Artículo Académico / Academic Paper
Recibido: 30-10-2020, Aprobado tras revisión: 23-07-2021
Forma sugerida de citación: Pozo, A.; Muñoz, E.; Ayala, E. (2021). “Direct Speed Control of a 9 MW DFIG Wind Turbine”.
Revista Técnica “energía”. No. 18, Issue I, Pp. 11-18
ISSN On-line: 2602-8492 - ISSN Impreso: 1390-5074
© 2021 Operador Nacional de Electricidad, CENACE
Direct Speed Control of a 9 MW DFIG Wind Turbine
Control de Velocidad Directo de un Aerogenerador de 9 MW
N. Pozo
1
E. Muñoz
1
E. Ayala
1
1
Universidad Politécnica Salesiana, Calle Vieja 12-30 and Elia Liut Ave., Cuenca - Ecuador.
Mechatronics Engineering Department.
E-mail: apozoa@est.ups.edu.ec;emunozp2@est.ups.edu.ec
eayala@ups.edu.ec
Abstract
This article proposes the implementation of a
Maximum Power Point Tracking (MPPT)
controller for a Doubly Fed Induction
Generator (DFIG) wind turbine using a
Luenberger observer. The implementation is
based on the Direct Speed Control (DSC)
strategy including a state observer which
operates according to the reference current
used for the power converter control.
Moreover, this controller allows to regulate the
Low Shaft Speed (LSS) for a better MPPT
within the operating zone before the pitch
controller command is activated, improving the
power extraction manifested in the Power
Coefficient (
) value.
This control strategy has been validated in a 9
MW DFIG wind turbine system by means of
the simulation in Matlab-Simulink and the
Fatigue, Aerodynamic, Structure and
Turbulence (FAST) software. The performance
of the technique has been evaluated under
normal operating conditions and the
incorporation of 10% white noise for testing the
robustness. Implementing the state observer,
the wind turbine system responds more
sensitively to the presence of disturbances in the
mechanical system. The technique allows to
increase the power extraction.
Resumen
Este articulo propone la implementación de un
control Seguimiento del Punto Máximo de
Potencia (MPPT) en un sistema de turbina de
viento basado en un Generador de Inducción
Doblemente Alimentado (DFIG), de sus siglas
en inglés, utilizando un observador de
Luenberger. La implementación se basa en la
estrategia de Control de Velocidad Directo
(DSC) con la incorporación de un observador
de estados el cual opera en función de la
corriente de referencia y la señal del Eje de Baja
Velocidad (LSS), este sistema permite un mejor
MPPT dentro de la zona de operación
mejorando la extracción de potencia reflejada
en el valor del Coeficiente de Potencia (
).
La estrategia de control fue validada en un
sistema de aerogenerador DFIG de 9 MW
mediante la simulación en Matlab-Simulink y
Fatigue, Aerodynamic, Structure and
Turbulence (FAST) y el desempeño de la
técnica fue evaluada en condiciones de
operación normal y la incorporación de ruido
blanco. Con el observador de estados, el sistema
de aerogenerador responde con mayor
sensibilidad ante la presencia de perturbaciones
en el sistema mecánico. La técnica mejora la
capacidad de respuesta en términos de
extracción de potencia.
Index terms Power coefficient, MPPT, direct
speed control, DFIG, wind turbine, white noise.
Palabras clave Coeficiente de potencia,
MPPT, control de velocidad directo, DFIG,
aerogenerador, ruido blanco.
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Edición No. 18, Issue I, Julio 2021
1. INTRODUCTION
Nowadays, energy production using wind turbines
has shown an accelerated growth rate in response to
climate change, for this reason government policies
increasingly favor the generation of energy using
renewable resources [1]. With the development of high-
power wind turbines, the implementation of more reliable
and efficient systems is necessary; currently the
development of wind turbine technology is aimed at
improving energy extraction systems [2].
The DFIG wind turbines have shown adequate
performance due to their good cost-benefit reliability [3];
this machine allows manipulating the slip angle at
approximately ±30% of the operating speed of the wind
turbine, this consideration provides greater flexibility in
speed control using techniques such as MPPT [4], [5].
For the implementation of this strategy, it is important to
consider the wind speed, rotor speed, the electromagnetic
torque generated by the rotation of the blades and the
extracted power; as well as the mechanical and electrical
components that characterize the behavior of the
analyzed wind turbine. Regarding the mechanical
characteristics, the model considers a system of two
coupled masses as detailed in [6]; electrical
characteristics include grid connection, a performance
factor for the wind turbine, as well as the power converter
[7].
The control strategy implemented consists of direct speed
control in a DFIG 9 MW wind turbine, considering the
LSS as an input variable to improve the extraction of
power measured as a function of
. For the simulation
of the model, the Matlab-Simulink and FAST software
have been used. It allows to represent the operation under
more realistic conditions considering aeroelastic aspects.
Regarding the validation of results, the controller has
been subjected to test signals referred to nominal
[8],
[9], [10]. In order to evaluate the responsiveness of the
controller, a white noise signal has been incorporated to
the wind speed input signal and mechanical disturbances.
In section two the components included in a DFIG wind
turbine are detailed, as well as the mechanical and
aerodynamic parameters that make up the turbine for the
simulation of DSC. In section three the control strategy
and simulations based on FAST and Matlab-Simulink are
described [11], and [12]. Finally, the results are presented
in section four.
2. DFIG SYSTEM DESCRIPTION
The proposed DFIG wind turbine system includes all
the mechanical and electrical components necessary to
evaluate the performance of the model under normal
operating conditions. The parameters considered to
obtain the maximum power point the model include: the
wind turbine blades, the turbine nacelle, the box of gears,
as well as the tower that supports the generator and the
connection to the grid. The systems involved in the
electrical generation: aerodynamic model, mechanical
system, electrical, and control system[4], [6], [13].
The proposed wind turbine model, as well as its
interaction with the systems, can be seen in Fig. 1.
Figure 1: Main components of a wind turbine
2.1 Mechanical Model
The mechanical model of a wind turbine is commonly
approximated to a two-mass power train model [14]. Fig.
2 displays the representation used for the model in this
proposal.
Figure. 2: Two-mass mechanical model
In Fig. 2, the high-speed component related to the
generator rotor is on the right side, while the low-speed
component related to the turbine is located on the left
side. From the general mechanical model and applying
Newton's second law, the equations of approximate the
behavior of the mechanical model are extracted [6], [7];
the moment of inertia and the torque generated are shown
in equation (1):
(1)
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Pozo et al. / Direct Speed Control of a 9 MW DFIG Wind Turbine
where
is equal to the High Shaft Speed (HSS) or the
angular speed at the machine side,
is the LSS or the
angular speed at the turbine side, the
is the
electromagnetic torque, and
and
are factors
related to the friction and inertia, respectively. The model
can be simplified by neglecting the damping coefficients
(
,
, and
); consequently, it results in a two-
inertia model (
, and
) and the stiffness constant (
)
[6].
2.2 Pitch Angle Control
The Pitch angle controller allows to regulate the
position of the blades for turbine’s speed control. Each
blade has its own pitch regulator, the independence of the
controllers allows the control of each blade separately; to
move the blades it is common to use hydraulic actuators
[6]. The control strategy implemented in the regulators is
shown in Fig. 3, where a traditional PI controller provides
a reference applied to the turbine model, the angle of
inclination is calculated by integrating the variation of the
angle. For the implementation, it is necessary to measure
the wind speed, the HSS, as well as the angle references
to regulate the speed of the turbine in an adequate way
[6], [15].
Figure 3: Pitch angle control strategy
2.3 Wind Turbine Aerodynamics
The ability to extract power from the wind turbine
depends directly on the rotation speed of the turbine, thus
the system converts wind energy into rotational energy.
In the conversion process there is a variation in the
pressure exerted by the air as can be seen in Fig. 4, this
behavior allows a high energy extraction capacity. This
representation indicates that the pressure after the wind
enters the wind turbine swept area decreases, the
remaining energy is transferred to the turbine by the
rotation of the blades.
Figure 4: Airflow through the turbine
The expressions that relate the behavior between the
aerodynamics and the power extracted from a wind
turbine are based in terms of: Cp, blade length, wind
speed and air density [6], [15]. As a result, the extracted
power can be formulated by the relationships described
in equation (2):
(2)
where
is the mechanical power of the rotor in (Watt),
is the wind speed in (m/s), ρ is the air density in
,
is the rotor power coefficient, is the distance
from the center of the rotor at the tip of the blade, or the
blade’s length in (m),
is the angular velocity of the
rotor in
, β is the pitch angle of the blades in
(degrees), λ is the tip speed ratio (TSR) which is defined
as the ratio between the speed of the tip of the blades and
the speed of the wind over the rotor [16], [17]. For a wind
turbine, the Betz limit establishes that the maximum
generation capacity is 59,26% of the energy transferred.
This limit is called the power coefficient and it relates the
length of the blade to its angle of incidence
[15]. The
expression is defined in equation (3):
(3)
3. INDIRECT SPEED CONTROL MODEL
The main benefit of applying the DSC technique is to
improve the response capacity of the wind turbine in
terms of
; the controller estimates the speed of the
turbine through the TSR, which is related to . For speed
estimation, the DSC control starts with
,
consequently, a state observer capable of estimating the
torque which is directly related to the optimal operating
speed. The equation that allows describing the optimal
reference speed
is shown in equation (4) [4], [6]:
(4)
where
is the estimated aerodynamic torque, is the
gearbox ratio, and
is the adjustment constant that
allows for the optimal electromagnetic torque. The
implemented control scheme is shown in Fig. 5. To
calculate
it is necessary to describe the
and
terms. For the proposal, the control technique
calculates the maximum Cp value, when this occurs the
torque can be expressed with equation (5); consequently,
the value of
can be calculated with equation (6).
(5)
(6)
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Edición No. 18, Issue I, Julio 2021
Figure 5: Control strategy implemented
3.1 Indirect Speed Control Implementation
The implemented DSC method seeks to estimate the
aerodynamic torque
which considers the angular
speed of the turbine as an input signal. With the estimated
torque the controller can regulate the electromagnetic
torque control signal with faster dynamics. For the
calculation of the error signal the estimation considers a
proportional gain Kp. For instance, in this analysis the
gain Kp is unitary [4], [6]. The state observer proposal is
shown in Fig. 6.
Figure 6: State observer configuration
For the implementation of the DSC within the DFIG
wind turbine, LSS is considered as an input signal,
together with equation (5),
can be calculated by means
of equation (6) in terms of the electromagnetic torque
generated as shown in equation (7), [6], [15].
(7)
Where
is the mutual inductance and
is the stator
inductance, p is the pole pairs of the wind turbine and
is the electromagnetic flux of the stator. A representation
of the direct speed controller for the proposed DFIG wind
turbine is shown in Fig. 7.
With respect to FAST, the software models the wind
turbine with rigid and flexible elements, this allows
evaluating the performance of the controller and
simulating the aeroelastic behavior of the turbine. In Fig.
8, you can see the FAST model [11] implemented in
Matlab-Simulink [12] to obtain an array of multiplexed
output variables that interact directly in the proposed
controller model.
Figure 8: Integration between Matlab-Simulink and FAST
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Pozo et al. / Direct Speed Control of a 9 MW DFIG Wind Turbine
4. SIMULATION RESULTS
The next subsection shows the DSC simulation with
uncorrelated white noise added to the input wind speed
signal to test the robustness of the system to naturally or
externally produced oscillations. Regarding the electrical
and mechanical parameters of the implemented wind
turbine, Table 1 describes the data used for the model [6].
Table 1: Wind turbine specifications
9 MW Wind turbine system characteristics
Parameter
Value
Synchronism
1800 rev/min
Rated Power
9 MW
Rated stator voltage
575 Vrms
p
3 pais
R
45 m
Rs
0,00706 p.u.
Lm
2,9 p.u.
Rr
0,005 p.u.
Ls
0,171 p.u.
Lr
0,156 p.u.
4.1 Variable Wind Speed with Transitions
Fig. 9 shows the behavior of the DFIG wind turbine
with an input signal with progressive variations. Thus,
with a deterministic signal, the response capacity of the
controller against disturbances can be evaluated; step
functions are traditionally used to evaluate the response
of the system with drastic variations in the system input.
The wind speed input signal varies between 11 and 17,5
m/s as shown in Fig. 9 (d). Regarding the extracted
power, the model shows an extraction of approximately
9 MW which indicates that the controller works properly
despite incorporating a noise signal. The response of Cp
according to the restrictions mentioned in the previous
sections reaches approximately 0,44 %. Fig. 9 (a) shows
the performance of the reference current and Fig. 9 (c),
displays the adequate generated power recovery despite
the oscillations of the error signal generated by the
disturbance.
(a)
(b)
(c)
(d)
(e)
(f)
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Edición No. 18, Issue I, Julio 2021
(g)
Figure 9: Steps wind speed: reference current signal in p.u. (a),
electromagnetic torque in Nm (b), active output power in Watt (c),
wind input in m/s (d), generator speed in rpm (e), noise input
signal (f), and power coefficient (g).
4.2 Variable Wind Speed with Realistic Input
To ensure good response dynamics under normal
operating conditions, the controller was subjected to a
realistic input signal varying between 10 and 17,5 m/s as
shown in Fig. 10 (d). Regarding the extracted power, the
model is able to respond quickly in terms of power
extraction as shown in Fig. 10 (c). Regarding the power
coefficient, the controller can respond with a faster
dynamic against disturbances, which indicates a more
robust system as shown in Fig. 10 (g).
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure 10: Realistic variables wind speed: reference current signal
in p.u. (a), electromagnetic torque in Nm (b), active output power
in Watt (c), wind input in m/s (d), generator speed in rpm (e),
noise input signal (f), and power coefficient (g).
5. CONCLUSIONS
In this research, the DSC has been implemented by using
FAST and Matlab for a 9 MW DFIG wind turbine.
Compared to the control with and without disturbances,
the results highlight an important improvement because
of the correct selection of optimal TSR and maximum
when calculating the optimal torque constant at the
controller stage. This implementation also considers the
initial LSS speed and inertia which produce an oscillation
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Pozo et al. / Direct Speed Control of a 9 MW DFIG Wind Turbine
during the first 20 seconds of simulation. Nevertheless,
after that period of time, the rotor speed becomes steady
and closer to the nominal value. The considered
simulations have the intention of demonstrating the
capability of this technique to provide a fast response,
even in the presence of disturbance in the system i.e. the
LSS. Furthermore, the pitch controller is able to limit the
speed of the shaft properly. The power and speed reach
are close to their nominal values and these simulations
demonstrate the correct operation of the wind turbine in
partial load conditions.
The DSC strategy based on the Luenberger state observer
has allowed the system to operate with a better power
extraction and
. Although the system is unstable during
the first seconds of simulation due to the initial
conditions, it rapidly reaches the optimum values. This
means more active power generation and more reactive
power absorption of wind turbine. The advantage of this
technique is that the observer does not require any
additional hardware implementation since it exploits
common wind turbine signal measurements such as the
HSS, the wind speed, torque, voltages, and currents. For
further implementations, it can be increased the number
of inputs intended to improve the accuracy of the
controller.
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Nicolás Pozo Ayala. - He was born
in Cuenca, Ecuador in 1998. He is
a Mechatronics student with a
major in Industrial Automation
since 2016 at the Salesian
Polytechnic University. His areas
of study are focused on the
automation of food processes and
the development of devices for people with disabilities.
Eduardo Muñoz Palomeque. -
He was born in Cuenca, Ecuador in
1997. Currently, he is completing
his undergraduate studies in
Mechatronics Engineering at the
Salesian Polytechnic University,
Cuenca del Ecuador Headquarters.
His research interests include
signal processing techniques, autonomous systems, and
power electronics.
Edy Ayala Cruz. - He was born in
Cuenca in 1987. He received his
degree in Electronic Technologist
in 2009 and Electronic Engineer in
2011, both from the Salesian
Polytechnic University of Ecuador;
and his Master of Engineering
Science (Electrical and Electronic)
degree from Swinburne University of Technology in
Australia, 2015. Currently, he is pursuing his Doctorate
studies in Engineering at the University of Ferrara. His
field of research is related to control systems in
engineering and renewable energies.
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