Intelligent speed sensorless maximum power point tracking control for wind generation system

Electrical Power and Energy Systems 42 (2012) 399–407

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Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes

Intelligent speed sensorless maximum power point tracking control for wind generation system Chiung Hsing Chen a, Chih-Ming Hong a,⇑, Fu-Sheng Cheng b a b

Department of Electronic Communication Engineering, National Kaohsiung Marine University, Kaohsiung, Taiwan, ROC Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 28 November 2008 Received in revised form 9 April 2012 Accepted 9 April 2012

Keywords: Model reference adaptive system (MRAS) Recurrent neural network (RNN) Particle swarm optimization (PSO) Wind turbine (WT) Induction generator (IG)

a b s t r a c t A sensorless vector-control strategy for an induction generator (IG) operating in a grid-connected variable speed wind energy conversion system is presented. The sensorless control is based on a model reference adaptive system (MRAS) observer for estimating the rotational speed. An on-line training recurrent neural network (RNN) controller using back-propagation learning algorithm with particle swarm optimization (PSO) is designed to allow the rotational speed adjustment for power regulation. The node connecting weights of the RNN are trained online by back-propagation (BP) methodology. The PSO is adopted to adjust the learning rates in the BP process to improve the learning capability. The proposed output maximization control is achieved without mechanical sensors such as wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The concept has been developed and analyzed using a turbine directly driven IG. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The induction machines are relatively inexpensive, robust, and require low maintenance. When induction machines are operated using vector-control techniques, fast dynamic response and accurate torque control are obtained [1]. All these characteristics are the advantages of variable-speed wind energy conversion systems (WECS). In these systems, the variable-speed generation system is more attractive than the fixed speed system because of the improvement in wind energy production and the reduction of flicker problems by adjusting the shaft speed. In order to deliver the maximum power, some control schemes have been studied. Many generators of research interests and for practical uses are the induction machines with wound-rotor or cage-type rotor [2]. The maximum power tracking control is to achieve optimum wind energy utilization and maintain the maximal aerodynamic efficiency, where the wind turbine generator must be operated in the variable-speed variable-frequency mode. The use of this encoder implies additional wiring, extra cost, extra space, and careful mounting which detracts from the inherent robustness of induction machines [3,4]. In recent years, the concept of incorporating fuzzy logic into a neural network has grown into a popular research topic. In contrast to the pure neural network or fuzzy system [5], the fuzzy neural network (FNN) possesses both advantages; it combines ⇑ Corresponding author. Tel.: +886 7 3617141x3318; fax: +886 7 3650833. E-mail address: [email protected] (C.-M. Hong). 0142-0615/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijepes.2012.04.019

the capability of fuzzy reasoning in handling uncertain information and the capability of artificial neural network networks in learning from the process. However, the major drawback of the existing FNN is that the application domain is limited to static problems due to their feedforward network structure. On the other hand, the RNN, that naturally involves dynamic elements in the form of feedback connections used as internal memories, has the same dynamic and robust advantages as the recurrent neural network. Variable speed generation can achieve maximum efficiency at all wind velocities. However, this system requires a rotor speed information for vector control purposes. In this paper, we propose a sensorless control structure based on a direct rotor flux-oriented (DRFO) vector-control system. A speed estimation, obtained from a MRAS, is used to control the electrical torque of the induction machine. The optimal rotor speed is determined using the estimated value, and a new technique is proposed to capture the maximum energy without using the wind speed sensor [6–10]. 2. Wind generation system 2.1. Composition of wind generation system The wind power generation system studied in this paper is shown in Fig. 1. The wind turbine is coupled to the shaft of an IG through a gear box, where the converter loss of the speedup gear is ignored in this study. The IG is connected with the power converter and inverter circuit, and the terminal voltage or the phase

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Nomenclature

r

A. General L0 ; Ls ; Lr magnetizing, rotor, stator inductance rotor, stator resistance Rr, Rs w rotor flux T r ¼ Lr =Rr rotor time constant

B. Superscripts ^ estimated value ⁄ reference value

current can control. Wind power (Pw) is converted into mechanical (Pm) then into electric power (Pe) to supply the power system. 2.2. Wind turbine characteristics For wind turbine control, the power electronic devices are generally installed between the WT and the grid where frequency is constant. The input of a wind turbine is the wind speed and the output is the mechanical power turning the generator rotor [11– 14]. For a wind turbine, the mechanical power output available from a wind turbine could be expressed as

Pm ¼

1 pqC p ðk; bÞR2 V 3x 2

ð1Þ

where q is air density (kg/m3), R is wind turbine blade radius, b is pitch angle, Vx is the wind velocity (m/s), and C p ðk; bÞ is called the power coefficient, and is given as a nonlinear function of the parameter k with

k¼

xr R

ð2Þ

Vx

Usually C p ðk; bÞ is approximated as C p ðk; bÞ ¼ c1 ðbÞk þ c2 ðbÞk2 þ c3 ðbÞk3 ; where c1(b), c2(b), and c3(b) are constructive parameters for a given turbine. Typical Cp versus k curve is shown in Fig. 2. Under the point of kopt , Cp = Cpmax, the maximal power can be captured at this time. It can be seen that Cpmax, the maximum value for Cp, is a constant for a given turbine. The dynamic performance of WT could be described as

J

induction machine leakage coefficient

dxr ¼ T m  Bxr  T e dt

ð3Þ

where J is the inertia moment of WT, Te is the electrical torque of the generator, and B is the friction coefficient. 3. Recurrent neural network (RNN) with PSO algorithm

turbine, e is speed error of turbine, and the RNN input is x11 and x12 with x11 ¼ eðNÞ and x12 ¼ ceðNÞ in this study. For the Nth sampling instant, the error and the change of error can be expressed as ^ r and the change of error can be expressed as eðNÞ ¼ xr  x ceðNÞ ¼ eðNÞ  eðN  1Þ, where eðN  1Þ is the error value in the previous sampling time. Subtracting rotor speed reference xr from ^ r gives rotor speed error e that turbine estimated rotor speed x  evaluates control law iqs via RNN speed control system. In the proposed RNN, the units in the input, hidden, and output layers are two, nine and one, respectively. The signal propagation and the basic function in each layer is introduced in the following. 3.2. Basic nodes operation 3.2.1. Layer 1 – input layer

net1i ðNÞ ¼ x1i ðNÞ

ð4Þ

O1i ðNÞ ¼ fi1 ðnet1i ðNÞÞ ¼

1 1

1 þ eneti ðNÞ

i ¼ 1; 2

;

ð5Þ

where N denotes the number of iterations; fi1 is the activation function, which is a sigmoidal function. 3.2.2. Layer 2 – hidden layer

net2j ðNÞ ¼ w2j O2j ðN  1Þ þ

X w2ij x2i ðNÞ

ð6Þ

i

O2j ¼ fj2 ðnet2j ðNÞÞ ¼

1 net2j ðNÞ

;

1þe

j ¼ 1; . . . ; n

ð7Þ

where w2j are the recurrent weight for the units in the hidden layer; w2ij are the connective weights between the input layer and the hidden layer; n is the number of neurons in the hidden layer; fj2 is the activation function, which is also a sigmoidal function.

3.1. Architecture of RNN 3.2.3. Layer 3 – output layer The architecture of RNN is illustrated in Fig. 3. The RNN has three layers, namely, input layer, hidden layer and output layer. A three-layer neural network shown in Fig. 3 is adopted to implement the proposed RNN controller for the turbine [15,16], where ^ r is estimated rotor speed of wind xr is rotor speed reference, x

net3k ðNÞ ¼ Rj w3jk x3j ðNÞ

ð8Þ 

O3k ðNÞ ¼ fk3 ðnet3k ðNÞÞ ¼ net3k ¼ iqs ;

Fig. 1. Wind generation system configuration.

k¼1

ð9Þ

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C.H. Chen et al. / Electrical Power and Energy Systems 42 (2012) 399–407

"

Dw3jk ¼ 

#

@E @E @O3k ¼ dk O2j ¼  @w3jk @O3k @net3k

ð12Þ

Hence, the weight can be updated by

w3jk ðN

þ 1Þ ¼ w3jk ðNÞ þ gjk Dw3jk ðNÞ

ð13Þ

where gw is the learning rate for adjusting the parameter wjk. 3.3.2. Layer 2: update w2j and w2ij The multiplication operation is done in this layer. The adaptive rule for w2j is

Dw2j

Fig. 2. Typical CP versus k curve.

" # 2 @E @E @O3k @Oj ¼ dk w2jk P2j ¼ ¼  3 @w2j @Ok @O2j @w2j

ð14Þ

and the adaptive rule for w2ij is

Dw2ij

" # 2 @E @E @O3k @Oj ¼ ¼  3 ¼ dk w3jk Q 2ij @w2ij @Ok @O2j @w2ij

ð15Þ

Thus the updated rules for w2j and w2ij are

w2j ðN þ 1Þ ¼ w2j ðNÞ þ gj Dw2j ðNÞ

ð16Þ

w2ij ðN þ 1Þ ¼ w2ij ðkÞ þ gij Dw2ij ðNÞ

ð17Þ

where gj and gjk are the learning rates for adjusting the parameters w2j and w2ij , respectively. With tuning parameters w2j , w2ij , and wjk , we can derive a learning algorithm that drives E to zero. 3.4. PSO algorithm definition The PSO is a population-based optimization method first proposed by Kennedy and Eberhart. PSO technique finds the optimal solution using a population of particles. Each particle represents a candidate solution to the problem. PSO is basically developed through simulation of bird flocking in two-dimensional space [17]. Step 1: define basic conditions.

Fig. 3. Architecture of the RNN.

where w3jk are the connective weights between the hidden layer and the output layer; fk3 is the activation function, which is set to be unit. x3j ðNÞ represents the Nth input to the node of output layer. 3.3. Supervised learning and training process

Step 2: initialize random swarm location and velocity.

Once the RNN has been initialized, a supervised learning law of gradient descent is used to train this system. The derivation is the same as that of the back-propagation algorithm. It is employed to adjust the parameters w3jk , w2j , and w2ij of the RNN by using the training patterns. By recursive application of the chain rule, the error term for each layer is calculated, and updated. The purpose of supervised learning is to minimize the error function E expressed as

1 1 E ¼ ðPw  Pm Þ2 ¼ e2m 2 2

ð10Þ

where Pw and Pm represent the wind power and the turbine output power. 3.3.1. Layer 1: update weight w3jk In this layer, the error term to be propagated is given by

dk ¼ 

@E @O3k

" ¼ 

@E @em @em @O3k

#

Then the weight wjk is adjusted by the amount

In the first step of PSO, one should determine the parameters that need to be optimized and give them minimum and maximum ranges. The number of groups, population size of each group, and initial radius of each gbest are also assumed in this step.

ð11Þ

To begin, initial location Rdi ðNÞ and velocities v di ðNÞ of all particles are generated randomly in whole search space. The generation particles are Rdi ¼ ½R1i ; R2i ; R3i , where R1i ; R2i ; R3i are the RNN learning rate, respectively. Then, the initial pbest of a particle is set by its current position. Then, the gbest of a group is selected among the pbests in the group. The random generation Rdi ðNÞ initial value range same as below:

  Rdi  U gdmin ; gdmax where gmin ; gmax are the lower and upper bound of learning rate.

Step 3: update velocity In the classical PSO algorithm, the velocity of a particle was determined according to the relative location from pbest and gbest, of which the relation was given as the following equation: During each iteration, every particle in the swarm is updated using (18) and (19). Two pseudorandom sequences r 1  Uð0; 1Þ and r2  Uð0; 1Þ are used to affect the stochastic nature of the algo-

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Fig. 4. Flowchart of MPSO.

Vw

(a) AC Grid

PWM Inverter

PWM Converter Tb

Ta

IG

Tc

Comparison Current Control

ia ib ic

va vb vc

Vω

ωr* =

ia*

* λoptVω ω r +

∑

R

-

ωr

RNN with PSO Controller

* ids

λopt C p max

* i qs

ib*

ic*

Coordinate Translator

Cp

MRAS Observer

θˆ e − ωb ω b

λ

Field-weakening

(b)

Fig. 5. Block diagram of direct field-oriented IG system (a) configuration of control system (b) simplified control system.

d

rithm. For all dimensions d, let Rdi , Pbesti ; and v di be the current position, current personal best position, and velocity of the jth dimension of the ith particle. The velocity update step is

v di ðN þ 1Þ ¼ wv di ðNÞ þ c1  r1  ðPbestdi  Rdi ðNÞÞ þ c2  r2 d

 ðGbest  Rdi ðNÞÞ

ð18Þ

Step 4: update position The new velocity is then added to the current position of the particle to obtain its next position

Rdi ðN þ 1Þ ¼ Rdi ðNÞ þ v di ðN þ 1Þ i ¼ 1; . . . ; P

ð19Þ

C.H. Chen et al. / Electrical Power and Energy Systems 42 (2012) 399–407

(a)

403

(b) Estimated rotor speed

Rotation speed error

Rotor speed reference and Actual rotor speed Error=actual rotor speed-estimated rotor speed

(c)

(d)

Estimated rotor speed

Rotor speed reference

Actual and Estimated rotor speed

Rotor speed reference and Actual rotor speed

Fig. 6. Simulation results of the first wind profile speed: (a) the first wind profile speed tracking with RNN and PSO controller, (b) speed estimation errors, (c) the wind profile speed tracking with RNN controller, (d) the wind profile speed tracking with PI controller.

Step 5: update pbests If the current position of a particle is located within the analysis space and does not intrude territory of other gbests, the objective function of the particle is evaluated. If the current fitness is better than the old pbest value, the pbest is replaced by the current position. The calculate fitness value of each particle is select as: FIT ¼ 0:1þabsð1x^ r x Þ r

The acceleration coefficient c1 and c2 control how far a particle will move in a single iteration. The inertia weight w in (18) is used to control the convergence behavior of the PSO. Small values of w result in more rapid convergence usually on a suboptimal position, while a too large value may prevent divergence. In general, the inertia weight w is set according to the following equation:

w ¼ wmax 

wmax  wmin  iter itermax

ð20Þ

where itermax is maximum number of iterations, and iter is the current iteration number.

Step 6: update gbests In the conventional PSO, gbest is replaced by the best pbest among the particles. Each particle Rdi memorises its own fitness value and chooses the maximum one that is the best so far as d

d

d

pbest i and the maximum vector in the population pbesti ¼ ½pbest 1 ; d d pbest 2 ; . . . pbest p  d directly to pbesti

is obtained. Moreover, each particle

Rdi

is set

in the first iteration, and the particle with the best fitness value among pbest is set to be the global best gbest. Step 7: repeat and check convergence

Steps 3–6 are repeated until all particles are gathered around the gbest of each group, or a maximum iteration is encountered. d The final Gbest i is the optimal learning rate ðgij ; gj ; gjk Þ of RNN. The flowchart of MPSO is shown in Fig. 4.

4. Control system for wind induction generator 4.1. Overall structure An appropriate model of the induction generator is the most complicated part of the total wind generation model. The model of such a system is well described in many books and papers [12]. The configuration of a field-oriented IG drive system is shown in Fig. 5a, which consists of an IG, a current-controlled PWM voltage source converter (VSC), and a field-orientation mechanism including the coordinate translator, and a speed control loop. By using the reference frame theory and the linearization technique, the field-oriented induction generator system can be reasonably represented by the control system block diagram shown in Fig. 5b, in which

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C.H. Chen et al. / Electrical Power and Energy Systems 42 (2012) 399–407

(a)

(b) Actual and Estimated rotor speed

Rotor speed reference

Rotation speed error

Error=actual rotor speed-estimated rotor speed

(c)

(d) Rotor speed reference

Rotor speed reference

Actual and Estimated rotor speed

Actual and Estimated rotor speed

Fig. 7. Simulation results of the second wind profile speed: (a) the second wind profile speed tracking with RNN and PSO controller, (b) speed estimation errors, (c) the wind profile speed tracking with RNN controller, (d) the wind profile speed tracking with PI controller.



T e ¼ K t iqs Hp ðsÞ ¼

1 Js þ B 

ð21Þ

the error between the reference flux and the flux estimated from (24). The error in a  b components is usually defined as

ð22Þ

e ¼ w^ a wb  wa w^ b



K t ¼ ð3np =4ÞðL2m =Lr Þids ; np is the number of pole pairs, iqs is the tor que current command generated from the speed controller, and ids is the flux current command. 4.2. MRAS observer A MRAS observer is used to extimate the rotational speed of the induction machine. This observer is based on two models, the voltage model and the current model [18–22]. The voltage model is used to obtain the rotor flux as

    dw Lr d V  Rs þ rLs ¼ is dt L0 dt

ð23Þ

The rotor flux is also calculated from the stator current, speed and machine inductances. The flux from the current model is obtained as

 ^  dw 1 ^ L0 w þ is ¼ jxr  dt Tr Tr

ð24Þ

In the MRAS observer, the flux obtained from (23) is used as the reference. By adjusting the rotational speed, it is possible to reduce

ð25Þ

Eqs. (23)–(25) are used to implement the MRAS speed observer. The error calculated using (25) is driven to zero by a PI controller. The voltage model is used to obtain the rotor flux w using a bandpass filter as a modified integrator to block the dc components of the measured voltages and currents. From the voltage model, the electrical angle he is calculated using the a  b components of the rotor flux.

5. Simulation results In this section, the MRAS speed observer as well as the RNN controller with PSO Algorithm were tested, including performance in the transition between power optimization and power limitation in rated wind speed. The proposed RNN controller with PSO Algorithm is augmented to the MRAS observer system to preserve the desired command tracking response under the occurrence of uncertainties. The control strategies proposed in this paper have been tested with several wind profile, and similar results have been achieved. The optimum rotational speed xr is obtained for each wind speed Vx, and used as a reference for the closed loop. Generally

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C.H. Chen et al. / Electrical Power and Energy Systems 42 (2012) 399–407

(a)

(a) Turbine power Pm

Turbine power Pm

Max. power reference Pw

Max. power reference Pw

Generator Power Pe

Generator Power Pe

(b)

(b)

Max. power reference Pw

Max. power reference Pw

Turbine power Pm

Turbine power Pm Generator Power Pe

Fig. 8. Simulation results of the proposed control for maximum power tracking: (a) slowly varying sinusoidal wind profile, (b) randomly varying wind profile.

Generator Power Pe

Fig. 9. Simulation results of RNN control for the maximum power tracking: (a) slowly varying sinusoidal wind profile, (b) randomly varying wind profile.

(b) RNN type controller the turbine is linked with the generator’s shaft using a gearbox, which imposes an additional transform relation in the model. Dynamics of this gearbox are considered unknown in this paper. The machine parameters are given in the Appendix.

Fig. 6c shows the performance of the RNN controller with MRAS observer control system. (c) PI type controller

5.1. Simulation of the variable wind speed Figs. 6a and 7a shows a wind profile with a 5 ms sampling time for the wind velocity. The results in this section have been obtained using this profile. The performance of the RNN controller with MRAS observer has been investigated emulating wind turbines of different inertia and friction coefficient. 5.1.1. Case #1: slowly varying sinusoidal wind profile (a) RNN with PSO algorithm type controller Fig. 6a shows the performance of the RNN with PSO algorithm controller and MRAS speed observer control system. In this case, the MRAS speed observer tracked the actual speed during the whole wind profile with very small errors, and is almost negligible. Fig. 6b shows that the tracking error is approximately 0.5 rad/s.

Fig. 6d shows the performance of the PI controller with MRAS observer control system. 5.1.2. Case #2: randomly varying wind profile (a) RNN with PSO algorithm type controller Fig. 7a shows the performance of the RNN with PSO algorithm controller and MRAS speed observer control system. In this case, the MRAS observer is tracking the actual speed during the whole wind profile with very small errors, and is almost negligible. Fig. 7b shows that the tracking error is approximately 0.6 rad/s. (b) RNN type controller Fig. 7c shows the performance of the RNN controller with MRAS observer control system.

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C.H. Chen et al. / Electrical Power and Energy Systems 42 (2012) 399–407 Table 2 Performance for various control methods (randomly varying wind profile).

(a) Turbine power Pm

RNN with PSO algorithm controller RNN controller PI controller

Max. power reference Pw

Average power

Max. speed tracking error

1276 W 1256 W 1221 W

0.6 rad/s 1.37 rad/s 5.0 rad/s

(c) PI type controller The two wind speed profiles verification of maximum power tracking control are shown in Fig. 10a and b. The simulation results show that the MRAS observer can track the speed of the wind turbine with an error of less than 0.6 rad/s for the whole speed range. Note that the actual speed is closely tracked by the estimation obtained from the MRAS. With the controlled rotor speed, the actual turbine power Pm and the generator power Pe can track the desired Pw closely. The system could capture the maximal wind energy shown in the figures. It shows a robust control performance of the proposed RNN with PSO algorithm controller and MRAS speed observer, both in the wind speed tracking and power regulation. Simulation results of performance for various control methods are shown in Tables 1 and 2.

Generator Power Pe

(b)

Max. power reference Pw

6. Conclusion

Turbine power Pm

Generator Power Pe

Fig. 10. Simulation results of PI control for the maximum power tracking: (a) slowly varying sinusoidal wind profile, (b) randomly varying wind profile.

Table 1 Performance for various control methods (slowly varying sinusoidal wind profile).

RNN with PSO algorithm controller RNN controller PI controller

Average power

Max. speed tracking error

814 W

0.5 rad/s

798 W 768 W

0.97 rad/s 1.3 rad/s

This paper has presented a sensorless vector-control strategy for an IG in a variable-speed WECS using a MRAS observer to estimate the rotational speed of the IG. Wind velocity sensorless operation for wind power generation system have been presented in this paper. We estimate the rotor position from flux linkages using the MRAS speed observer. MRAS based wind speed observer is developed to provide fast and accurate velocity information to avoid using anemometers. The dynamic performance can be used to obtain an accurate estimation of rotational speed not only in steady state but also when fast input changes as wind steps are applied to the WECS. The approach used, based on a combination of RNN and a MRAS speed observer, allowed fast convergence to a simple linear dynamic behavior, even in the presence of parameter changes and model uncertainties. However, the traditional PI controller cannot be sure of the uncertainty model for various wind speed. The proposed MRAS speed observer and RNN with PSO Algorithm controller are successfully implemented in this study for the speed control of WECS. This technique can maintain the system stability and reach the desired performance even with parameter uncertainties. Appendix

(c) PI type controller Fig. 7d shows the performance of the PI controller with MRAS observer control system.

The wind turbine generator system used for the simulation has the following parameters: (1) wind turbine parameters:

5.2. Simulation of the maximum power tracking (a) RNN with PSO algorithm type controller The two wind speed profiles of maximum power tracking control and the dynamic difference between the turbine power Pm and generator power Pe due to the system inertia and friction are also shown in Fig. 8a and b. (b) RNN type controller The two wind speed profiles verification of maximum power tracking control are shown in Fig. 9a and b.

Pm ¼ 1:5 KW; q ¼ 1:25 kg=m3 ; R ¼ 1:62 M; J ¼ 0:026244 Nm s2 ; B ¼ 0:007864 Nm s=rad. (2) generator parameters: Rs ¼ 1:1 X; Rr ¼ 1:3 X; Ls ¼ 0:1466 H; Lr ¼ 0:1466 H; Lm ¼ 0:136 H. References [1] Pena RS, Cardenas RJ, Asher GM, Clare JC. Vector controlled induction machines for stand-alone wind energy applications. Proc IEEE Ind Appl Annu Meeting 2000;3:1409–15.

C.H. Chen et al. / Electrical Power and Energy Systems 42 (2012) 399–407 [2] Morimoto S, Nakayama H, Sanada M, Takeda Y. Sensorless output maximization control for variable-speed wind generation system using IPMSG. IEEE Trans Ind Appl 2005;41(1):60–7. [3] Lin FJ, Wai RJ, Lin PC. Robust speed sensorless induction motor drive. IEEE Trans Aerosp Electron Systems 1999;35(2):532–42. [4] Cardenas R, Pena R, Proboste J, Asher G, Clare J. MRAS observer for sensorless control of standalone doubly fed induction generators. IEEE Trans Energy Convers 2005;20(4):710–8. [5] Simoes MG, Bose BK, Spiegel RJ. Fuzzy logic-based intelligent control of a variable speed cage machine wind generation system. IEEE Trans Power Electron 1997;12(1):87–95. [6] Li H, Shi KL, McLaren PG. Neural-network-based sensorless maximum wind energy capture with compensated power coefficient. IEEE Trans. Ind Appl 2005;41(6):548–1556. [7] Tan K, Islam S. Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors. IEEE Trans Energy Convers 2004;19(2):392–9. [8] Senjyu T, Tamaki S, Urasaki N, Uezato K, Higa H, Funabashi T, et al. Wind velocity and rotor position sensorless maximum power point tracking control for wind generation system. In: 35th Annual IEEE Power electron specialists conf, 2004; 2023–2028. [9] Mohamed MA, Hamdy A, Hamdy AE, Mohamed ES. Maximum power point tracking using fuzzy logic control. Int J Electr Power Energy Syst 2012;39(1): 21–8. [10] Ghedamsi K, Aouzellag D. Improvement of the performances for wind energy conversions systems. Int J Electr Power Energy Syst 2010;32(9):936–45. [11] Karrari M, Rosehart W, Malik OP. Comprehensive control strategy for a variable speed cage machine wind generation unit. IEEE Trans Energy Convers 2005;20(2):415–23.

407

[12] Bose BK. Power electronics and AC drive. New York: John Wiley; 1986. [13] Ahmed MK, Ali MY. Robust control of an isolated hybrid wind–diesel power system using linear quadratic gaussian approach. Int J Electr Power Energy Syst 2011;33(4):1092–100. [14] Mohsen R, Mostafa P. Dynamic behavior analysis of doubly-fed induction generator wind turbines – the influence of rotor and speed controller parameters. Int J Electr Power Energy Syst 2010;32(5):464–77. [15] Lu CH, Tsai CC. Adaptive predictive control with recurrent neural network for industrial processes: an application to temperature control of a variablefrequency oil-cooling machine. IEEE Trans Ind Electron 2008;55(3):1366–75. [16] Lin FJ, Lin CH, Hong CM. Robust control of linear synchronous motor servodrive using disturbance observer and recurrent neural network compensator. IEE Proc Electr Power Appl 2000;147(4):263–72. [17] Esmin AA, Torres GL, Souza CZ. A hybrid particle swarm optimization applied to loss power minimization. IEEE Trans Power Syst 2005;20(2):859–66. [18] Schauder C. Adaptive speed identification for vector control of induction motors without rotational transducers. IEEE Trans Ind Appl 1992;28(5): 1054–61. [19] Tajima H, Hori Y. Speed sensorless field orientation control of the induction machine. IEEE Trans Ind Appl 1993;29(1):175–80. [20] Yang G, Chin TH. Adaptive-speed identification scheme for a vector-controlled speed sensorless inverter-induction motor drive. IEEE Trans Ind Appl 1993;29(4):820–5. [21] Peng FZ, Fufao T. Robust speed identification for speed-sensorless vector control of induction motors. IEEE Trans Ind Appl 1994;30(5):1234–40. [22] Landau YD. Adaptive control – the model reference approach. Marcel Dekker. New York: John Wiley; 1979.