Modelling and Fuzzy Logic Control of the Pitch of a Wind Turbine

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 09 | Sep -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 662
Modelling and Fuzzy Logic Control of the Pitch of a Wind Turbine
Silpa Baburajan1, Dr. Abdulla Ismail2
1Graduate Student, Dept. of Electrical Engineering, Rochester Institute of Technology, Dubai, UAE
2Professor, Dept. of Electrical Engineering, Rochester Institute of Technology, Dubai, UAE
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Abstract - Recently, the renewable energy, especially
wind energy, has been paid much attention due to the
energy shortage and environmental concern. As the
penetration of the wind energy into the electrical power
grid is extensively increased, the influence of the wind
turbine systems on the frequency and voltage stability
becomes more and more significant [1]– [4]. Wind turbine
rotor bears different types of loads; aerodynamic loads,
gravitational loads and centrifugal loads. These loads cause
fatigue and vibration in blades, which cause degradation to
the rotor blades. These loads can be overcome and the
amount of collected power can be controlled using a good
pitch controller (PC) which will tune the attack angle of a
wind turbine rotor blade into or out of the wind. Each blade
is exposed to different loads due to the variation of the wind
speed across the rotor blades. For this reason, individual
electric drives can be used in future to control the pitch of
the blades in a process called Individual Pitch Control. In
this thesis work, a new pitch angle control strategy based on
the fuzzy logic control is proposed to cope with the
nonlinear characteristics of wind turbine as well as to
reduce the loads on the blades. A mathematical model of
wind turbine (pitch control system) is developed and is
tested with Fuzzy Controller and conventional PID
Controller. After comparing the proposed strategy, the
simulation results show that the Fuzzy logic controller has
the optimum response as it controls the pitch system as well
as the disturbances and uncertain factors associated with
the system.
Key Words: Energy, Wind-Power, Pitch Controller,
PID, Fuzzy Controller.
1. INTRODUCTION
Energy crisis is the one of the biggest problems faced by
people in the twenty-first century. The increase in the
demand for electric energy along with the availability of
limited fossil fuels have together contributed to the need
for shifting from the human dependency on conventional
resources for energy to the renewable energy resources.
There are three important renewable energy resources
available to us: solar, gravitational and geothermal energy.
Wind energy is one of the indirect consequence from the
incident solar energy, promoting air circulation between
hot and cold zones [1]. The kinetic energy present in the
wind can be converted to mechanical energy by using a
wind turbine and further into electrical energy by using
wind turbine generator.
2. MOTIVATION
To increase the power capacity of the wind turbine, larger
rotors are being built which causes an increase in
aerodynamics and other loads across the blades. The
aerodynamics and other loads contribute to fatigue failure
which results in decrease in the lifespan and efficiency of
the wind turbine [5]. With the help of good pitch angle
controllers, the lift profile of the rotor blades can have
altered which results in reduction of the aerodynamics
and other loads across the blades. This mechanism uses
the fact that much of the fatigue causing loads are partly
deterministic, periodic and vary slowly over a fixed time
[7]. Therefore, in this thesis the main goal is to design an
optimum controller to control the pitch angle of the wind
turbine system so that the loads on the blades are reduced.
Reduction in the loads on the blades will ultimately help to
improve the performance, efficiency and power output
daily of the wind turbine.
3. Control Techniques of Wind Turbine
System:
It The wind energy captured by the turbine can be
increased by the following two control strategies: pitch
control and stall control. The initial step in both strategies
is to check the turbine’s power output several times per
second using an electronic controller. In case the output
power is too high, a signal is send to the blade pitch
mechanism because of which the rotor blades turn slightly
out of the wind, adapting the attack angle. Once the wind
drops, these blades are turned back into the wind.
Turbines with this type of control mechanism is known as
pitch controlled wind turbines.
In the stall control technique, the rotor blades are fixed
onto the hub at a fixed angle. But the geometry of the rotor
blade is aerodynamically designed in such a way that it
ensures that from the moment the wind speed becomes
too high, it is caused turbulence on the side of the rotor
blade which is not facing the wind, creating a stall which
prevents the lifting force of the rotor blade from acting on
the rotor [1]. In the modern turbines, the pitch control
technique is used because it helps in controlling the output
power simultaneously operating at variable speed to
control tip speed ratio and so the power extraction for
different wind speeds [8].

4. Model of the Wind Turbine System
The block diagram of a typical wind turbine system
model is shown in the figure below [6]. In the following
sections, the pitch actuator model and the drive terrain
model are explained.
Figure 1: Wind Turbine System Feedback Control
System Model
5. Pitch Actuator Model
The pitch actuator is used to turn blades along their
longitudinal axis. The actuator model describes a dynamic
behavior between a pitch demand, from the pitch
controller and measurement of pitch angle [6]
The change in pitch angle is given by
This is the required Transfer Function. The value of
time constant of pitch actuator, T p can be calculated from
initial parameters of Wind Turbine [6] shown in Table I.
Table 1: Parameters of Wind Turbine
6. Drive Terrain Model
Figure 2: Mechanical model of drive train
The parameters taken while modelling the drive train are
shown in Table 2.
Table 2: Mechanical Model Parameters of Drive Train

The dynamics of drive-train are described by following
differential equations
This is the required first order Transfer function of
Drivetrain. This can also be represented as
Thus, the mathematical model of wind turbine is derived.
7. Implementation of Conventional PID
Controller
The Simulink model of wind turbine pitch control system
with conventional PID Controller is shown in Fig. 3 and the
control parameters for the PID controller are shown in
Fg.4.
Figure 3: Simulink diagram of PID Controller
Implementation
Figure 4: PID Controller Parameters
8. Implementation of Fuzzy Logic Controller
First the rules-surface diagrams for each parameter Kp, Ki,
Kd are shown in the figures 5,6 and 7. Then all the 49 rules
are displayed in the figure 8. Finally, the Simulink model of
wind turbine pitch control system with fuzzy logic is
shown in Fig. 9 and the subsystems are shown in Fig 10
and Fig 11.
Figure 5:Surface Rule Diagram for Kp
Figure 6:Surface Rule for Ki

Figure 7:Surface Rule Diagram for Kd
Figure 8:Rule Viewer for Fuzzy Controller
Figure 9: Simulation diagram of fuzzy controller for
pitch control system
Figure 10: Fuzzy Controller Subsystem
Figure 11: Plant Subsystem
10. Simulation and Result of System without
Controllers
The figures below show the Simulink model of wind
turbine without any controllers and the output graph
Figure 12: Wind Turbine System Without Controllers

Figure 13:The unit step response of wind turbine
without controllers
We see that the desired output is not reached as well
Settling time very high. Also the Overshoot and
Undershoot is high. Hence, we need to implement
controller to overcome these drawbacks.
11. Simulation of the Plant with PID
Controller
Figure 14:The unit step response of wind turbine with
PID controller
The unit step response of wind turbine pitch control
system is shown in Fig. 17. Time domain specifications are
observed from the response graphs and tabulated in Table
6. With PID controller, we observed less rise time, settling
time and peak overshoot when compared to the wind
turbine model without any controllers.
12. Simulation of the Plant with Fuzzy
Controller
Figure 15:The unit step response of wind turbine with
Fuzzy controller
The unit step response of wind turbine pitch control
system is shown in Fig.18. Time domain specifications are
observed from the response graphs and tabulated in Table
6. With fuzzy controller, we observed more rise time and
less settling time compared to conventional PID controller
and a very little overshoot.
14. Comparison of Simulation Results of the
Plant using PID, Fuzzy and an Adaptive
Fuzzy-PID Controller
Figure 16:Comparision of unit step response of wind
turbine pitch controllers

Table 3:Comparison of Time Domain Specifications of
Pitch Control System for Unit Step Input Using
From the Table above and Fig.20, we can see that the
Fuzzy Logic Controller has the best response when
compared to the PID controller. The settling time is fast
and overshoot is very less, hence the Fuzzy Logic
Controller gives a better control for the pitch angle of the
wind turbine system.
15. CONCLUSIONS AND FUTURE WORK
In this paper, we developed the wind turbine
pitch control system mathematical model and simulated
with conventional PID, fuzzy and fuzzy adaptive PID
controllers using MATLAB/Simulink to achieve optimum
response. We compared the responses in terms of time
domain specifications for unit step input using
conventional PID and fuzzy logic controllers. Even though,
the PID controller produces the response with lower delay
time and rise time, it has oscillations with a peak
overshoot of 11.8%, which causes the damage in the
system performance. To suppress these oscillations fuzzy
logic controller is proposed to use. From the results, it can
be observed that, this controller can effectively suppress
the oscillations and produces smooth response. This
technique is much better to realize the control of pitch
system and to guarantee the stability of wind turbine
output power.
In future, one can use artificial neural networks
to control the pitch angle of the wind turbine system and
check its performance with the Adaptive Fuzzy PID
controller. Also, Individual pitch control method can be
used along with the Adaptive Fuzzy PID controller to
improve the overall performance of the system.
REFERENCES
[1]. Civelek, Zafer, Ertuğrul Çam, Murat Lüy, and Hayati
Mamur. "Proportional–integral–derivative Parameter
Optimisation of Blade Pitch Controller in Wind Turbines
by a New Intelligent Genetic Algorithm." IET Renewable
Power Generation 10.8 (2016): 1220-228.
[2] C. Tan and H. Wang, "A Review on Pitch Angle Control
Strategy of Variable Pitch Wind Turbines", Advanced
Materials Research, vol. 772, pp. 744-748, 2013.
[3] Dunne, F. "Optimizing Blade Pitch Control of Wind
Turbines with Preview Measurements of the Wind." Order
No. 10108716 University of Colorado at Boulder, 2016.
Ann Arbor: ProQuest. Web. 4 Feb. 2017.
[4]. Han, Bing, Lawu Zhou, Fan Yang, and Zeng Xiang.
"Individual Pitch Controller Based on Fuzzy Logic Control
for Wind Turbine Load Mitigation." IET Renewable Power
Generation 10.5 (2016): 687-93. Kozak, Peter. "Blade Pitch
Optimization Methods for Vertical-axis Wind Turbines."
Thesis. ProQuest Dissertations Publishing, n.d. 2016.
[5] Jason T Brown. Montana environmental information
centre. http://meic.org, 2013.
[6] Novak P, Ekelund T, Jovik I, and Schmidt Bauer B.
"Modeling andControl of Variable-Speed Wind-Turbine
Drive-System Dynamics,"IEEE Control Syst Mag 1995;
15(4), pp. 28-38.
[7] Sharma, R.D. (2013). ‘Feedforward Learning Control
for Individual Blade Pitch Control of Modern Two-Bladed
Wind Turbines’.
[8] S.Suryanarayanan, A.Dixit, "Control of Large Wind
Turbines: Review and Suggested Approach to
Multivariable Design", Proc. of the American Control
Conference 2005, Portland, USA, pp. 686-690.
BIOGRAPHIES
Silpa Baburajan obtained her B.SC
Electrical Engineering degree from
American University of Sharjah (2015)
and is currently pursuing her Master’s
degree in Electrical Engineering,
specializing in Control System, at
Rochester Institute of Technology (RIT),
Dubai Campus.
Dr Abdulla Ismail obtained his B.Sc.
(’80), M.Sc. (’83), and Ph.D. (’86) in
electrical engineering, from the
University of Arizona, U.S.A. Currently,
he is working as a professor in the
Electrical Engineering Department
and assistant to the President at the
Rochester Institute of Technology,
Dubai, UAE

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