Intellegent control of induction generator used in a ...

12th International conference on Sciences and Techniques of Automatic control & computer engineering December 18-20, 2011, Sousse, Tunisia

Intellegent control of induction generator used in a stand-alone site Zeddini Med Ali1, Mimouni Med Faouzi1, Mansouri Med Najib1, 1 Ecole Nationale d’Ingénieurs de Monastir, Rue Ibn Jazrar, 5000 Monastir, Tunisie [email protected]

Abstract. This paper presents a new approach for the control of the terminal voltage and the frequency of a wind system dedicated to work in isolated site. This new approach uses first the static model of the Self Excited Induction Generator (SEIG) and the Genetic Algorithm (GA) to determinate the optimal values of the capacity assuring an almost a constants voltage and frequency. Then, we suggest controlling the frequency and the terminal voltage of the SEIG in dynamic regime by adapting the value of the excitement capacity of the Static VAR Compensator (SVC) using a Fuzzy Logic Controller (FLC). A simulated result has been elaborated under the MATLAB / SIMULINK environment for the validity of the used technique.

Keywords: SEIG, Fuzzy Logic, Genetic Algorithm, SVC

1. Introduction In our days, even if, industrially the " big wind turbines " of powers going of some hundreds of kW to some MW seem rapidly expanding, the " small wind turbines " of small and average powers constitute a good alternative, in particular when the constraints of the connecting to the network seems impossible, too expensive or too complex to realize. In both cases, the induction machine is widely used [1, 2]. However, this machine must be magnetized. When it is directly connected on the network, this one will supply the reactive power necessary for its magnetizing but, when it used in autonomous; a battery of capacitances must be connected in parallel with the stator. In this last case, it is important to indicate that the phenomenon of auto-excitement is difficult to master because the variations of the load and the wind speed are directly influential on the effective value and the frequency of the generated voltage. In a first shutter of this paper we study the static model of the induction generator by taking into account the saturation of the magnetizing inductance. We so develop a not linear model to create all the conditions relative to the auto-excitement of the generator. Then, we study the effect of the external parameters such as the wind speed and the nature of the load, on the effective value and the frequency of the STA'2011-PEC-1445, pages 1437-1447 Academic Publication Center of Tunis, Tunisia

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STA′2011 – Power systems, controls Electrical machines, pages 1 to 11

voltage supplied by the generator. The effect of these external parameters will be verified on the dynamic model of the generator. This theoretical work will be led together with works of simulation to bring to light the performances and the limits of functioning of such a configuration of wind turbine. This work aims at justifying the possibility of exploiting the induction machine in generative mode in applications of pumping or as auxiliary source in zones remote from the network.

2. Modeling of the studied structure An auto-excited wind turbine intended for the supply of the isolated sites consists mainly of a turbine coupled mechanically with an induction generator with rotor with cage or with rotor wound through a multiplier of speed. However, the generator must be magnetized. Besides, when the SEIG is autonomous, the reactive energy of magnetizing is supplied by a trendy battery of capacitance in parallel with the winding of the stator [1, 2, 3]. The Fig.1 illustrates the basic configuration of an autonomous wind turbine working in isolated site:

Fig.1. Basic configuration of an autonomous wind turbine 2.1. Static study of the induction generator The per-phase equivalent circuit of a three-phase SEIG with a R-L load and excitation capacitor is shown in Fig. 1, where R s , X s , R r , X r and X m represent the stator resistance, stator leakage reactance, rotor resistance, rotor leakage reactance, and magnetizing reactance respectively, R L , X L , and X C represent the load resistance, load reactance, and excitation capacitor reactance, respectively and F and v

Intellegent control of induction generator used in a stand-alone site3

represent the per unit(p.u.) frequency and speed, respectively. The reactances are specified at a base or rated frequency. The equivalent circuit is normalized to the base frequency by dividing all the parameters by the p.u. frequency [4, 5, 6, 7]

Fig.2. Per-phase equivalent circuit of a three-phase SEIG with a R-L load Loop equation in terms of stator current as obtained using Fig.1 is as; [4, 11, 12]

Zeq.Is=0

(1)

Zeq=Z1 +Z2 +Z3

(2)

-j.X C . ( R L +j.F.X L )

(3)

With:

Z1 =

(

)

F.  F.R L +j.F. F2 .X L -X C  Z2 =

Z3 =

Rs +j.XS F

j.X m . ( R r +j. ( F-v ) .X r )

(4)

(5)

R r +j. ( F-v ) . ( X m -X C )

Under steady state operation of SEIG, Is can not be equal to zero, therefore:

Zeq ( F,X m ,X C ) =0

(6)

4


This equation after separation into real and imaginary parts can be rearranged into two nonlinear equations, which may be solved (using optimization techniques) to estimate the excitation capacitance and generated frequency for the desired terminal voltage.

w1 ( F,X m ,X C , v ) =a1.F4 +a 2 .F3 +a 3 .F2 +a 4 .F+a 5 =0

(7)

w 2 ( F,X m ,X C , v ) =b1.F5 +b 2 .F4 +b3 .F3 +b 4 .F2 +b5 .F+b6 =0

(8)

Where ‘ai’ and ‘bi’ are defined in Appendix-I Then at the Constant-Voltage operation, the magnitude of terminal voltage is kept too constant, the previous equation becomes:

f ( F,X m ,X C ) = ( Vpu - VL Indeed, the expression of the terminal voltage

VL =

-j.X C . ( R L +j.F.X L )

(

F.R L +j. F2 .X L -X C

Where: Vg ( Xm= )

)

2

(9)

=0

VL can be written as:

(

F2 .R L +j.F. F2 .X L -X C

.

) ( R +j.X ) . ( F .X 2

s

s

L

)

(

)

-X C -F.X C . F.X L +j.R L

(10)

)

.Vg

2

i ∑ αi .Xm ,is the air gap voltage.

i =0

These cases, (7), (8), and (9) are to be solved simultaneously to find the values of Xm, F, and XC for given values of ZL and v. Thus, the objective function has optimized becomes [8,9]:

L= ( f,W1 ,W2 ) f ( F,Xm ,Xc ) + λ1 .W1 ( F,Xm ,Xc ) + λ2 .W2 ( F,Xm ,Xc ) With

λ1 and λ 2

(11)

are two constants of Lagrange.

2.2. Technique of optimization Seen the complexity of the problem to be resolved (not linearity of the equations,…), we are going to use the genetic algorithm. Indeed from an initial population of chromosomes (character strings) created randomly, AG generates new chromosomes to build a new generation, by making the genetic operations: selection and reproduction.


Fig.3. Mechanism of Genetic Algorithm Technical The selection. The operation of the selection allows chromosomes coding good structures to reproduce mostly that those that they are not it. The most used is the one of the proportional selection (selection by wheel of biased fortune). The reproduction. The operation of the reproduction allows the construction of a new generation, by applying the genetic operators to know the crossing and the transfer (transformation). The Crossing. The operator of crossing consists in exchanging genes between two individuals (parents) to obtain descendants who take the characteristics of their relatives (parents). The site of crossing is randomly chosen. The Fig4 schematizes clearly the various types of crossings.

Fig.4. various types of crossings The Mutation. The operator of mutation consists in distorting one or several genes chosen randomly in a chromosome. This change takes different forms according to the type of genes: A simple inversion for a Boolean coding (Fig5). A random edition in the discreet whole genes (whole coding). The addition of a Gaussian noise for a real coding.

Fig.5. A simple inversion for a Boolean coding

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3. Results of simulation 3.1. Static Regime The simulated results are obtained by using GA and Lagrange technical on machine with specifications given in Appendex II. Fig.6.a and Fig.6.b show the variation of terminal voltage and frequency with output power at rated speed (υ =1pu, υ =0.9 pu et υ =0.8 pu). The terminal voltage is considered constant (V=220V) but the frequency decreases as output power increases. Fig.7.a-Fig.7.b give the variation of optimal capacitance and the corresponding reactive power generated with different values of Load power. Fig.8.a and Fig.8.b show that the values of stator currents and rotor currents increase with increase in values of load power. Fig.9.a shows that the efficiency factor is maintained between 80% and 95%. The variation of magnetizing reactance of machine with output power is given by Fig.9.b. 240

50 Fréquency (Hz)

220 200 180

40 30

v=1.00 pu v=0.90 pu v=0.80 pu

20 10

160 500

1000

1500

2000

0 0

2500

Fig.6.a: Variation of terminal voltage with Load Power.

10000 Reactive Power (VAR)

Optimal Capacitor (uF)

80

v=1.00 pu v=0.90 pu v=0.80 pu

60 40 20 0

500

1000 1500 2000 Active Power (Watt)

2500

Fig.7.a: Variation of optimal Capacitor with Load Power.


2500

Fig.6.b: Variation of Frequency with Load Power .

120 100

500

8000

v=0.80 pu v=0.90 pu v=1.00 pu

6000 4000 2000 0

500


2500

Fig.7.b: Variation of Reactive Power with Load Power .


15

20

v=0.80 pu v=0.90 pu v=1.00 pu

Rotor Currents (Ampèrs)

Stator Currents (Ampèrs)

20

10 5 0 0

500


15 10 5 0 0

2500

Fig.8.a: Variation of Stator Currents

v=1.00 pu v=0.90 pu v=0.80 pu

500

85 80 75 0

500

1000

1500

2000

Active Power (Watt)

Fig.9.a: Variation of Efficiency with Load Power.

2500

145 Magnétisant Reactance

Efficiency (%)

with Load Power .

v=1.00 pu v=0.90 pu v=0.80 pu

90

2500

Fig.8.b: Variation of Rotor Currents

with Load Power. 95


140 135 130 125 0

v=1.00 pu v=0.90 pu v=0.80 pu 500


2500

Fig.9.b: Variation of Magnetizing Reactance with Load Power .

3.1. Dynamic Regime Fig. 10.b, Fig.11.a and Fig. 11.b show the dynamic characteristics of different greatnesses of the induction generator when connected R-L Load is given in Fig. 10.a. At t = 0 sec to t=0.2 sec R load with active power P=800 W is connected at the stator terminals of the induction generator. The value of terminal voltage is equal to 220V. At t=0.2 sec to t=0.4 sec we connect an R-L load with active Power P=1200W and reactive powers Q= 1200VAR. The generated voltage is kept too constant (V=220V),

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but the currents of load shown on Fig11.a and the currents of VCR system given in Fig11.b are changed. However, we notice that the terminal voltage is kept too constant whatever the variation of nature of load. 3000 Load Power (P,Q)

2500

500

Active Power Reactive Power

0

2000 -500 0

1500 1000

0.2

0.4 0.6 Times (Second)

0.8

1

-500 0.44

Fig.10.a: Variation of Active & Reactive Powers with Time.

0.8

1

0.445

0.45

0.455

0.46

0.465

0.47

10

0

0 0.4

0.6

0.8

1

0 0.445

0.45

0.455

0.46

0.465

0.47

-105

Curents (Ampèrs)

0.2

10

-10 0.44

0.6

Fig.10.b: Variation of Load Currents with Time.

10

-10 0

0.4

0

500 0 0

0.2

500

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0 -5 0.44

0.445

0.45

0.455 0.46 Times (Second)

0.465

Fig.11.a: Variation of Load

Fig.11.b: Variation of VCR

Currents with Time.

Currents with Time.

0.47

4. Conclusion Self excited induction generators seems to be the right choice for remote windy locations provided terminal voltage is maintained with load. In this paper a new and unique GA based modeling has been proposed to improve the voltage profile of SEIG. Genetic Algorithm is proposed for estimation and selection of shunt capacitance. It is found that proposed methodology results into a simultaneous


estimation for generated frequency, magnetizing reactance and excitation capacitance. A control strategy has been worked out to achieve the required performance of SEIG. The made works of simulation showed the efficiency of the chosen method. Indeed we managed to maintain the terminal voltage and the frequency to values normalized towards the used loads. Analysis proposed may be helpful for researchers to think over the implementation of such generators successfully in windy remote locations.

References [1] Driss SAIDANI «Modélisation, commande et expérimentation de la génératrice asynchrone auto-excitée pour la conversion de l’énergie éolienne dans les sites isolés, 2010, École Supérieure des Sciences et Technique de Tunis » [2] Dawit Seyoum «THE DYNAMIC ANALYSIS AND CONTROL OF A SELFEXCITED INDUCTION GENERATOR DRIVEN BY A WIND TURBINE, 2003, The University of New South Wales for the Degree of Doctor of Philosophy” [3] S.C. THRIPATHY “Wind Turbine Self Excited Induction Generator,” Energy Conversion, vol. 34, no. 8, pp. 641–648, Jun. 1993. [4] M. H. Haque, “A Novel Method of Evaluating Performance Characteristics of a Self-Excited Induction Generator,” IEEE Trans. Energy Conversion, vol. 24, no. 2, pp. 358–365, Jun. 2009. [5] Dheeraj JOSHI, “A Novel Method of Evaluating Performance Characteristics of a Self-Excited Induction Generator using Genetic Algorithm,” Turk J Elec Eng & Comp Sci, vol. 17, no. 1, Jun. 2009. [6] R. C. Bansal, “Three-Phase Self-Excited Induction Generators: An Overview,” IEEE Trans. Energy Conversion, vol. 20, no. 2, pp. 292–297, Jun. 2005. [7] Chan T.F., “Analysis of Self-excited Induction Generators using an Iterative Method,” IEEE Trans. Energy Conversion, vol. 10, no. 3, pp. 502–507, Jun. 1995. [11] Abdulrahman L. Alolah “Optimization-Based steady state of Three Phase SelfExcited Induction Generator ,” IEEE Trans. Energy Conversion, vol. 15, no. 1, pp. 61–65, Jun. 2000. [12] Milan Radić, “critical speed-capacitance requirements for self-excited induction generator,” Series: Automatic Control and Robotics, vol. 08, no. 1, pp. 502–507, Jun. 1995. [13] Ibrahim A.M, “Modeling and Simulation of a Self Excited Induction Generator/Inductive Load System,” International Journal of Electrical Power Engineering, vol. 5, no. 2, pp. 92–101, Jun. 2011. [14] Ibrahim A.M, “Modeling and Simulation of a Self Excited Induction Generator/Inductive Load System,” International Journal of Electrical Power Engineering, vol. 5, no. 2, pp. 92–101, Jun. 2011.

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[15] Shelly Vadhera and K. S. Sandhu, “Constant Voltage Operation of Self Excited Induction Generator using Optimization Tools,” INTERNATIONAL JOURNAL of ENERGY and ENVIRONMENT, Issue 4, vol 2, pp. 191–198, 2008. [16] S.C TRIPATHY, “Wind Turbine Driven Self-Excited Induction Generator,” Energy Convers. Mgmt , vol. 34, no. 8, pp. 641–648, 1993. [17] M.I.Mosaad, “Control of Self Excited Induction Generator using ANN based SVC,” International Journal of Computer Applications, vol. 23, no. 5, Jun 2011. [18] A.K . AI Jabri, “Capacitance requirement for isolated self excited induction generator ,” IEEE POCEEDINGS, vol. 137, no. 3, May. 1990. [19] Jayanta K. Chatterjee, “Analysis of Operation of a Self-Excited Induction Generator With Generalized Impedance Controller,” IEEE Trans. Energy Conversion, vol. 22, no. 2, pp. 307–315, Jun. 2006. [20] M.Senthilkumar, “Optimal Capacitor For Maximum Output Power Tracking Of Self Excited Induction Generator Using Fuzzy Logic Approach,” International Journal on Computer Science and Engineering, vol. 02, no. 5, pp. 1758–1762, 2010.

Appendex-I a1 =X m .X r . ( R L .X s +R s .X L ) a 2 =-X m .X r . ( R L .X s +R s .X L ) .v

a 3 =-  R r .R m . ( R L .X s +R s .X L ) +X m .X r . ( R L +R L ) .X C  a 4 =X m .X r . ( R L +R s ) .X c .v a 5 =R r .R m . ( R L +R s ) .X c b1 = ( X L .X m +X s .X r )

b 2 =-  X L .X s . ( R m .X m +R m .X r +R r .X m +X m .X r .v ) +R m .X L .X m .X r  b3 =[- ( R L .X s +R s .X L ) . ( R m .X m +R m .X r +X m .X r .v ) + X L .X s . ( R m .X m .v-R m .R r +R m .X r .v ) -X m .X r . ( R L .R s +X L .Xc+X s .X c ) -R m .X L .X m . ( R r -X r .v ) +R L .R m .X m .X r +X m .X r . ( R L .X s +R s .X L ) .v

]


b 4 =[ ( R L .X s +R s .X L ) . ( R m .X m .v-R m .R r +R m .X r .v ) +

(

+ ( R L .R s +X L .X c +X s .X c ) . R m .X m +R m .X r +R r .X m +X m .X r .v

)

+R m .R r . ( R L .X s +R s .X L ) +R L .R m .X m . ( R r -X r .v ) +R m .X m .X r .X c

]

b5 =[- ( R m .X m .v-R m .R r +R m .X r .v ) . ( R L .R s +X L .X c +X c .X s ) +

( R L .X c +R s .X c ) . ( R m .X m +R m .X r +X m .X r .v ) +R m .X m .X c . ( R r -X r .v ) -X m .X r .v. ( R L .X c +R s .X cr ) ] b6 =- ( R L .X c +R s .X c ) . ( R m .X m .v-R m .R r +R m .X r .v )

α 0 =1.12 ; α1 =0.078 ; α 2 =-0.146 Appendex-II Rating of Machine: Three Phase, 3KW, 220 V, 8 A, 50Hz, star connected, Squirrel cage induction Machine. Speed – 1500 r.p.m. The Machine parameters are:

Rs=5.7 Ω ; Rr=5 Ω ; x s =x r =0.038 Ω )