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Experimental Validation of a Marine Current Turbine
Simulator: Application to a Permanent Magnet
Synchronous Generator-Based System Second-Order
Sliding Mode Control
Seifeddine Benelghali, Mohamed Benbouzid, Jean Frédéric Charpentier, Tarek
Ahmed-Ali, Iulian Munteanu
To cite this version:
Seifeddine Benelghali, Mohamed Benbouzid, Jean Frédéric Charpentier, Tarek Ahmed-Ali, Iulian
Munteanu. Experimental Validation of a Marine Current Turbine Simulator: Application to a Per-
manent Magnet Synchronous Generator-Based System Second-Order Sliding Mode Control. IEEE
Transactions on Industrial Electronics, Institute of Electrical and Electronics Engineers, 2011, 58 (1),
pp.119-126. �10.1109/TIE.2010.2050293�. �hal-00564727�
118 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 58, NO. 1, JANUARY 2011
Experimental Validation of a Marine Current Turbine
Simulator: Application to a Permanent Magnet
Synchronous Generator-Based System
Second-Order Sliding Mode Control
Seifeddine Benelghali, Student Member, IEEE, Mohamed El Hachemi Benbouzid, Senior Member, IEEE,
Jean Frédéric Charpentier, Member, IEEE, Tarek Ahmed-Ali, and Iulian Munteanu, Member, IEEE
Abstract—This paper deals with the experimental validation
of a Matlab-Simulink simulation tool of marine current turbine
(MCT) systems. The developed simulator is intended to be used
as a sizing and site evaluation tool for MCT installations. For
that purpose, the simulator is evaluated within the context of
speed control of a permanent magnet synchronous generator-
based (PMSG) MCT. To increase the generated power, and there-
fore the efficiency of an MCT, a nonlinear controller has been
proposed. PMSG has been already considered for similar ap-
plications, particularly wind turbine systems using mainly PI
controllers. However, such kinds of controllers do not adequately
handle some of tidal resource characteristics such as turbulence
and swell effects. Moreover, PMSG parameter variations should
be accounted for. Therefore, a robust nonlinear control strategy,
namely second-order sliding mode control, is proposed. The pro-
posed control strategy is inserted in the simulator that accounts
for the resource and the marine turbine models. Simulations
using tidal current data from Raz de Sein (Brittany, France) and
experiments on a 7.5-kW real-time simulator are carried out for
the validation of the simulator.
Index Terms—Marine current turbine (MCT), modeling,
nonlinear control, permanent magnet synchronous generator
(PMSG), second-order sliding mode (SOSM), simulation.
Manuscript received July 4, 2009; revised November 15, 2009 and
February 1, 2010; accepted April 27, 2010. Date of publication June 1, 2010;
date of current version December 10, 2010. This work was supported in part
by Brest Métropole Océane (BMO), in part by the European Social Fund
(ESF), and in part by the GDR SEEDS CNRS 2994 under the Internal Project
HYDROLE.
S. Benelghali was with the EA 4325 LBMS, University of Brest, 29238
Brest Cedex 3, France. He is now with the French Naval Academy, 29240 Brest
Cedex 9, France (e-mail: seifeddine.ben_elghali@ecole-navale.fr).
M. E. H. Benbouzid is with the EA 4325 LBMS, University of Brest, 29238
Brest Cedex 3, France (e-mail: Mohamed.Benbouzid@univ-brest.fr).
J. F. Charpentier is with the French Naval Academy Research Institute
(IRENav EA 3634), French Naval Academy, 29240 Brest Cedex 9, France
(e-mail: jean-frederic.charpentier@ecole-navale.fr).
T. Ahmed-Ali is with the UMR CNRS 6072 GREYC, University of Caen,
14032 Caen Cedex, France (e-mail: Tarek.Ahmed-Ali@greyc.ensicaen.fr).
I. Munteanu was with the Grenoble Electrical Engineering Laboratory
(G2Elab), 38402 Grenoble Cedex 9, France. He is now with Dun
˘
area de
Jos University of Galat
,
i, 800008 Galat
,
i, Romania (e-mail: iulian.munteanu@
ugal.ro).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIE.2010.2050293
NOMENCLATURE
MCT Marine current turbine.
PMSG Permanent magnet synchronous generator.
MPPT Maximum power point tracking.
SOSM Second-order sliding mode.
SHOM French navy hydrographic and oceanographic
service.
BEM Blade element momentum
ρ Fluid density.
A Cross-sectional area of the marine turbine.
V
tide
Fluid speed.
C
p
Power coefficient.
C Tide coefficient.
V
st
(V
nt
) Spring (neap) tide current speed.
λ Tip speed ratio.
s, (r) Stator (rotor) index.
d, q Synchronous reference frame index.
V (I) Voltage (current).
P (Q) Active (reactive) power.
φ Flux.
φ
f
Permanent magnet flux.
T
em
(T
m
) Electromagnetic torque (mechanical torque).
R PMSG resistance.
L PMSG inductance.
ω Electrical angular speed.
f Viscous friction coefficient.
J Turbine rotor inertia.
p Pole pair number.
I. I
NTRODUCTION
T
HERE ARE basically two ways of generating electricity
from marine and tidal currents: 1) by building a tidal
barrage across an estuary or a bay in high tide areas or
2) by extracting energy from free-flowing water (tidal kinetic
energy). Within the last few decades, developers have shifted
toward technologies that capture tidally driven coastal currents
or tidal stream [1]. The astronomic nature of this resource
makes it predictable, to within 98% accuracy for decades, and
independent of prevailing weather conditions. This predictabil-
ity is critical to a successful integration of renewable energy
0278-0046/$26.00 © 2011 IEEE
BENELGHALI et al.: EXPERIMENTAL VALIDATION OF MARINE CURRENT TURBINE SIMULATOR 119
Fig. 1. Marine current turbine global block diagram.
in the electrical grid [2]. It is therefore obvious that there is
a need to quantify the potential of generating electricity from
these various sites [3].
This paper reports then on the experimental validation of a
practical Matlab-Simulink simulation tool based on the mod-
eling of the resource and the tidal turbine rotor. The BEM
approach is in this case used for the turbine modeling.
In this paper, the simulator is evaluated within the context of
the speed control of a permanent magnet synchronous gener-
ator (PMSG)-based marine current turbine (MCT). In previous
works, different control strategies of control, mainly for doubly-
fed induction generator-based MCT, have been tested to eval-
uate the generated power [4]–[6]. In this paper, and in order
to be able to compare and choose the adequate technology,
a robust nonlinear control of a PMSG-based MCT is carried
out [7], [8]. The adopted control strategy, namely second-order
sliding mode (SOSM), relies on the resource and the marine
turbine models that were validated by experimental data [9].
Simulations using tidal current data from Raz de Sein (Brittany,
France) and experiments on a 7.5-kW real-time simulator are
carried out for validation purposes.
II. M
ARINE CURRENT TURBINE MODELING [9]
The global scheme for a grid-connected MCT is given
by Fig. 1.
A. Resource Model
1) Resource Potential: The total kinetic power in an MCT
has a similar dependence to that of a wind turbine and is
governed by the following equation [10]:
P =
1
2
ρAV
3
tide
. (1)
However, a marine energy turbine can only harness a fraction
of this power due to hydrodynamic behavior and (1) is modified
as follows:
P =
1
2
ρC
p
AV
3
tide
. (2)
For marine turbines, C
p
is estimated to be in the range
0.35–0.5 [11].
2) Resource Model: Tidal current data are given by the
SHOM and are available for various locations in chart form.
The SHOM available charts give, for a specific site, the current
velocities for spring and neap tides. These values are given at
hourly intervals, starting at 6 h before high waters and ending
Fig. 2. Tidal velocity in Raz de Sein for March 2007.
6 h after. Therefore, knowing tide coefficients, it is easy to derive
a simple and practical model for tidal current speeds V
tide
V
tide
= V
nt
+
(C − 45)(V
st
− V
nt
)
95 − 45
(3)
where C is the tide coefficient which characterizes each tidal
cycle (95 and 45 are, respectively, the spring and neap tide
medium coefficient). This coefficient is determined by astro-
nomic calculation of earth and moon positions. V
st
and V
nt
are, respectively, the spring and neap tide current velocities for
hourly intervals starting at 6 h before high waters and ending
6 h after. For example, 3 h after the high tide in Brest, V
st
=
1.8 knots and V
nt
=0.9 knots. Therefore, for a tide coefficient
C =80, V
tide
=1.53 knots.
This first-order model is then used to calculate the tidal
velocity each hour. The implemented model will allow the user
to compute tidal velocities in a predefined time range. For
illustration, Fig. 2 shows the model output for a month (March
2007). This adopted resource model has several advantages,
including its modularity, not to mention its simplicity. Indeed,
the marine turbine site can be changed, the useful current
velocity can be adapted, and the time range taken into account
can also be adapted from one month to one year.
B. Turbine Rotor Model
The harnessing of the energy in a tidal flow requires the
conversion of kinetic energy in a moving fluid, in this case
water, into the motion of a mechanical system, which can then
drive an electrical generator. It is not too surprising, therefore,
that many developers suggest using technology that mirrors
that which has been successfully utilized to harness the wind,
which is also a moving fluid [1]. Moreover, much of the
technology is based upon the use of horizontal-axis turbines,
such as that shown in Fig. 3. There are, however, a number of
120 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 58, NO. 1, JANUARY 2011
Fig. 3. Horizontal-axis tidal turbine.
fundamental differences in the design and operation of marine
turbines. Particular differences entail changes in force loadings,
immersion depth, different stall characteristics, and the possible
occurrence of cavitations [12].
The BEM method has therefore been used for the marine
turbine rotor modeling. Indeed, it is widely used in the industry
as a computational tool to predict aerodynamic loads and power
of turbine rotors [13].
It is relatively simple and computationally fast meeting
the requirements of accuracy and control loop computational
speed.
C. PMSG Model
The generator chosen for the marine current system was
the PMSG [14]–[16]. Indeed, the benefit of using a PMSG in
renewable energy applications as an alternative to conventional
generators is its higher efficiency. Moreover, the elimination
of the gearbox and the introduction of variable speed control
would further increase the availability of the system, reducing
its active weight and the need for maintenance. A schematic
diagram of a PMSG-based generation system is shown in Fig. 4.
The PMSG dynamic equations are expressed in the
d–q reference frame. The model of electrical dynamics in terms
of voltage and current can be given as (4) [17]
V
d
= RI
d
+ L
d
dI
d
dt
− ωL
q
I
q
V
q
= RI
q
+ L
q
dI
q
dt
+ ωL
q
I
q
− ωφ
f
.
(4)
The electromagnetic torque in the rotor is written as
T
em
=
3
2
p [(L
d
− L
q
) I
d
I
q
− φ
f
I
q
] . (5)
III. C
ONTROL OF PMSG-BASED MCT
A. Problem Formulation
A common practice in addressing PMSG control problem
is to use a linearization approach [15], [17]. However, due to
the tidal resource characteristics such as turbulence and swell
effects and the inevitable uncertainties inherent in PMSG-based
marine current turbines, such control methods come at the price
of poor system performance and low reliability [18]. Hence,
there is the need for nonlinear and robust control to take into
account these control problems.
Although many modern techniques can be used for this
purpose, sliding mode control has proved to be particularly
appropriate for nonlinear systems, presenting robust features
with respect to system parameter uncertainties and external
disturbances [19]–[22].
Sliding mode control copes with system uncertainty, keep-
ing a properly chosen constraint by means of high-frequency
control switching. Featuring robustness and high accuracy, the
standard (first-order) sliding mode usage is, however, restricted
due to the chattering effect caused by the control switching and
the equality of the constraint relative degree to 1. Higher order
sliding mode approach (HOSM) suggests treating the chattering
effect using a time derivative of control as a new control, thus
integrating the switching [23], [24].
Up to now, a few SOSM control approaches have been
introduced for wind and marine applications [4], [25], [26].
B. Second-Order Sliding Mode Control Approach
As the chattering phenomenon is the major drawback of prac-
tical implementation of sliding mode control, the most efficient
way to cope with this problem is HOSM. This technique gener-
alizes the basic sliding mode idea by acting on the higher order
time derivatives of the sliding manifold, instead of influencing
the first time derivative as it is the case in the standard (first-
order) sliding mode. This operational feature allows mitigating
the chattering effect, keeping the main properties of the original
approach [25].
The proposed control strategy is based on a step-by-step
procedure.
1) First, the speed reference ω
ref
is generated by an MPPT
strategy [5].
2) Then, an optimal electromagnetic torque, which ensures
the rotor speed convergence to ω
ref
, is computed using
the following equation:
T
em_ref
= T
m
+ fω − α(ω − ω
ref
)+J ˙ω
ref
(6)
where α is a positive constant. Afterwards, current refer-
ences are derived to ensure the PMSG torque convergence
to the optimal one
I
d_ref
=0
I
q_ref
=
2
3
T
em
pφ
f
.
(7)
To ensure currents convergence to their references, a SOSM
strategy is used. Let us define the following sliding surfaces:
S
1
= I
d
− I
d_ref
S
2
= I
q
− I
q_ref
.
(8)
It follows that
˙
S
1
=
˙
I
d
−
˙
I
d_ref
¨
S
1
= ϕ
1
(t, x)+γ
1
(t, x)
˙
V
d
(9)
˙
S
2
=
˙
I
q
−
˙
I
q_ref
¨
S
2
= ϕ
2
(t, x)+γ
2
(t, x)
˙
V
q
(10)
BENELGHALI et al.: EXPERIMENTAL VALIDATION OF MARINE CURRENT TURBINE SIMULATOR 121
Fig. 4. Schematic diagram of a PMSG-based generation system.
Fig. 5. Proposed control structure.
where ϕ
1
(t, x), ϕ
2
(t, x), γ
1
(t, x), and γ
2
(t, x) are uncertain
bounded functions that satisfy
ϕ
1
> 0, |ϕ
1
| > Φ
1
, 0 < Γ
m1
<γ
1
< Γ
M1
ϕ
2
> 0, |ϕ
2
| > Φ
2
, 0 < Γ
m2
<γ
2
< Γ
M2
.
The main problem with HOSM algorithm implementations is
the increased required information. Indeed, the implementation
of an nth-order controller requires the knowledge of
˙
S,
¨
S,
...
S,
...,S
(n−1)
. The exception is the supertwisting algorithm,
which only needs information about the sliding surface S
[23], [24]. Therefore, the proposed control approach has been
designed using this algorithm. The proposed SOSM controller
contains two parts
V
d
= u
1
+ u
2
V
q
= w
1
+ w
2
(11)
where
˙u
1
= −α
1
sign(S
1
)
u
2
= −β
1
|S
1
|
ρ
sign(S
1
)
˙w
1
= −α
2
sign(S
2
)
w
2
= −β
2
|S
2
|
ρ
sign(S
2
).
In order to ensure the convergence of the sliding manifolds
to zero in finite time, the gains can be chosen as follows [24]:
⎧
⎪
⎨
⎪
⎩
α
i
>
Φ
i
Γ
mi
β
2
i
≥
4Φ
i
Γ
2
mi
Γ
mi
(α
i
+Φ
i
)
Γ
mi
(α
i
−Φ
i
)
0 <ρ≤ 0.5
i =1, 2.
The above proposed SOSM control strategy for a PMSG-
based MCT is illustrated by the block diagram in Fig. 5.
![Fig. 7. Tested marine turbine [28].](/figures/fig-7-tested-marine-turbine-28-29uuv94n.webp)
