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Alexander Andre Limas
Control in Low-Power Wind Turbines
Dynamic Torque Estimation to Improve Self-Sensing
Academic year 2014-2015
Faculty of Engineering and Architecture
Chairman: Prof. dr. ir. Jan Melkebeek
Department of Electrical Energy, Systems and Automation
Master of Science in Electromechanical Engineering
Master's dissertation submitted in order to obtain the academic degree of
Supervisors: Prof. dr. ir. Jan Melkebeek, Dr. ir. Frederik De Belie
Alexander Andre Limas
Control in Low-Power Wind Turbines
Dynamic Torque Estimation to Improve Self-Sensing
Academic year 2014-2015
Faculty of Engineering and Architecture
Chairman: Prof. dr. ir. Jan Melkebeek
Department of Electrical Energy, Systems and Automation
Master of Science in Electromechanical Engineering
Master's dissertation submitted in order to obtain the academic degree of
Supervisors: Prof. dr. ir. Jan Melkebeek, Dr. ir. Frederik De Belie
Permission of Use
The author gives permission to make this master dissertation available for consulta-tion and to copy parts of this master dissertation for personal use. In the case of anyother use, the limitations of the copyright have to be respected, in particular withregard to the obligation to state expressly the source when quoting results from thismaster dissertation.
The author,
Alexander Andre Limas
Acknowledgment
This internship has consumed a huge amount of work, research, and dedication. Yet,all would have not been possible without support of many individuals. Therefore, Iwould like to extend my sincere gratitude to all of them.
Prima facea, I am grateful to the Almighty God, for the opportunity, good healthand wellbeing to finally complete this thesis. Not to forget to thank my biggestsupport, which comes from my parents, who always listen to my good and bad sit-uations.
I would also like to express my sincere gratitutude towards my supervisors, Dr.ir. Frederik De Belie and Dr. ir. Pablo Garcia Fernandez, for providing informa-tional and essential guidance concerning projects continuation. Without their inputsand advices, the project would neither progress as it should be, nor finish on time;thus their assistance has been a great help.
I am extremely thankful to Ir. Araz Darba for the provision of expertise and tech-nical support in the implementation. Thank you for spending a lot of time figuringout what the problems are, explaining the BLDC concept, and how to program inFPGA, which assisted me in producing high quality outcomes.
Moreover, I take this opportunity to express appreciation to the technician, TonyBoone and Stefaan Dhondt, for their time in making the wind turbine. I could notimplement the project without them. I would also like to thank my close friends,Ir. Dimitar Bozalakov and Virginia Manzolini, for some great discussions about theproject and their companion during lunchtime.
I am also thankful to have supportive friends, such as Emillio, Thibaut, Lucasand Thomas, for their support and jokes during the difficult times in fulfilling thisthesis. Last but not least, I would also show my gratefulness for my girlfriend,Christie Mandasari, for her abundant love, support, and understanding, which havealways been a great motivation for me. Thank you for the great moments I haveexperienced these past few months and it was such a great pleasure to know andwork with each one of you.
Alexander Andre Limas04/09/2015
iii
Overview
Dynamic Load Estimation to Harvest Maximum Power in Low-Power
Wind Turbines
Alexander Andre Limas
Master’s dissertation submitted in order to obtain the academic degree ofMaster of Science in Electromechanical Engineering
Supervisor: Prof.dr.ir.Jan Melkebeek, Dr. ir. Frederik de BelieCounselors: ir. Araz Darba
Department of Electrical Energy, Systems and AutomationChairman: Prof. dr. ir. Jan MelkebeekFaculty of Engineering and ArchitectureAcademic year 2014-2015
A common method to commutate the stator current in a three-phase BrushlessDC ma-chine is by using the rotor position. Often this is done by using three Hall-effect sensor, detecting the permanent-magnet field fixed to the rotor. However,due to different issues related to these additional mechanical sensors, such as costand installation, this method is less preferred. Another way to detect the currentcommutation moments is by sensing the electrical states of the machine that dependon the rotor position such as the back-EMF voltage. Such a method is known assensorless or self-sensing method.By using a self-sensing method, the rotor position could be more accurately esti-mated. Hence, it would improve the current commutation also, which in its turnenhances the dynamic behaviour of the machine in both motor and generator mode.In addition, by using a load torque estimation, it would be possible to further im-prove the current commutation and dynamical behaviour of the machine duringspeed transients due to changes in the load torque (in motor mode or driving torquein generator mode). This load torque estimation also provides necessary informationto compute the optimal breaking torque in order to harvest maximum power andprevent possible instabilities during severe wind gusts.This project is focused on the load torque estimator for wind turbines and the cor-responding adaption of the current controller to the load torque in order to generatemore power for a given wind situation; the optimization of the wind blades andelectrical machine and fluid modeling is consequently not discussed here. At theend of the project, power extraction on a real low-cost low-power wind turbine isanalyzed. The estimation and control algorithms are implemented on an FPGAevaluation platform with Matlab/Simulink integrated.
Keywords
Distributed generation, wind turbines, grid voltage control, active and reactivepower control, power quality
iv
Dynamic Load Estimation to Harvest Maximum
Power in Low-Power Wind Turbines
Alexander Andre Limas
Supervisors: Ir. Araz Darba, Dr. ir. Frederik de Belie and Prof. Dr. ir. Jan Melkebeek
Abstract—A common method to commutate the stator currentin a three-phase Brushless DC ma-chine is by using the rotorposition. Often this is done by using three Hall-effect sensor,detecting the permanent-magnet field fixed to the rotor. However,due to different issues related to these additional mechanical sen-sors, such as cost and installation, this method is less preferred.Another way to detect the current commutation moments is bysensing the electrical states of the machine that depend on therotor position such as the back-EMF voltage. Such a methodis known as sensorless or self-sensing method. By using a self-sensing method, the rotor position could be more accuratelyestimated. Hence, it would improve the current commutationalso, which in its turn enhances the dynamic behavior of themachine in both motor and generator mode. In addition, byusing a load torque estimation, it would be possible to furtherimprove the current commutation and dynamical behavior ofthe machine during speed transients due to changes in the loadtorque (in motor mode or driving torque in generator mode).This load torque estimation also provides necessary informationto compute the optimal breaking torque in order to harvestmaximum power and prevent possible instabilities during severewind gusts. This project is focused on the load torque estimatorfor wind turbines and the corresponding adaption of the currentcontroller to the load torque in order to generate more powerfor a given wind situation; the optimization of the wind bladesand electrical machine and fluid modeling is consequently notdiscussed here. At the end of the project, power extraction on areal low-cost low-power wind turbine is analyzed. The estimationand control algorithms are implemented on an FPGA evaluationplatform with Matlab/Simulink integrated.
Index Terms—sensorless, BLDC generator, wind turbine, loadtorque estimation
I. INTRODUCTION
Nowadays, people are more aware of the energy sources.
Hence, renewable energy has become a growing trend and a
lot of research has been conducted since the past 20 years.
A wind turbine as an alternative energy to produce electricity
has been widely used nowadays. The wind turbines capture
the wind’s kinetic energy. This kinetic energy is translated into
rotation through the wind blades. The rotor shaft is coupled
mechanically to the electrical generator with a gearbox that can
transform slower generator’s shaft rotational speeds into higher
rotational speeds. A Brushless Alternative Current (BLAC)
drive is commonly used in wind turbine systems. However,
a Brushless Direct Current (BLDC) model by Araz Darba [1]
is carried out as a generator in this paper. The differences and
comparison of these two machines will not be discussed in
this paper. Furthermore, by considering the dynamics of the
generator and help of power electronics, current will flow from
the generator and electricity will be produced, likewise stored
in the battery or DC-link capacitor.
(1) Wind turbine (2) BLDC Generator (3) Rectifier (4)
DC-link Resistor/Capacitor
Fig. 1: Block Diagram of Wind Turbine Energy Conversion [2]
In order to control a BLDC machine, three Hall-effect
sensors are commonly used to get the information of the
rotor position. However, the recent technology’s objective is
to reduce the cost and due to mechanical issues such as the
sensors’ placement, the Hall-effect sensors are less used. To
replace the sensor, a method known as sensorless or self-
sensing is widely known. A sensorless method is a method
to estimate the rotor position by measuring the back-EMF
and the current commutation. By using an improved zero-
crossing self-sensing control of BLDC [1], it is able to estimate
the rotor position and current commutation more accurately.
Therefore, it enhances the dynamic behavior of the machine
in motor and generator mode. An improved dynamic behavior
of the machine is necessary if the machine has a dynamic load
such as varying wind speed.
A small wind turbine design is explained without consider-
ing the aerodynamic, and fluid modeling and an algorithm
based on the estimated load torque developed by [1] is
proposed in this paper. This algorithm will be able to absorb
maximum power and prevent the instabilities due to excessive
wind speed. The algorithm is tested on an FPGA with an
integrated MATLAB/Simulink programming. This paper is
divided into seven sections. The first chapter explain the model
of the wind turbine. The proposed algorithm and setup of the
experiment will be illustrated in the second and third chapter
respectively. Lastly, the results of the experiment are analyzed
and the outcome will be concluded in the last two sections.
II. WIND TURBINE MODEL
A. Static Model
The mechanical power extracted by the rotors and torque
of the wind turbine Pr and Tr can be expressed as follows:
Pr =1
2· ρ ·A · υ3 · Cp(λ, φ) (1)
Tr =1
2· ρ ·A · υ2 · Ct(λ, φ) (2)
where ρ is the air density (kg/m3), which equals 1.225
kg/m3 at a temperature of 150C, A is the circular turbine blade
swept area (m2), which is calculated from the turbine radius
R by πR2, υ is the wind speed (m/s). Furthermore, not all
the mechanical power from the wind can be extracted by the
turbine. Thus, a power coefficient or Betz factor, Cp is defined
to express the useful mechanical power. The power and torque
coefficient of the turbine, Cp and Ct respectively, represent
the aerodynamic efficiency. The maximum achievable Cp is
between 0.4 and 0.5 for high speed, two-blades turbines and
between 0.2 and 0.4 for slow speed turbines with more blades
[3].
Moreover, the mechanical model includes the turbine and
generator which shafts are connected together by a gearbox.
The rotor is modelled as two rotating masses connected to-
gether by shaft with a certain damping and stiffness coefficient
values. By neglecting the turbine and generator self-damping
(Dt and Dg), the shaft stiffness, damping and torsional oscil-
lations the mechanical equations can be simplified as (4).
ω =1
J
∫
(Tm − Te) (3)
where,
J = Jt + n2 · Jg (4)
J is the inertia of the wind turbine, Jt is the turbine inertia,
n is the gearbox ratio, and Jg is the generator inertia.
B. Control Algorithm
The main objective of controlling wind turbine is to capture
the effective power generated by the wind turbine. The wind
speed corresponds to active power generated by the wind
turbine system. In order to know the power generated by the
wind, three typical operating systems [3] [4]: I. low wind
speed, II. medium wind speed, and III. high wind speed, are
depicted in Figure 2.
For a small wind turbine, a high speed region is mostly
achieved. At high speed, a variable speed with pitch control is
used above the rated wind speed to reduce the aerodynamic ef-
ficiency and thus to ensure that the generator is not overloaded.
However, in small wind turbines, pitch control is normally not
available. To optimize the power, Cp is kept constant at region
below rated speed.
III. PROPOSED ALGORITHM
Many method to predict the wind speed have been proposed.
However, because of the stochastic and intermittent property
of wind speed, it makes it very difficult to predict. The
most commonly used method to predict the wind speed is
Neural Network (NN) algorithm. Nonetheless, these available
algorithm are complex and they will not be used in the
Fig. 2: Typical Wind Turbine Operating Systems [4]
implementation. The proposed algorithm will be based on
the available load torque estimation which was done by Araz
Darba [5].
A. Wind Speed Estimation and Prediction
In order to predict the wind speed at time instant tk+1, the
wind speed is estimated firstly. The wind speed is estimated at
time instant tk based on the wind turbine torque equation 2.
In motoring mode, the wind turbine becomes the load which
brakes the machine. While in generator mode, the wind turbine
becomes the drive of the machine. Assuming a static model of
wind turbine and assuming the torque produced by the wind
turbine is equal to the estimated load torque, the estimated
wind speed can be computed as:
ˆvest[k] =3
√
ˆT lest[k]. 2. ˆωest[k]
ρπR2Cp(5)
where ˆT lest and ˆωest is the estimated load torque and speed
respectively. ρ is the air density (1.225 kg/m3), R is the
radius of the wind blades, and Cp is the power coefficient.
Furthermore, the wind speed is predicted by taking the sample
of the estimated load torque and estimated rotor speed at the
next sample time k+1.
vpred[k + 1] = 3
√
T lpred[k + 1]. 2. ωpred[k + 1]
ρπR2Cp(6)
The predicted rotor speed can be derived at time instant k+1.
While, the estimated load torque at time instant k+1 can
be expressed by estimated load torque at time instant k+1;
referring these predicted rotor speed and estimated load torque
to [5]. By substituting these two variables to 6, the predicted
wind speed at time instant k+1 can be derived.
From the wind speed estimation equation (5), the estimated
value of the wind speed is achieved from the load torque. Once
the load torque changes, the wind speed reacts and updates its
value. By assuming Cp constant (Cp is not controlled), the
variation of the wind speed is caused by the the dynamic of
the load torque.
B. Load Torque Prediction
The load torque can be predicted at time instant k+1 by
using the predicted wind speed equation (6) at the same time
instant tk+1. The predicted load torque can be computed as:
ˆT lpred[k + 1] =( ˆvpred[k + 1])3ρπR2Cp
2 ˆωpred[k + 1](7)
The predicted load torque is updated when the predicted wind
speed changes because a change in the estimated load torque.
From the equation, it is shown that load torque is a factor
three of wind speed. The higher the wind speed, the bigger
the load produced to deliver the power.
C. Current Reference from Predicted Current
A load torque is corresponding to a DC-bus current if speed
is assumed to be constant. Moreover, from the torque equation,
the load torque at time instant k can be calculated as
Tl[k] = Te[k]− (J + Jturbine)dω
dt(8)
It can be observed that torque is proportional to the current
from equation (8), by substituting Te = 2ktiDC . Consequently,
by assuming the derivation of the rotor speed is equal to the
derivation of the predicted rotor speed, the predicted current
at time instant k+1 can be expressed:
ˆiDC,pred[k+1] =(J + Jturbine) ˆωpred[k + 1] + ˆT lpred[k + 1]
2kt(9)
The current is related to the load torque, so the bigger the
load torque, the bigger the current. In closed loop control, the
predicted current is used as the reference current in order to
harvest the maximum power produced by the wind turbine.
The current is predicted in order to know the next sampling
current needed to produce the torque.
Moreover, the produced torque corresponds proportionally
to the total harvest power. Hence, if the current is maximized,
the power extracted by the output bank, such as resistor or
capacitor bank will be topmost. In addition, the predicted
current can be used to prevent over load due to high current.
By saturating the reference current, the current will be limited
to the maximum allowed value by the machine. This will
prevent the machine to work in stable region and will avoid
overheating that may cause breaking the machine. The overall
schematic of the predicted algorithm based on the load torque
estimation is shown in Figure 3.
IV. TEST SETUP
A. Wind Turbine Design
There has been many wind turbine models available. A
wind turbine has to be perfectly designed in order to have
impeccable characteristics and high efficiency. A small wind
turbine is divided into several important parts. These parts are:
the blades, the tower and the base. Having a good efficiency
is needed in this project. However, designing a perfect wind
turbine is not the main purpose of this paper. The new wind
blades design is based on the kid’s windmill toy. Material
Fig. 3: Schematic of the Predicted Algorithm
used to make the blades is a stiff paper that can easily be bent
and strong enough to rotate the rotor. It has 4 blades with 30
cm diameter. The average height of a small wind turbine is
(a) The Blades (b) The Tower (c) The Mechanic
Fig. 4: Wind Turbine Design
80 feet or approximately 915 cm. At this height the average
wind speed is 5 m/s. Due to deficient wind and closed room
testing environment, the tower is designed using a tube with
a 2 cm diameter and 130 cm height. Moreover, the tower is
mounted on a block 18 cm x 7 cm x 10 cm.
B. Wind Speed Sensor
The wind speed sensor is calibrated by using an electric
bike. The speed shown in the speedometer will be assumed
proportional to the wind gust. Moreover, the wind speed sensor
is decided to be mounted close to the wind blades. The result
of the test shows that mounting the anemometer next to the
blades gives the most wind extraction and it is reliable to place
due to mechanical issue such as cable harnesses and rotating
cups. It is not suggested to mount the wind speed sensor at
the back of the wind blades and the machine because the wind
speed drops significantly. However, the wind speed sensor is
not mounted on the wind turbine as the fan is too small to
provide a balance wind blow from every angle.
C. Wind Source
The source of the wind comes from two fans. These fans
are used in order to give enough high wind speed, so that the
variation of the wind speed can be achieved. Three different
wind speeds are given by this fan. Furthermore, placement
of the fan is based on experiment [6] where at the closest
distance and the further distance, the lower the wind speed. In
the experiment, distance of 10 cm is chosen because it gives
the highest wind blow to the blades.
D. Electronics
The electronics necessary to implement the project are a
driver, four ADC converters, an isolator, a connector and an
FPGA. The driver controls the switch via three half-bridge
inverter. The DC-link can be measured too because the driver
is connected internally with a shunt-resistor. Four analog-to-
Fig. 5: Electronics Setup
digital converters are implemented to translate each voltage
output of the BLDC into digital value which can be read by
the FPGA. A galvanic isolator is used to separate the ground
between driver and FPGA by preventing unwanted current
flowing from the driver to the FPGA. In addition, the function
of the connector is to interface the ADCs and the FPGA. The
electronics’ setup is shown in Figure 5.
E. Software
One of the main drawbacks of modeling an FPGA in
MATLAB/Simulink is that it can not do complex mathemat-
ical calculation, such as a fractional power. Some variables,
such as rotor speed, predicted wind speed, measured current,
predicted current and torque constant, are used and calibrated
so that it matches the hardware measurement. Likewise, the
inertia of the wind turbine is calculated based on [7], where
J = kJML2. The mass of the blades, M , is measured 55
gram. The length of the blade, L, is 15 cm and the constant
kJ for 15 cm blade is 0.469722921. The result of the wind
blade’s inertia is 0.000581282. Due to simple mechanism, the
wind turbine’s inertia is equal to the wind blade’s inertia.
Moreover, the predicted current has to be filtered because
the predicted load torque is sampled at high frequency. A high
frequency reference current will result to a poor control of
the current, such as switching noise. In order to avoid this
situation, a biquad digital filter is carried out at sampling
frequency of 500 Hz and cut-off frequency of 2 Hz.
V. EXPERIMENTAL RESULTS
The proposed algorithm based on the load torque estimation
is programmed in generator mode and compiled to the FPGA.
A BLDC model, six-steps commutation, static model of wind
turbine, improved zero-crossing method and hysteresis current
control are implemented.
A. Open Loop and Controlled Loop Back-EMF, Current Com-
mutation and Measured Output Voltage
With the wind blades connected to the rotor, a correct
commutation has to be achieved in BLDC generator. With a
correct commutation and working hysteresis current control,
the output is chopped. The output of the controlled voltage is
depicted in Figure 6. The amplitude of the back-EMF depends
on the speed of the generator. The faster the rotor speed,
the higher the amplitude of the back-EMF. The back-EMF
determines the output voltage measured at the DC-link. As a
result, with a correct control and trapezoidal back-EMF shape,
an output voltage is measured at almost constant, giving a DC
output value. For such back-EMF output, the measured voltage
is shown in Figure 7.
Fig. 6: Controlled 3-Phase Back-EMF BLDC
Fig. 7: Measured Voltage
B. Speed Sensor and Predicted Speed
The proposed algorithm is based on the static model; Cp is
considered constant, the aerodynamic and air flow of the wind
blades are not taken into the account. However, this coefficient
has a big impact on the algorithm. The predicted wind speed
is an inverse function of Cp. As a consequence, Cp limits
the total wind prediction. On the other hand, placement of the
wind speed sensor or anemometer should be adjusted with this
limitation. If the anemometer is placed facing the highest wind
blow (tip of the fan’s blades) from the fan, the sensed wind
will be over the predicted wind speed. This situation results
to a high load torque prediction and uncontrolled current.
To prevent this condition, the anemometer should be
mounted to sense the wind blow from the middle of the fan.
On the other hand, with the proposed algorithm and correct
calibration, the predicted wind speed is able to approximate
the real wind speed. The wind speed sensor is tested and
the algorithm works if sensor’s output is below the maximum
positive predicted wind speed. Thus, according to the test, the
approximated wind speed is 80 percent below the maximum
predicted wind speed. Three different wind speeds given by
the fan are predicted to be approximately 4.5-5 m/s, 5-5.5 m/s,
and 5.5-6 m/s.
Fig. 8: 1st Level
Fig. 9: 2nd Level Fan Speed
C. Power Extracted by the Wind Turbine
In order to analyze the power extracted by the wind turbine,
the power balance equation is used. By comparing the power
produced by the wind turbine and the power supplied to
the wind turbine, the power balance is met. As the machine
acts as a generator, the supplied power is the mechanical
power given by the wind turbine with different wind speed
Fig. 10: 3rd Level Fan Speed
from the fan. The mechanical power is proportional to the
estimated load torque and the speed of the rotor, Pmech =T load.ωr. On the other hand, the produced power is the
electrical power produced by the power at the DC-link. The
electrical power is proportional to the voltage and current
measured on the generator, Pelec = V.imeasured/predicted.
With Ohm’s law, the electrical power can be expressed as
Pelec = imeasured/predicted2.R.
Two conditions have to be fulfilled in order to analyze the
power: current has to be controlled, and back-EMF has to be
trapezoidal. If these two conditions are not met, voltage and
current cannot be measured. Hence, power flow will not be
able to be calculated. To measure the current at the DC-link,
a resistor is connected. Two different value of resistors are
implemented; power resistor 1.2 ohm and regular resistor 0.5
ohm. Below is the result of mechanical power and electrical
power with 1.2 ohm. The experiments were done in 5 different
fan speed levels(1 st level, 2nd level, 3rd level, 3rd+1st level,
3rd+3rd level) where each level is taken 3 times, giving a total
of 15 experiments. The graph of the result is depicted in Figure
11.
Fig. 11: Electrical Power with R=1.2 ohm
As the wind speed increases, the the electrical power tends
to increase as well. However, at several points, the power goes
lower due to different absorption of the wind speed. This is
due to characteristics of the wind blades’ and wind turbine’s
design. The amount of absorbed power is proportional to: 1.
rotor speed 2. value of resistor. The power shown in the graph
is very small because the machine rotates at a low speed;
between 267 rpm to 355 rpm. On the other hand, the machine
is meant to rotate at a nominal 5240 rpm. At a very low speed,
the voltage at the terminal tends to be very low as well; as
back-EMF is proportional to the speed. In order to gain higher
power, three alternatives can be considered: a gearbox has to
be implemented or another motor has to be used, change the
resistor load, or use another power converter’s topology.
However, changing the design of the wind turbine and the
machine will change everything, such as inertia, machine’s
parameters, etc. Moreover, another cheaper possibility might
be reasonable by changing the power converter’s topology. The
use of a diode bridge and a boost converter is cheaper, but it
produces a large amount of input current harmonics, which
affects the performance of the system, higher output voltage
harmonic losses caused by the uncontrolled rectifier.
By changing the value of the resistor, the current flows will
change too. With a lower resistor value, a higher current will
flow through it. This gives result to a higher electrical power.
However, this resistor acts as a load. Thus, it is not a linear
relation. Due to this condition, a second resistor with lower
value is used to analyze the power stored in it.
In order to increase the power, the current and the power
will be analyzed. By subtracting the current flows to resistor
equals to 0.5Ω with the current flows to resistor equals to 1.2Ω,
the increased power between these two different value can be
calculated as well. Result shows that with a decreased resistor
value from 1.2Ω to 0.5Ω, an increased current of 0.2814 A can
be achieved. Likewise, an increased power of 0.0276 Watt is
achieved. Assuming a linear relation, the resistor’s value can
be calculated by evaluating the increased power needed and
the needed current. Table I shows the desired resistor’s value
for a desired 0.25, 0.5, 1, 5, and 10 Watt electrical power.
D. Efficiency of the Wind Turbine
The efficiency of the wind turbine can be evaluated by
knowing the output power and the input power. The output
power is the electrical power stored in the DC-link. While the
input power can be achieved by measuring the load torque
using the estimator and predictor and by measuring the rotor
speed of the machine using the estimator and predictor as well.
The efficiency rises if the total amount of load torque
decreases. It means that the total amount of input power is
small in order to produce the output power. The predicted
algorithm harvest more power and its efficiency is always
higher than the measured one. Furthermore, the efficiency at
lower wind blow is lower because the rotor rotates at low
speed. A low speed rotor gives a low voltage and the current
is difficult to be controlled. Thus, a total produced electrical
power is low and efficiency drops. In contrast, the higher the
wind speed, above 6.5 m/s, the efficiency starts to decline.
The reason is that the wind turbine’s design is not efficient
DesiredPower (W)
IncreasedPower (W)
Current(A)
Resistor(ohm)
0.25 0.1379 2.613 0.03661
0.5 0.3879 7.3504 0.00925
1 0.8879 16.8251 0.00353
5 4.8879 92.6222 0.00058
10 9.8879 187.3685 0.00028
TABLE I: DC-link Resistor Value
Fig. 12: Mechanical Power with R=1.2 ohm
because a high vibration from the wind turbine, such as the
tower, hub, etc. This gives a lot of lost in absorbing the power.
To increase the efficiency of the wind turbine at high speed,
the turbine has to be designed perfectly; stable tower, less
vibration and better wind blades. From the graph, the most
efficient to harvest the power is at 6-6.5 m/s wind speed.
Fig. 13: Efficiency with R=1.2 ohm
VI. CONCLUSION
This paper was contributed to develop a predicted algorithm
based on the estimated load torque to harvest maximum power
of the wind turbine and avoid instabilities caused by severe
wind speed.
A BLDC generator self-sensing model, improved zero-
crossing method by Araz Darba, and static wind turbine model
were used. The wind turbine was designed without considering
the aerodynamic, the fluid characteristic of the wind blades
and the electric drive in order to evaluate the power. A
kid’s windmill toy design, a wind turbine without gearbox,
a brushless direct current (BLDC) generator, two fans with
three different wind speed (4.5-5 m/s, 5-5.5 m/s and 5.5-6
m/s), and a wind speed sensor to sense the wind speed were
calibrated and implemented.
The wind speed sensor is suggested to be placed facing
the middle of the fan, but it is not mounted on the wind
turbine as the fan is too small to provide a balance wind blow
from every angle. Nonetheless, the predicted wind speed was
used to approximate the wind speed. The wind speed was
approximated to be 80 % below the maximum positive value
of the predicted wind speed. Moreover, in order to prevent
instabilities and overheating the machine, the predicted current
was filtered and feeded back as a reference current limited to
the nominal current value of the machine; 2.33 A. A hysteresis
controller was used to control the current by chopping the
voltage.
Two conditions have to be fulfilled in order to analyze
the power: 1.current has to be controlled, 2. back-EMF has
to be trapezoidal. The power was analyzed by using the
power balance equation. The outcome showed that harvested
power was higher using the predicted algorithm. In contrast,
the output power was low due high nominal speed of the
generator and low current flowed to the DC-link. These can be
uplifted by having a gearbox or change to another machine,
using another power converter’s topology and changing a
lower resistor’s load value. However, changing the design
will change the characteristics of the wind turbine, such as
inertia, speed, etc. Using diode rectifiers and boost converter
is cheaper but it produces high harmonics and losses which
affect the performance of the system.
Another way to lift up the power is to lower the power
resistor. The increase of the current was evaluated by having
two different resistors’ value; 1.2Ω and 0.5Ω. With these
evaluated current, by assuming a linear increasing current
and decreasing resistor’s value, a resistor’s value can be
approximated for a desired power.
The efficiency of the wind turbine was evaluated. At low
wind speed, only some fractions of the power will be harvested
due to poor control. This resulted to a low efficiency. On
the other hand, at wind speed above 6.5 m/s the efficiency
drops slightly due to inefficient wind turbine’s design, such
as vibration of the tower, unstable wind blades due to the
bearings, etc. To conclude, the most efficient region is when
the wind blows at 6-6.5 m/s.
REFERENCES
[1] D. Araz, “Current commutation and control of brushless direct currentdrives using back electromotive force samples,” Ph.D. dissertation, Univ.of Ghent, Gent, Aug. 2015.
[2] e. a. G.Gatto, “Predictive control of standalone brushless dc generators,”IEEE, 2009.
[3] E. U. Godswill Ofualagba, “The modeling and dynamic characteristicsof a variable speed wind turbine,” Journal of Energy Technologies and
Policy, vol. 1, no. 3, 2011.[4] M. Rasila, “Torque and speed control of a pitch regulated wind turbine,”
Master’s thesis, Chalmers University of Technology, Sweden, 2003.[5] F. B. J. M. A.Darba, P.D’haese, “Improving the dynamic stiffness in a
self-sensing bldc machine drive using estimated load torque feedforward,”IEEE Transactions on Industry Application, vol. 51, no. 4, 2015.
[6] e. a. S.W. Hong, “The design and testing of a small-scale wind turbinefitted to the ventilation fan for a livestock building,” Computers and
Electronics Agriculture, vol. 99, 2013.[7] M. B. P. A.G. Gonzalez Rodrıguez, A. Gonzalez Rodrıguez,
“Estimating wind turbines mechanical constants.” [Online]. Available:http://www.icrepq.com/icrepq07/361-gonzalez.pdf
Contents
Permission of Use ii
Acknowledgement iii
Overview iv
Extended abstract v
Abbreviations xiv
1 Introduction 1
1.1 Project Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Current Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Project Goals and Objectives . . . . . . . . . . . . . . . . . . . . . . 41.4 Working Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 State of the Art 6
2.1 Brushless DC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.1.1 General Information . . . . . . . . . . . . . . . . . . . . . . . 62.1.2 Working Principle . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.3 Four Quadrant Operating Region . . . . . . . . . . . . . . . . 102.1.4 Generation Mode . . . . . . . . . . . . . . . . . . . . . . . . . 102.1.5 Six Step Current Commutation . . . . . . . . . . . . . . . . . 112.1.6 BLDC Machines Model . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Torque, Current and Speed Control . . . . . . . . . . . . . . . . . . . 152.3 Self-Sensing Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Conventional Zero Crossing Method . . . . . . . . . . . . . . . 172.3.2 Back-EMF Threshold Method . . . . . . . . . . . . . . . . . . 18
2.4 Estimation of Speed, Position and Load Torque . . . . . . . . . . . . 192.4.1 Speed and Position Estimation . . . . . . . . . . . . . . . . . 192.4.2 Load Torque Estimation . . . . . . . . . . . . . . . . . . . . . 20
2.5 Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.5.1 Wind Turbine Model . . . . . . . . . . . . . . . . . . . . . . . 212.5.2 Static Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.5.3 Drive Train Model . . . . . . . . . . . . . . . . . . . . . . . . 242.5.4 Control Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 25
xii
3 Proposed Algorithm 26
3.1 Wind Speed Estimation and Prediction . . . . . . . . . . . . . . . . . 263.2 Load Torque Prediction . . . . . . . . . . . . . . . . . . . . . . . . . 273.3 Current Reference from Predicted Current . . . . . . . . . . . . . . . 27
4 Test Setup 29
4.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1.1 Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1.2 Wind Speed Sensor . . . . . . . . . . . . . . . . . . . . . . . . 314.1.3 Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.1.4 Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.2.1 Inertia of the Wind Turbine . . . . . . . . . . . . . . . . . . . 324.2.2 Speed, Predicted Wind Speed, Measured Current, Predicted
Current and Torque Constant Calibration . . . . . . . . . . . 334.2.3 Digital Predicted Current Filter . . . . . . . . . . . . . . . . . 334.2.4 Block Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5 Simulation and Implementation 35
5.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.2.1 Open Loop and Controlled Loop Back-EMF, Current Com-mutation and Measured Output Voltage . . . . . . . . . . . . 39
5.2.2 Speed Sensor and Predicted Speed . . . . . . . . . . . . . . . 415.2.3 Predicted Load Torque and Predicted Current . . . . . . . . . 435.2.4 Power Extracted by the Wind Turbine . . . . . . . . . . . . . 435.2.5 Efficiency of the Wind Turbine . . . . . . . . . . . . . . . . . 47
6 Conclusions 50
7 Future Developments 52
A Measurement Results 53
B Essential Components of the Test Setup 58
B.1 Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58B.2 BLDC Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58B.3 Driver Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59B.4 ADC Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60B.5 Digital Isolators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60B.6 FX2 Connector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61B.7 FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61B.8 Wind Speed Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Figures 63
Tables 64
Bibliography 65
xiii
Abbreviations
ADC Analog Digital Converter
BLAC Brushless Alternating Current
BLDC Brushless Direct Current
EELAB Electrical Energy Laboratory
EMF Electromotive Force
FPGA Field-Programmable Gate Array
HIL Hardware in the Loop
IGBT Insulated-Gate Bipolar Transistor
MMF Magnetomotive Force
MOSFET Metal-Oxide-Semiconductor Field-Effect Transistor
MPPT Maximum Power Point Tracking
NN Neural Network
PI Proportional-Integral
PVC Polyvinyl Chloride
PWM Pulse Width Modulation
TSR Tip Speed Ratio
xiv
Chapter 1
Introduction
Nowadays, people are more aware of the energy sources. Hence, renewable energyhas become a growing trend and a lot of research has been conducted since thepast 20 years. Wind turbine is one of the main sources to produce electricity frommechanical movement of the blades. At the University of Ghent, Belgium, a topicrelated to wind turbine has been raised and the internship has been carried out.
In order to control a BLDC machine, three Hall-effect sensors are commonly used toget the information of the rotor position. However, the recent technology’s objectiveis to reduce the cost and due to mechanical issues such as the sensors’ placement, theHall-effect sensors are less used. To replace the sensor, a method known as sensorlessor self-sensing is widely known. A sensorless method is a method to estimate the ro-tor position by measuring the back-EMF and the current commutation. By using animproved self sensing control of BLDC, it is able to estimate the rotor position andcurrent commutation more accurately. Therefore, it enhances the dynamic behaviorof the machine in motor and generator mode. An improved dynamic behavior ofthe machine is necessary if the machine has a dynamic load such as varying windspeed.
The main objective of the project is to build a small wind turbine setup and todevelop an algorithm based on the estimated load torque. This algorithm will beable to absorb maximum power and prevent the instabilities due to excessive windspeed. The algorithm is tested on an FPGA with an integrated MATLAB/Simulinkprogramming.
The thesis consists of seven chapters. Chapter 1 explains the description of theproject, the current situation, the project goals and objectives and the workingmethod. Chapter 2 describes the background information needed to understand thecomplete development process and the resulting test system. This includes a gen-eral explanation of the BLDC drives, i.e. the dynamic characteristics, the workingprinciple, the commutation, the operation mode, and the model itself. Furthermore,it describes a brief overview of the self sensing control method, followed by the loadtorque estimation method and a brief explanation of the wind turbine, i.e. the modeland the control algorithm. Chapter 3 describes the method to solve the issues andchallenges faced during the work and the adopted solutions. The development pro-cess of the complete setup, including the hardware arrangement and the writtensoftware to perform the test are explained in Chapter 4. Finally, some simulation
1
and measurement results are shown in Chapter 5. At last, conclusion of the overallthesis is in Chapter 6, followed by Chapter 7 which will explain the improvementand future work that can be done for this project.
1.1 Project Description
The Electrical Energy Laboratory (EELAB) at Ghent University (UGent) has beeninvolved in the development of self-sensing control of induction machines, permanent-magnet synchronous machines, switched reluctance machines and recently of BLDCin particular. A three phase BLDC has permanent magnets mounted on the rotor.In a six-step BLDC commutation, each phase in the stator is energized during in-tervals of 120 electrical degrees and current commutates each 60 electrical degrees,resulting in a rotating mmf field in the air gap. The permanent-magnet rotor fieldtries to align with this mmf field and in doing so produces a force according toLorentz Law which in rotating machines produces a torque. Moreover, the rotatingpermanent magnetic field on the rotor produces an induced voltage in the stator,known as back-EMF. Thus, in order to commutate the current or to know the totaltorque produced by the machine, the rotor position or the permanent-magnet fluxorientation has to be known. Often the rotor position is known by placing Hall-effectsensors in the motor. Such sensors increase the total price of the motor, demandadditional cabling and space, and reduces reliability of the drive. In order to over-come these disadvantages, another technique is used by estimating the rotor positionfrom electrical variables only, hence replacing the position sensors by software. Of-ten only sensors for stator voltages (back-EMF) and currents are used, resultingin stable control of the BLDC machine without using mechanical position or speedsensors. This method is widely known as “Self-Sensing or Sensorless” control.
As the back-EMF is proportional to the speed of the rotor, different rotor speedswill produce different back-EMF waveforms. The higher the rotor speed, the higherthe amplitude of the back-EMF and the steeper the back-EMF waveform during thetransient between positive and negative values. To obtain a self-sensing BLDC-drive,estimation of the phase current commutation is often done by using informationcoming from the speed-induced back-EMF. At EELAB, some improvements havebeen made for standard back-EMF based methods. The improvements are made toguarantee a smooth and stable operation during transient state operation resultingfrom speed drops and load torque variations. To further improve the behavior ofthe machine, in this project, dynamic load estimations will be made besides rotorposition estimations, allowing to predict the upcoming speed variations. From this,the resulting time deviations in the current commutation moments can be compen-sated as driving a BLDC requires very accurate commutation moments in order tomaintain an efficient torque production.
Methods developed will be tested on a low-power, low budget wind turbine in whichthe turbine blades, driven by a variable wind, generate a variable torque on the shaft.An FPGA test platform including a Simulink graphical programming will be usedduring the project for a rapid development of the load estimator in the self-sensingcontrol loop. The FPGA allows hardware-in-the-loop testing and can run differentturbine models at the same time if required, allowing to choose a proper model that
2
fits best the turbine characteristics. Likewise, control of different turbine bladescould be tested as well. Power extraction will be the key performance indicator ofthe control loop designed.
Despite that BLDC machines are widely used in the market due to higher powerdensity and simplicity in control compared to AC machines, using a BLDC machinein a drive for a wind turbine is not really common. Nonetheless, it is not a goal of theproject to search for a drive with the best performance for wind turbine application.A BLDC drive allows measuring the back-EMF directly and hence is very suitableto test the feasibility of the proposed load estimation algorithms. In future researchAC machines can be tested as well for which the back-EMF is measured indirectly.However, the latter is excluded from the project’s scope.
1.2 Current Situation
At the moment, the method of using the zero-crossing moments of the back-EMFwaveform to detect current commutation moments has been analyzed as a benchmark. It follows that such zero-crossing methods fail at lower speed due to a lowsignal to noise ratio and that stability is hard to guarantee at lower speed ranges.
A method has been evaluated by UGent which the transient from the positive tonegative back-EMF value and vice versa is used in threshold-tracking. This methodsamples the unexcited phase of back-EMF and keep an eye on the commutation untilit reaches a threshold value. The threshold value is calculated after switching eventand sampling delay as another negative sampled of back-EMF voltage amplitude.The sampling delay is due to spikes of the commutation which gives important noiseto the measurements.
However, even this method might not be accurate enough in estimating the commu-tation. Due to load variations altering the commutation moments, the symmetric-threshold tracker is affected. Research has been started at EELAB to estimate theload in order to compensate the errors in the commutation moments due to loadvariations. The method has been tested with controllable loads. The project in-tends to verify the self-sensing method with load torque estimation on low-powerwind turbines and to search for the required load models and characteristics. Themodel could be assisted with additional sensors if required such as a wind speedsensor.
Some issues have been found also that back-EMF waveform is not perfectly trapezoidwhich gives a distorted torque output when using square current waveforms andcan cause damage to the machine. Moreover, magnetic saliency along the air gapresults in a reluctance torque often working as a breaking torque. This study ishowever the outcome of a second master thesis running in parallel with the projectas described here. However, input of that project could be used to improve thetechniques discussed here when presented on time.
3
1.3 Project Goals and Objectives
The project is initiated and funded by University of Ghent, Belgium. The mainpurpose of the project is to complete the master thesis. Likewise, there are severalspecific goals and objectives that have to be achieved:
• Learning and studying self-sensing BLDC drive as a completion of the masterthesis.
• Studying in an analytic way and by time modeling the behavior of self-sensingBLDC drives using six-step commutation and symmetrical-threshold back-EMF tracking.
• Finding the relation between the wind speed and estimated load torque in a lowpower wind turbine by modeling and limiting the predicted current withoutstudying the optimization of the wind blades, the optimization of the electricalmachine and the fluid modeling.
• Controlling the machine in a dynamic way with a wind turbine as the load/drivingtorque while guaranteeing correct commutation moments. By having a precisecontrol, the power extraction is analyzed in different wind speed, the predictedcurrent is used to prevent instabilities due to extreme wind speed.
• Making a full complete prototype of the system.
• Report the full system and hand it over to the sending and receiving univer-sity’s mentor.
1.4 Working Method
The project was conducted in the Technologie Park Zwijnaarde, from March toSeptember 2015 for approximately 6 months. The entire project can be divided intoseveral phases:
Figure 1.1: Division of the Project
4
• Planning
10% of the project was spent for planning, such as creating a project plan, doingadministration and project management.
• Research
20% of the whole internship was spent to do the study of the BLDC, the wind turbinemodelling, the FPGA, the software implementation and how to write a report inLatex. These were achieved by reading literatures, papers, manuals, data sheets,manufacturer websites, internet documents, etc.
• Test Setup
20% of the project was used to develop the system: coding, debugging, installingthe instruments, installing the software, and constructing the wind turbine andhardware.
• Implementation
30% was spent on testing the test system, measuring the output of the coding, takingdata of the measurement.
• Documentation
20% of the internship was spent to write documentation, including writing thisreport.
5
Chapter 2
State of the Art
In this chapter, literature study related to the project is reported in order to help usunderstand the basic and/or important information. The objectives of the projectare to study the BLDC drive and the self-sensing control. The first section of thischapter will mainly focus on the BLDC drive, such as the general information ofdrives, the BLDC drive working principle, the operating region, the generation mode,the current commutation and the model of BLDC in discrete time. In addition, twodifferent self-sensing control techniques, commonly known as Zero Crossing methodand the improved one namely Threshold method, are discussed in the followingsection. The third section explains how the load torque estimation is achievedthrough speed and position estimation. Last not but least, dynamic load whichcauses dynamic in BLDC drive is a wind turbine in this project. The last sectiondescribes the static model, the drive train model and the typical control of the windturbine.
2.1 Brushless DC
2.1.1 General Information
There are different permanent magnet motors based on its operation. Permanentmagnets can be classified into two according to the supply source; line start andinverter fed. In this research report, we will not discuss the line start type of motors.On the other hand, inverted fed type will be classified into two depending on therotor construction; cage rotor and cageless rotor. The most common cage motoris a squirrel cage rotor. A squirrel cage motor has a rotating part (rotor) which ismade of a cylinder of steel with aluminium or copper conductors constructed in itssurface. A brushed motor has brushes inside the motor. These brushes are used todeliver current to the motor windings through commutator contacts. Furthermore,windings are installed on the rotor. In contrast, a cageless motor is known asa brushless motor. A cageless motor does not have any of the current carryingcommutators. The field inside a brushless motor is switched by a commutatingdevice, such as an optical encoder. The windings of brushless motor are on thestator. Due to these construction differences, brushless motors have overcome thedisadvantages of brushed motors. Brushless motors have higher efficiency and longlifespan compared to brushed motors, and low maintenance because there is nobrushes to be replaced. Other advantages of brushless motors are better speed
6
Figure 2.1: Permanent Magnet Motor by its Operation [1]
versus torque characteristics, high dynamic response, noiseless operation and higherspeed range [2]. Brushless motor has two type of source; AC and DC, they are calledAC brushless and DC brushless respectively. An AC brushless is fed by a sinusoidalsource. In contrast, DC brushless is fed by a trapezoidal source. Figure 2.1 showsus the permanent magnet motors classification based on the operation. A BLDCis a permanent magnet machine which already mentioned above that its coils andwindings are on the stator and its permanent magnet is on the rotor. The back-EMF produced by BLDC is trapezoidal. This back-EMF produces square currentwaveform in the stator. The operation of BLDC is discussed in detail in the nextchapter.
2.1.2 Working Principle
As discussed previously, brushless DC drive has normally 3-phase coils on the statorand permanent magnet on the rotor. The construction of the BLDC stator androtor can be seen in Figure 2.2a. As current is injected and passed through thestator coil, in such a way it creates a north pole in the air gap on one of the statorphases (e.g. phase b’) and south pole in the air gap on the complementary side ofthe stator phase (phase b). The rotor will rotate with the help of a prime mover.Furthermore, torque is produced as the rotor rotates. The torque will not perfectlytrapezoidal because the motor is hard to achieve uniform flux density in the air gap.
It can be seen from Figure 2.2b that the rotor is trying to move at torque equalto zero. This is because the permanent magnet pole is fighting against the statorpolarity. At position 180 electrical degree, the rotor is placed in the position whereit should be. The north pole of the rotor is attracted by the south pole of the stator.Whenever it rotates slightly off 180o, the rotor is trying to come back to the placewhere it wants to be at. However, all the forces being produces on the rotor is rightthrough the center of it. It results to a net torque equals to zero and thus torquewill not be produced. At this condition, the rotor will resist any rotation, eitherclockwise or counter clockwise. In order to force the motor to rotate another 180o,
7
(a) BLDC Construction [3] (b) Torque per Phase [4]
Figure 2.2: BLDC Construction and Single Phase Torque
it is necessary to change the direction of the current flow in the stator coils.Changing the current results in an opposite torque produced by the machine; it isillustrated as a dashed line in Figure 2.2b. Hence, this behavior is not desired in asingle phase machine.The solution is to introduce a 3-phase stator in order to pro-duce torque. To move the rotor, current is supplied to one phase and taken out toanother phase. In this example, a positive current is applied to phase b and southpole of the rotor will be aligned with phase b.
To move the rotor clockwise, current applied to phase b is taken out and negativecurrent is put into phase c. The south pole of the rotor will try to align with thecurrent at phase c. The process of taking out current from one phase and puttingit into another phase is called commutation. The current waveform is depicted in2.3b.This process keeps on repeating until 360o electrical degree and torque will beproduced.
(a) 3-Phase Torque (b) 3-Phase Current
Figure 2.3: Torque and Current of 3-Phase BLDC [4]
8
Nonetheless, this method is not efficient because only one phase is conducted at atime. Another method to lift up the efficiency is by energizing two coils in orderto have more torque[4]. The torque produced by the machine is depicted in Figure2.3a. It is shown that each phase is displaced by 120o.
The total torque is constant and the power can be calculated as:
P = 2Tωm (2.1)
where ωm is the mechanical speed. According to the power balance, P also equalsto 2epIp. The current, torque and power of three phase BLDC is shown in Figure2.4.
Figure 2.4: Torque, Current and Power Three Phase BLDC [4]
9
2.1.3 Four Quadrant Operating Region
BLDC as a machine is capable to work as a motor and generator. As discussedby [5], BLDC machine has four quadrant operations. Figure 2.5 shows the BLDCoperating region. The performance of the BLDC is examined under starting andno load, with load and regenerative braking. By neglecting friction, the operatingregion is based on the torque equation (2.2).
Figure 2.5: BLDC 4-Quadrant Operation [6]
Tem − Tl = Jdω
dt(2.2)
Under no load operation, BLDC works as a forward motoring with positive torqueand speed (Quad-I). Under load condition where the load torque drive the elec-tromagnetic torque, the summation torque will be negative. In this condition, twooperating regions can be achieved (Quad-II and III). If the motor undergoes negativespeed, the motor operates in a motoring condition. On the other hand, generatorcondition is achieved if the speed runs in a forward direction. Moreover, the regen-erative region (Quad-IV) is when the speed reference is reversely issued. The motoris braking in forward direction with speed tends to be zero and starts rotating inreverse direction as soon as the speed is zero.
2.1.4 Generation Mode
BLDC drive as a generator operation has been discussed in the previous chapter.In order to work in generation mode, the rotor has to be rotated by some loads. Atthe machine terminal, back-EMF will be produced and current will flow. The im-portant difference between motoring and generating operation is that the polarity ofthe phase current opposes the corresponding phase back-EMF [7]. A negative phasecurrent means that current flows from the machine to the DC-bus; shunt resistoror freewheeling capacitor. This resistor now becomes the load of the machine. Fur-thermore, the value of the load resistance determines the amount of current flowingto the load. In order to control this current, a current controller is implemented,explained in Chapter 2.2.
10
In generator mode, the energy is transferred back to the grid. Thus, BLDC pro-duces electrical energy from mechanical rotor rotation. The 3-phase currents andtheir corresponding back-EMFs waveform are shown in Figure 2.6. There are sev-eral conventional strategies to control BLDC drive such as control, high-gain currentregulated PWM (CRPWM), CRPWM with back-EMF compensation, etc [8]. Twomajor topologies are used to control the BLDC drive in generation mode: currenthysteresis control and PWM scheme. Both topologies have their own advantagesand disadvantages [9]. Although PWM scheme operates at constant switching, itis mainly used because hysteresis current control produces noises due to its varyingfrequency. According to [9], in order to operate in generation mode, an importantcondition has to be fulfilled: back-EMF has to be lower than the DC link voltageor else diodes conduct all the time and switches cannot be controlled [10]. A PWMscheme controls the duty cycle of the rectifier switching, in order to regulate thespeed of BLDC drive. The commutation technique is based on the six-step com-mutation which will be discussed below. The torque, speed and control of BLDCmachine will be discussed as well in Chapter 2.2.
Figure 2.6: Current and Back-EMF Waveform of BLDC in Generation Mode
2.1.5 Six Step Current Commutation
To control the BLDC, a right current commutation moment is a mandatory. In thissection, a Six Step commutation of the BLDC is explained. In a six-step BLDCcommutation, each phase in the stator is energized during intervals of 120 electricaldegrees and current commutates each 60 electrical degrees. The commutation ofBLDC stator coil connected in star configuration is shown below. The arrows depictthe current direction from a more energized phase (positive) to another less energizedphase (negative). In order to realize these commutations, value of each phase can becontrolled by regulating the switching of the inverter. Therefore, a model of inverterhas to be derived from the switching strategy. A commonly used topology is byusing three phase two level inverter; scheme of this topology is shown in Figure 2.7.
11
Figure 2.7: Three Phase Inverter, Controller and Driver of Typical BLDC Drive [4]
Each inverter switch includes a fully controlled power semiconductor device such asa Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) or an Insulated-Gate Bipolar Transistor (IGBT) with an anti-parallel diode. Figure 2.8 shows usthe state of the conduction in which the state of the semiconductor switches of thesame leg are complementary.
Figure 2.8: BLDC Stator Current Commutation States [4]
Due to the symmetry of the machine, each of commutation states is active during 60o
electrical. The commutation states are based on the arrangement of the three Hall-effect sensors and the switching of the inverter, which is determined by the currentdirection shown in Table 2.1. The results of these commutations are a square currentwaveform and trapezoidal back-EMF waveform.
Sequence Phase A Phase B Phase CActiveSwitches
0 1 -1 0 T1, T4 : ON
1 1 0 -1 T1, T6 : ON2 0 1 -1 T3, T6 : ON
3 -1 1 0 T3, T2 : ON4 -1 0 1 T5, T2 : ON
5 0 -1 1 T5, T4 : ON
Table 2.1: Digitalized Six Step Commutation Sequence
12
Figure 2.9: Six Step Conducting States [4]
Figure 2.10: Current and Back-EMF Waveform of BLDC [4]
Moreover, the commutations are digitalized, so that it can be translated into digitaldomain. The condition of each switching state and the current waveform, back-EMFwaveform of each phase in digitalized sequence are shown in figure 2.9 and Figure2.10 respectively. Table 2.1 shows the sequence in digital domain. 1 means thatthe phase is positively energized (connected to positive side of the supply), -1 meansthat the phase is negatively energized (connected to negative side of the supply) and0 means the phase is not energized. As shown here, every step has an un-energizedphase. Thus, only 2 phases are active in every step.
13
2.1.6 BLDC Machines Model
The dynamic of BLDC drives can be validated by continuous-time mathematicalmodel of BLDC by [4]. The continuous time model of BLDC is then discretized inorder to run on the computer time based. The machine model and parameters areused as Hardware-in-the-Loop platform. The model performs important dynamicand features while some simplifications and assumptions are made to simplify themodel. These simplifications and assumptions are discussed in [4]. By using Kir-choff’s law, the terminal voltage of each phase can be written as below
Va(t) = Ra · ia + La(θ)dia(t)
dt+ Ea(t) + Vn (2.3)
where Va(t), ia(t), Ea(t) are terminal voltage, phase current and back-EMF respec-tively. Ra is the phase resistance and La is the rotor position phase inductance.Vn isthe neutral voltage of the machine. The back-EMF is the voltage induced in the sta-tor winding by a changing of the magnetic field because of the rotation speed of therotor. Thus, its profile depends on the working speed and the design of the machine.In an ideal design it has a trapezoidal waveform. Each back-EMF is separated 120
one to another.
ea = Ke · ωm · F (θe)
eb = Ke · ωm · F (θe −2π
3)
ec = Ke · ωm · F (θe −4π
3)
(2.4)
The function F is a speed normalized back-EMF. It can be seen that back-EMF isdependent on the rotor speed and position of the rotor defined by function F. Thus,in order to achieve a trapezoidal back-EMF waveform, function F can be determinedby corresponding electrical rotor position as below
F (θe) =
1, 0 ≤ θe ≤2Π3
1− 6Π(θe −
2Π3), 2Π
3≤ θe ≤
4Π3
−1, Π ≤ θe ≤5Π3
−1 + 6Π(θe −
5Π3), 5Π
3≤ θe ≤ 2Π
(2.5)
Additionally, the mechanical can be equated as below
Jdωm
dt= Te − kf · ωm − Tl (2.6)
where ωm is the rotor speed, J is the inertia of the rotor and load, Te is the elec-tromagnetic torque and Tl is the load torque. In equation (2.6), a frictional torqueis also introduced. The frictional torque is proportional to the rotor speed, with afactor of kf . The electromagnetic torque depends on the magnetic flux in the airgap, which varies with respect to the rotor position. Thus, electromagnetic torqueis defined as following
Te = kt[F (θe) · ia + F (θe −2Π
3) · ib + F (θe +
2Π
3) · ic] (2.7)
14
where kt is the torque constant of the machine. Because the environment is in digitaldomain, the BLDC model has to be discretized using a zero discretization method.The following differentiation is one of the form used for the discretization:
dx
dt= Ax+Bu (2.8)
The general solution of this differential equation is given by
x(t) = eA(t−t0) · x(t0) +
∫ t
t0
eA(t−τ) ·Bu(τ)dτ (2.9)
2.2 Torque, Current and Speed Control
As mentioned in Section 2.1.4, two major topologies are used to control the current;current hysteresis control and Pulse-Width Modulation (PWM) control. The controltopologies is depicted in Figure 2.11. A common PI controller is used to control thecurrent flowing through one of the phases, the phase with positive current value,of the BLDC machine operated by six step commutation method. This current isequal to the magnitude of the DC-bus current. In order to do that, the controller istuned so that it follows the reference current. The DC-bus current can be measuredby the Hall-effect sensors or shunt resistor. The output of the PI controller is thencompared with a carrier signal which is a triangular wave in order to produce thePWM signal. This PWM signal produces the duty ratio which is applied to theinverter. The duty ratio determines the on time of a switch related to the switchingperiod of the inverter. During the on time, the supply voltage Vs is connected tothe machine phases. There are three common switching strategies [4]:bus clamped,independent, and complementary. These strategies will not be discussed further.
A hysteresis control sets the upper and lower band for the current. The current hasto be within these two limits. In order to have a correct output current, the averagevalue of the output current is controlled. This average output current is controlled bychopping the output voltage using the hysteresis. The hysteresis controls the currentby making the output voltage equals to 0 when the current exceeds Iref +
band2
andmakes output voltage equals to VDC when the output current becomes less thanIref − band
2. When the voltage is chopped, its average output current is lowered.
Hence, the chopping determines the duty ratio, e.g. with a duty ratio of 0.5, theaverage value becomes half of its maximum value. Moreover, the output is definedby the current reference; the lower the reference, the lower the output voltage.
(a) PWM PI-Controller (b) Hysteresis Control
Figure 2.11: Current Control of BLDC Machine [4]
15
Therefore, the average output current becomes lower. All in all, the hysteresis bandconfigures the chopping and further the duty ratio of the switching. The lower theband, the faster the inverter switches and vice versa. A faster switching gives a bettercontrol of the current. In contrast, faster switching results in higher switching lossesand noise which can be faulty to the system. By controlling the current and havingcurrent commutation between each phase in synchronism with the back-EMF vectordirection, the demanded torque can be controlled and achieved simultaneously.
Figure 2.12: Cascaded Speed Control [4]
Additionally, a conventional cascaded speed control is used. The speed which ismeasured by Hall-effect sensors or self-sensing method is controlled by PI controller.This PI output is cascaded to the current controller and the speed becomes thereference of the DC-bus current. Figure 2.12 depicts the speed control of BLDCmachine.
2.3 Self-Sensing Control
As the rotor is spinning, it is creating a back-EMF signal in the stator winding thatcan be amplified to give good speed information. If the motor going relatively slow,it is hard to sense the speed information from the Hall-effect transition as well. If themotor starts going at a really slow speed, these transitions are few and far between.As a result, the back-EMF signal can be used to get much better speed resolution.
Hall-effect sensor plays a pivotal role in the brushless DC motor. In fact, the sensoris used to sense the rotor position which can give us the way how to commutatethe machine. The problem is that the Hall-effect sensor is very expensive, it usesa disk which associates to a lot of wiring especially if the controller and motor isseparated by some distances. All of the wiring and harnessing have a significantrole in the motor. Furthermore, reliable issues such as bad connection between thesensor and the power supply to power the sensor and so on. These are some reasonswhy Hall-effect is not efficient to be used in sensing the rotor position and thus leadsto a sensorless method.
One of the method is based upon the back-EMF signal. In the commutation, there isalways one un-energized coil and listen to this coil in order to analyze the back-EMFsignature. From that, information about the rotor position can be achieved. Themost known method is zero back-EMF crossing. The zero crossing method and thethreshold sensorless method will be discussed in this section.
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Figure 2.13: Brushless DC Sensorless Scheme [11]
2.3.1 Conventional Zero Crossing Method
The conventional Zero Crossing method uses information from the back-EMF signal;it is when the back-EMF cross the zero point. This method has been investigatedand improved by [4]. In this thesis, the basic idea of zero-crossing is based on[4]. The zero crossing occurs 30o electrical before each commutation in BLDC ma-chine with commutation every 120o electrical. As mentioned above, this methoduses un-excited and compare it to a sample of the neutral point voltage in order toanalyze the back-EMF. A good back-EMF measurement method is needed; thereare already methods available to measure the back-EMF accurately [12][13]. Theback-EMF zero crossing instant occurs when the back-EMF voltage value equals thevoltage of the star point of the stator windings. A virtual neutral point is used indelta connected and machines without star point. The zero crossing can be detectedin many ways. Two common methods to detect the zero-crossing will be explainedbriefly.
First method is to detect the zero-crossing based on the commutation instant. Atimer starts to count when the previous commutation instant [n] occurs. On theother hand, the timer stops counting when the back-EMF hits zero-crossing at 30o
electrical. The time interval, Tz, 1, is then measured between this occurrence. Itis assumed that this interval is when the rotor position changes by 30 electrical.Consequently, a same time interval is used to trigger the next commutation instant[n+1]; for illustration see Figure 2.14a.
However, the measurement is not perfectly occurred due to commutation spikeswhich is caused by high frequency switching of the power converter. During thistime, the power converter controls the average value of the voltage applied to themachine. In order to overcome this occurrence, a low-pass filter is used to reducethe frequency [14]. As a result, the speed range is limited and a time delay, Td, isintroduced in the measurement. The second method is to use the time interval be-tween two successive zero-crossings, Tz, 2. Half of the time interval, Tz ,2
2, is assumed
to be 30o electrical, see Figure 2.14b.
17
(a) using time difference between commutationand zero-crossing instant
(b) time difference between successive zero-crossing instants
Figure 2.14: Different methods to determine commutation instants using back-EMFzero-crossing detection [4]
Nonetheless, using zero-crossing method is inaccurate to measured the correct cur-rent commutation instant due to an assumption of constant rotor speed during whichthe variation in rotor position is proportional to the corresponding time interval. Ina transient speed such as acceleration and deceleration, the assumption of Tz, 1 andTz ,22
is inaccurate to estimate the 30o electrical position. This commutation errormight have big consequences in the speed estimation and could result in unstablebehaviour of the machine. Another method to measure the position accurately isproposed by [15] and used in this thesis, namely the threshold technique. Thistechnique will be discussed in Section 2.3.2.
2.3.2 Back-EMF Threshold Method
To overcome faulty estimation of the 30o electrical position, a Back-EMF thresholdmethod is used. The method is explained thoroughly by [4]. In this method, theunexcited phase is measured during the transient of the back-EMF. Figure 2.15shows a decreasing back-EMF of one phase, occurs at commutation instant t0 to thenext commutation instant t4 during constant speed operation; referring this to thebasis of symmetrical threshold-tracking [15]. To avoid the high frequency switchingas discussed before, the unexcited phase voltage is sampled and the threshold iscomputed right after minimum δt of the previous commutation, which in this caseoccurs at t1. At this moment the back-EMF is measured and then monitored. Thesign of this sampled back-EMF is modified and the resulting value will be used as athreshold that will detect the next commutation instant. This threshold is comparedwith the measured back-EMF in order to decide the next commutation. As soonas the back-EMF voltage intersects with the threshold value t3, a time delay δt isapplied due to the switching noise. The next commutation instant arises after thistime delay. The occurrence of zero-crossing point t3 is not used.
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Figure 2.15: Threshold Method [4]
2.4 Estimation of Speed, Position and Load Torque
In order to estimate the load torque, a speed and position of the machine has to beestimated firstly. This method of estimating speed, position and load torque havebeen discussed and researched by [4].
2.4.1 Speed and Position Estimation
As discussed in Section 2.1.6, the speed ω and electrical position θe can be obtainedfrom the measured back-EMF through function F(θe). Likewise, the estimation ofthe back-EMF is achieved by having a correct successive sampling at k, e[k], and atk-1, e[k − 1], of the back-EMF [16]. Thus, the estimated back-EMF can be derived
∆e[k] = e[k]− e[k − 1] (2.10)
∆e[k] = ke(F (θe[k])− F (θe[k − 1]))ω[k] + keF (θe[k])(ω[k]− ω[k − 1])
= kemL(θe[k]− θe[k − 1])ω[k] + keF (θe[k])(ω[k]− ω[k − 1])(2.11)
where mL is the slope of the linear part of the back-EMF. In addition, ∆e[k] can bewritten in terms of speed and time constant between two back-EMF measurement,Tsω[k]. Considering that Ts is smaller in comparison with the mechanical timeconstant, ∆e[k] is approximated and derived from (2.11).
∆e[k] = keω[k](mLTsω[k]) + keF (θe[k])(ω[k]− ω[k − 1]) (2.12)
19
From (2.11), by assuming that electrical angle θe is in the linear part of F (θe) bymeans that the back-EMF is at the moment when the phase is unexcited. Likewise,supposing that τm ≫ τe, the rotor speed can be estimated as below:
ω[k] =
√
e[k]− e[k − 1]
kemLTs
(2.13)
Additionally, as mentioned before that the rotor position can be obtained fromthe measured back-EMF through function F (θe). Function F (θe) can be estimatedfrom the back-EMF measurement at that moment k sample time, e[k]. As a result,estimated function F (θe) is:
F (θe[k]) =e[k]
keω[k](2.14)
The rotor position is then estimated from the function F (θe) through a one-to-onerelationship between F and θe. An estimated function F implies an estimated rotorposition θe.
F → θe (2.15)
2.4.2 Load Torque Estimation
In order to estimate the load torque, the back-EMF has to be measured. In motoringmode, the evolution of the speed ω can be described by the BLDC mechanical torqueequation 2.6 by neglecting the friction.
dω
dt=
Tem − Tl
J(2.16)
It is shown that the change in speed over time is due to difference of electromagnetictorque Te and load torque Tl. From equation 2.7, Te is proportional to the measuredDC-bus current iDC [16]. Furthermore, as mentioned in the current commutation insection 2.1.5, two phase currents are energized and one is null. Thus, Te is estimatedby sampling the DC-bus current at that moment, k, multiplied by two due to twoenergized phase currents.
Te[k] = 2ktiDC [k] (2.17)
In furtherance of estimating the load torque Tl at a given instant tk = kTs, referredto as Tl[k], an update of the previous load torque estimation at k-1, Tl[k − 1], hasto be made. As discussed in speed estimation section 2.4.1, the estimated speed iscomputed by subtracting the the measured back-EMF at k, e[k] with the previousmeasured back-EMF at previous time instant By discretizing 2.16 and assuming thatthe electromagnetic torque and load torque remain constant at the next sampleperiod, the speed can be predicted at the next time instant tk+1. The predictedspeed is computed by updating the estimated speed with the discretized speed atthe given instant tk.
ˆωm,pred[k + 1] = ωm[k] + TsTe[k]− Tl[k]
J(2.18)
By substituting (2.17) to (2.18), the predicted speed can be expressed as
ˆωm,pred[k + 1] = ωm[k] + Ts2ktiDC [k]− Tl[k]
J(2.19)
20
In this way, the predicted speed at time instant k can be attained as well by updatingthe estimated values of speed with load torque computed at previous time instanttk−1.
ˆωm,pred[k] = ωm[k − 1] + TsTe[k − 1]− Tl[k − 1]
J(2.20)
By having a constant iDC in motoring mode, the electromagnetic torque Te remainsconstant as well. As a result, the variation between the estimated speed and thepredicted speed is caused by the variation of the load torque Tl over the time Ts.Hence, the updated estimated load torque at time instant tk, can be calculatedby subtracting the predicted speed with the estimated speed at time instant tk,
ˆωm,pred[k]− ωm[k]:
Tl[k] = Tl[k − 1] +J
Ts
( ˆωm,pred[k]− ωm[k]) (2.21)
2.5 Wind Turbine
A wind turbine as an alternative energy to produce electricity has been widely usednowadays. The wind turbines capture the wind’s kinetic energy. This kinetic energyis translated into rotation through the wind blades.
(1) Wind turbine (2) BLDC Generator (3) Rectifier (4) DC-link Capacitor
Figure 2.16: Block Diagram of Wind Turbine Energy Conversion [17]
Moreover, the rotor shaft is coupled mechanically to the electrical generator with agearbox that can transform slower rotational speeds of the generator’s shaft to higherrotational speeds. A Brushless Alternating Current (BLAC) drive is commonly usedin wind turbine systems. However, a Brushless Direct Current (BLDC) is carried outas a generator in this paper. Furthermore, considering the dynamics of the generatorand help of power electronics, current will flow from the generator and electricitywill be produced, likewise stored in the battery or DC-link capacitor. Figure 2.16depicts the block diagram of the energy conversion system.
2.5.1 Wind Turbine Model
As mentioned before, a wind energy conversion system is a complex system of con-verting wind energy into kinetic energy and further into electrical energy. The outputpower or torque of a wind turbine is determined by several factors, such as windvelocity, size, shape of the turbine, etc [18]. The wind turbine used in this project isa small wind turbine. Small wind turbines have different properties than large wind
21
turbines. For instance, a more fluctuating wind speed at low altitude and simplermechanical construction which results to a reduced cost, i.e. the construction has noactive pitch mechanism and a wind vane is often used as a passive yaw mechanism[19]. Moreover, a lower rated power designates a different design of generator andconverter. Due to a very unpredictable and uncontrollable wind, wind turbine isconsidered as a non-linear dynamic systems subjected to wind turbulence. In orderto control the wind turbine to operate at the desired characteristic, there are somedynamics which have to be taken into account. There are three sub-systems: thedrive-train dynamics, the wind turbine aerodynamics, and the generator dynamics[20] [21]. The generator dynamics was discussed before. Below we will see the drivetrain of a static wind turbine system.
2.5.2 Static Model
The mechanical power extracted by the rotors and torque of the wind turbine Pr
and Tr can be expressed as follows:
Pr =1
2· ρ · A · υ3 · Cp(λ, φ) (2.22)
Tr =1
2· ρ · A · υ2 · Ct(λ, φ) (2.23)
where ρ is the air density (kg/m3), which equals 1.225 kg/m3 at a temperatureof 150C, A is the circular turbine blade swept area (m2), which is calculated fromthe turbine radius R by πR2, υ is the wind speed (m/s). Furthermore, not all themechanical power from the wind can be extracted by the turbine. Thus, a powercoefficient or Betz factor, Cp is defined to express the useful mechanical power. Thiscoefficient is defined [22] and approximated [23] as:
power coefficient, Cp =turbinepowerpowerofwind
Cp(λ, φ) = 0.73(151
λi–0.58φ–0.002φ2.14–13.2) · e
−18.4
λi (2.24)
The power and torque coefficient of the turbine, Cp and Ct respectively, representthe aerodynamic efficiency. Both coefficients are the functions of tip speed ratio(TSR) λ which characterizes the air flow around the blades, and blade pitch angleφ which characterizes the position of the blades. Theoretically, the maximum valueof Cp is 0.59. However, the maximum achievable Cp is between 0.4 and 0.5 for highspeed, two-blades turbines and between 0.2 and 0.4 for slow speed turbines withmore blades [18]. The correlation of Cp and Ct is:
Cp = λ · Ct (2.25)
The TSR is calculated as the ratio between the speed of the tip of a blade Rω andthe wind speed υ, where R is the radius of the turbine blade in meters, ω is theturbine rotor angular speed in rad/s:
TSR(λ) =Rω
υ(2.26)
22
Thus, the torque (2.23) can also be expressed in Cp as:
Tr =12· ρ · A · υ3 · Cp(λ, φ)
ωg
(2.27)
Where ωg is generator rotor speed. For a fixed pitch wind turbine, the blade pitchangle φ is zero and hence Cp and Ct are only a function of TSR. The relation betweenCp and TSR (λ) is shown graphically in Figure 2.17.
Figure 2.17: Cp(λ) versus TSR Curve [24]
There are some optimization to get the maximum power of the wind turbine, oftenthe method is called MPPT (Maximum Power Point Tracking). In this paper,optimization will not be analyzed. On the other hand, an ideal TSR versus windspeed can be obtained in three level of wind speed; low wind zone υ < υci, normalwind zone υci < υ < υnom, and high wind zone υnom < υ [24]. The ideal TSR versuswind speed is shown in the following Figure 2.18.
Figure 2.18: Ideal TSR versus Wind Speed Curve [24]
23
2.5.3 Drive Train Model
The drive train includes the turbine and generator which shafts are connected to-gether by a gearbox. The rotor is modelled as two rotating masses connected to-gether by shaft with a certain damping and stiffness coefficient values. The drivetrain used for power system operation analysis is shown in Figure 2.19.
Figure 2.19: Physical Model of Drive Train [21]
The gearbox inertia is smaller by a factor 130
than the generator inertia, whichmeans it can be also included in the generator inertia [20]. Moreover, the model isparameterized and referred to the low speed side of wind turbine, and by neglectingthe turbine and generator self-damping (Dt and Dg), the mechanical equations ofthe turbine side and generator side are given by (2.28) and (2.29) respectively.
Jtdωt
dt= Tm −Ks(θr–θt)−Dm(ωr–ωt) (2.28)
Jgdωg
dt= −Te −Ks(θg–θt)−Dm(ωg–ωt) (2.29)
where, Ks is the shaft stiffness, Dm is the mutual shaft damping, θ is the rotorangle and Te is the electromagnetic torque. Suffix t denotes turbine and g denotesgenerator. By neglecting the shaft stiffness, damping and torsional oscillations givesa simplified equation, on a low speed shaft, as (2.31)
ω =1
J
∫
(Tm − Te) (2.30)
where,J = Jt + n2 · Jg (2.31)
In [25], the author estimated the inertia of the wind blade from the mass and thelength of the blade. The wind blades inertia can be estimated as:
J = kJML2 (2.32)
where kJ is a constant related to the length of the blade, M is the mass of the bladeand L is the length of the blade. The total inertia of the wind turbine is equal tothree times the blade moment of inertia plus the moments of the other rotating parts[26]. The inertia from the other rotating parts do not vary as blade angle varies,thus it can be considered a constant.
24
2.5.4 Control Algorithm
The main objective of controlling wind turbine is to capture the effective powergenerated by the wind turbine. The wind speed corresponds to active power gener-ated by the wind turbine system. The most effective operation of wind turbine hasbeen investigated by [27]. In order to know the power generated by the wind, threetypical operating systems [18] [27]: low wind speed, medium wind speed, and highwind speed, are depicted in Figure 2.20.
Three regions: I. low speed II. Medium speed and III. High Speed are shown in thefigure. The control of these three regions can be divided into two type of controls:fixed speed and variable speed. Based on [18] [27], at low wind speed, a variablespeed with generator torque control is implemented. This variable speed controlregulates the generator electric torque to optimize the energy capture or outputpower and to smooth the fluctuations on the electrical torque by slowly regulatingthe rotor speed. In this region, pitch is kept constant and Cp is maintained tobe at the most efficient point, this is due to variable speed control advantages [18].Moreover, at the second region, medium wind speed, the pitch angle is kept constantand speed is controlled to be at nominal speed by using a stall regulator.
Figure 2.20: Typical Wind Turbine Operating Systems [27]
At high speed, a variable speed with pitch control is used above the rated wind speedto reduce the aerodynamic efficiency and thus to ensure that the generator is notoverloaded. However, for small wind turbines, pitch control is normally not available.In conclusion, the region below the rated wind speed is a power optimization regionwhich Cp is constant. On the other hand, a region above the rated wind speed is apower limitation region which power is constant.
25
Chapter 3
Proposed Algorithm
The main idea of using a wind turbine is to get the maximum power produced bythe wind turbine. As discussed in the previous chapter, optimizing the power isdone at the first region which is below the rated wind speed. The technique used isnormally a fixed-pitch variable-speed control to keep Cp constant. Likewise, pitchcontrol is usually not available in small wind turbine.
The reason to keep the speed vary is that as the speed is controlled, the currentsupplied to the machine will be limited. Limiting the current will result in limitingthe total torque produced which is proportional to the total power produced by themachine. On the other hand, the current should be controlled to follow the referencecurrent which comes from the predicted current. In this case, the predicted currentprevents the machine to be working in unstable region if excessive wind occurs.Therefore, the proposed method is attained from the available wind speed informa-tion. By predicting the wind speed, the torque and the current will be predictedand updated for the next time instant.
Many method to predict the wind speed have been proposed. However, because ofthe stochastic and intermittent property of wind speed, it makes it very difficultto predict. The most commonly used method to predict the wind speed is NeuralNetwork (NN) algorithm. Nonetheless, these available algorithm are complex andthey will not be used in the implementation. The proposed algorithm will be basedon the available load torque estimation which was done by Araz Darba [4].
3.1 Wind Speed Estimation and Prediction
In order to predict the wind speed at time instant tk+1, the wind speed is estimatedfirstly. The wind speed is estimated at time instant tk based on the wind turbinetorque equation (2.27). In motoring mode, the wind turbine becomes the load whichbrakes the machine. While in generator mode, the wind turbine becomes the driveof the machine. Assuming a static model of wind turbine and assuming the torqueproduced by the wind turbine is equal to the estimated load torque, the estimatedwind speed can be computed as:
ˆvest[k] =3
√
ˆT lest[k]. 2. ˆωest[k]
ρπR2Cp
(3.1)
26
where ˆT lest and ˆωest is the estimated load torque and speed respectively. ρ is theair density (1.225 kg/m3), R is the radius of the wind blades, and Cp is the powercoefficient. Furthermore, the wind speed is predicted by taking the sample of theestimated load torque and estimated rotor speed at the next sample time k+1.
vpred[k + 1] = 3
√
T lpred[k + 1]. 2. ωpred[k + 1]
ρπR2Cp
(3.2)
The predicted rotor speed is derived from (2.19). While, the estimated load torqueat time instant k+1 can be expressed by estimated load torque at time instant kwhich equation based on (2.21). The estimated load torque at time instant k+1 canbe derived as:
T l[k + 1] = Tl[k] +(J + Jturbine)
Ts
( ˆωm,pred[k − 1]− ωm[k − 1]) (3.3)
By substituting the estimated load torque and predicted rotor speed at time instantk+1, the wind speed can be predicted as:
vpred[k+1] =3
√
√
√
√
(Tl[k] +J+Jturbine
Ts( ˆωm,pred[k − 1]− ωm[k − 1])).2.(ωm[k] + Ts
2ktiDC [k]−Tl[k]J+Jturbine
)
ρπR2Cp
(3.4)From the wind speed estimation equation (3.1), the estimated value of the windspeed is achieved from the load torque. Once the load torque changes, the windspeed reacts and updates its value. By assuming Cp constant (Cp is not controlled),the variation of the wind speed is caused by the the dynamic of the load torque.
3.2 Load Torque Prediction
The load torque can be predicted at time instant k+1 by using the predicted windspeed equation (3.2) at the same time instant tk+1. The predicted load torque canbe computed as:
T lpred[k + 1] =(vpred[k + 1])3ρπR2Cp
2ωpred[k + 1](3.5)
The predicted load torque is updated when the predicted wind speed changes becausea change in the estimated load torque. From the equation, it is shown that loadtorque is a factor three of wind speed. The higher the wind speed, the bigger theload produced to deliver the power.
3.3 Current Reference from Predicted Current
A load torque is corresponding to a DC-bus current if speed is assumed to be con-stant. Moreover, from the torque equation, the load torque at time instant k can becalculated as
Tl[k] = Te[k]− (J + Jturbine)dω
dt(3.6)
It can be observed that torque is proportional to the current from equation (3.6),by substituting Te = 2ktiDC . Consequently, by assuming the derivation of the rotor
27
speed is equal to the derivation of the predicted rotor speed, the predicted currentat time instant k+1 can be expressed:
iDC,pred[k + 1] =(J + Jturbine)ωpred[k + 1] + T lpred[k + 1]
2kt(3.7)
The current is related to the load torque, so the bigger the load torque, the biggerthe current. In closed loop control, the predicted current is used as the referencecurrent in order to harvest the maximum power produced by the wind turbine. Thecurrent is predicted in order to know the next sampling current needed to producethe torque.
Moreover, the produced torque corresponds proportionally to the total harvestpower. Hence, if the current is maximized, the power extracted by the outputbank, such as resistor or capacitor bank will be topmost. In addition, the predictedcurrent can be used to prevent over load due to high current. By saturating thereference current, the current will be limited to the maximum allowed value by themachine. This will prevent the machine to work in stable region and will avoidoverheating that may cause breaking the machine.
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Chapter 4
Test Setup
The setup to implement the proposed algorithm is described in this chapter. Thesetup will be divided into hardware and software. The hardware consisted of thewind turbine and the electronics used to interface the FPGA with the wind turbine,control the wind turbine with the proposed algorithm implemented in the FPGA.The software setup explains the needed calibration and the software block diagramof the proposed algorithm.
4.1 Hardware
4.1.1 Wind Turbine
There has been many wind turbine models available. A wind turbine has to beperfectly designed in order to have impeccable characteristics and high efficiency.A small wind turbine is divided into several important parts. These parts are: theblades, the tower and the base. Having a good efficiency is needed in this project.However, designing a perfect wind turbine is not the main purpose of this thesis.The wind turbine is desired to produce 4 to 5 kW. A commonly used material tomake the blades is PVC. However, using PVC needs strong wind to rotate the bladesin which it is not efficient and test can’t be done. During the process, 3 differentPVC wind blades with different diameter were made. Finding out that these bladesare not efficient, another design was proposed. The new wind blades design is basedon the kid’s windmill toy. Material used to make the blades is a stiff paper that caneasily be bent and strong enough to rotate the rotor. It has 4 blades with 30 cmdiameter. A square paper 30 x 30 is cut diagonally in each tip, making each sideinto two section separated. One of the sections is folded to the middle and screwedtogether with a long M4 size bolt. The bolt is connected on the middle of the squareand screwed over together to a hub which mounted on the rotor. For a video howto build the wind blades, see Science Kids [28]. The average height of a small windturbine is 80 feet or approximately 915 cm. At this height the average wind speedis 5 m/s. Due to deficient wind and closed room testing environment, the tower isdesigned using a tube with a 2 cm diameter and 130 cm height. Moreover, the toweris mounted on a block 18 cm x 7 cm x 10 cm.
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(a) The Blades (b) The Tower (c) The Mechanic
Figure 4.1: Wind Turbine Design
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4.1.2 Wind Speed Sensor
Last but not least, the wind speed sensor is calibrated. As there is no wind tunnelavailable to test the anemometer, the way to calibrate the anemometer is by using atraditional method. The anemometer is mounted on the electric bicycle as shown inFigure 4.2. The speed shown in the speedometer will be assumed proportional to thewind gust. Measurements were taken at different roads around the neighborhood.The result shows that the analog output of the sensor is increasing linearly withrespect to the wind speed. The result is shown in Appendix A.
Figure 4.2: Anemometer Calibration
Moreover, the wind speed sensor is decided to be mounted close to the wind blades.The result of the test shows that mounting the anemometer next to the blades givesthe most wind extraction and it is reliable to place due to mechanical issue suchas cable harnesses and rotating cups. It is not suggested to mount the wind speedsensor at the back of the wind blades and the machine because the wind speed dropssignificantly. However, the wind speed sensor is not mounted on the wind turbineas the fan is too small to provide a balance wind blow from every angle. The overalldesign is shown in Figure 4.1.
4.1.3 Fan
The number of the fan used in this experiment is two. Two fans are used in orderto give enough high speed, so that the variation of the wind speed can be achieved.Three different wind speeds are given by this fan. The fan is placed 10 cm awayfrom the wind blades. For different distance with respect to wind speed producedby the fan is already explained by [29] where at the closest distance the wind speedis low. Likewise, the further the fan, the lower the wind speed. For the experiment,10 cm is chosen because it gives the highest wind blow to the blades.
4.1.4 Electronics
The electronics necessary to implement the project are a driver, four ADC convert-ers, an isolator, a connector and an FPGA. Features and properties of the compo-nents can be found in Appendix B. The driver controls the switch via three half-bridge inverter. The DC-link can be measured too because the driver is connected
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internally with a shunt-resistor. Four analog-to-digital converters are implemented
Figure 4.3: Electronics Setup
to translate each voltage output of the BLDC into digital value which can be readby the FPGA. A galvanic isolator is used to separate the ground between driverand FPGA by preventing unwanted current flowing from the driver to the FPGA.In addition, the function of the connector is to interface the ADCs and the FPGA.The electronics’ setup is shown in Figure 4.3.
4.2 Software
One of the main drawbacks of modeling an FPGA in MATLAB/Simulink is that itcan not do complex mathematical calculation, such as a fractional power. In theproposed algorithm, a power of one third is formulated by Taylor series of expo-nential fractions. In this section, some variables are used and calibrated so that itmatches the hardware measurement. Likewise, the block diagram of the design willbe explained.
4.2.1 Inertia of the Wind Turbine
The inertia of the wind turbine is calculated by a formula discussed in Section 2.5.3.where the inertia of the blade is calculated based on the mass and the length of theblade, Jturbine = kJML2. The mass of the blades is measured 55 gram. The lengthof the blade is 15 cm and the constant for 15 cm blade is 0.469722921. The resultof the wind blade inertia is 0.000581282. Due to simple mechanism connection,without any gear ratio, of the wind blades to the rotor, it is assumed that the windturbine’s inertia is equal to the wind blade’s inertia.
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4.2.2 Speed, PredictedWind Speed, Measured Current, Pre-
dicted Current and Torque Constant Calibration
Some calibration are done to synchronize the output of the measurement with thesoftware design. Measurement is taken in motor mode and without any load insteady state. The speed is measured by a tachometer, the wind speed is measuredby an anemometer and current is measured by multimeter in series. The rotor speedis calibrated so that it matches the measured speed. The predicted wind speed iscalibrated as well in order to match the measured wind speed by the anemometer.Moreover, the measured current is calibrated because it is based on a different valueof DC-link resistor. The smaller the value of the resistor bank, the higher thevalue of the current. As a measured current changes, the predicted current has tobe calibrated as well in order to follow the measured current. This calibration isneeded in order to have control of the current; predicted current is the reference ordemanded current of the controller. Moreover, the torque constant (Kt) is takenbased on the electrical constant (Ke). The electric constant is a unit that showsthe relation of voltage achieved by a certain speed in kilo rpm (V/krpm). Thevoltage is measured between one of the three phases and a neutral. By using a drill,the machine is rotated constantly and measured by the tachometer. The electricconstant is then calculated and the result is Ke = 1.026. This value is less thanthe value stated on the datasheet because of demagnetization. On the other hand,Kt is usually equal to Ke in steady state. However, due to different load, the totalamount of torque produced by a certain current might vary slightly with respect tothe Ke. The Kt is calibrated and the result is Kt = 0.9375.
4.2.3 Digital Predicted Current Filter
The predicted current is a function of the predicted load torque which is sampled athigh frequency, 7.5 kHz. This high frequency will result to a high frequency predictedcurrent. As mentioned, the predicted current is used as a reference current. A highfrequency reference current will result to a poor control of the current, such asswitching noise. In order to avoid this situation, a biquad digital filter is carried outat sampling frequency of 500 Hz and cut-off frequency of 2 Hz.
4.2.4 Block Diagram
The overall schematic of the predicted algorithm based on the load torque estimationis shown in Figure 4.4. From the figure, the wind turbine torque is estimated andused to predict the wind speed which blows to the wind turbine. As the estimatedtorque is updated, the predicted wind speed is updated to predict the load torque.The update of the load torque prediction will give the total predicted current. Thispredicted current becomes the reference current which will be supplied to the ma-chine. In order to damage the machine from severe wind blow, the current will belimited up to the allowed nominal current of the machine.
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Figure 4.4: Schematic of the Practical Realization of Predicted Algorithm
34
Chapter 5
Simulation and Implementation
5.1 Simulation
The simulation is done in MATLAB/Simulink. The BLDC works as a motor. Thewind turbine becomes the load to the BLDC machine. A static model of the windturbine is applied as a load. Moreover, torque produced by the wind turbine acts asa braking torque. This means that the electromagnetic torque drives the machineand speed becomes positive and higher if the electromagnetic increases proportionalto the current supplied to the machine.
Input of the wind turbine model is the wind which is a sinusoidal. Wind gust doesnot vary regularly in a low power wind turbine, so a sinusoidal waveform is simu-lated. A PI current controller is used with Kp = 2.5 and Ki = 0.25. The radius ofthe wind blades is 0.15 m, Cp is assumed to be constant at 0.5, and turbine inertiais 0.000581282. With a correct zero-crossing moment and precise sampling instantof the estimated load torque, the wind speed is predicted and shown in Figure 5.1.Additionally, with an assumption of the static model wind turbine, a sinusoidaltorque waveform is achieved from a sinusoidal wind speed input. Likewise, with aprecise sampling of the estimated load torque, the predicted load torque follows theestimated load torque. Figure 5.2 shows the estimated load torque and the predictedwhich are the braking torque produced by the wind turbine.
The current is also of interest here. The current is controlled by a PI controllerand limited to 2.33 A with respect to the maximum continuous current allowed bythe BLDC machine. This limitation will prevent the machine from overheating andunstable region. With a sinusoidal wind speed input, the current is expected tovary sinusoidally as well because it is proportional to the torque. The predictedcurrent and measured current are depicted in Figure 5.3. The spikes are due tothe commutation instant between two phases; from phase A to phase B, etc. Thezoomed figure can be seen in Figure 5.4. These spikes can be minimized, however itis not considered in this thesis.
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Figure 5.1: Sinusoidal Wind Speed Input vs Wind Speed Prediction
Figure 5.2: Sinusoidal Load Torque Estimation vs Load Torque Prediction
Figure 5.3: Measured Current vs Predicted Current
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Figure 5.4: Zoom of Measured Current and Predicted Current
As discussed, the speed will not be controlled in order to absorb the maximumtotal power. The current determines the driving torque produced by the machineto overcome the load. As the current increases by giving higher voltage supply tothe machine, the electromechanical torque as a driving torque is higher comparedto the load of the wind turbine. It results to an increase of the speed. On theother hand, lowering voltage supply gives a lower current and the speed of the ma-chine as well. This behavior is expected from the machine without any speed control.
Moreover, as the back-EMF is a function of the speed, an expected increase of theback-EMF is achieved too. Figure 5.5 and Figure 5.7 show the result of the speedand back-EMF produced by the machine respectively. A jump on the speed at thebeginning is due to transient.
Figure 5.5: Measured Speed vs Predicted Speed
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Figure 5.6: Zoom of Measured Speed and Predicted Speed
Figure 5.7: Back-EMF Produced by the Machine
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5.2 Implementation
The proposed algorithm based on the load torque estimation is programmed ingenerator mode and compiled to the FPGA. In this case, quadrant 2 is implementedwhere the current is supposed to be positive and the speed is supposed to be negative.This means that the load (the wind turbine) acts as a driving load, giving a highertorque load compared to the electromagnetic torque which then satisfies a negativespeed. The measurement is done in different wind speed which is supplied by the fan.Different blade’s size will not be analyzed, since the program has to be calibratedfor a different blade’s size. The experiment is done with the fan placed 10 cmaway from the wind blades. A BLDC model, six-steps commutation, static modelof wind turbine, improved zero-crossing method and hysteresis current control areimplemented.
5.2.1 Open Loop and Controlled Loop Back-EMF, Current
Commutation and Measured Output Voltage
With the wind blades connected to the rotor, a correct commutation has to beachieved in BLDC generator. Figure 5.8 shows that using zero-crossing method inopen loop, the commutation is attained correctly. This can be shown by measuringthe back-EMF of each phase. The spikes are due to commutation from one phaseto another phase.
Figure 5.8: Open-Loop 3-Phase Back-EMF BLDC
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Furthermore, as mentioned in the previous chapter, in order to make the currentcontroller, the current has to be calibrated at first. If the current is controlled, theoutput voltage is chopped; referring hysteresis control to Chapter 2.2. The outputof the controlled voltage is depicted in Figure 5.9. The amplitude of the back-EMFdepends on the speed of the generator. The faster the rotor speed, the higher theamplitude of the back-EMF. The back-EMF determines the output voltage measuredat the DC-link. The highest value of the back-EMF of a phase is chosen to beconnected to the output voltage. As a result, with a correct control and trapezoidalback-EMF shape, an output voltage is measured at almost constant, giving a DCoutput value. For such back-EMF output, the measured voltage is shown in Figure5.10.
Figure 5.9: Controlled 3-Phase Back-EMF BLDC
Figure 5.10: Measured Voltage
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5.2.2 Speed Sensor and Predicted Speed
In this thesis, the proposed algorithm is based on the static model; Cp is consideredconstant, the aerodynamic and air flow of the wind blades are not taken into theaccount. However, this coefficient has a big impact on the algorithm. The predictedwind speed is an inverse function of Cp. As a consequence, Cp limits the total windprediction. On the other hand, placement of the wind speed sensor or anemome-ter should be adjusted with this limitation. If the anemometer is placed facing thehighest wind blow (tip of the fan’s blades) from the fan, the sensed wind will be overthe predicted wind speed. This situation results to a high load torque predictionand uncontrolled current.
To prevent this condition, the anemometer should be mounted to sense the wind blowfrom the middle of the fan. On the other hand, with the proposed algorithm andcorrect calibration, the predicted wind speed is able to approximate the real windspeed. The wind speed sensor is tested and the algorithm works if sensor’s output isbelow the maximum positive predicted wind speed. Thus, according to the test, theapproximated wind speed is 80 percent below the maximum predicted wind speed.Three different wind speeds given by the fan are predicted to be approximately 4.5-5m/s, 5-5.5 m/s, and 5.5-6 m/s.
Figure 5.11: 1st Level
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Figure 5.12: 2nd Level Fan Speed
Figure 5.13: 3rd Level Fan Speed
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5.2.3 Predicted Load Torque and Predicted Current
With the proposed algorithm, the predicted load torque has to have a same waveformas the estimated load torque. Figure 5.14 and 5.15 depict the measured and predictedload torque. Moreover, the predicted current has to be precisely calibrated in orderto follow the measured current. Otherwise, the current will not be controlled. Ifcurrent is not controlled, the power will not be harvested. Figure 5.16 shows thepredicted and measured current when it is controlled.
5.2.4 Power Extracted by the Wind Turbine
In order to analyze the power extracted by the wind turbine, the power balanceequation is used. By comparing the power produced by the wind turbine and thepower supplied to the wind turbine, the power balance is met. As the machine actsas a generator, the supplied power is the mechanical power given by the wind tur-bine with different wind speed from the fan. The mechanical power is proportionalto the estimated load torque and the speed of the rotor, Pmech = T load.ωr. Onthe other hand, the produced power is the electrical power produced by the power
Figure 5.14: Estimated and Predicted Load Torque
Figure 5.15: Zoomed Estimated and Predicted Load Torque
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Figure 5.16: Measured and Predicted Current
electronics at the DC-link. The electrical power is proportional to the voltage andcurrent measured on the generator, Pelec = V.imeasured/predicted. With Ohm’s law,the electrical power can be expressed as Pelec = imeasured/predicted
2.R.
Two conditions have to be fulfilled in order to analyze the power: current has tobe controlled, and back-EMF has to be trapezoidal. If these two conditions are notmet, voltage and current cannot be measured. Hence, power flow will not be ableto be calculated. To measure the current at the DC-link, a resistor is connected.Two different value of resistors are implemented; power resistor 1.2 ohm and regularresistor 0.5 ohm. Below is the result of mechanical power and electrical power with1.2 ohm. The experiments were done in 5 different fan speed levels(1 st level, 2nd
level, 3rd level, 3rd+1st level, 3rd+3rd level) where each level is taken 3 times, givinga total of 15 experiments. The graph of the result is depicted in Figure 5.17 and theresults can be found in Appendix A.
Figure 5.17: Electrical Power with R=1.2 ohm
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As the wind speed increases, the the electrical power tends to increase as well. How-ever, at several points, the power goes lower due to different absorption of the windspeed. This is due to characteristics of the wind blades’ and wind turbine’s design.The amount of absorbed power is proportional to: 1. rotor speed 2. value of resistor.The power shown in the graph is very small because the machine rotates at a lowspeed; between 267 rpm to 355 rpm. On the other hand, the machine is meant torotate at a nominal 5240 rpm. At a very low speed, the voltage at the terminaltends to be very low as well; as back-EMF is proportional to the speed. In orderto gain higher power, a gearbox has to be implemented or another motor has to beused. Nonetheless, choosing a right machine for this application is not part of thethesis. Furthermore, changing the design of the wind turbine and the machine willchange everything, such as inertia, machine’s parameters, etc.
In spite of changing everything, the second way to lift up the electrical power is bychanging the resistor. By changing the value of the resistor, the current flows willchange too. With a lower resistor value, a higher current will flow through it. Thisgives result to a higher electrical power. However, this resistor acts as a load. Thus,it is not a linear relation. Due to this condition, a second resistor with lower valueis used to analyze the power stored in it. The experiments were done in 5 differentfan speed in which 3 times each. The graph of the result is depicted in Figure 5.18and the results can be found in Appendix A.
In order to increase the power, the current and the power will be analyzed. Bysubtracting the current flows to resistor equals to 0.5Ω with the current flows toresistor equals to 1.2Ω, the increased power between these two can be calculated aswell. Figure 5.19 and Figure 5.20 show the increased current and increased powerbetween two different resistor respectively.
Figure 5.18: Electrical Power with R=0.5 ohm
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Considering the highest (3rd+3rd) fan speed level, with a decreased resistor valuefrom 1.2Ω to 0.5Ω, an increased current of 0.2814 A can be achieved. Likewise, anincreased power of 0.0276 Watt is achieved. Thus, to gain a higher power, a decreaseof resistor’s value has to be chosen. Assuming a linear relation, the resistor’s valuecan be calculated by evaluating the increased power needed and the needed current.Based on measurement of 0.5Ω resistor, the needed resistor’s value for 0.25, 0.5, 1,5, and 10 Watt desired power are calculated and approximated in Table 5.1.
Moreover, another cheaper possibility might be reasonable by changing the powerconverter’s topology. At the moment, a bidirectional inverter is implemented. Yet,another alternative which relay on the use of a diode bridge and a boost converter ischeaper because simple rectifiers are used. Nonetheless, some disadvantages of usingthis topology might cause a drop in the power because diode rectifier produces a largeamount of input current harmonics, which affects the performance of the system,higher output voltage harmonic losses caused by the uncontrolled rectifier. Thecomparison of using this topology will not be discussed further in this thesis.
Figure 5.19: Increased Current
Figure 5.20: Increased Power
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DesiredPower (W)
AchievedPower (W)
IncreasedPower (W)
Current (A)Increased
Current (A)Resistor(ohm)
- 0.0845 - 0.2416 - 1.2- 0.1121 0.0276 0.523 0.2814 0.5
0.25 - 0.1379 2.613 - 0.036610.5 - 0.3879 7.3504 - 0.009251 - 0.8879 16.8251 - 0.003535 - 4.8879 92.6222 - 0.0005810 - 9.8879 187.3685 - 0.00028
Table 5.1: DC-link Resistor Value
5.2.5 Efficiency of the Wind Turbine
The efficiency of the wind turbine can be evaluated by knowing the output powerand the input power. The output power is the electrical power stored in the DC-link. While the input power can be achieved by measuring the load torque using theestimator and predictor and by measuring the rotor speed of the machine using theestimator and predictor as well. As in generator, load torque is the driving torque;driven by the wind blades and wind blow. This torque will produce the mechanicalpower, which is the input power of the system. The result of the estimated andpredicted power are shown in Figure 5.21 for 1.2Ω resistor and Figure 5.22 for 0.5Ωresistor. This load torque is a function of rotor speed. The rotor speed rotatesbased on the wind blades and wind blow. Thus, the more efficient the blades’ design(including the gearbox if it is available), the less friction and fluctuation of torqueis attained. However, the aerodynamic and fluid characteristic of the blades are outof the limit of this thesis.
Figure 5.21: Mechanical Power with R=1.2 ohm
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Figure 5.22: Mechanical Power with R=0.5 ohm
It can be seen that efficiency rises if the total amount of load torque decreases. Itmeans that the total amount of input power is small in order to produce the outputpower. The predicted value harvest more power and its efficiency is always higherthan the measured one. Furthermore, the efficiency at lower wind blow is lowerbecause the rotor rotates at low speed. A low speed rotor gives a low voltage andthe current is difficult to be controlled. Thus, a total produced electrical power islow and efficiency drops. In contrast, the higher the wind speed, above 6.5 m/s, theefficiency starts to decline. The reason is the wind turbine’s design is not efficientbecause a high vibration from the wind turbine, such as the tower, hub, etc. Thisgives a lot of lost in absorbing the power. To increase the efficiency of the windturbine at high speed, the turbine has to be designed perfectly; stable tower, lessvibration and better wind blades. From the graph, the most efficient to harvest thepower is at 6-6.5 m/s wind speed.
Figure 5.23: Efficiency with R=1.2 ohm
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Figure 5.24: Efficiency with R=0.5 ohm
49
Chapter 6
Conclusions
A BLDC self-sensing model by Araz Darba, improved zero-crossing method by ArazDarba, and static wind turbine model were used. In addition, my contributionthrough this thesis was to develop a predicted algorithm based on the estimatedload torque to harvest maximum power of the wind turbine and avoid instabilitiescaused by severe wind speed. To harvest maximum power in low power wind turbine,a variable speed and a fixed pitch are needed. In this way, there is no need of speedcontrol and current reference comes from the predicted current. These models andalgorithm were implemented by an FPGA with MATLAB/Simulink integrated. Thealgorithm was simulated in motor mode and implemented in generator mode. Thedifference between these two modes is:
• motor: the wind turbine becomes the load or braking of the BLDC machine.
• generator: the wind turbine becomes the driving torque of the BLDC machine.The algorithm has to be suited in quadrant two where the speed is negative,yet the torque stays positive.
The simulation showed that with a sinusoidal wind speed, the estimated load torquewas sinusoidal as well. Furthermore, with a precise sampling of the estimated loadtorque, the predicted load torque followed the estimated load torque; sinusoidalwaveform. Likewise, as torque is a function of current, with a PI control, the pre-dicted current was sinusoidally shaped. The existence of the spikes was becauseof the current commutation. Besides current and load torque, the speed was alsoanalyzed. The current has to be always increased in order to overcome the load.Increasing the current is done by giving higher voltage to the machine, which resultsto an expected increase of the speed.
The generator mode was implemented by a wind turbine which was designed todrive the BLDC machine. The wind turbine was designed without considering theaerodynamic, the fluid characteristic of the wind blades and the electric drive in or-der to evaluate the power. Due to practical limitation, such as low wind blow givenby the fan, the wind blades was designed to suit the limitation. A kid’s windmill toydesign, a wind turbine without gearbox, a brushless direct current (BLDC) genera-tor, two fans with three different wind speed (4.5-5 m/s, 5-5.5 m/s and 5.5-6 m/s),and a wind speed sensor to sense the wind speed were calibrated and implemented.The wind speed sensor is suggested to be placed facing the middle of the fan, butit is not mounted on the wind turbine as the fan is too small to provide a balance
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wind blow from every angle. Nonetheless, the predicted wind speed was used toapproximate the wind speed. The wind speed was approximated to be 80 % belowthe maximum positive value of the predicted wind speed. Moreover, in order toprevent instabilities and overheating the machine, the predicted current was filteredand feeded back as a reference current limited to the nominal current value of themachine; 2.33 A. A hysteresis controller was used to control the current by choppingthe voltage.
The aim of the thesis is to evaluate the power harvested by the proposed predictedalgorithm. Two conditions have to be fulfilled in order to analyze the power: 1.cur-rent has to be controlled, 2. back-EMF has to be trapezoidal. The power wasanalyzed by using the power balance equation, where electrical power is a functionof voltage and current flows to the DC-link and mechanical power is a function ofload torque and rotor speed. The outcome showed that harvested power was higherusing the predicted algorithm. In contrast, the output power was low due highnominal speed of the generator and low current flowed to the DC-link. These canbe uplifted by having a gearbox.
However, changing the design will change the characteristics of the wind turbine,such as inertia, speed, etc. So, another way to lift up the power is to lower thepower resistor. By lowering the resistor value, the current flows to the resistor willbe higher. However, it is not a linear relation because the resistor now acts as aload to the wind turbine. The increase of the current was evaluated by having twodifferent resistors’ value; 1.2Ω and 0.5Ω. With these evaluated current, by assuminga linear increasing current and decreasing resistor’s value, a resistor’s value can beapproximated for a desired power. The last option to boost the power is by us-ing another cheaper topology; diode rectifiers and boost converter. However, thistopology has its own disadvantages, such high harmonics and losses which affect theperformance of the system.
In generator mode, the electrical power is the output power at the DC-link and themechanical power is the input power from the rotation of the wind blades. Throughthis relation, the efficiency of the wind turbine could be evaluated. At low windspeed, rotor rotated at low speed and resulted to a low voltage. Furthermore, at lowspeed it was hard to control. Fulfilling the two conditions, the power will only bestored if it is controlled. Thus, only some fractions of the power will be harvested.This resulted to a low efficiency. On the other hand, the efficiency drops as thewind blows faster. At wind speed above 6.5 m/s the efficiency drops slightly dueto inefficient wind turbine’s design, such as vibration of the tower, unstable windblades due to the bearings, etc. To conclude, the most efficient region is when thewind blows at 6-6.5 m/s.
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Chapter 7
Future Developments
The main focus of this thesis is to absorb maximum power by a wind turbine. Inthis thesis, developing a new algorithm to gain higher absorbed power than themeasured one has been achieved. However, there are still a lot of assumption for thesuccess of the result. These assumption can be investigated for further improvementto harvest maximum power, such as:
• Assumption of a static wind turbine. Using this assumption results to a con-stant power coefficient Cp, static wind turbine’s power and torque equation,which has an impact on the algorithm, e.g. the predicted wind speed. Study-ing the impact of dynamic wind turbine will give a better precision of theproposed algorithm.
• The limited research of this thesis regarding the aerodynamic and fluid charac-teristics of the wind turbine’s design. In order to analyze the relation betweenthe wind speed and the load torque, wind blades and wind turbine’s designhave to be investigated. By knowing the relation of the wind blades design andhaving an improved wind turbine’s design, the wind energy can be absorbedand converted into electrical energy more efficiently.
• Choosing a perfect electric drive for this application is not considered as well inthis thesis. As already mentioned in Chapter 5, the motor has a high nominalspeed. This characteristic of the machine is not considered in this thesis. Asa result, a low power was harvested. In the future, a better generator can beinvestigated for wind turbine’s application.
• The limitation of the sources, such as sources of the wind blow; at the momentit comes from a fan. This limitation makes it difficult to place the speed sensor.The speed on the middle of the fan and the tip of the fan is different. In fact,the placement of the speed sensor is important as well. For a better accuracy,placement of wind speed sensor has to be investigated and supported by betterwind sources.
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Appendix A
Measurement Results
Speed (km/h) Speed (m/s) Output(V)0 0 0.44 14.4 0.4275 18 0.4537 25.2 0.4588 28.8 0.48812 43.2 0.51214 50.4 0.54715 54 0.56717 61.2 0.57618.5 66.6 0.58219.5 70.2 0.59120 72 0.6123 82.8 0.6224 86.4 0.6327 97.2 0.666
Table A.1: Anemometer Output
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Figure A.1: Electrical Power with R=1.2 ohm
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Figure A.2: Mechanical Power with R=1.2 ohm
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Figure A.3: Electrical Power with R=0.5 ohm
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Figure A.4: Mechanical Power with R=0.5 ohm
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Appendix B
Essential Components of the Test
Setup
B.1 Fan
The fan is used to provide wind to rotate the blades. A fan with several wind speedoptions was chosen to provide different wind speed which is needed to analyze varyingcharacteristics of the wind turbine. Due to smaller diameter of the fan comparedto the wind blades’ diameter, 3 fans are used in order to have more accurate windspeed sensing. The fan brand is Portland and its specifications are as below
Parameter ValueBlades diameter 40 cmPower 42.9 WMaximum Air Speed 2.48 m/sMaximum Air Flow 45.00 m3/min
Table B.1: Fan Specifications
B.2 BLDC Generator
The generator used in the setuo needs to be low in cogging torque. The reason isthat low cogging torque motor is easier to be rotated by the wind. If cogging torqueis high, a really high wind speed and a big wind blades are needed. Moreover,the higher the rotation of the motor, the more power can be absorbed and thusincreases the efficiency of the system. In this thesis, choosing and analysing themachine is not part of the discussion. The machine is given by the university ofGent. The generator is a Maxon EC 45 flat 42.8 mm, brushless, 50 Watt. It hasHall-Sensor output for each phase. The generator specifications are described inTable B.2 and the datasheet is available online at: http://www.maxonmotor.com/medias/sysmaster/root/8816806854686/15-262-EN.pdf
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Parameter ValueStator Resistance 1.03 ΩStator Inductance 0.572 mHRotor Inertia 1.35 x 10 5 kgm2
Number of Pole Pairs 8Nominal Voltage 24VNominal Current 2.33 ANominal Load 83.4 mNmTorque Constant 33.5 Nm/AVoltage Constant 3.51 V/krpm
Table B.2: Maxon Descriptions
Figure B.1: Maxon Generator
B.3 Driver Board
The switching of the generator is performed by three half-bridge inverter. Theinverter is included in ATMEL MC300 control driver board. This board has fourhalf-bridges with independent control of high and low side. Moreover, this boardinterface on MC300 connector is split into four eight-pin connectors. The driver canbe used in motor mode which can supply voltage up to 40 V and motor current upto 30 A. It is also possible to utilize it as generator with a DC output connectedto resistor or capacitor bank. The datasheet of the ATMEL MC300 driver board isavailable online at: http://www.atmel.com/images/doc8124.pdf
Figure B.2: Driver Board [6]
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B.4 ADC Modules
The driver board has output pins available for 4 phase voltages which 3 of them areused for 3-phase BLDC machine. A shunt resistor is connected to another pin that
Figure B.3: PmodAD1TM ADC module
makes us able to measure the DC-link. These analog outputs have to be convertedinto digital, so that it can be read by the FPGA. 4 ADCs are used for 3-phaseBLDC output voltages, DC-link output, and wind speed sensor output. The ADCsused in the setup are from Digilent, Digilent PmodAD1TM. Each module has twoAD7476A 12-bit A/D converter chips with a maximum sampling rate of one millionsamples per second. The datasheet of the Digilent PmodAD1TM ADC modulesis available online at: http://www.digilentinc.com/Data/Products/PMOD-AD1/
Pmod%20AD1_rm.pdf
B.5 Digital Isolators
IL715 modules manufactured by NVE Corporation are used to have an isolationbetween the driver stage and the FPGA board. These modules are produced withNVE’s patented IsoLoop R©, which transfers the measured signals using a magneticfield and FM modulation. The properties of the isolator are as followed:
Figure B.4: IL75 Isolator
• High speed transfer rates of 110Mbps over full temperature and supply voltagerange.
• 1.2 mA/channel typical quiescent current.
• A propagation delay of 10ns and pulse width distortion of 2ns.
• 2500 Vrms isolation voltage.
• A barrier life of 44000 year.
The datasheet of the IL715 digital isolators is available online at: http://www.nve.com/Downloads/il71x.pdf
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B.6 FX2 Connector
An interface between the ADC modules and the FPGA is needed. As the ADC ismanufactured by Digilent, the connector FX2 MIBTM is produced by Digilent aswell. FX2 MIBTM features are:
• one FX2 peripheral board connector which provides forty general purpose I/Osignals, three clock signals, JTAG signals, and power busses.
• four 12-pin and two 6-pin Pmod connectors.
• two power busses, VCC at 3.3V and VCCFX2 at 5V.
The datasheet of the FX2 MIBTM connector is available online at: https://www.
digilentinc.com/Data/Documents/Product%20Documentation/FX2%20MIB_rm_RevA.
Figure B.5: FX2 MIBTM connector
B.7 FPGA
The FPGA boards used to control the generator of the set-up are Xilinx VirtexII-Pro Evaluation boards with a XC2VP30 FPGA chips. The XC2VP30 FPGAhas 13696 slices, 438 Kbits of RAM memory and 136 multipliers. The datasheetof the Virtex II-Pro FPGA family is available online at: http://www.xilinx.com/support/documentation/data_sheets/ds083.pdf The FPGA is program in MAT-LAB/Simulink Xilinx integrated. A System Generator and ISE are used to generatethe program. Additionally, a Xilinx ChipScope Analyzer software is used to captureand show the internal signals of the FPGA.
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Figure B.6: Virtex II-Pro FPGA
B.8 Wind Speed Sensor
An anemometer is a device used for measuring wind speed, and is a common weatherstation instrument. An anemometer provides information of the wind speed. Thehardware has three cups of half circle to capture the wind. Additionally, an analogoutput is available proportional to the rotation of these cups. By coupling theanalog output to ADC module, these values are digitalized and further needed bythe FPGA. The sensor needs to be calibrated in order to have a perfect relationbetween the wind speed and the digitalized values. Information about the sensor isavailable online at: http://www.adafruit.com/products/1733
Parameter ValueTesting Range 0.4-2 VStart Wind Speed 0.5-50 m/sResolution 0.2 m/sMax Wind Speed 70 m/sInput Voltage 7-24 V DC
Table B.3: Anemometer Parameters
Figure B.7: Wind Speed Sensor/Anemometer
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List of Figures
1.1 Division of the Project . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 Permanent Magnet Motor by its Operation [1] . . . . . . . . . . . . . 72.2 BLDC Construction and Single Phase Torque . . . . . . . . . . . . . 82.3 Torque and Current of 3-Phase BLDC [4] . . . . . . . . . . . . . . . . 82.4 Torque, Current and Power Three Phase BLDC [4] . . . . . . . . . . 92.5 BLDC 4-Quadrant Operation [6] . . . . . . . . . . . . . . . . . . . . . 102.6 Current and Back-EMF Waveform of BLDC in Generation Mode . . 112.7 Three Phase Inverter, Controller and Driver of Typical BLDC Drive [4] 122.8 BLDC Stator Current Commutation States [4] . . . . . . . . . . . . . 122.9 Six Step Conducting States [4] . . . . . . . . . . . . . . . . . . . . . . 132.10 Current and Back-EMF Waveform of BLDC [4] . . . . . . . . . . . . 132.11 Current Control of BLDC Machine [4] . . . . . . . . . . . . . . . . . 152.12 Cascaded Speed Control [4] . . . . . . . . . . . . . . . . . . . . . . . 162.13 Brushless DC Sensorless Scheme [11] . . . . . . . . . . . . . . . . . . 172.14 Different methods to determine commutation instants using back-
EMF zero-crossing detection [4] . . . . . . . . . . . . . . . . . . . . . 182.15 Threshold Method [4] . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.16 Block Diagram of Wind Turbine Energy Conversion [17] . . . . . . . 212.17 Cp(λ) versus TSR Curve [24] . . . . . . . . . . . . . . . . . . . . . . 232.18 Ideal TSR versus Wind Speed Curve [24] . . . . . . . . . . . . . . . . 232.19 Physical Model of Drive Train [21] . . . . . . . . . . . . . . . . . . . . 242.20 Typical Wind Turbine Operating Systems [27] . . . . . . . . . . . . . 25
4.1 Wind Turbine Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.2 Anemometer Calibration . . . . . . . . . . . . . . . . . . . . . . . . . 314.3 Electronics Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.4 Schematic of the Practical Realization of Predicted Algorithm . . . . 34
5.1 Sinusoidal Wind Speed Input vs Wind Speed Prediction . . . . . . . 365.2 Sinusoidal Load Torque Estimation vs Load Torque Prediction . . . . 365.3 Measured Current vs Predicted Current . . . . . . . . . . . . . . . . . 365.4 Zoom of Measured Current and Predicted Current . . . . . . . . . . . 375.5 Measured Speed vs Predicted Speed . . . . . . . . . . . . . . . . . . . 375.6 Zoom of Measured Speed and Predicted Speed . . . . . . . . . . . . . 385.7 Back-EMF Produced by the Machine . . . . . . . . . . . . . . . . . . 385.8 Open-Loop 3-Phase Back-EMF BLDC . . . . . . . . . . . . . . . . . 395.9 Controlled 3-Phase Back-EMF BLDC . . . . . . . . . . . . . . . . . . 405.10 Measured Voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.11 1st Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
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5.12 2nd Level Fan Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.13 3rd Level Fan Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.14 Estimated and Predicted Load Torque . . . . . . . . . . . . . . . . . 435.15 Zoomed Estimated and Predicted Load Torque . . . . . . . . . . . . . 435.16 Measured and Predicted Current . . . . . . . . . . . . . . . . . . . . 445.17 Electrical Power with R=1.2 ohm . . . . . . . . . . . . . . . . . . . . 445.18 Electrical Power with R=0.5 ohm . . . . . . . . . . . . . . . . . . . . 455.19 Increased Current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465.20 Increased Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465.21 Mechanical Power with R=1.2 ohm . . . . . . . . . . . . . . . . . . . 475.22 Mechanical Power with R=0.5 ohm . . . . . . . . . . . . . . . . . . . 485.23 Efficiency with R=1.2 ohm . . . . . . . . . . . . . . . . . . . . . . . . 485.24 Efficiency with R=0.5 ohm . . . . . . . . . . . . . . . . . . . . . . . . 49
A.1 Electrical Power with R=1.2 ohm . . . . . . . . . . . . . . . . . . . . 54A.2 Mechanical Power with R=1.2 ohm . . . . . . . . . . . . . . . . . . . 55A.3 Electrical Power with R=0.5 ohm . . . . . . . . . . . . . . . . . . . . 56A.4 Mechanical Power with R=0.5 ohm . . . . . . . . . . . . . . . . . . . 57
B.1 Maxon Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59B.2 Driver Board [6] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59B.3 PmodAD1TM ADC module . . . . . . . . . . . . . . . . . . . . . . . . 60B.4 IL75 Isolator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60B.5 FX2 MIBTM connector . . . . . . . . . . . . . . . . . . . . . . . . . . 61B.6 Virtex II-Pro FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . 62B.7 Wind Speed Sensor/Anemometer . . . . . . . . . . . . . . . . . . . . 62
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List of Tables
2.1 Digitalized Six Step Commutation Sequence . . . . . . . . . . . . . . 12
5.1 DC-link Resistor Value . . . . . . . . . . . . . . . . . . . . . . . . . . 47
A.1 Anemometer Output . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
B.1 Fan Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58B.2 Maxon Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59B.3 Anemometer Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 62
65
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