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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 60, NO. 4, APRIL 2013
A Maximum Power Point Tracking Technique for
Partially Shaded Photovoltaic Systems in Microgrids
Bader N. Alajmi, Khaled H. Ahmed, Member, IEEE, Stephen J. Finney, and Barry W. Williams
Abstract—A modified fuzzy-logic controller for maximum
power point (MPP) tracking is proposed to increase photovoltaic
(PV) system performance during partially shaded conditions.
Instead of perturbing and observing the PV system MPP, the
controller scans and stores the maximum power during the perturbing and observing procedures. The controller offers accurate
convergence to the global maximum operating point under different partial shadowing conditions. A mathematical model of the PV
system under partial shadowing conditions is derived. To validate
the proposed modified fuzzy-logic-based controller, simulation and
experimentation results are provided.
Index Terms—Boost converter, fuzzy-logic controller (FLC),
maximum power point (MPP) tracker (MPPT), partial shadowing,
photovoltaic (PV).
I. I NTRODUCTION
T
HE limitation of conventional energy sources and environmental issues is motivating the world toward the development of microgrid systems. Microgrids can be defined as
a system with at least one distributed generation source, associated loads, and storage systems and should be able to operate in
grid-connected and island modes [1]–[5]. The operation of microgrids offers different advantages to customers and utilities,
such as improved service reliability, better economics, minimization of energy consumption, and environmental impact
[6], [7]. Renewable energies like solar and wind are commonly
used as distributed generation sources in microgrid systems,
because they are environmentally friendly, a sustainable source
of energy, and constructed on either the consumer or utility side
[8]–[10]. Photovoltaic (PV) systems are considered to be one of
the most efficient and well-accepted renewable energy sources
because of their suitability in distributed generation, mobile applications, transportation, and satellite systems [11]–[14]. However, PV systems suffer from a major drawback which is the
nonlinearity between the output voltage and current particularly
under partially shaded conditions [15]. During partially shaded
Manuscript received April 28, 2011; accepted August 18, 2011. Date of
publication September 19, 2011; date of current version November 22, 2012.
B. N. Alajmi, S. J. Finney, and B. W. Williams are with the Department
of Electronic and Electrical Engineering, University of Strathclyde, G11XW
Glasgow, U.K. (e-mail: Bader.alajmi@eee.strath.ac.uk; s.finney@eee.strath.
ac.uk; b.w.williams@eee.strath.ac.uk).
K. H. Ahmed is with the Electrical Engineering Department, Faculty
of Engineering, Alexandria University, Alexandria 21544, Egypt (e-mail:
khaled.ahmed@ieee.org).
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.2011.2168796
conditions, the system P –V characteristic curve has multiple
peaks. Therefore, a conventional maximum power point (MPP)
tracker (MPPT) such as hill climbing, incremental conductance,
and ripple correlation could miss the global maximum point
[16]–[19].
A study of partial shadowing conditions in [20] shows that
using a conventional MPPT during partial shadowing could
result in significant losses of PV output power. Therefore,
different researchers have investigated the limitation to
improve tracking efficiency. Noguchi et al. [21] propose
a short-circuit pulse-based MPPT with fast scan on the
P –V curve to identify the proportional parameter which is
commonly used in a current-based MPPT [22]. The global
maximum point is found; however, an additional switch in
parallel with the PV source is required to compute the shortcircuit current every few minutes. Therefore, such a method
causes momentary power losses and requires additional cost.
To avoid using an extra switch, the authors in [23] propose a
controller that swings the converter’s duty cycle from zero to
one to measure the open-circuit voltage and the short-circuit
current and then computes the optimum voltage and current.
From the computed values, the operating point is moved in
one step to the optimal operating point. The conventional
hill climbing algorithm is used to maintain operation around
the maximum point. Local maxima are avoided; however,
significant loss in power is experienced during the computation
of the open-circuit voltage and the short-circuit current.
Based on observation and investigation of the P –V characteristic, the authors in [24] claim that, on either side of the
global maximum point, the local maxima consistently decrease.
Therefore, an MPPT scheme for a PV system under partial
shadowing conditions is proposed based on this observation. In
[25], a simulation of a partially shaded PV system rejects the
observation in [24], and an MPPT based on conventional hill
climbing and a partial shadowing identifier has been proposed.
Also, it is observed [25] that the global maximum point of
a PV system under nonuniform conditions is always located
to the left of maximum power at normal weather conditions.
Therefore, a trajectory line of the PV system under different
isolation levels is stored in a data-based memory to identify the
partially shaded conditions. Such a technique could save the
controller from having unnecessary global scan; however,
the trajectory line is different between PV systems, and also,
the PV parameters vary with time.
A line search algorithm with Fibonacci sequence has been
employed to track the global power point under partial shadowing conditions. However, this method can miss the global MPP
0278-0046/$26.00 © 2011 IEEE
ALAJMI et al.: MPP TRACKING TECHNIQUE FOR PARTIALLY SHADED PHOTOVOLTAIC SYSTEMS IN MICROGRIDS
in some partial shadowing conditions [26]. In [27], a direct
search algorithm is employed to search the Lipschitz function
which describes the PV power and voltage relationship in an
interval. In order for this method to ensure finding the global
maximum power, the initial point must be carefully selected;
otherwise, the controller may be trapped at a local MPP. Practical swarm optimization has been implied to track the maximum
global operating point under abnormal weather conditions. A
large time delay is required to allow agents to compute the
global maximum point, resulting in a long computation time
to reach the maximum operating point [28].
A two-stage MPPT using monitoring cells is proposed in
[29], where the operating point in the first stage moves to
the MPP by assuming that the P –V curve is uniform. In the
second stage, the increment resistor method is used to locate
the real MPP. It could overlook the global MPP in some partial
shadowing conditions, and also, open-circuit voltage and shortcircuit current measurements are required.
To reduce the partial shadowing effect, an adaptive solar PV
array is proposed [30]. A model-based control algorithm is used
to control a switching matrix that connects a solar adaptive bank
to a fixed part of the PV array. Similarly, dynamic electrical
array reconfiguration is proposed [31] to improve the PV energy
production during partial shadowing conditions. A controllable
switching matrix is inserted between the PV generator and the
central inverter to allow electrical reconnection of the available
PV modules. In these approaches, conventional MPPT can be
applied to extract the MPP; however, power stage complexity
and cost are increased.
In this paper, a fuzzy-logic-based hill climbing technique is
proposed to track the global maximum point in a nonuniform
P –I curve characteristic. The proposed technique identifies
the global maximum among any number of local maxima.
Unlike conventional MPPT where the PV system operating
power is perturbed and observed to track the MPP, the proposed
controller scans and stores the maximum power during the
perturbation and observation stages. Moreover, PV modeling
during partial shading conditions is proposed. To demonstrate
the effectiveness and robustness of the proposed controller
method, computer-aided simulation and experimentation are
used to validate the results.
II. S YSTEM S TRUCTURE
A PV system consists of a solar array which is a group of
series-/parallel-connected modules, where the basic building
block within the module is a solar cell. Commercially, the solar
cell rated power varies between 1 and 2 W depending on the
solar cell material and the surface area; therefore, to design a
solar module, the solar cell’s power is measured, and then, the
modules are connected in series based on the desired output.
Fig. 1 shows a PV system.
Generally, the electrical characteristics of the PV system are
represented by power versus voltage (current/duty cycle) and
by current versus voltage. The characteristic curves of the solar
cell are nonlinear because of operational physical phenomena.
By using the equivalent circuit of the solar cell shown in Fig. 2,
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Fig. 1. PV array system.
Fig. 2. Equivalent circuit of a PV cell.
the mathematical model of the generated current in a PV system
is represented by
q(Vpv +Rs Ipv )
(Vpv + Rs Ipv )
−1 −
Ipv = np Iph − Io e AkT ns
ns Rsh
(1)
where Vpv and Ipv represent the PV array output voltage and
current, respectively. Rs and Rsh are the solar cell series and
shunt resistances, respectively. q is the electron charge (1.6 ×
10−19 C), Iph is the light generated current, Io is the reverse
saturation current, A is a dimensionless junction material factor,
k is Boltzmann’s constant (1.38 × 10−23 J/K), T is the temperature (in kelvins), and np and ns are the numbers of cells
connected in parallel and series, respectively.
The characteristic curves of the PV array system depend
on the radiation and temperature of the PV system. For a
given system, during normal conditions where the radiation is
equally distributed among the PV modules, the power–dutycycle (P –D) characteristics under varying weather conditions
are shown in Fig. 3. However, when the radiation is not equally
distributed, local and global maxima are introduced in the
characteristic curves. In order to understand such phenomena,
a PV array system with nine modules connected in series and
parallel is considered, as shown in Fig. 4. There are different
possibilities for the radiation distribution among the PV modules; the following five cases are randomly considered.
Case 1) One module in each column is completely shaded
(viz., modules 1, 4, and 7).
Case 2) One module in each column is partially shaded with
equal radiation levels (viz., modules 2, 5, and 8).
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Fig. 5.
PV output power characteristics under the five cases.
shaded modules result in a reduction of the PV output power,
creating local maxima, where the number of local maxima
increases as the variation of the radiation levels on each module
increases. The PV output power characteristics for the five
different cases are shown in Fig. 5.
III. PV M ODELING U NDER PARTIAL S HADING
Fig. 3. Influences of (a) temperature (T ) and (b) solar radiation (G) on the
P –D characteristics.
Fig. 4. PV array system with nine modules connected in series and parallel.
Case 3) One module in each column is partially shaded with
unequal radiation levels (viz., modules 3, 6, and 9).
Case 4) Two modules in the first column and one module in
each other column are partially shaded with equal
radiation levels (viz., modules 1, 2, 4, and 7).
Case 5) All modules are partially shaded with different radiation levels.
Simulation results for the five cases indicate that a completely shaded module causes a reduction of the PV output power without creating local maxima. However, partially
During shadowing conditions, the PV system mathematical
model in (1) is no longer valid because different radiation
levels are distributed around the PV system. Therefore, a new
mathematical model is required to represent the PV system
under partial shadowing conditions. An extensive study has
been undertaken for different PV module connections to derive
a general PV mathematical model under shadowing conditions.
For simplicity, three series-connected PV modules are considered. Based on the data for the Shell SP150-PC, the shortcircuit current and the open-circuit voltage for each PV module
under rated radiation level are 4.4 A and 43.4 V, respectively.
One PV module is partially shaded, and it receives a radiation
of 500 W/m2 , while the other two modules receive the rated
radiation, which is 1000 W/m2 . The I–V characteristic and PV
system block diagram are shown in Fig. 6. The three points in
Fig. 6(b) are considered: point 1 where the current equals Isc
and the voltage equals zero, point 2 where the current equals
Istep and the voltage equals Vstep , and point 3 where the current
equals zero and the voltage equals Voc . By inspection, the
previous variables can be defined as follows.
1) Isc is the short-circuit current of the unshaded PV
modules.
2) Istep is the short-circuit current of the shaded PV module.
3) Vstep is the summation of the open-circuit voltages of the
unshaded modules.
4) Voc is the summation of the open-circuit voltages of the
shaded and unshaded modules.
From these observations, the mathematical model of the
given PV system is (2), shown at the bottom of the next page,
s
where nus
s and ns are the numbers of series unshaded and
shaded modules, respectively, and λus and λs are the radiation
levels on the unshaded and shaded modules, respectively.
Equation (2) is valid for two radiation levels distributed
around the series-connected PV modules. Therefore, (2) is
ALAJMI et al.: MPP TRACKING TECHNIQUE FOR PARTIALLY SHADED PHOTOVOLTAIC SYSTEMS IN MICROGRIDS
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Fig. 7. Characteristic curve of the PV output current and voltage.
Fig. 6. (a) Block diagram of the three-series-connected-PV-module system.
(b) Characteristic curve of the PV output current and voltage.
extended to handle three radiation levels, as in (3). When the
radiation distribution levels increase, the number of voltage/
current steps increases. Fig. 7 shows the I–V characteristic of
three series-connected PV modules with different shadowing
levels. The PV system shown in Fig. 6(a) is tested under different partial shadowing conditions. Two PV modules are partially
shaded and receive two different radiation levels, which are 500
and 300 W/m2 , and the third module receives rated radiation,
which is 1000 W/m2 . Two more observations are added to the
previous observations as follows.
1) Istep2 is the short-circuit current of the shaded PV modules with the highest radiation level.
Vpv
Vpv
⎧
Isc ·λus −Ipv
AkT nus
s
⎪
,
ln
⎨
q
Io
=
⎪
I ·λus −I
s
⎩ AkT nus
ln sc Io pv +
q
2) Vstep1 is the open-circuit voltage of the unshaded modules plus the summation of the shaded module opencircuit voltages without the open-circuit voltage of the
lowest radiation module.
Therefore, the mathematical model for three different radiation levels on three series-connected PV modules is (3), shown
at the bottom of the page, where ns1
s is the number of partially
shaded PV modules with the lowest radiation level and ns2
s is
the number of partially shaded PV modules with the highest
radiation level. λs1 is the lowest radiation level, and λs2 is the
highest radiation level.
From (2) and (3), the general mathematical model of n seriesconnected PV modules in a PV system is (4), shown at the
bottom of the next page, where nsN
s is the number of partially
shaded PV modules with the highest radiation level and λsN
is the highest radiation level. N is the number of distributed
radiation levels.
Usually, the PV system consists of parallel-/series-connected
PV modules. Therefore, to drive a mathematical model for
the overall PV system, the previous three series-connected PV
modules are connected in parallel with another three seriesconnected PV modules, as shown in Fig. 8(a). For simplicity,
the radiation levels are distributed as follows.
In the first branch, modules 1 and 2 receive a radiation of
1000 W/m2 , and the shaded module receives 500 W/m2 . The
Ipv > Istep
AkT nss
q
⎧
Isc ·λun −Ipv
AkT nus
s
⎪
ln
,
⎪
q
Io
⎪
⎨
un
s1
I
·λ
−I
I ·λs −I
AkT nus
s
=
ln sc Io pv + AkTq ns ln sc Io pv ,
q
⎪
⎪
s1
⎪
I ·λ−I
I ·λs1 −I
⎩ AkT nus
s
ln sc Io pv + AkTq ns ln sc Io pv +
q
ln
Isc ·λs −Ipv
Io
(2)
,
Ipv < Istep
Ipv > I2step
AkT ns2
s
q
ln
Isc ·λs2 −Ipv
Io
I1step < Ipv < I2step
,
Ipv < I1step
(3)
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 60, NO. 4, APRIL 2013
IV. P ROPOSED M ETHOD
Fig. 8. (a) Block diagram of the series-/parallel-connected-PV-module system. (b) Characteristic curve of the PV output current and voltage.
three PV modules in the second branch are partially shaded to
receive a radiation of 300 W/m2 . The I–V characteristic of the
PV system is shown in Fig. 8(b). Curve A represents the I–V
characteristic of the whole PV system, curve B represents the
I–V characteristic of branch 1, and curve C represents the I–V
characteristic of branch 2. Clearly, the PV output current is the
summation of the instant currents from each branch
IpvTotal = Ibranch1 + Ibranch2 .
(5)
From (5), the general mathematical model of N parallelconnected PV modules in the PV system is
IpvTotal = Ibranch1 + Ibranch2 + · · · + IbranchN .
Vpv
(6)
Unlike conventional MPPTs where the PV system operating
power is perturbed and observed to track the MPP, scanning,
storing, perturbing, and observing the operating power of the
PV system are used for the proposed MPPT. The proposed
method is able to track the MPP under any weather conditions,
particularly partial shadowing where local and global maximum
points exist. During the initial condition or varying weather
conditions, the proposed MPPT makes a wide range search to
scan and store the maximum power value on the PV system. A
preset value which represents the accepted difference between
the identified maximum power and the operated power is stored
to decide the controller rules. If the difference between the
identified maximum power and the operated power is greater
than the preset value, the duty cycle is increased; otherwise,
fuzzy-logic-based MPPT is applied. In this case, the algorithm
ensures that the MPPT is not trapped by local maxima and
quickly recovers the new global maximum point during varying
weather conditions. Unlike conventional scanning MPPT, the
essentiality of using a long time delay is not required because
the controller scans the P –V curve while perturbation and
observation are carried out. Fig. 9 shows the flowchart of
the proposed method, where Vpv and Ipv are the PV output
voltage and current, respectively, D is the duty cycle, Pm is the
global MPP, and ΔPm is a constant that identifies the allowable
difference between the global maximum point and the operating
power point.
Three scanning and storing techniques are proposed to identify the global maximum power during initial conditions or
varying weather conditions.
The first technique is to initialize the system with maximum
duty cycle since the PV output power usually takes some
samples before reaching the operating point at maximum duty
cycle. The P –V characteristic curve is scanned, and the global
MPP is stored.
The second technique is to increase the duty cycle from
a minimum to a maximum value with a fixed step. In
this case, the P –V curve is scanned, and the global MPP
is stored.
The last technique is to apply a large initial perturbation
step to make a wide search range on the PV power locus.
Unlike the previous two proposed techniques, scanning and
storing the PV power are accomplished during perturbation
and observation. The three techniques guarantee to find and
store the global MPP; however, the global identification time
is different from one to another. Moreover, the duty cycle must
⎧
Isc ·λun −Ipv
AkT nus
s
⎪
,
ln
⎪
⎪
q
Io
⎪
⎪
⎪
.
⎨
..
= AkT
Isc ·λun −Ipv
Isc ·λs −Ipv
nus
AkT ns1
s
s
⎪
+
,
ln
ln
⎪
q
I
q
I
o
o
⎪
⎪
⎪
s1
us
s1
⎪
I ·λ−I
I ·λ −I
AkT ns
⎩
ln sc Io pv + AkTq ns ln sc Io pv + · · · +
q
AkT nsN
s
q
ln
Isc ·λsN −Ipv
Io
Ipv > IN step
..
.
I1step < Ipv < I2step
,
Ipv < I1step
(4)
ALAJMI et al.: MPP TRACKING TECHNIQUE FOR PARTIALLY SHADED PHOTOVOLTAIC SYSTEMS IN MICROGRIDS
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TABLE I
F UZZY-L OGIC RULES
is considered in the proposed MPPT design. The inputs to the
fuzzy-logic controller (FLC) are
ΔP = P (k) − P (k − 1)
(7)
ΔI = I(k) − I(k − 1)
(8)
ΔPM = Pm (k) − P (k)
(9)
and the output equation is
Fig. 9.
Proposed method flowchart.
Fig. 10. PV array system block diagram and the proposed MPPT controller.
return to a minimum value whenever it exceeds the maximum
value to track the global MPP.
Any conventional MPPT method can be used with the
proposed method; however, fuzzy-logic-based MPPT [32] is
preferred specially when using the third technique because
the tracking speed is not constant. Therefore, during initial
conditions or varying weather conditions, the initial tracking
speed should be fast enough to make a wide range power scan
and store the maximum available power. On the other side,
when the operating point reaches the global maximum, the
tracking speed decreases to minimize any oscillation around the
global maximum point. The PV system block diagram, along
with the proposed controller, is shown in Fig. 10.
ΔD = D(k) − D(k − 1)
(10)
where ΔP and ΔI are the PV array output power change and
current change, respectively, ΔPM is the difference between
the stored global maximum power (PM ) and the current power,
and ΔD is the boost converter duty cycle change. To ensure that
the PV global maximum power is stored during the scanning
procedure, a fast initial tracking speed is used. The variable
inputs ΔP and ΔI are divided into four fuzzy subsets: positive
big (PB), positive small (PS), negative big (NB), and negative
small (NS). The variable input ΔPM is divided into two fuzzy
subsets: PB and PS. The output variable ΔD is divided into
six fuzzy subsets: PB, positive medium (PM), PS, NB, negative
medium (NM), and NS. Therefore, the fuzzy algorithm requires
32 fuzzy control rules; these rules are based on the regulation
of a hill climbing algorithm along with the reference power.
To operate the fuzzy combination, Mamdani’s method with
max–min is used. The fuzzy rules are shown in Table I.
After simulating the PV system and studying the behavior of
the controller inputs and output, the shapes and fuzzy subset
partitions of the membership function in both of the inputs and
output are shown in Fig. 11.
The last fuzzy controller stage is defuzzification where the
center-of-the-area algorithm is used to convert the fuzzy subset
duty cycle changes to real numbers
n
μ(Di )Di
i
ΔD =
n
i μ(Di )
(11)
where ΔD is the fuzzy controller output and Di is the center of
the max–min composition at the output membership function.
V. FLC D ESIGN
VI. S IMULATION R ESULTS AND D ISCUSSION
Modification to the fuzzy-logic-based MPPT algorithm, using the scanning and storing procedures, is proposed to quickly
locate the global MPP. Fuzzification of the flowchart in Fig. 9
The tested PV array is composed of ten series modules with a
rated power of 850 W, where the design specification and circuit
parameters are shown in Table II. The simulation results are
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 60, NO. 4, APRIL 2013
Fig. 11. Membership functions: (a) Input ΔP , (b) input ΔI, (c) input ΔPM ,
and (d) output ΔD.
Fig. 13. PV output power for the proposed MPPT under partially shaded
conditions along with the PV power locus.
TABLE II
D ESIGN S PECIFICATION AND C IRCUIT
Fig. 14. Output power for a partially shaded PV system under two radiation
levels along with the PV power locus.
Fig. 12. PV output power characteristic for the proposed MPPT under two
radiation levels.
carried out using Matlab/Simulink to validate the performance
of the proposed MPPT. A large initial perturbation step is used
to scan and store the global MPP.
A. Normal Weather Conditions
The proposed system is tested under two equally distributed
radiation levels: 500 and 1000 W/m2 . As shown in Fig. 12, the
global maximum scan causes some power loss during initial
and varying weather conditions. Nevertheless, the proposed
MPPT still attains the MPP in a relatively short time, with small
oscillation in steady state.
Partial Shadowing Condition: To validate the performance
of the proposed MPPT during partial shading, the PV system
is tested under different distributed radiation levels; seven
unshaded modules receive 1000 W/m2 while the other three
modules are partially shaded with radiation levels of 800, 500,
and 100 W/m2 . The PV output power, along with the system
power locus, is shown in Fig. 13. Clearly, the local maxima do
not prevent the proposed controller from reaching the global
MPP. In addition, the tracking of the global maximum is fast
with small oscillations at steady state.
For further verification, the same partial shadowing conditions are repeated with different radiation levels on the
unshaded modules; the radiation level varies from 500 to
1000 W/m2 at 0.5 s and then from 1000 to 500 W/m2 at 1 s.
From Fig. 14, the proposed MPPT scans and then tracks the
global maximum point in a relatively short time even under
varying weather conditions.
To clarify and analyze the behavior of the proposed MPPT,
Fig. 15 shows the duty cycle of the PV system converter under
varying weather conditions of three partially shaded modules.
In the initial stage, a wide duty cycle range search is applied to
scan for the global maximum point. Once the global maximum
power is found, the controller stores the value and compares it
with the current operation power. If the difference between the
stored global maximum power and the current operating power
is greater than a preset value, the duty cycle increases. On the
other side, the fuzzy-logic control perturbs and observes the
PV system to keep the operation at the optimum duty cycle.
During varying weather conditions, the controller resets the
stored global maximum value and repeats the same process to
find the new global MPP.
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Fig. 15. Duty cycle behavior of the proposed MPPT.
Fig. 16. PV output power for a partially shaded PV system under the three
scanning techniques.
B. Comparison Between the Three Proposed Scanning
Techniques
As mentioned, three scanning techniques can be used to
identify the global maximum power during the initial condition
or varying weather conditions.
1) Initialize the system with a duty cycle close to the maximum allowable duty cycle.
2) Increase the duty cycle from minimum to maximum with
a fixed step.
3) Apply a large perturbation step during initial or varying
weather conditions.
A comparison between these scanning techniques is tested
for a PV system under different distributed radiation levels;
seven unshaded modules receive 1000 W/m2 while the other
three modules are partially shaded with radiation levels of 800,
500, and 100 W/m2 . As shown in Fig. 16, the global maximum
is attained for the three proposed scanning techniques. However, the techniques offer different scanning ranges and speeds.
For the first two techniques, the PV power locus is fully scanned
to identify the global maximum, while the third technique scans
only a wide range of the PV power locus. Therefore, the third
technique is slightly faster with less power losses during the
initial and varying weather conditions. In the three techniques,
the duty cycle must return to a minimum value whenever it
exceeds the maximum value to track the global MPP.
Fig. 17. (a) Hardware diagram arrangement. (b) Test rig photograph.
VII. E XPERIMENTAL R ESULTS
Experimental verification of the proposed MPPT is achieved
with the configuration shown in Fig. 17. The experimental
setup consists of an Agilent modular solar array simulator to
generate the PV system I–V curves, a boost converter with
4-kHz switching frequency to boost the output voltage and track
the MPP, and a battery load to fix the boost converter output
voltage and store the PV system energy. An Infineon TriCore
TC1796 is used to realize the proposed MPPT.
Different I–V curves are programmed into the PV source
simulator to test the experimental system under different
weather conditions. The results of the proposed MPPT under
two uniform insolation conditions are shown in Fig. 18. The
MPP is attained after scanning and tracking the I–V curve in
a relatively short time, with small oscillation in steady state.
When the radiation level varies, the proposed MPPT is reset
and repeats the scanning and tracking procedures.
To confirm the effectiveness of the proposed MPPT, (4) is
programmed into the PV source simulator to emulate the PV
system characteristic curve under partial shadowing conditions.
As shown in Fig. 19, the proposed fuzzy-logic-based MPPT
with a large initial perturbation step scans and stores the power
locus during perturbation and observation. The local maxima in
the P –V characteristic do not prevent the proposed MPPT from
successfully capturing the global MPP in a relatively short time,
with small oscillation around the MPP.
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Fig. 18.
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 60, NO. 4, APRIL 2013
Experimental results of the proposed MPPT under two radiation
Fig. 20. Experimental results. (a) I–V characteristic curve of a real PV
system. (b) Proposed MPPT under real PV conditions.
VIII. C ONCLUSION
Fig. 19. Experimental results of the proposed MPPT under partial shading.
TABLE III
PV M ODULE DATA
For further verification of the proposed MPPT, a roofinstalled PV array at our university building is used to collect
data. The PV array system consists of eight PV modules
connected in series with the specification, at standard test
conditions, as shown in Table III.
The PV array system is artificially shaded to generate an I–V
characteristic with three local MPPs as shown in Fig. 20(a).
After programming the I–V characteristic in the PV simulator,
the proposed MPPT tracks the global MPP by applying a large
perturbation step to the system duty cycle, to scan and store
the global MPP. Once the global maximum is found, the fuzzy
perturbation and observation take over to move the operating
point to the global MPP, with a small oscillation around it. The
power, voltage, and current curves of the PV system with the
proposed MPPT are shown in Fig. 20(b).
In this paper, a fuzzy-logic-based MPPT has been proposed
to extract the global MPP under partially shaded PV system
conditions. The proposed MPPT has been implemented by
combining fuzzy-logic-based MPPT with a scanning and
storing system. Three scanning techniques have been proposed
to scan the PV power characteristic curve and store the
maximum power value, during initial and varying weather
conditions. A new mathematical model has been proposed
to represent the behavior of the P –V characteristic under
partial shadowing conditions. Matlab/Simulink simulations
and practical experiments of a partially shaded PV system have
been carried out to validate the proposed MPPT. The results
show that the proposed MPPT is able to reach the global MPP
under any partial shading conditions. Moreover, the controller
exhibits a fast converging speed, with small oscillation around
the MPP during steady state.
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Bader N. Alajmi received the B.Sc. and M.Sc. degrees from California State University, Fresno, in
2001 and 2006, respectively. He is currently working
toward the Ph.D. degree in electrical engineering in
the Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, U.K.
His research interests are digital control of power
electronic systems, microgrids, and distributed generation, photovoltaic inverters, and dc–dc converters.
Khaled H. Ahmed (S’08–M’10) received the B.Sc.
(first-class honor) and M.Sc. degrees from the Faculty of Engineering, Alexandria University, Alexandria, Egypt, in 2002 and 2004, respectively, and the
Ph.D. degree in electrical engineering from the Department of Electronic and Electrical Engineering,
University of Strathclyde, Glasgow, U.K., in 2008.
In 2009, he was appointed Lecturer at Alexandria
University. His research interests are digital control
of power electronic systems, power quality, microgrids, and distributed generation. He has published
over 21 technical papers in refereed journals and conferences in the last three
years.
Dr. Ahmed is a Reviewer for various IEEE T RANSACTIONS and several
conferences.
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 60, NO. 4, APRIL 2013
Stephen J. Finney received the M.Eng. degree
from Loughborough University of Technology,
Loughborough, U.K., in 1988 and the Ph.D. degree from Heriot-Watt University, Edinburgh, U.K.,
in 1995.
For two years, he was with the Electricity Council Research Centre laboratories near Chester, U.K.
He is currently a Professor with the University of
Strathclyde, Glasgow, U.K. His areas of research interest are soft-switching techniques, power semiconductor protection, energy recovery snubber circuits,
and low-distortion rectifier topologies.
Barry W. Williams received the M.Eng.Sc. degree
from The University of Adelaide, Adelaide, Australia, in 1978 and the Ph.D. degree from the University of Cambridge, Cambridge, U.K., in 1980.
After seven years as a Lecturer at Imperial
College, University of London, London, U.K., he
was appointed Chair of electrical engineering at
Heriot-Watt University, Edinburgh, U.K., in 1986.
He is currently a Professor with the University
of Strathclyde, Glasgow, U.K. His teaching covers
power electronics (in which he has a free Internet
text) and drive systems. His research activities include power semiconductor
modeling and protection, converter topologies, soft-switching techniques, and
application of application-specific integrated circuits and microprocessors to
industrial electronics.