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ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
A NOVEL CONTROL TECHNIQUE FOR WIND TURBINE
POWER SYSTEM
A.Fathima Barveen
Department of Electrical and Electronics Engineering
National College of Engineering
Maruthakulam, Tirunelveli
Abstract— This paper suggests implementing the
Artificial Neural Network Controller (ANNC) and
fuzzy logic controller (FLC) for variable speed
and variable pitch wind turbine system to achieve
better power stability at all various wind speed
conditions. The current closed loop system design
can be upgraded by introducing the ANNC and
FLC at the grid side. Using MATLAB the
comparative analysis has been done and the
results are comparing.
Keywords—Artificial neural network (ANN),
Fuzzy Logic controller (FLC), DFIG, variable speed
and variable pitch wind turbine (VSVP), PI
controller.
I. INTRODUCTION
Wind energy is a source of renewable power
which comes from air current flowing across the
earth’s surface. Wind turbines harvest this kinetic
energy and convert it into usable power which can
provide electricity. Present growth and relates
installed power level need to develop wind farm
control capabilities and strategies that has features
which is equivalent to the conventional power plants.
The important features are to control the output
power of the wind farm, control of the reactive power
[1]-[2].
With the increasing of wind power into electrical
grids doubly fed induction generator (DFIG) wind
turbines are mostly used due to its special
characteristics. The working and operation of DFIG
can be studied clearly from the literature “Study of
Doubly-Fed Induction Generator for variable speed
wind energy conversion systems” [3]. The
information about the modelling of the DFIG and the
control operation of DFIG are provided by “Control
of a Doubly-Fed Induction Generator for wind energy
conversion system” [4].
A.A.Mohamed Faizal
Department of Electrical and Electronics Engineering
National College of Engineering
Maruthakulam, Tirunelveli
The paper “Doubly-Fed Induction Generator
using back-to-back PWM converter and its
application to variable speed wind energy generation”
provides the information about the converters used
for DFIG [5]. The literature “A robust stable PI
controller for the Doubly-Fed Induction machine”
gives the brief idea about the closed loop control
system using PI controller [6]. Artificial Intelligence
techniques were used for the purpose of control
system which requires intelligent system such as
fuzzy logic, neural networks, genetic algorithms and
particle swarm optimization. “Neural Network based
control of DFIG in wind turbines power generation”
gives better idea about the neural network closed loop
control system [7].
Three power control techniques were used one is
PI controller and second one is artificial neural
networks and third one is fuzzy logic controller. The
dynamic control joins different strategies that will
ensure better stability and power regulation generated
by the wind turbine. This paper presents the design
and implementation of intelligent control scheme
using ANNC and FLC for DFIG based variable speed
wind turbine system. The first part of the paper
explains the modelling of wind turbine using PI
controller and the second part illustrates the
modelling and implementation of ANN based
intelligent rotor control of DFIG. And the third part
illustrates the modelling and implementation of FLC
based intelligent rotor control of DFIG. It is
demonstrated through results that the ANN based
control scheme ensures better stability and regulation
of the power generated by the DFIG based wind
turbine system.
II. MODELLING OF WIND TURBINE
Wind turbine converts the kinetic energy present in
the wind into mechanical energy by means of
producing torque. Since the energy contained by the
134
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
wind is in the form of kinetic energy, its magnitude
depends on the air density and wind velocity. The
wind power developed by the wind turbine is given
by the equation (1)
for grid is to achieve the constant DC voltage in the
dc link capacitor. With the use of two converters the
voltage fluctuations, frequency and the output power
from the wind turbine can be controlled. The simple
block diagram for DFIG is shown in figure 1.
P = 0.5Cp (λ β)ρAV³
(1)
Where cp is the power co-efficient, ρ is the air
density in kg/m³, A is the area of the turbine blade in
m2, and V is the wind velocity in m/sec. The power
co-efficient is defined as the power output of the
wind turbine to the available power in the wind
regime. This co-efficient determines the “ maximum
power ” the wind can absorb from the available wind
power at a given wind speed. It is the function of the
tip speed ratio ( λ ) and blade pitch angle ( β ). The
blade pitch angle can be controlled by using a “ pitch
controller” and the tip speed ratio is given as,
Fig 1. Simple block diagram of DFIG wind turbine
λ = ωR / (2)
IV. CONTROL STRATEGIES OF DFIG
where ω is the rotational speed of the generator
and R is the radius of the rotor blades. Hence, the tip
speed ratio can be controlled by controlling the
rotational speed of the generator.
III.DOUBLY-FED INDUCTION GENERATOR
Most of the wind energy conversion systems are
based on DFIG. The stator and rotor windings of
DFIGs are connected to grid. The stator has direct
connection with the grid while the rotor of the DFIG
connection is made through back-to-back converters.
The back-to-back converter consists of two
converters namely rotor side converter (RSC) and
grid side converter (GSC), the two converters are
connected back-to-back. The dc link capacitor is
incorporated between two converters. The DFIG
control has been done by controlling the rotor current
which can be handled using power electronic
converter. In the DFIG scheme, it is possible to
extracting the maximum energy from the wind for
low wind speeds by optimizing the turbine speed.
Another advantage of DFIG scheme is the ability for
power electronic converters to generate or absorb
reactive power.
The back-to-back converters are simply
AC/DC/AC converter which uses the PWM
techniques. The converter for rotor is used to control
the wind turbine output power and the voltage
measured at the grid terminals. The converter used
A. Rotor Side Controller diagram
The rotor side converter is used to control the output
power from the generator and also the voltage
available at the grid terminals. For the rotor-side
controller the d-axis of the rotating reference frame
used for d-q transformation is aligned with air-gap
flux. The measured actual electrical output power,
available at the grid terminals of the wind turbine, is
added to the total power losses (mechanical and
electrical).This measures power is compared with the
reference power obtained from the tracking
characteristic. The power error is reduced to zero
with the help of PI controller. The output obtained
from this PI regulator is the reference rotor current
Iqr_ref that must be injected in the rotor by converter
Crotor. This is the current component that produces the
electromagnetic torque Tem. The actual Iqr
component and the reference Iqr_ref component is
compared. The occurred error is again reduced to
zero by a current regulator which is nothing but a PI
controller only. The output of this current controller
is the voltage Vqr generated by Crotor. The current
regulator is assisted by feed forward terms which
predict Vqr. The voltage at grid terminals is controlled
by the reactive power generated or absorbed by the
converter Crotor. The power (Q) is exchanged between
Crotor and the grid, through the generator. In the
exchange process the generator absorbs reactive
power to supply its mutual and leakage inductances.
135
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
The excess of reactive power is sent to the grid or to
Crotor..
1.
A measurement system measuring the d and q
components of AC currents to be controlled as
well as the DC voltage Vdc.
2.
An outer regulation loop consisting of a DC
voltage Regulator.
3. An inner current regulation loop consisting of a
current Regulator
The current regulator controls the magnitude
and phase of the voltage generated by converter Cgrid
(Vgc) from the Idgc_ref produced by the DC voltage
regulator and specified Iq_ref reference. The current
regulator is assisted by feed forward terms which
predict the Cgrid output voltage.
Fig 2. Rotor Side Controller Block Diagram
When the wind turbine is operated in var
regulation mode the reactive power at grid terminals
is kept constant by a var regulator. The output of the
voltage regulator or the var regulator is the reference
d-axis current Idr_ref that must be injected in the rotor
by converter Crotor. The same current regulator as for
the power control is used to regulate the actual Idr
component of positive-sequence current to its
reference value. The output of this regulator is the daxis voltage Vdr generated by Crotor. The current
regulator is assisted by feed forward terms which
predict Vdr. Vdr and Vqr are respectively the d-axis
and q-axis of the voltage Vr.
B. Grid Side Controller Diagram
V.ARTIFICIAL
CONTROLLER
NEURAL
NETWORK
ANNs are powerful tools for modeling. ANNs
can identify and learn correlated patterns between
input data sets and corresponding target values. When
the training is completed, ANNs can be used to give
the outcome of new independent input data. In this
paper, the data set from Conventional PI controller is
used to train ANN structure. The presented ANNC
has one input layer, one output layer and two hidden
layers. The algorithm used for training is back
propagation algorithm. During training the ANN,
from the available Pi data values, 70% of
conventional controller values are used to train the
ANN, 15 % data can be used for validate the ANN,
and 15 % data values can be used for testing purpose.
The ANN speed control technique block in a vector
controlled drive system is shown in Fig. 5. The
controller observes the pattern of speed loop error
signal and correspondingly updates the output so that
actual speed matches the reference speed.
Fig 3. Grid Side Controller Block Diagram
The Grid side converter is used to regulate the
voltage of the DC bus capacitor. For the grid-side
controller the d-axis of the rotating reference frame
used for d-q transformation is aligned with the
positive sequence of grid voltage. This controller
consists of:
Fig 5. Proposed ANNC
136
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
VI.FUZZY LOGIC CONTROLLER
VII.MATLAB MODEL
The control system is based on fuzzy logic. This
type of control, approaching the human reasoning
that makes use of the tolerance, uncertainty,
imprecision and fuzziness in the decision-making
process, manages to offer a very satisfactory
performance, without the need of a detailed
mathematical model of the system, just by
incorporating the experts’ knowledge into fuzzy
[5],[6].
As presented in Fig.3, the fuzzy logic control
system based on mamdani fuzzy model. This system
consist four main components. First, using input
membership functions, inputs are fuzzified then based
on rule bases and inference system, outputs are
produced and finally the fuzzy outputs are defuzzified
and applied to the main control system. Error of
inputs from their references and error deviations in
any time interval are chosen as inputs. These parts are
simulated in MATLAB. The output of fuzzy
controller is the value that should be added to the
prior output to produce new reference output.
a.
Grid side converter control system using
PIcontroller
Fig 7. Simulation diagram of grid side control system
controller
Using
PI
Fig.6. the fuzzy logic control blocks based on mamdani's
system.
137
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
b.
Rotor side converter control system using PI
controller
Fig 8. Simulation diagram of rotor side control system
Using PI controller
c.
Grid side converter control system using
ANNC
d.
Grid side converter control system using FLC
Fig 10. Simulation diagram of grid side control system Using FLC
VIII.RESULTS AND DISCUSSION
Fig 11. Response of PI controller
Fig 9. Simulation diagram of grid side control system Using
ANNC
138
All Rights Reserved © 2015 IJARTET
ISSN 2394-3777 (Print)
ISSN 2394-3785 (Online)
Available online at www.ijartet.com
International Journal of Advanced Research Trends in Engineering and Technology (IJARTET)
Vol. II, Special Issue XXIII, March 2015 in association with
FRANCIS XAVIER ENGINEERING COLLEGE, TIRUNELVELI
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN COMMUNICATION SYSTEMS AND
TECHNOLOGIES
(ICRACST’15)
25TH MARCH 2015
and two hidden layers. The ANN controller has been
used in the grid side converter of DFIG wind turbine.
Similarly the fuzzy logic controller is used in grid
side converter of DFIG. Fig 11 and Fig.13 shows the
wave forms of PI controller and Fuzzy logic
controller is not uniformed. But fig. 12 represents the
ANNC strategy is more effective compared to PI and
fuzzy controller.
IX.CONCLUSION
The ANNC and FLC based speed control scheme
for variable speed and variable pitch wind turbine has
been developed. The system is modelled and checked
with the help of MATLAB/SIMULINK simulation.
Based on conventional PI controller data values, the
ANNC has been trained. The comparison of three
controller outputs have been analyzed which shows
that ANNC gives very effective stabilization of the
system.
Fig 12. Response of ANNC
References
[1]
[2]
[3]
[4]
Fig 13. Response of FLC
[5]
Fig.11 and 12 represents the system response with
PI controller and ANN controller. Fig.13 shows the
system response with FLC. Based on PI controller
and ANN controller, the DFIG wind turbine was
modelled. The training of neural network was
achieved by PI controller data values. The trained
neural network has one input layer, one output layer
[6]
[7]
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