Damping of Wind Turbine Tower Oscillations through Rotor Speed ...

March 29 – April 1
International Conference on
Ecologic Vehicles & Renewable Energies
Damping of Wind Turbine Tower Oscillations
through Rotor Speed Control
Copyright © 2007 MC2D & MITI
Mate Jelavić, Nedjeljko Perić, Ivan Petrović
Faculty of Electrical Engineering and Computing
University of Zagreb, 10000 Zagreb, Croatia
E-mail: mate.jelavic@fer.hr
nedjeljko.peric@fer.hr
ivan.petrovic@fer.hr
Abstract: To enable wind turbines to produce power under great variety of wind conditions a
sophisticated control system is needed. Wind turbine system is highly nonlinear and its dynamic
changes rapidly with the change of wind speed. Besides this, wind turbine mechanical structure is very
flexible due to its great height and tends to oscillate. All this makes the design of wind turbine control
system very demanding task. In this paper three controller design methods are compared. It is shown
how with adequate controller design it becomes possible to control the wind turbine under various
operating conditions reducing the structural oscillations at the same time.
Keywords: Wind turbine, pitch control, tower oscillations, pole placement, full state feedback
1. Introduction
Modern wind turbines have to operate in
wide range of operating conditions determined
primarily by wind speed. To make it possible
for wind turbine to produce power in such a
variety of operating conditions a sophisticated
control system is needed that will account for
changes in operating conditions and
accompanying changes in wind turbine
dynamics.
Wind power or the power of air that moves
at speed vw over the area swept by turbine rotor
with radius R is given by [1]:
1 2 3
Pw = ρairR πvw,
(1)
2
where ρ air is density of air. From expression
(1) it is clear that wind energy increases rapidly
with increase in wind speed. This results in two
very different operation regions of wind
turbine, each of them placing specific demands
upon control system. During weak winds
power contained in the wind is lower than the
rated power output of wind turbine generator.
Therefore, the main task of the control system
in this region is to maximize wind turbine
power output by maximizing wind energy
capture. It can be shown [1] that for each value
of wind speed energy conversion efficiency is
maximal for only one particular value of rotor
speed. Since modern wind turbines are
connected to grid using AC-DC-AC frequency
converters, generator frequency is decoupled
from grid frequency what enables variable
speed operation. Therefore it becomes possible
to vary the rotor speed and to maintain optimal
energy conversion during varying wind speeds.
On the other hand, during strong winds power
of the wind is greater than the rated power
output of wind turbine generator. Therefore,
the wind energy conversion has to be
constrained in this region to assure generator
operation without overloading. Very efficient
method for constraining wind energy
conversion is pitching the rotor blades around
their longitudinal axis what deteriorates their
aerodynamic efficiency and therefore only a
part of wind energy is used for driving the
generator.

The borderline between two operation
regions is the lowest wind speed at which
turbine generator reaches its rated power
output. This wind speed is termed rated wind
speed [1]. Hence the wind turbine operation
regions are known as below and above rated
regions. In this paper we focus on the above
rated operation region and describe design of
controllers used in that region.
The paper is organized as follows. Section 2
gives a general description of the wind turbine
control system. Section 3 introduces
mathematical model of the wind turbine that is
used for controller design. Section 4 describes
the design of PID controller which is nowadays
standard solution for wind turbine control. In
section 5 and 6 two alternative control concepts
are proposed and their performances are
compared with the performance of PID
controller. Conclusions are given in section 7.
2. Wind turbine control system
The main task of wind turbine control system
is to obtain continuous power production under
operating conditions determined by various
wind speeds. As turbine power is directly
proportional to its speed, power control can be
done by controlling turbine speed. The
principle scheme of wind turbine speed control
system is shown in fig. 1. As it can be seen in
this figure turbine speed can be influenced and
Figure 1: Principle scheme of wind turbine control
system.
thus controlled by two means – by generator
electromagnetic torque Mg which opposes rotor
driving torque Mr and by pitch angle β which
alters the wind energy conversion. For this
reason turbine speed control system consists of
two control loops: torque control loop and pitch
control loop. Those control loops operate
simultaneously but depending on operation
region one of them is dominant. In the below
rated operation region the torque control loop is
used to control turbine speed to values that will
result in maximal wind power capture. This
control loop is not in the scope of this paper.
Details on its specifics can be found in e.g. [2].
In the above rated region this control loop just
holds generator torque at its rated value.
The pitch control loop is used for setting the
adequate pitch angle that will keep turbine
speed at its reference value under all operating
conditions determined by various winds. Below
rated wind speed this loop sets pitch angle to
value that assures maximal wind power capture
which is usually around 0 o . In this paper we
assume that all blades have the same pitch
angle what is known as "collective pitch".
Controller in this loop, although used to control
turbine speed, is commonly termed pitch
controller. Blade positioning is mostly done
using electrical servo drives that rotate blades
by means of gearboxes and slewing rings.
Position control of servo drives is usually
achieved using frequency converters. This
control loop design is rather simple and is not
in the scope of the paper. For simulation
analysis it is modeled as second order system:
SD
( )
G s
( s)
( )
β
ω
= =
β ζω ω
ref s
2
s + 2 β
2
nβ
nβs+ 2
nβ
. (2)
In this paper we use ω nβ =6.28 rad/s and
ζ β = 2/2 what is fairly good approximation
of real pitch positioning system.
3. Wind turbine model
The first step in control system design is
construction of a suitable process model for
wind turbine in scope. Wind energy
conversion process is highly nonlinear and
difficult for mathematical description. It can be
described quite well using combined blade
element and momentum theory [1]. However
this approach yields implicit mathematical
expressions that can only be solved iteratively.
This form, although very common in
simulation tools, is not suitable for controller
design. So in this paper we use another
somewhat simplified mathematical model that
is usual in the literature dealing with controller
design. Here we describe it briefly, while
details on it can be found in [3] and [4].
Power contained in wind given by
expression (1) can never be completely
transformed into wind turbine power and
afterwards into electrical power. The amount of
wind power that is converted into turbine
power Pr can be described by means of
performance coefficient CP [1]:

Pr = Pw⋅ CP.
(3)
The theoretical maximum for CP is
determined by the Betz' law [1] and equals
16/27. In practice wind turbines don't reach this
limit but approach the value of 0.5 at best.
CP is not a constant parameter but its value is
dependant on wind speed v w , rotor speed ω
and blade pitch angle β . Wind speed and rotor
speed are usually bound together introducing
parameter λ that is called tip speed ratio which
represents the ratio between blade tip speed and
wind speed [1]:
ωR
λ = . (4)
v
Typical dependence of performance
coefficient upon tip speed ratio with pitch angle
used as a parameter is shown in fig. 2.
Figure 2: Performance coefficient as a function of
tip speed ratio.
Aerodynamic torque Mr that drives wind
turbine rotor and thus generator is given by:
M
r
w
( , )
2 3
P 1 ρairR πv r
wCPλ β
= = . (5)
ω 2 ω
Rearrangement of expression (5) yields:
M
r
( , )
3 2
1 ρairR πvwCPλ β
= . (6)
2 λ
A quotient of performance coefficient and tip
speed ratio forms a new dimensionless
parameter that is known as torque coefficient
CQ [1]:
C
Q
( λβ , )
C
( λ, β )
P
= . (7)
Having aerodynamic torque calculated
according to (6) rotor speed can easily be found
using principle equation of motion:
λ
J � ω = M −M −M
, (8)
t r g l
where Mg is generator electromagnetic torque,
Jt is total moment of inertia of rotor and
generator while Ml is loss torque. Loss torque
Ml, caused mostly by friction is rather small
and will be neglected here.
In this paper we consider wind turbine with
generator that is directly coupled with turbine
rotor. This turbine setting known as direct drive
system uses synchronous multipole generator
that rotates at small speed of turbine rotor.
Since rotor and generator speeds are the same
no distinction between them is made
throughout the paper. Because there is no
gearbox between rotor and generator their
moments of inertia can just be summed
together in order to calculate total moment of
inertia Jt. The coupling of rotor to the generator
in direct drive solutions is very stiff and it can
be considered as rigid thus removing any
torsional oscillations what simplifies the
control system design.
Before going further an important issue has
to be addressed. Namely, expressions (4), (5)
and (6) in this form would be valid only for
structure with rigid tower and blades. In real
situation the absolute wind speed v w in
mentioned expressions has to be replaced by
wind speed that is "seen" by rotor blades. This
wind speed seen by the rotor is the resultant of
three factors: absolute wind speed v w , speed
of the tower movement perpendicular to wind
speed (i.e. tower nodding speed) x� t and speed
of blade movement perpendicular to wind
speed (i.e. speed of blade flapwise movement).
Influence of tower nodding on wind turbine
control is much more pronounced than
influence of blade flapwise movement.
Therefore we focus only on tower nodding
considering rotor blades as rigid.
Tower nodding originates from the fact that
wind turbine tower is very lightly damped
structure due to its great height (more than 100
meters in modern wind turbines) and need for
moderate mass. To model the wind turbine
tower precisely we would have to use model
with distributed parameters and to describe it
in terms of mass and stiffness distribution.
Such a model wouldn't be very suitable for
controller design so it has to be substituted by

model with concentrated parameters. This can
be done using modal analysis that is very
common tool in wind turbine analysis [1], [3].
It describes a complex oscillatory structure as
a composition of several simple oscillatory
systems each of them being described by
means of mass, stiffness and damping. By this
representation complex tower oscillations are
seen as a sum of many simple oscillations
characterized by their modal frequencies
which are one of the most important structural
properties of wind turbine. It has been shown
in practice [5] that fairly good modeling of
wind turbine tower nodding can be achieved
using two modal frequencies (two modes).
Since we are here primarily interested in
building model suitable for controller design
we use only the first modal frequency. The
justification for this lies in the fact that for the
turbine in scope second modal frequency is
more than 6 times greater than the first modal
frequency and therefore falls out of the
controller frequency bandwidth.
By using only one modal frequency tower
dynamics can be described as:
Mx �� + Dx� + Cx = F , (9)
t t t
where M, D, and C are modal mass damping
and stiffness respectively and F is the
generalized force that is originated by wind
and that causes wind turbine tower
oscillations. Tower modal properties in
expression (9) are related to first tower modal
frequency ω0t as follows [4]:
D= 2 ζ ω ⋅M,
t 0t
2
0t
( ω )
C = ⋅M,
(10)
where ζ t is structural damping. For steel
structure structural damping is mostly set to
0.005 [4]. Modal mass M can be calculated as
[1]:
ht
0
( ) ( ) 2
φ
M = ∫ m h h dh,
(11)
where ht is the height of the tower, m(h) is the
mass distribution along the tower and φ ( h)
is
the tower's first mode shape. Note that actual
distribution of mass along tower has to be
modified in order to include mass of the rotor
and the nacelle which is assumed to be
concentrated at the tower top.
Driving force F is mostly the rotor thrust force
Ft caused by wind. It can be shown [4] that
thrust force, similar to aerodynamic torque,
depends upon wind speed, rotor speed and
pitch angle. So similarly to (6) it can be
expressed as [4]:
1 2 2
Ft = ρairR πvwCt( λ, β)
, (12)
2
where Ct is so called thrust coefficient.
Expressions (6), (8), (9) and (12) form the
simplified nonlinear model of wind turbine
that is used in the following sections for
controller design. Model is summarized below
taking into account the fact that wind speed
seen by the rotor is a sum of wind speed and
tower nodding speed:
J � tω= Mr −Mg −Ml,
1 3
2
Mr = ρairR πCQ( λ, β)
⋅( vw −x�t)
,
2
. (13)
1 2
2
Ft = ρairR πCt( λ, β)
⋅( vw −x�t)
,
2
F = Mx �� + Dx�+ Cx.
t t t t
Torque and thrust coefficients Cq and Ct are
usually provided by wind turbine blade
manufacturers or can be calculated using
professional simulation tools.
4. PID Controller
The PID controllers are still by far the most
used controllers for wind turbine speed and
power control. This is due to their simplicity
and rather high robustness. The small number of
parameters makes possible for designer to
quickly arrive at satisfactory, although in many
cases suboptimal, system behavior. As stated
before wind turbine dynamics change in
nonlinear fashion with change in wind speed.
To control the wind turbine with linear
controllers gain scheduling has to be used [3].
Although it seems straightforward to use wind
speed as scheduling criterion this is not an
appropriate solution since the wind speed is not
measured fast and accurately enough. Therefore
measured pitch angle is usually used as
scheduling variable.
For the design of a PID controller using
analytical methods process model (13) has to
be linearised around chosen operating point.
After linearization of expressions (13) and

transition to Laplace domain simple algebraic
manipulations yield transfer functions that are
needed for controller design. Those transfer
functions are:
and
( )
G s
β
w
( )
G s
Δω
=
Δβ
w
( s)
( s)
( s)
( )
Δω
=
Δv
s
(14)
. (15)
These transfer functions are of the third order.
A good insight in system properties of the
wind turbine can be gained if we examine
frequency characteristics of transfer function
(14) shown in fig. 3.
Figure 3: Frequency characteristics of G β .
It can be observed that frequency
characteristics of transfer function (14) at
tower modal frequency exhibits magnitude and
phase drop. Similar phenomena are present in
frequency characteristics of (15) as well. This
fact makes the pitch controller design very
difficult. Physical explanation for observed
effects becomes clear from the following
analysis. Change in wind speed causes change
in rotor speed what requires controller action
and pitching of the blades in order to regulate
the rotor speed to its rated value. Pitching the
rotor blades, besides the aerodynamic torque,
alters the thrust force significantly. Thrust
force, according to (13), causes change in wind
turbine tower top speed and thus the wind
speed seen by the rotor is changed. This alters
the aerodynamic conversion and in this way a
feedback is formed. For this reason wind
turbine can easily be driven into oscillatory
behavior if the pitch controller is not designed
properly.
To prevent the pitch controller from driving the
wind turbine into oscillatory behavior it must be
assured that system frequency bandwidth is
below the first tower modal frequency.
Moreover, sufficiently small magnitude is
required at the first modal frequency. As it can
be seen from fig. 3. the first modal frequency of
the turbine in scope is 3 rad/s so a bandwidth of
1 rad/s was chosen. PID controller was designed
to assure phase margin of around 60 o what gives
satisfactory behavior of the system.
To fully explore the system behavior with
chosen controller simulation tool GH Bladed
was used. GH Bladed is professional simulation
package designed for wind turbine simulations
and load calculations [5]. It relies upon very
complex mathematical model based on
combined blade element and momentum theory
[1]. Structural properties of the wind turbine are
modeled in detail and inertial and gravitational
loads are taken into account along with
aerodynamic ones. Extensive testing showed
that simulation results obtained in Bladed are in
accordance with measurements taken on actual
wind turbines what was recognized by major
standardization and certification institutions
(e.g. Germanischer Llyod).
To model the structural properties of explored
turbine many modes are used [5]. Tower
nodding is modeled with two modes as well as
tower naying (tower side-side motion). Rotor
blades' motion in flapwise direction is modeled
with 6 modal frequencies while blades' motion
in edgewise direction (displacement of the rotor
blades in the plane of rotation) is modeled with
5 modal frequencies. Wind shear and tower
shadow are included in the model as well. PID
controller designed based on linearised model
(14) and (15) was implemented in C and
included as external discrete time controller. In
that way we could use Bladed for controller
testing.
In the following figures behavior of the system
when PID controller is used for rotor speed
control is shown for one representative
operating point ( v w = 15 m/s). Wind that was
used for simulation observed positive and
negative stepwise change shown in fig. 4.
Responses of rotor speed, pitch angle and tower
top displacement are shown in figs. 5. 6. and 7.
respectively. From these figures it can be seen
that rotor speed is well regulated and quickly
compensated for the influences of wind speed
changes. The pitch control actions are moderate
without any oscillations. Similar results were
obtained for all operating points throughout
wind turbine operating range.

Wind speed [m/s]
18
17
16
15
14
13
12
0 10 20 30 40 50 60 70
t [s]
Figure 4: Wind speed used for simulation in Bladed.
Rotor speed [rpm]
25
24.5
24
23.5
23
22.5
22
0 10 20 30 40 50 60 70
t [s]
Figure 5: Response of rotor speed of the system
controlled with PID controller.
Pitch angle [deg]
16
15
14
13
12
11
10
9
8
7
0 10 20 30 40 50 60 70
t [s]
Figure 6: Response of pitch angle of the system
controlled with PID controller.
Tower top displacement [m]
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
0 10 20 30 40 50 60 70
t [s]
Figure 7: Response of tower top displacement of the
system controlled with PID controller.
While rotor speed and pitch angle behavior is
satisfactory, attention should be paid to the
tower top oscillations shown in fig. 7. It can be
observed that tower top experiences lightly
damped oscillatory behavior. These lasting
oscillations, although small in magnitude,
contribute to material fatigue and can lead to
structure premature failure. So in the following
sections two methods for controller design that
aim at reduction of tower top oscillations are
proposed.
5. Input-output pole placement
controller
In this section the pitch controller with inputoutput
structure is designed using well known
pole placement method [6]. As design objective
in this method the desired behavior of the
closed loop system has to be chosen. The pitch
controller GPC is then designed to assure that
closed loop system behaves in the chosen
manner. Transfer functions (14) and (15) form
the linearised wind turbine model that can be
described with the principle scheme shown in
fig. 8.
Figure 8: Principle scheme of the linearised wind
turbine model.
From the principle scheme given in fig. 8 closed
loop transfer function with respect to wind
speed change can be derived:
G
CL_ w
Δω()
s
Gw() s
= =
. (16)
Δ v () s 1 + G () s G s G () s
w CL PC SD
( )
The pitch controller GPC has to assure that
closed loop transfer function (16) is equal to the
chosen model transfer function Gm:
( ) !
G s = G () s . (17)
CL _ w m
β

From (16) and (17) it follows that pitch
controller has a form of :
1 Gw() s −Gm()
s
GPC() s = ⋅ . (18)
G s G () s G () s
( )
SD β
m
It should be noted that the above expression
often needs to be modified to assure that the
controller is causal. Details on this design
method can be found in [6].
The crucial step in controller design using
described method is the choice of model
transfer function Gm. It is important to choose
model transfer that is achievable for the system
in scope and that assures its satisfactory
behavior. One possibility that is very common
in the literature is the use of standard forms
such as Butterworth or binomial form.
However, these forms can be rather difficult to
relate to system physical properties. So another
approach is used in this paper. As previously
said our primary goal is the reduction of tower
oscillations. Tower oscillations can be reduced
if the tower modal damping increases what
would require change in tower structural
parameters such as mass and stiffness
distributions. Our goal is to achieve similar
increase of tower damping by means of pitch
controller actions without change in tower
structural parameters. In that sense as a
controller design objective we set a desired
increase of tower modal damping. In other
words tower modal damping D in the last
expression in (13) is replaced with desired
modal damping D' thus forming system model
with new set of parameters. This model is
linearised and transfer functions (14) and (15)
are calculated. Using calculated transfer
functions PID controller is designed following
the guidelines described in section 4. Having
the controller designed it is possible to calculate
closed loop transfer function (16). This closed
loop transfer function, obtained using system
with increased damping and PID controller is
then regarded as desired model transfer
functions Gm for the real system. In other words
our goal is to design a controller that assures
that real system behaves as its tower damping
has increased. The system response with Pole
placement controller was simulated in Bladed
using previously described full featured model.
Wind used for simulation was the same as in
section 4. Simulation results are given in the
figures 9-11. From these figures it can be seen
that pole placement controller achieves almost
the same regulation of rotor speed as PID
Rotor speed [rpm]
25
24.5
24
23.5
23
22.5
22
0 10 20 30 40 50 60 70
t [s]
Figure 9: Response of rotor speed of the system
controlled with input-output pole placement
controller.
Pitch angle [deg]
16
15
14
13
12
11
10
9
8
7
0 10 20 30 40 50 60 70
t [s]
Figure 10: Response of pitch angle of the system
controlled with input-output pole placement
controller.
Tower top displacement [m]
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
0 10 20 30 40 50 60 70
t [s]
Figure 11: Response of tower top displacement of
the system controlled with input-output pole
placement controller.
controller does. This was to be expected since
the PID controller is the "core" of the pole
placement controller design. At the same time
tower oscillations are more damped. Since the
tower structure remains the same this damping
is achieved by higher pitch control activity than
in the case of PID controller.
The question that naturally appears is the limit
to which extend the tower oscillations can be
damped in this way. Answer to this question can

e obtained analyzing fig. 12. This figure shows
comparison of Bode plots of controllers
designed with different values of desired tower
damping D'.
Figure 12: Bode plots of input-output pole placement
controllers designed to achieve different values of
tower damping D'.
From fig. 12 it becomes clear that increased
tower damping results in high pass controller
behavior. This has a consequence of increased
pitch activity which in turn results in additional
oscillations and finally cancels out the
advantages of proposed design methodology. So
a tradeoff between desired increase in tower
damping and pitch activity has to be made.
6. Full state feedback controller
The input-output pole placement controller
described in section 5 has shown some
promising results but its ability to damp the
tower oscillations is limited. Key cause for this
is the fact that it doesn't use information about
actual tower oscillations but only rotor speed
feedback. Damping of tower oscillations by
addition of tower top speed feedback to PID
controller is proposed in [1]. Tower top speed
measurement can be obtained from tower top
acceleration measured by accelerometers that
are nowadays almost a standard part of wind
turbine control system. In our approach we use
slightly different methodology. Instead of
extended PID controller we use full state
feedback controller designed using pole
placement method. Desired closed loop
behavior is chosen in the same way as for
described pole placement controller. For this
purpose process model (13) has to be rewritten
in the state space form:
x� = A⋅ x+ B⋅u, y = C⋅ x+ D⋅u. (19)
State variables used for system description and
control are rotor speed ω , rotor acceleration
ω� , tower top speed x� t and tower top
acceleration x�� t , while system inputs are wind
speed v w , pitch angle β and generator torque
M g :
⎡ω⎤ ⎢ ⎡ v ⎤ w
� ω
⎥
⎢ ⎥
x = ⎢ ⎥,
u = β
x ⎢ ⎥.
(20)
⎢�⎥ t
⎢ ⎥ ⎢M⎥ g
x ⎣ ⎦
⎣��t ⎦
Rotor speed and tower acceleration are
measured variables while other two states are
derived from them. Using Ackermann's formula
[6] vector of feedback gains for selected states
can be calculated. System with such a controller
was tested in Bladed using the same wind
stepwise change as in sections 4 and 5.
Simulation results are shown in the figures 13-
15. From these figures it can be seen that full
state feedback controller maintains good rotor
speed regulation while at the same time
achieving better damping of the tower
oscillations and practically removing the
oscillatory movement of the tower. The pitch
activity in this case increases considerably so a
tradeoff between tower oscillations' damping and
pitch activity is necessary.
To examine the behavior of three described
controllers in more realistic conditions the
system behavior in 3D turbulent wind field was
simulated in Bladed. Further information about
simulations of 3D turbulent wind fields can be
found in e.g. [7]. Let's just mention here that
turbulent wind field was generated in Bladed
Rotor speed [rpm]
25
24.5
24
23.5
23
22.5
22
0 10 20 30 40 50 60 70
t [s]
Figure 13: Response of rotor speed of the system
controlled with full state feedback controller.

Pitch angle [deg]
16
15
14
13
12
11
10
9
8
7
0 10 20 30 40 50 60 70
t [s]
Figure 14: Response of pitch angle of the system
controlled with full state feedback controller.
Tower top displacement [m]
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
0 10 20 30 40 50 60 70
t [s]
Figure 15: Response of tower top displacement of
the system controlled with full state feedback
controller.
using Kaimal spectrum recommended by the
international standards for wind turbine design
[8]. To gain valid information about system
behavior in 3D turbulent conditions it is
necessary for the simulations to last for at least
10 minutes what generates lots of data.
Therefore, simulation results are not presented
here due to limited space. Simulations in 3D
turbulent wind field have shown that inputoutput
pole placement controller in turbulent
conditions achieves only modest improvement of
tower oscillations when compared to the PID
controller. The reason for this is the mentioned
fact that input-output controller doesn't use
information about actual tower oscillations. On
the other hand full state feedback controller
maintains shown ability to reduce tower
oscillations even under turbulent winds. The cost
of that is increased pitch activity what must be
taken into account to avoid pitch system
excessive wear. Simple design method proposed
in this paper makes it possible to easily change
the desired increase in tower damping and to
achieve a tradeoff between reduction of tower
oscillations and increase of pitch activity.
7. Conclusion
The control system for variable speed pitch
controlled wind turbine is presented. The wind
turbine system is highly nonlinear and its
parameters change significantly with change of
wind speed. Furthermore wind turbine
mechanical structure is very flexible and can
easily be driven into oscillatory behavior. All
this makes the controller design a very
demanding task. In this paper three methods
for wind turbine pitch controller design are
compared. It is shown that classic PID
controller can assure good rotor speed
regulation but tower oscillations are very
pronounced. To reduce these undesired
oscillations two alternative control structures
are investigated: input-output pole placement
controller and full state feedback controller.
The design objective for these controllers,
besides good rotor speed regulation, was the
increase of tower damping. Both controllers
have shown that owing to increased pitch
control activity it becomes possible to damp
the tower oscillations. To test the controllers'
performances in more realistic conditions
simulation under 3D turbulent wind field were
conducted in Bladed. It was observed that
under such conditions only slight reduction of
tower oscillations is achieved by input-output
pole placement controller. On the other hand
full state feedback controller has shown that is
capable of reducing the tower oscillations even
under turbulent conditions. The achieved
damping of tower oscillations leads to fatigue
reduction what enables production of lighter
and less expensive wind turbines. This can
result in reduction of the price for electrical
energy generated by wind turbines.
Acknowledgements
This work was financially supported by Končar –
Electrical Engineering Institute and the Ministry of
Science Education and Sports of the Republic of
Croatia.
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