Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
Intelligent Control of DFIG based Variable
Speed Wind Turbine System using Artificial
Neural Network
Sathansa and Jitender Rohillab
a.
is Professor with the Department of Electrical Engineering, National Institute of Technology, Kurukshetra-136119, INDIA (phone:
+91-1744-233390; fax: +91-1744-238050; e-mail: sathans@ rediffmail.com).
b.
Jitender Rohilla was M. Tech. Student with the Department of Electrical Engineering, National Institute of Technology,
Kurukshetra-136119, INDIA (e-mail: jiturohilla@gmail.com).
Abstract— In this paper, intelligent control scheme using artificial
particle swarm optimization (PSO) to design the optimal PI
controllers for the rotor side converter and grid side converter
of DFIG for a particular wind speed. In [8], the authors used
intelligent control techniques for the variable speed cage
machine used for the wind generation system, in which a cage
induction machine is considered and a fuzzy control system is
used to drive the wind energy conversion system to the point
of maximum energy capture for a given wind velocity. Fuzzy
logic is used to develop an advanced and intelligent control
strategy for a line excited cage generator system used for wind
power applications in [9]. In [10], ANN control technique has
been developed for DFIG based wind energy generation
system.
This paper presents the design and implementation of
intelligent control scheme using ANN for DFIG based
variable speed wind turbine system. The first part of the paper
explains the modeling of wind turbine and ANN based rotor
loop design and the second part illustrates the modeling and
implementation of ANN based intelligent rotor control of
DFIG. The conventional vector control scheme is also
implemented on the same system to present the comparative
analysis of performance with the proposed ANN based control
scheme. It is demonstrated through results that the ANN
based control scheme ensures better stability and regulation of
the power generated by the DFIG based wind turbine system.
neural network (ANN) is proposed for doubly fed induction generator
(DFIG) based variable speed wind turbine system. ANN based rotor
loop design is developed for variable speed wind turbine and the
rotor side controller is proposed for DFIG to improve its transient
performance in all wind speed conditions. For comparative analysis,
the conventional vector control scheme is also implemented for the
system under investigation. It is observed, from the results, that the
dynamic performance of the DFIG is improved with the proposed
intelligent control scheme, as compared to the conventional control.
The simulations are carried out using MATLAB platform.
Keywords— Artificial neural network (ANN), Doubly fed
induction generator (DFIG), Intelligent control, Variable speed wind
turbine, Vector control.
I.
INTRODUCTION
I
N recent years, exploitation of fossil fuel is on the rise
leading to the increased air pollution by greenhouse gases.
As an alternative, renewable energy systems, especially wind
energy generation, have attracted great interests. Large wind
farms have been installed or planned across the globe and the
power ratings of the wind turbines are increasing. Wind power
installed capacity is growing at the rate of 20% annually on
the average around the world, and its cost has decreased 50%
in the last 10 years [1]. In wind farms, wind turbines based on
DFIG, with converters rated at 25% - 30% of the generator
rating, are used as compared to wind turbines using a fixed
speed induction generator, due to variable speed operation,
four-quadrant active and reactive power capabilities, lower
converter costs, lower power losses, higher efficiency, reactive
power production, and flexible control of DFIG [2-4]. Many
researchers have attempted to improve DFIG transient
performance by developing different control schemes in the
past. In [5], the authors investigated and compared the
different crow bar protections and rotor side converter restart
schemes to improve DFIG transient performance. A new
series and parallel connected grid side converters with
conventional DFIG was developed to damp out the stator and
rotor currents oscillations in [6]. The authors in [7] used
ISBN: 978-1-61804-221-7
II.
MODELING OF WIND TURBINE
Wind turbines produce electric power by using the power of
the wind to drive an electrical generator. The power contained
in the wind is given by the kinetic energy of the flowing air
mass per unit time [11] and is given as:
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Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
Under rated wind speed conditions, pitch angle β is
considered
zero.
Therefore,
the
three-dimensional
relationship of Cp, λ and β turns to the two-dimensional
relationship of Cp and λ, and for wind speed conditions
Although Eq. (1) gives the power available in the wind,
above rated speed, pitch angle β is controlled to get
the power transferred to the wind turbine rotor is reduced
steady
by the power coefficient,
output
power.
Because
the
wind
power
generation is a complex nonlinear system, ANN based
control method is proposed in this paper. ANN offers
many advantages over nonlinear adaptive controllers in
terms of robustness, improved performance, learning
capabilities, and increased flexibility [13]. Fig. 1 shows
the systematic diagram of ANN implementation to obtain
Cp vs. λ characteristics of wind turbine at different β.
Where,
is the power contained in wind (in watts) ,
ρ is the air density (1.225 kg/m3 at 15°C and normal
λ
pressure), A is the swept area in square meter, and V is
β
the wind velocity in m/sec. Fixed pitch wind turbine has
ANN Cp
been modeled to drive the DFIG. The number of blades
Fig 1 ANN block for Cp vs. λ characteristics
are considered as 3, blade radius is considered as 13m
with fixed pitch as ( = 0) [12]. Cp is calculated as
The Cp (λ, β) curves are known for some discrete
values of β( β =0….20). The discrete values are used as
training data for the ANN. The neural network has two
inputs, λ and β, and one output Cp, and it consists of two
Where,
C1=0.5176, C2=116, C3=0.4, C4=5, C5=21, C6=0.0068
hidden layers. The standard back-propagation algorithm
is used for learning. The neural network, so designed, is
able to produce a power coefficient for any value of tip
speed ratio in the range [0...20] and for any continuous
And turbine torque is
pitch value in the range [0...20]. Obtained with ANN, the
Fig 2 shows the Cp vs. λ characteristics of wind turbine at
different β.
Where,
The relationship of Cp, λ and β is nonlinear. The tip
speed ratio
is given by:
Where, R= radius to tip of rotor
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Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
referred to the stator side. The stator and rotor voltages
Cp(lambda)
0.5
B=0
B=5
B=10
B=15
B=20
0.45
0.4
0.35
are expressed as [15]:
Cp
0.3
0.25
0.2
0.15
0.1
0.05
0
0
2
4
6
8
lambda
10
12
14
16
Fig 2. Cp vs. λ characteristics of wind turbine at different β.
III.
The flux linkage equations of the stator and rotor can be
related to their currents and are expressed as:
MODELLING OF DFIG
DFIG is basically a wound rotor induction machine in
which stator is directly connected to the grid, and the
connection of the rotor to the grid is via a back- to-back
(PWM) convertor as shown in Fig. 3.
Where Lss = Ls +Lm and Lrr = Lr + Lm
The electromagnetic torque developed is expressed as:
Where, Tm is positive for motoring operation and
negative for generator operation. Equations (8) to (16)
are the set of differential equations which represent a 4th
order model for describing the dynamic behavior of
DFIG. These equations are simulated in MATLAB to
Fig.3 Schematic diagram of a DFIG-based wind energy
develop a model of the DFIG for analysis. In the
generation [14].
simulated model, the mechanical torque, the stator and
rotor input voltages and the synchronous speed are the
The DFIG is modeled same as induction machine.
inputs and the electromagnetic torque, the stator and
Stator and rotor windings are identical, sinusoidally
rotor currents and the rotor speed act as the outputs.
distributed and displaced 120 degree apart. In order to
The model can be run in sub-synchronous as well as in
explain the actual behavior of the DFIG, dynamic
super-synchronous mode. In sub-synchronous mode (ωe
equations are considered for more realistic observation.
>ωr ) positive load torque will operate the model as a
From the control point of view of the machine, the d-q
motor while in super-synchronous mode (ωr >ωe ) a
representation of an induction machine leads to control
negative load torque will operate the model as a
flexibility. The dynamic behavior of the DFIG in
generator. The power flow scheme of both operating
synchronous reference frame can be represented by the
modes is shown in Fig 4 [16].
Park equations provided all the rotor quantities are
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Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
And
Now by using eq. (21-24) the eq. (19-20) are:
Fig.4 Power flow of a DFIG system [16].
IV. CONTROL SCHEME OF DFIG
Power regulation between the generator and the grid is an
essential requirement when DFIG is connected to an existing
grid [17]. The control technique can be applied on both rotor
side converter (RSC) and grid side converter (GSC). The
objective of the RSC is to control both the active and reactive
powers independently; while the objective of the GSC is to
keep the dc-link voltage constant, regardless of the magnitude
and direction of the rotor power. This control technique is
called vector control technique and is a conventional
technique. In this paper, the main focus is the design of the
ANN based RSC controller.
by solving these eq. we have:
A. Conventional RSC Controller Design
In order to achieve a decouple control of active and reactive
power; stator flux oriented vector control scheme is adopted.
In the stator-flux oriented reference frame, the d-axis is
aligned with the stator flux linkage vector
s,
namely,
ds
= s and qs = 0. Stator voltage drop across resistance has
been neglected [18]. The frequency and amplitude of the stator
or grid voltage is assumed constant. Thus
Vds = 0, Vqs = Vs and ds = s , qs = 0. Neglecting the stator
resistance, Rs =0, then eq. (8) to (15) become
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Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
Fig.5 Vector control scheme of RSC
0
B. Proposed ANN controller
-1
ANNs are powerful tools for modelling. ANNs can identify
and learn correlated patterns between input data sets and
corresponding target values. After training, ANNs can be used
to predict the outcome of new independent input data [19].
In this paper, the data set from Conventional PI controller is
used to train ANN structure, with one input layer and one
output layer and number of hidden neurons as two, using
Levenberg-Marquardt back propagation method. In the
training process, 70% data is used for training, 15% is used for
validation and 15% is used for testing. The ANN speed control
technique block in a vector controlled drive system is shown
in Fig. 6. The controller observes the pattern of speed loop
error signal and correspondingly updates the output so that
matches the reference speed
.
actual speed
Torque
-2
-3
-4
-5
-6
-7
0
0.2
0.4
0.6
0.8
1
Time
1.2
1.4
1.6
1.8
2
7
8
9
10
8
9
10
Fig. 8 Mechanical input torque
ωr_ref +
*
output irq +
Input
-
2500
-
ANN
2000
ωr
irq
Wr
1500
1000
Fig.6 Proposed ANN control structure.
500
V.
RESULTS AND DISCUSSION
0
Simulation studies are carried out on a 3hp DFIG and its
control scheme is implemented in MATLAB environment.
The parameters used are given in Appendix. When, after 5 sec,
there is a step change in wind velocity from 12 m/s to 10 m/s,
the simulation results with conventional PI tuned controller
are shown in Fig. 9-12.
0
1
2
3
4
5
Time
6
Fig. 9 Rotor speed with conventional PI controller
180
160
140
13
120
100
Te
12.5
12
80
60
wind speed
11.5
40
11
20
0
10.5
-20
10
9.5
9
0
1
2
3
4
5
Time
6
7
Fig.10 Torque with conventional PI controller
0
1
2
3
4
5
time
6
7
8
9
10
Fig. 7 Wind velocity
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Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
100
200
80
60
150
Electromagnetic torque (Te)
Rotor Current (Ir)
40
20
0
-20
-40
-60
-80
-100
100
50
0
0
0.2
0.4
0.6
0.8
1
Time
1.2
1.4
1.6
1.8
2
-50
Fig.11 Rotor current without ANN
0
1
2
3
4
5
Time
6
7
8
9
10
1.4
1.6
1.8
2
1.8
2
Fig. 14 Torque with ANN control scheme
100
80
60
Stator Current (Is)
40
20
100
0
80
-20
60
-40
40
-80
20
-100
0
0.2
0.4
0.6
0.8
1
Time
1.2
1.4
1.6
1.8
2
Rotor Current
-60
0
-20
-40
Fig.12 Stator current with conventional PI controller
-60
-80
The results with the proposed ANN control scheme are shown
in Fig. 13-16.
-100
0
0.2
0.4
0.6
0.8
2500
1.2
Fig. 15 Rotor current with ANN control scheme
2000
Rotor speed (Wr)
1
Time
1500
100
80
1000
60
0
0
1
2
3
4
5
Time
6
7
8
9
10
Stator Current (Is)
40
500
20
0
-20
-40
-60
Fig.13 Rotor speed with ANN control scheme
-80
-100
0
0.2
0.4
0.6
0.8
1
Time
1.2
1.4
1.6
Fig. 16 Stator Current with ANN control scheme
From Fig. 9-16, it is evident that the disturbances caused in the
rotor speed, electromagnetic torque, stator and rotor current
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Proceedings of the 2014 International Conference on Power Systems, Energy, Environment
[12] K. Trinadha, A. Kumar, K. S. Sandhu, “ Study of Wind Turbine based
SEIG under Balanced/Unbalanced Loads and Excitation” International
Journal of Electrical and Computer Engineering (IJECE) Vol.2, No.3,
June 2012, pp. 353~370 ISSN: 2088-8708
[13] WEI Zhi-nong, YU Xiao-yong, WU Jia-jia, HAN Lian-shan, XIE Xiang,
CHE Dan, WANG Yue “The Intelligent Control of DFIG-Based Wind
Generation”.
[14] Tarek Medalel Masaud1, Student Member, IEEE And P.K. Sen2, Fellow
IEEE”Modeling And Control Of Doubly Fed Induction Generator For
Wind Power”
[15] Md. Arifujjaman, m.T. Iqbal,john E. Quaicoe “Vector Control Of A
Dfig Based Wind Turbine” Istanbul University Journal Of Electrical &
Electronics Engineering.
[16] Srirattanawichaikul,W, Kumsuwan Y, Premrudeepchacharn S,and Wu
B,”A Vector Control of A Grid Connected 3L-NPC-VSC with DFIG
Drives.”,(ECTI-CON) International Conference, pp .828,May 2012.
[17] Lie Xu, Cartwright P, “Direct Active and Reactive Power Control of
DFIG for Wind Energy Generation”, IEEE Transactions on Energy
Conversion, Vol. 21( 3), September 2006.
[18] Mohamed, M.B., Jemli, M., Gossa, Jemli, K., “Doubly fed induction
generator (DFIG) in wind turbine modeling and power flow control,”
Proceedings of the IEEE International Conference on Industrial
Technology 2004, AL; USA, Vol: 2, pp. 580-584, 2004.
[19] S. Haykin, “Neural Networks: A Comprehensive Foundation,” New
Jersey: Prentice-Hall, 1999.
are smoothened and with the use of the ANNs and less
oscillatory as compared to conventional PI controller.
VI. CONCLUSION
An ANN based speed control scheme of DFIG, driven by a
wind turbine, has been developed. The developed ANN
control scheme has reasonable accuracy and simple structure.
The comparative analysis of the performance with the
intelligent and conventional controllers shows that ANN is
very effective on the stabilization of the system.
APPENDIX
Stator Voltage
Rs
Rr
Xs
Xm
Xr
No of pole pair
J (Inertia constant)
220V
0.435 ohm
0.816 ohm
0.446 ohm
0.43 ohm
0.446 ohm
2
0.08 kg.m2
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