Doubly Fed Induction Generator-Based Wind Turbine Modelling and Simulation Using MATLAB/Simulink

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Doubly Fed Induction Generator-Based Wind Turbine Modelling and
Simulation Using MATLAB/Simulink
Abstract - The generator acts as the primary component in
a wind turbine (WT) system since it transforms mechanical
energy into electrical energy. Most wind turbine malfunctions
are caused by an unreliable generator. As a result, it is now
more important than ever to understand the specific
characteristics of the generator in wind turbines in order to
avoid errors. The objective of this paper is to create a
mathematical model of a wind turbine generator that is
simply changeable to apply any generator fault for the
research of the dynamic WT system. This is because the
majority of developed WT models are either too simplistic in
generator modelling or have intellectual property protection.
MATLAB/Simulink was used in this research to create the
mathematical model of the induction generator based on a
wind turbine. The wind turbinemodelthatwasbuiltcomprises
of an induction generator model, an aerodynamic model, and
a wind turbine drive train based on two mass models.
Electrical equations in Park's reference frame served as the
basis for the development of the induction generator.
Electrical and mechanical subsystems make upthemodel. The
proposed model of the wind turbine with an induction
generator was then verified using a thorough MATLAB model
of a wind farm with a doubly-fed induction generator (DFIG).
Comparisons were made between the two models' simulated
responses for mechanical torque, electrical torque, generator
speed, and power. The outcome demonstrates that both WT
models' simulated responses sharedthesamewaveformshape
and dynamic behavior due to variable configurationorrating.
Key Words: Wind turbine (WT), Doubly-fed induction
generator (DFIG), WindEnergyConversion Systems(WECS),
Renewable energy sources (RESs), Synchronous rotating
reference frame, Matlab / Simulink.
1. INTRODUCTION
The need for the generation of renewable energy is
increasing in recent years due to social and environmental
reasons, such as climate change and the dangers of fossil
fuels. Given this increase in demand, a number of nations,
including China, the USA, and Europe, have demonstrated
the efficiency and cleanliness of producing electricity from
wind turbine systems [1]. Globally, the advancement of
renewable energy in contemporary production power
systems has multiplied due to the rise in atmospheric
concentrations of greenhouse gases, which are incredibly
harmful to our planet. When compared to other sustainable
power sources, wind energy hasemergedasa viablesolution
for generating clean energy and is now the source that is
developing the fastest. However, theusageoftheenergythat
is available is dependent on the weather (wind speed), and
its integration results in volatility in the power system. The
generation of power and the reduction of CO2 could change
as a result of the integration of renewable energies with
network connectivity, intelligent control, and storage
systems. The worldwide energy storage has previously
conducted an analysis of the need for a 100% renewable
electricity supply [2,3]. The author of International
Electrotechnical Commission claims that advances in smart
grids and rising use of renewable energy aretoblameforthe
demand for energy storage [4]. Renewable energy sources
(RESs) are predicted to be able to supply 70% of the world's
energy requirements by 2050. The most significant energy
sources will be wind, solar, and storage technologies. The
utilisation of these alternative energy sources reduces
dependence on fossil fuels and greenhouse gas emissions in
the electrical industry [5]. The majority of nuclear and fossil
fuel sources will also be totally replaced, with wind energy
likely being the most prevalent and the first viable power of
a worldwide energy system. Numerous academics are
currently interested in initiatives to increase and improve
the wind sector's participation in the power generation
industry [6]. A squirrel-cage induction generator (SCIG), a
doubly fed induction generator (DFIG), and a direct-drive
synchronous generator (DDSG) are a few examples of the
several types of generators utilised in the wind power
industries [7–9]. The advantagesofDFIGincludeitsabilityto
independently manage active and reactive power output, a
tiny power converter rated at 30% of the generator to
handle the rotor power for excitation, and the capacity to
control terminal voltage via reactive power control. The
main cause of problems in WT is faulty generators. The
generator, which is a key element in a wind turbine and is
responsible for converting the mechanical energy of the
wind into electrical energy, is the heart of the wind turbine.
The costs of operation and maintenance will rise as a result
of the rapid breakdown in WT. Therefore, a fault
investigation of the generator model in the wind turbine is
required to learn the specific characteristics in order to
avoid the generator from breaking. The majority of WT
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 07 | Jul 2023 www.irjet.net p-ISSN: 2395-0072
Mohamed Gad EL-Moula Abd-Rabou1*, Fathy Abd El-Lateif Elmisery2, Asmaa Salah Farag Saad3,
Saber. M. Saleh4
1Post Graduate Student, Electrical Engineering Dept., Faculty of Engineering, Fayoum University, Egypt
---------------------------------------------------------------------***---------------------------------------------------------------------
2
3
4
Doctor, Electrical Engineering Dept., Faculty of Engineering, Beni-Suef University, Egypt
Doctor, Electrical Engineering Dept., Faculty of Engineering, Fayoum University, Egypt
Associate Professor, Electrical Engineering Dept., Faculty of Engineering, Fayoum University, Egypt

models are either too simplistic in their generatormodelling
or include intellectual property protection. Therefore, it is
challenging to apply any generator flaws for the aim of WT
dynamic study. In order to examine faults, a mathematical
model of the induction generator in the wind turbine was
created in this research. The model was subsequently
confirmed using the thorough MATLAB software model of
the doubly-fed induction generator (DFIG) WT.
2. The wind power system
The mechanical power generated by a wind turbine is
determined by aerodynamics, and [10, 11]:
(1)
(2)
and, (3)
The expressions in Eqs. (2) and (3) show that Cp depends
on the blade pitch angle β, and the tip speed ratio λ, which
is defined as:
Where the angular velocity of wind turbine and R is
Fig -1: shows the relation between Cp and λ for different
pitch angles. The maximum value of the power coefficient,
≈ 0.48, is obtained at β = 0° and λ = ≈ 8.1.
The torque produced by the wind turbine is given by
the following equation [12]:
Fig -2: Simulink model of wind turbine
2.1 Mechanical drive train model
The dynamics of a wind turbine are frequently represented
using a two-mass model, as in [13]. What distinguishes the
two-mass model of the wind turbine from other models is
the benefit of its controllers' universal design, which can be
applied in wind turbines of various sizes. The two-mass
model incorporates the wind turbine's adaptability as long
as the modes are present [14]. Equation (6) gives the
mechanical model of a two-mass wind turbine.
(6)
Fig -3: Configuration of drive Train With wind turbine
Where ρ is the air density (kg/m3), is the wind speed
(m/s), (A= π R²) is swept area covered by the rotor (m2),
and Cp is the power coefficient which is a function of both
tip speed ratio, λ, and blade pitch angle β (deg). The
efficiency of the wind turbine blades' power coefficient Cp
can be analytically estimated as [16]:
blade radius. The turbine power characteristics are
illustrated as shown in Figure 2. The pitch angle of 0° is
used in this study because it results in the highest power
coefficient.

Fig -4: Simulink model of Drive Train
Where  is the mechanical speed of the shaft, P is the
number of poles of the machine, is the friction coefficient,
J is the inertia of the rotor, Tm is the mechanical torque
generated by wind turbine, and is the electromagnetic
torque generated by the machine. Two-mass drive-train
model is used for the simulation study in this paper.
2.2 Modelling of DFIG
The DFIG is composed of the up of the rotor and stator
windings. Slip rings are present. The three-phase covered
windings of the stator are connected to the grid by a three-
phase transformer. The rotor consists of three-phase
insulated windings, just like the stator. Slip rings and
brushes are used to connect the rotor windings to an
external fixed circuit, allowingthecontrol rotorcurrentto be
injected into or removed from the windings [15–18].
Following assumptions form the basis of the DFIG model.
Figure 5. Below shows the DFIG's stator can's steady state
equivalent electric circuit.
Fig -5: Equivalent circuit of the DFIG referred to stator.
The equations are calculated usingdirect(d)andquadrature
(q) axis representation in the synchronous reference frame.
The stator and rotor voltages are given by:
Where , , , and : stator and rotor voltages in the
dq frame, respectively. , ,
, and : stator and rotor current in the frame,
respectively. , , and :stator and rotor phase
resistances and angular velocity, respectively.
Where is the flux linkages are given by the expressions:
Where , are the fluxes along the dq axis stator. ,
are the fluxes along with the dq axis rotor. , are
stator and rotor phase leakage inductances, respectively,
is stator–rotor mutual inductance.
Where and are the self-inductances of the stator
and the rotor respectively.
The developed electromagnetic torque is given by:
(17)
A decoupled control of the active and reactive power by the
stator flux orientation to obtain separate control of the
powers generated by the wind system. Controlling the dq-
axes rotor currents of the DFIG will also allow for control of
the stator reactive power and electromagnetic torque. The
stator field revolves continuouslyatsynchronousspeed. The
stator flux vector, which depicts the phase and amplitude of
the flux, serves as the field symbol. Selecting the two-phase
dq and placing the stator flux vector on the d-axis will allow
it to write the two-phase dq related to the rotating stator
field.

Fig -6: d-axes aligned with the stator flux space vector.
3. SIMULATION RESULTS
The simulations are executed out using MATLAB/Simulink
software. In order to validate the modelingofDFIG indicated
in this research, the operating point where thewindspeedis
constant is at 12 m/s. The DFIG and wind turbine
characteristics for this simulation are reported in (Tables 1
and 2).
Table -1: The Parameters of the two-mass wind turbine
mathematical model
Parameters Constant value
Rated wind speed 12 m/s
Rated capacity 1.5 MW
Rotor radius 30.66 m
Gearbox ratio 71.28
Turbine side inertia 18.7 kg.m2
Air density 1.225 kg/m3
No of poles 4
Table -2: The Parameters OF DFIG
Parameters Constant Value
Rated Voltage (line to line) 690 V
Stator resistance 2.3 m 
Rotor resistance 2 m 
Stator inductance 2.93 mH
Rotor referred inductance 2.97 mH
Mutual inductance 2.88 mH
Base Frequency 60 Hz
dc-link Voltage 937 V
dc-link capacitor 60 mF
MATLAB / SIMULINK software has been used to design and
simulate the modeling of wind turbine model based on DFIG
with a power of 1.5 MW.
Fig .8 Overall Simulink model of wind turbine with DFIG.
Chart -1: Response of the turbine to changes in wind
speed

Chart -2: Electrical and Mechanical Torque.
Chart -3: DFIG AC Voltage Vabc.
Chart -4: DFIG AC Current Iabc.
Chart -5: Mechanical Power Response.
4. CONCLUSIONS
In order to evaluate the responses and ensure the electrical
distribution with the wind in terms of grid voltage and
frequency fluctuations, the DFIG and dynamics operation
were modelled in this study. Additionally, the primary goal
of this essay is to investigate the usefulness and optimum
performance of the DFIG characteristic analysis in
determining the effects of a wind farm's numerous wind
turbines on output variables. In order to avoid these
situations and others involving fluctuation, the continuation
of the research's proposal calls for the creation of a hybrid
system made up of multiple wind turbines and an energy
storage system. This hybrid system will givea preciseidea of
the distribution and level of fluctuations as presented in the
analysis and simulation in the pitch angle, current, and
voltage curves. Non-isolated power converters will be used
to regulate each subsystem, and the inverter will be used to
connect them to the grid or a local load.
Energy management across all hybrid system components
will enhance simulations of the intelligent control system.
Therefore, it is not sufficient to say that one energy storage
technology is superior to all others for each generator;
rather, it is more accurate to say that each of them performs
better and is more appropriate for particular applications.
To overcome the drawbacks of DFIG generating wind
turbines and enable the use of electronic power converters,
several configurations will be researched. The storage
components of the hybrid system increase its adaptability
and capacity to control and regulate its active power
generation in order to meet changing grid demand.
Abbreviations
The following abbreviations and notations are used in this
manuscript:
DFIG Doubly fed induction generator
RES Renewable energy sources
PWM Pulse width modulation
WECS Wind energy conversion systems
Rotational speed of turbine
Vw Wind speed
𝝺 Tip speed ratio
β Blade pitch angle
U Voltage
R Resistance
Pw Power wind turbine
CP Polynomial function of λ and β
REFERENCES
[1] International Energy Agency. Global Energy Review.
2021.Availableonline:https://www.iea.org/reports/
global-energyreview-2021/renewables(accessedon10
January 2022).

[2] Pleßmann, G.; Erdmann, M.; Lusaka,M.;Breyer,C.Global
energy storage demand for a 100% renewable
electricity supply. Energy Procedia 2014, 46, 22–31.
294, Dec. 2001, pp. 2127-2130,
doi:10.1126/science.1065467.
[3] Bussar, C.; Moos, M.; Alvarez, R.; Wolf, P.;Thien,T.;Chen,
H.; Cai, Z.; Leuthold, M.; Sauer, D.U.; Moser, A.
Optimal allocation and capacity of energy storage
systems in a future European power system with 100%
renewable energygeneration. EnergyProcedia2014,46,
40–47.
[4] IEC White Paper EnergyChallenge:2010. Copingwiththe
Energy Challenge The IEC’s Role from 2010 to 2030;
IEC: Geneva, Switzerland, 2010.
[5] IRENA. Global Energy Transformation: A Roadmap to
2050, 2018th ed.; IRENA: Abu Dahbi, UAE, 2018.
[6] Sun, Z.; Wang, H.; Li, Y. Modelling and simulation of
doubly-fed induction wind power system based on
Matlab/Simulink. IET Conf. Publ. 2012.
[7] Rolán, A.; Pedra, J.; Córcoles, F. Detailed study of DFIG-
based wind turbines to overcome the most severe
grid faults. Int. J. Electr. Power Energy Syst. 2014, 62,
868–878.
[8] Fernández, L.M.; García, C.A.; Saenz, J.R.; Jurado, F.
Equivalent models of wind farms by using aggregated
wind turbines and equivalent winds. Energy Convers.
Manag. 2009, 50, 691–704.
[9] Krim, Y.; Abbes, D.; Krim, S.; Mimouni, M.F. Intelligent
droop control and power management of active
generator for ancillary services under grid instability
using fuzzy logic technology. Control Eng. Pract. 2018,
81, 215–230.
[10] Boukhezzar B, Siguerdidjane H. Nonlinear control of a
variable-speed wind turbine using a two-mass model.
IEEE Trans Energy Convers 2011; 26(1):149–62.Mar.
[11] Ghasemi S, Tabesh A, Askari-Marnani J. Application of
fractional calculus theory to robust controllerdesignfor
wind turbine generators. IEEE Trans Energy Convers
2014; 29(3):780–7. Sep.
[12] Liu J, Gao Y, Geng S, Wu L. Nonlinear control of variable
speed wind turbines via fuzzy techniques. IEEE Access
2017; 5:27–34.
[13] Novak P, Ekelund T, Jovik I, Schmidtbauer B. Modeling
and control of variable-speed wind-turbine drive-
system dynamics. IEEE Control Syst 1995;15(4):28–
38.Aug.
[14] Boukhezzar B, Siguerdidjane H, Hand MM. Nonlinear
control of variable-speed wind turbines for generator
torque limiting and power optimization. J Sol Energy
Eng 2006; 128(4):516–30. Aug.
[15] Hore, D.; Sarma, R. Neural Network–based Improved
Active and Reactive Power Control of Wind-Driven
Double Fed Induction Generator under Varying
Operating Conditions. Wind Eng. 2018, 42, 381–396.
[16] Carroll, J.; McDonald, A.; McMillan, D. Reliability
Comparison of Wind Turbines withDFIGandPMGDrive
Trains. IEEE Trans. Energy Convers. 2015, 30, 663–670.
[17] Hosseini, S.M.H.; Rezvani, A. ModelingandSimulationto
Optimize Direct Power Control of DFIG in Variable-
Speed PumpedStorage Power Plant Using Teaching–
learning-Based Optimization Technique. Soft Comput.
2020, 24, 16895–16915.
[18] Xia, Y.; Chen, Y.; Song, Y.; Strunz, K. Multi-ScaleModeling
and Simulation of DFIG-Based Wind Energy Conversion
System. IEEE Trans. Energy Convers. 2020, 35, 560572.

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