Dynamic Response Improvement of DFIG Driven by

International Review of Automatic Control (I.RE.A.CO.), Vol. 4, N. 5 September 2011

Dynamic Response Improvement of DFIG Driven by Wind Turbines Using a Novel Algorithm Mostafa Eidiani1, Natan Asghari1, Hossein Zeynal2 Abstract – The frequency converter is the most sensitive part in the variable-speed wind turbine generator system equipped with a double-fed induction generator (DFIG). The frequency converter is normally controlled by a set of PI controllers. In order to improve the response of DFIG when subjected to system disturbances, the best way is to tune the PI controllers of the frequency converter. Due to the high complexity of the system, the tuning of these PI controllers is very difficult. In this paper an approach is offered to improve the response of DFIG when subjected to system disturbances using Hybrid Particle Swarm Optimization and Genetic Algorithm (PSO-GA). In this case, tuning all PI controllers parameters is considered. The results show that the proposed algorithm is well suited in terms of accuracy and quick response. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved. Keywords: Double-Fed Induction Generator (DFIG), Variable-Speed Wind Turbine, Electrical Grids, Genetic Algorithm (GA), Particle Swarm Optimization (PSO)

Most of the major wind turbine manufactures are developing new larger wind turbines in the 3-5 MW range [2]. These large wind turbines are all based on variable-speed operation with pitch control using a directly driven synchronous generator (without gearbox) or a double-fed induction generator (DFIG). Fixed-speed induction generators with stall control are regarded as unfeasible [3] for these large wind turbines. Today, double-fed induction generators are commonly used by the wind turbine industry for larger wind turbines [4],[5]. Compared to the variable speed wind turbine equipped with a synchronous generator (SG), in which a full load variable frequency control (VFC) is connected directly between the generator stator and the grid, the VFC of the DFIG is smaller in size and therefore much cheaper. In the DFIG concept, the induction generator (IG) is gridconnected at the stator terminals, but the rotor terminals are connected to the grid via a partial-load variable frequency AC/DC/AC converter (VFC) and a transformer. The VFC only needs to handle a fraction (25-30%) of the total power to achieve full control of the generator. Compared to the fixed-speed wind turbine with IG, the VSWT with DFIG can provide decoupled control of the active and reactive power of the generator, more efficient energy production, improved. The behavior of the VFC and the associated WTGS relies on the performance of its control system. Using well designed controllers, it is possible to increase the chance of the WTGS to remain in service during grid disturbances. In the last decade, various modern control techniques such as adaptive control, variable structure control and intelligent control [6]-[8], have been intensively studied for controlling the nonlinear components in power systems. However, these control

Nomenclature PSO GA DFIG APSO Pb Pg D n Xi Pib Vi Vmax Vmax w tr ts ‫ݐ‬௖ Ess

Particle Swarm Optimization Genetic Algorithm Double Fed Induction Generator Adaptive particle swarm optimization Best position Neighborhood best position Dimension of a searching space Total number of particles Position of ith particle Best position of ith particle being searched Velocity of the ith particle Minimum velocity Maximum velocity Inertial weight Rise time Settling time Critical time Steady state error

I.

Introduction

Wind turbines can either operate at fixed speed or variable speed. For a fixed speed wind turbine, the generator is directly connected to the electrical grid. For a variable speed wind turbine, the generator is controlled by power electronic equipment. There are several reasons for using variable-speed operation of wind turbines; among those are the possibilities of reducing stresses of the mechanical structure, acoustic noise reduction and the possibility of controlling active and reactive power [1].

Manuscript received and revised August 2011, accepted September 2011

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Mosstafa Eidiani, Natan Asgharri, Hossein Zeeynal

techniques have h few reall applicationss probably duue to their compliccated structurres or the lackk of confidencce in their stabilityy. Therefore, the conventioonal PI controollers are still the most commoonly used conntrol techniquees in power system ms due to theeir simple struuctures, as caan be seen in the control c of the WTs equippeed with DFIG Gs [9, 10]. Unfortuunately, tuningg the PI conttrollers is teddious and might bee difficult to tuune the PI gains properly duue to the nonlinearrity and high complexity c off the system. Over O the years, heuristic h searrch-based alggorithms suchh as genetic algoorithms (GAss), tabu searcch algorithm and simulated annnealing (SA A) have beenn used for poower system stabillizers (PSS) design d [11]. However, H whenn the parameters which have been optim mized are hiighly correlated, the t performannce of thesee heuristic seearch algorithms degrades d [12]. Recently, a new n techniquee has been successsfully used for f single- annd multi-objective nonlinear opptimization, based b on sw warm intelliggence called particle swarm opttimization (PS SO) [13]. Thee use of PSO for designing a single PID controller inn the automatic voltage v reguulator (AVR R) system of o a conventionall turbo generaator has beenn reported in [14]. This design, however, is based b on the step responsee and did not invvestigate the transient perrformance off the controller. Inn [15] an appproach is preesented to usee the particle swaarm optimization algorithm m to designn the optimal PI controllers for the rotor side converter of the DFIG. In [221] it is shoown that thee particle sw warm optimization (PSO) wass demonstratted to convverge rapidly durinng the initial stages of a global g search, but around globaal optimum, the t search proocess will beccome very slow [227]-[30]. In thhis paper, thee hybrid PSO O-GA algorithm is used to find the optimal parameters off the various PI controllers for f the rotor and grid side converters of the vaariable frequuency convverter simultaneoussly.

III.

II.1.

M Modeling andd Control of DF FIG

The T basic connfiguration of a DFIG driven by a windd turb bine is shownn in Fig. 2. T The wound ro otor inductionn macchine in this configuration c is fed from bo oth stator andd roto or sides. The stator is direectly connecteed to the gridd while the rotor is i fed via a V VFC. In ordeer to producee elecctrical power at a constant vooltage and frequency to thee utiliity grid overr a wide opperation rang ge from sub-syncchronous to suuper-synchronnous speed, th he power flow w betw ween the rotorr circuit and tthe grid must be controlledd both h in magnitudde and in dirrection. Conssequently, thee VFC C consists of two fouur-quadrant IGBT I PWM M conv verters connected back-to-bback by a dc-link capacitorr [10]]. The crow-bbar is used to short-circuit the rotor-sidee conv verter (RSC) in order to protect the RS SC from over-currrent in the rottor circuit durring grid faultts. Control off the VFC includess the RSC conntrol [9], [10] and the grid-sidee converter (G GSC) control [10]. The objjective of thee RSC C is to governn both the stattor-side activee and reactivee pow wers independdently [24]-[226]. Figure 3 shows thee overrall vector conntrol scheme oof the RSC.

Fig. 2. Configguration of a DFIG G driven by a win nd turbine

Power Network Model M

Figure 1 shows s the singgle-line diagraam of a large wind w farm conneccted to a poower networkk. Wind farm m is represented by b an aggregated model inn which hunddreds of individuall wind turbines and DFIGs are modelleed as one equivaleent DFIG drivven by a single equivalent wind w turbine. The system is sim mulated in the Matlab /Simuulink environment. The paraameters of the system components are provided in the t Appendix.

Fig. 3. Rotor side conntrol Scheme [22]]

In n order to acchieve indepenndent control of the statorr activ ve power Ps (by ( means of speed control) and reactivee pow wer Qs by means m of rotoor current reegulation, thee instaantaneous threee-phase rotorr currents irab bcare sampledd and transformed to d-q compponents idran nd iqr in thee stato or-flux oriennted referencee frame. The actual d-qq currrent signals (iidr and iqr) aree later comparred with theirr refeerence signalss (idr* and iqqr*) to generrate the errorr sign nals, which are a passed thhrough one PI controllerr (thro ough a Demuux) to form thhe voltage sig gnal vdr1, vqr1. Thee two voltage signals (vdr1 and vqr1) are compensatedd by the t correspondding cross couupling terms (vdr2 and vqr2)

Fig. 1. Single-line diagrram of a wind farrm connected to a power network [23]

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Internnational Review oof Automatic Con ntrol, Vol. 4, N. 5

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Mosstafa Eidiani, Natan Asgharri, Hossein Zeeynal

the population arre identical, aand represent a suboptimall solu ution to the problem. p A G GA is govern ned by threee facttors: the mutaation rate, thhe crossover rate, and thee population size. The T implemenntation of GA is detailed inn [14]]. GA is one of o the effective methods forr optimizationn prob blems especcially in nnon-differentiaal objectivee funcctions with discrete d or coontinues decission variabless [15]]. As A with any seearch algorithm, the optimu um solution iss obtaained only affter much iteeration. The speed of thee iteraations is deterrmined by the length of thee chromosomee and the size of the populations. There are a two mainn metthods for GA to generate iitself, namely y generationall and steady state. In n generationaal, an entire ppopulation is replaced r afterr iteraation (generation). Whereass in steady staate, only a few w mem mbers of the population p arre discarded.th he populationn sizee remains consstant [16]. Onne of the draw wbacks of GA A is itts possibility to t converge prrematurely to a suboptimall solu ution [17]. Annother drawbaack of this alg gorithm is itss high h sensitivity too initial population [16], [1 18]. There aree a few w main limitaations of a GA A when applied to problemss [16]] including: • The T fitness fuunction must bbe well-written n • blind and unddirected searchh • stochastic seaarch s to innitial parameteers • sensitive • computationa c lly expensive • unclear u stoppiing criterion

to form the d-q d voltage siggnals vdr and vqr. They are then used, in form m of abc, by thhe PWM moduule to generate the IGBT gate control c signalss to drive thee IGBT conveerter. The purposee of the GSC is to keep thhe dc-link volltage constant regaardless of the magnitude annd direction of o the rotor power [8]. In this paaper, the GSC control schem me is also designedd to regulate the reactive power. p This might m be necessaryy to keep the voltage v within the desired raange, when the DF FIG feeds intoo a weak pow wer system witthout any local reaactive compennsation. Whenn the DFIG feeds f into a strong power system m, the reactivee power comm mand of Qgcan be simply set to zero. This meeans that referrence iqgcan be sett to zero. Figure 4 shows s the overrall control sccheme of the GSC. G The actual signals of the dc-link voltaage and the q--axis current (Vdc and iqg) are compared c withh their comm mands * to form thee error signals,, which are paassed (Vdc* and iqg*) through the PI P controller to generate thee reference siggnals for the d-axis and q-axis current com mponents (idg* and p iqg*), respecttively. The insstantaneous ac-side three phase current of thhe GSC are saampled and transformed intto daxis and q--axis currentt componentss idg and iqgg by applying thee synchronouusly rotating reference frame fr transformatioon. The actuaal signals (idg and iqg) are then compared with w the correesponding refference signalls to form the errror signals, which pass through onee PI controller. The T voltage signals (vdgg1 and vqg1) are compensatedd by the correesponding crooss coupling teerms to form the d-q voltage signals vdgannd vqg. Theyy are then used by the PWM moodule to generrate the IGBT gate control signaals to drive thee IGBT conveerter.

II.3.

PSO P is an algoorithm to searrch for the best solution byy simu ulating the movement m annd flocking of o birds. Thee algo orithm works by initializingg a flock of biirds randomlyy overr the searchinng space, in w which every biird is called a ‘‘paarticle’’. Thesse ‘‘particles’’ fly at a cerrtain velocityy and find the globbal best position after somee iteration. Att each h iteration, each particle cann adjust its veelocity vector,, baseed on its mom mentum and thhe influence off Pb as well ass Pg, and then com mpute a new pposition that th he ‘‘particle’’’ is to o fly to. Assum ming D, n, theerefore Xi = (xxi1,xi2, . . .,xiD);; the best position of the ith paarticle being searched s untill now w is denoted as Pib = (pi1,ppi2, . . .,piD), and the bestt posiition of the tootal particle sw warm being searched s untill now w is denoted as vector Pg = (pg1,pg2, . . .,pgD); thee velo ocity of the itth particle is represented as a vector Vi = (vi1, vi2. . . viD). Then T the origginal PSOA iss described ass [19]]:

Fig. 4. Oveerall control schem me of the Grid Siide Converter [222]

II.2.

Particle Swarm Optimization (PSO)

Genettic Algorithm (GA)

The GA iss a search algoorithm based on the mechannism of natural sellection and naatural geneticss [13]. In a sim mple GA, individuuals are similaar to a chrom mosome that codes c for the variaables of the problem. Thhe strength of o an individual is i the objecctive functionn that mustt be optimized. Population P of candidates evvolves by gennetic operators: mutation, m croossover, andd selection. The characteristiccs of good caandidates havee more chancees to be inherited, because goodd candidates live l longer. Soo the average streength of the population rises r throughh the generations. Finally, the population stabbilizes, becausse no better individual can bee found. Att that stage, the algorithm haas converged, and most of the individuaals in

vid ( t + 1) = = w ⋅ vid ( t ) + c1 ⋅ rand (

) ⋅ ⎡⎣ pid ( t ) − xid ( t )⎤⎦ + + c2 ⋅ randd ( ) ⋅ ⎡⎣ p gd ( t ) − xid ( t ) ⎤⎦ xid ( t + 1) = xid ( t ) + vid ( t + 1) 1≤ i ≤ n 1≤ d ≤ D

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(1))

(2))

Internnational Review oof Automatic Con ntrol, Vol. 4, N. 5

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Mostafa Eidiani, Natan Asghari, Hossein Zeynal

converter’s performance during the transient disturbances [22]. However, tuning controllers is difficult to achieve a set of optimal parameters manually. In this section, the PSO-GA algorithm is applied to find the optimal parameters of the RSC and GSC controllers simultaneously. The genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population [21]. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. However if the initial population is well selected, the performance of the algorithm may enhance. PSO, on the other hand, is not as sensitive as GA to initial population. One of the characteristics of PSO is its fast convergence towards global optima in the early stage of the search and its slow convergence near the global optima. The idea behind this paper is the combination of the PSO and GA algorithm in such a way that the performance of the newly established algorithm is better than the PSO or GA algorithm. The objective of the PSO is to find the optimal parameters of the PI controllers. Generally, the PI controller performance in the time domain can be measured by a set of parameters: the overshoot Mp, tr, ts, and Ess. In this paper, the objective is to reduce the over-current in the rotor circuit during the grid faults. Therefore, a performance measure function is defined as follows [23]:

where c1, c2 are the acceleration constants with positive values; rand() is a random number between 0 and 1; w is the inertia weight. In addition to the parameters c1, and c2 parameters, the implementation of the original algorithm also requires placing a limit on the velocity (Vmax). After adjusting the parameters w and Vmax, the PSO can achieve the best search ability. The APSO algorithm is based on the original PSO algorithm, firstly proposed by Shi and Eberhart in 1998 [20]. The APSO can be described as follows:

vid ( t + 1) = = w ⋅ vid ( t ) + c1 ⋅ rand (

) ⋅ ⎡⎣ pid ( t ) − xid ( t )⎤⎦ + + c2 ⋅ rand ( ) ⋅ ⎡⎣ pgd ( t ) − xid ( t ) ⎤⎦ xid ( t + 1) = xid ( t ) + vid ( t + 1)

(3)

1≤ i ≤ n 1≤ d ≤ D in which w is a new inertial weight. These algorithms can reduce w gradually as the generation increases by adjusting the parameter w. In the searching process of the PSO algorithm, the searching space will reduce gradually as the generation increases. So the APSO algorithm is more effective, because the searching space reduces step by step nonlinearly, so the searching step length for the parameter w here reduces correspondingly. Similar to GA, after each generation, the best particle of particles in the last generation will replace the worst particle of particles in current generation, thus a better result can be achieved. Generally, in the beginning stages of algorithm, the inertial weight w should be reduced rapidly, around optimum, the inertial weight w should be reduced slowly [20]. II.4.

f ( x ) = c1 ⋅ ∆I r ,max + (1 − c1 ) ⋅ ( ts − t0 ) + c2 ⋅ Ess

(4)

III. Simulation Results and discussion Fig. 1 presents the simulated network. A 9 MW wind farm consisting of six 1.5 MW wind turbines connected to a 25 kV distribution system exports power to a 120 kV grid through a 30 km, 25 kV feeder. The wind speed is maintained constant at 10 m/s. The control system uses a torque controller in order to maintain the speed at a specified rate. The reactive power produced by the wind turbine is regulated at 0 MVAr. The corresponding turbine speed is 1.09 pu of generator synchronous speed. The DC voltage is regulated at 1200 V and reactive power is kept at 0 MVAr. At t=0.2 second the positive sequence voltage suddenly drops to 0.5 p.u. leading to an oscillation on the DC bus voltage and on the DFIG output power. During the voltage sag, the control system regulates DC voltage and reactive power at their set points (1200 V, 0 MVAr). The system recovers in approximately 100ms. In this paper, the effect of separate PI Controllers, rotor-side controllers, grid-side controllers, and all PI controllers on reduction of rotor current taken into account. In order to tune the parameters of the controllers, a PSO approach is used. To verify the results and for comparison Real Code Genetic Algorithm is also used. In this paper, the objective is to reduce the over current in the rotor circuit during grid faults.

Tuning the Parameters of the PI Controllers Using Hybrid PSO-GA

In the wind turbine equipped with the DFIG, the variable frequency converter and its power electronics (IGBT-switches) are the most sensitive part. The converter action will probably determine the operation of the wind turbine during transient disturbances in the power grid. Grid faults, for example, even far away from the location of the wind farm, can cause voltage sags at the connection point of the wind turbine. This voltage sag will result in an imbalance between the turbine input power and the generator output power and therefore a high current in the stator windings of the DFIG. Because of the magnetic coupling between stator and rotor, this current will also flow in the rotor circuit and the converter. Since the power rating of the IGBT converter is only 25-30% of the induction generator power rating, this over-current can lead to the destruction of the converter. On the other hand, the behaviour of the converter depends on the control system. If the controllers are tuned properly, it is possible to limit the over current of the rotor circuit and therefore improve the Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Automatic Control, Vol. 4, N. 5

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Mostafa Eidiani, Natan Asghari, Hossein Zeynal

III.1. Case1: Effect of grid-side PI controllers

III.2. Case2: Effect of rotor-side PI controllers

In this case the effect of grid side PI controllers on rotor current overshoot is considered. By tuning the parameters of grid side PI controllers ([Kp,vdc, KI,vdc, KP,grid, KI,grid]) using PSO-GA a 30.04% reduction is obtained (Fig. 5). Following Table I shows the tuned parameters.

In this case the effect of rotor-side PI controllers on rotor current overshoot is considered. By tuning the parameters of rotor side PI controllers ([Kp,Q, KI,Q, KP,rotor, KI,rotor]) using PSO-GA a 29.86% reduction in rotor current overshoot is obtained (Fig. 6). Following Table II shows the tuned parameters.

Fig. 5. Effect of grid-side PI controllers

Fig. 6. Effect of rotor-side PI controllers

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International Review of Automatic Control, Vol. 4, N. 5

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Mostafa Eidiani, Natan Asghari, Hossein Zeynal

TABLE I PI PARAMETERS OF CASE1 RELATED TO FIG. 5 kP,Vdc

kI,Vdc

kP,grid

kI,grid

kP,Q

kI,Q

kP,rotor

kI,rotor

Initial design

0.0001

0.15

3

80

0.07

7

0.4

0.07

Optimal design

0.0278

0.4957

1.9234

269.01

0.07

7

0.4

0.07

TABLE II PI PARAMETERS OF CASE2 RELATED TO FIG. 6 kP,Vdc

kI,Vdc

kP,grid

kI,grid

kP,Q

kI,Q

kP,rotor

kI,rotor

Initial design

0.0001

0.15

3

80

0.07

7

0.4

0.07

Optimal design

0.0001

0.15

3

80

0.0032

8.12

1.72

9.072

IV.

R1 = R2 = 0.1153 ohms/km, L1 = L2 = 1.05 e-3 H/km, line Length = 30 km.

Conclusion

Wind farm is represented by an aggregated model in which hundreds of individual wind turbines and DFIGs are modelled as one equivalent DFIG driven by a single equivalent wind turbine. The control system of the DFIG consists of the control of two back-to-back connected IGBT PWM converters, namely, the rotor-side converter (RSC) and the grid side converter (GSC). The linear PI controllers are used to control both converters and their parameters are initially designed at a specific operating point. In this paper, the effect of rotor-side controllers, grid-side controllers, and all PI controllers on over current in the rotor circuit is studied. The hybrid particle swarm optimization and Genetic algorithm (PSO-GA) is then used to find the optimal parameters of the PI controllers for both the RSC and GSC. To verify the results and for comparison Real Code Genetic Algorithm is also used. We executed the program in a Pentium IV 2GHz. The PSO-GA is approximately 3 times faster than RCGA and the accuracy of PSO-GA is better. The results show that PI controller of Vdc regulator is the most effective part in reducing the over current of the rotor circuit. And PI controllers of the grid-side converter reduced the over current by 30.04% while PI controllers of the rotor-side converter reduced the over current by 29.86%.

Acknowledgements Authors gratefully acknowledgeBojnourd Branch, Islamic Azad University, as well as Science and Research Branch, Islamic Azad University, Hesarak, Tehran, Iran, for providing financial supports and relevant research environment.

References [1]

Akhmatov, Analysis of Dynamic Behavior of Electric Power Systems with Large Amount of Wind Power, Ph.D. thesis, Technical University of Denmark, Kgs. Lyngby, Denmark, Apr. 2003. [2] M. V. A. Nunes, J. A. Pecas Lopes, H. H. Zurn, U. H. Bezerra, and R. G. Almeida, “Influence of the variable-speed wind generators in transient stability margin of the conventional generators integrated in electrical grids,” IEEE Trans. Energy Conversion, vol. 19, no. 4, Dec. 2004, pp. 692-701. [3] T. Ackermann and L. S¨oder, “An overview of wind energy-status 2002” Renew. Sustain. Energy Rev., vol. 6, no. 1–2, pp. 67–128, Feb./Apr. 2002. [4] [Online]. Available: http://www. Nordex –online .com /e /online service/ download/ dateien/PB N80 GB.pdf [5] [Online].http://www.vestas.com/pdf/produkter/AktuelleBrochurer /v120/V120%20UK.pdf [6] M. Lown, E. Swidenbank, B. W. Hogg, “Adaptive fuzzy logic control of a turbine generator system,” IEEE Trans. Energy Conversion, vol. 12, no. 4, Dec. 1997, pp. 394-399. [7] Y.-Y. Hsu, C.-H. Cheng, “Variable structure and adaptive control of a synchronous generator,” IEEE Trans. Aerospace and Electronic Systems, vol. 24, no. 4, July 1988, pp. 337-345. [8] G. K. Venayagamoorthy, R. G. Harley, and D. C. Wunsch, “Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrolof a turbogenerator,” IEEE Trans. Neural Networks, vol. 13, no. 3, May 2002, pp. 764- 773 [9] J. Morren and S. W. H. de Haan, “Ridethrough of wind turbines with doublyfed induction generator during voltage dip,” IEEE Trans. Energy Conversion, vol. 20, no. 2, Jun. 2005, pp. 435-441. [10] R. Pena, J. C. Clare, and G. M. Asher, “Doubly fed induction generator using backto- back PWM converters and its application to variable-speed wind-energy generation,” IEE Proceedings – ElectRic Power Applications, vol. 143, no. 3, May 1996, pp. 231241.

Appendix Induction generator: Rated power =10MW, Rated voltage = 575 V, Rs = 0.00706 pu, Lls = 0.171 pu, Rr = 0.005 pu, Llr = 0.156 pu, Lm = 2.9 pu, H = 5.04 s. Converter: PWM frequency = 1620 Hz, Rated voltage DC = 1200 V, Capacitor DC link= 6e-4 Farads, Power network: 2500MVA, 120kV base,

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[11] M. A. Abido, “Robust design of multimachine power system stabilizers using simulated annealing,” IEEE Trans. Energy Conversion, vol. 15, no. 3, Sept. 2000, pp. 297-304 [12] D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, New York: IEEE Press, 2000, ISBN 0-7803-5379-X. [13] K.F.Man, K.S.Tang and S.Kwong, "Genetic Algorithm concepts and application", IEEE Trans.On Industrial Electronics, vol 43 no.5, Oct 1996,pp.519-534. [14] J.H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press, 1975. [15] T.S. Chung, Y.Z. Li," A Hybrid GA Approach for OPF with Consideration of FACTS Devices", IEEE Power Engineering Review, February 2001. [16] BetramKoh Lin Hon "Accelerated Genetic Algorithm in Power System Planning" Electrical Engineering thesis 2003 [17] StephaneGerbex, Richard Cherkaoui and Alain.J.Germond "Optimal location of multi-type FACTS devices by means of Genetic algorithm" IEEE Trans. Power system Vol.16 ,August 2001, pp 537-544. [18] Jose Miva, Jose Ramon, Alvarez "Artificial Intelligence and Knowledge Engineering Applications" NewYork, 2002. [19] James Kennedy and Russel Eberhart, "Particle Swarm Optimization" Proc. of IEEE International conference on neural networks, Vol 14, pp 1942 – 1948 December 1995. [20] Yuhui Shi, Russel.C.Eberhart," Empirical study of particle swarm optimization", Proc. of the congress on Evolutionary computation, Vol.13, pp 1945-1950, July 1999. [21] A.Mohammadi, M. Jazaeri, "A hybrid particle swarm optimization-genetic algorithm for optimal location of SVC devices in power system planning",Universities Power Engineering Conference, 2007. UPEC 2007. 42nd International , sep 2007, pp: 1175 – 1181. [22] M. Fendereski, A. Mohammadi, M.T. Arabyarmohammadi, S.Amirkhan, M. Sadighi, "Response Improvement of Doubly Fed Induction Generators Driven by Wind Turbines Using PSO and RCGA Algorithms"Iranian student conference, 2002. [23] Wei Qiao; Venayagamoorthy, G.K.; Harley, R.G. "Design of Optimal PI Controllers for Doubly Fed Induction Generators Driven by Wind Turbines Using Particle Swarm Optimization", International Joint Conference on Neural Networks, pp:19821987. [24] M.Eidiani, “A Reliable and efficient method for assessing voltage stability in transmission and distribution networks”, International Journal of Electrical Power and Energy Systems, Vol.33, No.3, pp: 453-456, 2011. [25] M. Eidiani and M.H.M. Shanechi, “FAD-ATC, A New Method for Computing Dynamic ATC”, International Journal of Electrical Power & Energy System, Vol.28, No.2, pp: 109-118, 2006. [26] M. Eidiani, A New Method for Assessment of Voltage Stability in Transmission and Distribution Networks, International Review of Electrical Engineering (IREE), Vol.5, No.1, pp:234-240, 2010. [27] H.Zeynal, A.K.Zadeh, K. M. Nor, M. Eidiani, “Locational Marginal Price (LMP) AssessmentUsing Hybrid Active and Reactive Cost Minimization”, International Review of Electrical Engineering (IREE.),Vol.5, No.5, pp: 2413-2418, October 2010. [28] N. Aouani, F. Bacha, R. Dhifaoui, “Control Strategy of a Variable Speed Wind Energy Conversion System Based on a Doubly Fed Induction Generator”,International Review of Automatic Control (IREACO), Vol. 2. n. 2,pp. 197-204,March 2009. [29] RouzbehJahani, Heidar Ali Shayanfar, OmidKhayat,”GAPSOBased Power System Stabilizer for Minimizing the Maximum Overshoot and Setting Time”, International Review of Automatic Control (IREACO),Vol. 3. n. 3,pp. 270-278,May 2010. [30] HengamehKojooyanJafari, Ahmed Radan,”Comparison between Self Tuning PI Voltage Control of DFIG and a Combinational Control for Improved Wind Turbines”,International Review of Automatic Control (IREACO),Vol. 3. n. 5,pp. 480-484,September 2010.

Authors’ information 1

Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran. Tel.: +98-584-2296982-94 Fax: +98-584-2296982-95 E-mails: [email protected] [email protected] 2 Department of Electrical Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Hesarak, Tehran, Iran. Tel.: +98-21-47911 Fax: +98-21-47912 E-mail: [email protected]

Mostafa Eidiani (M’05, IEEE) was born in Mashhad, Iran. He received his B.Sc. degree in 1994 and his M.Sc. degree in 1996both in Electrical Power Engineering from Ferdowsi University of Mashhad, Iran, and then his Ph.D. degree in Electrical Engineering from Science and ResearchBranch, IslamicAzad University, Tehran, Iran, in 2004. He is currently an Associate Professor at Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University ,Bojnourd, Iran.His current research interests are in renewable energy, voltage stability, ATC assessments and power system security evaluation. Natan Asghari (StM’11, IEEE) was born in Mashhad, Iran. He obtained his B.Sc. degree in Electrical Power Engineering from Bojnourd Branch, Islamic Azad University, Iran. He is currently pursuing his Master’s degree at department of Electrical Engineering in the same university. His research interests include power system dynamics and control, wind energy, Artificial intelligence techniques application in power system control. Hossein Zeynal (StM’08, IEEE) was born in Bojnourd, Northern Khorasan, Iran. He obtained his B.Sc. degree in 2005 from Bojnourd Branch, Islamic Azad University, Iran, and later his M.Eng degree in 2007 from UniversitiTeknologi Malaysia (UTM), Johor Bahru, Malaysia. Both degrees are in Electrical Power Engineering. His current research embraces high performance parallel computing, and optimal generation scheduling, Unit Commitment. He is also a member of ACM and USENIX.

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International Review of Automatic Control, Vol. 4, N. 5

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