application of allied genetic algorithms in sensor less speed adjustment control for im drive system

8/14/2019 Application of Allied Genetic Algorithms in Sensor Less Speed Adjustment Control for IM Drive System

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Application of Allied Genetic Algorithms in Sensorless Speed AdjustmentControl for Induction Motor Drive System

Gs rs+Q -& M P +i:sI]{:+r$p

Feng Lin, Qi-Wen Yang

(3 )

College of Electrical Engineering, Zhejiang University, 3 10027, P.R.China,E-mail: eeflin@,emb.zju.edu.cn

Abstract-This paper presents a Genetic tuning digitalPI controller for the sensorless speed vector control ofinduction motor drive system. In order to preventpremature convergency we introduce the alliedstrategy of human being into GAS, and presentparallel genetic algorithm based on the allied strategy(PGAAS). Based on the designed control systemstructure, the system is simulated. The simulationresults show that the system has a strong robust to theparameter variation and is insensitive to the loaddisturbance.Index Terms--sensorless speed control, geneticalgorithms,vector control, allied strategy

1. INTRODUCTIONRecently , vector control without a speed sensor

for an inverter-induction moto r system has receivedwide spread research interests . Many methods toestimate the speed from terminal electricalmeasurements have been proposed since 1 9 7 5 . Here ascheme by which the rotor speed is described. Thisapproach utilize motor parameter values (e.g., the rotortime constant and the magnetizing inductance) toimplement nonlinear feed forward calculation of th eproper motor sl ip frequency so that the speed of rotor isachieved.In this paper, we applied Genetic Algorithms tocope with this problem. We employed a parallelgenetic algorithm based on the allied strategy(PGAAS) to the classical PI controllers. The approachhaving ability for global optimization and with goodrobustness is expected to overcome som e weakness ofconventional approaches and to be more acceptable forindustrial practices. Simulations are carried out to verifythe feasibility of this sensorless control under vectorcontrol conditions. The simulation results demonstratethat this PGAAS- ased approach is really analternative and potential method for induction motordrive system .

2. THE MATHEMATICAL MODEL ANDVECTOR CONTROL

3. SPEED ESTIMATIONA block diagram of the speed estimation is show nin Fig. 2 . In this system, two stator voltages uaC and

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Ubc and two stator currents & an d 4 are measured.These voltages and currents are transformed into d-qcomponents , nd the flux linkage of the rotor and themotor speed are estimated.

To derive the equations for the speed calculation,

Fig. 1 Configuration of control system

F R O MM :-f$-B-=ENSOR d-q SPEEDF R O M

U &T%%ONVOLTAGEIM SENSOR CALCULATE

Fig. 2 Block diagram o f speed estimationthe voltage equation of a squirrel cage induction motor,as is well known is used. Which is expressed as follows:

Where . .u s = + j u q r, s = i d s+ j i q s ,w , = W d r + j w , , = M i , + L , i , ,(3 = 1 - M / ( L s L , ) , (3 , yr&, = d / dt .

The motor speed are given byO r = - P w d r / q r - r (W dr-Midr ) /W qr (6)w , = P W q r / W d r - o r ( - v q r M i q s ) / v d r (7 )

In the steady state, when the currents, thevoltages ,and the flux linkage have sinu soidal waves, (6 )and (7 ) become as follows:w r = - T r ( W d r - > /w q r (8 )

(9)r = - Or (-w qr + M i s > / d r4. TEE ADAPTIVE PI CONTROLLER

BASED ON ALLIED GENETICALGORITHMS

4.1 Genetic AlgorithmGAS has been form ally provided for more than 20years. Nowadays, people bring our a lot of modifying

algorithms according to GASs problems and faults,which include development in the following directions:1 the modification of genetic strategy (for example,

Elitist Strategy, GAGMS). 2.the modification of geneticoperator and its operating probability (genetic operatorsvary with the coding method, e.g. AGA). Thesestrategies greatly improve the optimizing ability of Gas.Because GA S has the inborn ability of collateralprocessing, with the popularization of paralleldistributing computer, people pay attention to GASgradually. In order to increase the exploring efficiencyof G AS, in this article, we advance modifying methodsto genetic strategy and genetic operator respectively,and we get the Parallel Genetic Algorithm Based onAllied Strategy, PGAA S).4.2 Allied StrategyJ.Craig Potts brought out a parallel-realizableGAMAS algorithm based on transference and manualselection. This algorithm uses four genetic groups withdifferent functions, maintains the individuals diversitythrough the purposeful movement of best individualsbetween genetic groups, and seeks for new bestindividuals on the base of the above work. T he failuresof GAMAS also exist: the number of genetic groups istoo large. The work of this algorithm is huge. Theconvergent speed is slow because of the use of SGA inthe inter-genetic-group development, the relationshipamong different genetic groups is loosen, and there isno inter-effects between best individuals in a generation.As a result, the effective genes, which are carried bybest individuals, are not explored out.As we know, during the course of creatureevolution, this situation exist: the natures of theoffspring who bore as a result of close relativepropagation are always no better than their parents, and,on the contrary, the nature of offspring whose parentsare widely departed is extremely good. So, people alsochoose this kind of marriage, and they try their best notto get marry with c lose relatives.In the GAS of single group, the offspring isreproduced by definitive parents because the size ofgenetic group is limited, and a fair number of offspringresult from the same parents with the homologic geneticstructure, which will cause the premature convergenceof algorithm. In this article, we introduce the alliedmethod of people into G AS, and bring o ut a G AS basedon parellel evolution among multi-genetic groups: For,M genetic groups evolve parellelly, when therelationship between genetic groups satisfy a certainrequirements, the recent two best individuals ofdifferent genetic groups ally. And the best individualswill exist in the offspring, transfer to the related groups,and engage in the evolution of next generation. Becauseallied offspring carry the genes of another group, alliedstrategy can both assure the diversity of genes, avoidthe danger brought by the propagation of closerelatives, and speed the exploring process of GA S withthe introduction of good genes from other groups. Inorder to prevent the best individuals from rapid

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vanishing, we extend the best-individual-maintaining-strategy of single genetic grou p GAS: By com paring thebest offspring of original group and allied offspring, weselect the excellent one and put it into the nextevolution process. This is the main idea of alliedstrategy.

Stator resistanceRotor res is tance

Select the best oneW

rs 0.5616Qrr 0.5345Q

Genetic grouplc7Stator inductanceRotor inductance

Genetic group0L, 0.009348HL 0.005636H

I .(Seed

Fig. 3 the model of allying4.3 Logical Operator

According to this GAS based on binary code,crossbreed operator is usually expressed as 1 pointcrossbreed, 2-point crossbreed, multipoint crossbreedand uniform crossbreed. In these four operators, thegenes constituted of {O,l} act as symbols to takefunction. But, as we all know, 0 and 1 are twological states in computer technology, and theirbehavior manner can be fulfilled by logic operations ofdifferent definitions. In this article, we endue the logicfunctions of 0 and 1 to the genes in the GAS, sowe get a novel genetic operator----logic operator:

I I O I O O ~ O I oioiooooi in~uperariun01 1101001 111101101 u#uperaiioncrussbreed =3

llo!o?lol ~ I O O O ~ I O ~nut-u3uperatiot01 1101001 01 1 1 0 0 0 0 1 and-uPoperatiutmufa f iun =$. .

4.3 The Description of PGAAS and theProcedure of AlgorithmAccording to the description of single group GASof article [3], the PGAA S can be expressed as:

,=IIn this equation, M is the genetic group size; Pi (0)is the initial genetic group; rJ is the size of everygenetic group; h is the length of chromosome, in orderto assure the achievement of the allying, h must beequal. S; is the genetic strategy, allied strategy is onetoo. gi is the genetic operator and a is the geneticprobability. fi is the proper-value algorithm; q is theallying condition. The t is the terminal standard of thealgorithm.The procedure of PGAAS is following:1 M genetic groups initiate respectively.2.Every genetic group evolves by itself (single

3.if the allying condition q is satisfied, the geneticgenetic group GAS).groups marry on e to the other (see figure 3).

4.judge the terminal standard t of the algorithm, if thecondition is satisfied, the program ends; otherwise,turn to 2.The structure of controller in this article is showedin figure 4. The parameters of the PI controller areadjusted gradually through the allied GAS.In the control method of this article, the geneticoptimizing procedure is used in online estimation, thecontrol signal is sent by traditional PI co ntroller. At first,we use allied GAS on original parameter to fulfill offlineoptimizing, then we add these into control system. O none hand, we propose the best PI parameter, on theother hand, the system is studying further, adjust its PIparameter to satisfy the change of the controlled object.

I Magnetizing inductance I M I 0.008415H I5. SIMULATION RESULTS

Using the Genetic tuning controllers, simulation ofa vector control induction motor ratings of 15KW werecarried out. The current source inverter drive withvector control and the speed estimation is used in thissystem. The constant used for simulations is listed ontable 1. The calculation of this control schemes in lessthan lms is possible using a digital signal processor@SP).The simulation of the overall closed-loop syst emis accomp lished using Runge-Kutta numerical method.Now the cha nge interval o f Ti is [0,50], the length 11=16;Ki is [0.1,0.42], i=l O, Tn is [O.OS,l.OS], b=lO; & is[O.6,1.83],l4=1O, ( M , N , A ,Pc, P,)= (2, 30 ,45 ,0. 6,0 .05 ).

According to the parameters value mentionedabove, a series of comparison curves of the speedcontrol responses between the conventional PIcontroller and the new Genetic tuning PI controllers areshown in Fig.4 to 7 .Fig. 4 and Fig. 5 show the variation of the speedresponses when each of the different rr is set for thesame system. In Fig. 4, the simulation curves are causedby using the conventional PI controllers, and in Fig. 5by using the Allied Genetic tuning PI controllers. Thesecurves show that the system with the Allied Genetictuning PI controllers is insensitive to the change ofmotor parameters.In Fig. 6, the curves are corresponding to thestarting response of comm and speed caused by the PIand the new Allied Genetic tuning PI controllers. 1:Allied Genetic tuning PI controllers 2: conventional PIcontrollers. The simulation curves show that the

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overshot are reduced in the response and the settingtime is shorter by using the Allied Genetic tuning PIcontrolle rs than using the others.In Fig. 7 the curves are corresponding to thetransient response in change of the load (from 100% to120%) caused by the PI and the new Allied Genetictuning PI controllers. 1:Allied Genetic tuning PI

0 Am Lm LEm 2mtfmsFig.7

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controllers 2: conventional PI controllers. Thesimulation curves shows that the system with newcontrollers is insensitive to the load disturbance.

6. CONCLUSIONThe proposal of genetic algorithms (G AS) isresulted from the evolution of creatures. The GASfollows the evolution principle of creatures at great level.

So, every principle which benefits the creature smultiplying and evolution can be regarded as theameliorating method of GAS, which is the main cause ofthe opinion that GA S is not a simple optimizingalgorithm, but a novel general methodology based onevolution ideal . Human beings marrying manner isthe outcome of accumulation of tens of thousandsyears evolution. We improve stability and theconvergent speed of the algorithm by introducing themarring idea of humans. Because the genetic groups,which are independent to each other, are marrying inappropriate time, PGAAS is used in parallel calculationof multi-processor. In the same time, we can build afundamental of the hard ware realization by substitutingtraditional operator with logical operator. So we canbelieve that due to the convenient realizing method andthe global quickly convergent characteristic divorcedfrom the controlled object model, GAS have powerfullife-force although they need be ameliorate furthermore.GAS introduces a special method to the design ofcontrol system, and it must be widely used in theelectrical drive system. .

REFERENCESNicholas P. Rubin, Ronald G. Harley, and GregoryDiana. Evaluation of Various Slip EstimationTechniques for an Induction Machine OperatingUnder Field-Oriented Control Condition s, IEEETram.onLA., vol. 28, 1992,pp. 1367-1375

Geng Yang and Tung-Hai Chin, Ada ptive-Speed Identification Scheme for a Vector-Controlled Speed Sensorless InverterInductionMotor Drive, IEEE fianx on LA., vol. 29, 1993,pp. 820-824.A. E.Eiben, E. H Aarts and Van Hee K. M., GlobalConvergence o Genetic Algorithms.. An InfiniteMarkov Chain Anabsis. Parallel ProblemSolvingfrom Nature.Heidelberg, Berlin: Springer-Verlay, 1991 pp. 4- 12.M. Srinvas, and L. M. Patnaik, AdaptiveProbabilities of Crossover and Mutation inGenetic Algorithms, IEEETrans. on SMC, vol. 4,1994, pp. 656-666.

application of allied genetic algorithms in sensor less speed adjustment control for im drive system

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