efficiency improvement in solar power

1. Enhanced Energy Output From a PV System Under Partial
Shaded Conditions Using Fuzzy Logic
SINGARAVELAN.S
Final year, EEE department
M.KUMARASAMY COLLEGE OF ENGINEERING,KARUR. SUBRAMANI.K
Final year, EEE department
M.KUMARASAMY COLLEGE OF ENGINEERING,KARUR
Abstract—For the maximum utilization of solar energy,
photovoltaic (PV) power generation systems are operated at the
maximum power point (MPP) under varying atmospheric
conditions, and MPP tracking (MPPT) is generally achieved
using several conventional methods. However, when partial
shading occurs in a PVsystem, the resultant power–voltage (P–V)
curve exhibits multiple peaks and traditional methods that need
not guarantee convergence to true MPP always. This paper
proposes an fuzzy logic algorithm for global MPP (GMPP)
tracking under conditions of in-homogenous insulations. The
formulation of the problem, application of the ABC algorithm,
and the results are analyzed in this paper. The numerical
simulations carried out on two different PV configurations under
different shading patterns strongly suggest that the proposed
method is far superior to existing MPPT alternatives.
Experimental results are also provided to validate the new
dispensation.
Keywords—Maximum power point tracking (MPPT), Fuzzy logic
optimization methods, photovoltaic (PV) systems
I. INTRODUCTION
The increased energy demand coupled with limited
stock and rising cost of conventional sources, such as coal,
petrol, etc., has increased the contribution of renewable energy
sources to the total energy consumption. The photovoltaic
(PV) energy becomes a promising alternative as it is
omnipresent, freely available, environment friendly, and has
less operational and maintenance costs. To optimize the
utilization of large PV modules, maximum power point
tracking (MPPT) is generally employed in conjunction with a
power converter (dc–dc converter and/or inverter). The MPPT
scheme ensures that the system can always harvest maximum
power generated by the PV system independent of change in
environmental conditions namely ambient temperature and
solar insulation. Since the power–voltage (P–V) characteristic
curve varies nonlinearly with solar irradiation and atmospheric
temperature conditions, tracking MPP is a challenge and has
been successfully achieved using several methods. In large PV
installations, several PV modules are interconnected in series
and/or parallel to cope with desired voltage and current
capacity. At times, due to moving clouds, shadows of trees,
buildings, and other neighboring objects, some parts of the PV
array receive nonuniform sunlight leading to partial shaded
condition (PSC). The PV modules are equipped with bi-pass
diodes under PSC to avoid hotspots, and this result in multiple
maxima in the P–V curve of the PV system. The presence of
multiple peaks reduces the effectiveness of conventional
MPPT techniques due to their inability to distinguish between
the local and global peaks. Thus, if a conventional MPPT
technique such as multisearch perturb and observe (PO)
method or modified versions of PO method is employed under
PSC, this may result in the significant reduction of generated
power and further brings down the reliability of PV power
generation systems. The drop in PV power generation due to
PSC can be alleviated either through PV array
reconfigurations, system architectures, converter circuit
topologies, or improved methods of MPPT techniques. The
development of enhanced MPPT algorithms is more attractive
due to simplicity of implementation, reduced cost, and the
immediate adoption to existing system. Several MPPT
techniques are recently developed under PSC and are reported
in. Among these, particle swarm optimization (PSO) has been
extensively employed to track the global MPP (GMPP) in a
PV system under PSC.
II. CHARACTERISTICS OF PV SYSTEM
The basic photovoltaic device is the Structure for PV
modules. All modules contain cells. The group of panels
comprises the complete PV generating unit. A photovoltaic
cell is a semiconductor p-n junction expose to light into
electricity. Single cells are connected in series or parallel
combination to form a module to achieve certain voltage or
current. Photovoltaic cell is made from different types of
semiconductors using manufacturing processes. Generally,
mono crystalline and poly crystalline are used in commercial
level. When light falls on the cell, it generates charge carriers
that originate the electric current, if the cell is short-circuited.
To draw the real model of a PV cell, it is necessary to take into
the account, the losses due to leakage current in the diode. So,
one resistor connected in series, its value will be low and
another resistor will be connected in parallel, its value will be
high It is clear that the current I that flows to the external
circuit is flows through the diode and produces an opencircuit
voltage Voc of about 0.5-0.6V. If the solar cell is short
circuited, then no current flows through the diode, and all of
the short-circuit current ISC flows through the short circuit.

2. Figure-1. Equivalent circuit of Solar Panel.
III. EFFECT OF PARTIAL SHADING
When one or more PV cells are shaded, bypass
diodes are added in parallel for protection, prevent from the
damage due to overheating, when cells are connected in series
Figure-2. Characteristics of different irradiance conditions.
Partial shading may occur due to environmental
conditions, such as clouds, dirt and dust, trees and buildings.
However, in power voltage characteristics curve also changes
rapidly, multiple peaks are obtained. When the PSC occurs,
the shaded PV cell act as a load instead of power flow,
generating multiple peaks in the I-V characteristic curve
and multiple peak values in the P-V curve. To prevent this
problem, PV module is comprised of parallel connected
bypass diodes. In multiple peaks, one GMPP are obtained in
the curve.
IV. DC-DC CONVERTER
In the solar PV system, the obtained output is of DC
which is unregulated in nature. Therefore, the unregulated
output has been converted into regulated DC output by means
of a converter called DC-DC Converter. According to DC-DC
converter, the unregulated DC voltage takes as input and
converts the DC in to AC voltage. After the conversion
process, the obtained voltage is transformed and rectified to
desired DC output voltage. It is provided to regulate the
constant output voltage under various operating conditions.
Electrically, this converter Can produce high power, light
weight and noise free. The features of converters are
 Wide range of input voltage
 Over voltage protection.
The converter has two tasks, interface a PVmodel, grid and
drive the operating point of the PV panelto MPP. Converter
classified into categories of application, types of switching and
types of current modes. DC-DC converter types are Non-
isolated and
Isolated. Isolation refers to the electrical barrier separating
the input and output of the converter.In the buck converter, the
output voltage will be less than that of input voltage. It can be
used for connecting high module, voltage to low load. These
converters can modulate the input voltage through PWM
to generate the output voltage required to cause the panel
to operate in MPP . The step up converter, the output
voltage is higher than that of input voltage magnitude.
This converter used to connect high load and low module
voltages. Many research workers developed the applications
for Boost converter in PV systems. There are four different
categories for low-cost and high efficiency boost converters.
 Coupled inductor
 Switched capacitor
 Inductor and Switched capacitor and
 Coupled inductor and switched capacitor.
The three phase system has three levels, boosting
of MPPT control. Three level boost converters reduce diode
reverse recovery losses and reduce the input filter size. Several
controlled voltage levels are needed with self-balancing and
unidirectional current flow, such as PV systems. When applied
to the MOSFET, boost converter balance the voltage. It avoids
over voltage due to the leakage inductor. In buck-boost
converter, the output voltage magnitude may be higher or
lower than the input
voltage; it can be used in connecting nearly matched battery
and module voltage. In Cuk converter performs like buck-
boost converter. It is capable of stepping up or down input
voltage with reverse polarity through the common terminal of

3. input voltage. In SEPIC Converter, the input current will be
continuous and it draws the ripple free current from the PV
panel. SEPIC Converter will use, when the battery voltage will
be higher than the PV module voltage. In front of the inverter,
high step up converter will be required to improve power
conversion efficiency and stable DC link to invert from the
panel low voltage to a high voltage level. This converter
achieves a high step up voltage conversion ratio; the leakage
inductor energy of coupled inductor is recycled to load . The
converter has been built in two different
ways, such as, to maximize the efficiency, the possibility of
implementing MPPT, low price, reliability, flexible converter
run with a wide range of input, output voltage and power .The
bidirectional Cuk converter used as the bypass converter and a
terminal Cuk converter was referred. In bypass converter,
performance can be evaluated, and efficiency will be better
under partial shading conditions. Due to the additional power
converter, cost of building the type of PV system will be
higher than conventional ones using only by-pass diodes.
When connected to a variable current source like a PV panel,
behaviors will not be expected, but this converter designed to
operate with constant voltage source. For converting the low
panel voltage in to high DC-link voltage with a high voltage
conversion ratio is necessary,
and this converter reduces the switching power losses by soft-
switching operation of power devices. High voltage gain
interleaved boost converter is a nonisolated boost converter,
level up 12V DC input voltage to 36V DC output voltage. In
long time operation, it gives better reliability and small size
due to the simplicity of installation; switching devices can be
controlled. Two switch buck boost DC-DC Converter and low
cost 8bit micro controller are referred . This converter is more
flexible and it can perform both step-up and step-down
functions. It is able to bend through an entire voltage range of
PV panel. In novel boost-half bridge micro inverter and
repetitive current controller were explained . The minimum
use of semiconductor devices, circuit simplicity, and easy
control was achieved. The boost half-bridge micro inverter
possesses features of low cost and high reliability. In the
boost-half-bridge dc-dc converter over the wide operation
range, high efficiency (97.0%-98.2%) is obtained; current
injected to the grid is regulated precisely and stiffly. The
variable step size technique provides a fast tracking speed and
high MPPT
efficiency. In a low-cost, high efficiency current measurement
technique using a resistor and bypass switch for PV power
systems with MPPT control. Because of this technique, it can
reduce the power loss significantly for feedback control
systems using a DSP decreases the size and material cost.
80W prototype hardware has been implemented for PV MPPT
verification of low power loss current measurement technique
V. MPPT TECHNIQUES
The earliest MPPT method published in 1960s.There
are different types of MPPT algorithm have been discussed in
literature. It is broadly classified into two types.
 Conventional methods and
 Soft computing methods
For conventional MPPT, the methods include incremental
conductance, perturb and observe, hill combining, short circuit
current, open circuit voltage, ripple correlation control, current
sweep method. These methods are satisfied under uniform
solar irradiance conditions. In normal condition, it is able to
track efficiently, but continuous oscillation around MPP, loss
of power in a steady state condition. These techniques are
failing to track GMPP and cannot capable of handling partial
shading conditions. In soft computing method, the methods
such as artificial neural network, fuzzy logic controller,
particle swarm optimization, Ant-colony optimization and
differential evolution. Recent approaches
in software computing methods are cuckoo search and firefly
algorithm. Compare with conventional MPPT, soft computing
method able to track the GMPP in multiple peaks.
VI. FUZZY LOGIC ALGORITHM
It is one of the most popular control algorithm methods
which are known by its multimode based variable control
algorithm. It provides more accurate for MPPT problems, but
it is more complicated in implementations. It changes the duty
cycle of the converter according to the voltage error input such
that panel output voltage becomes equal to the voltage
corresponding to maximum voltage. The fuzzy logic control
algorithm is a Photovoltaic array dependent. It is based on the
operator’s experience, because it is followed by certain rules
that are given by the operator. It helps to improve the response
of a Photovoltaic system. The main disadvantage of this
method is that the efficiency of the whole system which
depends on the operator's performance and the precision of the
rules. Fuzzy logic control mainly consists of four stages,
namely, Fuzzification, rule base, inference, defuzzification.
First, we have to initialize the inputs to the Fuzzy logic
controller. Fuzzy logic- based hill combining. Algorithm is
introduced, where all the MPP’s values are periodically stored
in advanced micro controller and fuzzy logic is later
implemented to track the GMMP. The use of fuzzy logic also
involves the complicated fuzzification and defuzzification.
Compare with conventional nonlinear controllers, these
methods will work with variable inputs, no need of an accurate
mathematical model, handling nonlinearity and more robust.
Compared with P&O algorithm, it is more complicated and
possesses some advantages such as, better performance, good
stability and fast response
VII. HARDWARE
Latching relay, dust cover removed, showing pawl
and ratchet mechanism. The ratchet operates a cam, which
raises and lowers the moving contact arm, seen edge-on just
below it. The moving and fixed contacts are visible at the left
side of the image. A latching relay has two relaxed states

4. bistable. These are also called "impulse", "keep", or "stay"
relays. When the current is switched off, the relay remains in
its last state. This is achieved with a solenoid operating a
ratchet and cam mechanism, or by having two opposing coils
with an over-center spring or permanent magnet to hold the
armature and contacts in position while the coil is relaxed, or
with a remanent core. In the ratchet and cam example, the first
pulse to the coil turns the relay on and the second pulse turns it
off. In the two coil example, a pulse to one coil turns the relay
on and a pulse to the opposite coil turns the relay off. This
type of relay has the advantage that it consumes power only
for an instant, while it is being switched, and it retains its last
setting across a power outage. A remanent core latching relay
requires a current pulse of opposite polarity to make it change
state.
Figure-3.Expremental model.
Microcontrollers are destined to play an increasingly
important role in revolutionizing various industries and
influencing our day to day life more strongly than one can
imagine. Since its emergence in the early 1980's the
microcontroller has been recognized as a general purpose
building block for intelligent digital systems. It is finding
using diverse area, starting from simple children's toys to
highly complex spacecraft. Because of its versatility and many
advantages, the application domain has spread in all
conceivable directions, making it ubiquitous. As a
consequence, it has generate a great deal of interest and
enthusiasm among students, teachers and practicing engineers,
creating an acute education need for imparting the knowledge
of microcontroller based system design and development. It
identifies the vital features responsible for their tremendous
impact, the acute educational need created by them and
provides a glimpse of the major application area. Liquid
crystal displays (LCDs) have materials which combine the
properties of both liquids and crystals. Rather than having a
melting point, they have a temperature range within which the
molecules are almost as mobile as they would be in a liquid,
but are grouped together in an ordered form similar to a
crystal.
An LCD consists of two glass panels, with the liquid
crystal material sand witched in between them. The inner
surface of the glass plates are coated with transparent
electrodes which define the character, symbols or patterns to
be displayed polymeric layers are present in between the
electrodes and the liquid crystal, which makes the liquid One
each polarisers are pasted outside the two glass panels. These
polarisers would rotate the light rays passing through them to
a definite angle, in a particular direction
When the LCD is in the off state, light rays are
rotated by the two polarisers and the liquid crystal, such that
the light rays come out of the LCD without any orientation,
and hence the LCD appears transparent.
When sufficient voltage is applied to the electrodes,
the liquid crystal molecules would be aligned in a specific
direction. The light rays passing through the LCD would be
rotated by the polarisers, which would result in activating /
highlighting the desired characters.
The LCD’s are lightweight with only a few
millimeters thickness. Since the LCD’s consume less power,
they are compatible with low power electronic circuits, and
can be powered for long durations.
The LCD’s don’t generate light and so light is needed
to read the display. By using backlighting, reading is possible
in the dark. The LCD’s have long life and a wide operating
temperature range.
Changing the display size or the layout size
is relatively simple which makes the LCD’s more customer
friendly.crystal molecules to maintain a defined orientation
angle
Figure-4.Circuit model.
uniquely decoded ‘E’ strobe pulse, active high, to accompany
each module transaction. Address or control lines can be
assigned to drive the RS and R/W inputs.
Utilize the Host’s extended timing mode, if available,
when transacting with the module. Use instructions, which
prolong the Read and Write or other appropriate data strobes,
so as to realize the interface timing requirements. If a parallel
port is used to drive the RS, R/W and ‘E’ control lines, setting
the ‘E’ bit simultaneously with RS and R/W would violate the
module’s set up time. A separate instruction should be used to
achieve proper interfacing timing requirements.
VII. CONCLUSION
A new approach based on ABC has been proposed in this
paper for GMPP tracking in a PV power generation system.
Numerical simulations carried out on two different

5. configurations with varying shading patterns clearly
demonstrate the improved performance of the proposed
algorithm in comparison with the existing methods of PSO
and EPO. The experimental results clearly demonstrate faster
tracking characteristics of the proposed algorithm with
reduced PV output power oscillations. The developed
methodology is also shown to improve energy saving and
increased revenue generation when compared with alternative
schemes of MPPT.
REFERENCES
[1] M. Z. S. EL-Dein, M. Kazerani, and M. M. A. Salama, “Optimal
photovoltaic
array reconfiguration to reduce partial shading losses,” IEEE
Trans. Sustain. Energy, vol. 4, no. 1, pp. 145–153, Jan. 2013.
[2] T. Esram and P. L. Chapman, “Comparison of photovoltaic array
maximum
power point tracking techniques,” IEEE Trans. Energy Convers.,
vol. 22, no. 2, pp. 439–449, Jun. 2007.
[3] B. Subudhi and R. Pradhan, “A comparative study on maximum power
point tracking techniques for photovoltaic power systems,” IEEE Trans.
Sustain. Energy, vol. 4, no. 1, pp. 89–98, Jan. 2013.
[4] M. Aureliano, L. Galotto, Jr., L. P. Sampaio, G. de Azevedo e Melo,
and C. Alberto Canesin, “Evaluation of the main MPPT techniques for
photovoltaic applications,” IEEE Trans. Ind. Electron., vol. 60, no. 3,
pp. 1156–1166, Mar. 2013.
[5] P. Sharma and V. Agarwal, “Exact maximum power point tracking of
grid-connected partially shaded PV source using current compensation
concept,” IEEE Trans. Power Electron., vol. 29, no. 9, pp. 4684–4692,
Sep. 2014.
[6] K. Sundareswaran, S. Peddapati, and S. Palani, “MPPT of PV systems
under partial shaded conditions through a colony of flashing fireflies,”
IEEE Trans. Energy Convers., vol. 29, no. 2, pp. 463–472, Jun. 2014.
[7] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams, “A
maximum power point tracking technique for partially shaded photovoltaic
systems in microgrids,” IEEE Trans. Ind. Electron., vol. 60, no. 4,
pp. 1596–1606, Apr. 2013.
[8] Y.-H. Liu, S.-C. Huang, J.-W. Huang, andW.-C. Liang, “A particle swarm
optimization-based maximum power point tracking algorithm for PV systems
operating under partially shaded conditions,” IEEE Trans. Energy
Convers., vol. 27, no. 4, pp. 1027–1035, Dec. 2012.
[9] M.Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximum
power point tracking of multiple photovoltaic arrays: A PSO approach,”
IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380,
Jan. 2011.
[10] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particle
swarm optimization (PSO)-based MPPT for PV with reduced steady-state
oscillation,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627–3638,
Aug. 2012.
[11] K. Ishaque and Z. Salam, “A deterministic particle swarm optimization
maximum power point tracker for photovoltaic system under partial shading
condition,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3195–3206,
Aug. 2013.
[12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm
theory,” in Proc. 6th Int. Symp. MHS, 1995, pp. 39–43.
[13] D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial bee
colony (ABC) algorithm,” Appl. Soft Comput., vol. 11, no. 1, pp. 652–657,
Jan. 2011.
[14] B. Babar and A. Cr˘aciunescu, “Comparison of artificial bee colony
algorithm
with other algorithms used for tracking of maximum power point of
photovoltaic arrays,” presented at the Int. Conf. Renew. Energies Power
Qual. (ICREPQ’14), Cordoba, Spain, Apr. 8–10, 2014.
[15] B. Bilal, “Implementation of artificial bee colony algorithm on maximum
power point tracking for PV modules,” in Proc. 8th Int. Symp.
Adv. Topics Elect. Eng. (ATEE), Bucharest, Romania, May 23–25, 2013,
pp. 1–4.
[16] D. Karaboga and B. Akay, “A comparative study of artificial bee colony
algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, Aug.
2009.
[17] Q. Pan, L. Wang, K. Mao, J. Zhao, and M. Zhang, “An effective artificial
bee colony algorithm for a real-world hybrid flowshop problem
in steelmaking process,” IEEE Trans. Autom. Sci. Eng., vol. 10, no. 2,
pp. 307–322, Apr. 2013.
[18] F. S. Abu-Mouti and M. E. El-Hawary, “Optimal distributed generation
allocation and sizing in distribution systems via artificial bee colony
algorithm,”
IEEE Trans. Power Del., vol. 26, no. 4, pp. 2091–2101, Oct.
2011.
[19] L. D. S. Coelho and P. Alotto, “Gaussian artificial bee colony algorithm
approach applied to Loney’s solenoid benchmark problem,” IEEE Trans.
Magn., vol. 47, no. 5, pp. 1326–1329, May 2011.
[20] M. Seyedmahmoudian, S. Mekhilef, R. Rahmani, R. Yusof, and
E. T. Renani, “Analytical modeling of partially shaded photovoltaic
systems,” Energies, vol. 6, no. 1, pp. 128–144, Jan. 2013.
[21] K. S. Tey and S. Mekhilef, “Modified incremental conductance
MPPT algorithm to mitigate inaccurate responses under fast-changing
solar irradiation level,” Solar Energy, vol. 101, no. 1, pp. 333–342,
Mar. 2014.
[22] H. Patel and V. Agarwal, “Maximum power point tracking scheme for
PV systems operating under partially shaded conditions,” IEEE Trans.
Ind. Electron., vol. 55, no. 4, pp. 1689–1698, Apr. 2008.
[23] W. Gao, S. Liu, and L. Huang, “A novel artificial bee colony algorithm
based on modified search equation and orthogonal learning,” IEEE Trans.
Cybern., vol. 43, no. 3, pp. 1011–1024, Jun. 2013.
[24] N. Femia, G. Petrone, G. Spagnuolo, andM. Vitelli, “Optimization of
perturb
and observe maximum power point tracking method,” IEEE Trans.
Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005.

6. configurations with varying shading patterns clearly
demonstrate the improved performance of the proposed
algorithm in comparison with the existing methods of PSO
and EPO. The experimental results clearly demonstrate faster
tracking characteristics of the proposed algorithm with
reduced PV output power oscillations. The developed
methodology is also shown to improve energy saving and
increased revenue generation when compared with alternative
schemes of MPPT.
REFERENCES
[1] M. Z. S. EL-Dein, M. Kazerani, and M. M. A. Salama, “Optimal
photovoltaic
array reconfiguration to reduce partial shading losses,” IEEE
Trans. Sustain. Energy, vol. 4, no. 1, pp. 145–153, Jan. 2013.
[2] T. Esram and P. L. Chapman, “Comparison of photovoltaic array
maximum
power point tracking techniques,” IEEE Trans. Energy Convers.,
vol. 22, no. 2, pp. 439–449, Jun. 2007.
[3] B. Subudhi and R. Pradhan, “A comparative study on maximum power
point tracking techniques for photovoltaic power systems,” IEEE Trans.
Sustain. Energy, vol. 4, no. 1, pp. 89–98, Jan. 2013.
[4] M. Aureliano, L. Galotto, Jr., L. P. Sampaio, G. de Azevedo e Melo,
and C. Alberto Canesin, “Evaluation of the main MPPT techniques for
photovoltaic applications,” IEEE Trans. Ind. Electron., vol. 60, no. 3,
pp. 1156–1166, Mar. 2013.
[5] P. Sharma and V. Agarwal, “Exact maximum power point tracking of
grid-connected partially shaded PV source using current compensation
concept,” IEEE Trans. Power Electron., vol. 29, no. 9, pp. 4684–4692,
Sep. 2014.
[6] K. Sundareswaran, S. Peddapati, and S. Palani, “MPPT of PV systems
under partial shaded conditions through a colony of flashing fireflies,”
IEEE Trans. Energy Convers., vol. 29, no. 2, pp. 463–472, Jun. 2014.
[7] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams, “A
maximum power point tracking technique for partially shaded photovoltaic
systems in microgrids,” IEEE Trans. Ind. Electron., vol. 60, no. 4,
pp. 1596–1606, Apr. 2013.
[8] Y.-H. Liu, S.-C. Huang, J.-W. Huang, andW.-C. Liang, “A particle swarm
optimization-based maximum power point tracking algorithm for PV systems
operating under partially shaded conditions,” IEEE Trans. Energy
Convers., vol. 27, no. 4, pp. 1027–1035, Dec. 2012.
[9] M.Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximum
power point tracking of multiple photovoltaic arrays: A PSO approach,”
IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380,
Jan. 2011.
[10] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particle
swarm optimization (PSO)-based MPPT for PV with reduced steady-state
oscillation,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627–3638,
Aug. 2012.
[11] K. Ishaque and Z. Salam, “A deterministic particle swarm optimization
maximum power point tracker for photovoltaic system under partial shading
condition,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3195–3206,
Aug. 2013.
[12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm
theory,” in Proc. 6th Int. Symp. MHS, 1995, pp. 39–43.
[13] D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial bee
colony (ABC) algorithm,” Appl. Soft Comput., vol. 11, no. 1, pp. 652–657,
Jan. 2011.
[14] B. Babar and A. Cr˘aciunescu, “Comparison of artificial bee colony
algorithm
with other algorithms used for tracking of maximum power point of
photovoltaic arrays,” presented at the Int. Conf. Renew. Energies Power
Qual. (ICREPQ’14), Cordoba, Spain, Apr. 8–10, 2014.
[15] B. Bilal, “Implementation of artificial bee colony algorithm on maximum
power point tracking for PV modules,” in Proc. 8th Int. Symp.
Adv. Topics Elect. Eng. (ATEE), Bucharest, Romania, May 23–25, 2013,
pp. 1–4.
[16] D. Karaboga and B. Akay, “A comparative study of artificial bee colony
algorithm,” Appl. Math. Comput., vol. 214, no. 1, pp. 108–132, Aug.
2009.
[17] Q. Pan, L. Wang, K. Mao, J. Zhao, and M. Zhang, “An effective artificial
bee colony algorithm for a real-world hybrid flowshop problem
in steelmaking process,” IEEE Trans. Autom. Sci. Eng., vol. 10, no. 2,
pp. 307–322, Apr. 2013.
[18] F. S. Abu-Mouti and M. E. El-Hawary, “Optimal distributed generation
allocation and sizing in distribution systems via artificial bee colony
algorithm,”
IEEE Trans. Power Del., vol. 26, no. 4, pp. 2091–2101, Oct.
2011.
[19] L. D. S. Coelho and P. Alotto, “Gaussian artificial bee colony algorithm
approach applied to Loney’s solenoid benchmark problem,” IEEE Trans.
Magn., vol. 47, no. 5, pp. 1326–1329, May 2011.
[20] M. Seyedmahmoudian, S. Mekhilef, R. Rahmani, R. Yusof, and
E. T. Renani, “Analytical modeling of partially shaded photovoltaic
systems,” Energies, vol. 6, no. 1, pp. 128–144, Jan. 2013.
[21] K. S. Tey and S. Mekhilef, “Modified incremental conductance
MPPT algorithm to mitigate inaccurate responses under fast-changing
solar irradiation level,” Solar Energy, vol. 101, no. 1, pp. 333–342,
Mar. 2014.
[22] H. Patel and V. Agarwal, “Maximum power point tracking scheme for
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