Solution to Direct Torque Control of Induction Motor with Artificial Bee Colony Algorithm

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1 Solution to Direct Torque Control of Inuction Motor with Artificial Bee Colony Algorithm 1 T L V Naga Lathish, 2 G Ram Mohan Assistant professor, Assistant professor, Dept. of EEE, Gurunanak Institutions Technical Campus, DVR & Dr. HS MIC College of Technology, Anhra Praesh, Inia. Abstract: The unesire flux ripple an torque may occur in conventional irect torque control (DTC) inuction motor rive. DTC can improve the system performance at low spees by continuously tuning the regulator by ajusting the Kp, Ki values. In this Artificial Bee Colony Algorithm (ABC) is propose to ajust the parameters (Kp, Ki) of the spee controller in orer to minimize torque ripple, flux ripple, an stator current istortion.the ABC base PI controller has resulte is maintaining a constant spee of the motor irrespective of the loa torque fluctuations. Keywors: Artificial Bee Colony Algorithm, Forager Bee, Scout Bee Direct Torque Control, PI controller. I. INTRODUCTION Inuction motors are the most wiely use machines in AC rives because of their rugge construction an cost. To control the torque an flux of the inuction motor ifferent strategies are available as per the literature. DTC was patente by Manfre Depen brock in the US an in Germany, the latter patent having been file on October 20, 1984, both patents having been terme irect self-control (DSC). However, Isao Takahashi an Toshihiko Noguchi escribe a similar control technique terme DTC in an IEEJ paper presente in September 1984 an in an IEEE paper publishe in late The DTC innovation is thus usually creite to all three iniviuals. The only ifference between DTC an DSC is the shape of the path along which the flux vector is controlle, the former path being quasi-circular whereas the latter is hexagonal such that the switching frequency of DTC is higher than DSC. DTC is accoringly aime at low-to-mi power rives whereas DSC is usually use for higher power rives. Direct torque control is one of the methos which is use in variable frequency rives for the control of the inuction motor. Direct torque control has emerge over the last ecae to become one possible alternative to the well-known Vector Control of Inuction Machines. In DTC, the stator flux an the torque are irectly controlle by selecting the appropriate inverter state. The output of the spee regulator (PI controller) results in generation of the reference torque. However the PI controller cannot result in perfect control if its parameters Kp, Ki are not properly chosen. The unesire torque an flux ripple may occur in conventional irect torque controlle inuction motor rive. DTC can improve the system performance at low spees by continuously tuning the regulator by ajusting the Kp, Ki values. Many artificial intelligence techniques an ranom search methos have been employe to improve the control parameters. Artificial Bee Colony Algorithm (ABC) is propose to ajust the parameters (Kp, Ki) of the spee controller in orer to minimize torque ripple, flux ripple, an stator current istortion. ABC is generally consiere as a reliable, accurate, robust an fast optimization technique. ABC has been successfully applie to solve a wie range of numerical optimization problems. II. MATHEMATICAL MODELING OF INDUCTION MOTOR Mathematical moelling of the inuction motor was one base on the equations (1) (5). V qs = Rsi qs + φ qs t (1) V s = Rsi s + φ s t (2) 0 = Rri qr + φqr ωrφ r t (3) 0 = Rri r + φr ωrφ qr t (4) 3 p T e = (φsiqs φqsi s) 2 2 (5) JETIRJ Journal of Emerging Technologies an Innovative Research (JETIR) 191

2 III. CONVENTIONAL DIRECT TORQUE CONTROL 3.1. DTC Strategy: Fiel Oriente metho ecouples stator current vector into -q components. FOC uplicate the DC motor ynamics. Unlike FOC, DTC oes not uplicate the DC motor ynamics, but DTC metho chooses the voltage vectors accoring to the emane flux an torque in orer to keep them within hysteresis bans. The torque evelope by the inuction motor is given by 3 p Lm T e= λ s λ r sinθ (6) 22σLsLr From the above equation we can say that the torque prouce by the inuction motor epens upon the stator flux, rotor flux an phase angle between them. The inuction motor stator voltage equation is given by λs V s = - Isr s (7) t Change in flux can be expresse as Δλ s = V s t (8) This means that the voltage vector changes the flux vector. It is well know that two level inverter is capable of proucing eight voltage vectors. A switching table is generate which etermines the voltage vector that has to be applie. The selection of the voltage vector epens on the position of the stator flux an torque. Voltage vector selection table can be expane to inclue more number of voltage vectors by three level inverter. The use of PI controllers to comman a high performance DTC of inuction motor rive is often characterize by an overshoot uring start up. This is mainly cause by the fact that the high value of the PI generates a positive high torque error. This will let the DTC scheme take control of the motor spee riving it to a value corresponing to the Fig.1.1.Conventional DTC of Inuction motor. reference stator flux. At start up, the PI controller acts only on the error torque value by riving it to the zero borers. When this borer is crosse, the PI controller takes control of the motor spee an rives it to the reference value. Another main problem of the Conventional PI controller is the correct choice of the PI gains. Traitional PI controller using fixe gains may not provie the require control performance for the reason that the inuction motor parameters are changing on ifferent operating conitions. To tune the PI controller, lots of strategies have been propose. The most famous, which is frequently use in inustrial applications, is the Ziegler-Nichols metho which oes not require a system moel an control parameters are esigne from the plant step response. Tuning using this metho is characterize by a goo isturbance rejection but on the other han, the step response has a large percentage overshoot in aition to a high control signal that is require for the aequate performance of the system. Another technique uses frequency response methos to esign an tune PI controller gains base on specifie phase an gain margins as well as crossover frequency. Furthermore, root locus an pole assignment esign techniques are also propose in aition to transient response specifications. All these methos are consiere as moel base strategies an then the efficiency of the tuning law epens on the accuracy of the propose moel as well as the assume conitions with respect to actual operating conitions. All these techniques takes a more time fortuning the PI controller. To overcome the state problems, an aaptive PI controller has been propose to replace the classical PI controller where the proportional an integrator gains are tune by the Differential Evolution algorithm. IV. ARTIFICIAL BEE COLONY ALGORITHM The Artificial Bee Colony algorithm propose by Dervis Karaboga in 2005 for real-parameter optimization is a recently introuce optimization algorithm which simulates the foraging behaviour of bee colony. In the ABC algorithm, the JETIRJ Journal of Emerging Technologies an Innovative Research (JETIR) 192

3 foraging artificial bees are ivie into three groups: employe bees, unemploye bees an scout bees. One half of the colony size of the ABC algorithm represents the number of employe bees, an the secon half stans for the number of unemploye bees. The employe bees are responsible for exploiting the explore foo sources an passing their foo information to onlooker bees. The onlooker bees will make a move to choose a foo source on this information, an then further exploit the foos aroun the chosen foo source. The employe bee change to a scout bee when it abanons a foo source an search the environment surrouning the nest (up to a 14 km raius) for the new foo sources. The etails of the algorithm are as follows. 4.1 Foo source sites initialization In the initialization of the algorithm, a set of foo source sites ( eb ) are create ranomly. Let s consier u th foo source in the population as u = u,1, u,2, u,3,.., u,n (9) An each foo source site is create as per the Eq. (10) uv = min + ran(0,1)( max min ) (10) Where u signifies the size of foo source sites, u=1, 2, 3... eb, v signifies the parameters to be optimize, v =1, 2, 3,, ncv u max & u min are the upper an lower bouns for the imension u. After initialization of the foo source sites fit u amounts are calculate Employe bee forager A new caniate foo sources is create by moification of u of its current position an then calculate nectar or fit u amount. The position of the new foo source is efine as w u,v = u,v + ξ u,v ( u,v q,v ) (11) Where q = 1, 2,3,..., eb is a ranomly chosen inex that has to be ifferent from u, istribute real ranom number in the range [-1, 1]. 1, if obj fit u = { 1+obj u 0 u 1 + abs(obj u ), if obj u < 0 u,v is a uniformly (12) Where obj u is the cost value or objective value of the solution w u. If the fit u of w u is equal or better than that of u it will be replace by the new caniate foo source position w u otherwise the previous position is kept in memory. 4.3 Onlooker probabilities After all employe bees complete the search process, each onlooker bee chooses a foo source. The probability that a foo source will be chosen by the onlooker bee is calculate. prob u = fit u eb (13) fit u u=1 JETIRJ Journal of Emerging Technologies an Innovative Research (JETIR) 193

4 Fig.2 Flow Chart of Artificial Bee Colony Algorithm 4.4 Onlooker bee forager This is also similar to employe bee forager step. Here, caniate foo source is create of its current position as per Eq. (14) an calculate fit u value. If the new caniate foo source has equal or better fit u value than the ol source, it is replace with the ol one in the memory. Otherwise, the ol one is retaine in the memory. This process is repeate until all onlookers are istribute onto foo source sites. 4.5 Scout bee forage If the fit u value of the employe bees oes not improve by a continuous preetermine number of iterations, those foo sources are abanone. The foo source abanone by its bee is replace with a new foo source iscovere by the scout as per Eq. (10) V. RESULTS AND DISCUSSIONS The optimize values can be obtaine by using ABC Program, that values substitute in the DTC system an finally observe results of ABC base DTC system at ifferent loa torques. The applie loa torques an the motor spee wave forms of ABC base DTC with Conventional DTC shown in the 3(a),4(a), 5(a), 6(a) an 3(b), 4(b), 5(b), 6(b) respectively. Fig.3 (a) Applie loa torque Fig.3 (b).spee wave forms of conventional an ABC base DTC JETIRJ Journal of Emerging Technologies an Innovative Research (JETIR) 194

5 Fig.4 (a) Applie loa torque Fig.4 (b).spee wave forms of conventional an ABC base DTC Fig.5 (a) Applie loa torque Fig.5 (b).spee wave forms of conventional an ABC base DTC Fig.6 (a) Applie loa torque Fig.6 (b).spee wave forms of conventional an ABC base DTC VI. CONCLUSION Base on the DTC inuction motor, ABC tune PI controller is propose in this paper. The spee ajustment capability of the DTC system improve by using ABC tune PI controller. From the simulation results of ABC base DTC it has been observe that an improve torque an flux response was achieve. The comman flux optimization scheme has reuce the torque ripple. By comparing the ABC base DTC metho is better with the other conventional DTC system. REFERENCES [1] Abou El Ela AA, Abio MA, Spea SR. Optimal power flow using ifferential evolution algorithm, Electr Power Syst Res, Vol.80, pp , [2] K. Ohyama, G. M. Asher, an M. Sumner, Comparative analysis of Experimental Performance an stability of sensorless inuction motor Drives, IEEE Trans. In. Electron.Vol. 53, no. 1, pp , Feb [3] Naia Salvator Anrea Caponio, Stuent, FerranteNeri, Silvio Stasi, an Giuseppe Leonaro Cascella, Optimization of Delaye- State Kalman- Filter- BaseAlgorithm via Differential Evolution for Sensorless Control of Inuction Motors IEEE transactions on Inustrial Electronics, Vol. 57, No. 1, January [4] C.Thanga Raj, member, IAENG,S.P.Srinivastava,an pramo Agarwal Differential Evolution Base optimal control of Inuction Motor By Serving tom Textile Inustry, IAENG International Journal of Computer Science, 35:2, IJCS [5] K. V. Price, Differential evolution: A practical approach to global optimization, Springer, Berlin, [6] Fang Wang, YuhuiQiu, A moifie particle swarm optimizer with Roulette selection Operator, IEEE conference proceeings of NLP-KE, 2005, pp [7] Hassan FarhanRashag,S.P.Koh,Ahme N.Aballa,NaiaM.L.Tan,K.H.Chong an S.K.Tiong. DTC torque ripples minimization base on PSO-PID controller Scientific Research an Essays Vol. 7(15), pp.1572,23,april,2009 JETIRJ Journal of Emerging Technologies an Innovative Research (JETIR) 195

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