An Improved Perturb and Observe MPPT for Photovoltaic Systems
using Fuzzy Step Size
SALAH ANIS KRIM, FATEH KRIM, HAMZA AFGHOUL, FERIEL ABDELMALEK
University of Setif-1,
Faculty of Technology,
Laboratory of Power Electronics and Industrial Control, 19000 Setif,
ALGERIA
Abstract: - Photovoltaic (PV) systems have emerged as a promising energy resource that caters to the future
needs of society, owing to their renewable, inexhaustible, and cost-free nature. The output power of these
systems relies on solar cell radiation and temperature. To mitigate the dependence on atmospheric conditions
and enhance power tracking, a conventional approach has been improved by integrating various methods. The
Maximum Power Point Tracking (MPPT) algorithm is employed to optimize power extraction from PV
systems. To overcome limitations such as steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely Perturb and Observe (P&O) and Fuzzy Logic Controller (FLC), have been
modified. This research work aims to simulate and validate the fuzzy step size of the proposed modified P&O
and FLC techniques within the MPPT algorithm using Matlab/Simulink™ for efficient power tracking in PV
systems.
Key-Words: - Photovoltaic system; Matlab/Simulink; Perturb and Observe; MPPT; DC converter; Fuzzy.
Received: March 19, 2023. Revised: January 4, 2024. Accepted: February 17, 2024. Published: April 2, 2024.
1 Introduction
Research and development of alternative energy
sources that are renewable, cleaner, and have less
impact on the environment, have been prompted by
the rising demand for energy and the potential for a
reduction in the availability of traditional fuels, as
evidenced by the petroleum, coal, and natural gas
crisis, [1], [2], [3]. Additionally, among the
alternative energy sources, the currently thought to
be a more practical natural energy source is the
generation of electrical energy from PV cells
because it is plentiful, available for free, clean, and
is dispersed throughout the earth. It also plays a
crucial role in all other processes of energy
production on Earth. Therefore, harnessing solar
energy through PV cells has gained significant
attention in the search for sustainable energy
solutions. Besides, it is believed that solar energy
incident on the Earth’s surface is 10,000 times
larger than global energy consumption, despite the
phenomena of sunlight reflection and absorption by
the atmosphere, [4].
Evaluation of a PV source due to its nonlinear
output features which change with atmospheric
temperature and solar irradiation are another crucial
component of using a PV source. The characteristics
become more complex, especially when the PV
array receives non-uniform insolation, such as in
partially shaded conditions, resulting in multiple
peaks, [5]. The efficiency may reduce due to the
existence of numerous peaks. Therefore, various
methods have been developed to track the maximum
power point (MPP), including the P&O algorithm
and FLC, which are commonly used in PV systems.
The P&O algorithm can be presented by
processing actual values of PV current and voltage,
regardless of atmospheric circumstances, type of PV
panel, or aging, to track the MPP continuously. Due
to its easy implementation and simplicity, it has
been a common method used in the PV system. The
method involves perturbing the current or voltage of
the PV array, either by decreasing or increasing its
value, and comparing the resulting PV output power
with the power from the previous perturbation cycle,
[6]. The control system inclined the PV array
operating point in that way if the operating voltage
changes and the power increases; otherwise, the
operating point is moved in the opposite direction.
The next perturbation cycle of the algorithm is
conducted in the same way. The benefits of the
P&O method include its simplicity, ease of
implementation and control, low cost, and high
output power, [7], [8].
The FLC has also been widely adopted in PV
systems to track the MPP because it is easy to
develop, robust, and capable of tolerating
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Salah Anis Krim, Fateh Krim, Hamza Afghoul, Feriel Abdelmalek
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nonlinearity and working with imperfect inputs
without the need for a precise mathematical model,
[9], [10]. The FLC technique consists of three
stages: fuzzification, aggregation, and
defuzzification. A membership function is created
during the fuzzification stage to convert the
numerical input variables. The input and output
systems are linguistically related. Rules are the
relationships and a fuzzy set is the result of each
rule. Therefore, numerous rules are applied to
improve conversion efficiency. A separate output of
a fuzzy set is created by aggregating the fuzzy sets
produced by each rule, which is called as
aggregation process. The defuzzification method
subsequently sharpens the output from the fuzzy set,
[11], [12], [13].
Driven by the literature survey mentioned
earlier, in this paper, a modified method combining
both the P&O algorithm and FLC has been
developed. A modified fuzzy logic controller-based
P&O for MPPT has been developed based on fuzzy
variable step size due to limitations of traditional
P&O approach such as delayed convergence or
ascent to the MPP, oscillation of PV power around
the MPP under steady state that results in loss
power, and rapid changes in MPP position due to
fluctuating atmospheric conditions. This paper is
structured as follows. It consists of 5 parts,
following the introduction, section 2 presents the PV
system description which consists of the PV system,
PV panel model, and power converter. Besides,
section 3 presents the proposed Fuzzy Logic-based
variable step size P&O MPPT, while section 4
consists of the discussion of the simulation
outcomes and findings which are obtained from
Matlab/Simulink™. Lastly, the conclusion is
presented in section 5.
2 PV System Description
2.1 PV System
Fig. 1: Proposed PV System
Figure 1 illustrates the proposed PV system
integrated with an MPPT controller. When
designing a PV system, two key aspects need to be
considered: the modeling of the MPPT boost DC-
DC converter and the modeling of the PV array. The
objective is to optimize power transmission by
adjusting the load impedance to coincide with the
operating point with the MPP, [14].
2.2 PV Panel Model
Electrical energy can be generated through the
conversion of solar energy, facilitated by solar PV
technologies. These technologies rely on solar cells
to directly convert sunlight exposure into electrical
energy in the form of direct current (DC). Figure 2
illustrates the circuit model of a PV panel, which
comprises diodes, resistors, and a current source. PV
cells employ a semiconductor structure, typically a
p-n junction, to harness the energy from photons in
sunlight. When exposed to solar radiation, the cells
absorb photons, causing the mobilization of
electrons and the subsequent generation of
electricity. As a result, when a load is connected to a
PV cell during the period of irradiance, electric
charges flow as direct current. To achieve the
desired current and voltage levels, the cells can be
connected in either parallel or series configurations.
Connecting the cells in series allows for higher
output voltage while connecting them in parallel
enables higher output current
Fig. 2: PV array modeling circuit
Figure 2 illustrates the circuit model of the PV
array, which enables the determination of
representing the output current of the PV array. Equ.
(1) provides the derivation of ℎ, which represents
the photogenerated current and is expressed as
follows:
Where is the short circuit current of PV
system, is the short circuit current coefficient,
is the absolute operating temperature, is the
temperature at standard test condition (STC) @
25°C, G is the irradiance and is the irradiance
at standard test condition (STC) @ 1000W/m². But,
in indoor cconditions the ℎ ≈ 0, where the I-V
characteristics of the PV array is expressed by Equs.
2, 3, 4 as:
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Where, represents the dark saturation current,
is the output current, is the panel series
resistance, ℎ is the panel shunt or parallel
resistance, is the number of cells connected in
series, is the junction thermal voltage that is
given by equation of , where is the
Boltzmann’s constant of and
is the elementary charge of The
parameters of the PV array under STC are presented
in Table 1.
Table 1. Parameters of the solar panel 1 Soltech
1STH-250-WH at STC
Electrical Characteristics
Parameters
Rated maximum power (Pmax)
250.205W
Open-circuit voltage (Voc)
37.3V
Short-circuit current (Isc)
8.66A
Voltage at MPP (Vmpp)
30.7V
Current at MPP (Impp)
8.15A
Voltage temperature coefficient
-0.36901%/°C
Current temperature coefficient
0.086998
2.3 Power Converter
Power electronics is essentially employed in PV
panels, wind turbines, and geothermal resources
which need power conditioning systems, improve
grid integrations. Energy conversions phenomena
occur to be usable and user-friendly. For example
consider a PV generator which provides DC power,
to obtain AC power here a power electronic
converter called inverter is used. A power converter
is a power electronic circuit that receives a DC input
and generates a DC output with different voltage.
This transformation is achieved through high-
frequency switching actions that involve inductive
and capacitive filter elements. The purpose of a
power converter is to convert electrical energy from
one form to an optimized form that suits the specific
load requirements. In the context of PV systems,
one commonly used type of power converter is the
DC-DC boost converter, [15]. Figure 3 illustrates
the basic configuration of a DC-DC boost converter.
It comprises two semiconductor devices, such as a
transistor and a diode/IGBT, as well as an inductor,
input and output capacitors, and a DC load
connection. The boost converter operates by
increasing the input DC voltage, making it a step-up
converter, as the output voltage is greater than the
source voltage, [16].
Fig. 3: DC-DC boost converter
The equation of the DC-DC boost converter is
derived as follows, where the boost level of the
output voltage is determined by the duty ratio of the
switch and the applied input voltage:
When the condition of the IGBT/diode is on and
is reverse biased in (6), (7) and (8), the output
voltage is obtained from the derivation input voltage
and duty cycle from the equation below:
Equs. (9), (10) are derived by correlating the
relationship between the changing of inductor
current with time and PV voltage with inductor
when the condition of IGBT/diode turned off and
is forward biased.
By altering the duty cycle , the power
converter is in charge of controlling the energy
transmission from the input source to the load.
Since in steady state the integral of the induction
voltage over one time period must be zero, we
obtain Equ. 11. Equ. (12) shows the simplified
version of Equ. (11), where PV voltage of cell is
excluded.
The general equation of period is stated in (13)
where the turn-on time is summed with the turn-off
time. Then, Equ. (14) represents the ratio of turn on
time to period called as duty cycle, .
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Then, from Equ. (12), the voltage produced can
be derived as (15) where the output voltage is
determined from the input voltage of the solar cell
and duty cycle.
3 Fuzzy Logic based Variable Step
Size P&O for MPPT
3.1 Perturb and Observe Description
P&O techniques are commonly employed to extract
the maximum power point in a PV system due to
their simplicity and minimal parameters
requirement. The voltage of the array is periodically
perturbed by either increasing or decreasing it, and
the P&O algorithm compares the PV output power
with the power from the previous perturbation cycle,
[17]. If the power increases, the perturbation
continues in the same direction; otherwise, it
changes direction. As a result, each MPPT cycle
induces a change in the terminal voltage of the
array. In situations where atmospheric conditions
exhibit continuous or gradual changes, the P&O
algorithm will subsequently adapt, potentially
leading to a loss of PV power, [18].
Fig. 4: P&O MPPT operation
Figure 4 illustrates the operation of P&O
MPPT, taking into account the I-V and P-V
characteristics curves and the step size of voltage
perturbation. It clearly demonstrates that the
electrical behavior of a solar PV system under
varying solar irradiance is described by the output
current and voltage. The MPP is achieved when the
terminal voltage of the PV source is effectively
controlled to maintain a value that maximizes the
product of PV current and voltage. As shown in
Figure 4, the knee point of the standard I-V curve
for PV diodes is indicated, with the limits displayed
for short circuit current () and open circuit
voltage (), [19].
The basic concept behind the P&O approach for
MPPT is to analyze the voltage and output power
derivatives of the PV array, which determine the
shift in the operating point. This method involves
periodically adjusting the PV array voltage by either
increasing or decreasing it. If an increase in the
operating voltage leads to a rise in output power, the
operating point will be located to the left of the
MPP, necessitating further voltage perturbations to
reach the MPP on the right side. On the other hand,
if an increase in voltage results in a decrease in
power, the operating point will be positioned to the
right of the MPP, requiring additional perturbations
to move towards the left side and approach the
MPP, [20], [21].
3.2 Fuzzy Logic Controller Description
The FLC is a well-known artificial intelligence-
based control technique used in MPPT. Fuzzy logic,
or fuzzy set theory, is a novel approach to achieving
peak power point tracking. In Figure 5, the block
diagram of the FLC illustrates the mapping of input
variables, such as the first perturbation step size and
the instantaneous measured slope of PV power, into
linguistic values through fuzzification. This process
involves the use of linguistic variables and fuzzy
sets, which represent smooth changes in
membership rather than abrupt transitions, forming
the basis for fuzzy logic controllers, [22]. The
inference engine in the controller assesses the fuzzy
rules and linguistic variable definitions to make
decisions and determine the appropriate fuzzy
control action. To obtain a non-fuzzy (crisp) control
action that closely resembles the fuzzy one, a
defuzzification technique is applied since a fuzzy
controller produces a fuzzy set as its output. The
final step involves obtaining the crisp value for the
variable step size, as the output of the controller.
Fig. 5: Fuzzy logic controller block diagram
FLC is a heuristic approach that allows the
incorporation of human thinking and knowledge
into the design of nonlinear controllers, [23].
Typically, fuzzy controller rules are expressed using
linguistic terms. There are two types of fuzzy
inference systems commonly used: Mamdani and
Sugeno. The Mamdani inference system synthesizes
a collection of linguistic control rules defined by
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expert human operators, with each rule producing a
fuzzy set as its output. This system is particularly
suitable for expert system applications, such as
medical diagnostics, where the rules are based on
human expertise and are relatively straightforward
to understand, [24]. On the other hand, the Sugeno
inference system, also known as the Takagi-Sugeno-
Kang inference, uses singleton output membership
functions that can be either linear functions or
constants of the input values. Unlike the Mamdani
system, which computes the centroid of a two-
dimensional area, a Sugeno system employs a
weighted sum or average of a small number of data
points, making it more computationally efficient,
[25].
Table 2 shows the fuzzy rules table for MPPT.
There are about 25 rules developed in the fuzzy
logic toolbox to prescribe the conclusion of the
instantaneous voltage of the variable step size. The
inputs indicate the step size perturbation and P-V
curve slope while one output indicates variable step
size.
Table 2. Fuzzy rules table for MPPT
Δe = S(k)
E = Voltage
Step
PVS
PS
PM
PH
PVH
PVS
PVH
PVS
PVS
PS
PS
PS
PVH
PVS
PVS
PS
PS
PM
PS
PS
PS
PVH
PVH
PH
PS
PS
PVH
PVH
PVH
PVH
PVS
PVS
PVH
PVH
PVH
where PVS = Positive Very Small, PS =
Positive Small, PM = Positive Medium, PH =
Positive High and PVH = Positive Very High
Fig. 6: Flowchart of the proposed FLC-based P&O
Figure 6 illustrates the flowchart of the
proposed FLC-based P&O algorithm. This later
evaluates power variations and adjusts the
operational voltage of a PV system by modifying
the effective input resistance of the boost converter
through the duty cycle adjustment of the switching
device. The system initiates by measuring two
parameters: voltage and current from the PV system.
The flowchart provides a detailed explanation of the
process.
Firstly, the voltage and current measurements
lead to two distinct paths: the P&O method and
FLC. Various calculations are performed based on
the measurements to determine the actual power
(Ppv (k)), the changes in power (Δ Ppv (k)), and the
changes in voltage (Δ Vpv (k)). These calculations
involve combining the instantaneous current and
voltage values with their respective previous values.
The FLC receives two inputs: the slope, which is the
result of the division between ΔP and ΔV, and the
perturbation step size.
The output of the FLC is the variable step size
for making small changes in voltage, which is added
to the PV voltage. This action also modifies the duty
cycle of the PV voltage based on the two inputs.
The PV panel is considered to operate at the MPP
condition when the delta power equals zero. When
ΔP is greater than zero, the sign is positive, and vice
versa. Similarly, when ΔV is positive, the voltage is
updated by adding the small changes derived from
the output of the FLC. The design of fuzzy logic-
based P&O for PV MPPT is implemented and
simulated in Matlab/Simulink and is discussed in
the following section.
4 Outcomes and Discussion
4.1 PV System Circuit Model
Fig. 7: A circuit simulation model
The PV system circuit model is then presented
using Matlab/Simulink™ software to determine
system performance based on variable conditions.
The model consists of a PV model of 1Soltech
1STH-250-WH, a boost converter, loads, and fuzzy
logic controller-based P&O MPPT algorithm,
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Figure 7. The PV array with a capacity of 250.205W
consists of one series modules and one parallel
string. The loads considered in this model are 5Ω,
30Ω, and 100Ω while the power converter used is
IGBT with diode boost converter.
Fig. 8: MPPT controller subsystem
4.2 Fuzzy Rule
The fuzzy rule is constructed using the fuzzy logic
designer in Matlab/Simulink™, as shown in Figure
8. The membership functions involve two input
variables and one output variable for the FIS. The
first input variable represents the perturbation step
size, labeled as FS and depicted in Figure 9. The
second input, denoted as S in Figure 10, corresponds
to the slope of the P-V curve or ΔP/ΔV. The fuzzy
logic controller generates an output called the
variable step size (VSS), as illustrated in Figure 11.
Fig. 9: Input variable of perturbation step size, FS
Fig. 10: Input variable of P-V curve slope, S
Fig. 11: Output variation of variable step size, VSS
When the design of fuzzy logic is finished, the
rules and surface viewer are presented in Figure 12
and Figure 13, respectively. There are 25 different
rules corresponding between the inputs and output
of FIS variables. An example of an if-then rule is
stated below:
1. If (A is X1) and (B is Y1) then (C is A1)
……
25. If (A is X5) and (B is Y5) then (C is A25)
where A = First input, X1 = First variable of
first input, B = Second input, Y1 = First variable of
second input, C = Output, A1 = First output and
A25 = 25th output.
The fuzzy rule consists of fixed variables A, B,
and C, along with changing variables X1, Y1, and
A1~A25, which represent the variable relationship
according to the fixed variables. These rules are
visualized in a 3-D dimension due to the presence of
three different FIS variables, as shown in Figure 12.
The complete set of rules can be seen in the rule
viewer depicted in Figure 13. The inference process
of the fuzzy system involves adjusting the two
inputs to observe the corresponding output for each
fuzzy rule, including the aggregated output fuzzy set
and defuzzified output values. The output of the
fuzzy logic controller represents the change in the
duty cycle (ΔD), which completes the P&O
algorithm. Therefore, this method is designed in the
proposed Fuzzy Logic-based P&O approach to
ensure that the PV output always remains in an
optimal state.
Fig. 12: 3D Dimensions of fuzzy rule
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4.3 Fuzzy Rule
4.3.1 P-V and I-V curves
The graphs of Figure 14 and Figure 15 are plotted
using the parameters of the 1Soltech 1STH-250-WH
array and are displayed for two specific conditions:
array @ 25°C with specified irradiances and array
@ 1000 W/m² with specified temperatures. Various
irradiance and temperature values are examined to
track different states of the maximum power point.
In Figure 14, the irradiance levels are varied from
1000 W/m² to 400 W/m², while in Figure 15, the
temperatures range from 85°C to 25°C. The red dot
indicates the maximum power point and the
corresponding maximum current at different
voltages. These curves are correlated with the
simulation results of the PV system circuit model.
Furthermore, a comparison is made between the
outputs of the boost converter with loads and the
input of PV power.
Fig. 13: Rule viewer in MATLAB windows of
fuzzy logic
Fig. 14: I-V and P-V curve characteristics for
changing irradiance and fixed temperature
4.3.2 Changing Irradiance and Fixed
Temperature
Figure 16 represent irradiance and temperature
profiles. We focus on the changing irradiance with a
fixed temperature of 25°C. The blue line in Figure 17,
Figure 18 and Figure 19 represents the PV array's
initial condition, while the red line represents the
boost and load variables.
Fig. 15: I-V and P-V curve characteristics for
changing temperature and fixed irradiance
Fig. 16: Changing irradiance and fixed temperature
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Fig. 17: PV power and load power
Fig. 18: PV voltage and load voltage
Figure 17 shows a "ladder down-shape" profile,
indicating that the PV power varies with the
different irradiance levels.
At t = 0.1 s, when the irradiance is 1000 W/m²,
the power at the maximum power point is
approximately 250 W. However, when the
irradiance decreases to 800 W/m² at t = 0.2 s, the
power drops to around 200 W due to reduced
irradiance reception. Both graphs demonstrate
similar outputs in controlling the PV power to
maintain stability and avoid voltage fluctuations.
Fig. 19: PV current and load current
Fig. 20: Duty ration change with irradiation
Table 3. Key results
The explanation for these power outputs is
provided in Figure 18 and Figure 19. Figure 18
shows that at an irradiance of 1000 W/m², the PV
voltage is 31.54 V, while the load voltage is 60.95
V, as a result of the boost converter's nature to step
up the system voltage. Similarly, Figure 19
illustrates that the PV current is 7.85 A, and the load
current is 4.064 A, which is less than the input
current due to the voltage increase in the boost
converter at 1000 W/m². This relationship aligns
with Ohm's Law, where power is the product of
voltage and current, as stated in the P&O subsystem.
To achieve the maximum power point, the voltage
or current needs to increase or decrease
simultaneously. Hence, when the voltage reaches its
maximum or rises, the current decreases. Finally,
Figure 20 shows the variation of the duty ratio,
which follows the irradiance level. The initial duty
Irradiance (W/m²) and 25°C
1000
800
600
400
PV
Load
PV
Load
PV
Load
PV
Load
P
(W)
247
247
199
198
149
149
98.
98
V
(V)
31
60
31
54
30
47
29
38
I
(A)
7.85
4.064
6.388
3.636
4.827
3.153
3.298
2.56
D
0.4808
0.4305
0.3502
0.2198
Fig. 20: Duty cycle D
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cycle is 0.4808 and decreases proportionally with
decreasing irradiance, Table 3.
Hence, the simulation results indicate that the
proposed modified P&O-based fuzzy logic
controller exhibits excellent system performance by
minimizing steady-state oscillations near the
maximum power point and demonstrating a prompt
response to variations in irradiance.
5 Conclusion
PV is undeniably one of the most significant
alternative methods for generating renewable
energy. However, a PV system without an MPPT
algorithm faces challenges in harnessing the
maximum power potential. An MPPT algorithm is
essential to ensure that the PV array operates at its
maximum power point. In this regard, an enhanced
P&O MPPT algorithm, incorporating a fuzzy logic
controller with a variable step size, was developed
and implemented to overcome the limitations of the
traditional fixed step size approach. Simulation
results demonstrate that the proposed method
reduces steady-state oscillations around the MPP
and exhibits a faster response to changes in
irradiance. The main objectives of this work were to
evaluate and simulate the variable step size
modifications of the P&O algorithm in a PV system.
Three criteria were analyzed, including power
generated, current, voltage, and duty cycle, by
comparing them with the P-V and I-V curve
characteristics of the PV panel. The results reveal a
trade-off between minimizing convergence time
towards the maximum power point and reducing
oscillations in the photovoltaic array's power output
around the maximum power point, addressing some
of the drawbacks associated with using a fixed step
size in MPPT. Consequently, the primary goal of
this paper, which aimed to examine the
effectiveness of the modified P&O-based fuzzylogic
controller with a variable step size in a PV system,
has been achieved. In future work, MPPT with the
hybrid HBA-COA technique will be evaluated on an
experimental hardware platform using a PV
emulator. MPPT based on deep learning will be
developed and compared to the proposed technique.
References:
[1] M. A. Abo-Sennah, M. A. El-Dabah, and M.
A. El-Biomey, "Maximum power point
tracking techniques for photovoltaic systems:
a comparative study," International Journal of
Electrical & Computer Engineering, vol. 11,
no. 1, pp. 2088–8708, 2021.
[2] L. Abualigah, R. A. Zitar, K. H. Almotairi, A.
M. Hussein, M. Abd Elaziz, M. R. Nikoo, and
A. H. Gandomi, “Wind, solar, and
photovoltaic renewable energy systems with
and without energy storage optimization: A
survey of advanced machine learning and
deep learning techniques,” Energies, vol. 15,
no. 2, pp. 578, 2022.
[3] R. Kumar, and S. K. Singh, “Solar
photovoltaic modeling and simulation: As a
renewable energy solution,” Energy Reports,
vol. 4, pp. 701–712, 2018.
[4] S. S. Nadkarni, S. Angadi, and A. B. Raju,
“Simulation and Analysis of MPPT
Algorithms for Solar PV based Charging
Station,” Conference: 2018 International
Conference on Computational Techniques,
Electronics and Mechanical Systems
(CTEMS), pp. 45–50, 2018.
[5] B. E. Elnaghi, M. E. Dessouki, M. N. Abd-
Alwahab, and E. E. Elkholy, “Development
and implementation of two-stage boost
converter for single-phase inverter without
transformer for PV systems,” International
Journal of Electrical & Computer
Engineering, vol. 10, no. 1, pp. 2088–8708,
2022.
[6] A. Mohapatra, B. Nayak, and C. Saiprakash,
“Adaptive perturb & observe MPPT for PV
system with experimental validation,” in 2019
IEEE International Conference on
Sustainable Energy Technologies and Systems
(ICSETS), Feb. 2019, pp. 257–26.
[7] D. Pilakkat and S. Kanthalakshmi, “Study of
the Importance of MPPT Algorithm for
Photovoltaic Systems under Abrupt Change in
Irradiance and Temperature Conditions,”
WSEAS Transactions on Power Systems,
vol.15, pp. 8-20, 2020,
https://doi.org/10.37394/232016.2020.15.2.
[8] B. Sankar, A. Yappan and R. Seyezhai,
“Comparative analysis of Maximum Power
Point Tracking Algorithms for photovoltaic
applications,” WSEAS Transactions on Power
Systems, vol.15, pp. 161-171, 2020,
https://doi.org/10.37394/232016.2020.15.20.
[9] A. S. Samosir, H. Gusmedi, S. Purwiyanti,
and E. Komalasari, “Modeling and simulation
of fuzzy logic based maximum power point
tracking (MPPT) for PV application,” Int. J.
Electr. Comput. Eng., vol. 8, no. 3, pp. 1315–
1323, 2018.
[10] A. Al-Gizi, A. H. Miry, and M. A. Shehab,
“Optimization of fuzzy photovoltaic
maximum power point tracking controller
Fig. 20 : Duty cycle profile.
WSEAS TRANSACTIONS on POWER SYSTEMS
DOI: 10.37394/232016.2024.19.13
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using chimp algorithm,” International Journal
of Electrical & Computer Engineering, vol.
12, no. 5, pp. 2088–8708, 2023.
[11] N. K. Pandey, R. K. Pachauri, S. Choudhury,
and R. K. Sahu, “Asymmetrical interval Type-
2 Fuzzy logic controller based MPPT for PV
system under sudden irradiance changes,”
Materials Today: Proceedings, vol. 80, pp.
710–716, 2023.
[12] R. Arulmurugan, “Optimization of perturb
and observe based fuzzy logic MPPT
controller for independent PV solar system,”
WSEAS Transactions on Power Systems, vol.
19, pp. 159-167, 2020,
https://doi.org/10.37394/23202.2020.19.21.
[13] S. D. Al-Majidi, M. F. Abbod, and H. S. Al-
Raweshidy, “A Modified P&O-MPPT based
on Pythagorean Theorem and CV-MPPT for
PV Systems,” in 2018 53rd International
Universities Power Engineering Conference
(UPEC), Sept. 2018, pp. 1–6.
[14] Z. M. S. Elbarbary, and M. A. Alranini,
“Review of maximum power point tracking
algorithms of PV system,” Frontiers in
Engineering and Built Environment, vol. 1,
no. 1, pp. 68-80, 2021.
[15] R. Arulmurugan, “Comparative evaluation of
new FLC controller based MPPT for a DC to
DC buck-boost zeta converter,” WSEAS
Transactions on Power Systems, vol.11,
pp.27-34, 2016.
[16] U. Yilmaz, A. Kircay, and S. Borekci, “PV
system fuzzy logic MPPT method and PI
control as a charge controller,” Renewable
and Sustainable Energy Reviews, vol. 81, pp.
994–1001, 2018.
[17] S. Singh, S. Manna, M. I. H. Mansoori, and A.
K. Akella, “Implementation of perturb &
observe MPPT technique using boost
converter in PV system,” in 2020
International Conference on Computational
Intelligence for Smart Power System and
Sustainable Energy (CISPSSE), July 2020, pp.
1–4.
[18] M. Jiang, M. Ghahremani, S. Dadfar, H. Chi,
Y. N. Abdallah, and N. Furukawa, “A novel
combinatorial hybrid SFL–PS algorithm based
neural network with perturb and observe for
the MPPT controller of a hybrid PV-storage
system,” Control Engineering Practice, vol.
114, pp. 104880, 2021.
[19] N. Kumar, I. Hussain, B. Singh, and B.K.
Panigrahi, “Framework of Maximum Power
Extraction From Solar PV Panel Using Self
Predictive Perturb and Observe Algorithm,”
IEEE Trans. Sustain. Energy 2018, vol. 9, pp.
895–903.
[20] M. N. Ali, K. Mahmoud, M. Lehtonen, and
M. M. Darwish, “An efficient fuzzy-logic
based variable-step incremental conductance
MPPT method for grid-connected PV
systems,” IEEE Access, vol. 9, pp. 26420–
26430, 2021.
[21] M.A. Abdourraziq, M. Maaroufi, M. Ouassaid
and M. Tlemcani “Maximum Power Point
Tracking Method Based Fuzzy Logic Control
for Photovoltaic Systems,” WSEAS
Transactions on Power Systems, Vol. 12, pp.
324-334, 2017.
[22] X. Li, Q. Wang, H. Wen, and W. Xiao,
“Comprehensive studies on operational
principles for maximum power point tracking
in photovoltaic systems,” IEEE Access, vol. 7,
pp. 121407–121420, 2019.
[23] José Fernando Silva, Sónia F. Pinto, “Linear
and Nonlinear Control of Switching Power
Converters”, Power Electronics Handbook
(Fourth Edition).
[24] Mamdani, E.H., and S. Assilian. “An
Experiment in Linguistic Synthesis with a
Fuzzy Logic Controller,” International
Journal of Man-Machine Studies 7, no. 1
(January 1975): 1–13.
[25] M. Sugeno, Industrial Applications of Fuzzy
Control, Amsterdam, New York, N.Y., U.S.A,
Elsevier Science Pub. Co, 1985.
Contribution of Individual Authors to the
Creation of the Article
- Salah Anis Krim carried out the design of the
system.
- Hamza Afghoul achieved the simulation of the
system.
- Fateh Krim proposed an improved algorithm.
- Feriel Abdelmalek achieved the discussion of the
outcomes.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
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DOI: 10.37394/232016.2024.19.13
Salah Anis Krim, Fateh Krim, Hamza Afghoul, Feriel Abdelmalek
E-ISSN: 2224-350X
114
Volume 19, 2024
