Real Time Implementation of an Artificial Immune
System Based Controller for a DSTATCOM in an
Electric Ship Power System
Pinaki Mitra, Student Member, IEEE and Ganesh K. Venayagamoorthy, Senior Member, IEEE
Real-Time Power and Intelligent Systems Laboratory
Missouri University of Science and Technology, Rolla, MO 65409, USA.
pm33d@mst.edu & gkumar@ieee.org
Abstract— A new adaptive control strategy based on
Artificial Immune System (AIS) for a DSTATCOM in an electric
ship power system is presented in this paper. DSTATCOM is a
shunt compensation device, which can be used to improve the
power quality during the pulse power requirements in a naval
shipboard system. The role of DSTATCOM controller is very
important to meet this objective. In this paper, the DSTATCOM
controller parameters are first tuned by Particle Swarm
Optimization (PSO) technique, so that it can provide innate
immunity to common system disturbances. Then, these optimum
parameters are modified online by an artificial immune system
(AIS), which provides adaptive immunity to unusual system
disturbances. To evaluate the performance of the proposed
control strategy, a simplified model of the ship system consisting
of a 45 MVA main generator, a 5 MVA auxiliary generator and a
36 MW propulsion motor is simulated in a real-time
environment. The effectiveness of the PSO and AIS based
adaptive controller is demonstrated on a Real Time Digital
Simulator based test system for pulsed loads of different
magnitudes and durations.
Keywords- artificial immune system; DSTATCOM; electric ship
power system; intelligent control; particle swarm optimization;
power quality
I. INTRODUCTION
In an all-electric ship power system, the loads like rail guns,
aircraft launchers, etc. are referred as pulsed loads [1]. These
pulsed loads are generally associated with severe voltage dips
due to its high power requirement for a very short period of
time. Therefore, in order to increase the survivability of the all-
electric navy ships in battle condition, the negative effects of
pulsed loads are to be minimized. DSTATCOM or distribution
static compensator can be a probable solution to this problem.
DSTATCOM is a Voltage Source Inverter (VSI) based shunt
compensation device, which can inject power to the bus as per
requirement [2]. The sophisticated power electronics based
control of DSTATCOM helps it to regulate the bus voltage by
controlling the injected power efficiently. The typical structure
of a DSTATCOM is shown in Fig. 1.
The performance of the DSTATCOM is very much
dependent on the DSTATCOM controller. The investigations
on the control strategies of DSTATCOM primarily focus on its
topology and the type of application. For example, papers [2]-
[5] present different control strategies based on the multi-level
inverter topologies of shunt compensators. Attempts have been
made to make the controller robust by applying sliding mode
control strategy as in [6] and [7]. But, these control strategies
are not adaptive to the system dynamics. Also, most of the
conventional control schemes of DSTATCOM have several PI
controllers. The tuning of PI controllers is a complex task for
the systems having power electronics equipments. In order to
overcome these problems, Computational Intelligence (CI)
techniques can be used. There are not so many attempts of
using CI techniques in DSTATCOM control. References [8]
and [9] are based on Artificial Neural Networks (ANNs). In
[8], the PI controllers are replaced by an ANN which is trained
using a backpropagation algorithm. But, the training is carried
out offline and hence the ANN based controller is not adaptive.
In [9], an ANN based reference current generator is used,
which is a partially adaptive control strategy. Here, though the
reference generator adapts its ANN weights online, but the DC
voltage regulation is handled by conventional PI controllers.
In this paper an adaptive control strategy for a
DSTATCOM based on Artificial Immune System (AIS) is
presented. Most of the CI techniques are offline and require
prior knowledge of the system behavior. But AIS, which is
inspired by theoretical immunology and observed immune
functions, principles and models, has the potential for online
adaptive system identification and control [10]. Abnormal
changes in the system response are identified and acted upon
without having any prior knowledge [11]. AIS controller
parameters are first tuned by particle swarm optimization
(PSO), so that it can provide innate immunity to common
system disturbances. As a result, the AIS based DSTATCOM
controller exhibits both innate and adaptive immune system
behaviors.
The proposed control strategy of DSTATCOM in ship
power system is validated on a Real-Time Digital Simulator
(RTDS) platform. The advantage of RTDS is that, it can
represent the dynamics of a system almost as close as the real
system. The fast acting power electronic switching devices are
also simulated in such a way that it can be interfaced with a
practical hardware system any time. The tuning of the
controller parameters using PSO is carried out on a digital
signal processor (DSP) interfaced to the RTDS. The AIS based
control strategy is also implemented on a DSP.
This work was supported by a US Office of Naval Research YIP award
# N00014-07-1-0806 and a NSF CAREER grant ECCS # 0348221, both
awarded to Dr. Venayagamoorthy.
978-1-4244-2279-1/08/$25.00 © 2008 IEEE 1
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Figure 1. Schematic diagram of DSTATCOM.
II. DSTATCOM AND ITS CONTROL STRUCTURE
The simplest structure of a DSTATCOM is shown in Fig. 1.
The principle of operation of DSTATCOM is based on the fact
that the real and reactive power can be adjusted by adjusting
the voltage magnitude of the inverter (VC) and the angle
difference between the bus and the inverter output (α). The
equations for active and reactive power are:
sinV VPCC CP
X
α
= (1)
( cos )PCC PCC CV V VQ
X
α−
= (2)
Where
P = Active Power,
Q = Reactive Power,
VC = Inverter voltage,
VPCC = Voltage at the Point of Common Coupling,
α = Angle of VPCC with respect to VC,
X = Reactance of the branch and the transformer.
The control strategy for the DSTATCOM adopted in this
paper is represented by Fig. 2. Here, the PLL generates a
reference angle. This reference angle is used to calculate d-q
component of the DSTATCOM current using a-b-c to d-q-0
transformation. Also this angle is used to calculate the a-b-c
voltage from its d and q components and to generate a
triangular wave for the sine-triangle modulator to produce
required firing pulses. The controller uses a two layer
decoupled control scheme to keep the bus voltage and the DC
capacitor voltage at constant level [12]. The PI controllers of
the outer layer (PI(1) and PI(2)) generate the reference currents
Id_ref and Iq_ref for the inner loop. The other two PI controllers
(PI(3) and PI(4)) just keeps track of the reference.
III. PSO BASED TUNING OF DSTATCOM CONTROLLER
Particle swarm optimization is a population based search
algorithm modeled after the motion of flock of birds and school
of fish [13], [14]. A swarm is considered to be a collection of
particles, where each particle represents a potential solution to
a given problem. The particle changes its position within the
swarm based on the experience and knowledge of its
neighbors. Basically it ‘flies’ over the search space to find out
the optimal solution [15].
Initially a population of random solutions is considered. A
random velocity is also assigned to each particle with which
they start flying within the search space. Also, each particle has
a memory which keeps track of its previous best position and
the corresponding fitness. This previous best value is called the
‘pbest’ of a particle. The best of all the ‘pbest’ values is called
‘gbest’, of the swarm. The fundamental concept of PSO
technique is that the particles always accelerate towards their
‘pbest’ and ‘gbest’ positions at each search instant k. Fig. 3
demonstrates the concept of PSO, where
Figure 2. Control Structure for the DSTATCOM.
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Figure 3. Concept of changing a particle’s position in two dimensions.
a) xid(k) is the current position of ith particle with d
dimensions at instant k.
b) xid(k+1) is the position of ith particle with d dimensions
at instant (k+1).
c) vid(k) is the initial velocity of the ith particle with d
dimensions at instant k.
d) vid(k+1) is the initial velocity of the ith particle with d
dimensions at instant (k+1).
e) w is the inertia weight which stands for the tendency of
the particle to maintain its previous position.
f) c1 is the cognitive acceleration constant, which stands
for the particles’ tendency to move towards its ‘pbest’
position.
g) c2 is the social acceleration constant which represents
the tendency of the particle to move towards the ‘gbest’
position.
The velocity and the position of a particle are updated
according to the following equations. The velocity of the ith
particle of d dimension is given by:
1 1 _
2 2 _
( 1) ( ) ( ( ) ( ))
( ( ) ( ))
id id best id id
best id id
v k w v k c rand p k x k
c rand g k x k
+ = ⋅ + ⋅ ⋅ −
+ ⋅ ⋅ − (3)
The position vector of the ith particle of d dimension is
updated as follows:
( 1) ( ) ( 1)id id idx k x k v k+ = + + (4)
In this paper, the first two PI controllers PI(1) and PI(2) of
Fig. 2 are tuned by PSO. In order to find out the optimum
controller parameters, the PSO algorithm is implemented on an
Innovative Integration M67 which is based on the Texas
Instruments TMS3206701 DSP. The M67 operates at 160 MHz
and is equipped with two A/D conversion and D/A conversion
modules. The rest of the system is built in RSCAD software,
which is the RTDS software. The analog signal provided to the
M67 is the AC bus voltage deviation, which comes from the
RTDS. This is converted to digital signal through the A/D
block of the DSP and is used to calculate the fitness value of
the controller parameters. The four parameters (Kp1 =
proportional gain of PI(1), Ki1 = integral gain of PI(1), Kp2 =
proportional gain of PI(2), Ki2 = integral gain of PI(2)) are the
dimensions of each particle of the swarm. The particle
positions are initiated randomly inside the DSP and are sent to
the RTDS as analog voltage signals within the range of -10 to
+10 Volts. These voltages are scaled proportionately inside the
RTDS and used as the PI controller parameters for each
iteration. The calculation of pbest and gbest, the update of
position and velocity, all are performed inside the DSP. The
hardware set up of the experiment carried out including the
RTDS and DSP is shown in Fig. 4.
Here, bus voltage regulation is one of the main objectives
of the DSTATCOM. Hence the cost function is considered in
such a way that the minimization of the cost function gives
better regulation. The mathematical expression for the cost
function is as follows:
/
2
1
( ( ))
T t
k
J v k t
Δ
=
= Δ ⋅Δ∑ (5)
Where
T = Total time of simulation after the application of
pulsed load
tΔ = Sampling interval
k = Sampling instant
( )v kΔ = Bus voltage deviation at kth sampling instant.
To have a fast PSO search performance, the values of w, c1
and c2 are kept fixed at 0.8, 2.0 and 2.0 respectively and the
number of particles is taken to be 20. The optimum PI
controller parameters found by PSO are:
Kp1 = 30.0,
Ki1 = 50.02,
Kp2 = 124.7,
Ki2 = 2.08.
IV. BIOLOGICAL IMMUNE SYSTEM AND ADAPTIVE
CONTROLLER DESIGN
The natural immune system of a human body is basically
the interaction of various cells. Among these, T and B cells
play the most vital roles. B cells secrete antibodies, whereas, T
cells are made of three types of cells: a) helper T cells, b)
suppressor T cells, c) killer T cells. Within the immune system,
there is a feedback mechanism. When a non-self cell (antigen)
is identified in a human body by APC (Antigen Presenting
Cell), it activates helper T cells. Those helper T cells then
stimulate the B cells, the killer T cells and the suppressor T
cells. Activation of B cell is the most important feedback
mechanism of the immune system and it is basically
responsible for elimination of antigens. Again, when the
number of antigens is reduced, the suppressor T cells inhibit the
activities of all other cells. As a result of this inhibitive
feedback mechanism, the action of immune system is
tranquilized [10].
In this paper, to adapt the four PI controller parameters,
which are already found by PSO, the approach described below
is followed:
The amount of foreign material (antigen) at kth generation is
defined here as the deviation in the bus voltage )(kVbΔ and also
the deviation of the capacitor voltage )(kVCAPΔ . The first PI
controller (PI(1)) works with an objective of keeping the
capacitor voltage constant, i.e. it will try to keep
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)(kVCAPΔ equal to zero. Similarly, other PI controllers (PI(2))
tries to keep )(kVbΔ equal to zero. In terms of Artificial
Immune System the aforesaid functions of the PI controllers
can be made adaptive considering the helping and suppressing
actions of the helper and suppressor T cells. The mathematical
representation shown here is only for the antigen )(kVbΔ . The
same analysis will also hold for the antigen )(kVCAPΔ .
The output from the helper T cells stimulated by the
antigen )(kVbΔ is given by
)()( kVmkTH bΔ= (6)
where ‘m’ is the simulation factor whose sign is positive.
The suppressor T cells inhibit the other cell activities and its
effect can be represented by
)(
)1(
)()( ' kV
kV
kVfmkTS b
b
b Δ⎟⎟⎠
⎞
⎜⎜⎝
⎛
−Δ
Δ
= (7)
Where m’ is positive suppression factor. f(x) is a non-linear
function which is defined as
)exp()( 2xxf −= (8)
The output of the function is limited within the interval [0,
1]. The total stimulation received by the B cells is based on
immune based feedback law which is given by
)(
)1(
)()(
)()()(
' kV
kV
kVfmmkB
kTSkTHkB
b
b
b Δ⎥⎥⎦
⎤
⎢⎢⎣
⎡
⎟⎟⎠
⎞
⎜⎜⎝
⎛
−Δ
Δ
−=
−=
(9)
So, the mechanism basically consists of two actions: once
the antigens are found, the TH cells work to eliminate them,
whereas the TS cells work to inhibit the actions of other cells.
Fig. 5 illustrates this action of immune based adaptive
controller for the disturbance )(kVbΔ , where the parameters for
the PI controller 2 are modified online following AIS. A
similar figure can be drawn for the disturbance )(kVCAPΔ ,
which dynamically modifies the parameters associated with PI
controllers 1.
In this paper, the AIS based control strategy is implemented
inside the DSP. Each AIS based PI controller is associated with
four ‘m’ constants as shown in Fig. 5. So there are as a whole
eight ‘m’ constants (m1 to m8) for two PI controllers which are
to be tuned using PSO. These eight ‘m’ constants are the
dimensions of each particle of the swarm and are initiated
randomly inside the DSP. Here the signals )(kVbΔ and
)(kVCAPΔ are sent to the DSP from RTDS in order to take the
helping and suppressing actions. Also, )(kVbΔ is used for the
calculation of cost function as before. The control signal ( )B k ,
which is basically the adaptive deviation in the values of
proportional and integral gains of the PI controllers, are
generated by the AIS based controller inside the DSP. These
signals are scaled and brought within the range -10 to +10
Volts and sent back to RTDS. Inside the RTDS, those signals
are again restored to their original values and added to the
optimal values of the PI controller parameters to make them
adaptive.
Figure 4. Laboratory hardware set-up including RTDS and DSP.
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Figure 5. Adaptive PSO-AIS based controller for a DSTATCOM.
V. TEST SYSTEM
To validate the performance of the proposed AIS based
controller of DSTATCOM in ship power system, a model of an
electric ship is built in a real-time environment. The advantage
of the RTDS is that, it can represent the dynamics of a system
almost as close as the real system. Since the ship power system
has a symmetrical network; the impact of the pulsed loads and
the effects of DSTATCOM can easily be demonstrated by
considering two generators and one propulsion motor. The test
system in this paper consists of one main generator of 45
MVA, one auxiliary generator of 5 MVA and one propulsion
motor of 36 MW with voltage source converter drives. Fig. 6
shows the schematic diagram of the test system and Fig. 7
shows the RSCAD model used in this paper. Small time-step
model (1μs) of the propulsion motor and the Voltage Source
Converter (VSC) are built up and interfaced with the remaining
large time-step portion of the model through two interfacing
transformers (Fig. 8).
VI. RESULTS
The optimal PI controller parameters and the ‘m’ constants
in Fig. 5 are found using PSO for a pulsed load of 20 MW and
50 MVAR with a duration of 40 cycles. The performance of
the AIS based controller is compared with a system having no
DSTATCOM connected to it. Fig. 9 shows the characteristics
of bus voltage deviation with the disturbance for which the
controller parameters are tuned. It is clearly observed that the
AIS based DSTATCOM reduces the voltage dip as well as the
peak overshoot by a substantial amount. To check the
robustness of this controller, pulsed loads of different
magnitude and duration are now applied. To represent a less
severe pulsed load, the duration is made 30 cycles and the
reactive power is changed to 40 MVAR keeping the active
power constant. Then both the magnitude and duration of the
pulsed loads are increased in steps. Two more cases of severe
pulsed loads (40 cycles and 60 MVAR and 50 cycles and 60
MVAR) are simulated. Figs. 10, 11 and 12 represent the bus
voltage characteristics for the above mentioned cases. Again, it
is found that in all the cases, the AIS based control strategy is
performing well which establishes the effectiveness of this
control strategy for a ship power system. Finally, the dynamic
variations of the two PI controller parameters are shown for a
pulsed load of 40 cycles and 20MW/60 MVAR. The adaptive
modification of the parameters in running condition due to the
AIS control strategy is clearly observed from Fig. 13.
Figure 6. Schematic diagram of the test system
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Figure 7. RTDS model of the test system depicting the different modules.
Figure 8. Small time-step model of the propulsion motor with VSC.
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0 1 2 3 4 5 6 7
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Time (sec.)
Bu
s
V
ol
ta
ge
(p
.
u
.
)
without DSTATCOM
with AIS controller
based DSTATCOM
Figure 9. Performance comparison for pulsed load of 20 MW/50 MVAR for
40 cycles.
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