A New Adaptive Anti-windup Controller for Wind Energy Conversion System Based on PMSG

International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol. 9, No. 3, September 2018, pp. 1321 – 1329
ISSN: 2088-8694, DOI: 10.11591/ijpds.v9.i3.pp1321-1329 1321
A New Adaptive Anti-Windup Controller for Wind Energy
Conversion System Based on PMSG
Ed-dahmani Chafik, Mahmoudi Hassane, Bakouri Anass, and El Azzaoui Marouane
Power Electronics and Control Laboratory, Electric Department, Mohammed V University, Morocco
Article Info
Article history:
Received Aug 10, 2017
Revised Nov 27, 2017
Accepted Aug 6, 2018
Keyword:
Anti-windup controller
PI controller
PMSG
WECS
ABSTRACT
In this paper, an adaptive anti-windup control strategy for permanent magnet synchronous
generator dedicated for wind energy conversion systems. The proposed control has the ad-
vantage to suppress the performance deterioration caused by the overshooting phenomenon,
and optimize the controller gains using the particle swarm optimization algorithm. The
scheme of the speed controller is implemented on field orientation control in the generator
side converter. A simulation of the proposed scheme is carried out in SIMULINK-MATLAB
in order to evaluate the effectiveness of the control against the saturation and the parameter
optimization.
Copyright c

2018 Insitute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ed-dahmani Chafik
Power Electronics and Control laboratory of Mohammadia school of engineers,
Mohammed V University, Rabat, Morocco
+212674343036
Email: chafik.eddahmani@research.emi.ac.ma
1. INTRODUCTION
In the last decades, there has been a growing interest in wind turbines. Electrical generators and control
strategies should respond to the needs of wind power applications. The permanent magnet synchronous generator
(PMSG) and doubly fed induction generator (DFIG) are widely used in wind energy conversion systems (WECS)
because they offer the possibility to work with variable wind speed [1]. Also, they can offer an improvement on
production of wind energy, and the ability to achieve maximum energy conversion efficiency.
Comparing to doubly fed induction generator (DFIG), the PMSG can provide a high-efficiency and high
reliability power generation, low maintenance required, and the electrical losses in the rotor are eliminated [2],[3].
Due to the mentioned advantages, the PMSG becomes an interesting solution for wind turbine applications. For
a special architecture with a high number of poles pairs, the PMSG offers the possibility to eliminate the gearbox
system as shown in Figure 1, that allows a higher efficiency. Several authors [4],[5] have discussed about the Variable
wind speed conversion systems based on PMSG.
The conventional control strategy of PMSG is based on field oriented control (FOC) with PI controller [6].
This controller is easy to apply, and present a good performance in linear region [7],[8]. But, it suffers from non-linear
effects such as saturation, when the saturation is neglected in the design phase it causes instability during closed-loop
where the output system response takes a long time to stabilize in the steady state. In other words, this phenomenon is
caused by the windup integrator contained in the PI controller, which keeps integrating the tracking error even if the
input is saturating [9]–[10].
In order to overcome the windup phenomenon, several researches have proposed anti-windup techniques to
deal with input saturation, where some proposals involve a complicated design [11],[12]. Thus, the average strategy
to handle the integrator windup by tuning the controller disregarding the saturation caused by the integrator, and then
adds an anti-windup compensator to avoid performance graduation [13]. Basically, the classical anti-windup strategies
come with two different approaches, namely, conditional integration and tracking back-calculation.
In this paper, the speed control is based on adaptive anti-windup PI controller. The adaptation controller
gains are gotten with search technique known as Particle Swarm Optimization technique (PSO), this technique was
developed by Eberhart and Kennedy in 1995 [14],[15]. This technique is an optimization tool based on population,
Journal Homepage: http://iaesjournal.com/online/index.php/IJPEDS

1322 ISSN: 2088-8694
and the system is initialized with a population of random solutions and can search for optima by the updating of
generations. The PSO algorithm has been used in power system for tuning control purpose in [16],[17],[18].
The aim of this paper is to implement, discuss and compare the racking performance of adaptive anti-windup
speed controller with conventional linear PI. The proposed scheme of anti-windup eliminates the overshooting with
a simple structure existing in PI controllers, and guarantees the independence between the desired performance and
operating conditions. The simulation is realized with Simulink in order to verify the performance improvement com-
paring to conventional PI controller.
2. MODELING THE WECS CONCEPTS
2.1. Wind turbine model
A wind turbine is composed of many parts to convert a kinetic-to-electrical energy. The mechanical power
and torque delivered by a wind turbine is given by (1) and (2).
Pt = 0.5ρCpπR2
v3
(1)
Tt = 0.5ρCpπR2 v3
wt
(2)
Cp = 0.5

116
λi
− 0.4β − 5

· exp

−21
λi

+ 0.0068λ (3)
With: 1
λi
= 1
λ+0.08β − 0.035
1+β3
ρ is the air density, R is the blade radius, v and wt are respectively the wind and turbine speed, Cp is the power
coefficient, Pt is the turbine power, and Tt is the turbine torque. The tip speed ratio (TSR: λ) is an important parameter
in wind energy systems. It is defined as the ratio of the blade speed to the speed of incoming wind.
PMSG
Grid
RL filter
AC-DC-AC Converters
Figure 1. WECS based on direct drive PMSG.
0 20 40 60 80 100 120
wt
( rad.s-1
)
0
500
1000
1500
2000
2500
P
t
(W)
12 m/s
13.5 m/s
11.5 m/s
10.5 m/s
9 m/s
Figure 2. Turbine power evolution curve.
2.2. PMSG Model
Similarly to the induction generator (IG), the construction of the stator in PMSG is essentially the same. On
the other side, the rotor magnetic flux is constant and generated by permanent magnets [19]. Also, depending on
magnets architecture, the PMSG can be classified into surface-mounted and inset PM generators. The mathematical
model of PMSG in synchronous rotating dq-reference is given by the following equations.
(
Vsd = −Rs.isd − Ld
did
dt + wrLq.isq
Vsq = −Rs.isq − Lq
diq
dt − wrLd.isd + wrφf
(4)
Te =
3P
2
((Ld − Lq)isd.isq + φf .isq) (5)
J
dwm
dt
= Tt − Te − Bwm (6)
Where, Rs is the stator resistance, Ldq are the inductances in dq-reference, φf is the PM-flux, P is the pole
pair number, wr and wm are electrical and mechanical speed, Vdq,Idq are the stator voltages and currents components
in dq-reference, Te and Tt are respectively the electromagnetic and turbine torque, J and B are respectively the
equivalent system inertia and viscous damping.
IJPEDS Vol. 9, No. 3, September 2018: 1321 – 1329

IJPEDS ISSN: 2088-8694 1323
The equations (4) represent the electrical behavior of the PMSG which gives the possibility to control the
dq-currents components. Also, the speed controller is based on (6). It should be noted that the electromagnetic torque
Te may be controlled directly by the quadrature current component in case of surface mounted PM or zero direct
current control (ZDC). Then, the expression of Te is given by (7).
Te =
3P
2
φf .isq = Kt.isq (7)
3. WECS CONTROL STRATEGIES
3.1. Wind turbine control
The purpose by turbine control is to produce a maximum power, this is achieved for a particular value of
power coefficient, called Cp−max, the maximal power coefficient is set to 0.48, and it is given for a specified TSR
λopt = 8.1 with pitch angle β = 0. The maximum power operation can be achieved with optimal torque control
according to (9).
λopt =
R.wm−opt
v
(8)
Tt−opt = 0.5ρCp−maxπR5
w2
t−opt
λopt
(9)
Also, to guarantee the safety working of the turbine against strong wind, a pitch angle controller is imple-
mented in Figure 4. The working principle of this controller is by comparing the generated power Pt with the nominal
power, then it generates a pitch angle response.
Eq.2
Eq.7
+
-
Te
β
Turbine model
Shaft
MPPT
Eq.9
Eq.10
wt
Tt
wm
v
Cp
λ
Figure 3. Turbine model diagram scheme.
+
-
Pitch angle
controller
Pnom
Pt
β
Figure 4. pitch angle controller scheme
3.2. Speed control of PMSG
The windup phenomenon comes when the output current command of the speed controller is limited to a
maximum value, and the integral component becomes very large because is not compatible with the plant input.
Which causes a large overshoot and slow settling time in the speed response.
In this paper, the speed-loop controller has been designed using an adaptive gains anti-windup algorithm. It has
an advantage to eliminate the overshooting phenomenon during operation that can deteriorate the power generation
performance. The aim of anti-windup control for nonlinear system with saturating actuators is to modify the control
in order to limit the overshoot.
A New Adaptive Anti-Windup Controller for WECS Based on PMSG (Chafik ed-dahmani)

1324 ISSN: 2088-8694
The proposed anti-windup strategy by [7] as shown in fig.5 can switch between the P and PI modes according
to the working states. For the PI mode, it is necessary to initialize the integrator, and in case of P mode the initial value
of isq−i(0) is inserted, where the PI mode can utilize this value. A low pass filter is used to avoid an abrupt change of
current by loading the initial value isq−i(0) in the LPF.
3.3. Closed loop identification
According to the mechanical equations of the PMSG (6),(7), and for simplicity, the viscous damping B is
neglected, the new expression of mechanical equation of WECS is given as follow
J
dwm
dt
= Kt.isq − Tt (10)
The quadrature stator current component (isq) can be written for the PI controller as (12).





isq = isq−i + isq−p
isq−p = Kp (wm−ref − wm)
isq−i = Ki
s (wm−ref − wm) +
isq(0)
s
(11)
Where, wm−ref is the reference speed of the PMSG which is the optimal speed derived from the MPPT of
turbine, isq−p and isq−i are the proportional and integral components of isq, Kp and Ki are the PI controller gains.
By implementing the Laplace transform on (10) and substituting by (11), the transfer function of the closed loop for
the mechanical is expressed by (12).
J (swm − wm(0)) =
−Tt
s
+ Kt

Kp +
Ki
s

(wm−ref − wm) +
isq(0)
s

(12)
wm(0) and isq(0) denotes the initial states values of mechanical speed and quadrature stator current. Then,
the mechanical speed wm(s) can expressed as a function of the input arguments shown in (13).
wm(s) =





Ki.Kt+sKt.Kp
Js2+KtKp.s+KtKi
Kt
Js2+KtKp.s+KtKi
Js
Js2+KtKp.s+KtKi
−1
Js2+KtKp.s+KtKi





T
.




Wm−ref
isq−i(0)
wm(0)
Tt



 (13)
In the steady state, according to (10) and (11), the proportional and integral terms of quadrature current are
expressed as:
(
isq−psteady = 0
isq−isteady = Tt
Kt
(14)
When the PI mode is activated, the initial integral term of isq is defined by (15).
isq−i(0) =
Tt
Kt
− m (wm−ref − wm(0)) (15)
The first term of (15) signify the required current at steady state according to (14), for the second term it is
assigned to the compensation term for the overshoot. With m is the anti-windup controller gains. Substituting (15) in
(13).
wm(s) =
h
Kt(Ki+s(Kp−m))
Js2+KtKp.s+KtKi
Kt.m+J.s
Js2+KtKp.s+KtKi
i
.

Wm−ref
wm(0)

(16)
Then, (16) can be simplified during the PI mode to the final expression as:
wm(s) −
wm(0)
s
=
Kt (Ki + s(Kp − m))
Js2 + KtKp.s + KtKi
.

wm−ref −
wm(0)
s

(17)

IJPEDS ISSN: 2088-8694 1325
3.4. Controller design
In order to establish a good performance for speed controller, the anti-windup controller gain m should be
determined in the PI mode. According to (12), the transfer function can be rewritten as
H(s) =
Kt (Ki + s(Kp − m))
Js2 + KtKp.s + KtKi
= −
p1.p2
z
.
s − z
(s − p1)(s − p2)
(18)
Where z is the zero, and p1,2 are the poles of transfer function.



z = − Ki
Kp−m
p1,2 =
−Kt.Kp±
√
(Kt.Kp)2−4J.Kt.Ki
2J
(19)
+
-
+
+
m-ref
w ∆w
p
K
s
i
K
m
w
1/Kt
e-ref
T isq
isq−ref
wc
wc+s
LP filter
isq−i(0)
PI mode
P mode
isq−i
isq−p
max
I
Figure 5. Diagram scheme of the proposed anti-windup
speed controller.
+_ +_
Anti-windup
speed controller
PMSG
dq
abc
dq
abc
Current
controller
idq
+_
Generator Side control
Tt
C
GSC
isd-ref
DC-Bus
isd
isq
wm
wm-ref
vd-ref
vq-ref
ed
eq
wt
Figure 6. Global structure of the generator side based on
the proposed control for the mechanical speed.
As mentioned in [7] and [9], the pole is a function of PI gains, and the zero-location determined by the anti-
windup and PI gains. In order to simplify the transfer function to first order as shown in (21), it is assumed that z = p1
in such away |p1| |p2|, which represents a first order low pass filter without saturation. In that case, the anti-windup
gain is given by (20). If the gain is smaller than the specified value, a higher overshoot is provided. Else, a large value
can result a slow response.
m = Kp +
Ki
p1
(20)
H(s) =
p2
p2 − s
(21)
The next step is to determine the initial value of the input arguments. In transition state between the P mode
to PI mode, the initial integral term of q-axis current component is given as follow:
isq−i(0) = −Imax − Kp (wm−ref − wm(0)) (22)
Where Imax is the maximum limited current. Also, according to (10), the turbine torque can be expressed by
in steady state. Using (15) and (22), the new expression of isq−i(0) is given by
isq−i(0) =
Kp.isq−isteady + m.Imax
Kp − m
(23)
When the P mode is selected, the initial integral current isq−i(0) is generated by the low-pass filter. The
initial mechanical speed wm(0) can be expressed as follow
wm(0) = wm−ref +
isq−isteady + Imax
Kp − m
(24)
3.5. Particle Swarm Optimization
In the previous part, the controller has been designed. The anti-windup controller parameters gains, as Kp,
Ki and m determine the performance system. The selection of parameters is a task that can be difficult. In order to
guarantee a fast-dynamic response and optimal control, PSO comes to solve the problem of parameters estimation.
PSO derived from social, psychological theory. It imitates the natural process of group communication to share

1326 ISSN: 2088-8694
individual experience flocking, migrating, or hunting. Basically, it searches for the optimal solution from a population
of moving particles. In PSO, starting with a randomly initialized population called a swarm, each member called
particle flies through the searching space, where is positioned by xi vector, evaluating the fitness, and remember the
best position xgbest on which it has the best fitness. This information is shared by all particles and adjust their positions
xi, and velocities vi according to the information. The velocity adjustment is based on the historical behaviors of the
particles themselves and their companions. The particles tend to fly better to best the positions [17],[18]. The velocity
and current position respectively of every particle are evaluated by (25) and (26).
vi(t + 1) = w.vi(t) + a [r1 (xpbest − xi(t)) + r2 (xgbest − xi(t))] (25)
xi(t + 1) = xi(t) + vi(t) (26)
Where, t is time step, w the inertia weight factor, a acceleration constant, r1, r2 are random functions in the
range of [0,1], xi the position of ith
particle, xpbest the best previous position of ith
particle, xgbest the position of
best particle among the entire population, and vi the velocity for the ith
particle.
The adaptive weighted PSO has been proposed in (27) to improve the reaching capability.
a = a0 +
t
Nt
(27)
With Nt indicate the iterations number, t is the current step, and a0 is a constant in [0.5,1]. It should be noted
that the inertia weight changes at every step by (28).
w = w0 + r3 (1 − w0) (28)
With, w0 is a positive constant chosen in [0.5,1], and r3 is a random function in the range of [0,1].
4. SIMULATION RESULTS
To verify the effectiveness of the PI anti-windup speed controller, a simulation of the proposed scheme is car-
ried out in Simulink. The PMSG and turbine data are listed in Table 1.In order to evaluate the controller performance
in extreme cases, a step change is applied for the speed reference and load torque. Figure 7 shows a comparison of the
tracking performance of mechanical speed responses by the conventional PI and anti-windup controllers with different
iteration numbers for a step changing speed response 70rad/s → 157rad/s → 120rad/s. By comparing the speed
responses, in the adaptive anti-windup controller, the saturation input is limited which can guarantee a better stability
and high tracking performance. Also, compared to the conventional PI controller, the PSO improve the controller
performance, by selecting the optimal gains. With the proposed method, the steady-state is quickly established for
an optimal value of the iterations number.Therefore, the anti-windup controller provides the optimal dynamic perfor-
mance in term of convergence, saturation, and robustness compared to conventional PI controller. The simulation of
generator side converter for the WECS is based on the wind speed profile of Figure 8, it should be noted that the
nominal speed of the wind is chosen vnom = 12m.s−1
. From Figure 9(a)-9(b), the power coefficient and tip speed
ratio-TSR are maintained at their optimal values by the MPPT control. The pitch angle controller is activated when
the wind speed exceeds the nominal speed. Then, the power coefficient and tip speed ratio are decreased in order to
kept the extracted power at the nominal value. Figure 9(c) represents the pitch angle controller response for variable
wind speed. The mechanical turbine power is illustrated in Figure 9(d) changed according to the wind speed variation,
also it is maintained in nominal state when the pitch angle controller is activated.

IJPEDS ISSN: 2088-8694 1327
Figure 7. Comparison Simulation responses for conven-
tional PI speed-controller and the speed anti-windup con-
troller with different iterations number Nt
0 2 4 6 8 10 12 14 16 18 20
Time (s)
2
4
6
8
10
12
14
v
(m.s
-1
)
Wind speed
Nominal speed
Figure 8. wind speed profile.
0 2 4 6 8 10 12 14 16 18 20
Time (s)
0
0.1
0.2
0.3
0.4
0.5
C
p
(a) Power coefficient response
0 2 4 6 8 10 12 14 16 18 20
Time (s)
0
5
10
15
(b) Tip speed ratio response
0 2 4 6 8 10 12 14 16 18 20
Time (s)
0
1
2
3
4
(c) Pitch angle response
0 2 4 6 8 10 12 14 16 18 20
Time (s)
0
500
1000
1500
2000
P
t
(W)
(d) Turbine power response
Figure 9. Turbine dynamic performance using the MPPT and pitch angle controllers
Table 1. Turbine and PMSG data
Nominal power Pn 1.7kW
Turbine radius R 1.04m
Air density ρ 1.22kg/m3
Gearbox gain G 1.7
Equivalent system inertia J 0.35N.m.rad−1
.s2
Maximum power coefficient Cp−max 0.48
Optimal speed ratio λopt 8.1
Nominal current Inom 5A
Stator resistance Rs 2.7Ω
Stator inductance Ld,q 3.1mH
PM flux φf 0.341Wb
Pole pairs number P 4
Nominal speed wnom 157.1rad.s−1
Nominal frequency fr 100Hz
Figure 10(a) shows the high tracking performance of mechanical speed response of the proposed controller
under a variable turbine speed, it should be noted that the refence speed is given by the MPPT bloc controller. Also, the
mechanical speed response is stable and tracks the reference value by using the selected value of the PSO algorithm.

1328 ISSN: 2088-8694
In Figure 10(b), the electromagnetic torque is identical to the reference value. When, the pitch control is activated
the PMSG speed and electromagnetic torque are kept at the nominal values, which implies that the extracted power
is maximal. Figure 10(c) shows current responses in dq frame. The FOC with ZDC is applied, where the reference
current component of d-axis is set to zero (isd−ref = 0) as shown in Figure 6, and the quadrature current is propor-
tional to the turbine torque as mentioned in (7). For the three-phase stator current response is shown in Figure 10(d).
Where, the current amplitude and frequency are proportionals respectively to electromagnetic torque Te and generator
velocity wm.
0 2 4 6 8 10 12 14 16 18 20
Time (s)
0
50
100
150
w
t
,w
m
,w
m-opt
(rad.s
-1
)
optimal speed Wm-opt
actual speed Wm
turbine speed Wt
2 3 4
100
120
140
(a) Mechanical speed response of PMSG
0 2 4 6 8 10 12 14 16 18 20
Time (s)
0
5
10
15
20
T
t
,T
e
,T
e-ref
(N.m)
Te
Te-ref
Tt
0 1 2
6
8
10
(b) Electromagnetic torque response
0 2 4 6 8 10 12 14 16 18 20
Time (s)
-2
0
2
4
6
8
10
I
dq
(A)
Id
Iq
(c) Current response in dq plan
0 2 4 6 8 10 12 14 16 18 20
Time (s)
-10
-5
0
5
10
I
abc
(A)
14 14.02 14.04
-5
0
5
(d) Stator current response
Figure 10. PMSG dynamic performance with the anti-windup speed controller
5. CONCLUSION
The adaptive anti-windup was proposed in this paper to replace the conventional PI controller for the speed
controller in the direct drive PMSG field oriented control. The initial values of the integrator current and mechanical
speed are determined. The PSO algorithm is used to estimate the optimal parameters of the proposed controller, which
gives a high tracking and dynamic performance, and fast response with least overshoot. The anti-windup controller
is designed to give a best tracking speed comparing to the conventional linear PI controllers. The proposed control
is implemented for the speed-loop. The simulation results confirm the effectiveness of the anti-windup controller
regarding the saturation phenomenon and fast responses.
REFERENCES
[1] I. Boldea, Variable Speed Generators, Nov. 2005.
[2] A. Bakouri, H. Mahmoudi, and A. Abbou, “Intelligent Control for Doubly Fed Induction Generator Connected to
the Electrical Network,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 7, no. 3,
pp. 688–700, Sep. 2016.
[3] M. E. Azzaoui, H. Mahmoudi, and K. Boudaraia, “Backstepping Control of Wind and Photovoltaic Hybrid
Renewable Energy System,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 7,
no. 3, pp. 677–687, Sep. 2016.
[4] H. Polinder, F. F. A. v. d. Pijl, G. J. d. Vilder, and P. J. Tavner, “Comparison of direct-drive and geared generator
concepts for wind turbines,” IEEE Transactions on Energy Conversion, vol. 21, no. 3, pp. 725–733, Sep. 2006.
[5] M. Chinchilla, S. Arnaltes, and J. C. Burgos, “Control of permanent-magnet generators applied to variable-speed
wind-energy systems connected to the grid,” IEEE Transactions on Energy Conversion, vol. 21, no. 1, pp. 130–
135, Mar. 2006.
[6] J. Liang and B. Whitby, “Field Oriented Control of a Permanent Magnet Synchronous Generator for use in a
Variable Speed Tidal Stream Turbine,” in Universities’ Power Engineering Conference (UPEC), Proceedings of
2011 46th International, Sep. 2011, pp. 1–6.

IJPEDS ISSN: 2088-8694 1329
[7] J. W. Choi and S. C. Lee, “Antiwindup Strategy for PI-Type Speed Controller,” IEEE Transactions on Industrial
Electronics, vol. 56, no. 6, pp. 2039–2046, Jun. 2009.
[8] H. B. Shin and J. G. Park, “Anti-Windup PID Controller With Integral State Predictor for Variable-Speed Motor
Drives,” IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1509–1516, Mar. 2012.
[9] S. Tarbouriech and M. Turner, “Anti-windup design: an overview of some recent advances and open problems,”
IET Control Theory Applications, vol. 3, no. 1, pp. 1–19, Jan. 2009.
[10] R. J. Wai, J. D. Lee, and K. L. Chuang, “Real-Time PID Control Strategy for Maglev Transportation System via
Particle Swarm Optimization,” IEEE Transactions on Industrial Electronics, vol. 58, no. 2, pp. 629–646, Feb.
2011.
[11] F. C. Ferreira, T. R. Nascimento, M. F. Santos, N. F. S. Bem, and V. C. Reis, “Anti wind-up techniques applied
to real tank level system performed by PI controllers,” in 2016 20th International Conference on System Theory,
Control and Computing (ICSTCC), Oct. 2016, pp. 263–268.
[12] L. Meng and M. Li, “A new antiwindup pi controller for direct torque control system,” TELKOMNIKA Indonesian
Journal of Electrical Engineering, vol. 12, no. 7, pp. 5268–5274, 2014.
[13] A. Visioli, “Modified anti-windup scheme for PID controllers,” IEE Proceedings - Control Theory and Applica-
tions, vol. 150, no. 1, pp. 49–54, Jan. 2003.
[14] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in , Proceedings of the Sixth Inter-
national Symposium on Micro Machine and Human Science, 1995. MHS ’95, Oct. 1995, pp. 39–43.
[15] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evo-
lutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360),
May 1998, pp. 69–73.
[16] Y. d. Valle, G. K. Venayagamoorthy, S. Mohagheghi, J. C. Hernandez, and R. G. Harley, “Particle Swarm Op-
timization: Basic Concepts, Variants and Applications in Power Systems,” IEEE Transactions on Evolutionary
Computation, vol. 12, no. 2, pp. 171–195, Apr. 2008.
[17] W. Qiao, G. K. Venayagamoorthy, and R. G. Harley, “Design of Optimal PI Controllers for Doubly Fed Induction
Generators Driven by Wind Turbines Using Particle Swarm Optimization,” in The 2006 IEEE International Joint
Conference on Neural Network Proceedings, 2006, pp. 1982–1987.
[18] M. Yang, X. Wang, and K. Zheng, “Adaptive backstepping controller design for permanent magnet synchronous
motor,” in 2010 8th World Congress on Intelligent Control and Automation, Jul. 2010, pp. 4968–4972.
[19] C. Ed-dahmani, H. Mahmoudi, and M. Elazzaoui, “Direct torque control of permanent magnet synchronous
motors in MATLAB/SIMULINK,” in 2016 International Conference on Electrical and Information Technologies
(ICEIT), May 2016, pp. 452–457.

A New Adaptive Anti-windup Controller for Wind Energy Conversion System Based on PMSG

More Related Content

What's hot (20)

Similar to A New Adaptive Anti-windup Controller for Wind Energy Conversion System Based on PMSG (20)

More from International Journal of Power Electronics and Drive Systems (20)

Recently uploaded (20)

A New Adaptive Anti-windup Controller for Wind Energy Conversion System Based on PMSG