

-e!9 ~~ ~ _____[6 Issue 2, May 2005 Page(s):1070 - 1078----------------Kioskeridis,-------I;--Margaris,-----N.;Kio"Losses1; Mminimizationsesmniinatscailar-ar

7 (F")controlled induction motor drives with search controllers"(b) Power Electronics, IEEE Transactions on, Volume: 11, Issue:

Pli i ilst-t, Cp,, FVI E-=32, March 1996 Pages: 213-220-0--73 i V,~~~~~~~~~_i.[] ui axi; han Cengui;ZhoFM;IOptmaleficinc

---------control of field-oriented induction motor drive and rotor-

--------resistance adaptive identifying" Power Electronics andMotion Control Conference, 2004. IPEMC 2004. The 4th

-r (F- LJ) ~~~~~[8] Vukosavic, S.N.; Levi, E.; "A method for transient torque(c) response improvement in optimum efficiency induction

____ -. ~~~~~~~~~motordrives" Energy Conversion, IEEE Transactionson, Volume: 18, Issue: 4, Dec. 2003 Pages: 484 - 493

----------_-drives" Industry Applications, IEEE TransactionsE= ~~~ ~ ~ ~ ~ ~ ~~~~o,Volume: 39, Issue: 4, July-Ag 03Pgs 0017

[10] Vukosavic, S.N.; Levi, E.; "Robust DSP-based efficiency0.20.3 0.4 0.7 F.U optimization of a variable speed induction motor drive''

(d) Industrial Electronics, IEEE Transactions on, Volume:Figure 6 the comparison between MES and MWCS of the 50, Issue: 3, June 2003 Pages: 560 - WOinduction motor at rated rotor speed. a- Stator current b- [11] M. Kouki, and all. "High-Response Flux Control of Direct-Efficiency c-power factor d-operating cost saving Field-Oriented Induction Motor with High Efficiency Taking7. CONCLUSONSCor Lose itoAcout" IEEE Trns n nusr