Said AZZOUZ1,2, Sabir MESSALTI1, Abdelghani HARRAG3
Electrical Engineering Department, Faculty of Technology, Msila University, Algeria (1), LGE Laboratory, Msila University, Algeria (2) ,
CCNS Laboratory, Electronics Department, Faculty of Technology, Ferhat Abbas University, 19000 Setif, Algeria (3)
doi:10.15199/48.2019.08.26
Innovative PID-GA MPPT Controller for Extraction of Maximum
Power From Variable Wind Turbine
Abstract. Although the multitude benefit of wind power, the randomness of wind speed and the fluctuations of wind power are the most
disadvantages of wind energy. So, for more efficiency and better performances, wind rotor must be driven at specific optimal rotational speed under
each particular wind speed. Therefore, to extract the maximum power from wind turbine, a Maximum Power Point Tracking (MPPT) controller is
required. In this paper, modeling of wind energy conversion system WECS using tip speed ratio (TSR) MPPT controller using PID controller tuned by
genetic algorithm is investigated. The wind energy conversion is based on a doubly-fed induction generator (DFIG), which it is controlled by robust
sliding mode control technique using a generator of 3.6 MW . The obtained results are presented and analyzed, where the performances of both
proposed control strategies (MPPT based PID-GA, sliding mode control) have been shown
Streszczenie. W pracy przedstawiono system energii wiatrowej wykorzystujący sterownik śledzący szcztową prędkość . W sterowniku zastosowano
regulator PID strojony z wykorzystaniem algorytmu generycznego. Jako generator wykorzystano układ DFIG sterowany za pośrednictwem
sterownika ślizgowego. Nowa koncepcja sterownika PID-GA MPPT do zapewnienia maksymalnej mocy farmy wiatrowej o zmiennej
szybkości wiatru
Keywords: MPPT based PID-GA, genetic algorithm, DFIG, tip speed ratio (TSR) , active and reactive power control, sliding mode control.
Słowa kluczowe: MPPT – maksimum power tracking, farma wiatrowa, sterownik z algorytmem PID-GA, DFIG.
Introduction
The growing demand of energy and the successive oil
shocks since the 70s have demonstrated the economic and
geopolitical risks of energy production based on the
exploitation of fossil fuels, whose reserves are unevenly
distributed and exhaustible. Renewable energy is the
energy comes from natural resources such solar energy,
wind, rain, tides, geothermal heat and various forms of
biomass .These resources are renewable and can be
naturally replenished continuously [1-3]. Therefore,
development of new forms of energy sources must take a
huge consideration as solution in order to cover the future
demands and the huge disturbances. Wind energy source
is regarded as one of the most important renewable energy
source; it can be used today in many applications [4]. Due
to previous, many countries have made great progress in
wind power technology such as Denmark which produces
40% of its electricity from wind, and at least 83 other
countries around the world are using wind power to supply
their electricity grids. The global wind power capacity
expanded 16% to 369,553 MW. As shown in fig.1.Although
several advantages of wind turbine, its random nature of
wind and nonlinear characteristic (Power-speed) are the
main drawbacks. Therefore, the wind turbine system must
be designed to operate at their maximum power for different
conditions. So over the last few decades, considerable
progress has been made in the MPPT techniques and
consequently many Maximum Power Point Tracking
(MPPT) methods have been developed [6-10 ].
Fig.1. Global wind power cumulative capacity [5]
The doubly feed induction generator is widely used in
variable speed wind turbine systems owing to their ability to
maximize wind power extraction and to their capability to
fulfill the basic technical requirements set by the system
operators and contribute to power system security [11-12].
Usually, a DFIG wind turbine is shown in Figure 2.
Fig.2. Wind Turbine System
A lot of works have been presented with diverse control
diagrams of DFIG (Fig.2), these control diagrams are
usually based on vector control (Field oriented control) [12].
Field oriented control using PI controllers makes the DFIG
achieve good performance in the wind energy generation.
In the vector control scheme, a complex voltage is
synthesized from two quadrature components, one of which
is responsible for the active power in the generator , and
another which controls the reactive power production by the
generator [11-15]. But, it may be difficult to adjust PI gains
properly due to the nonlinearity and system complexity. In
addition, the obtained performances using PI controller
depends heavily on accurate of the machine parameters,
for this reason many techniques have been developed
nonlinear control laws with parameter identification and
state estimation to replace PI type controllers [15,16], as
artificial neural network control, fuzzy logic control, sliding
mode control ...etc. Sliding mode control is one of the best
techniques that can offer many advantages[12,16].
Moreover, recently a number of important applications of
the theory in the field of power electronics, motion control,
robotics, bioprocess, etc. Therefore, the use of the
PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 95 NR 8/2019
115
0.5
0.45
0.4
0.35
0.3
Cp
nonlinear sliding mode method provides very satisfactory
performance for DFIG control, it is an universal
approximators of nonlinear dynamic systems [19]. So the
use of robust control methods like sliding mode control is
necessary [11].
This paper presents modeling and simulation of wind
energy conversion system (WECS), which it driven by tip
speed ratio PID (TSR) MPPT controller based on genetic
algorithm. This paper is organized in fourth parts: The first
part shows modeling of wind turbine, the second part
presents the tip speed ratio (TSR) MPPT controller. The
third part shows the DFIG model and control based on
sliding mode control strategy. The discussion and analysis
are presented in the fourth part.
0.2
0.15
0.1
0.05
0
area m
Cp
0.2
0.1
m / s ; : Blade
Cp
is often given as a function of the tip speed ratio
0
5
: is the performance coefficient of the
λ; : is the ratio of blade tip speed to wind speed defined
by
(2)
0.3
0
pitch angle (deg);
turbine,
; v w ind : is the wind speed
R t
V w ind
where: Ω: the wind turbine rotational speed (rad /sec); R:
the wind turbine radius.
The power coefficient Cp of turbine 3 MW is defined by
[12] :
0.1
(3)
Cp (,) 0.350.0167( 2) *sin
0.00184 3 ( 2)
14.340.3( 2)
The power coefficient of two wind turbine 1.5 MW and 3
MW for different pitch angles is presented in Fig.3
R .
V
10
(b)
Fig.3. Power coefficient based on the speed ratio for different pitch
angles (a)1.5MW wind turbine, (b) 3MW wind turbine
Modeling MPPT Controller Using PID Controller
Tuned By Genetic Algorithm
The produced power from a given wind turbine depends
mainly on wind speed, speed ratio. As these quantities vary
with time, maximum power point tracking control algorithm
is necessary to control and adjust continuously the rotor
speed to the corresponding MPP value at any given time
and under rapidly varying environmental conditions. From
fig.3, there is a unique operating point called the maximum
power point (MPP), where the power generation is
maximum. The block diagram of speed ratio PID (TSR)
MPPT controller using PID controller tuned by genetic
algorithm is shown in Fig.4:
1
G
Cp
C aer
1
C p . .S .V .
2
3
opt V
.
R
t *
1
G
G
15
Speed ratio (Lamda)
turb
t u rb
15
Beta=2
Beta=4
Beta=6
Beta=8
Beta=10
Beta=12
0.4
1
C p ( , ) Sv w3 ind
2
10
0.5
where: Paer is the extracted power from the wind turbine;
: is the air density
K g / m 3 ; S : is the turbine swept
2
5
(a)
P ow er coefficient
Paer
0
Lumda
Modeling of Wind Turbine
The mechanical power extraction from the wind can be
expressed as follows [11]:
(1)
0.25
*mec
1
Js f
Cg
C em
mec
Cem*
PI
mec
Fig.4. Proposed MPPT controller
The genetic algorithms are a family of computational
models inspired by evolution and is a search heuristic that
mimics the process of natural selection, which is routinely
used to generate useful solutions to optimization and
116
search problems. These algorithms encode a potential
solution to a specific problem on a single chromosome and
apply recombination operators to them so as to preserve
critical information.
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The PID controller genetic algorithm tuning procedure is
designed, and then is embedded into wind energy
conversion system, which the fitness of each chromosome
is evaluated by converting its binary string into a real value
which represents PID gains [20-22]. Each set of PID
parameters is passed to PID controller in order to compute
a complete response of the system as described in Fig. 5 .
The system of equations can be written as:
dX
1
1
L . Z .X L .U
dt
(7)
Where:
X I sd I sq I rd I rq
U V V
V
V
rq
sd sq rd
(8)
Ls
0
L M
0
(9)
Fig.5. The block diagram of proposed Genetic speed ratio MPPT
PID-GA controller
The simulated GA algorithm parameters used to
initialize the GA algorithm parameters and generating an
initial random population of individuals representing the PI
gains (Kp and Ki) are defined in Table I.
Table 1. GA parameters.
Description
Population size
Maximum iteration
Crossover probability
Mutation probability
Number of bits per chromosome
Parameters
20
50
0.5
0.01
16
The PI controller genetic algorithm tuning procedure is
evaluated by repeated simulations in an offline mode to find
the optimal PI parameters. Once found, the optimal PI
controller is used in the online mode to track the MPP point.
The PI controller genetic algorithm tuning procedure is
evaluated by repeated simulations in an offline mode to find
the optimal PI parameters. Once found, the optimal PI
controller is used in the online mode to control the DFIM
speed.
Modeling of Wind Turbine Doubly Fed Induction
Modeling
The general model of the DFIG obtained using Park
transformation is given by the following equations [12-17].
V sd
V sq
V
rd
V rq
(4)
(5)
d sd
s sq
dt
d sq
R s . I sq
s sd
dt
d rd
R r . I rd
rq
dt
d rq
R r . I rq
rd
dt
R s . I sd
M
0
0
Lr
M
0
0
M
0
Lr
sLs
0
sM
Rs
L
sM
0
Rs
s s
Z
0 ( )M R ( )L
s
r
s
r
(s )Lr
Rr
0
(s )M
The DFIG is very common because they are
inexpensive and robust and used for electrical energy
production [12,15]. In order to easily control the production
of electricity by the wind turbine, we will carry out an
independent control of active and reactive powers by the
technique called vector control.
Based on stator flux oriented, the following equations
can be obtained
(11)
and
ds s
qs 0
Therefore, the stator active and reactive power, can be
written as:
M
Ps V s L I rq
s
2
V s V M I
s
rd
s s L s
Ls
(12)
Equations showing the relationship between the rotor
currents and voltages are written as:
(13)
2
2
M dI rd
M
V rd R r I rd (L r L ) dt g s ( L r L ) I rq
s
s
2
2
V R I ( L M ) dI rq g ( L M ) I g MV s
r rq
r
s
r
rd
rq
L s dt
Ls
Ls
The block diagram of the DFIG is illustrated in the Fig 6.
sd -L s I sd MI rd
-L I MI
sq
s sq
rq
L
I
MI
r rd
sd
rd
rq L r I rq - MI sq
The active and reactive stator powers for the DFIG are:
(6)
(10)
0
Ls
Ps V sd .I sd
Q s V sq .I sd
V sq .I sq
V sd .I sq
Fig.6. Block diagram of vector control for DFIG.
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117
Sliding mode control of DFIG
Sliding mode control is one of the effective control
methodologies for DFIG drive control among nonlinear
control strategies, because of its disturbance rejection,
strong robustness subject to system parameter variations
and uncertainties and particularly its simplicity of practical
implementation . SMC algorithm consists to calculate the
equivalent and discontinuous components of control
variable from an adequate surface of sliding mode chosen.
In this case we chose the error as being the sliding surface.
The control algorithm is defined by the relation:
(14)
u u eq u n
(26)
u is the control vector, u eq – is the equivalent
n
control vector, u – the switching part of the control (the
eq
correction factor), u can be obtained by considering the
condition for the sliding regime, s 0 .
(27)
where: –
The control law is defined as follows:
(15)
u n u max sat s ( X ) / ,
if
s
if
s
s X
d
dt
KV rq
V rqn K V rq sign s P
positive constant.
Reactive power control
For n 1 ,the sliding surface representing the error
between the measured and reference reactive power is
given by this relation:
s ( ) s ref s
The derivative of the surface is given by:
(28)
s ( ) s ref s
I rd
1
V rd R r I rd
Lr
We take :
n 1
(30)
V rd V rd eq V rd att
(31)
V M
s ( ) s ref s
V rdeq V rdn R r I rd
Ls L r
L
L
(32)
V rdeq s ref s r R r I rd
e
The convergence condition is defined by the equation
Lyapunov s ( x ). s ( x ) 0 .
VsM
Active power control
For n 1 the sliding surface representing the error
between the measured and reference active power is
given by this relation:
s ( P ) P s ref P s
(18)
L s L r
R r I rq
V sM
V rqeq P s ref
Therefore, the correction factor is given by:
(29)
General equation given by J.J.Slotine to determine the
sliding surface given by:
(17)
(25)
By following the same steps and from the equation 12:
(16)
sign ( s )
sat s ( X ) /
s /
Where the equivalent control is:
By the convergence condition defined by the equation
Lyapunov s ( X ) s ( X ) 0 .
(33)
V rdn K V rd sign s
The Simulink model of DFIG sliding mode control is
illustrated in Fig .7
The derivative of the surface is given by:
s ( P ) P s ref P s
(19)
By replacing active power equation in the equation of
the switching surface, the expression of the surface
becomes:
V M
s ( P ) P s ref s
I rq
Ls
(20)
From the equation 12, during the sliding mode and in
permanent regime we find:
I rq
(21)
1
. V rq R r I rq
Lr
We take:.
V rq V rq eq V rq att
(22)
Fig.7. Model of the sliding mode control of DFIG
We have:
(23) s (P ) P s ref
VsM
V rqeq V rqn R r I rq
Ls L r
During the sliding mode and in permanent regime, we
have:
(24)
118
s ( P ) 0, s ( P ) 0,
V rqn 0.
Simulation Results
To analyze the efficiency of both proposed controllers
(sliding mode control for the generator and the proposed
PID-GA MPPT for wind turbine system), a set of simulation
tests have been performed. Simulations have been
investigated with a 3.6 MW generator connected to a
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6
Active power [W]
x 10
x 10
2.5
2
1.5
1
0.5
P-mes
P-ref
5
0
-0.5
-1
0
10
20
30
40
0
50
Time (s)
60
70
80
90
100
Fig.12. Produced wind turbine using proposed GA-PID MPPT
controller.
-5
0
5
10
15
20
Time[s]
Fig.8. Active power of the DFIG using S.M.C.
6
Reactive power [VAR]
6
3
Power (W)
690V/50Hz grid. The machine's parameters are given next
in appendix I.
The simulation results active and reactive power control
using sliding mode control is presented in the following
figures, in which we can observe that both active and
reactive power produced by DFIG track perfectly their
reference.
8
x 10
Q-mes
Q-ref
6
4
2
0
-2
-4
0
5
10
15
20
Time[s]
Fig.9. Reactive power of the DFIG using S.M.C.
10
9.5
The planned strategy PID-GA MPPT is tested under
different wind conditions. The wind speed profile variation is
presented in Fig.10. While both power coefiicient and
produced wind turbine using proposed GA-PID MPPT
controller are presented in Fig.11 and Fig.12 respectively.
From fig 11, it’s clear that we have reached the
maximum power coefficient 0.35 (confirmed from turbine
DATA) very fast , consequently the wind turbine operates at
its optimal power.
Conclusion
In this paper, new method for extraction of maximum
power of wind energy conversion system WECS has been
proposed and tested, which the new MPPT uses a PID
controller tuned by genetic algorithm. The modeling of wind
turbine and doubly-fed induction generator have been
demonstrated. Simulation results has been carried out
using Simulink/matlab , which perfect control of active and
reactive power has been proved. In addition , the wind
energy conversion system WECS has provided its
maximum power and operates at optimal condition under
variable wind speed since it driven by PID-GA MPPT
controller. So, better operation of the available wind energy
is achieved, particularly under variable wind speeds using
the proposed PID-GA MPPT controller.
Speed(m/s)
9
Appendix I.
8.5
8
7.5
7
0
10
20
30
40
50
Time(s)
60
70
80
90
100
Fig.10. wind profile.
Parameters
Nominal power
Stator voltage
Stator frequency
Number of pairs poles
Stator resistance
Rotor resistance
Stator inductance
Rotor inductance
Mutual inductance
Rated values
3.6 MW
4.16 kV
60 Hz
2
0.0079 Ω
0.025 Ω
0.07937 H
0.04 H
0.0045 H
0.4
Authors
0.35
Said Azzouz was born in Bou Saâda, Algeria, in 1985. He
received the Licence degree in Electrical Engineering from M’sila
University, Algeria in 2012, his Master degree from M’sila
University in 2014. He is currently working towards his PhD degree
in Electrical Engineering from M’sila University, Algeria. His current
research interest includes power electronics, Electrical Drives and
Process Control, modelling and control of wind turbines, artificial
intelligence and Renewable energies, control of electrical
machines.(said.azzouz@univ-msila.dz)
0.3
Cp
0.25
0.2
0.15
0.1
0.05
0
0
10
20
30
40
50
Time(s)
60
70
80
90
100
Fig.11. Power coefiicient using proposed GA-PID MPPT controller.
Sabir Messalti was born in setif (Algeria), he obtained his Master
and PhD in Electrical Engineerig from Setif University. He has
worked as an engineer in power system department. Today he is
full Professor of Power Systems at Electrical Engineering
department of Msila University (Algeria). His research interests
includes wind turbines, power systems planning, control of
electrical machines, PV systems, FACTS, HVDC, control of voltage
PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 95 NR 8/2019
119
and frequency, etc. He is author of about 30 papers published on
international journals or presented in various national and
international conferences. (sabir.messalti@univ-msila.dz)
Abdelghani Harrag was born in Setif, Algeria. He received BSc,
Magister and PhD Degrees in Electronics from Ferhat Abbas
University (UFAS), Setif, Algeria, in 1995, 1998 and 2011,
respectively. In 1998, he was awarded the best Magister thesis
prize in Electronics by UFAS. He worked as Project Manager
during more than 10 years in France with French and American
Societies. He is the creator of the standard Arabic langage on all
mobile and intelligent systems sold by Alcatel Lucent all over the
world including Arab countries from Atlantic ocean to Arabic gulf.
He taught at University Pierre Mendes France and Joseph Fourier
1999–2000, Grenoble, France, at Ferhat Abbas University 1996–
1999, Setif, Algeria. In 2009, he joined Mohamed Boudiaf
University, Msila, Algeria, where he works currently as Associate
Professor. He supervised many BSc, Master and PhD students. His
research interests mainly concerned intelligent control, renewable
energy, heuristic and evolutionary optimization, embedded systems
and signal processing. He is currently interested in bioinformatics
and control applied to renewable energy systems. He is member of
several research projects at University of Msila and CCNS
Laboratory at UFAS University.(abdelghani.harrag@gmail.com)
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PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 95 NR 8/2019
Sabir MESSALTI