This document presents a uniform implementation scheme for applying evolutionary algorithms like ant colony optimization (ACO) to maximum power point tracking (MPPT) in photovoltaic (PV) systems. The key points are: 1) A uniform procedure is proposed that allows various evolutionary algorithms to be applied to PV systems with different architectures (centralized, string-based, module-level etc.) using only one MPPT controller. 2) This reduces the number of required voltage/current sensors compared to individual MPPT controllers at each string/module. 3) The effectiveness of an ACO-based MPPT is demonstrated through simulations and experimental testing on a PV string under uniform and partial shading conditions.

1. A Uniform Implementation Scheme for Evolutionary
Optimization Algorithms and the Experimental
Implementation of an ACO Based MPPT for PV
Systems under Partial Shading
Lian Lian Jiang
School of Computer Engineering,
Nanyang Technological University,
Singapore.
ljiang2@e.ntu.edu.sg
Douglas L. Maskell
School of Computer Engineering,
Nanyang Technological University,
Singapore.
asdouglas@ntu.edu.sg
Abstract—Partial shading is one of the important issues in maxi-
mum power point (MPP) tracking (MPPT) for photovoltaic (PV)
systems. Multiple peaks on the power-voltage (P-V) curve under
partial shading conditions can result in a conventional MPPT
technique failing to track the global MPP, thus causing large
power losses. Whereas, evolutionary optimization algorithms
exhibit many advantages when applying them to MPPT, such as,
the ability to track the global MPP, no requirement for irradi-
ance or temperature sensors, system independence without
knowledge of the PV system in advance, reduced current/voltage
sensors compared to conventional methods when applied to PV
systems with a distributed MPPT structure. This paper presents
a uniform scheme for implementing evolutionary algorithms into
the MPPT under various PV array structures. The effectiveness
of the proposed method is verified both by simulations and exper-
imental setup. The implementation of the ant colony optimization
(ACO) based MPPT is conducted using this uniform scheme. In
addition, a strategy to accelerate the convergence speed, which is
important in systems with partial shading caused by rapid irra-
diance change, is also discussed.
Keywords—photovoltaic (PV) systems; ant colony optimi-
zation (ACO); global maximum power point tracking
(MPPT); partial shading conditions; computational intelli-
gence optimization algorithms;
I. INTRODUCTION
Photovoltaic (PV) systems represent the fastest growing
renewable energy segment, with operating capacity increasing
at approximately 58% annually [1]. One of the reasons for the
popularity of solar PV is its relatively low carbon footprint and
energy payback time (EPBT) [2]. Maximizing the energy out-
put of PV systems is important both from an operating and
energy payback perspective. During the last two decades,
many strategies have been proposed to improve the efficiency
of the PV systems. They mainly include: different PV array
configurations, such as series-parallel (SP), total-cross-tied
(TCT), bridge-linked (BL) [3] and reconfigurable PV arrays
[4, 5]; PV system architectures, including centralized [6, 7],
string-level [7, 8], and module-level microconverters [9] or
microinverters [10]; and maximum power point tracking
(MPPT) algorithms, which are necessary to increase the power
efficiency of the PV system due to the non-linear characteris-
tics of the solar modules. As more and more PV systems are
installed worldwide, often in locations with a less than optimal
solar irradiance profile (such as in the tropics [11]), it is be-
coming more important that appropriate techniques for max-
imizing the energy capture be employed. Tropical climates are
characterized by rapid irradiance changes [11] due to the pre-
vailing environmental conditions. These irradiance changes
contribute to partial module/array shading and rapid ramp
rates, both of which can impact on the energy capture.
A large number of MPPT techniques have been proposed
during last two decades. Among the algorithms, the classic
MPPT techniques such as perturb and observe (P&O) [6, 7,
12] and incremental conductance (IncCond) [13] are the most
popular MPPT techniques in commercial systems due to their
simple implementation and high efficiency. However, the
tracking ability is limited by the perturbation step size: a large
step size results in a fast tracking speed but means that there is
a large steady state oscillation around the maximum power
point (MPP); with the opposite for a small step size. To over-
come this problem, the variable step P&O [7, 14] and variable
step IncCond algorithms [15] were proposed to accelerate the
tracking speed while minimizing the steady state oscillations.
Extremum seeking control (ESC) [16, 17] and ripple correla-
tion control (RCC) [18, 19], which have similar control strate-
gies, are two other techniques for the uniform irradiance con-
ditions. They have among the fastest tracking speeds to date,
but their performance largely depends on the choice of dither-
ing frequency and the parameters of the filters. Since these
methods were originally developed for relatively constant ir-
radiance conditions, generally they are not suitable for track-
ing the global MPP under non-uniform conditions. More re-
cent MPPT techniques have been specifically developed for
partial shading conditions. These include power sweep based
MPPTs [6, 20], load line based MPPTs [21, 22] and computa-
tional intelligence based methods [8, 16-19, 23-33], and all
have certain advantages and disadvantages. For example, the
power sweep method [6, 20] guarantees good accuracy in
tracking the global MPP but has a slow tracking speed and
978-1-4799-4546-7/14/$31.00 ©2014 IEEE

2. requires additional devices. Load line based MPPTs [21, 22]
do not require additional equipment and have relative simple
implementations, but are unable to track the global MPP under
all shading patterns. Fibonacci search [23], dividing rectan-
gles (DIRECT) [24] and voltage window search (VWS) meth-
ods [25] have a fast convergence speed, but, similar to the load
line based MPPTs, cannot guarantee to track the global MPP
for all shading patterns. Artificial neural network (ANN) [26,
27] and fuzzy logic (FL) [28] based MPPTs can determine the
global MPP with good accuracy and tracking speed, but are
only suitable for PV systems where suitable training data or
knowledge of the required fuzzy rules is available. The
MLAM+HF hybrid MPPT [29], which combines an MPP-
locus accelerated method and IncCond, and MPPTs using the
parametric search algorithm [30] provide good accuracy but
both need predefined locus equations or a knowledge of the
system which makes them system dependent.
Evolutionary algorithm based MPPT methods, such as the
genetic algorithm (GA) [31], particle swarm optimization
(PSO) [32], differential evolution (DE) [33, 34], and ant colo-
ny optimization (ACO) [8], have an advantage over other
MPPT techniques due to their excellent ability to track the
global MPP with a system independent implementation, while
requiring no additional devices and fewer voltage and current
sensors. The relatively slow tracking speed associated with the
original algorithm implementations, compared to the ESC and
RCC methods, may limit their application when rapid irradi-
ance changes are prevalent. However, the combination of an
evolutionary algorithm with another technique, such as P&O,
narrows the search range thus significantly reducing the search
speed [35]. Concerns have been raised [20, 25] that the com-
plexity of newer computational based algorithms make MPPT
implementations based on these techniques inferior to methods
such as RCC, ESC and VWS. However, the objective of an
MPPT is to extract the maximum energy possible from the PV
system and thus the need, or otherwise, for processing power
is secondary, even as embedded processor cost is falling while
processor reliability is improving. In extracting the maximum
energy, particularly under partial shading conditions, the main
objective of the MPPT is how to converge to the global MPP
in the minimum number of steps. The fewer steps needed by
the MPPT algorithm, the better the algorithm’s performance.
Since many related evolutionary algorithms can also be
used in MPPT for PV systems with various architectural con-
figurations, we propose a uniform implementation scheme for
evolutionary algorithms to be applied to PV systems. Previ-
ously we proposed an ACO based MPPT [8] and verified its
effectiveness under various shading patterns using simulation.
In this paper, the experimental implementation of the ACO
based MPPT using the proposed uniform implementation
structure is conducted. In addition, techniques for accelerating
the convergence of the algorithm are also presented. The rest
of this paper is organized as follows. Section II introduces the
uniform implementation procedure of evolutionary algorithm
based MPPT and the techniques for accelerating the search
speed. Section III briefly presents the ACO background and
fundamentals based on our previous work published in [8]. In
Section IV, a simulation using datasheet parameters from an
actual PV module is conducted. In Section V, the experimental
implementation and results of ACO based MPPT, using the
uniform implementation scheme under both uniform and non-
uniform shading patterns, are presented. Finally, the work is
summarized and conclusions are made in Section VI.
II. UNIFORM IMPLEMENTATION SCHEME
PV systems can be broadly categorized as: centralized,
string-based and distributed architectures. Centralized archi-
tectures are typically characterized as being large systems with
multiple PV strings and a single centralized MPPT, as shown
in Fig. 1 (a). String-based systems are typically smaller mid-
sized systems with one PV string (or a small number of PV
strings) and a centralized inverter, as shown in Fig. 1 (b). Dis-
tributed architectures are characterized by module-level MPPT
(possibly a small number of modules may share an MPPT) and
can be configured as cascaded converter-MPPT architectures
(as in Fig. 1 (c)) and module-MPPT architectures (as in Fig. 1
(d)). Fig. 1 shows a grid-tied system using DC-AC inverters
connecting directly to the AC grid, although other structures
which connect to DC microgrids are also possible. In this situ-
ation, the DC-AC inverter is replaced by a DC-DC converter.
However, in all scenarios, as the MPPT is on the PV module
DC side of the inverter/converter, the final connection is un-
important to this study.
Fig.1. Different PV system structures: (a) centralized-MPPT, (b) string-based-
MPPT, (c) cascaded converter-MPPT and (d) module-MPPT.
Since we can consider the MPPT problem as a multi-
dimensional optimization problem and use an evolutionary
algorithm to solve it. Thus, many related evolutionary optimi-
zation algorithms can be applied to solve this problem. There-
fore, we present a uniform scheme for the application of evo-
lutionary optimization algorithms to MPPT. This requires a
change to the PV architecture, such that multiple MPPTs are
replaced by a single central MPPT which then provides indi-
vidual control signals to each power converter, as shown in
Fig. 2. That is, in all the MPPT structures, only one MPPT is
used to control the entire PV array. For the centralized-MPPT,
the control variable is scalar. For the string-based-MPPT, cas-
caded converter-MPPT and module-MPPT, the control varia-
ble (u) is multiple dimensional (u = [u1, u2, … , un]). The con-
trol variable could be the current/voltage of the PV ar-
ray/module or the duty ratio of the power converter. In our
work, the control variable for the PV string is the duty ratio
(D) of the PWM signal sent to the DC-DC converter. Since a

3. single PV string is tested, the control variable is a scalar. This
will be explained in the following section (Section III B).
There are two fundamental advantages to this approach. First-
ly, the global power point for each power converter is able to
be determined using just a single controller, and secondly, the
number of voltage and current sensors is able to be significant-
ly reduced, from a voltage and current sensor for each power
converter (each MPPT in Fig. 2) to just a single voltage and
current sensor at the AC (or DC microgrid) power bus.
Fig.2. The implementation of evolutionary optimization based MPPT into PV
systems with (a) centralized-MPPT, (b) string-based-MPPT, (c) module-
MPPT and (d) cascaded converter-MPPT architectures.
A uniform implementation procedure for evolutionary op-
timization is proposed in Fig. 3. This process starts with the
initialization of the algorithm parameters and the initial posi-
tion of each particle (also called the possible solutions in the
population). Then each particle is applied successively to the
PV system. When the implementation of the last particle is
completed, a new particle population is generated for the next
generation. Updating particles in the new generation continues
until the termination condition or the maximum number of
iterations is reached. By using this scheme, a range of evolu-
tionary optimization algorithms can be applied to MPPT, such
as DE, PSO, and ACO, etc.
Fig.3. Uniform procedure of applying particle based optimization for MPPT in
PV systems.
III. ACO BASED MPPT
A. Background of ACO algorithm
The ant colony algorithm is a probabilistic algorithm for
finding optimal paths based on the behavior of ants searching
for food [36]. It was introduced in the early 1990s and was
originally used to solve difficult combinatorial optimization
problems. In recent years, this technique has been extended to
continuous optimization [37, 38] and has been successfully
applied in many disciplines. In the combinational optimization
problem, the collective behaviors of a large number of ants
form a positive feedback phenomenon: ants initially search the
path randomly and lay down pheromone for other ants to fol-
low. The more ants that travel a path, the higher the density of
pheromone on the path, and as a result, the greater likelihood
that subsequent ants will choose the path. Finally, most of the
ants follow the trail until an ant individual achieves the short-
est path through the exchange of such information. For the
continuous problem, the solution construction is a little differ-
ent from the combinational problem. An pheromone archive is
defined in the process of solution generation as shown in Fig.
4. The vectors si (i = 1, 2, …, j, …, K) and f(si) are the K pos-
sible solutions and their corresponding fitness functions in the
archive, respectively. In this work, the fitness function is the
power output from the PV string. It is assumed that, for an N -
dimensional problem, the number of ants (population size) is
NP. The optimal solution is obtained by updating the possible
solutions in the archive continuously until the termination
condition is satisfied. The detailed procedure for the solution
construction of an ACO based MPPT can be described as fol-
lows [37].
f(s1)
f(s2)
.
.
.
f(sj)
.
.
.
f(sk)
s1
1 s1
2 ... s1
i ... s1
n
s2
1 s2
2 ... s2
i ... s2
n
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
sj
1 sj
2 ... sj
i ... sj
n
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
͘
sk
1 sk
2 ... sk
i ... sk
n
ω1
ω2
.
.
.
ωj
.
.
.
ωk
s1
s2
sj
sk
G1 G2 GnGi
Fig. 4. The archive of solution generation process in ACOR.
Step 1: Initialization: Set the initial values for the parame-
ters, such as the number of ants (NP), size of the archive (K),
balance coefficient (Q), maximum number of generations (e.g.
iterations) (maxIter) and the convergence speed constant (EP).
Generate K random solutions and store them in the solution
archive with size K (K ≥ NP), and then rank these K solutions
according to the fitness value (f(si)) (from best to worst, in the
minimization problem, )()()()( 21 Kl sfsfsfsf ≤≤≤≤≤ LL ).
Step 2: Generation of new solutions: Generate a new so-
lution by sampling the Gaussian kernel probability density
function for each dimension in two steps. The first step is to
choose the Gaussian probability density subfunction and the
second step is to sample the chosen Gaussian probability den-
sity subfunction according to the parameterized normal distri-
bution. In the first step, when the lth Gaussian probability den-

4. sity subfunction is chosen, the subcomponent of the solution sl
in each dimension will be used to calculate the parameters for
the chosen Gaussian subfunction in the following steps. The
probability density function of each dimension, which is a
Gaussian function and consists of multiple (K) Gaussian sub-
functions, is given by:
)
2
)(
exp(
2
1
)()( 2
2
11
i
l
i
l
K
l
i
l
l
K
l
i
ll
i x
xgx
σ
μ
πσ
ωω
−
−== ∑∑ ==
G (1)
where Gi
(x) is the Gaussian kernel for the ith dimension of the
solution, gl
i
(x) is the lth sub-Gaussian function for the ith di-
mension of the solution, μl
i
and σl
i
are the ith dimensional
mean value and the standard deviation for the lth solution,
respectively. Three parameters of the Gaussian kernel for each
dimension in Eq. (1) (mean, μi
; standard deviations, σi
;
weight, ωl ), are calculated based on the solutions in the ar-
chive. They are given by:
},,,{},,,{ 11
i
K
i
l
ii
K
i
l
ii
sss LLLL == μμμμ (2)
∑=
−
−
=
K
j
i
l
i
ji
l
K
ss
1
1
ξσ (3)
)
2
)1(
exp(
2
1
22
2
KQ
l
QK
l
−
−=
π
ω , ( 12 ωωωω ≤≤≤≤≤ LL lK ) (4)
where si
l, the ith dimensional value for the lth solution, is con-
sidered as the mean value for each sub-Gaussian function, the
standard deviation for the ith dimension of the lth solution (σl
i
)
is calculated by multiplying the average distance from the cho-
sen solution sl to other solutions in the archive with the parame-
ter ξ, which represents the speed of convergence (the higher the
value of ξ, the lower the convergence speed) [37, 38]. Here, ωl
is the weight of solution sl. It is considered as a value of the
Gaussian function with a variable l, mean value of ‘1’ and
standard deviation of ‘QK’, where l is the rank of solution sl,
and Q is the algorithm parameter which represents the im-
portance of the best ranked solutions and is also designed to
control the diversification of the search process. When Q is
small, the possibility of choosing the best ranking solution is
larger. When it is large, the chance of choosing all the solutions
is equal. More discussion about the parameters of Q and ξ can
be found in [37]. The Gaussian subfunction is randomly cho-
sen based on the following probability:
∑
=
=
= Kr
r r
l
lp
1
ω
ω
(5)
Step 3: Ranking and archive updating: By repeating above
process, NP new solutions are generated. Add the newly gen-
erated solutions to the original solutions in the archive, rank the
NP + K solutions and keep only the K best solutions in the ar-
chive.
Step 4: Go to step 2 and stop when the maximum genera-
tion is reached or the termination conditions (|Vref(k) - Vref(k-1)|
< ε) are satisfied.
B. Implementation of ACO
Our proposed ACO technique for MPPT uses a fitness
function f(k) defined as the power output from the PV array,
f(k) = I(k) × V(k). For an evolutionary optimization algorithm
based MPPT with a population size NP and a maximum num-
ber of iterations maxIter, each particle is applied successively
as in Fig. 5.
1
1D 1
2D 1
NPD
1
1f 1
2f
1
NPf 2
1f 2
2f
2
NPf
2
2D
2
NPD2
1D maxIter
1D maxIter
2D maxIter
NPD
maxIter
1f maxIter
2f maxIter
NPf
Fig. 5. The execution series of particles using particle based optimization.
where Popi
(i = 1, 2, …, maxIter) is the population in each
generation, and Di
g
and fi
g
(i = 1,2,…, NP, g = 1,2,…, maxIter)
are the ith particle (a possible duty ratio candidate) and its
corresponding fitness (power) value at the gth generation, re-
spectively. The flowchart of the ACO based MPPT is shown
in Fig. 6. The algorithm starts by initializing parameters such
as the initial particle values in the archive (X_total) and the
newly generated ones (X_ant), the fitness values of particles in
the archive (fx_total) and the newly generated ones (fx_ant),
lower and upper limits of the duty ratio (L and H), the archive
size (K), convergence speed constant (ξ), locality of the search
process (Q) and the maximum number of iterations (maxIter).
Subsequently, the voltage and current values of the PV array
output are measured to calculate the fitness value for the cur-
rent particle. After applying all the newly generated particles
(ants) in the current generation, all the fitness values of the
newly generated particles and the particles in the archive are
put together and ranked in ascending order. Finally, the K best
solutions are kept in the archive. Thus, with these steps, the
particles in the current archive are updated in the first genera-
tion. For the next generation, the new particles are generated
based on Eqs. (1) - (5). The same process is repeated until the
termination condition of the algorithm or the maximum itera-
tions is satisfied. In the flowchart, due to the successive oper-
ating process of each particle, flag_ant and flag_Iter are used
to mark the current particle and the generation, respectively.
C. Acceleration of the convergence speed
One of the major concerns of using evolutionary optimiza-
tion for MPPT is the algorithm speed. In fact, the speed of all
three of the MPPT algorithms can be accelerated using a two
stage search process by narrowing down the search range.
Before executing the particle based algorithms, the variable
step P&O, or IncCond, algorithm can be used to search for the
first and last local MPP, as shown in Fig. 7. These two local
MPPs occur in the voltage range close to the Isc and nVoc, re-
spectively, where Isc, Voc and n are the short circuit current, the
open circuit voltage of the PV module and the number of
modules connected in series. Subsequently, the power values
of these two local MPPs are compared. If the local MPP at Isc
side has a larger power value than the other local MPP, the
local MPP at the Isc side is then kept as the lower boundary of
the search range (L). The local MPP at the Voc side is then used
to calculate the upper boundary. For instance, as shown in Fig.
7, if the power value of the first possible local MPP (A) is
larger than the other local MPP (B), the lower boundary of the
searching range will be the voltage of point A (VA). Since the

5. minimum voltage difference between two local MPP is Voc,
and when considering a safety margin, the upper range is de-
fined as:
H= VB – 0.8Voc (6)
where VB is the voltage value of point B. For I-V curves with
more local MPPs, it may not exhibit much acceleration due to
the fact that the first and the last local MPPs will be located
very near to the Isc and Voc, respectively, which means the
searching range is not reduced by very much, while for the I-V
curves with fewer local MPPs, it provides a significant ad-
vantage over the original algorithms.
Fig.6. Flowchart of ACO based MPPT.
0 5 10 15 20 25 30 35 40 45
0
10
20
30
40
V (V)
P(W)
Fig. 7. The process of narrowing down the searching range for the MPPT
using particle based optimization algorithms.
IV. SIMULATION AND RESULTS
The ACO based MPPT is simulated in MATLAB
/Simulink using a PV array system consisting of two parallel
PV strings, each of which has 12 PV modules connected in
series. The parameters of the Silevo Triex U300 module used
in this simulation are shown in Table 1. While there are many
different possibilities for the irradiance distribution on the PV
array, we only consider two cases. One is a uniform irradiance
distribution (of 1000W/m2
) on all 24 PV modules, while the
other is a nonuniform irradiance distribution, where the two
strings receive different irradiance values, Irr_s1 = [1000,
1000, 100, 100, 800, 800, 600, 600, 600, 600, 200, 200] and
Irr_s2 = [1000, 1000, 1000, 1000, 800, 800, 600, 600, 600,
600, 200, 200]. The PV array configuration and the string
characteristic for the different shading patterns are shown in
Fig.8 and Fig. 9, respectively. A mathematical model of the
PV array with uniform shading can be determined based on
the two diode model [39]. As each PV string is connected to
an individual DC-DC converter, the total power produced is
the sum of the power output from the two strings at each con-
verter’s optimal operating point.
TABLE 1. PARAMETERS OF THE SILEVO TRIEX U300 PV MODULE.
Solar module
Triex
U300
Short circuit current
( Isc )
5.57A
Maximum power
(Pmax)
300W
Number of cells in each
module
96
Optimum voltage
(Vmp)
57.5V Number of bypass diode 4
Optimum current
(Imp)
5.23A
Temp. coefficient of Isc
% /o
C
0.04
Open circuit
voltage (Voc)
69.0V
Temp. coefficient of Voc
% /o
C
-0.262
M1,1
M2,1
M12,1
.
.
.
DC-DC
Converter
M1,2
M2,2
M12,2
.
.
.
DC-DC
Converter
Fig.8. The configuration of the tested PV array in the simulation.
0 100 200 300 400 500 600 700 800
0
1
2
3
4
V (V)
P(W)
0 100 200 300 400 500 600 700 800
0
2
4
6
I(A)
Fig.9. The I-V and P-V characteristics under different irradiance distributions.
For simplicity, we use the current value as the control vari-
able in our proposed MPPT algorithm, which means that the

6. power output of the PV system is calculated based on a pair of
current values. The control variable could also be the duty
cycle or the voltage of each PV string. If the control variable is
the voltage, that is, a pair of voltage values defines the operat-
ing point for the two PV strings, the PV array model can be
realized using a Table Block in MATLAB/Simulink where the
I-V points over the entire voltage range of the PV array are
generated and stored before the simulation starts. The updating
period of MPPT algorithm (Tmppt) is set to 0.12s. The archive
size and the newly generated solution in the archive for a new
generation are set to 7 and 5, respectively. At 15s, the shading
pattern changes from a uniform distribution with 1000W/m2
to
a partial shading condition with different irradiance values on
each PV string, Irr_s1 and Irr_s2. The performance of the ACO
based MPPT, implemented according to the process described
in section III. B, is shown in Fig.10. Here, we see that the
ACO based MPPT is able to operate at the global MPP under
both uniform and nonuniform irradiance patterns. The power
values are 6873.2W (uniform irradiance) and 3233.5W (nonu-
niform irradiance). Under uniform irradiance, the current val-
ues of the two PV strings are equal (at 5.23A). After 15s, the
current values for these two strings are 3.18A (I1) and 3.20A
(I2), respectively, which means each PV string is working at its
optimal operating point as shown in Fig.9.
0
5
I1
(A)
0 5 10 15 20 25 30 35 40 45
0
5
t (s)
I2
(A)
0
2
4
6
8
P(W)
V_string1
Fig.10. The power output and the current values of each PV string produced
by the ACO based MPPT when the shading pattern changes from uniform to a
nonuniform irradiance condition.
V. EXPERIMENTAL IMPLEMENTATION AND RESULTS
A. Experimental setup
The proposed ACO based MPPT [8] is implemented to de-
termine their performance for MPPT in PV systems. The
MPPT algorithms are implemented into a simple experimental
prototype inverter as shown in Fig. 11. A single string of the
PV array is simulated using a Chroma 62150H-600S pro-
grammable PV array simulator. The PV string consists of four
series connected PV modules, each of which has a bypass di-
ode connected in parallel to avoid module/cell hotspots due to
cell reverse bias during shading [22, 40]. Because of power
limitations in the converter used in the prototype and in the
range of the voltage and current sensors used to determine the
MPPT operating point we use a very small PV module, with
specifications given in Table 2. The two-diode model [39] is
used to generate I-V curves for a number of module shading
patterns. These are then uploaded into the PV array simulator
to emulate a real PV array. The PV array simulator is able to
store a maximum of 100 I-V curves (with current and voltage
points) of a PV array, thus simulating an array under various
operating conditions. A buck converter is used to step down
the voltage from the PV array to a Chroma 63802 programma-
ble DC electronic load working as a 12 volt lead-acid battery.
The switching frequency of the buck converter is set to 50
kHz. The component values used in the buck converter are
given in Table 3. A Texas Instruments TMF28335 DSP is
used to implement the evolutionary algorithm MPPT. Two
ADC channels are used to input the current and voltage values
measured at the output of the PV simulator. A photograph of
the prototype system is shown in Fig. 12.
Fig.11. The schematic diagram of the tested PV system.
Fig. 12. The photo of prototype system.
TABLE 2. PARAMETERS OF THE TESTED PV MODULE.
Maximum power
(Pmax)
15W
Short circuitcurrent
( Isc )
1.90A
Optimum voltage
(Vmp)
8.55V
Number of cells in each
module
18
Optimum current
(Imp)
1.75A
Temp. coefficient of Isc
A /o
C
1.50×10-3
Open circuit voltage
(Voc)
10.55V
Temp. coefficient of Voc
V /o
C
-0.04
The experimental prototype is used to determine the per-
formance of the ACO based MPPT. Additional guidelines on
methods to choose the parameters for ACO can be found in
[41]. The MPPT update period (Tmppt) is set to 0.12s. This re-
quirement ensures that the system reaches steady state before
the next perturbation. The maximum iteration (maxIter) is set

7. to 200, which is much larger than the number of iterations
actually needed for convergence. The population size (NP) is
5. The initial operating voltage of the particles are set to [0.4
0.6 0.7 0.8 0.9] nVoc, where nVoc is the open circuit voltage of
the whole PV string. The lower and upper limit of the search
range is set to Vo/nVoc and 0.99, respectively, where Vo is the
output voltage of the buck converter. The algorithm parame-
ters are listed in Table 4.
TABLE 3. PARAMETERS OF THE BUCK CONVERTOR.
Components Symbol Value
Inductance L 453μH
Diode D 600V/8A
Input capacitor Cin 2200μF
Output capacitor Cout 22 μF
Mosfet switch M 500V/13A
Switching frequency fhz 50KHz
MPPT update rate fMPPT 0.12sec
TABLE 4. ALGORITHM PARAMETERS OF ACO IN THE EXPERIMENT.
Parameters Values
Archive size (K) 8
Convergence speed constant (ξ) 0.25
Balance coefficient (Q) 0.8
In the experiment, two types of irradiance conditions,
namely uniform (SP1) and nonuniform (SP2 and SP3) shading
patterns, are tested, as in Table 5. SP1 has a unique MPP on
the P-V curve. For the other two nonuniform shading patterns,
there are multiple peaks, equal to the number of different irra-
diance values, in the shading pattern. The P-V curve for each
shading pattern, along with the global MPP (the dot on the P-
V curve), is given in Fig 13.
TABLE 5. THE SHADING PATTERNS TESTED IN THE SIMULATION AND
EXPERIMENT.
Shading pattern
number
Shading patterns [M1, M2, M3, M4]
SP1 [600.0, 600.0, 600.0, 600.0]
SP2 [900.0, 400.0, 800.0, 800.0]
SP3 [400.0, 400.0, 100.0, 100.0]
0 5 10 15 20 25 30 35 40
0
10
20
30
40
V (V)
P(W)
Fig. 13. P-V curves for under shading patterns, SP1, SP2, and SP3.
The search time depends not only on the tracking speed of
the algorithm but also on the MPPT update period. The num-
ber of steps required to converge to the global MPP is 41 for
shading patterns change SP1 to SP2 and 33 for SP1 to SP3. It
should be noted that the number of ‘steps’ required till con-
vergence for the different MPPT algorithms is not simply the
number of iterations or generations. This is because each par-
ticle is operated in a successive manner, and thus the number
of steps is the result of multiplying ‘iterations/generations’
with the population size (Nstep = Iter × NP). Convergence time
could then be calculated as t = Nstep × Tmppt. The corresponding
experimental power curves when the shading pattern changes
from SP1 to SP2 and SP1 to SP3 are shown in Fig. 14 and Fig.
15, respectively. The power readings are recorded by the
Chroma PV array simulator every 1 second. In each case, the
shading pattern changes at the 15 second interval. Before the
shading pattern changes, the system is stable and operating
under the uniform irradiance condition SP1. Because the sam-
pling time for recording the data point is 1s for the experiment,
the experimental results show less transient oscillation than the
simulation results. From Figs. 14 and 15, we can see that the
output power value changes to the new global MPP, from
35.0W to 35.9W for the shading pattern change SP1 to SP2
and from 35.0W to 10.7W for SP1 to SP3.
0 5 10 15 20 25 30 35 40 45
0
10
20
30
40
t (s)
P(W)
Fig. 14. The experimental power output by ACO based MPPTs when the
shading pattern changes from SP1 to SP2.
0 5 10 15 20 25 30 35 40 45
0
10
20
30
40
t (s)
P(W)
Fig. 15. The experimental power output by ACO based MPPTs when the
shading pattern changes from SP1 to SP3.
VI. CONCLUSION
In this paper, we propose a uniform implementation
scheme for various evolutionary optimization algorithms ap-
plied to MPPT in PV systems with different structural config-
urations. Our previously proposed ACO based MPPT is im-
plemented using the proposed scheme. The uniform imple-
mentation scheme provides a general way of implementing
various evolutionary optimization algorithms into MPPT. For
the centralized MPPT, the current/voltage output of the PV
array or duty ratio applied to the converter is considered as a
control scalar (or a particle). These control scalars are applied
to the PV system in a successive manner. For the distributed
MPPT, these algorithms can be applied by combining each
control variable for the individual PV module as a control vec-
tor (or particle). Additionally, a method for accelerating the
search speed using a two-stage scheme is proposed. Simula-
tions applying ACO based MPPT to PV systems are conduct-
ed and the results show that the ACO based MPPT using our
uniform implementation scheme is able to track the global

8. MPP. The MATLAB simulation code can be downloaded
from [42]. The experimental results show the effectiveness of
the ACO based MPPT, however we believe it is possible to
further improve the performance of these particle optimization
based MPPT implementations. Therefore, our future work will
focus on improving the tracking ability of the MPPT by utiliz-
ing a variant of ACO or combining the advantages of different
particle optimization algorithms, and applying particle optimi-
zation techniques to different structures of distributed MPPT,
where we consider a control vector for each PV module, and
their performance comparison will also be investigated.
Acknowledgement
This research is supported by the Singapore National Re-
search Foundation under NRF2012EWT-EIRP001.
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