Artículo Académico / Academic Paper
Recibido: 29-10-2020, Aprobado tras revisión: 11-01-2021
Forma sugerida de citación: Ayala, E.; Simani, S.; Pozo, A.; Muñoz, E. (2021). “Indirect Speed Control Strategy for Maximum
Power Point Tracking of the DFIG Wind Turbine System”. Revista Técnica “energía”. No. 17, Issue II, Pp. 92-101
ISSN On-line: 2602-8492 - ISSN Impreso: 1390-5074
© 2021 Operador Nacional de Electricidad, CENACE
Indirect Speed Control Strategy for Maximum Power Point Tracking of the
DFIG Wind Turbine System
Estrategia de Control de Velocidad Indirecto para el Seguimiento del Punto
Máximo de Potencia de un Sistema Eólico DFIG
E. Ayala
1
S. Simani
2
N. Pozo
3
E. Muñoz
4
1,2,4
Universidad Politécnica Salesiana, Calle Vieja 12-30 and Elia Liut Ave., Cuenca, Ecuador
3
Ferrara University, Via Savonarola, 9, 44121 Ferrara FE, Italy
E-mail: eayala@ups.edu.ec, apozoa@est.ups.edu.ec
silvio.simani@unife.it,emunozp2@est.ups.edu.ec
Abstract
In this article, a control strategy for Maximum
Power Point Tracking (MPPT) of a wind turbine
system based on a Doubly Fed Induction Generator
(DFIG) is presented. The proposed strategy consists
of the Indirect Speed Control (ISC) taking the Low
Speed Shaft (LSS) as variable input. Even though the
pitch control mainly influences the power extraction,
this implementation allows the MPPT to optimize
the Power Coefficient (Cp) in terms of dynamic
response. The controller has been designed in order
to allow the wind turbine to reach the MPPT along
the partial load operation. For these experiments, a
1.5 MW wind turbine was modeled and simulated by
using Matlab and Fatigue, Aerodynamic, Structure
and Turbulence (FAST) software. In order to
present the achieved results, a comparison between
the ISC and a classical PI controller is made. The Cp
curves as well as the output power display an
important improvement in terms of stability. These
results are possible because the appropriate values
of optimal Tip Speed Ratio (TSR) and maximum Cp
have been properly established.
Resumen
En el siguiente artículo, se propone una estrategia de
control para el seguimiento del punto máximo de
potencia (MPPT) de un sistema de energía eólica con
generador de inducción doblemente alimentado
DFIG. La estrategia propuesta está basada en un
Control de Velocidad Indirecto (ISC) tomando la
velocidad baja del eje como variable de entrada. A
pesar que el control de pitch influye principalmente
en la extracción de potencia, esta implementación
permite realizar un seguimiento del MPPT de modo
que el Coeficiente de Potencia (Cp) del sistema se
optimice en términos de respuesta dinámica. El
controlador ha sido diseñado para permitir que la
turbina eólica alcance este punto máximo a lo largo
de la zona de operación de carga parcial. Para
realizar los experimentos, se seleccionó un
aerogenerador de 1,5 MW y el modelo se implementó
en una simulación por medio de Matlab y el software
de Fatiga, Aerodinámica, Estructuras y Turbulencia
(FAST) para el análisis y posterior validación de
resultados. La curva de Cp ha sido comparada con
un controlador PI mostrando una mejora
importante en términos de estabilidad y potencia
activa de salida. Estos resultados son posibles debido
a que se ha seleccionado apropiadamente los valores
de Tip Speed Ratio (TSR) y Cp máximo.
Index terms Power Coefficient, MPPT, Indirect
Speed Control, DFIG, Wind Turbine
Palabras clave Coeficiente de Potencia, MPPT,
Control de Velocidad Indirecto, DFIG,
Aerogenerador
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Edición No. 17, Issue II, Enero 2021
1. INTRODUCTION
Nowadays, wind energy has grown because of the
high demands of renewable energies worldwide. In this
context, energy efficiency must be improved in order to
obtain as much energy as possible from the source [1],
[2], [3]. For wind turbines, the transition from no load to
full load operation is crucial for the control of energy
production. Particularly, one of the characteristics that
allows more energy production is the control strategy
during partial load operation also known as MPPT.
The dynamics of the system is one of the most important
characteristics because the control strategy is designed by
using mechanical and electrical models. The mechanical
load must be modeled depending on the number of
masses, the gearbox ratio, and the friction losses along
the system. With regard to the electric characteristics, the
grid connection, the power electronic converter, and the
generator losses are included. Although there are
different types of electric generators, there are, in the
market, there two main popular wind turbines: The
Permanent Magnet Synchronous Generator (PMSG) and
the Doubly Fed Induction Generator (DFIG). PMSG,
which works through a wind turbine, is usually selected
for minimizing the cost of maintenance and
implementation whereas DFIGs are common for high
power applications [4], [5], [6]. The DFIG wind turbine
configuration has the advantage of permitting high
amounts of power extraction; thus, minimizing the cost
of power electronic devices. Further, the control strategy
can be widely adjusted since the slip angle is adaptable
in a typical range of +/-30% from the operational speed
around the synchronous speed [7]. The MPPT control is
usually designed by considering the wind speed, rotor
speed, output power, and pitch angle. One simple control
strategy consists of tracking the electromagnetic torque
vs Low Shaft Speed (LSS) curve; thereupon, allowing the
system to reach the MPPT using the natural dynamic
response. This method is also known as Indirect Speed
Control (ISC) [7], [8]. In this article, an ISC strategy for
a DFIG wind turbine in order to attain the MPPT is
presented. For this simulation, a 1,5MW wind turbine
using Matlab and FAST software has been selected [9],
[10].
In section two, the analysis and modeling of the
subsystems directly involved in the operation of the wind
turbine are detailed. Moreover, the parameters of
description that have been used for the ISC simulation are
explained. In section three, the control strategy and the
simulations based on FAST and Simulink are described.
Finally, the results are presented in section four.
2. WIND TURBINE MODEL
Basically, the wind turbine transforms the energy of
air mass movement into electrical energy. The
electromechanical model of the wind turbine depends on
the parameters related to the aerodynamics and the size
of the generator. In order to allow the maximum power
extraction, it is necessary to obtain the most accurate
mathematical model in order to implement an appropriate
MPPT control. The most relevant variables involved are
the nominal speed of the turbine, the gearbox ratio, the
electromagnetic torque of reference, the pitch angle, and
the TSR [11], [12], [13]. In Fig. 1, a description of the
basic elements of a wind turbine is detailed.
Figure 1: Basic elements of a wind turbine.
2.1 Aerodynamic Model
Aiming to produce the movement of the wind turbine
rotor, the air enters the swept rotor area which is a
circumference created by the rotation of the blades. The
rotor captures the energy of the air, causing a decrease of
pressure once the energy is transferred to the mechanism
of the wind turbine detailed in Fig. 2 [7].
Figure 2: Air flowing through the swept area
The aerodynamic model represents the energy
extraction of the rotor, and calculates the mechanical
torque as a function of the air flow in each propeller. The
wind speed can be considered as the average speed of the
incident wind in the area swept by the propellers with the
purpose of evaluating the average torque in the low speed
axis [5]. The mechanical power can be determined by the
expression in equation (1) [1], [3], [14].
(1)
However, the wind turbine can recover only part of
available power because of the Betz limit shown in (2).
Here is where the term Cp affects the wind turbine
performance due to mechanical and electrical losses.
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Ayala et al. / Indirect Speed Control Scheme for Maximum Power Point Tracking of the DFIG Wind Turbine System
(2)
Where, R is the wind turbine blade radius, ρ is the air
density,
is the wind speed, and Cp is the power
coefficient.
For a wind turbine, Betz's law establishes that 59.26%
of the kinetic energy of the wind can be converted into
useful mechanical energy [7]. The other losses of the
system described by the parameter Cp are defined by the
relationship in (3).
(3)
For a wind turbine, Cp is, on a regular basis,
experimentally by the manufacturers. The Cp depends on
TSR and pitch angle β described in equation (4). The
general equation (5) makes reference to the family of
curves that relates TSR to Cp of a wind turbine [7], [13].
(4)
(5)
Where
(6)
2.2 Mechanical Model
For the mechanical model, the two-mass
representation of the speed joint in the power
transmission and linked to the gearbox which transmits
the torque caused by the impact of the wind has been
modeled in this work. The mechanical model is displayed
in Fig. 3.
Figure 3: Two-mass mechanical model
The left side of Fig. 3 represents the low-speed part,
where the propellers are generating the low-speed
rotational movement. On the right, the high-speed side
which is attached to the generator rotor is located. The
moment of inertia and torque are detailed in equations (7)
[5], [7].
(7)
Where:
= Low Shaft Speed LSS
= High Shaft Speed HSS
The model can be simplified by discarding the
damping coefficients (Dt, Dm, and Dtm); hence,
resulting in a two-inertia model (Jt and Jm) and the
stiffness constant (Ktm).
2.3 Pitch Control Model
The objective of the controller is to manipulate the
pitch of the propellers at different angles to increase or
decrease the wind turbine speed. The β-pitch controllers
allow independent control of each propeller usually
through hydraulic actuators. This permits the reduction
of the tension generated in the entire system. Pitch angle
regulation is modeled, as shown in Fig. 4, by a PI
controller which generates a reference rate. This
reference is limited, and a first order system provides the
dynamic behavior of the speed control of the wind
variation. The angle of inclination itself is then obtained
by integrating the variation of the angle. The control of a
variable-speed wind turbine is needed to calculate both
the generator torque and the pitch angle references in
order to comply with the generator speed regulation [7].
Figure 4: Pitch angle control system
2.4 Wind Turbine Speed Control
The wind turbine benchmark is oriented to design
high power systems (1 MW onwards). For such
applications, DFIGs are popular as they can generate
high controllable power thanks to the size of the power
electronics converters compared to other wind turbine
technologies [7], [15]. For instance, it can be seen, in
Fig. 5, a general control scheme for the operation of the
Wind Turbine at variable speed, where there are two
important input signals for the controllers: the generator
electromagnetic reference torque and the pitch angle
command.
Figure 5: Wind turbine
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Edición No. 17, Issue II, Enero 2021
The main goal is to extract the maximum energy from
the wind, keep the wind turbine in a safe operating mode
(power, speed and torque below limits), and mechanical
stress reduction on the drivetrain. The turbine control
operation zones used in the development of this work is
illustrated in Fig. 6 and consists of four areas of operation
[7] where four zones are displayed: 1) Minimum
operating speed zone restriction, 2) MPPT zone into
partial load operation for variable speed operation until
nominal speed is achieved, 3) the maximum speed in
partial load operation and 4) full load operation before
the brake is applied. The objective of the proposed
controllers is to regulate the operation of the wind turbine
so that it works in its second operating zone, this will
allow obtaining the maximum power monitoring.
Figure 6: Operation zones of the wind turbine description
3. INDIRECT SPEED CONTROL STRATEGY
The indirect speed control strategy is based on the
relationship between the electromagnetic torque Tem and
the angular velocity Ωt. Nonetheless, the relation
between the electromagnetic torque and the angular
velocity does not have a direct dynamic relationship due
to the inertia involved. This leads to a much slower
response of the system as the mechanical coupling is not
canceled [7]. The ISC modifies the dq frame currents in
order to command the back to back converter allowing
the controllability of the slip angle which also affects the
speed and torque of the generator. The scheme to be
implemented is shown in Fig. 7.
Figure 7: Indirect Speed Control strategy general block diagram
When the wind turbine operates at optimal
conditions, the parameters allow finding a constant value
called
which is used for the optimal electromagnetic
torque reference calculation. The general description can
be found in equations (8). According to [7], the ISC can
be achieved by measuring the LSS and subtracting the
mechanical losses as shown in Figure. 7. The
Electromagnetic torque of reference
is calculated in
terms of
and the LSS
. In equation (9) and (10),
it is shown how the
is determined. The ISC strategy
also considers the mechanical losses at the gearbox
established at block D including friction and damping
factors [13], [16].
(8)
(9)
(10)
For
calculation it is necessary to establish the
optimal TSR
and the corresponding
. These
parameters can be either calculated experimentally or
provided by the manufacturer. In this work, multiple
simulations using Matlab and FAST where used to
estimate
,
and compared to manufacturers
specifications found in [17] (see Table 1).
Table 1: Parameters for Iqr calculation
3.1 Indirect Speed Control Implementation
FAST models a wind turbine as a composition of rigid
and flexible elements. For example, two-blade turbines
are modeled as four rigid and four flexible bodies. The
rigid bodies are the ground, the nacelle, the hub, and the
tip brakes (point masses) whereas flexible bodies
comprise blades, tower, and transmission system. The
components of both flexible and rigid bodies include the
following variables: tower bending, blade bending,
nacelle yaw, rotor position, rotor speed, and driveshaft
torsional flexibility. The tower bending has two modes,
each in the front-to-back and side-to-side directions, and
flexible blades have two modes of rotation and one edge
per blade mode. In Fig. 8, the FAST model [18]
implemented in Simulink [10] can be observed to obtain
a matrix of output variables which directly interact in the
proposed controller model.
Parameter Value Description
Cp_max 0,5 Maximum Power Coefficient
Air density 1,225 Air density configured to FAST
R 41,25 Blade lenght
7,4 Optimal Tip Speed Ratio
N 72 Gearbox ratio
LSS Input variable Low Shaft Speed
wr Input variable Angular rotor speed
Power Input variable Output power
iqr* Output variable Reference current iq
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Figure 8: Simulink and FAST integration.
For the implementation of the proposed control
algorithm, the angular velocity at low speed is initially
considered, and together with equation (10), the control
block is carried out; thus, importing the variables
calculated in FAST [8]. It is worth noting that the
controller was designed in Matlab. The implementation
of the control loop is observed in Fig. 9.
Figure 9: Indirect Speed Controller
For the calculation of Iqr, which enters the PI control
block, the expression (11) is used. This expression was
calculated from the generated electromagnetic torque [3].
(11)
Where,
p = Generator pair of poles
= Stator frame flux
= Mutual Inductance
= Stator Inductance
4. SIMULATION RESULTS
ISC MPPT simulation strategy is based on a DFIG
electric machine of 1,5MW output power at nominal
speed. Some wind turbine parameters for FAST and
Matlab configurations are displayed in Table 2. The ISC
MPPT technique depends deeply on the dynamics of the
system because the optimal constant calculated in
equation (10) remains constant for all possible wind
variations. The advantage of this method consists in its
simplicity of implementation and conceptually it is easier
to understand the response and calibrations over some
parameters can be performed intuitively. This method
requires precise characteristics of the wind turbine and
evidently can be improved using Direct Speed Control
(DSC) methods based on complex observers. It is
important to note that ISC as well as DSC do not require
to measure the wind for the control strategy. Instead, it
can be calculated, predicted or estimated depending on
the approach. Considering that dynamics of the wind
turbine system and the corresponding MPPT ISC
controller require to be tested at multiple wind
conditions, three different wind speed signals were
configured for analysis and validation. These signals
where generated and imported to FAST where the
outputs are calculated considering mainly partial load
operations. The signals are generated considering
different dynamic responses such as step, triangular and
realistic functions. All the signals were created for
evaluation purposes considering real ranges such as
levels or slopes. Furthermore, there are some
considerations for establishing the wind speed profiles.
For instance, according to [7], the low speed region
oscillates between 3,5 m/s and 5,5 m/s. Partial load
operations where the MPPT ISC controller acts from a
input wind speed of 5,5 to 11 m/s and the constant or
nominal speed is established between 11 and 12 m/s. The
Cp is plotted from time zero. Besides, the initial inertia,
wind, and rotor speed cause the Cp to initiate in an
unrealistic quantity; however, after ten seconds, the Cp
swings between 0,4 and 0,5 which is desirable for this
wind turbine (see Table 2). All the following experiments
compare a traditional PID with the ISC controller.
Table 2: Wind Turbine Specification
4.1 Steady wind speed with ramp type transitions
The first wind speed signal used for simulations
consists of different levels of steady wind speed with
ramp type transitions. The idea, here, is to provide a more
deterministic signal to evaluate the controller response. It
is well known that step shape functions are often used in
order to determine the system response for drastic input
variations. The wind speed input signal varies from 8 to
10 m/s and the plot can be observed in Fig. 10 (d). For
the study`s purpose, 100 seconds of simulation have been
tested. Thereon, the following outcomes were obtained.
First, the output power remains about 1,275 MW in
average. It is important to note that even though the wind
turbine is at rated speed of 11 m/s, the simulation only
considers the partial load. When the rated speed is
Parameter Value
Synchronism 1200 rev/min
Rated power 1,5 MW
Rated stator voltage 575 Vrms
Rated torque 10 KNm
p 3 pairs
R 41,25 m
Rs 0,006352 pu
Lm 2,613233 pu
Rr 0,004496 pu
Ls 0,154253 pu
Lr 0,1406427 pu
NI 49.130E3 Kg m^2
GI 960 Kg m^2
1,5 MW Wind Turbine System
Parameters
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Edición No. 17, Issue II, Enero 2021
attained, the output power reaches near 1,5MW, and
these wind speed profiles generate a stable Cp near 0,5.
In terms of the pitch controller, it also operates, but the
simulations are intended to reduce its direct influence
over the MPPT controller. In this test, the main
achievement is the stability of the Cp signal in Fig. 10
(g), the ISC MPPT can be observed even though the Cp
average value has short increase. However, the output
power shown in Fig. 10 (c) also describes a small
increment in level an also stability. Fig 10 (a) shows the
Iq reference current in per units which is similar for both
controllers, however, for the ISC the current is saturated
between 50 and 70 seconds because the wind speed is
over the partial load limit. For this experiment, pitch
angle controller is activated when the magnitude of the
wind is above 9,5 m/s which corresponds to the
manufacturer specifications. The pitch angle can be
observed in Fig. 10 (f) where the pitch angle executes
three corrections before the steady state for traditional PI
control and two corrections for the case of the ISC. This
leads to a less pitch dependent approach reducing the
stress of the components of the overall system.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure 10: Steady wind speed with ramp type transitions:
Reference signal
in p.u. (a), electromagnetic torque in Nm (b),
active output power in W (c), wind input in m/s (d), generator
speed in rpm (e), pitch angle in degrees (f) and
(g)
4.2 Variable wind speed with positive and negative
ramps
The second wind speed signal used for simulations
consists of a variable wind speed with triangular type
signal. The idea, here, is to test the system with a more
realistic oscillatory signal in order to evaluate the
dynamic response using triangular input winds. This
signal simulates an increasing wind speed profile which
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Ayala et al. / Indirect Speed Control Scheme for Maximum Power Point Tracking of the DFIG Wind Turbine System
varies between 6,5 and 8,5 m/s. To comply with the
study`s guidelines, 100 seconds of simulation have been
tested. The power remains about 0,9 MW in average
during MPPT zone. It is fundamental to emphasize that
even though the wind turbine has been configured at the
rated speed of 11 m/s, the simulation is intended to
evaluate the dynamic response during the variations of
wind. Moreover, although the initial inertia and torque
are low for testing the dynamics during partial load, a
very strong oscillation at the beginning of the simulation
which corresponds to the initial inertia configuration in
FAST can be observed. When the rated speed is attained,
the output power reaches near 0,75 MW, and the wind
speed profiles generate a stable Cp near 0,5. With respect
to the pitch controller, it also acts when the wind speed
increases, and because of this, the system is tested mainly
during partial load without a straight impact of pitch
control. In Fig. 11 (a) it is shown the triangular input
signal with small slopes stating with 7 m/s speed. For this
experiments the pitch angle does not actually work
because the wind speed is low as shown in Fig. 11 (f).
The output power clearly increases after 80 seconds and
remains higher for a steady state condition after 90
seconds as shown in Fig. 11 (c). Iqr displays smooth
variations in Fig. 11 (a), however, it is able to produce
changes also in the Cp which also has rapid variations
following the response of the wind in the triangular shape
function.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure 11: Steady wind speed with positive and negative ramp:
Reference signal
in p.u. (a), electromagnetic torque in Nm (b),
active output power in W (c), wind input in m/s (d), generator
speed in rpm (e), pitch angle in degrees (f) and
(g)
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4.3 Variable Wind Speed with realistic input
The third wind speed signal used for simulations
consists of a variable wind speed with transitions. The
idea, here, is to test the system with a more realistic
oscillatory signal in order to evaluate the dynamic
response. The oscillatory signal simulates an increasing
wind speed profile which varies from 7 to 10 m/s.
Thereupon, 100 seconds of simulation have been tested.
Under those circumstances, the following results were
obtained. First, the output power remains about 0,9MW
in average during MPPT zone. At this instant, it is
important to highlight that even though the wind turbine
is at rated speed of 11 m/s, the simulation only considers
the partial load and the initial conditions. Moreover, the
initial inertia and torque are low for testing the dynamics
during partial load. When the rated speed is attained, the
output power reaches near 1,25MW due to the slip, and
these wind speed profiles generate a stable Cp near 0,5.
Regarding the pitch controller, it also acts when the wind
speed increases, and because of this, the system is tested
mainly during partial load without a straight impact of
pitch control. The generated realistic input wind speed
can be observed in Fig. 12 (d). The improvements of the
Cp are observed in Fig. 12 (g) along basically all the
curve. The pitch angle also remains zero because of the
range of the wind speed as shown in Fig. 12 (f). Probably
the best outcome during this experiment is the
improvement of the output power along the curve
following the variations of the inputted wind.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure 12: Steady wind speed with realistic inputs: Reference
signal
in p.u. (a), electromagnetic torque in Nm (b), active
output power in W (c), wind input in m/s (d), generator speed in
rpm (e), pitch angle in degrees (f) and
(g)
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5. CONCLUSIONS
In this work, the ISC has been implemented by using
FAST and Matlab for a DFIG 1,5MW wind turbine.
Compared to the classical PI control, the results display
an important improvement because of the correct
selection of optimal TSR and maximum Cp when
calculating the optimal constant at the controller stage.
This implementation also considers the initial LSS speed
and inertia which produce an oscillation during the first
20 seconds of simulation. Nevertheless, after that period
of time, the rotor speed becomes steady and close to the
nominal value. The aforementioned tests have the
intention of demonstrating the capability of this
technique for responding fast, even for disturbed wind
input. Furthermore, the pitch control is able to perform a
correct control for limiting the speed of the shaft. This is
important since the Cp performance is also affected by
the pitch angle. After the simulation enters a full load
operation, the power and speed become nominal values,
but in these experiments, the idea is to demonstrate the
correct operation of the wind turbine along the partial
load operation.
The DFIG electric machine could also have a different
configuration for the connection to the grid; thus,
allowing the system to provide more power depending on
the active and reactive power. The ISC improvements
compared with traditional PID controllers can be
quantified in terms of stability but mainly of Cp
maximization. This can be observed in the results when
the average output active power is measured where the
ISC allows more power extraction. Because of the nature
of the wind turbine components, the dynamic error
represents all possible stages of the wind turbine where
the energy is transformed. The variations in the wind
speed input produces many transients over the output
power and power coefficient consequently. The wind
speed is not usually constant for real scenarios and it is
mostly considered as an oscillatory signal which
produces a complex dynamic. The ISC could be
improved when the knowledge of all possible variables is
available since it is very sensitive to disturbances and
implementation of DSC methods are recommended for
better dynamic performance.
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Edy Ayala Cruz.- Nació en
Cuenca en 1987. Recibió su título
de Tecnólogo Electrónico en 2009
y de Ingeniera Electrónico en 2011,
ambos de la Universidad
Politécnica Salesiana del Ecuador;
y su título de Máster of Engineering
Science (Electrical and Electronic)
de Swinburne University of Technology en Australia,
2015. Actualmente, se encuentra cursando sus estudios
de Doctorado en Ingeniería en la Universidad de Ferrara.
Su campo de investigación se encuentra relacionado con
sistemas de control en ingeniería y energías renovables.
Silvio Simani.- Nació en Ferrara,
Italia en 1971. Obtuvo su título en
Ingeniería Eléctrica por la
Università Degli Studi Di Ferrara
(Italia) en 1996; y su Ph.D. en
Ciencias de la Información:
Control Automático de la
Universidad de Ferrara y Modena
(Italia). Sus intereses de investigación incluyen el
diagnóstico de fallas de procesos dinámicos, el modelado
e identificación de sistemas y los problemas de
interacción entre la identificación y el diagnóstico de
fallas.
Nicolás Pozo Ayala.- Nació en
Cuenca, Ecuador en 1998. Es
estudiante de Mecatrónica con
mención en Automatización
Industrial desde el año 2016 en la
Universidad Politécnica Salesiana.
Sus áreas de estudios se focalizan
en la automatización de procesos
alimenticios y desarrollo de dispositivos para personas
con discapacidad.
Eduardo Muñoz Palomeque.-
Nació en Cuenca, Ecuador en
1997. Actualmente, se encuentra
culminando sus estudios de grado
en la carrera de Ingeniería
Mecatrónica en la Universidad
Politécnica Salesiana Sede Cuenca
del Ecuador. Sus intereses en áreas
de investigación incluyen técnicas de procesamiento de
señales, sistemas autónomos y electrónica de potencia.
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