Review on Wind Power Generation With Doubly Fed
Induction Generator
Mridul Kanti Malakar
Praveen Tripathy
Department of Electronics and Electrical Engineering
Indian Institute of Technology Guwahati
Guwahati, India
E-mail: m.mridul@iitg.ernet.in
Department of Electronics and Electrical Engineering
Indian Institute of Technology Guwahati
Guwahati,India
E-mail: ptri@iitg.ernet.in
Abstract—This paper presents the review of the Doubly fed
Induction Generator (DFIG) based Wind Energy Conversion
System (WECS). Basically the various challenges of the grid
connected DFIG based wind turbine has been highlighted in this
review. Nonlinear model of wind turbine with DFIG
configuration are explained and a 1.2 MW, 50 Hz, 6 pole DFIG
simulated using MATLAB/SIMULINK. Some important control
issues are pointed in this paper.
Key words—Variable speed constant frequency, doubly fed
induction generator, MRAS.
I. INTRODUCTION
power is today's fastest growing renewable energy
WIND
source. The Global Wind Energy Council (GWEC) [1]
and Electrical India magazine [2] reported that worldwide
installed capacity of wind power reached 293 GW by the end
of 2011. India (14550 MW) is in 5 th position for installed
capacity. China (44733 MW), US (40180 MW), Germany
(27215 MW) and Spain (20676 MW) are advanced than India.
Present target of India declare that by the end of 2012,
installed capacity will be increased 6000 MW. Variable speed
operation is introduced to capture the maximum possible
energy available from the wind. Due to enormous advantages
over the other types of generator, DFIG is recently most
popular trend to use for extracting more wind energy with
variable speed constant frequency (VSCF).
Though basic concepts and features are explained in many
literatures and books, some of specific issues and the remedial
actions taken by the researcher are highlighted in the section
(II). Section (III) and (IV) describes the nonlinear behavior of
wind turbine with state space model and the basic
configuration of DFIG. A specified rated DFIG model has
been simulated and simulation results are given in the section
(V). Some challenging issues have been discussed in the
section (VI) and lastly important conclusions on the WECS
using DFIG are presented in the section (VII).
II. LITERATURE REVIEW
Leonhard [3] and P. Vas [4] well explained the vector
control techniques used for the independent control of torque
and excitation current in the synchronous reference frame.
Yamamoto and Motoyushi [5] have shown the technique to
decouple control of active and reactive powers of the machine.
In this technique used reference frame is fixed to the air-gap
flux and rotor side is connected to grid via cycloconverter. The
converter design and control techniques are described by Ned
Mohan et.al [6]. Pena, Clare and Asher [7] gave the detail
design of DFIG using back-to-back PWM voltage source
converters in the rotor circuit and also they have validated the
system experimentally considering a grid connected system
and stand-alone system.
The instantaneous position of the rotor is required with
respect to the stator for decoupled control of active and
reactive power. In conventional field oriented control
schemes, the instantaneous rotor position is estimated by using
absolute encoder fitted to the shaft. A high resolution position
encoder is more expensive as well as reduces the system
reliability. However, in DFIG wind turbine system the encoder
mounting is not easy task. The encoder must be mounted in
such way that angle between rotor and stator axis can be
access directly. P. Vas [4] described the sensor less vector
control of the machine. V.T.Ranganathan [8] also
implemented a position sensor less algorithm for rotor side
field oriented control of the machine. Pena [9] used model
reference adaptive system (MRAS) to estimate the same.
Some techniques are proposed by different researchers
[10],[11] to estimate rotor position, rotor currents and torque
angle. DFIG based system is dominating in grid connected
modern WECS. It has excellent advantages, such as maximum
power point tracking (MPPT) capability [12], variable speed
constant frequency (VSCF) operation [13] and independently
active-reactive power regulation [14]. Since the stator side of
DFIG is directly connected to the grid and the rotor side via
back-to-back converter, DFIG system is very sensitive to grid
faults [15]. Even though if severely symmetrical/asymmetrical
fault occurs, the grid voltage dips [15] obviously but wind
turbine must have the fault ride through (FRT) [16] capability
to continue power supply to grid and return back to normal
operation after clearance of the fault.
Therefore, the control strategy must be designed in such
way that DFIG can operate not only in normal grid conditions,
as well as in faulty grid conditions. There are so many
1
literatures explained FRT with converter protection
technology, low voltage ride through [17], voltage-frequency
regulations [18], uninterrupted power flow operations under
balanced or unbalanced gr id faults [19] as well as island
operations in the distributed energy generation systems.
However, during this fault it is required to keep the DC-link
voltage stable and limit the current fluctuations of the
generator side converter (GSC). After removing the fault,
GSC operatesagain in normal conditions. During this
operation DC-link voltage fluctuates and so that the rotor
current control strategy might be influenced which is
undesirable. Again for the high voltage applications the
electrolytic capacitor size has to be optimized because of
heavy, expensive and unreliability. Some of literature [20]–
[22] proposed the improved control strategies [21] such as
direct capacitor current control [20] and current injection [22].
But these are not fully sufficient control strategy for desired
performance as well as stability.
However, proposed technologies and algorithms are
explained in the above literatures so far do not address to all
the requirements of VSCF DFIG system. Recently, in order to
improve the steady state and transient stability, dynamic
performance and power quality of the DFIG system as well as
to produce more power output, lots of control strategies have
been proposed. Implementation of such control algorithms
requires faster real time computations. High-speed digital
signal processors (DSP) are recently introduced to overcome
such computational burden. DSP based implementation
approach has been described in the paper [23]. In this paper
[23] the performance of three control strategy namely vector
control (VC), direct torque control (DTC) and direct power
control (DPC) have been compared. Sliding mode robust
control technique has been introduced to reduce the torque
fluctuations [24] and maximum power utilization [25] , [26] In
this strategy parametric uncertainty of the system has been
described and also improved vector control strategy has been
proposed [27]. Multiple Model Predictive Control algorithms
has been suggested [28] for maximum energy extraction from
the wind turbine and reduction of drive train torsional torque.
Improved hysteresis based current regulator control strategy in
vector control has been proposed [29] and compares transient
and steady-state performances with the conventional PI based
current regulators.
III. NONLINEAR VARIABLE SPEED VARIABLE
PITCH WIND TURBINE SYSTEM
Variable pitch wind turbine [30] is used to extruct more
wind energy within the wide range of wind speed.
Aerodynamic blade pitch angle β is continuously controlled to
regulate the turbine rotor speed 𝝎𝒕 as well as captured wind
energy. The mechanical power Pmech produced by the wind
turbine is defined as,
𝑷𝒎𝒆𝒄𝒉 = 𝑻𝒕 𝝎𝒕 (1)
𝑇𝑡 =
𝐶 (𝜆,𝛽 )
1
𝜌𝐴𝑅3 𝑝 3 𝜔𝑡2 (2)
2
𝜆
Where, Tt , A, Cp(λ, β) and ρ is the turbine aerodynamic
torque, rotor swept area, power coefficient for a variable pitch
windturbine and air density respectively. The tip speed ratio,λ
=Rωt /ϑ. Where, R and ϑ stands for turbine rotor radius and
wind speed respectively. Using blade element momentum
(BEM) [30], [31] theory the pitch angle dynamics can be
derived as,
𝛽 = 𝐾1 (𝜔𝑡 − 𝜔𝑟 )(1 −
𝛽
𝐾2
)
(3)
The turbine rotor position can be expressed by δ and the
dynamic model [30] of variable pitch variable speed wind
turbine can be written as,
𝛿 = 𝜔𝑡
𝜔𝑡 =
1
𝐽
(4)
−𝐾𝑟 𝛿 − 𝐵𝑟 𝜔𝑡 + 𝑇𝑡 − 𝑇𝑒
𝛽 = 𝐾1 (𝜔𝑡 − 𝜔𝑟 )(1 −
𝛽
𝐾2
)
(5)
(6)
where K r , Br , J and Te is the rotor shaft torsion, rotor shaft
friction, rotor inertia and electromagnetic torque respectively.
K1 and K 2 is integration constant and the gain reduction
constant that reduces the pitch rate at high pitch angles
respectively. The parameters are not precisely known and
depending on several factors such as aerodynamic conditions,
in addition introduces uncertainty in the nonlinear wind
turbine dynamics [32], [33].
IV. DFIG CONFIGURATION
The generator configured by using wound rotor induction
generator (WRIG) with the stator windings directly connected
to the grid and rotor windings connected to the stator terminal
through the AC/DC/AC converter. A transformer is used to
match the voltage levels between grid and grid side converter.
The arrangement of DFIG configuration is depicted in Fig.1
Fig. 1: DFIG Configuration.
The DFIG system (Fig.1)presents enormous flexibility in
terms of control of active and reactive powers in variable
speed applications. The mode of operation [13], [34] can be
explained by in tabular form Table I,
TABLE I: MODE OF OPERATIONS
Slip
Mode
Torque 𝑃𝑚𝑒𝑐ℎ 𝑃𝑠𝑡𝑎𝑡𝑜𝑟 𝑃𝑟𝑜𝑡𝑜𝑟
motor
0 <S< 1
>0
<0
<0
>0
0 <S< 1 generator
<0
>0
>0
<0
motor
S <0
>0
>0
<0
<0
generator
S <0
<0
>0
>0
>0
2
The back-to-back converter is a bi-directional power
converter consisting of two conventional pulse width
modulation (PWM) voltage source converters and a common
DC bus employing a DC-link capacitor. Back-to-back
converters are widely using in DFIG based system for wind
power applications to produce morepower output. Depending
upon the decoupled control strategy and complexity, the DFIG
can be operated in both sub and super synchronous speed
range. This type of converter system consists of two PWM
converters with a DC-link capacitor as shown in Fig. 1. The
GSC is to be controlled to maintain constant DC-link voltage
and it is responsible also for reactive power control of the
DFIG and grid. The rotor side converter (RSC) control
strategy is basically taking care about electromagnetic torque
control and the rotor excitation currents. The whole control
strategy is defined in synchronous reference frame which are
decomposed into d-q components. For the GSC control, d-axis
voltage vector represents the DC-link voltage control and qaxis current is used to control the electromagnetic torque and
q-axis current controls the power factor which tries to
maintain unity power factor. It is important to ensure that the
dynamics of the speed controller are not extremely fast, else
large transients in generator torque may occur.
The rating of the converter is around 33% of total generator
power. It is dependent on the selected speed range or the slip
power. Due to the bi-directional power flow ability of the
converter, the DFIG may operate as a generator or motor in
both sub-synchronously (0 < s < 1) and super-synchronously
(s < 0) (explained in table: I). For the shake of simplicity
neglecting all the losses, the power relation [34] can be
obtained,
𝑃𝑟𝑜𝑡𝑜𝑟 ≃ −𝑠𝑃𝑠𝑡𝑎𝑡𝑜𝑟
(7)
𝑃𝑠𝑡𝑎𝑡𝑜𝑟 ≃
𝑃 𝑔𝑟𝑖𝑑
1−𝑠
(8)
1−𝑠
𝑃𝑚𝑒𝑐 ℎ ≃ −𝑃𝑟𝑜𝑡𝑜𝑟
(9)
𝑠
= 𝑃𝑠𝑡𝑎𝑡𝑜𝑟 + 𝑃𝑟𝑜𝑡𝑜𝑟
(10)
According to the conventional voltage equations of the
induction generator [9] and flux linkage equations [35], the
mathematical model of DFIG can be expressed in a d-q
reference frame rotating at synchronous speed in equations
(11) and (12).
𝑑𝜓𝑑𝑠
𝑉𝑑𝑠 = 𝑅𝑠 𝑖𝑑𝑠 +
− 𝜔𝑠 𝜓𝑞𝑠
𝑑𝑡
𝑑𝜓𝑞𝑠
𝑉𝑞𝑠 = 𝑅𝑠 𝑖𝑞𝑠 +
− 𝜔𝑠 𝜓𝑑𝑠
𝑑𝑡
𝑑𝜓𝑑𝑟
𝑉𝑑𝑟 = 𝑅𝑠 𝑖𝑑𝑟 +
− ( 𝜔𝑠 − 𝜔𝑟 )𝜓𝑞𝑟
𝑑𝑡
𝑑𝜓 𝑞𝑟
𝑉𝑞𝑟 = 𝑅𝑠 𝑖𝑞𝑟 +
− ( 𝜔𝑠 − 𝜔𝑟 )𝜓𝑑𝑟
(11)
respectively.
Slip, s =
𝜔𝑠 − 𝜔𝑟
𝜔𝑠
𝜔𝑠 and 𝜔𝑟 stands for synchronous speed (50Hz supply
frequency) and rotor speed respectively in (rad/s) . Subscripts
s, r and m denote the stator, rotor and the mutual inductances
respectively. The electromagnetic torque can be written as,
3
𝑇𝑒 = 𝑝(𝜓𝑑𝑟 𝑖𝑞𝑟 − 𝜓𝑞𝑟 𝑖𝑑𝑟 )
(13)
2
and the electrical active and reactive power (P,Q) for stator
and rotor can be defined as,
3
𝑃𝑠 = (𝑉𝑑𝑠 𝑖𝑑𝑠 + 𝑉𝑞𝑠 𝑖𝑞𝑠 )
2
3
𝑃𝑟 = (𝑉𝑑𝑟 𝑖𝑑𝑟 + 𝑉𝑞𝑟 𝑖𝑞𝑟 )
2
3
𝑄𝑠 = (𝑉𝑞𝑠 𝑖𝑑𝑠 − 𝑉𝑑𝑠 𝑖𝑞𝑠 )
2
3
𝑄𝑟 = (𝑉𝑞𝑟 𝑖𝑑𝑟 − 𝑉𝑑𝑟 𝑖𝑞𝑟 )
(14)
2
where, p is the number of pole pairs of the generator. The
subscripts s and r denote the stator and rotor quantities.
V. SIMULATION RESULTS
A 1.2 MW, 575 V, 6 pole, 50 Hz DFIG has been simulated
using MATLAB/SIMULINK whose stator resistance, rotor
resistance, stator inductance, rotor inductance and magnetizing
inductance are 0.023 pu, 0.015 pu, 0.18 pu, 0.16 pu and 2.9
purespectively. Based on 575 V and 1.2 MW, the machine
parameters are taken in per unit (pu). DC-link voltage has
been set at 1200 V with 60000 µF. The mechanical power at
the generator shaft is 1.2 MW. The controller gain has been
chosen to run the DFIG at steady state. In the simulation after
0.2 second DFIG runs at steady state. The controller gains are
given in the TableII. The simulated dynamic performances of
the DFIG are shown in Fig: [2-9].
With extensive simulation result these initial PI-controller
parameters are chosen to get the particular satisfactory results
for DFIG systemwhile wind speed varies from 12-18.5 m/s.
TABLE II: CONTROLLER GAINS
Controller
DC-link voltage regulator gain
GSC current regulator gain
RSC speed regulator gain
Reactive power controller gain
Pitch controller with angle compensation gain
𝐾𝑝
8
0.83
3
0.05
150
𝐾𝑖
400
5
0.6
20
30
𝑑𝑡
𝜓𝑑𝑠 = 𝐿𝑠 𝑖𝑑𝑠 + 𝐿𝑚 𝑖𝑑𝑟
𝜓𝑞𝑠 = 𝐿𝑠 𝑖𝑞𝑠 + 𝐿𝑚 𝑖𝑞𝑟
𝜓𝑑𝑟 = 𝐿𝑟 𝑖𝑑𝑟 + 𝐿𝑚 𝑖𝑞𝑟
𝜓𝑞𝑟 = 𝐿𝑟 𝑖𝑞𝑟 + 𝐿𝑚 𝑖𝑞𝑟
(12)
Where, the symbols V, i, R, ψ and L stands for the voltages,
currents, resistances, flux-linkages and winding inductances
Fig. 2: Mechanical power of wind turbine to the DFIG shaft
3
Fig. 7: Stator voltage of DFIG
Fig. 3: Stator power of DFIG
Fig. 4: Rotor power of DFIG
Fig. 5: Total power output of DFIG (super-synchronous
generating mode
Fig. 6: DC-link voltage of back to back converter
VI. CHALLENGES
For the partial steady load, the primary objective is to
control the turbine rotor speed to maximization of turbine
aerodynamic efficiency. For the partial steady systematic load,
first task is to control the turbine rotor speed to maximize the
aerodynamic efficiency. This can be done by manipulating
generator torque and blade pitch angle. In the same regime
more challenging is to design controller to maximize
conversion efficiency while keeping transient load minimum.
It is required to have trade-off between maximum conversion
efficiency and minimum transient load. For the full load, due
to unpredictable variations in the wind speed, one of the most
challenging task is to regulate both DFIG power output and
generator speed at their rated value in presence of severe
fluctuations in the turbine power. This fluctuation in the power
leads to large variations in the torque and grid which is the
cause of flicker problem. The cyclic aerodynamic torque [36]
variation also increases the dynamic loads and voltage flicker.
In presence of nonlinearity in the system dynamics as well
as in environment and continuous variation of the operating
point, it is the more challenges to design a controller.
Sensorless control strategies are usually used to estimate the
DFIG flux and rotor position using observer design [37] which
reduces the cost, size and maintenance of the drive system.
Due to nonlinearity of the wind turbine dynamics and
stochastic behavior of the wind, it is not so simple to estimate
the rotor position. It is very difficult to calculate the angular
difference with large range of vector angle. Another big
challenge to estimate the rotor position as well as machine
flux. Generally, open loop [8], [10], [11], [38] and [39]
observers are often used to estimate the rotor position. This is
directly observed by measurement of parameters and reference
frame transformation. The rotor speed is obtained by
differentiation of rotor position, which introduces noise into
the speed estimator. Moreover, open loop estimators are
highly dependent on machine parameters and the accuracy of
estimator is not guaranteed.
Closed-loop MRAS observers [9], [37] and [40] using
different output variables for speed adaptation were analyzed
and compared. But all MRAS observer models are based on
static flux-current relations. Therefore, they are very sensitive
to machine inductance. Another drawback is that all adaptive
4
observers are implemented in the stationary reference frame,
where the electrical states are usually sinusoidal functions of
time in steady state. Hence it is very difficult to design
controller parameters and the observer might become
inaccurate or even unstable in digital implementations.
VII. CONCLUSION
This paper is mainly focused on various challenges in grid
connected nonlinear wind turbine as per research work. It also
addressed about important control challenges of the DFIG
machine connected with wind turbine. However, in order to
enhance the efficiency of the DFIG power output, control
strategy must be improved.
REFERENCES
[1] www.gwec.net, 2012.
[2] Electrical India, 2012.
[3] W. Leonard, “Control of Electrical Drives” .Spinger,
New York, USA, 1985.
[4] P. Vas, “Sensorless Vector and Direct Torque Control” .
Oxford University Press, 1998.
[5] M. Yamamoto and O. Motoyoshi, “Active-reactive power
control for doubly fed wound rotor induction generator,”
IEEE Trans. Power Electronics, vol. 6, no. 4, pp. 624–
629, 1991.
[6] N. Mohan, T. M. Undeland, and W. P. Robbins, “Power
Electronics:Converters,Applications and Design” .
Clarendon Press, Oxford, UK,1989.
[7] R. Pena, J. C. Clare, and G. M. Asher, “Doubly fed
induction generator using back-to-back pwm converters
and its application to variable-speed wind-energy
generation,” IEEE Proc. Elect. Power Appl., vol. 143, pp.
231–241, 1996.
[8] Datta R. and Ranganathan V.T., “A simple position
sensor less algorithmfor rotor side field oriented
control of wound rotor induction machine,”IEEE
Trans. Ind. Electron, vol.48, no. 4, pp. 710–718, 2001.
[9] Cardenas R., Pena R., Proboste J., Asher G., and Clare J.,
“Mras observer for sensorless control of standalone
doubly fed induction generators,”
IEEETrans.Energy Conversion, vol. 20, no. 4, 2005.
[10] L. Xu and W. Cheng, “Torque and reactive power control
of a doubly fed induction machine by position sensorless
scheme,” IEEE Trans. Ind. Appl., vol. 31, no. 3, pp. 636–
641, 1995.
[11] L. Morel, H. Godfroid, A. Mirzaian, and J.M.
Kauffmann, “Doubly fed induction machine: converter
optimization and field orientated control without position
sensor,” Proc. Inst. Electr. Eng. Electr. Power Appl., vol.
145, no. 4, pp. 360–368, 1998.
[12] B. A. Chen, T. K. Lu, Y. Y. Hsu, W. L. Chen, and Z. C.
Lee, “An analytical approach to maximum power
tracking and loss minimization of a doubly fed induction
generator considering core loss,” IEEE Transactions,
EnergyConversion, vol. 27, no. 2, pp. 449 –456, june
2012.
[13] M. Tazil and V. Kumar et.al, “Three-phase doubly fed
induction generators: an overview,” IET Electric Power
Applications, vol. 4, pp. 75–89, 2010.
[14] J. Hu, H. Nian, B. Hu, Y. He, and Z. Q. Zhu, “Direct
active and reactive power regulation of dfig using slidingmode control approach,” EnergyConversion, IEEE
Transactions on, vol. 25, no. 4, pp. 1028 –1039,
dec.2010.
[15] J. Lopez, Sanchis P., Roboam X., and Marroyo L.,
“Dynamic behaviour of the doubly fed induction
generator during three phase voltage dips,” IEEETrans.
Energy Conversion, vol. 22, pp. 709–717, 2007.
[16] A. Mullane, Lightbody G., and Yacamini R., “Wind-turbi
ne fault ride-through enhancement,” IEEE Trans. Power
Syst, vol. 20, pp. 1929–1937, 2005.
[17] O. Abdel Baqi and A. Nasiri, “A dynamic lvrt solution for
doubly fed induction generators,” IEEE Trans. Power
Electron, vol. 25, no. 1, pp. 193–196, Jan 2010.
[18] Z. Wang, Y. Sun, G. Li, and B. T. Ooi, “Magnitude and
frequency control of grid connected doubly fed induction
generator based on synchronized model for wind power
generation,” IET Renewable Power Generation, vol. 4,
no. 3, pp. 232–241, may 2010.
[19] L. Xu and Y. Wang, “Dynamic modeling and control of
dfig-based wind turbines under unbalanced network
conditions,” IEEE Trans. Power Syst., vol. 22, no. 1, pp.
314–323, Feb 2007.
[20] Bon-GwanGu and K. Nam, “A dc link capacitor
minimization method through direct capacitor current
control,” IEEE Trans. Ind. Appl., vol. 42, no. 2, 2006.
[21] J. Yao, H. Li, and Z. Chen, “An improved control strategy
of limiting the dc-link voltage fluctuation for doubly fed
induction wind generator,” IEEETrans. Power
Electronics, vol. 23, no. 3, 2008.
[22] A. G. Abo-Khalil, “Current injection-based dc-link
capacitance estimation using support vector regression,”
IET Power Electronics, vol. 5, 2012.
[23] E. Tremblay, S. Atayde , and A. Chandra, “Comparative
study of control strategies for the doubly fed induction
generator in wind energy conversion systems: A dspbased implementation approach,” IEEE Trans.
SustainableEnergy., vol. 2, no. 3, pp. 288 –299, 2011.
[24] S. Z. Chen, N. C. Cheung, K. C. Wong, and J. Wu,
“Integral sliding-mode direct torque control of doubly-fed
induction generators under unbalanced grid voltage,”
IEEE Trans.Energy Conversion., vol. 25, no. 2, pp. 356–
368, 2010.
[25] B. Beltran, Md. E. H. andTarek Ahmed-Ali, “ Second
order sliding mode control of a doubly fed induction
generator driven wind turbine,” IEEE Trans. Energy
Conversion, 2011, Doi. 10.1109/TEC.2011.2181996.
[26] M. Itsaso, G. Tapia, A. Susprregui and R. P. Swatloski, “
Control of DFIG wind turbine with direct current vector
control configuration ,”IEEE trans. Sustainable Energy,
Vol. 2, no.3, pp. 215-225, 2011.
5
[27] S. Li, T. A. Haskew, K A. Williams, and R.P. Swatloski,
“C ontrol of dfig wind turbine with direct-current vector
control configuration,” IEEE Trans.Sustainable Energy.,
vol. 3, no. 1, pp. 1–11, 2012.
[28] M. Soliman, O.P.Malik, and W. T. Dvid, “Multiple model
predictive control for wind turbines with doubly fed
induction generators,” IEEE Trans.Sustainable Energy.,
vol. 2, no. 3, pp. 215–225, 2011.
[29] M. Mohensi, Syed M. Islam, and Md. A S Masoum,
“Enhanced hysteresis-based current regulators in vector
control of dfig wind turbines,” IEEETrans. Power
Electronics., vol. 26, no. 1, pp. 223–234, 2011.
[30] J.K. Sethi, D. Deb, and M. Malakar, “Modeling of a wind
turbine farm in presence of wake interactions,” Energy,
Automation, and Signal (ICEAS),2011 International
Conference on, pp. 1–6, 2011.
[31] J. F. Manwell, J. G. McGowan , and A. L. Rogers,
“Wind
energy
explained:Theory,
design
and
application” . John Wiley and Sons ltd., UK, 2004.
[32] R. Vepa, “Nonlinear, optimal control of a wind turbine
generator,” IEEETrans.Energy Convers, vol. 26, no. 2,
pp. 468–478, 2011.
[33] Salman S. K. and Teo A. L. J., “Wind mill modeling
consideration and factors influencing the stability of a
grid connected wind power based embedded generator,”
IEEE Trans.Power System, vol. 18, no. 2, pp. 793– 802,
2003.
[34] Boldia, “Variable Speed Generators: the electrical hand
book” . Taylor and francis, New York, USA, 2006.
[35] Rouco L. and Zamora L. “Dynamic patterns and model
order reduction in a small signal models of doubly fed
induction generator for wind power applications,” IEEE
PES General Meeting, pp. 1-8, 2006.
[36] T. Sun, Z. Chen and F. Blaabjerg, “Flicker study on
variable speed wind turbines with doubly fed induction
generator,” IEEE Trans. Power Electronics, Vol. 26, no.
1, pp. 896-905, 2011.
[37] Daniel G. Forchetti, Guillermo O. G, and Maria Ines
Valla, “Adaptive observer for sensorless control of standalone doubly fed induction generator,” IEEE Trans. Ind.
Electron, vol. 56, no. 10, pp. 4174 – 4180, Oct 2009.
[38] B. Hopfensperger, D. J. Atkinson, and R. A. Lakin, “Stat
or-flux oriented control of a doubly-fed induction
machine with and without position encoder,” Proc. Inst.
Electr. Eng. Electr. Power Appl., vol. 147, no. 4, pp. 241–
250, 2000.
[39] M. Abolhassani, P. Enjeti, and H. Toliyat, “Integrate d
doubly fed elctricalternator/active filter (idea), a variable
power quality s olution, for wind energy conversion
system,” IEEE Trans. Energy Conversion, vol. 23, no. 2,
pp. 642–650, 2008.
[40] Sheng Y. and Venkataramana A., “A speed-adaptive
reduced-order observer for sensorless vector control of
doubly fed induction generator-based variable speed wind
turbine,” IEEE Trans.EnergyConvertion, vol. 25, no. 3,
2010.
MridulKantiMalakarreceived the M.Tech. degreefrom
theNational Institute of Technology Agartala, Agartala,
India, in 2010. He is currently working toward the Ph.D.
degree in electrical engineering with theDepartmentof
Electronics and Electrical Engineering of IndianInstitute of
Technology Guwahati, Assam.
Hisresearchinterestsincludepower electronics and drives,
Powersystem, wind energy conversion system and control.
Praveen Tripathy (M’11) received his PhD in Electrical
Engineering from Indian Institute of Technology Kanpur,
India in 2011. He is presently working as Assistant Professor
in the Department of ElectronicsandElectrical Engineering
at the Indian Institute of Technology Guwahati, India.
His research interests include wide area monitoring and
control, power system dynamics, power system operation and
control, FACTS, distribution automation and power system
planning.
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