Design Optimal of Adaptive Control and Fuzzy Logic Control on

Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
Design Optimal of Adaptive Control and
Fuzzy Logic Control on Torque-Shaft
Small Scale Wind Turbine
Ali Musyafa(1), I.Made Yulistiya Negara (2), Imam Robandi(3)
Abstrac – In this paper carried adaptive controller design
and fuzzy logic control (FLC) is applied to the wind
turbine shaft torque. Design goal to improve the
performance of wind turbines to increase efficiency.
Control system designed to facilitate adaptive estimation
of the "shaft torque" wind turbines. Estimated torque
reference applaid to provide torque to the subsequent
induction machine connected to the turbine through a
gearbox arm. Adaptive controllers, linear feedback,
designed to ensure the existence of a linear relationship to
the turbine system with a guarded the turbine speed and
load changes from energy users. Fuzzy Logic Control
aalso developed which is intelligent control in wind
turbine systems. Changes input are designed based on
wind data from the next field to find out for imbalance,
dynamic stability and tracking of changes in turbine
speed. Reference speed controller is a function of wind
speed varied selected to ensure that the system can
produce optimal energy. The results show that the
performance of control systems designed to give a good
response to fluctuating wind speeds. FLC able to produce
a better system performance in line with expectations.
Keywords - Wind turbine, Adaptive control, Linear
feedback, PID Control , Fuzzy Logic Control (FLC).
I.
INTRODUCTION
Wind speed and other variables that will determine
influence appropriate alternative for the implementation
of control systems in a plant[1-2].The geographical
position of a region also affects the wind speed, for
example in the upstream or coastal areas. Local topology
can be used to conduct a study of wind turbine
applications[2-3]. There are many types of wind turbine
configurations that can be used to extract the energy and
connection with the use of synchronous or asynchronous
machines, stall regulation and pitch regulation system [45]. Variations in wind speed can produce exctract for
further wind power into electrical energy transformation
and electricity sent through the network with a particular
specification. [6-7]. Wind speed data used in this study is
the sampled data from the Meteorological Station:
Surabaya Juanda Meteorology Station, Location: '070 23
'05 "70 S: 1120 47' 02” 68 E, elevation: 28 meters,
Element: Wind, measuring tool: Anemometer, Units:
Knots, data from BMG converted into units meters per
second (m/s) [8]. Wind speed data are grouped into 3
zones based on the level of speed. 1zone speed range (0,12) m/ s. 2 zone speed range (2,1-3,9) m/s, and 3 zone
speed range (4-7) m/s. One of the wind speed profile is
shown in Fig.1.[1-2].
6.5
______________________________________________________________________
6
1
5.5
5
V (m/s)
Ali Musyafa’ is with Department of Electrical
Engineering, Institut Teknologi Sepuluh Nopember,
Kampus ITS Keputih Sukolilo, Surabaya, Indonesia,
60111 and Engineering Physics Department, Institut
Teknologi Sepuluh Nopember, Kampus ITS Keputih
Sukolilo, Surabaya, Indonesia, 60111 (corresponding)
phone: +62-31-5947188, fax: +62-31-5923626; e-mail:
musyafa@ep.its.ac.id).
I. Made Yulistya Negara2 and Imam Robandi3 are with
Department of Electrical Engineering, Institut Teknologi
Sepuluh Nopember, Kampus ITS Keputih Sukolilo,
Surabaya, Indonesia, 60111.
4.5
4
3.5
3
2.5
2
0
50
100
150
200
time (s)
Fig. 1. INPUT WIND SPEED (m/s)
202
250
Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
Wind turbine components comprising a rotor consisting
of a number of fins (blade) and mounted on the front of a
swivel shaft (hub/shaft) connected at the rear of the
turbine through a gear box. Axis swivel out of the
gearbox is coupled to a generator which then generators
change mechanical energy into electrical energy [9-10]. A
rotor blade’s wind turbine is composed of 3 fins which
serve to capture wind energy in the form of mechanical
energy. The problem in this section is the aerodynamic
design of effective and efficient, and tries hard materials
durability and fins and a propeller. Gearbox: gear system
includes a low turn change the speed of the propeller
(about 100 rpm) to a high turn rate (> 500 rpm) for input
generator.[11-14].
Fig. 2. BASIC CONCEPTS ADAPTION
II. METODOLOGI
Control algorithm in the adaptive system consists of two
parts; basic algorithms and adaptation algorithms. In
adaptive control system there are two levels of the system.
The lower level are the basic controls that are (executor of
the control algorithm) that directly receive data from the
plant and determine the control decision, and a higher
level called adaptation (executor has the adaptation
algorithm). The second level of control both the basic
control level and control level of adaptation, they will
improve the system performance is often referred to as a
two-tier system. The system shown in Figure 2. Adaptive
general controls consist of a control algorithm based on
the adaptation algorithm. In this study the basic control
algorithm is Feedback Linearization. The use of favorable
feedback, because linearization control linear relationship
obtained from additional input v (which is previous
controller output signal) of the variable speed wind
turbine angle (ω). Fig. 3 show a model of wind turbine
system and Table 1. Show wind turbine parametres.
Feedback linearization is a control system that can
manipulate variable (MV) of torque is the model for
process variable (PV) is a constant (ω). In the wind
turbine control system based on ω braking system has a
central role in the manipulation of the turbine torque.
Original torque (Tt) is derived from turbine gearboxes,
while ( Tem ) torsion sub-system. The equation as show as
eq. 1. Things that need to be observed is the condition Tt
changing conditions caused by changing wind speeds at
all times. Thus, to obtain the optimum control results,
required sensing system that monitors the change of Tt,
and will affect the magnitude Tem. Thus the adaptation
algorithm is needed to monitor changes in Tt is caused by
input changes. Furthermore adaptation algorithm as show
in eq. 2-3. Understanding adaptive mentioned is a change
/ update the quantities here`in`after Tt change (adaptive)
to wind speed input.
Fig. 3. A PROTOTYPE OF WIND TURBINE
Tt is very usefull adaptation for the manipulation Tem , to
obtain an optimal control system. In the overall system
there are two Tt; Tt = results from turbine gearboxes and
bars Tt =
, from the adaptation law.
J R ω& = Tt − Tem
(1)
e& = −ke −
1 ~
Tt
JR
& γe
Tˆt =
JR
(2)
(3)
The wind feed on the blade will form the aerodynamic lift
and thrust produced. This situation resulted from the rotor
wind turbine torque and wind power show in eg.4-5 ;
203
Trot =
Paero
ω rot
1
= πρR 3C p (λ ,θ pitch )
2
1
3
Paero = πρR 2 u eq C p (λ , θ pitch )
2
(4)
(5)
Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
The relationship of torque and angular velocity of the
turbine is shown as ;
J R ω& = Tt − Tem
(6)
e& = − ke −
1 ~
Tt
JR
(7)
γe
&
(8)
Tˆt =
JR
JR is a combination of generator and turbine inertia is
delivered by the shaft, a block diagram of field oriented
control as follows, Block diagram of linear feedback
systems, with a selected constant γ = 2 x106.
Relationship adaptation system shown in the eq.7-8;
Table 1
WIND TURBINE PARAMETERS [2]
Item
Value
Wind turbine model
Rated power (kW),v=5 m/s
Rotor diameter (m)
Hub heigh (m)
Swept area of rotor (m2)
Cut-in wind speed ( m/s)
Rated wind speed ( m/s)
Cut-out-wind speed (m/s)
Rotor speed ( rpm )
Tower type
Generator type
Rating power (KW)
Rating volt (V)
Rating amp (A)
RPM
(N*M)
Power
Weight (Kgs)
ALFA-IR 001
50 W
1
10
0,785
3
4
10,0 m/s
20-500
Turbular
ALD-50.PMA.
0,05
14/28
3,57/1,79
500
< 0,15
>65 %
6
Integral Time Absolute Error (ITAE) is one of the
methods used to measure the absolute error of the
performance of a system. Shown in eq.4 ; This criterion
is the performance index of the most easily applied. If this
criterion is applied, the damping system for damping
down and cannot be optimum. Optimum systems based
on these criteria are a system that has damping
characteristics and meet the following transient response ;
Like the previous criteria, a large initial error in the unitstep response has a small weight, and the error happens
next will affect the transient response.
( 9)
III. CONTROL SYSTEM DESIGN
Control system block diagram-torque shaft designed in
wind turbine using adaptive feedback linearization control
shown in Fig. 4. Wind velocity (ω), is the system input
variables. Changes input system will be controlled by a
PID controller and feedback system will next controlled
by the adaptive law, so the system will continually adapt,
or to vary the amount of torque from a wind turbine
gearboxes. With the entry has changed and still be able to
produce output torque in accordance with the Field
Oriented Control (FOC). Fig.5. Therefore a system
actuators turbine braking system, the process variable in
the equation of wind turbine model will generate output
control system of the shaft angular velocity. Fuzzy logic
is a methodology of problem-solving control system that
can be applied to human language (high, low, long, short,
etc.) Which allows eliminating the difficulty of
mathematical language Fuzzification is a process of
mapping from the input of the set of strict (crisp) into the
form of fuzzy sets for a particular conversation universe?
The data was then converted into mapping linguistic
forms according to the labels of fuzzy sets that have been
defined for the system input variables Fuzzy Inference.
Engine (Fuzzy processing) is the core of a fuzzy logic
controller that has the ability as humans to make decisions
Defining the size of membership and degree of linguistic
variables of the action undertaken to control each of the
control rules based on implication functions are used.
Defuzzification has transformed the functions are fuzzy
conclusions into crisp signals (which are real) by using
the defuzzification operator. Mapping ripples fuzzy
control action (fuzzy domain name) into the control action
of the non-fuzzy (crisp) defuzzification base( Table-2).
Fuzzy logic control strategy proposed for the control of
wind turbine shaft torque. By combining PI-fuzzy
controller, the control point can be introduced, which are
designed carefully to both the on-line and off-line which
can then be applied to the rotation shaft torque actuation.
FLC strategy can overcome the possible parameters of
uncertainty, lack precision and what was not sure in some
mathematical models based on the human knowledge.
Vwind
Fig. 4. BLOCK DIAGRAM OF ADAPTIVE SYSTEM
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Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
FLC used for shaft speed control to tuning torque from
wind turbines. More FLC to variations robust turbine
parameters and have a better ability to resist interference
in the internal and external, and provide fast dynamic
response of stability. FLC allows can be used for speed
control PI tuning on-line and can accept the values that
have been scale in principal wich corelation in error
veleocity and delta error velocity. Output here updated by
a gain PI controller (∆Kp and ∆Ki) which is based on a
set of rules to maintain the exellent control perform
remain even when the parameters and the lack variation
linear actuation Each input from FLC consists of five
triangular membership functions with the same width and
overlapping. The first output (∆Kp) has three triangular
membership functions, while the second output (∆Ki) has
five memberships function. Inference rules based on rule
25. Parameters of the FLC calculated by trial and error to
ensure optimum performance.
Table 2.
RULES BASE A TWO-DIMENSIONAL LINIER
Fig. 6. LINIER FEEDBACK SYSTEM
Fig. 7. CONFIGURATION CONTROL ADAPTIVE
Fig. 8. BLOCK DIAGRAM PID-ADAPT. CONTROL
Fig. 9. BLOCK DIAGRAM OF FLC.
Fig. 5. BLOCK DIAGRAM OF FOC.
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Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
Block diagram of ccontrol system in wind turbine shaft
torque for PID Adaptive control is shown in Figure 8. and
block diagram for the fuzzy logic control showed in
Figure 9. The two systems were running to determine the
performance of each system block and overall system
performance. As a tool to test the control strategies and
evaluate the performance of control systems using fuzzy
logic rule base as shown in Table- 2.
Fig. 12. ADAPTIVE CONTROL RESPONSE FOR
WIND SPEED INPUT (4.0 -7.0) m/s
IV ANALYSIS
At this stage testing control systems with Simulink, the
first test conducted on the open loop system. At this stage
the obtained parameters Kp, Ti, and Td, which in turn is
used for PID control tuning. Further testing PID control
system using 3 types of range: (0-2m/s),(2.13.9m/s),and(4-7 m/s).Each
range tested by giving
interference with the value 0%, 10%, 20%, and 30% of
set point. PID tuning methods used are continuous
method Cycling Method (oscillation method). My next
calculated value and Tu. Further included in the
formulation of the PID in Table 3, and the PID parameter
values obtained; Kp, Ti, and Td. By the same way, obtain
PID parameters in range 1-3.
Table 3. PID TUNING WITH CONTINUOUS
CYCLING METHOD
Kp
Ti
Td
Range 1
417.6400
0.2100
0.0525
Range 2
423.5290
0.2050
0.0513
Range 3
441.1765
0.2000
0.0500
The respons of system would like to identify the system
performance satisfaction in the PID-adaptive and FLC
controllers with test in all range wind speed as follows
Fig. 10-15.
Fig. 13. FLC CONTROL RESPONSE FOR WIND
SPEED INPUT (0-2) m/s
Fig. 14. FLC CONTROL RESPONSE FOR WIND
SPEED INPUT (2.1 -3.9) m/s
Fig. 12. ADAPTIVE CONTROL RESPONSE FOR
WIND SPEED INPUT (4.0 -7.0) m/s
From the eksperiment, the performance index wind
turbine system with PID-adaptive and FLC can
demontrated. Performance test that is performed when
the system is running without any interruption and
interference with the system. The criteria used as a
benchmark assessment of the maximum overshoot (%),
settling time (s), and steady state error (%) and ITAE.
From the calculations can be show on the table 4-5.
Table 4. PERFORMANCE 0F PID-ADAPTIVE AND
FLC CONTROL
Fig. 10. ADAPTIVE CONTROL RESPONSE FOR
WIND SPEED INPUT (0-2) m/s
Fig. 11. ADAPTIVE CONTROL RESPONSE FOR
WIND SPEED INPUT (2.1 -3.9) m/s
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Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
Table 5. COMPARISON VALUE ITAE OF
ADAPTIVE AND FLC CONTROL
V. CONCLUTION
The FLC action showed a better response than PID
adaptive control, this is indicated by the time the
achievement of steady state (ts) that is shorter, the
maximum overshoot (Mp), and steady state error (ess.) A
smaller. ITAE value of the FLC smaller than the PIDadaptive controller system, this shows that the resistance
(robustness) systems using FLC better than PID-adaptive
controllers, both associated with a disturbance and no
disturbance.
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th
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Robndi,2010, ”Pitch Angle Control of Variable Low
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Annexes ;
207
Fig.13 . Wind turbine experimen
Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011
BIOGRAPHIES
Ali Musyafa’, Was born in 1960 in
Jombang, Indonesia. He recived
B.Sc. degree in Engineering Physics
from Sepuluh Nopember Institute of
Technolgy , Surabaya, in 1986, and
M,Sc,
degree
in
Electrical
Enginering from Bandung Institute
of Technology, Indonesia in 1990.
He is currently
P hD. Student in
Department
of
electrical
engineering, Sepuluh Nopember
Institute of Technology. His current
reasearch
interest
includes
renewable energy generation and
control.
I Made Yulistya Negara, Was born
in 1970 in Negara, Indonesia. He
recived
B.Sc. degree in power
engineering from Sepuluh Nopember
Institute of Technolgy , Surabaya,
Indonesia in 1994, and M,Sc, degree
in Electrical Enginering from
Untersuchungen
zu
Teilentladungsmessungen
bei
Gleichspannung, in Universitaet
Karlsruhe, Deutsch, in 2001. and
PhD degree in Department of
Electrical Engineering from the
Kyushu University, Japan,2006.
Imam Robandi, Was born in 1963 in
Gombong, Indonesia. He recived
B.Sc. degree in power engineering
from Sepuluh Nopember Institute of
Technolgy, Surabaya, in 1989, and M,
Eng., degree in Electrical Enginering
from Bandung
Institute of
Technology, Indonesia in 1994. and
Dr.Eng. degree in Department of
Electrical Engineering from the
Tottori university, Japan, 2002. He is
currently
Profesor in Department
of Electrical Engineering, Sepuluh
Nopember Institute of Technology.
His current reasearch interest includes
Stability analysis of multimachine
power system using LQR, fuzzy logic,
and artificial intelegent control.
208