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www.etasr.com Kebbab et al.: A Comparative Analysis of MPPT Techniques for Grid Connected PVs 

 

A Comparative Analysis of MPPT Techniques for 

Grid Connected PVs 
 

Fatima Zohra Kebbab 

Department of Electrical Engineering 
DAC HR Laboratory 

Ferhat Abbas University Setif I 

Setif, Algeria  
fatimazohra.kebbab@univ-setif.dz 

Louarem Sabah  

Department of Electrical Engineering 
LAS Laboratory 

Ferhat Abbas University Setif I 

Setif, Algeria 
slouarem@univ-setif.dz

Hamou Nouri 

Department of Electrical Engineering 
LAS Laboratory 

Ferhat Abbas University Setif I 

Setif, Algeria 
hnouri19@univ-setif.dz 

 

Received: 19 December 2021| Revised: 8 January 2022 and 13 January 2022 | Accepted: 18 January 2022 

 

Abstract- Maximum Power Point Tracking (MPPT) is essential 
for the application of a photovoltaic (PV) energy system in order 

to extract the maximum possible power under variable conditions 

of irradiation and temperature. This paper deals with the 

implementation of different MPPT algorithms for a PV array 

installed for a system connected to the Grid: Perturb and 

Observe (P&O), Fuzzy Logic Control (FLC), Cuckoo Search 
(CS), and Beta algorithms were simulated in Matlab/Simulink 

and the results were analyzed and compared. Beta algorithm 

proved to have greater tracking power, minor power loss, great 

tracking speed, less time, and less oscillation than the other 
techniques. 

Keywords-beta algorithm; cuckoo search; fuzzy logic 
controller; grid; photovoltaic; MPPT; P&O; THD 

I. INTRODUCTION  

Global increasing energy consumption and inevitable 
reduction of fossil fuel resources has pushed scientists to find 
and develop novel renewable energy resources [1, 2]. Solar 
energy is the most abundant renewable energy source [3, 4]. 
Photovoltaic (PV) energy becomes an important alternative as 
it is ubiquitous, environment friendly, freely available, it has 
low operational and maintenance costs, and less balance of 
systems [5]. Therefore, the technological innovations with the 
lower cost of PV modules lead to the implementation of PV 
grid-connected systems, especially small ones with Low 
Voltage (LV), which have gradually emerged [6]. However, the 
efficiency of a PV system is low, the output characteristics of 
solar arrays are nonlinear, and they vary with solar irradiance 
and temperature [7]. In order to make the PV system fully 
exploited, it must operate at its Maximum Power Point (MPP).  

There are two categories of Maximum Power Point 
Tracking (MPPT) techniques: conventional MPPT algorithms 
which are simple and low-cost, yet they have poor efficiency. 
The second category consists of the MPPT methods based on 
intelligent control. These methods are complex with high 
efficiency [8]. Many research studies on the MPPT techniques 
have been reported. Authors in [9] fixed the duty cycle, in the 
simplest of methods that does not require any feedback, in 
which the load impedance is adjusted only once for the MPP 
and it is not adjusted again. Authors in [10], proposed an 
Adaptive Reference Voltage (ARV)-based MPPT technique to 
improve the performance of the constant voltage tracking 
technique by making it adaptable to weather conditions. 
Constant voltage control can be easily implemented with 
analog hardware. However, its MPPT tracking efficiency is 
low relative to those of other algorithms [9]. Authors in [11] 
used the SimPowerSystems platform in Matlab to design a PV 
system and a new GMPPT algorithm based on the open circuit 
voltage. The open circuit voltage method was adapted to a 
system under shading condition in [12]. This method has high 
tracking accuracy but does not always track the real peak and 
instead it causes the system to operate at a point close to the 
MPP. Authors in [13] proposed a hybrid MPPT method 
combined with the conventional Short Current Pulse (SCP) 
MPPT method and Perturb & Observe (P&O) method [14-15]. 
This algorithm is the most commonly used because of its ease 
of implementation for standalone PV systems. However, P&O 
has limitations that reduce its MPPT efficiency [16] while it 
requires complex control circuits [17]. 

Conventional MPPT methods are used satisfactorily, but 
they have the problem of low speed of convergence and high 
oscillations around the MPP. Since the P-V characteristic of a 

Corresponding author: Fatima Zohra Kebbab



Engineering, Technology & Applied Science Research Vol. 12, No. 2, 2022, 8228-8235 8229 
 

www.etasr.com Kebbab et al.: A Comparative Analysis of MPPT Techniques for Grid Connected PVs 

 

PV module is not linear, the conventional MPPT control 
method is not adequate. To resolve this problem, alternative 
MPPT control methods based on intelligent algorithms have 
been widely proposed and successfully applied .The most 
known intelligent methods for MPPT are Fuzzy Logic Control 
(FLC), Artificial Neural Networks (ANNs), and meta-heuristic 
algorithms [18]. Authors in [19] proposed ANNs as a global 
theoretical approach for predicting and tracking the MPP of 
solar panels. ANNs have been extensively used to model the 
complicated relationship between the inputs and outputs of 
nonlinear systems [19]. Several microcontrollers use FLC for 
MPPT. The FLC is preferred when the mathematical model of 
the control plant is difficult to obtain. This technique has been 
proposed in several research works to achieve the MPP. 
Authors in [20] mentioned that FLC can track the MPP quickly 
while it has better dynamic and steady-state performance. 
However, the main disadvantages of FLC are that the variation 
of irradiance can cause drift and the implementation is 
complex. The Particle Swarm Optimization (PSO) -based 
MPPT approach has been presented in [21, 22]. In [7], a 
modified version of PSO is developed. The PSO is highly 
potential due to its simple structure, easy implementation, and 
fast computation capability [23]. 

The main contribution of this paper is to determine the most 
efficient method to track the MPP of a grid connected PV 
system. The proposed methods are the Cuckoo Search (CS) 
algorithm [24-27], the Beta algorithm [28], P&O method, and 
FLC. 

II. MODEL DESCRIPTION OF THE PV MODULE 

A simple equivalent circuit model for a PV cell is shown in 
Figure 1 [29]. The ideal case is presented by a current source in 
parallel with a diode, the real case of a PV cell is represented 
by the insertion of the resistances Rs and Rsh [30-31]. 

 

 
Fig. 1.  Electric model equivalent of a PV cell. 

The current of the PV panel is given by [32-33]: 

I	 �	I�	–	I	�exp	.��.
�������.� � � 1�–	�
��	��	�
���

�    (1) 
The current through the diode is given by: 

I� � 	I �exp	.��.
��������.� � � 1�    (2) 
The solar cell output current is : 

I	 � I�	–	I�	–	I��    (3) 
where I is the solar cell current (A), I� is the light generated 
current (A), I�  is the diode saturation current (A), R�  is the 
solar cell series resistance (Ω), q is the electron charge (1.6× 
10

-19
C), K is the Boltzman's constant, T represents the cell 

temperature (K), V is the solar cell's output voltage (V), and 
R�� is the solar cell's shunt resistance (Ω). 
In this paper, the solar panel SPR-305E-WHT-D is 

considered. The specifications of this solar panel at steady state 
conditions (STC) of 25

o
C and 1000W/m

2
 are the parameters 

used in simulations and are shown in Table I [34]. 

TABLE I.  PARAMETERS OF THE CONSIDERED PV PANEL [34] 

Information Value 

Open circuit voltage 64.2V 

Short circuit current 5.96A 

Voltage at MPP 54.7V 

Current at MPP 5.58A 

Maximum power 305.226W 

Parallel strings 66 

Series-connected modules per string 55 

 

III. MPPT TECHNIQUES 

A. Perturbation and Observation (P&O)  

The P&O method is one of the best MPPT approaches used 
to track the MPP in PV panels, combining easy implementation 
and simplicity of execution [35]. In P&O technique, we 
introduce a minor -predefined change to perturb the system and 
consequently cause the power variation of the PV module [30]. 
The PV output power is measured periodically and is compared 
with the previous power value [36-37]. If the power output 
increases, the same process continues, otherwise, the 
perturbation direction is reversed [36]. 

B. Cuckoo Search (CS) 

The CS algorithm [38] was proposed as an efficient 
metaheuristic optimization mechanism. This algorithm 
emulates the behavior of cuckoo birds in their reproduction 
process [24-25]. The cuckoo bird lays its eggs inside the nests 
of other birds instead of building its own [24]. It will choose 
the best nest for her eggs to hatch safely and breed a new 
generation of cuckoos. In some cases, in order to increase the 
chance of hatching, the cuckoo bird will make some effort as it 
lays its eggs strategically in a good position and sometimes 
drops the eggs of the host birds outside the nest. Some cuckoo 
species even produce eggs similar to the eggs of other bird 
species [25]. Upon hatching, the young cuckoos sometimes 
destroy the eggs of the host birds to increase the chance of 
getting more food. In some cases, host birds discover and 
destroy cuckoo eggs and may abandon their nests [27]. The CS 
optimization algorithm was developed, to track the MPPT of 
the solar PV. In order to make this algorithm suitable, some 
simplifications of the actual behavior are needed [39]. This 
algorithm works based on three rules [40] 

• Each cuckoo bird will only lay one egg in a random host 
nest. 

• Only the best nest, containing high quality organisms, will 
transmit the next generation of cuckoo birds. 

• Finally, host networks are considered to have a fixed value. 
Levy flights are used to create new eggs and a step size of 
the cuckoo is determined with the Levy function as follows:  



Engineering, Technology & Applied Science Research Vol. 12, No. 2, 2022, 8228-8235 8230 
 

www.etasr.com Kebbab et al.: A Comparative Analysis of MPPT Techniques for Grid Connected PVs 

 

Levy
β� � L%&     , 1 ' β ( 3    (4) 
where L is the flight length. The optimizing flight size 
coefficient α is regarded as a fraction of excreted eggs (P+), 
where P+ 	∈	-0,10. 
C. Fuzzy Logic Controller (FLC) 

The main task of the FLC is to reach the MPP. However, 
the performance of the controller depends principally on 
human expertise. Fuzzy logic can work with imprecise inputs, 
it does not need the precise mathematical model of the system 
[41]. E(t) and 12
3� are the FLC inputs given by (5) and (6) 
[42]: 

E
t� � 678
9�%678
9%:��78
9�%�78
9%:� �
∆6
∆�    (5) 

dE
t� � E
t� � E
t � 1� � ∆E    (6) 
where 	P6�
t� and V6�
t�	are the output power and the voltage 
of the PV module. 

The FLC output is dD, which is the variable step size of the 
duty cycle: 

dD � D
t� � D
t � 1�    (7) 
In this work, the triangular membership function is chosen 

for the inputs, due to its simplicity [43]. The proposed control 
model simulated in Matlab/Simulink is shown in Figure 2. 

 

 
Fig. 2.  Simulink model of the fuzzy logic controller. 

D. Beta Method 

The Beta method has been detailed in [44]. In this method, 
an intermediate variable β is used to track the MPP [45]. The 
intermediate variable β is given as [29, 46]: 

β � lnA BCDECDF �c H vIE    (8) 
c � q/
NsAKT�    (9) 

where ipv and vpv are the PV current and voltage, c is the diode 
constant, q is the electron charge, A is the diode ideality factor, 
K the Boltzmann's constant, T is the temperature of the p-n 
junction, and Ns is the cell number of the PV module. 

The Beta-based MPPT algorithm consists in tracking the 
reference β* that is calculated at the MPP [47]: 

β∗ � lnABCDRCCECDRCCF � c H vIERCC    (10) 
where vIERCC and iIERCCare respectively the MPP voltage and 
current of the PV array at STC. 

IV. SIMULATION RESULTS 

This paper aims to test the feasibility and effectiveness of 
the considered MPPT methods to track the MPP of a grid-
connected PV panel. The simulation of the overall system 
depicted in Figure 3 was carried out in Matlab/Simulink. The 
considered MPPT methods for this comparative study are: 
P&O, FLC, CS, and Beta. The PV array is modeled to have 66 
strings and connected to the boost converter that operates with 
the MPPT controller. The linguistic variables in this study are 
defined as Positive Big (PB), Negative Big (NB), and Zero 
(ZE). Figure 4 presents the grid details connected with the PV. 

In order to examine the effect of solar irradiation on module 
characteristic, the temperature of the cells was kept constant at 
25

o
C. In Figures 5 and 6, the obtained I-V and P-V 

characteristics at different irradiation levels are shown. 

 

 
Fig. 3.  100 kW PV grid-connected PV array. 



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www.etasr.com Kebbab et al.: A Comparative Analysis of MPPT Techniques for Grid Connected PVs 

 

 
Fig. 4.  Grid details. 

 
Fig. 5.  I-V characteristic of the PV module at constant temperature and 
variable irradiation level. 

 
Fig. 6.  P-V characteristic of the PV module at constant temperature and 
variable irradiation level. 

A DC-DC boost converter is utilized as a power-
conditioning unit between the PV module and the grid. The 
degree of the membership function of the inputs E, dE and the 
output dD is shown in Figure 7. The output voltage and current 
of the converter are converted by a three-phase inverter, to 
produce AC signals. This PV system is connected to the 
distribution line via a 100kVA transformer. To examine the 
robustness and the efficacy of the considered techniques in the 
tracking of MPP and re-tracking with changes in the weather 
conditions, the temperature of the cells is kept constant at 25°C 
and the irradiance level changes. Sun insulation of 1000W/m² 
is applied to the PV system in t ϵ [0, 1s]. Then, it is changed to 
400W/m

2
 between 1 and 1.5s. Finally it is stepped to 

1000W/m
2
 between 1.5 and 3s as shown in Figure 8. 

 
Fig. 7.  Membership function inputs E, dE and output dD. 

 
Fig. 8.  Irradiation variation. 

Figure 9 shows the output power of the considered 
methods. In Figure 9(a), we see the calculation of the 
Maximum Power Efficiency (MPE), that presents the main 
evaluation parameter of the MPPT in PV systems, in STC 
conditions. The MPE measures the percentage of the created 
power at certain time mentioned to the theoretical maximum 
created power at the same time. At STC, the MP (maximum 
power) is 100.7Kw. The power achieved by Beta, FLC, P&O, 
and CS was: 100.4, 100.3, 100.2, and 100.18KW respectively. 
The efficiency achieved by Beta, FLC, P&O, and CS was 
99.7%, 99.6%, 99.5%, and 99.48% respectively. We see that 
Beta achieved higher efficiency than the other techniques .We 



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www.etasr.com Kebbab et al.: A Comparative Analysis of MPPT Techniques for Grid Connected PVs 

 

can clearly note that in a transient state large oscillations can be 
observed in CS and FLC, which cause power loss. 

 

(a) 

 

(b) 

 

Fig. 9.  PV system’s power output: (a) STC, (b) sudden irradiance change. 

In order to extent the robustness of the MPPT techniques, 
settling time is an essential tool. The settling time in STC 
(Figure 9(a)) for Beta, FLC, CS, and P&O is 0.0256s, 0.1614s, 
0.2411s, and 0.0914s respectively. So, Beta ensures the 
convergence to the MPP more rapidly.  

In the zoomed part (1-1.4s) of Figure 9(b) the first step 
change is introduced at 1s. We stepped the irradiance from 
1000W/m

2
 to 400W/m

2
. The FLC and Beta reach the MPP 

without an overshoot in short time, but the CS and P&O reach 
the MPP with large overshoot. In the interval from 2 to 3s, 
irradiance changed from 400W/m

2
 to 1000W/m

2
. With the CS 

algorithm, the oscillations around the MPP introduce a power 
with an average value equal to 98.3KW which creates a static 
error equal to 1.7kW. These results have confirmed the good 
performance and the high effectiveness of FLC and Beta. At 
t=2.6s, the CS reaches the MPP. CS takes a relatively long time 
to reach the MPP without an overshoot. P&O presents large 
oscillations around the MPP. The major inconvenience of P&O 
algorithm is its poor behavior following a sudden change in 
irradiance. 

For the PV voltage (V6�) and the PV current (I6�) shown in 
Figure 10, we observe that in the transient state of PV voltage 
(V6�), the CS and FLC algorithms present large oscillations 
between t=0 and t=0.2s. In the steady state, FLC, CS, and Beta 
are stable, but P&O in the intervals of the change of irradiance, 
presents large oscillations. 

Figure 11 shows the DC output voltage regulation to 
connect the PV with the grid. In the zoomed part, with the 
quick change of irradiance at t = 1 s, Beta reached the reference 
voltage 500VDC quicker than the other algorithms. 

(a) 

 

(b) 

 

Fig. 10.  (a) PV current, (b) PV voltage. 

 
Fig. 11.  The DC output voltage regulation at 500VDC. 

 
Fig. 12.  Power injected into the grid. 

Figure 12 shows the power injected into the grid. In the 
zoomed part (2.8-3s), P&O presents a large oscillation. The 
power achieved by Beta, FLC, P&O, and CS is 98.73, 98.72, 
98.58, and 98.57kW respectively. This confirms the higher 
efficacy of the Beta algorithm. 



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www.etasr.com Kebbab et al.: A Comparative Analysis of MPPT Techniques for Grid Connected PVs 

 

(a) 

 

 

(b) 

 

 

(c) 

 

 

(d) 

 

 
Fig. 13.  AC output current waveform with grid connection, THD 
performance: (a) P&O, (b) CS, (c) FLC, (d) Beta. 

The injected current to the grid has the shape of a sinusoidal 
wave, as shown in Figure 13 that confirms the effectiveness of 
the considered proposed controllers. To evaluate the quality of 
the injected current, the Fast Fourier Transform (FFT) analysis 
of the current is also shown in Figure 13. All the considered 
techniques show low THD , within the acceptable limit as per 
the standards IEC 61727, IEEE 1547 and IEEE 929 [48]. Beta 
and P&O perform better than the other algorithms with  
THD = 2.13%. 

V. DISCUSSION 

We note that the Beta method has satisfactory application in 
PV systems but with load resistance. In this paper our model is 
realized with a 100kW grid-connected PV system. The 
simulation results showed that the Beta method can address the 
two problems the existing MPPT methods face, namely the 
tradeoff between the steady-state oscillations and dynamic 
behavior and the tradeoff between high computational load and 
accuracy, since it shows a fast tracking speed in the transient 
stage, smaller oscillations in the steady-state and medium 
complexity of implementation. The performance of the Beta 
method as an MPPT technique is evaluated with respect to 
other known existing techniques, namely P&O [4], which 
introduces considerable oscillations near the MPP that lead to 
power losses, the FLC [6], and CS algorithm which presented 
large oscillations around the MPP contrarily to the results 
presented in [25]. 

To confirm the efficiency of proposed method, two 
comparisons were conducted: 

• Power efficiency and settling time: The main evaluation 
parameters of the MPPT in PV systems are power 
efficiency and settling time. In this paper, the efficacy of 
the Beta technique was proved with efficiency of 99.7% 
and settling time of 0.0256s 

• THD comparative study: Another criterion taken into 
account in evaluating the performance of the considered 
control algorithms is the rate of distortion of the network 
currents (THD). The results of the FFT analysis of the 
current were compared and validated. 

VI. CONCLUSION 

The efficiency of a PV system is an essential indicator for 
assessing the power of grid-connected PV systems. MPPT 
performance is a key indicator. A MPPT algorithm must be 
used to track the operating point with the highest power output. 
In this esteem, this paper presented and compared four MPPT 
algorithms: P&O, FLC, CS, and Beta with regard to the 
achievement of the MPPT and the injection of sinusoidal 
current to the electric grid. 

The simulation results showed the superior performance of 
the Beta algorithm in effectively and efficiently following the 
MPP with minimum oscillations and highest speed, even 
during a quick change in the solar irradiance. 

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Fuzzy THD= 2.34%

0 200 400 600 800 1000

Frequency (Hz)

0

0.5

1

1.5

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F
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n
d
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