Indirect power control of DFIG based on wind turbine operating in MPPT using backstepping approach

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 3, June 2021, pp. 1951~1961
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i3.pp1951-1961  1951
Journal homepage: http://ijece.iaescore.com
Indirect power control of DFIG based on wind turbine
operating in MPPT using backstepping approach
Yahya Dbaghi, Sadik Farhat, Mohamed Mediouni, Hassan Essakhi, Aicha Elmoudden
Laboratoire des Sciences de l’Ingénieur et Management de l’Energie (LSIME) ESTA Ibn Zohr University,
Agadir, Morocco
Article Info ABSTRACT
Article history:
Received Jul 15, 2020
Revised Dec 12, 2020
Accepted Dec 22, 2020
This paper describes a MPPT control of the stator powers of a DFIG
operating within a wind energy system using the backstepping control
technique. The objective of this work consists of providing a robust control
to the rotor-side converter allowing the stator active power to be regulated at
the maximum power extracted from the wind turbine, as well as maintaining
the stator reactive power at zero to maintain the power factor at unity, under
various conditions. We have used the Matlab/Simulink platform to model the
wind system based on a 7.5 kW DFIG and to implement the MPPT control
algorithm in a first step, then we have implemented the field-oriented control
and the backstepping controller in a second step. The simulation results
obtained were very satisfactory with a fast transient response and neglected
power ripples. They furthermore confirmed the high robustness of the
approach used in dealing with the variation of the internal parameters of the
machine.
Keywords:
Backstepping control
DFIG
Lyapunov stability
MPPT
Wind turbine
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yahya Dbaghi
Department of Electrical Engineering
Ibn Zohr University
Ecole Supérieure de Technologie d'Agadir, B.P 33/S, Agadir 80000, Morocco
Email: yahya.dbaghi@edu.uiz.ac.ma
1. INTRODUCTION
The investigated wind power system consists of a wind turbine driven by the DFIG shaft via a
gearbox as shown in Figure 1. The generator's stator is wired straight into the electrical grid while its rotor
dissipates or absorbs its power into the electrical grid through a back-to-back converter, which is composed
of an AC/DC converter on the rotor side, a DC link capacitor on the DC bus side, and a DC/AC converter on
the grid side. This configuration allows the rotor speed to be adjustable according to the wind speed [1-3].
Variable speed allows the wind turbine to operate over a wider range of wind speeds and to extract
the maximum possible power for each wind speed. Compared to some synchronous machines, which use
converters traversed by the entire rated power, DFIG's converters are sized to allow only 25% of the rated
power to pass through [4]. They are therefore less cumbersome and less expensive [5, 6].
The research work on the control of the DFIG is almost all based on the field oriented control. This
technique which makes the DFIG similar to the DC machine through the separation of the rotor and stator
fluxes provides the basis for the implementation of the control methods [7]. In the recent years, the shift
towards non-linear control methods by researchers has reached an interesting place in the control scope of
converters in systems with sustainable energy, precisely in photovoltaics and wind turbines. Their robustness
against disturbances and their ability to withstand non-linear systems renders these control methods an

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 3, June 2021 : 1951 - 1961
1952
efficient tool that satisfies the requirements demanded and offers solutions to issues experienced by the linear
PID [8, 9].
Although various control methods have recently been proposed in the literature, the linear PI with
vector control is still the most famous one, but its drawbacks are numerous, namely its lack of robustness in
rejecting internal and external disturbances. Nowadays, researchers have switched to intelligent control
strategies such as fuzzy logic, neural networks, and genetic algorithms. While these methods are very robust
and efficient, their implementation is not obvious, it requires a high level of expertise on the one hand and
they require very fast and powerful calculation processors on the other hand. Lyapunov's stability based
nonlinear control methods such as SMC and backstepping remain a good compromise that is easily
implementable, robust and offers high satisfaction. These methods have already been proposed by
researchers as an application in the control of DFIG-based wind energy systems. In [10] Zamzoum et al. have
proposed the technical adaptive sliding mode as a control solution, its results showed that the use of high
order sliding mode control offers robustness against variations of internal machine parameters with fast
transient response. The backstepping control of the DFIG which was treated by Mensou et al. in [11] also has
showed a great robustness against external disturbances as well as offering a very fast response time.
In this paper a backstepping control has been proposed in order to control the rotor-side converter to
ensure a smooth regulation of the DFIG stator active power to follow the variation of the maximum power
extracted by the turbine. And with the aim of validating the robustness of this control technique we have
tested its behaviour under the variation of the internal parameters of the DFIG. This paper is organized as
follows: Section 2 deals with wind turbine modeling and the MPPT algorithm. The modeling of the DFIG
with field oriented control is presented in section 3. Section 4 describes the design of the backstepping
controller. Section 5 is reserved for the presentation and discussion of the simulation results, and the last
section gives a conclusion of this work.
Figure 1. Studied wind turbine system
2. MODELING OF WIND TURBINE AND MPPT ALGORITHM
The power generated by the wind turbine is defined by the formula (1) [12]:
Paer = Cp(λ, β).
ρ.S.V3
2
=
1
2
Cp(λ, β). ρ. π. R2
. V3
(1)
where ρ is the air density, S is the surface swept by the turbine, V is the wind speed, β is the angle of the
orientation of the blades.
λ is the speed ratio defined by (2) [13] :
λ =
R.Ωt
V
(2)
where Ωt is the angular velocity of the turbine shaft.

Int J Elec & Comp Eng ISSN: 2088-8708 
Indirect power control of DFIG based on wind turbine operating in MPPT using ... (Yahya Dbaghi)
1953
Cp(λ, β) is the power coefficient and refers to the efficiency of the wind turbine in terms of energy
conversion, which is expressed (3) [14] :
{
Cp(λ, β) = C1(C2 × A − C3β − C4)e−c5×A
+ C6λ
A =
1
λ+0.08β
−
0.035
β3+1
(3)
with C1= 0.5109, C2= 116, C3= 0.4, C4= 5, C5= 21, C6= 0.0068.
The power coefficient evolutions as a function of the speed ratio λ and the beta β are illustrated in
Figure 2. It is shown on this figure that for operation in MPPT mode with β=0°, the optimum speed ratio is
λopt=8.1 for Cpmax=0.48.
Figure 2. Power coefficient Cp
The expression of the aerodynamic torque is (4):
Taer =
Paer
Ωt
=
1
2
Cp(λ, β).
ρ.π.R2.V3
Ωt
(4)
The rotary speed and torque at the generator shaft are defined as (5) [15]:
{
Ωmec = G. Ωt
Tg =
Taer
G
(5)
where
Ωmec: mechanical speed of the generator.
Tg: The generator torque.
Applying Newton's second law of motion to the generator shaft gives:
{
Tg − Tem − fΩ = J
dΩmec
dt
J =
Jt
G
+ Jg
(6)
with:
J, Jg,Jt : the total inertia, the generator inertia, and the turbine inertia.
f: Damping coefficient.
Tem: the electromagnetic torque of the generator.
We can eventually express the optimum electromagnetic torque corresponding to the maximum
power as shown in (7) [16]:

 ISSN: 2088-8708
1954
Temopt =
Cpmax(λ,β)
λopt
3 ×
ρ.π.R5
2
×
Ωmec
2
G3 (7)
The block diagram of the wind turbine with the MPPT algorithm is shown in Figure 3.
Figure 3. Block diagram of the wind turbine system with MPPT
3. MODELING AND VECTOR CONTROL OF THE DFIG
DFIG’s rotor and stator voltages equations in the park frame are expressed as (8) [17]:
{
Vds = RsIds +
d
dt
φds − ωsφqs
Vqs = RsIqs +
d
dt
φqs + ωsφds
Vdr = RrIdr +
d
dt
φdr − ωrφqr
Vqr = RrIqr +
d
dt
φqr + ωrφdr
(8)
where Rr and Rs are respectively the rotor and stator resistances per phase.
Rotor and stator flux are defined as (9) [18]:
{
φds = LsIds + MIdr
φqs = LsIqs + MIqr
φdr = LrIdr + MIds
φqr = LrIqr + MIqs
(9)
where Lr, Ls are respectively the rotor and stator inductances. M is the machine mutual inductance. Idr, Iqr, Ids,
Iqs, are respectively the (d, q) rotor and stator currents.
The stator active and reactive powers are defined by (10):
{
Ps = VdsIds + VqsIqs
Qs = VqsIds − VdsIqs
(10)
Electromagnetic torque is expressed as [19]:
Tem =
pM
Ls
(Idrφqs − Iqrφds) (11)
where p is the pair pole number:

1955
By aligning the d-axis in the direction of the stator flux [20], we obtain:
φds = φs , φqs = 0 (12)
As a consequence, the stator flux expressions become:
{
φs = LsIds + MIdr
0 = LsIqs + MIqr
(13)
The stator resistance is negligible because of its small value. In this sense, the stator voltages can be described
as (14):
{
Vds = 0
Vqs = Vs = ωsφs
(14)
From (13) and (14) the stator currents can be expressed as:
{
Ids =
φs
Ls
−
M
Ls
Idr
Iqs = −
M
Ls
Iqr
(15)
By combining (10), (14) and (15) the stator active and reactive powers become:
{
Ps = −Vs
M
Ls
Iqr
Qs =
Vs²
ωsLs
− Vs
M
Ls
Idr
(16)
Hence, we can express the rotor voltages (d, q) in (17):
{
Vdr = RrIdr + (Lr −
M2
Ls
)
dIdr
dt
+ eq
Vqr = RrIqr + (Lr −
M2
Ls
)
dIqr
dt
+ ed
(17)
In which the coupling voltages ed and eq are expressed as:
{
eq = −ωr (Lr −
M2
Ls
) Iqr
ed = ωr (Lr −
M2
Ls
) Idr +
gMVs
Ls
(18)
where g is the slip.
4. BACKSTEPPING CONTROL IN THE LOOP OF INDIRECT FIELD ORIENTED CONTROL
The fundamental idea of backstepping is to synthesize the control law in a recursive way, at each
step a system state is stabilized, assuming the next state as a virtual control. The objective of the method is to
design a control law that brings the system to a desired state, which is generally a stable closed-loop steady
state [21]. These can be achieved by using Lyapunov's functions which ensure stepwise stability [22].
- Step 1 Active and reactive power control
The active and reactive power errors are defined as:
{
z1 = Ps
∗
− Ps
z3 = Qs
∗
− Qs
(19)
By deriving the error we obtain:
{
ż1 = Ṗs
∗
− Ps
̇
ż3 = Q̇ s
∗
− Qs
̇
(20)

 ISSN: 2088-8708
1956
The first Lyapunov candidate function is:
V1 =
1
2
z1
2
+
1
2
z3
2
(21)
The derivative of the function V1 is expressed as:
V̇1 = z1ż1 + z3ż3 (22)
Therefore V1 can be expressed as:
V̇1 = −C1z1
2
− C3z3
2
(23)
C1 and C3 are positive constants ensuring the Lyapunov stability condition [23-24]. The derivative of the
power errors becomes:
{
ż1 = Ṗs
∗
+
VsM
Ls
İqr = −C1z1
ż3 = Q̇ s
∗
+
VsM
Ls
İdr = −C3z3
(24)
From (24) rotor currents references are expressed as (25):
{
İdr
∗
=
Ls
VsM
(−Q̇ s
∗
− C3z3)
İqr
∗
=
Ls
VsM
(−Ṗs
∗
− C1z1)
(25)
- Step 2 Rotor currents control
The rotor currents errors are defined as:
{
z2 = Iqr
∗
− Iqr
z4 = Idr
∗
− Idr
(26)
The derivative of the rotor currents errors is:
{
ż2 = İqr
∗
− İqr
ż4 = İdr
∗
− İdr
(27)
with:
{
İdr =
1
Lrσ
(Vdr − RrIdr − eq)
İqr =
1
Lrσ
(Vqr − RrIqr − ed)
(28)
The second Lyapunov function chosen is defined as:
V2 =
1
2
(z1
2
+ z2
2
+ z3
2
+ z4
2) (29)
The derivative of V2 is defined as (30):
V̇2 = z1ż1 + z2ż2 + z3ż3 + z4ż4 (30)
We can rewrite (30) is being as:
V̇2 = −C1z1
2
− C2z2
2
− C3z3
2
− C4z4
2
(31)
C2 and C4 are positive constants ensuring the Lyapunov stability condition.

1957
{
ż2 =
Ls
VsM
(−Ṗs
∗
− C1z1) −
1
Lrσ
(Vqr − RrIqr) = −C2z2
ż4 =
Ls
VsM
(−Q̇ s
∗
− C3z3) −
1
Lrσ
(Vdr − RrIdr) = −C4z4
(32)
with σ = (1 −
M2
LrLs
).
Then the references rotor voltages are given by:
Vdr
∗
= Lrσ (
Ls
VsM
(−Q̇ s
∗
− C3z3) +
Rr
Lrσ
Idr + K4z4)
Vqr
∗
= Lrσ (
Ls
VsM
(−Ṗs
∗
− C1z1) +
Rr
Lrσ
Iqr + K2z2)
(33)
The Figure 4 illustrates the indirect flux oriented control of DFIG using the backstepping method.
The diagram contains the calculation blocks for the desired rotor currents and voltages, the PARK
transformation blocks, a PWM inverter on the rotor side, and the model of the DFIG in the PARK frame dq.
Figure 4. Block diagram of IFOC backstepping control
5. SIMULATION RESULTS AND DISCUSSION
5.1. Normal test simulation
This part presents the results of the simulation of the wind turbine assembly with DFIG. We have
applied a wind profile to the turbine illustrated by the Figure 5. Figure 6 presents the mechanical speed
response of the DFIG.
Figure 5. Wind speed Figure 6. Mechanical speed response
Time (s)
Wind speed profile (m/s)
Time (s)
Mechanical speed (rd/s)

 ISSN: 2088-8708
1958
It can be clearly seen on the figure that the DFIG angular velocity follows perfectly its reference
speed image of the MPPT, as validated by maintaining of the power coefficient Cp around its optimal value
of 0.48 as shown in Figure 7. The maximum power produced by the wind turbine is therefore the reference
applied to the active stator power control loop of the DFIG, the simulation results as presented in Figure 8
show a perfect tracking of the MPPT power by the DFIG while the response time is tr5%=0.4 (ms) and some
neglected power oscillations, on the same figure one can see the variation of the rotor active power which
varies according to the operating mode of the machine (hypo-synchronous or hyper-synchronous). Figure 9
also shows a very satisfactory regulation of the reactive stator power at a zero value, this being confirmed by
Figure 10 which shows that as a result the power factor is maintained at unity. The coupling terms presented
in (17) are perfectly compensated and this is of obvious benefit by the use of indirect flux control. In
Figure 11 the variations of the stator current injected into the electrical grid are shown, they are smooth and
present a very low THD%=0.75 according to the FFT analysis Figure 12.
Figure 7. Power coefficient response Figure 8. Active stator and rotor power response
Figure 9. Reactive stator power response Figure 10. Power factor response
Figure 11. Stator currents response Figure 12. FFT analysis of stator currents
Power coefficient Cp
Time (s) Time (s)
Active powers (W)
Reactive stator power (var)
Time (s) Time (s)
Power factor
Time (s)
Stator currents (A)

1959
5.2. Robustness test simulation
In order to test the robustness of the Backstepping controller against unexpected variations in the
internal parameters of the DFIG, caused by an increase in temperature or other events. We have elaborated a
robustness test in which the rotor resistance and inductance values are increased by 50%:
{
L′r = Lr × 1.5
R′r = Rr × 1.5
(34)
Figures 13-15 illustrate the simulation results. It is immediately observed that the regulations have
been fully ensured both for the active and reactive power but there is some slight degradation of the
performances obtained previously. At the level of active power we have an increase in response time to
tr5%=1.7 (ms) with an overshoot of 100 (W) which remains very negligible for a few (ms). For the reactive
power we also have an overshoot of -100 (var) during the first 40ms. These results confirm that the
backstepping control is highly robust through indirect field oriented control. It offers a perfect decoupling
between the d and q axes, a much reduced response time and minimal power oscillations in the face of
disturbances resulting from variations in the internal DFIG’s parameters.
Figure 13. Active stator and rotor power response in
robustness test
Figure 14. Reactive stator power response
in robustness test
Figure 15. Reactive stator power response in robustness test
6. CONCLUSION
This work presents the modeling and the control of a DFIG-based wind energy system using
backstepping technique under variable wind speed. The maximum power point tracking was achieved by
using the tip speed ratio algorithm. The results of the simulation of the indirect field oriented control with
backstepping controller showed a high stability of the system as well as a very fast response, with a very low
total harmonic distortion of the stator currents injected into the power grid, even with the variation of the
internal parameters of the DFIG. However, these results confirm the efficiency of this non-linear
backstepping control which is easy to implement and gives sometimes better results than the use of the new
control based on artificial intelligence such as fuzzy logic or neural network control.
Active powers (W)
Time (s) Time (s)
Reactive stator power (var)
Power factor
Time (s)

 ISSN: 2088-8708
1960
APPENDIX
The following Table 1 presents the nominal values of the different parameters of the studied wind
turbine system.
Table 1. DFIG and wind turbine parameters [25]
Wind turbine parameters Values DFIG parameters Values
Rated power 7.5KW Rated power 7.5KW
Damping coefficient 0,024 N.m.s.rad-1 Rated stator voltage 220/380 V
Air density 1.225Kg.m-3 Number of pole pairs p = 2
Blade radius 3 m Rotor resistance Rr = 0.19 Ω
Mechanical speed multiplier 5.4 Stator resistance Rs = 0.455 Ω
Moment of inertia 0,042 Kg.m-1 Stator inductance Ls = 0.07H
Rotor inductance Lr= 0.0213H
Mutual inductance M = 0.034H
REFERENCES
[1] S. A. Davari, “Predictive rotor control of DFIG supplied with back-back converters for variable speed wind turbine
application,” 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), Istanbul, 2014,
pp. 1415–1419.
[2] M. Taoussi, M. Karim, D. Hammoumi, C. E. Bekkali, B. Bossoufi, and N. E. Ouanjli, “Comparative study between
backstepping adaptive and field-oriented control of the DFIG applied to wind turbines,” 2017 International
Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Fez, 2017, pp. 1–6.
[3] S. Labdai, B. Hemici, L. Nezli, N. Bounar, A. Boulkroune, and L. Chrifi-Alaoui, “Robust Control based on
Backstepping and adaptive neural network for the DFIG based WECS,” 2019 International Conference on Control,
Automation and Diagnosis (ICCAD), Grenoble, France, 2019, pp. 1–6.
[4] S. Farhat, R. Alaoui, A. Kahaji, and L. Bouhouch, “Wind Turbine MPPT Strategy with DFIG Vector Control,”
International Review on Modelling and Simulations (IREMOS), vol. 11, no. 6, p. 406, 2018.
[5] A. Daoud and N. Derbel, “Direct Power Control of DFIG Using Sliding Mode Control Approach,” Modeling,
Identification and Control Methods in Renewable Energy Systems, pp. 193–204, 2018.
[6] D. Porate, S. P. Gawande, K. B. Porate, R. N. Nagpure, M. A. Waghmare, and S. G. Kadwane, “Performance of
PMSG Based Variable Speed WECS with Parallel Back-to-Back Converters Using Separate Zero d-axis Current
Control,” 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES),
Chennai, India, 2018, pp. 1–6.
[7] I. Karaoui, M. Maaroufi, and B. Bossoufi, “Robust power control methods for wind turbines using DFIG-
generator,” International Journal of Power Electronics and Drive Systems(IJPEDS), vol. 10, pp. 2101–2117, 2019.
[8] J. E. Son, “Adaptive and Robust Controls for Uncertain Linear and Nonlinear Systems,” [Gainesville, Fla.]:
University of Florida, 2012.
[9] B. Bhalerao and V. V. Khatavkar, “A Literature Review on Modelling and Control of Solar-Wind Hybrid System
using PID & Fuzzy-PID Controller,” 2018 International Conference on Recent Innovations in Electrical,
Electronics & Communication Engineering (ICRIEECE), Bhubaneswar, India, 2018, pp. 2416–2421.
[10] O. Zamzoum, Y. El Mourabit, M. Errouha, A. Derouich and A. El Ghzizal, “Active and Reactive Power Control of
Wind Turbine based on Doubly Fed Induction Generator using Adaptive Sliding Mode Approach,” International
Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 2, 2019.
[11] Mensou S., Essadki A., Nasser T., Bououlid I. B, and Ben T. L., “Dspace DS1104 implementation of a robust
nonlinear controller applied for DFIG driven by wind turbine,” Renewable Energy, vol. 147, pp. 1759–1771, 2020.
[12] E. Elhaji, “Modeling and Control of Wind Turbine to Damp the Power Oscillation,” Stability Control and Reliable
Performance of Wind Turbines, 2018. Available: 10.5772/intechopen.74835.
[13] I. Idrissi, H. Chafouk, R. El Bachtiri, and M. Khanfara, “Modeling and Simulation of the Variable Speed Wind
Turbine Based on a Doubly Fed Induction Generator,” Modeling of Turbomachines for Control and Diagnostic
Applications, IntechOpen, 2019.
[14] Y. Dbaghi, S. Farhat, and M. Mediouni, “First Order Sliding Mode and Super-twisting Sliding Mode Control of
Doubly Fed Induction Generator Driven by Wind Turbine System: A Comparative Study,” Journal of Advanced
Research in Dynamical and Control Systems, vol. 12, no. 04, pp. 1656–1667, 2020.
[15] B. Bossoufi, M. Karim, A. Lagrioui, M. Taoussi, and A. Derouich, “Observer backstepping control of DFIG-
Generators for wind turbines variable-speed,FPGA-based implementation,” Renewable Energy, vol. 81,
pp. 903–917, 2015.
[16] D. Kumar and K. Chatterjee, “A review of conventional and advanced MPPT algorithms for wind energy systems,”
Renewable and Sustainable Energy Reviews, vol. 55, pp. 957–970, 2016.
[17] M. Bouderbala, B. Bossoufi, A. Lagrioui, M. Taoussi, H. Aroussi, and Y. Ihedrane, “Direct and indirect vector
control of a doubly fed induction generator based in a wind energy conversion system,” International Journal of
Electrical and Computer Engineering (IJECE), vol. 9, no. 3, pp. 1531-1540, 2019.

1961
[18] C. Ahlem, B. A, D. I and B. Barkati, “Comparative study of two control strategies proportional integral and fuzzy
logic for the control of a doubly fed induction generator dedicated to a wind application,” International Journal of
Power Electronics and Drive Systems (IJPEDS), vol. 11, no. 1, pp. 263-274, 2020.
[19] Y. Bakou et al., “DTC Control of the DFIG, Application to the Production of Electrical Energy,” 2019 8th
International Conference on Renewable Energy Research and Applications (ICRERA), Brasov, Romania, 2019,
pp. 910-915.
[20] F. Senani et al., “A Complete Modeling and Control for Wind Turbine Based of a Doubly Fed Induction Generator
using Direct Power Control,” International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 8,
pp. 1954-1962, 2017.
[21] A. Haruna, Z. Mohamed, M. Efe, and M. Basri, “Improved integral backstepping control of variable speed motion
systems with application to a laboratory helicopter,” ISA Transactions, vol. 97, pp. 1-13, 2020.
[22] S. Sastry, “Lyapunov Stability Theory,” Interdisciplinary Applied Mathematics, pp. 182-234, 1999.
[23] M. Makhad, M. Zazi, and A. Loulijat, “Nonlinear control of WECS based on PMSG for optimal power extraction,”
International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 3, pp. 2815-282, 2020.
[24] O. Boughazi, “Sliding Mode Backstepping Control of Induction Motor,” International Journal of Power
Electronics and Drive System (IJPEDS), vol. 4, no. 4, pp. 481-488, 2014.
[25] W. Ayrir and A. Haddi, “Fuzzy 12 sectors improved direct torque control of a DFIG with stator power factor
control strategy,” International Transactions on Electrical Energy Systems, vol. 29, no. 10, 2019.

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